Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation

Brain parcellations play a key role in functional connectomics. A set of standard neuro-anatomical brain atlases are in common use in most studies. In addition, data-driven parcellations computed from fMRI data using a variety of clustering algorithms have also been used. Recent studies set out to determine the best parcellation in terms of quality and reliability have remained inconclusive without a clear winner. In this work, we investigated the utility of test-retest reliability of functional connectivity as an evaluation metric for comparing parcellations. Specifically, using data from the human connectome project, we compared a data-driven parcellation and a geometric parcellation using Intraclass Correlation Coefficient (ICC). We also investigated the impact of parcellation granularity on the test-retest reliability. We observed that the ICCs for geometric parcellation are better than those of a data-driven parcellation, suggesting that the FCs computed using regular parcels in the geometric atlases are more reliable than those computed using a data-driven parcellation.

[1]  Bruce R. Rosen,et al.  fMRI at 20: Has it changed the world? , 2012, NeuroImage.

[2]  Mark W. Woolrich,et al.  Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models , 2009, NeuroImage.

[3]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[4]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[5]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[6]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[7]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[8]  J. Ford,et al.  Widespread cortical dysfunction in schizophrenia: the FBIRN imaging consortium. , 2009, Schizophrenia bulletin.

[9]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[10]  Jean-Baptiste Poline,et al.  Which fMRI clustering gives good brain parcellations? , 2014, Front. Neurosci..

[11]  Vipin Kumar,et al.  The Brain-Network Paradigm: Using Functional Imaging Data to Study How the Brain Works , 2016, Computer.

[12]  K. Amunts,et al.  Centenary of Brodmann's Map — Conception and Fate , 2022 .

[13]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[14]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[15]  Antonio Napolitano,et al.  Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review , 2015, Journal of Neuroscience Methods.

[16]  B. Biswal,et al.  Characterizing variation in the functional connectome: promise and pitfalls , 2012, Trends in Cognitive Sciences.

[17]  Stephen M. Smith,et al.  Spatially constrained hierarchical parcellation of the brain with resting-state fMRI , 2013, NeuroImage.

[18]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[19]  Daniel Rueckert,et al.  Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex , 2017, NeuroImage.

[20]  N. Filippini,et al.  Group comparison of resting-state FMRI data using multi-subject ICA and dual regression , 2009, NeuroImage.

[21]  Andreas Heinz,et al.  Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.

[22]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[23]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[24]  Simon B Eickhoff,et al.  Imaging-based parcellations of the human brain , 2018, Nature Reviews Neuroscience.

[25]  Oliver Grimm,et al.  Test–retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state , 2014, NeuroImage.

[26]  Paul M. Thompson,et al.  Emerging Global Initiatives in Neurogenetics: The Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) Consortium , 2017, Neuron.

[27]  O. Dietrich,et al.  Test–retest reproducibility of the default‐mode network in healthy individuals , 2009, Human brain mapping.

[28]  R. Adolphs,et al.  Building a Science of Individual Differences from fMRI , 2016, Trends in Cognitive Sciences.