Variational dimensions of cingulate cortex functional connectivity and implications in neuropsychiatric disorders.

Significant variations in brain functional connectivity exist in the healthy population, rendering the identification and characterization of their abnormalities in neuropsychiatric disorders difficult. Here, we proposed a new principal component analysis (PCA) approach to study variations in functional connectivity, focusing on major hubs of the salience network and default mode network, namely the anterior and posterior cingulate cortices. We analyzed the intersubject variability of human functional magnetic resonance imaging connectivity obtained from healthy, autistic, and schizophrenic subjects. Utilizing data from 1000 Functional Connectomes Project, COBRE, and ABIDE 1 database, we characterized the normal variations of the cingulate cortices with respect to top PCA dimensions. We showed that functional connectivity variations of the 2 cingulate cortices are constrained, in a parallel manner, by competing or cooperating interactions with different sensorimotor, associative, and limbic networks. In schizophrenic and autistic subjects, diffuse and subtle network changes along the same dimensions were found, which suggest significant behavioral implications of the variational dimensions. Furthermore, we showed that individual dynamic functional connectivity tends to fluctuate along the principal components of connectivity variations across individuals. Our results demonstrate the strength of this new approach in addressing the intrinsic variations of network connectivity in human brain and identifying their subtle changes in neuropsychiatric disorders.

Edge-based network analysis reveals frequency-specific network dynamics in aberrant anxiogenic processing in rats.

Uncovering interactions between edges of brain networks can reveal the organizational principle of the networks and also their dysregulations underlying aberrant behaviours such as in neuropsychiatric diseases. In this study, we looked into the applicability of edge-based network analysis in uncovering possible network mechanisms of aberrant anxiogenic processing. Utilizing a rat model of prodromal Parkinson's disease we examined how a dorsomedial striatum-tied associative network (DSAN) may mediate context-based anxiogenic behaviour. Following dopamine depletion in the dorsomedial striatum, an exaggerated bottom-up signalling (posterior parietal-hippocampal-retrosplenial to anterior prefrontal-cingulate-amygdala regions) and gradient specific to the theta frequency in this network was observed. This change was accompanied by increased anxiety behaviour of the animals. By employing an edge-based approach in correlating informational flow (phase transfer entropy) with functional connectivity of all edges of this network, we further explore how the abnormal bottom-up signalling might be explained by alterations to the informational flow-connectivity motifs in the network. Our results demonstrate usage of edge-based network analysis in revealing concurrent informational processing and functional organization dynamics across multiple pathways in a brain network. This approach in unveiling network abnormalities and its impact on behavioural outcomes would be useful in probing the network basis of neuropsychiatric conditions.