Michael P. Harms

YNIMG Journal 2023 Journal Article

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

• Barnaly Rashid
• Matthew F. Glasser
• Thomas Nichols
• David Van Essen
• Meher R. Juttukonda
• Nadine A. Schwab
• Douglas N Greve
• Essa Yacoub

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity.

YNIMG Journal 2022 Journal Article

Empirical transmit field bias correction of T1w/T2w myelin maps

• Matthew F. Glasser
• Timothy S. Coalson
• Michael P. Harms
• Junqian Xu
• Graham L. Baum
• Joonas A. Autio
• Edward J. Auerbach
• Douglas N. Greve

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.

YNIMG Journal 2022 Journal Article

Reliability and stability challenges in ABCD task fMRI data

• James T. Kennedy
• Michael P. Harms
• Ozlem Korucuoglu
• Serguei V. Astafiev
• Deanna M. Barch
• Wesley K. Thompson
• James M. Bjork
• Andrey P. Anokhin

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

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

Test-retest reliability of fMRI-measured brain activity during decision making under risk

• Ozlem Korucuoglu
• Michael P. Harms
• Serguei V. Astafiev
• James T. Kennedy
• Semyon Golosheykin
• Deanna M. Barch
• Andrey P. Anokhin

Neural correlates of decision making under risk are being increasingly utilized as biomarkers of risk for substance abuse and other psychiatric disorders, treatment outcomes, and brain development. This research relies on the basic assumption that fMRI measures of decision making represent stable, trait-like individual differences. However, reliability needs to be established for each individual construct. Here we assessed long-term test-retest reliability (TRR) of regional brain activations related to decision making under risk using the Balloon Analogue Risk Taking task (BART) and identified regions with good TRRs and familial influences, an important prerequisite for the use of fMRI measures in genetic studies. A secondary goal was to examine the factors potentially affecting fMRI TRRs in one particular risk task, including the magnitude of neural activation, data analytical approaches, different methods of defining boundaries of a region, and participant motion. For the average BOLD response, reliabilities ranged across brain regions from poor to good (ICCs of 0 to 0. 8, with a mean ICC of 0. 17) and highest reliabilities were observed for parietal, occipital, and temporal regions. Among the regions that were of a priori theoretical importance due to their reported associations with decision making, the activation of left anterior insula and right caudate during the decision period showed the highest reliabilities (ICCs of 0. 54 and 0. 63, respectively). Among the regions with highest reliabilities, the right fusiform, right rostral anterior cingulate and left superior parietal regions also showed high familiality as indicated by intrapair monozygotic twin correlations (ranging from 0. 66 to 0. 69). Overall, regions identified by modeling the average BOLD response to a specific event type (rather than its modulation by a parametric regressor), regions including significantly activated vertices (compared to a whole parcel), and regions with greater magnitude of task-related activations showed greater reliabilities. Participant motion had a moderate negative effect on TRR. Regions activated during decision period rather than outcome period of risky decisions showed the greatest TRR and familiality. Regions with reliable activations can be utilized as neural markers of individual differences or endophenotypes in future clinical neuroscience and genetic studies of risk-taking.

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

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

• Donald J. Hagler
• SeanN. Hatton
• M. Daniela Cornejo
• Carolina Makowski
• Damien A. Fair
• Anthony Steven Dick
• Matthew T. Sutherland
• B.J. Casey

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

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

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

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 2016 Journal Article

ConnectomeDB—Sharing human brain connectivity data

• Michael R. Hodge
• William Horton
• Timothy Brown
• Rick Herrick
• Timothy Olsen
• Michael E. Hileman
• Michael McKay
• Kevin A. Archie

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3.

YNIMG Journal 2015 Journal Article

Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy

• Javier Arnedo
• Daniel Mamah
• David A. Baranger
• Michael P. Harms
• Deanna M. Barch
• Dragan M. Svrakic
• Gabriel A. de Erausquin
• C. Robert Cloninger

Fractional anisotropy (FA) analysis of diffusion tensor-images (DTI) has yielded inconsistent abnormalities in schizophrenia (SZ). Inconsistencies may arise from averaging heterogeneous groups of patients. Here we investigate whether SZ is a heterogeneous group of disorders distinguished by distinct patterns of FA reductions. We developed a Generalized Factorization Method (GFM) to identify biclusters (i. e. , subsets of subjects associated with a subset of particular characteristics, such as low FA in specific regions). GFM appropriately assembles a collection of unsupervised techniques with Non-negative Matrix Factorization to generate biclusters, rather than averaging across all subjects and all their characteristics. DTI tract-based spatial statistics images, which output is the locally maximal FA projected onto the group white matter skeleton, were analyzed in 47 SZ and 36 healthy subjects, identifying 8 biclusters. The mean FA of the voxels of each bicluster was significantly different from those of other SZ subjects or 36 healthy controls. The eight biclusters were organized into four more general patterns of low FA in specific regions: 1) genu of corpus callosum (GCC), 2) fornix (FX)+external capsule (EC), 3) splenium of CC (SCC)+retrolenticular limb (RLIC)+posterior limb (PLIC) of the internal capsule, and 4) anterior limb of the internal capsule. These patterns were significantly associated with particular clinical features: Pattern 1 (GCC) with bizarre behavior, pattern 2 (FX+EC) with prominent delusions, and pattern 3 (SCC+RLIC+PLIC) with negative symptoms including disorganized speech. The uncovered patterns suggest that SZ is a heterogeneous group of disorders that can be distinguished by different patterns of FA reductions associated with distinct clinical features.

YNIMG Journal 2015 Journal Article

HPA axis genetic variation, pubertal status, and sex interact to predict amygdala and hippocampus responses to negative emotional faces in school-age children

• David Pagliaccio
• Joan L. Luby
• Ryan Bogdan
• Arpana Agrawal
• Michael S. Gaffrey
• Andrew C. Belden
• Kelly N. Botteron
• Michael P. Harms

Accumulating evidence suggests a role for stress exposure, particularly during early life, and for variation in genes involved in stress response pathways in neural responsivity to emotional stimuli. Understanding how individual differences in these factors predict differences in emotional responsivity may be important for understanding both normative emotional development and for understanding the mechanisms underlying internalizing disorders, like anxiety and depression, that have often been related to increased amygdala and hippocampus responses to negatively valenced emotional stimuli. The present study examined whether stress exposure and genetic profile scores (10 single nucleotide polymorphisms within four hypothalamic–pituitary–adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predict individual differences in amygdala and hippocampus responses to fearful vs. neutral faces in school-age children (7–12year olds; N =107). Experience of more stressful and traumatic life events predicted greater left amygdala responses to negative emotional stimuli. Genetic profile scores interacted with sex and pubertal status to predict amygdala and hippocampus responses. Specifically, genetic profile scores were a stronger predictor of amygdala and hippocampus responses among pubertal vs. prepubertal children where they positively predicted responses to fearful faces among pubertal girls and positively predicted responses to neutral faces among pubertal boys. The current results suggest that genetic and environmental stress-related factors may be important in normative individual differences in responsivity to negative emotional stimuli, a potential mechanism underlying internalizing disorders. Further, sex and pubertal development may be key moderators of the effects of stress-system genetic variation on amygdala and hippocampus responsivity, potentially relating to sex differences in stress-related psychopathology.

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 2013 Journal Article

Function in the human connectome: Task-fMRI and individual differences in behavior

• Deanna M. Barch
• Gregory C. Burgess
• Michael P. Harms
• Steven E. Petersen
• Bradley L. Schlaggar
• Maurizio Corbetta
• Matthew F. Glasser
• Sandra Curtiss

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.

YNIMG Journal 2013 Journal Article

Human Connectome Project informatics: Quality control, database services, and data visualization

• Daniel S. Marcus
• Michael P. Harms
• Abraham Z. Snyder
• Mark Jenkinson
• J. Anthony Wilson
• Matthew F. Glasser
• Deanna M. Barch
• Kevin A. Archie

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

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 2009 Journal Article

Neuroanatomical asymmetry patterns in individuals with schizophrenia and their non-psychotic siblings

• Anqi Qiu
• Lei Wang
• Laurent Younes
• Michael P. Harms
• J. Tilak Ratnanather
• Michael I. Miller
• John G. Csernansky

Neuroanatomical endophenotypes may reveal insights into the processes by which genetic factors increase the risk of developing schizophrenia. To determine whether patterns of neuroanatomical asymmetries may be useful as schizophrenia-related endophenotypes, we compared patterns of structural asymmetries in patients with schizophrenia, healthy controls, and their respective siblings. The surfaces of the left and right amygdala, hippocampus, thalamus, caudate nucleus, putamen, globus pallidus, and nucleus accumbens were assessed in 40 pairs of healthy comparison controls (CON) and their siblings (CON-SIB) and 25 pairs of patients with schizophrenia (SCZ) and their siblings (SCZ-SIB) in magnetic resonance (MR) images using large deformation diffeomorphic metric mapping (LDDMM) and parallel transport techniques. The within-subject asymmetry deformation of each structure was first measured via LDDMM, and then translated to a global template via parallel transport for evaluation of the patterns of asymmetry both within and across siblings. Our results revealed that asymmetries observed in CON subjects occurred in the amygdala and the anterior segment of the hippocampus with more pronounced expansion deformation in the right-sided structures (R>L asymmetry) but not in the basal ganglia and thalamus. Disturbance in this pattern of asymmetries was observed in both SCZ and SCZ-SIB subjects. More specifically, exaggerations and reductions in the normative pattern of asymmetries were observed in the amygdala–hippocampus formation, basal ganglia, and thalamus. These altered patterns of asymmetries are present in subjects with schizophrenia and their siblings, and therefore may represent a schizophrenia-related endophenotype.

YNIMG Journal 2001 Journal Article

Sound level representations in the human auditory pathway investigated using fMRI

• Irina Sigalovsky
• Monica L. Hawley
• Michael P. Harms
• Jennifer R. Melcher