Literature DB >> 28825322

On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture.

Maxime Chamberland1,2, Gabriel Girard3,4, Michaël Bernier1, David Fortin5, Maxime Descoteaux3, Kevin Whittingstall1.   

Abstract

Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. However, the source of FC variability inferred from resting-state functional magnetic resonance imaging remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC with SC is challenging given the many potential biases associated with quantifying their respective strengths. In an attempt to circumvent this, we employed a recently proposed test-retest approach that better quantifies inter-subject variability by first correcting for intra-subject nuisance variability (i.e., head motion, physiological differences in brain state, etc.) that can artificially influence FC and SC measures. Therefore, rather than directly comparing the strength of FC with SC, we asked whether brain regions with, for example, low inter-subject FC variability also exhibited low SC variability. From this, we report two main findings: First, at the whole-brain level, SC variability was significantly lower than FC variability, indicating that an individual's structural connectome is far more similar to another relative to their functional counterpart even after correcting for noise. Second, although FC and SC variability were mutually low in some brain areas (e.g., primary somatosensory cortex) and high in others (e.g., memory and language areas), the two were not significantly correlated across all cortical and sub-cortical regions. Taken together, these results indicate that even after correcting for factors that may differently affect FC and SC, the two, nonetheless, remain largely independent of one another. Further work is needed to understand the role that direct anatomical pathways play in supporting vascular-based measures of FC and to what extent these measures are dictated by anatomical connectivity.

Entities:  

Keywords:  connectivity; diffusion MRI; inter-subject variability; resting-state fMRI; tractography

Mesh:

Year:  2017        PMID: 28825322     DOI: 10.1089/brain.2017.0539

Source DB:  PubMed          Journal:  Brain Connect        ISSN: 2158-0014


  10 in total

1.  Refined measure of functional connectomes for improved identifiability and prediction.

Authors:  Biao Cai; Gemeng Zhang; Wenxing Hu; Aiying Zhang; Pascal Zille; Yipu Zhang; Julia M Stephen; Tony W Wilson; Vince D Calhoun; Yu-Ping Wang
Journal:  Hum Brain Mapp       Date:  2019-07-29       Impact factor: 5.038

2.  The morphology of the human cerebrovascular system.

Authors:  Michaël Bernier; Stephen C Cunnane; Kevin Whittingstall
Journal:  Hum Brain Mapp       Date:  2018-09-28       Impact factor: 5.038

3.  Tensor network factorizations: Relationships between brain structural connectomes and traits.

Authors:  Zhengwu Zhang; Genevera I Allen; Hongtu Zhu; David Dunson
Journal:  Neuroimage       Date:  2019-04-25       Impact factor: 6.556

4.  Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering.

Authors:  Fan Zhang; Ye Wu; Isaiah Norton; Yogesh Rathi; Alexandra J Golby; Lauren J O'Donnell
Journal:  Hum Brain Mapp       Date:  2019-03-15       Impact factor: 5.038

Review 5.  Making Sense of Connectivity.

Authors:  Andreas Hahn; Rupert Lanzenberger; Siegfried Kasper
Journal:  Int J Neuropsychopharmacol       Date:  2019-03-01       Impact factor: 5.176

6.  Interindividual Variability of Functional Connectivity in Awake and Anesthetized Rhesus Macaque Monkeys.

Authors:  Ting Xu; Darrick Sturgeon; Julian S B Ramirez; Seán Froudist-Walsh; Daniel S Margulies; Charles E Schroeder; Damien A Fair; Michael P Milham
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2019-03-11

7.  Multimodal mapping of the face connectome.

Authors:  Yin Wang; Athanasia Metoki; David V Smith; John D Medaglia; Yinyin Zang; Susan Benear; Haroon Popal; Ying Lin; Ingrid R Olson
Journal:  Nat Hum Behav       Date:  2020-01-27

8.  Quantitative Multi-Parameter Mapping Optimized for the Clinical Routine.

Authors:  Graham Cooper; Sebastian Hirsch; Michael Scheel; Alexander U Brandt; Friedemann Paul; Carsten Finke; Philipp Boehm-Sturm; Stefan Hetzer
Journal:  Front Neurosci       Date:  2020-12-07       Impact factor: 4.677

9.  The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure.

Authors:  Esin Karahan; Luke Tait; Ruoguang Si; Ayşegül Özkan; Maciek J Szul; Kim S Graham; Andrew D Lawrence; Jiaxiang Zhang
Journal:  Commun Biol       Date:  2022-09-23

10.  Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity.

Authors:  Martin Cole; Kyle Murray; Etienne St-Onge; Benjamin Risk; Jianhui Zhong; Giovanni Schifitto; Maxime Descoteaux; Zhengwu Zhang
Journal:  Hum Brain Mapp       Date:  2021-05-06       Impact factor: 5.038

  10 in total

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