Xia Liang

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

The human brain is topologically organized into a set of spatially distributed, functionally specific networks. Of these networks, the default-mode network (DMN), executive-control network (ECN), and salience network (SN) have received the most attention recently for their vital roles in cognitive functions. However, very little is known about whether and how the interactions within and between these 3 networks would be modulated by cognitive demands. Here, we employed graph-based modularity analysis to identify the DMN, ECN, and SN during an N-back working memory (WM) task and further investigated the modulation of intra- and inter-network interactions at different cognitive loads. As the task load elevated, functional connectivity decreased within the DMN while increased within the ECN, and the SN connected more with both the DMN and ECN. Within-network connectivity of the ventral and dorsal posterior cingulate cortex was differentially modulated by cognitive load. Further, the superior parietal regions in the ECN showed increased internetwork connections at higher WM loads, and these increases correlated positively with WM task performance. Together, these findings advance our understanding of dynamic integrations of specialized brain systems in response to cognitive demands and may serve as a baseline for assessing potential disruptions of these interactions in pathological conditions.

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Conceptual processing is a crucial brain function for humans. Past research using neuropsychological and task-based functional brain-imaging paradigms indicates that widely distributed brain regions are related to conceptual processing. Here, we explore the potential contribution of intrinsic or spontaneous brain activity to conceptual processing by examining whether resting-state functional magnetic resonance imaging (rs-fMRI) signals can account for individual differences in the conceptual processing efficiencies of healthy individuals. We acquired rs-fMRI and behavioral data on object conceptual processing tasks. We found that the regional amplitude of spontaneous low-frequency fluctuations in the blood oxygen level-dependent signal in the left (posterior) middle temporal gyrus (LMTG) was highly correlated with participants' semantic processing efficiency. Furthermore, the strength of the functional connectivity between the LMTG and a series of brain regions-the left inferior frontal gyrus, bilateral anterior temporal lobe, bilateral medial temporal lobe, posterior cingulate gyrus, and ventromedial and dorsomedial prefrontal cortices-also significantly predicted conceptual behavior. The regional amplitude of low-frequency fluctuations and functionally relevant connectivity strengths of LMTG together accounted for 74% of individual variance in object conceptual performance. This semantic network, with the LMTG as its core component, largely overlaps with the regions reported in previous conceptual/semantic task-based fMRI studies. We conclude that the intrinsic or spontaneous activity of the human brain reflects the processing efficiency of the semantic system.