Stephen M. Smith

YNIMG Journal 2023 Journal Article

Amplitudes of resting-state functional networks – investigation into their correlates and biophysical properties

• Soojin Lee
• Janine D. Bijsterbosch
• Fidel Alfaro Almagro
• Lloyd Elliott
• Paul McCarthy
• Bernd Taschler
• Roser Sala-Llonch
• Christian F. Beckmann

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.

YNICL Journal 2022 Journal Article

Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic

• Ludovica Griffanti
• Grace Gillis
• M. Clare O'Donoghue
• Jasmine Blane
• Pieter M. Pretorius
• Robert Mitchell
• Nicola Aikin
• Karen Lindsay

The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB). In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants). Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients' tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data. Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65-101 years, average 78.3 years) and UKB participants (44-82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.

YNIMG Journal 2022 Journal Article

Causal inference on neuroimaging data with Mendelian randomisation

• Bernd Taschler
• Stephen M. Smith
• Thomas E. Nichols

While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.

YNIMG Journal 2022 Journal Article

Deep neural networks learn general and clinically relevant representations of the ageing brain

• Esten H. Leonardsen
• Han Peng
• Tobias Kaufmann
• Ingrid Agartz
• Ole A. Andreassen
• Elisabeth Gulowsen Celius
• Thomas Espeseth
• Hanne F. Harbo

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data — the brain age delta — has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

YNIMG Journal 2021 Journal Article

Confound modelling in UK Biobank brain imaging

• Fidel Alfaro-Almagro
• Paul McCarthy
• Soroosh Afyouni
• Jesper L.R. Andersson
• Matteo Bastiani
• Karla L. Miller
• Thomas E. Nichols
• Stephen M. Smith

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

YNIMG Journal 2021 Journal Article

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

• Seyedeh-Rezvan Farahibozorg
• Janine D. Bijsterbosch
• Weikang Gong
• Saad Jbabdi
• Stephen M. Smith
• Samuel J. Harrison
• Mark W. Woolrich

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

YNIMG Journal 2021 Journal Article

Learning patterns of the ageing brain in MRI using deep convolutional networks

• Nicola K. Dinsdale
• Emma Bluemke
• Stephen M. Smith
• Zobair Arya
• Diego Vidaurre
• Mark Jenkinson
• Ana I.L. Namburete

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12, 802 T1-weighted MRI images and a further 6, 885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors Δ B r a i n A g e = A g e P r e d i c t e d − A g e T r u e correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between Δ BrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the Δ BrainAge from models such as this network were predictive of any health outcomes.

YNIMG Journal 2021 Journal Article

The Human Connectome Project: A retrospective

• Jennifer Stine Elam
• Matthew F. Glasser
• Michael P. Harms
• Stamatios N. Sotiropoulos
• Jesper L.R. Andersson
• Gregory C. Burgess
• Sandra W. Curtiss
• Robert Oostenveld

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

YNIMG Journal 2020 Journal Article

A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration

• Frederik J. Lange
• John Ashburner
• Stephen M. Smith
• Jesper L.R. Andersson

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour.

YNIMG Journal 2020 Journal Article

Denoising scanner effects from multimodal MRI data using linked independent component analysis

• Huanjie Li
• Stephen M. Smith
• Staci Gruber
• Scott E. Lukas
• Marisa M. Silveri
• Kevin P. Hill
• William D.S. Killgore
• Lisa D. Nickerson

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

YNIMG Journal 2020 Journal Article

Modelling subject variability in the spatial and temporal characteristics of functional modes

• Samuel J. Harrison
• Janine D. Bijsterbosch
• Andrew R. Segerdahl
• Sean P. Fitzgibbon
• Seyedeh-Rezvan Farahibozorg
• Eugene P. Duff
• Stephen M. Smith
• Mark W. Woolrich

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al. , 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git. fmrib. ox. ac. uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al. , 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al. , 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

YNIMG Journal 2020 Journal Article

Optimising network modelling methods for fMRI

• Usama Pervaiz
• Diego Vidaurre
• Mark W. Woolrich
• Stephen M. Smith

A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e. g. , behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼ 14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets ( ∼ 1000 subjects) to evaluate the generalisability of proposed network modelling methods.

YNIMG Journal 2020 Journal Article

Permutation inference for canonical correlation analysis

• Anderson M. Winkler
• Olivier Renaud
• Stephen M. Smith
• Thomas E. Nichols

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.

YNIMG Journal 2020 Journal Article

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

• Sean P. Fitzgibbon
• Samuel J. Harrison
• Mark Jenkinson
• Luke Baxter
• Emma C. Robinson
• Matteo Bastiani
• Jelena Bozek
• Vyacheslav Karolis

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.

YNIMG Journal 2019 Journal Article

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

• Matteo Bastiani
• Jesper L.R. Andersson
• Lucilio Cordero-Grande
• Maria Murgasova
• Jana Hutter
• Anthony N. Price
• Antonios Makropoulos
• Sean P. Fitzgibbon

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

YNIMG Journal 2019 Journal Article

Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power

• Matthew F. Glasser
• Timothy S. Coalson
• Janine D. Bijsterbosch
• Samuel J. Harrison
• Michael P. Harms
• Alan Anticevic
• David C. Van Essen
• Stephen M. Smith

We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.

YNIMG Journal 2019 Journal Article

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

• Soroosh Afyouni
• Stephen M. Smith
• Thomas E. Nichols

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i. e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors – before or after Fisher's transformation – becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

YNIMG Journal 2019 Journal Article

Estimation of brain age delta from brain imaging

• Stephen M. Smith
• Diego Vidaurre
• Fidel Alfaro-Almagro
• Thomas E. Nichols
• Karla L. Miller

It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5, 792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2, 641 multimodal brain imaging-derived phenotypes, with data from 19, 000 participants in UK Biobank.

YNICL Journal 2019 Journal Article

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank

• Lisa Nobis
• Sanjay G. Manohar
• Stephen M. Smith
• Fidel Alfaro-Almagro
• Mark Jenkinson
• Clare E. Mackay
• Masud Husain

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.

YNIMG Journal 2019 Journal Article

The Lifespan Human Connectome Project in Aging: An overview

• Susan Y. Bookheimer
• David H. Salat
• Melissa Terpstra
• Beau M. Ances
• Deanna M. Barch
• Randy L. Buckner
• Gregory C. Burgess
• Sandra W. Curtiss

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al. , 2018), and the image acquisition protocol common to both studies (Harms et al. , 2018).

YNIMG Journal 2018 Journal Article

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

• Jelena Bozek
• Antonios Makropoulos
• Andreas Schuh
• Sean Fitzgibbon
• Robert Wright
• Matthew F. Glasser
• Timothy S. Coalson
• Jonathan O'Muircheartaigh

We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36–44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.

YNIMG Journal 2018 Journal Article

Directed functional connectivity using dynamic graphical models

• Simon Schwab
• Ruth Harbord
• Valerio Zerbi
• Lloyd Elliott
• Soroosh Afyouni
• Jim Q. Smith
• Mark W. Woolrich
• Stephen M. Smith

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0. 4–0. 8 s offset between connected nodes), our method has a sensitivity of 72%–77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i. e. , directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

YNIMG Journal 2018 Journal Article

Disambiguating brain functional connectivity

• Eugene P. Duff
• Tamar Makin
• Michiel Cottaar
• Stephen M. Smith
• Mark W. Woolrich

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, analyses assessing changes in correlation fail to distinguish effects produced by sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterizes FC changes into certain prevalent classes of signal change that involve the input of additional signal to existing activity. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, suggesting that it could clarify our current understanding of FC changes in many contexts. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

YNIMG Journal 2018 Journal Article

Discovering dynamic brain networks from big data in rest and task

• Diego Vidaurre
• Romesh Abeysuriya
• Robert Becker
• Andrew J. Quinn
• Fidel Alfaro-Almagro
• Stephen M. Smith
• Mark W. Woolrich

Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

YNIMG Journal 2018 Journal Article

Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

• Michael P. Harms
• Leah H. Somerville
• Beau M. Ances
• Jesper Andersson
• Deanna M. Barch
• Matteo Bastiani
• Susan Y. Bookheimer
• Timothy B. Brown

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22–35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

YNIMG Journal 2018 Journal Article

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

• Fidel Alfaro-Almagro
• Mark Jenkinson
• Neal K. Bangerter
• Jesper L.R. Andersson
• Ludovica Griffanti
• Gwenaëlle Douaud
• Stamatios N. Sotiropoulos
• Saad Jbabdi

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100, 000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10, 000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

YNIMG Journal 2018 Journal Article

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks

• Giles L. Colclough
• Mark W. Woolrich
• Samuel J. Harrison
• Pedro A. Rojas López
• Pedro A. Valdes-Sosa
• Stephen M. Smith

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al. 's Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.

YNIMG Journal 2018 Journal Article

Multimodal surface matching with higher-order smoothness constraints

• Emma C. Robinson
• Kara Garcia
• Matthew F. Glasser
• Zhengdao Chen
• Timothy S. Coalson
• Antonios Makropoulos
• Jelena Bozek
• Robert Wright

In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

YNIMG Journal 2018 Journal Article

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

• Antonios Makropoulos
• Emma C. Robinson
• Andreas Schuh
• Robert Wright
• Sean Fitzgibbon
• Jelena Bozek
• Serena J. Counsell
• Johannes Steinweg

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

YNIMG Journal 2018 Journal Article

The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds

• Leah H. Somerville
• Susan Y. Bookheimer
• Randy L. Buckner
• Gregory C. Burgess
• Sandra W. Curtiss
• Mirella Dapretto
• Jennifer Stine Elam
• Michael S. Gaffrey

Recent technological and analytical progress in brain imaging has enabled the examination of brain organization and connectivity at unprecedented levels of detail. The Human Connectome Project in Development (HCP-D) is exploiting these tools to chart developmental changes in brain connectivity. When complete, the HCP-D will comprise approximately ∼1750 open access datasets from 1300 + healthy human participants, ages 5–21 years, acquired at four sites across the USA. The participants are from diverse geographical, ethnic, and socioeconomic backgrounds. While most participants are tested once, others take part in a three-wave longitudinal component focused on the pubertal period (ages 9–17 years). Brain imaging sessions are acquired on a 3 T Siemens Prisma platform and include structural, functional (resting state and task-based), diffusion, and perfusion imaging, physiological monitoring, and a battery of cognitive tasks and self-reports. For minors, parents additionally complete a battery of instruments to characterize cognitive and emotional development, and environmental variables relevant to development. Participants provide biological samples of blood, saliva, and hair, enabling assays of pubertal hormones, health markers, and banked DNA samples. This paper outlines the overarching aims of the project, the approach taken to acquire maximally informative data while minimizing participant burden, preliminary analyses, and discussion of the intended uses and limitations of the dataset.

YNIMG Journal 2018 Journal Article

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data

• Matthew F. Glasser
• Timothy S. Coalson
• Janine D. Bijsterbosch
• Samuel J. Harrison
• Michael P. Harms
• Alan Anticevic
• David C. Van Essen
• Stephen M. Smith

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e. g. , blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

YNIMG Journal 2017 Journal Article

Hand classification of fMRI ICA noise components

• Ludovica Griffanti
• Gwenaëlle Douaud
• Janine Bijsterbosch
• Stefania Evangelisti
• Fidel Alfaro-Almagro
• Matthew F. Glasser
• Eugene P. Duff
• Sean Fitzgibbon

We present a practical “how-to” guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

YNIMG Journal 2017 Journal Article

Heterogeneous fractionation profiles of meta-analytic coactivation networks

• Angela R. Laird
• Michael C. Riedel
• Mershack Okoe
• Radu Jianu
• Kimberly L. Ray
• Simon B. Eickhoff
• Stephen M. Smith
• Peter T. Fox

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10, 000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i. e. , d=20–300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

YNIMG Journal 2017 Journal Article

Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

• An T. Vu
• Keith Jamison
• Matthew F. Glasser
• Stephen M. Smith
• Timothy Coalson
• Steen Moeller
• Edward J. Auerbach
• Kamil Uğurbil

Whole-brain functional magnetic resonance imaging (fMRI), in conjunction with multiband acceleration, has played an important role in mapping the functional connectivity throughout the entire brain with both high temporal and spatial resolution. Ultrahigh magnetic field strengths (7T and above) allow functional imaging with even higher functional contrast-to-noise ratios for improved spatial resolution and specificity compared to traditional field strengths (1. 5T and 3T). High-resolution 7T fMRI, however, has primarily been constrained to smaller brain regions given the amount of time it takes to acquire the number of slices necessary for high resolution whole brain imaging. Here we evaluate a range of whole-brain high-resolution resting state fMRI protocols (0. 9, 1. 25, 1. 5, 1. 6 and 2mm isotropic voxels) at 7T, obtained with both in-plane and slice acceleration parallel imaging techniques to maintain the temporal resolution and brain coverage typically acquired at 3T. Using the processing pipeline developed by the Human Connectome Project, we demonstrate that high resolution images acquired at 7T provide increased functional contrast to noise ratios with significantly less partial volume effects and more distinct spatial features, potentially allowing for robust individual subject parcellations and descriptions of fine-scaled patterns, such as visuotopic organization.

YNIMG Journal 2016 Journal Article

Faster permutation inference in brain imaging

• Anderson M. Winkler
• Gerard R. Ridgway
• Gwenaëlle Douaud
• Thomas E. Nichols
• Stephen M. Smith

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM — Permutation Analysis of Linear Models.

YNIMG Journal 2016 Journal Article

The brain functional connectome is robustly altered by lack of sleep

• Tobias Kaufmann
• Torbjørn Elvsåshagen
• Dag Alnæs
• Nathalia Zak
• Per Ø. Pedersen
• Linn B. Norbom
• Sophia H. Quraishi
• Enzo Tagliazucchi

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)—a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

YNIMG Journal 2015 Journal Article

Attentional load modulates large-scale functional brain connectivity beyond the core attention networks

• Dag Alnæs
• Tobias Kaufmann
• Geneviève Richard
• Eugene P. Duff
• Markus H. Sneve
• Tor Endestad
• Jan Egil Nordvik
• Ole A. Andreassen

In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional task-fMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts.

YNIMG Journal 2015 Journal Article

Large-scale Probabilistic Functional Modes from resting state fMRI

• Samuel J. Harrison
• Mark W. Woolrich
• Emma C. Robinson
• Matthew F. Glasser
• Christian F. Beckmann
• Mark Jenkinson
• Stephen M. Smith

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

YNIMG Journal 2015 Journal Article

Multi-level block permutation

• Anderson M. Winkler
• Matthew A. Webster
• Diego Vidaurre
• Thomas E. Nichols
• Stephen M. Smith

Under weak and reasonable assumptions, mainly that data are exchangeable under the null hypothesis, permutation tests can provide exact control of false positives and allow the use of various non-standard statistics. There are, however, various common examples in which global exchangeability can be violated, including paired tests, tests that involve repeated measurements, tests in which subjects are relatives (members of pedigrees) — any dataset with known dependence among observations. In these cases, some permutations, if performed, would create data that would not possess the original dependence structure, and thus, should not be used to construct the reference (null) distribution. To allow permutation inference in such cases, we test the null hypothesis using only a subset of all otherwise possible permutations, i. e. , using only the rearrangements of the data that respect exchangeability, thus retaining the original joint distribution unaltered. In a previous study, we defined exchangeability for blocks of data, as opposed to each datum individually, then allowing permutations to happen within block, or the blocks as a whole to be permuted. Here we extend that notion to allow blocks to be nested, in a hierarchical, multi-level definition. We do not explicitly model the degree of dependence between observations, only the lack of independence; the dependence is implicitly accounted for by the hierarchy and by the permutation scheme. The strategy is compatible with heteroscedasticity and variance groups, and can be used with permutations, sign flippings, or both combined. We evaluate the method for various dependence structures, apply it to real data from the Human Connectome Project (HCP) as an example application, show that false positives can be avoided in such cases, and provide a software implementation of the proposed approach.

YNIMG Journal 2014 Journal Article

Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

• Gholamreza Salimi-Khorshidi
• Gwenaëlle Douaud
• Christian F. Beckmann
• Matthew F. Glasser
• Ludovica Griffanti
• Stephen M. Smith

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) – one of the most widely used techniques for the exploratory analysis of fMRI data – has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB's ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e. g. , what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.

YNIMG Journal 2014 Journal Article

Group-PCA for very large fMRI datasets

• Stephen M. Smith
• Aapo Hyvärinen
• Gaël Varoquaux
• Karla L. Miller
• Christian F. Beckmann

Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA.

YNIMG Journal 2014 Journal Article

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

• Ludovica Griffanti
• Gholamreza Salimi-Khorshidi
• Christian F. Beckmann
• Edward J. Auerbach
• Gwenaëlle Douaud
• Claire E. Sexton
• Enikő Zsoldos
• Klaus P. Ebmeier

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

YNIMG Journal 2014 Journal Article

MSM: A new flexible framework for Multimodal Surface Matching

• Emma C. Robinson
• Saad Jbabdi
• Matthew F. Glasser
• Jesper Andersson
• Gregory C. Burgess
• Michael P. Harms
• Stephen M. Smith
• David C. Van Essen

Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.

YNIMG Journal 2014 Journal Article

Permutation inference for the general linear model

• Anderson M. Winkler
• Gerard R. Ridgway
• Matthew A. Webster
• Stephen M. Smith
• Thomas E. Nichols

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm.

YNIMG Journal 2013 Journal Article

Evaluation of slice accelerations using multiband echo planar imaging at 3T

• Junqian Xu
• Steen Moeller
• Edward J. Auerbach
• John Strupp
• Stephen M. Smith
• David A. Feinberg
• Essa Yacoub
• Kâmil Uğurbil

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan.

YNIMG Journal 2013 Journal Article

Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration

• Marius de Groot
• Meike W. Vernooij
• Stefan Klein
• M. Arfan Ikram
• Frans M. Vos
• Stephen M. Smith
• Wiro J. Niessen
• Jesper L.R. Andersson

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a “skeleton projection” that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R2 0. 993; 0. 931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.

JMLR Journal 2013 Journal Article

Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models

• Aapo Hyvärinen
• Stephen M. Smith

We present new measures of the causal direction, or direction of effect, between two non-Gaussian random variables. They are based on the likelihood ratio under the linear non-Gaussian acyclic model (LiNGAM). We also develop simple first-order approximations of the likelihood ratio and analyze them based on related cumulant-based measures, which can be shown to find the correct causal directions. We show how to apply these measures to estimate LiNGAM for more than two variables, and even in the case of more variables than observations. We further extend the method to cyclic and nonlinear models. The proposed framework is statistically at least as good as existing ones in the cases of few data points or noisy data, and it is computationally and conceptually very simple. Results on simulated fMRI data indicate that the method may be useful in neuroimaging where the number of time points is typically quite small. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

YNIMG Journal 2013 Journal Article

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

• Kamil Uğurbil
• Junqian Xu
• Edward J. Auerbach
• Steen Moeller
• An T. Vu
• Julio M. Duarte-Carvajalino
• Christophe Lenglet
• Xiaoping Wu

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2mm isotropic resolution in 0. 7s for fMRI, and 1. 25mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

YNIMG Journal 2013 Journal Article

Resting-state fMRI in the Human Connectome Project

• Stephen M. Smith
• Christian F. Beckmann
• Jesper Andersson
• Edward J. Auerbach
• Janine Bijsterbosch
• Gwenaëlle Douaud
• Eugene Duff
• David A. Feinberg

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3T, with a spatial resolution of 2×2×2mm and a temporal resolution of 0. 7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

YNIMG Journal 2013 Journal Article

Spatially constrained hierarchical parcellation of the brain with resting-state fMRI

• Thomas Blumensath
• Saad Jbabdi
• Matthew F. Glasser
• David C. Van Essen
• Kamil Ugurbil
• Timothy E.J. Behrens
• Stephen M. Smith

We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels. A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.

YNIMG Journal 2013 Journal Article

The WU-Minn Human Connectome Project: An overview

• David C. Van Essen
• Stephen M. Smith
• Deanna M. Barch
• Timothy E.J. Behrens
• Essa Yacoub
• Kamil Ugurbil

The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.

YNIMG Journal 2012 Journal Article

Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure

• Adrian R. Groves
• Stephen M. Smith
• Anders M. Fjell
• Christian K. Tamnes
• Kristine B. Walhovd
• Gwenaëlle Douaud
• Mark W. Woolrich
• Lars T. Westlye

Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0. 95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e. g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail.

YNIMG Journal 2012 Journal Article

FSL

• Mark Jenkinson
• Christian F. Beckmann
• Timothy E.J. Behrens
• Mark W. Woolrich
• Stephen M. Smith

FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on “20 years of fMRI” we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.

YNIMG Journal 2012 Journal Article

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI

• Eugene P. Duff
• Aaron J. Trachtenberg
• Clare E. Mackay
• Matt A. Howard
• Frederick Wilson
• Stephen M. Smith
• Mark W. Woolrich

Functional Magnetic Resonance Imaging (fMRI) shows significant potential as a tool for predicting clinically important information such as future disease progression or drug effect from brain activity. Multivariate techniques have been developed that combine fMRI signals from across the brain to produce more robust predictive capabilities than can be obtained from single regions. However, the high dimensionality of fMRI data makes overfitting a significant problem. Reliable methods are needed for transforming fMRI data to a set of signals reflecting the underlying spatially extended patterns of neural dynamics. This paper demonstrates a task-specific Independent Component Analysis (ICA) procedure which identifies signals associated with coherent functional brain networks, and shows that these signals can be used for accurate and interpretable prediction. The task-specific ICA parcellations outperformed other feature generation methods in two separate datasets including parcellations based on resting-state data and anatomy. The pattern of response of the task-specific ICA parcellations to particular feature selection strategies indicates that they identify important functional networks associated with the discriminative task. We show ICA parcellations to be robust and informative with respect to non-neural artefacts affecting the fMRI series. Together, these results suggest that task-specific ICA parcellation is a powerful technique for producing predictive and informative signals from fMRI time series. The results presented in this paper also contribute evidence for the general functional validity of the parcellations produced by ICA approaches.

YNIMG Journal 2012 Journal Article

The future of FMRI connectivity

• Stephen M. Smith

“FMRI connectivity” encompasses many areas of research, including resting-state networks, biophysical modelling of task-FMRI data and bottom-up simulation of multiple individual neurons interacting with each other. In this brief paper I discuss several outstanding areas that I believe will see exciting developments in the next few years, in particular concentrating on how I think the currently separate approaches will increasingly need to take advantage of each others' respective complementarities.

YNIMG Journal 2011 Journal Article

A Bayesian model of shape and appearance for subcortical brain segmentation

• Brian Patenaude
• Stephen M. Smith
• David N. Kennedy
• Mark Jenkinson

Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0. 7 and 0. 9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.

YNIMG Journal 2011 Journal Article

Adjusting the effect of nonstationarity in cluster-based and TFCE inference

• Gholamreza Salimi-Khorshidi
• Stephen M. Smith
• Thomas E. Nichols

In nonstationary images, cluster inference depends on the local image smoothness, as clusters tend to be larger in smoother regions by chance alone. In order to correct the inference for such nonstationary, cluster sizes can be adjusted according to a local smoothness estimate. In this study, adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. We find that TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. We also find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator.

YNIMG Journal 2011 Journal Article

Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner

• Karla L. Miller
• Charlotte J. Stagg
• Gwenaëlle Douaud
• Saad Jbabdi
• Stephen M. Smith
• Timothy E.J. Behrens
• Mark Jenkinson
• Steven A. Chance

Diffusion imaging of post mortem brains has great potential both as a reference for brain specimens that undergo sectioning, and as a link between in vivo diffusion studies and “gold standard” histology/dissection. While there is a relatively mature literature on post mortem diffusion imaging of animals, human brains have proven more challenging due to their incompatibility with high-performance scanners. This study presents a method for post mortem diffusion imaging of whole, human brains using a clinical 3-Tesla scanner with a 3D segmented EPI spin-echo sequence. Results in eleven brains at 0. 94×0. 94×0. 94mm resolution are presented, and in a single brain at 0. 73×0. 73×0. 73mm resolution. Region-of-interest analysis of diffusion tensor parameters indicate that these properties are altered compared to in vivo (reduced diffusivity and anisotropy), with significant dependence on post mortem interval (time from death to fixation). Despite these alterations, diffusion tractography of several major tracts is successfully demonstrated at both resolutions. We also report novel findings of cortical anisotropy and partial volume effects.

YNIMG Journal 2011 Journal Article

Network modelling methods for FMRI

• Stephen M. Smith
• Karla L. Miller
• Gholamreza Salimi-Khorshidi
• Matthew Webster
• Christian F. Beckmann
• Thomas E. Nichols
• Joseph D. Ramsey
• Mark W. Woolrich

There is great interest in estimating brain “networks” from FMRI data. This is often attempted by identifying a set of functional “nodes” (e. g. , spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e. g. , correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e. g. , Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.

YNIMG Journal 2010 Journal Article

Crossing fibres in tract-based spatial statistics

• Saad Jbabdi
• Timothy E.J. Behrens
• Stephen M. Smith

Voxelwise analysis of white matter properties typically relies on scalar measurements derived, for example, from a tensor model fit to diffusion MRI data. These are spatially matched across subjects prior to statistical modelling. In this paper, we show why and how this can be improved through the use of directionally dependent measurements. In the case where different orientations relate to different fibre populations (e. g. , in the presence of crossing fibres), distinguishing and matching those populations of fibres across subjects are important prior to any statistical modelling. It allows one to compare measurements that are related to the same fibres across subjects. We show how this framework applies to the parameters of a crossing fibre model and discuss its implications for voxelwise analysis of the white matter.

YNIMG Journal 2009 Journal Article

Bayesian analysis of neuroimaging data in FSL

• Mark W. Woolrich
• Saad Jbabdi
• Brian Patenaude
• Michael Chappell
• Salima Makni
• Timothy Behrens
• Christian Beckmann
• Mark Jenkinson

Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.

YNIMG Journal 2009 Journal Article

Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies

• Gholamreza Salimi-Khorshidi
• Stephen M. Smith
• John R. Keltner
• Tor D. Wager
• Thomas E. Nichols

With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at σ =15 mm, KDA at ρ =25–30 mm and MKDA at ρ =15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0. 45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.

YNIMG Journal 2008 Journal Article

Enhanced brain extraction improves the accuracy of brain atrophy estimation

• Marco Battaglini
• Stephen M. Smith
• Susanna Brogi
• Nicola De Stefano

BET (Brain Extraction Tool) is a widely used computer program to automatically separate brain from non-brain structures in MR images. This procedure is used in SIENAX and SIENA, which are robust approaches to quantifying brain volume (atrophy state) and volume change (atrophy rate), respectively. Occasionally, however, BET produces imperfect results (e. g. , inclusion of non-brain structures). This is usually either ignored (if inaccuracies are small) or corrected by manual adjustment, with the disadvantages of user intervention. We describe here a new, automated option in BET. This is based on the original BET, but uses standard-space masking to remove tissue around the eyes, and further morphological operations and thresholding to refine eyeball removal and eliminate additional non-brain tissues. To assess whether the new BET procedure improves brain volume measurements, this was compared with the traditional and manual editing procedures in SIENA and SIENAX. Measures of atrophy rate and state were significantly higher with the traditional procedure than with the manual editing and new procedures. In contrast, both atrophy measures were almost identical and highly correlated when the manual editing and new procedures were used. The voxels excluded with these two procedures showed close overlap, as judged by the Dice overlap coefficient. We conclude that, in SIENA and SIENAX, the proposed BET procedure shows results matching those obtained after manual editing, thus more closely approximating the “true” brain volume. Multicentre studies monitoring brain atrophy in clinical trials may receive benefit by using this unbiased, fully automated procedure.

YNIMG Journal 2007 Journal Article

Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills

• Heidi Johansen-Berg
• Valeria Della-Maggiore
• Timothy E.J. Behrens
• Stephen M. Smith
• Tomáš Paus

Variation in brain structure may reflect variation in functional properties of specific brain systems. Structural variation may therefore reflect variation in behavioural performance. Here, we use diffusion-weighted magnetic resonance imaging to show that variation in white matter integrity in a specific region in the body of the corpus callosum is associated with variation in performance of a bimanual co-ordination task. When the callosal region showing this association is used as a seed for probabilistic tractography, inter-hemispheric pathways are generated to the supplementary motor area and caudal cingulate motor area. This provides further evidence for the role of medial wall motor areas in bimanual co-ordination and supports the idea that variation in brain structure reflects inter-individual differences in skilled performance.

YNIMG Journal 2007 Journal Article

Longitudinal and cross-sectional analysis of atrophy in Alzheimer's disease: Cross-validation of BSI, SIENA and SIENAX

• Stephen M. Smith
• Anil Rao
• Nicola De Stefano
• Mark Jenkinson
• Jonathan M. Schott
• Paul M. Matthews
• Nick C. Fox

Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimer's disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.

YNIMG Journal 2007 Journal Article

Regional brain atrophy development is related to specific aspects of clinical dysfunction in multiple sclerosis

• Bas Jasperse
• Hugo Vrenken
• Ernesto Sanz-Arigita
• Vincent de Groot
• Stephen M. Smith
• Chris H. Polman
• Frederik Barkhof

Brain atrophy in multiple sclerosis (MS) is thought to reflect irreversible tissue damage leading to persistent clinical deficit. Little is known about the rate of atrophy in specific brain regions in relation to specific clinical deficits. We determined the displacement of the brain surface between two T1-weighted MRI images obtained at baseline and after a median follow-up time of 2. 2 years for 79 recently diagnosed, mildly disabled MS patients. Voxel- and cluster-wise permutation-based statistics were used to identify brain regions in which atrophy development was significantly related to Expanded Disability Status Scale (EDSS), Timed Walk Test (TWT), Paced Auditory Serial Addition Test (PASAT) and 9-Hole Peg Test (HPT). Clusters were considered significant at a corrected cluster-wise p-value of 0. 05. Worse EDSS change-score and worse follow-up EDSS were related to atrophy development of periventricular and brainstem regions and right-sided parietal, occipital and temporal regions. Worse PASAT at follow-up was significantly related to atrophy of the ventricles. A worse TWT change-score and worse follow-up TWT were exclusively related to atrophy around the ventricles and of the brainstem. Worse HPT change-score and worse follow-up HPT of either arm were significantly related to the atrophy of widely distributed peripheral regions, as well as atrophy of periventricular and brainstem regions. Our findings suggest that decline in ambulatory function is related to atrophy of central brain regions exclusively, whereas decline in neurologically more complex tasks for coordinated hand function is related to atrophy of both central and peripheral brain regions.

YNIMG Journal 2007 Journal Article

Signal and noise characteristics of SSFP FMRI: A comparison with GRE at multiple field strengths

• Karla L. Miller
• Stephen M. Smith
• Peter Jezzard
• Graham C. Wiggins
• Christopher J. Wiggins

Recent work has proposed the use of steady-state free precession (SSFP) as an alternative to the conventional methods for obtaining functional MRI (FMRI) data. The contrast mechanism in SSFP is likely to be related to conventional FMRI signals, but the details of the signal changes may differ in important ways. Functional contrast in SSFP has been proposed to result from several different mechanisms, which are likely to contribute in varying degrees depending on the specific parameters used in the experiment. In particular, the signal dynamics are likely to differ depending on whether the sequence is configured to scan in the SSFP transition band or passband. This work describes experiments that explore the source of SSFP FMRI signal changes by comparing SSFP data to conventional gradient-recalled echo (GRE) data. Data were acquired at a range of magnetic field strengths and repetition times, for both transition band and passband methods. The signal properties of SSFP and GRE differ significantly, confirming a different source of functional contrast in SSFP. In addition, the temporal noise properties are significantly different, with important implications for SSFP FMRI sequence optimisation.

YNIMG Journal 2006 Journal Article

Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data

• Stephen M. Smith
• Mark Jenkinson
• Heidi Johansen-Berg
• Daniel Rueckert
• Thomas E. Nichols
• Clare E. Mackay
• Kate E. Watkins
• Olga Ciccarelli

There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the “mean FA skeleton”). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.

YNIMG Journal 2004 Journal Article

Advances in functional and structural MR image analysis and implementation as FSL

• Stephen M. Smith
• Mark Jenkinson
• Mark W. Woolrich
• Christian F. Beckmann
• Timothy E.J. Behrens
• Heidi Johansen-Berg
• Peter R. Bannister
• Marilena De Luca

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

YNIMG Journal 2004 Journal Article

Constrained linear basis sets for HRF modelling using Variational Bayes

• Mark W. Woolrich
• Timothy E.J. Behrens
• Stephen M. Smith

FMRI modelling requires flexible haemodynamic response function (HRF) modelling, with the HRF being allowed to vary spatially and between subjects. To achieve this flexibility, voxelwise parameterised HRFs have been proposed; however, inference on such models is very slow. An alternative approach is to use basis functions allowing inference to proceed in the more manageable General Linear Model (GLM) framework. However, a large amount of the subspace spanned by the basis functions produces nonsensical HRF shapes. In this work we propose a technique for choosing a basis set, and then the means to constrain the subspace spanned by the basis set to only include sensible HRF shapes. Penny et al. [NeuroImage (2003)] showed how Variational Bayes can be used to infer on the GLM for FMRI. Here we extend the work of Penny et al. to give inference on the GLM with constrained HRF basis functions and with spatial Markov Random Fields on the autoregressive noise parameters. Constraining the subspace spanned by the basis set allows for far superior separation of activating voxels from nonactivating voxels in FMRI data. We use spatial mixture modelling to produce final probabilities of activation and demonstrate increased sensitivity on an FMRI dataset.

YNIMG Journal 2004 Journal Article

Multilevel linear modelling for FMRI group analysis using Bayesian inference

• Mark W. Woolrich
• Timothy E.J. Behrens
• Christian F. Beckmann
• Mark Jenkinson
• Stephen M. Smith

Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects variance components. Inferring on these models is nontrivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and top-level regression parameters. Due to the typically small numbers of sessions or subjects in neuroimaging, the choice of prior is critical. To alleviate this problem, we introduce to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an information-theoretic sense. We propose two inference techniques at the top level for multilevel hierarchies (a fast approach and a slower more accurate approach). We also demonstrate that we can infer on the top level of multilevel hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a noncentral multivariate t distribution between them.

YNIMG Journal 2003 Journal Article

General multilevel linear modeling for group analysis in FMRI

• Christian F. Beckmann
• Mark Jenkinson
• Stephen M. Smith

This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established “summary statistics” approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.

YNIMG Journal 2003 Journal Article

On Bias in the Estimation of Autocorrelations for fMRI Voxel Time-Series Analysis

• Jonathan L. Marchini
• Stephen M. Smith

For fMRI time-series analysis to be statistically valid, it is important to deal correctly with temporal autocorrelation in the noise. Most of the approaches in the literature adopt a two-stage approach in which the autocorrelation structure is estimated using the residuals of an initial model fit. This estimate is then used to “prewhiten” the data and the model before the model is refit to obtain final activation parameter estimates. An assumption implicit in this scheme is that the residuals from the initial model fit represent a realization of the “true” noise process. In general this assumption will not be correct as certain components of the noise will be removed by the model fit. In this paper we examine (i) the form of the bias induced by the initial model fit, (ii) methods of correcting for the bias, and (iii) the impact of bias correction on the model parameter estimates. We find that while bias correction does result in more accurate estimates of the correlation structure, this does not translate into improved estimates of the model parameters. In fact estimates of the model parameters and their standard errors are seen to be so accurate that we conclude that bias correction is unnecessary.

YNIMG Journal 2002 Journal Article

Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis

• Stephen M. Smith
• Yongyue Zhang
• Mark Jenkinson
• Jacqueline Chen
• P.M. Matthews
• Antonio Federico
• Nicola De Stefano

Quantitative measurement of brain size, shape, and temporal change (for example, in order to estimate atrophy) is increasingly important in biomedical image analysis applications. New methods of structural analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method of longitudinal (temporal change) analysis, SIENA, was presented previously. In this paper, improvements to this method are described, and also an extension of SIENA to a new method for cross-sectional (single time point) analysis. The methods are fully automated, robust, and accurate: 0. 15% brain volume change error (longitudinal): 0. 5–1% brain volume accuracy for single-time point (cross-sectional). A particular advantage is the relative insensitivity to differences in scanning parameters. The methods provide easy manual review of their output by the automatic production of summary images which show the results of the brain extraction, registration, tissue segmentation, and final atrophy estimation.

YNIMG Journal 2001 Journal Article

Hierarchical fully bayesian spatio-temporal analysis of FMRI data

• Mark Woolrich
• Michael Brady
• Stephen M. Smith

YNIMG Journal 2001 Journal Article

Investigating the intrinsic dimensionality of FMRI data for ICA

• Christian F. Beckmann
• J.Alison Noble
• Stephen M. Smith

YNIMG Journal 2001 Journal Article

Spatio-temporal accuracy of ICA for FMRI

• Christian F. Beckmann
• J.Alison Noble
• Stephen M. Smith

YNIMG Journal 2001 Journal Article

Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data

• Mark W. Woolrich
• Brian D. Ripley
• Michael Brady
• Stephen M. Smith

In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or “coloring, ” attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or “prewhitening, ” gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 × 10−5.

YNIMG Journal 2000 Journal Article

Global optimisation for robust affine registration

• Mark Jenkinson
• Stephen M. Smith

YNIMG Journal 2000 Journal Article

Nonparametric estimation of temporal autocorrelation in FMRI

• Mark W. Woolrich
• Brian D. Ripley
• J.M. Brady
• Stephen M. Smith

YNIMG Journal 2000 Journal Article

Robust automated brain extraction

• Stephen M. Smith

AIJ Journal 1999 Journal Article

A non-rigid registration algorithm for dynamic breast MR images

• Paul M. Hayton
• Michael Brady
• Stephen M. Smith
• Niall Moore

Magnetic resonance image analysis is a promising technique for diagnosing breast cancer, particularly in women for whom X-ray mammography is ineffective. If breast motion is not corrected for, diagnostic accuracy is significantly reduced. In this paper, we analyse the kinds of motion that arise during image formation and we describe a model based non-rigid registration algorithm to estimate and correct for breast motion. Registration of breast MR images is complicated by the use of a contrast agent which results in a non-uniform increase in intensity across the image. The work described here forms part of an implemented breast MR analysis system which allows automatic detection and segmentation of regions of focal enhancement and non-rigid image registration.