Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere

Abstract

A reliable set of functional brain networks is found in healthy people and thought to underlie our cognition, emotion, and behavior. Here, we investigated these networks by quantifying intrinsic functional connectivity in six individuals who had undergone surgical removal of one hemisphere. Hemispherectomy subjects and healthy controls were scanned with identical parameters on the same scanner and compared to a large normative sample (n = 1,482). Surprisingly, hemispherectomy subjects and controls all showed strong and equivalent intrahemispheric connectivity between brain regions typically assigned to the same functional network. Connectivity between parts of different networks, however, was markedly increased for almost all hemispherectomy participants and across all networks. These results support the hypothesis of a shared set of functional networks that underlie cognition and suggest that between-network interactions may characterize functional reorganization in hemispherectomy.

Keywords: brain networks; fMRI; functional connectivity; hemispherectomy; plasticity.

Figure 1.. Hemispherectomy Brain Anatomy

Six adult participants with left (n = 2, HS2 and HS3) or right (n = 4, HS1, HS4, HS5, and HS6) hemispherectomy. Axial slices were taken minimally above the anterior/posterior commissure line. L, left; R, right.

Figure 2.. Parcellation Scheme

Displayed as example on the left inflated hemisphere (fsaverage6 template) are (A, upper row) seven color-coded resting-state-derived connectivity networks (Yeo et al., 2011) (see color to network legend on the right) and (B, lower row) 200 outlined parcels (from the 400 whole-brain parcellation; Schaefer et al., 2018).

Figure 3.. Connectivity Control Analyses

(A) Homogeneity of vertex to parcel time series responses within the originally assigned parcel (within), to all parcels inside the parcel’s network (inside), and to all parcels outside the network (outside) for the GSP (gray), CNT (blue) and HS (red) participants. Strengths of correlation (Z) for each comparison in HS were within the normal range of the CNT sample (see Table S1 for statistics). Each data point represents the average correlation for all vertices that comprise a given parcel (200 data points per subject/hemisphere). Boxplots represent distribution of the GSP data. (B) Differences in strength of correlations between homogeneity comparisons (inside versus outside network, within parcel versus inside network, and within parcel versus outside network) were positive for all HS and control participants. Data points represent individual differences between averaged homogeneity comparisons per hemisphere. Boxplots represent distribution of the GSP data. (C) Functional connectome fingerprinting per hemisphere. All but one hemisphere in each of the CNT and HS samples (CNT4L and HS5) showed successful connectome fingerprinting; i.e., the functional connectome was most similar across two runs of the same participant (large dots) than in comparison with any other participant (small dots). Boxplots represent distribution of the GSP data. CNT, Caltech control group; GSP, Brain Genomics Superstruct control group; HS, hemispherectomy; L, left hemisphere; R, right hemisphere; Z, Fisher’s r to z transformed strength of correlation coefficient.

Figure 4.. Functional Connectivity

(A) Between- and within-network functional connectivity averaged across networks per group (GSP, CNT, and HS) (seven data points, one for each network, per participant). CNT and HS showed similar within-network connectivity as compared to the large GSP sample, while overall between-network connectivity was notably stronger for HS participants. (B) High between-network connectivity was evident across all networks and in all but one (HS3) hemispherectomy participant. Boxplots represent distribution of the GSP data. FC, functional connectivity; Sal/VAttn, salience and ventral attention network; DorsAttn, dorsal attention network; SomMot, somatosensory/motor network; z, Fisher’s r to z transformed correlation coefficient. See also Tables S4, S5, and S9.

Figure 5.. Functional Connectivity Correlation Matrices across Networks

Upper row: averaged connectivity between networks (diagonal = within, off diagonal = between) for the CNT control group (left) showed typical relations between known functional networks (e.g., anticorrelation of default and attention networks). Comparable yet overall stronger connectivity was found across the HS sample (middle). Differences between CNT and HS connectivity did not seem to be pronounced in connections that show greater variance in controls (right). Middle and lower row: connectivity matrix per hemispherectomy participant revealed individual characteristics; between-network connectivity patterns of HS2, HS3, and HS5 were most comparable to controls, while HS4 showed weaker anticorrelations between default and attention networks. HS1 and HS6 showed the strongest connectivity between almost all networks. Sal/VAttn, salience and ventral attention network; DorsAttn, dorsal attention network; SomMot, somatosensory/motor network; V, variance; Z, Fisher’s r-to-z transformed strength of correlation. See also Table S6.

Figure 6.. Network Analyses of Functional Integration and Segregation Metrics

Each data point represents data from one participant’s hemisphere. (A) Global efficiency. All hemispherectomy participants showed relative efficient global information processing. (B) Modularity. Functional segregation of networks was very typical in hemispherectomy participants as compared to both control samples. Sal/VAttn, salience and ventral attention network; DorsAttn, dorsal attention network; SomMot, somatosensory/motor network. Boxplots represent distribution of the GSP data. See also Tables S7 and S8.