3D statistical neuroanatomical models from 305 MRI volumes

Evans, Alan C.; Collins, D. Louis; Mills, Susan; Brown, Elizabeth D.; Kelly, Rachel; Peters, Terry M.

doi:10.1109/nssmic.1993.373602

Cited by 1,044 publications

(913 citation statements)

References 5 publications

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“…1. The most commonly used cortical templates are based on either the anatomy of a single subject (Tzourio-Mazoyer et al, 2002) or the ensemble average over many subjects, such as the ICBM-152 (Evans et al, 1993) that defines the Montreal Neurological Institute (MNI) space. Such templates can be viewed as two extremes in GM representation: single subject templates take no account for inter-subject variability, and the group averaged templates, such as the ICBM-152, lack fine anatomical detail of the cerebral GM, which makes both categories unsuitable for our purpose.…”

Section: Accounting For Inter-subject Gm Variabilitymentioning

confidence: 99%

“…The spatially unbiased infra-tentorial (SUIT) cerebellum template (Diedrichsen, 2006) is the most accurate cerebellar template available to date. Compared to the ICBM 152 template (Evans et al, 1993) that is designed through averaging of T1 scans from 152 different subjects, SUIT is constructed from scans of 20 subjects, and at the same time, has the unique feature of being spatially unbiased; that is, the location of each of the structures is equal to its expected location in the MNI space across subjects (Diedrichsen, 2006). Therefore, we propose the use of the SUIT atlas as the basis for defining a canonical cerebellar subgraph and the DARTEL for constructing study-dependent template cerebral subgraphs.…”

Section: Accounting For Inter-subject Gm Variabilitymentioning

confidence: 99%

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Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

et al. 2015

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A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar gray matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.

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Section: Accounting For Inter-subject Gm Variabilitymentioning

confidence: 99%

Section: Accounting For Inter-subject Gm Variabilitymentioning

confidence: 99%

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

et al. 2015

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“…By the end of the year 2008 approximately 9400 fMRI studies investigating human cognition and action will have been published in English language journals (Appendix). An estimated 74% of these studies (Appendix) report the locations of statistically significant peak activations in a 3-D reference space (Evans et al, 1993;Mazziotta et al, 2001;Talairach & Tournoux, 1988;see Lancaster et al, 2007 …”

Section: One Of the Great Advantages Of Neuroimaging Research Is Thementioning

confidence: 99%

Lost in localization: The need for a universal coordinate database

Derrfuß

Mar

2009

NeuroImage

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“…All 99 sessions were realigned (motion-corrected) to the same target (the first scan of the first session of the first day) and then a mean over all 99 sessions was created. This was used to find normalization (to a T2-weighted target in MNI space [Evans et al, 1993]) parameters for all 99 sessions (using 12-parameter affine followed by 7 ϫ 8 ϫ 7 basis-function nonlinear registration). Sinc interpolation on final output was used.…”

Section: Original Experiments and Analysismentioning

confidence: 99%

Variability in fMRI: A re‐examination of inter‐session differences

Smith

Beckmann

Ramnani

et al. 2005

Human Brain Mapping

167

116

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Abstract:We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708 -734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results. Hum Brain Mapp 24:248 -257, 2005.

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3D statistical neuroanatomical models from 305 MRI volumes

Cited by 1,044 publications

References 5 publications

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

Lost in localization: The need for a universal coordinate database

Variability in fMRI: A re‐examination of inter‐session differences

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