TY - JOUR
T1 - A cross-modal, cross-species comparison of connectivity measures in the primate brain
AU - Reid, Andrew T.
AU - Lewis, John
AU - Bezgin, Gleb
AU - Khundrakpam, Budhachandra
AU - Eickhoff, Simon B.
AU - McIntosh, Anthony R.
AU - Bellec, Pierre
AU - Evans, Alan C.
N1 - Funding Information:
This research was enabled in part by the support provided by Calcul Quebec ( http://www.calculquebec.ca ) and Compute Canada ( http://www.computecanada.ca ).
Funding Information:
ATR was supported by a Canadian Institutes of Health Research ( CIHR ) Fellowship. SBE and ATR are supported by Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1 , LA 3071/3-1 ; EI 816/6-1 ), the National Institute of Mental Health ( R01-MH074457 ), the Helmholtz-Portfolio Project on “Supercomputing and Modeling for the Human Brain” and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2016/1/15
Y1 - 2016/1/15
N2 - 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.
AB - 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.
KW - CoCoMac
KW - Connectomics
KW - Cortical thickness
KW - Diffusion-weighted MRI
KW - Hierarchical clustering
KW - K-Means clustering
KW - Resting state functional MRI
KW - Structural covariance
KW - Tract tracing
UR - http://www.scopus.com/inward/record.url?scp=84945545942&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2015.10.057
DO - 10.1016/j.neuroimage.2015.10.057
M3 - Article
C2 - 26515902
AN - SCOPUS:84945545942
SN - 1053-8119
VL - 125
SP - 311
EP - 331
JO - Neuroimage
JF - Neuroimage
ER -