Literature DB >> 26515902

A cross-modal, cross-species comparison of connectivity measures in the primate brain.

Andrew T Reid1, John Lewis2, Gleb Bezgin3, Budhachandra Khundrakpam4, Simon B Eickhoff5, Anthony R McIntosh6, Pierre Bellec7, Alan C Evans8.   

Abstract

In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CoCoMac; Connectomics; Cortical thickness; Diffusion-weighted MRI; Hierarchical clustering; Resting state functional MRI; Structural covariance; Tract tracing; k-Means clustering

Mesh:

Year:  2015        PMID: 26515902     DOI: 10.1016/j.neuroimage.2015.10.057

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  32 in total

1.  Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains".

Authors:  Gleb Bezgin; Ana Solodkin; Rembrandt Bakker; Petra Ritter; Anthony R McIntosh
Journal:  Hum Brain Mapp       Date:  2017-01-05       Impact factor: 5.038

2.  Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia.

Authors:  Wei Zhao; Shuixia Guo; Zeqiang Linli; Albert C Yang; Ching-Po Lin; Shih-Jen Tsai
Journal:  Schizophr Bull       Date:  2020-02-26       Impact factor: 9.306

3.  Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis.

Authors:  Carolina Makowski; John D Lewis; Claude Lepage; Ashok K Malla; Ridha Joober; Martin Lepage; Alan C Evans
Journal:  Cereb Cortex       Date:  2019-12-17       Impact factor: 5.357

4.  Toward neuroimaging-based network biomarkers for transient ischemic attack.

Authors:  Yating Lv; Xiujie Han; Yulin Song; Yu Han; Chengshu Zhou; Dan Zhou; Fuding Zhang; Qiming Xue; Jinling Liu; Lijuan Zhao; Cairong Zhang; Lingyu Li; Jinhui Wang
Journal:  Hum Brain Mapp       Date:  2019-04-19       Impact factor: 5.038

5.  Relationship between structural and functional connectivity change across the adult lifespan: A longitudinal investigation.

Authors:  Anders M Fjell; Markus H Sneve; Håkon Grydeland; Andreas B Storsve; Inge K Amlien; Anastasia Yendiki; Kristine B Walhovd
Journal:  Hum Brain Mapp       Date:  2016-09-22       Impact factor: 5.038

6.  Resting state functional connectivity of the amygdala and problem drinking in non-dependent alcohol drinkers.

Authors:  Sien Hu; Jaime S Ide; Herta H Chao; Simon Zhornitsky; Kimberly A Fischer; Wuyi Wang; Sheng Zhang; Chiang-Shan R Li
Journal:  Drug Alcohol Depend       Date:  2018-02-07       Impact factor: 4.492

Review 7.  Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective.

Authors:  Lisa E Mash; Maya A Reiter; Annika C Linke; Jeanne Townsend; Ralph-Axel Müller
Journal:  Dev Neurobiol       Date:  2017-12-27       Impact factor: 3.964

8.  A seed-based cross-modal comparison of brain connectivity measures.

Authors:  Andrew T Reid; Felix Hoffstaedter; Gaolang Gong; Angela R Laird; Peter Fox; Alan C Evans; Katrin Amunts; Simon B Eickhoff
Journal:  Brain Struct Funct       Date:  2016-07-02       Impact factor: 3.270

9.  Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey.

Authors:  Chad J Donahue; Stamatios N Sotiropoulos; Saad Jbabdi; Moises Hernandez-Fernandez; Timothy E Behrens; Tim B Dyrby; Timothy Coalson; Henry Kennedy; Kenneth Knoblauch; David C Van Essen; Matthew F Glasser
Journal:  J Neurosci       Date:  2016-06-22       Impact factor: 6.167

10.  Aberrant multimodal brain networks in patients with anti-NMDA receptor encephalitis.

Authors:  Jinhui Wang; Yunyun Duan; Tian Zhang; Jing Huang; Zhuoqiong Ren; Jing Ye; Ningkai Wang; Yinzhi Li; Xiaoya Chen; Peiyi Gao; Kuncheng Li; Yaou Liu
Journal:  CNS Neurosci Ther       Date:  2021-03-13       Impact factor: 5.243

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