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. 2020 Jun;10(6):e01647.
doi: 10.1002/brb3.1647. Epub 2020 Apr 30.

Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset

Ying-Chia Lin  1   2 , Steven H Baete  1   2 , Xiuyuan Wang  1   2 , Fernando E Boada  1   2
Affiliations

Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset

Ying-Chia Lin et al. Brain Behav. 2020 Jun.

Abstract

Introduction: Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets.

Materials and methods: In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts.

Results: Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077).

Conclusions: At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.

Keywords: brain behavior; brain networks; connectivity; connectome fingerprint; functional connectivity; functional structural connectome; individual difference; neuroplasticity; structure connectivity.

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Conflict of interest statement

No conflict of interest has been declared by the author(s).

Figures

Figure 1
Figure 1
Behavioral traits are correlated with connectivity measures using two methods: FC using FC‐network parcellation (a) and SC using SC‐bundle parcellation (b). These areas are colored according to the cognitive network or majority white matter bundles that they are most connected to. FC‐network parcellation: medial frontal network (MFN), frontal‐parietal network (FPN), default mode network, subcortical network (SUB), somatosensory motor network (SMN), ventral attention network (VAN), visual network (VN), and dorsal attention network (DAN). SC‐bundle parcellation: corpus callosum (CC), cingulum (Cingulum), optic radiation (OR), fornix (Fx)+ posterior (CP)+ anterior commissure (CA), middle + superior+inferior cerebellar peduncle (MCP + SCP+ICP), cortical‐spinal tract + frontal+parietal‐occipital pontine tract (CST + FPT+POPT), and uncinated + superior longitudinal + inferior longitudinal fasciculus (UF + SLF+ILF)
Figure 2
Figure 2
The LOOCV identification of the language test results based on the negative correlations of the language test results with functional (a) and structural (b) parcellations
Figure 3
Figure 3
The connectome fingerprints calculated using (a) FC and (b) SC parcellations showing positive correlations (in red) and negative correlations (in blue) with individual behavior traits in LOOCV model fitting. FC networks and SC bundles (horizontal axis) are related to behavioral traits (vertical axis) and highlight highly significant traits, p < .05 with FDR correction (Tables S2‐S3)
Figure 4
Figure 4
Language‐specific SC ROI used in this study (a), (b) streamlines connecting the language subregions in (a) in a healthy individual. The streamlines are filtered from one million generated streamlines and displayed with (left) and without (right) the subregions. The analysis focuses on eight subnetworks of SC language network. The complete language network, connecting 52 parcels, consists in total of (52x52)/2 connections assigned to 8 subnetworks
Figure 5
Figure 5
The LOOCV modeling of a language measurement (Pic Vocab) calculated using (a) FC based on the positive (red) and negative (blue) correlations of the language test and related to the most significant language subregions. (b) The connectome fingerprints calculated using FC showing positive correlations (in red) and negative correlations (in blue) with individual behavior. Language subnetworks (horizontal axis) are related to behavioral traits (vertical axis), and we highlight highly significant traits, p < .05 with FDR correction (Table S4). The FC analyses focus on eight language subnetworks (superior temporal gyrus (STG), superior temporal sulcus (STS), middle temporal gyrus (MTG), superior temporal pole (STP), IFG triangularis (IFGt), IFG orbitalis (IFGo), middle, frontal gyrus (MFG), angular gyrus (AG))
Figure 6
Figure 6
LOOCV modeling of a language measurement (Pic Vocab) calculated using (a) SC based on the positive correlations of the language test results with QA (quantitative anisotropy), ML (mean streamline length), and NS (normalized number of streamlines). Significant language subnetworks are indicated (lower). (b) The connectome fingerprints calculated using SC showing positive and negative correlations with individual behavior traits. Subnetworks (horizontal axis) are related to behavioral traits (vertical axis), and we highlight highly significant traits, p < .05 with FDR correction (Tables S5‐S7)
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