Thomas Nichols

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 2019 Journal Article

Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power

• Tamás Spisák
• Zsófia Spisák
• Matthias Zunhammer
• Ulrike Bingel
• Stephen Smith
• Thomas Nichols
• Tamás Kincses

The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test. Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting. The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data. The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster “leaking” than TFCE and, in many realistic cases, further improves its sensitivity. Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow. Implementation of pTFCE is available at https: //spisakt. github. io/pTFCE.

YNICL Journal 2015 Journal Article

Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

• Kerstin Bendfeldt
• Renata Smieskova
• Nikolaos Koutsouleris
• Stefan Klöppel
• André Schmidt
• Anna Walter
• Fabienne Harrisberger
• Johannes Wrege

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

YNIMG Journal 2015 Journal Article

FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data

• Meiyan Huang
• Thomas Nichols
• Chao Huang
• Yang Yu
• Zhaohua Lu
• Rebecca C. Knickmeyer
• Qianjin Feng
• Hongtu Zhu

More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC >12 million known variants) associations with signals at millions of locations (NV ~106) in the brain from thousands of subjects (n ~103). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC ) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC + NV )n 2) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193, 275voxels in RAVENS maps, and 501, 584 SNPs, and the total processing time was 203, 645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.

YNIMG Journal 2014 Journal Article

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease

• Arash Nazeri
• Habib Ganjgahi
• Tina Roostaei
• Thomas Nichols
• Mojtaba Zarei

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1year follow-up. Group comparisons at baseline and changes over 1year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

YNIMG Journal 2009 Journal Article

Anatomically-distinct genetic associations of APOE ɛ4 allele load with regional cortical atrophy in Alzheimer's disease

• Nicola Filippini
• Anil Rao
• Sally Wetten
• Rachel A. Gibson
• Michael Borrie
• Danilo Guzman
• Andrew Kertesz
• Inge Loy-English

APOE ɛ4 is the best-established genetic risk factor for sporadic Alzheimer's disease (AD). However, while homozygotes show greater disease susceptibility and earlier age of onset than heterozygotes, they may not show faster rates of clinical progression. We hypothesize that there are differential APOE ɛ4 allele-load dependent influences on neuropathology across the brain. Our aim was to define the relationship between APOE ɛ4 allele load and regionally-specific brain cortical atrophy in Alzheimer's Disease (AD). For this reason voxel-based morphometry (VBM) was performed using T1-weighted MR images from 83 AD patients, contrasting regional cortical grey matter by APOE ɛ4 load according to either dominant or genotypic models. Patients fulfilled NINCDS-ADRDA criteria and were genotyped for APOE ɛ4 (15 ɛ4/ɛ4, 39 ɛ4/− and 29−/−). We observed that grey matter volume (GMV) decreased additively with increasing allele load in the medial (MTL) and anterior temporal lobes bilaterally. By contrast, a 2 degree-of-freedom genotypic model suggested a dominant effect of the APOE ɛ4 allele in the left temporal lobe. Brain regions showing a significant APOE ɛ4 allele load effect on GMV in AD included only some of those typically described as having greatest amyloid plaque deposition and atrophy. Temporal regions appeared to show a dominant effect of APOE ɛ4 allele load instead of the additive effect previously strongly associated with age of onset. Regional variations with allele load may be related to different mechanisms for effects of APOE ɛ4 load on susceptibility and disease progression.

YNIMG Journal 2009 Journal Article

Simple group fMRI modeling and inference

• Jeanette A. Mumford
• Thomas Nichols

While many advanced mixed-effects models have been proposed and are used in fMRI, the simplest, ordinary least squares (OLS), is still the one that is most widely used. A survey of 90 papers found that 92% of group fMRI analyses used OLS. Despite the widespread use, this simple approach has never been thoroughly justified and evaluated; for example, the typical reference for the method is a conference abstract, (Holmes, A. , Friston, K. , 1998. Generalisability, random effects & population inference. NeuroImage 7 (4 (2/3)), S754, proceedings of Fourth International Conference on Functional Mapping of the Human Brain, June 7–12, 1998, Montreal, Canada.), which has been referenced over 400 times. In this work we fully derive the simplified method in a general setting and carefully identify the homogeneity assumptions it is based on. We examine the specificity (Type I error rate) of the OLS method under heterogeneity in the one-sample case and find that the OLS method is valid, with only slight conservativeness. Surprisingly, a Satterthwaite approximation for effective degrees of freedom only makes the method more conservative, instead of more accurate. While other authors have highlighted the inferior power of the OLS method relative to optimal mixed-effects methods under heterogeneity, we revisit these results and find the power differences very modest. While statistical methods that make the best use of the data are always to be preferred, software or other practical concerns may require the use of the simple OLS group modeling. In such cases, we find that group mean inferences will be valid under the null hypothesis and will have nearly optimal sensitivity under the alternative.

YNIMG Journal 2006 Journal Article

Twelfth Annual Meeting of the Organization for Human Brain Mapping

• Maurizio Corbett
• Thomas Nichols
• Pietro Pietrini

YNIMG Journal 2005 Journal Article

Valid conjunction inference with the minimum statistic

• Thomas Nichols
• Matthew Brett
• Jesper Andersson
• Tor Wager
• Jean-Baptiste Poline

In logic a conjunction is defined as an AND between truth statements. In neuroimaging, investigators may look for brain areas activated by task A AND by task B, or a conjunction of tasks (Price, C. J. , Friston, K. J. , 1997. Cognitive conjunction: a new approach to brain activation experiments. NeuroImage 5, 261–270). Friston et al. (Friston, K. , Holmes, A. , Price, C. , Büchel, C. , Worsley, K. , 1999. Multisubject fMRI studies and conjunction analyses. NeuroImage 10, 85–396) introduced a minimum statistic test for conjunction. We refer to this method as the minimum statistic compared to the global null (MS/GN). The MS/GN is implemented in SPM2 and SPM99 software, and has been widely used as a test of conjunction. However, we assert that it does not have the correct null hypothesis for a test of logical AND, and further, this has led to confusion in the neuroimaging community. In this paper, we define a conjunction and explain the problem with the MS/GN test as a conjunction method. We present a survey of recent practice in neuroimaging which reveals that the MS/GN test is very often misinterpreted as evidence of a logical AND. We show that a correct test for a logical AND requires that all the comparisons in the conjunction are individually significant. This result holds even if the comparisons are not independent. We suggest that the revised test proposed here is the appropriate means for conjunction inference in neuroimaging.

YNIMG Journal 2002 Journal Article

Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate

• Christopher R. Genovese
• Nicole A. Lazar
• Thomas Nichols

Findingobjective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e. g. , Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.

YNIMG Journal 2000 Journal Article

Assessment of medication effects in neuroimaging using quadratic models

• Christopher Gottschalk
• Thomas Nichols
• Alan Feingold
• Lisa Mauzi
• Thomas Kosten

YNIMG Journal 1996 Journal Article

Anterior cingulate gyrus dysfunction and attentional pathology in schizophrenia

• Cameron Carter
• Mark Mintun
• Jonathan Cohen
• Thomas Nichols
• Marybeth Wiseman