Brain hothubs and dark functional networks: correlation analysis between amplitude and connectivity for Broca's aphasia

Abstract

Source localization and functional brain network modeling are methods of identifying critical regions during cognitive tasks. The first activity estimates the relative differences of the signal amplitudes in regions of interest (ROI) and the second activity measures the statistical dependence among signal fluctuations. We hypothesized that the source amplitude-functional connectivity relationship decouples or reverses in persons having brain impairments. Five Broca's aphasics with five matched cognitively healthy controls underwent overt picture-naming magnetoencephalography scans. The gamma-band (30-45 Hz) phase-locking values were calculated as connections among the ROIs. We calculated the partial correlation coefficients between the amplitudes and network measures and detected four node types, including hothubs with high amplitude and high connectivity, coldhubs with high connectivity but lower amplitude, non-hub hotspots, and non-hub coldspots. The results indicate that the high-amplitude regions are not necessarily highly connected hubs. Furthermore, the Broca aphasics utilized different hothub sets for the naming task. Both groups had dark functional networks composed of coldhubs. Thus, source amplitude-functional connectivity relationships could help reveal functional reorganizations in patients. The amplitude-connectivity combination provides a new perspective for pathological studies of the brain's dark functional networks.

Keywords: Aphasia; Functional brain networks; Functional connectivity; Magnetoencephalography; Network hubs; Phase-locking value; Source amplitude.

Figure 1. Experiment paradigm.

Figure 2. Data processing workflow.

The dashed curved arrow denotes that the final reported m776 and m388 islands are selected from a series of m values by interpreting the Figs. S1 and S2. MEG, magnetoencephalography; MRI, magnetic resonance imaging; DEG, weighted degree; BET, weighted betweenness; TRA, weighted transitivity; KVL, k-value of coreness; LAP, Laplacian centrality; EIG, eigenvector centrality; PART, participation coefficient; GATE, gateway coefficient; WMDZ, within module degree z-score.

Figure 3. Relations between activations (picoampere) and module-independent graph measures in m776-islands.

Activations are the estimated electric densities (in a physical unit of picoampere) in the source space. The partial Pearson coefficients between the activations and graph measures are calculated for m776-islands at different stages. The significant coefficients (p < 0.05) are marked with asterisks. The 95% confidence intervals having significant coefficients are marked by transparent colored bars. DEG, weighted degree; BET, weighted betweenness; TRA, weighted transitivity; KVL, k-value of coreness; LAP, Laplacian centrality; EIG, eigenvector centrality; t1: 0–119 ms, visual feature extraction; t2: 120–150 ms, object recognition; t3: 151–190 ms, memory access; t4: 191–320 ms, semantic processing; t5: 321–480 ms, phonological encoding; t6: 481–535 ms, articulation. A positive coefficient marked with an asterisk denotes that strongly activated brain regions are more likely to be highly connected hubs. A negative coefficient marked with an asterisk suggests that highly connected hubs are more likely to be with weak intensities of activation. The separation of the confidence intervals having opposite values of coefficients infers that the two groups have significantly different amplitude–connectivity relationships. One significant correlation having another nonsignificant correlation also implies that there are interconditional differences of amplitude–connectivity relationships.

Figure 4. Relations between activations (picoampere) and module-dependent graph measures in m388-islands.

Activations are the estimated electric densities (in a physical unit of picoampere) in the source space. The partial Pearson coefficients between the activations and graph measures are calculated for m388-islands at different stages. The significant coefficients (p < 0.05) are marked with asterisks. The 95% confidence intervals having significant coefficients are marked by transparent colored bars. PART: participation coefficient; GATE: gateway coefficient; WMDZ: within module degree z-score; t1: 0–119 ms, visual feature extraction; t2: 120–150 ms, object recognition; t3: 151–190 ms, memory access; t4: 191–320 ms, semantic processing; t5: 321–480 ms, phonological encoding; t6: 481–535 ms, articulation. A positive coefficient marked with an asterisk denotes that strongly activated brain regions are more likely to be highly connected hubs. A negative coefficient marked with an asterisk suggests that highly connected hubs are more likely to be with weak intensities of activation. The separation of the confidence intervals with opposite values of coefficients infers that the two groups have significantly different amplitude–connectivity relationships. One significant correlation with another nonsignificant correlation also implies that there are interconditional differences of amplitude–connectivity relationships.

Figure 5. Hothubs in m776-islands.

^a Significantly lower z-scores for amplitudes of Broca group than those of the control group (permutation t-test, 1,000 randomizations). ^b Significantly higher z-scores for amplitudes of control group than those of Broca group (permutation t-test, 1,000 randomizations). (A) t1(0–119 ms, visual feature extraction): InfOcciGyr_VenPst_R (p = 0.016), SupPariGyr_Pst_R (p = 0.008), PreCune_Sup_L (p = 0.02). (B) t2 (120–150 ms, object recognition): PreCune_Sup_R (p = 0.046), MidOcciGyr_DsoAnt_L (p = 0.004). (C) t3 (151–190 ms, memory access): SupPariGyr_Ant_R (p = 0.016), PreCune_Sup_L (p = 0.024), PreCune_Sup_R (p = 0.017), SupFrtGyr_Ant_L (p = 0.039). (D) t4 (191–320 ms, semantic processing). (E) t5 (321–480 ms, phonological encoding). (F) t6 (481–535 ms, articulation). Colors for functional modules: Pink: attention; orange: auditory; yellow: cinguloopercular; blue: frontoparietal; green: medial default mode; grey: motor and somatosensory; lilac: ventral temporal association; white: visual.

Figure 6. Sample amplitude and linking strength distributions in t4.

The t4 is the semantic processing window of 191–320 ms. There are four node types: hothubs (red), coldhubs (green), non-hub hotspots (yellow), and non-hub coldspots (blue). Hotspots are identified based on their amplitudes that exceeded the mean-plus-one standard deviation. Hubs are identified based on their eigenvector centralities as defined in Pajek, with the same number of hotspots at each stage. The top-weighted 100 edges are plotted, and the edges are colored according to their terminals. For the amplitude weighted layouts, node areas are in proportion to the z-scores of their amplitudes, including top-down view (A) and left lateral view (C) of the Broca group, and top-down view (E) and left lateral view (G) of the control group. For the linking strength weighted layouts, node areas are in proportion to the total linking strengths (i.e., weighted degrees), including top-down view (B) and left lateral view (D) of the Broca group, and top-down view (F) and left lateral view (H) of the control group. The amplitude weighted layouts are remarkably different from the linking strength weighted layouts, suggesting that it is necessary to reconsider the role of coldhubs in the functioning brain. Arrow ^a Left SupFrtGyr_Pst (left superior frontal gyrus posterior). Arrow ^b Left MidTempGyr_Mid (left middle temporal gyrus middle).