Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of a distributed nonlinear process (dynamical systems theory). The former predicts that tasks that recruit similar brain areas should have an equivalent degree of similarity in their connectivity. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views by comparing activation and connectivity patterns from a large sample of healthy subjects (N=242) that performed two executive control tasks, color-word Stroop task and Multi-Source Interference Task (MSIT), known to recruit similar brain areas. Using a measure of instantaneous connectivity based on edge time series as outcome variables, we estimated task-related network profiles as connectivity changes between incongruent and congruent information conditions. The degree of similarity of such profiles at the group level between both tasks was substantially smaller than their overlapping activation responses. A similar finding was observed at the subject level and when employing a different method for defining task-related connectivity. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be understood at both node and edge (connectivity) levels.

Examining the Neurobiology of Non-Suicidal Self-Injury in Children and Adolescents: The Role of Reward Responsivity

Although prior work has shown heightened response to negative outcomes and reduced response to positive outcomes in youth with a history of non-suicidal self-injury (NSSI), little is known about the neural processes underlying these responses. Thus, this study examined associations between NSSI engagement and functional activation in specific regions of interest (ROIs) and whole-brain connectivity between striatal, frontal, and limbic region seeds during monetary and social reward tasks. To test for specificity of the influence of NSSI, analyses were conducted with and without depressive symptoms as a covariate. We found that NSSI was associated with decreased activation following monetary gains in all ROIs, even after controlling for depressive symptoms. Exploratory connectivity analyses found that NSSI was associated with differential connectivity between regions including the DS, vmPFC, insula, and parietal operculum cortex when controlling for depressive symptoms. Disrupted connectivity between these regions could suggest altered inhibitory control of emotions and pain processing in individuals with NSSI. Findings suggest dysfunctional reward processes in youth with NSSI, even very early in the course of the behavior.

Auditory cortical micro-networks show differential connectivity during voice and speech processing in humans

The temporal voice areas (TVAs) in bilateral auditory cortex (AC) appear specialized for voice processing. Previous research assumed a uniform functional profile for the TVAs which are broadly spread along the bilateral AC. Alternatively, the TVAs might comprise separate AC nodes controlling differential neural functions for voice and speech decoding, organized as local micro-circuits. To investigate micro-circuits, we modeled the directional connectivity between TVA nodes during voice processing in humans while acquiring brain activity using neuroimaging. Results show several bilateral AC nodes for general voice decoding (speech and non-speech voices) and for speech decoding in particular. Furthermore, non-hierarchical and differential bilateral AC networks manifest distinct excitatory and inhibitory pathways for voice and speech processing. Finally, while voice and speech processing seem to have distinctive but integrated neural circuits in the left AC, the right AC reveals disintegrated neural circuits for both sounds. Altogether, we demonstrate a functional heterogeneity in the TVAs for voice decoding based on local micro-circuits.

The fronto-parietal network connects more strongly to central than peripheral V1

ABSTRACTCentral and peripheral vision are important for distinct aspects of everyday life. We use central vision to read and peripheral vision to get the gist of a scene. To understand how these differences are reflected in connectivity between V1 and higher-order cognitive areas, we examined the differential connectivity of V1 that represent central and peripheral vision. We used diffusion-weighted-imaging and resting-state blood-oxygen-level-dependent data to examine structural and functional connectivity. The present results demonstrate strong evidence that centrally-representing portions of V1 are more strongly functionally and structurally connected to the fronto-parietal network than are peripherally representing portions of V1. This suggests that these patterns of connections between central V1 and the fronto-parietal network are direct and support attention-demanding visual tasks. Overall, our findings contribute to understanding how the human brain processes visual information and forms a baseline for any modifications in processing that might occur with training or experience.

On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

The intrinsic neonatal hippocampal network: rsfMRI findings

Many adults cannot voluntarily recall memories before the ages of 3-5, a phenomenon referred to as “infantile amnesia” The development of the hippocampal network likely plays a significant part in the emergence of the ability to form long-lasting memories. In adults, the hippocampus has specialized and privileged connections with certain cortical networks, which presumably facilitate its involvement in memory encoding, consolidation, and retrieval. Is the hippocampus already specialized in these cortical connections at birth? And are the topographical principles of connectivity (e.g. long-axis specialization) present at birth? We analyzed resting-state hippocampal connectivity in neonates scanned within one week of birth (Developmental Human Connectome Project) and compared them to adults (Human Connectome Project). We explored the connections of the whole hippocampus and its long-axis specialization to seven canonical cortical networks. We found that the neonatal hippocampal networks show clear immaturity at birth: adults showed hippocampal connectivity that was unique for each cortical network, whereas neonates showed no differentiation in hippocampal connectivity across these networks. Further, neonates lacked long-axis specialization (i.e., along anterior-posterior axis) of the hippocampus in its differential connectivity patterns to the cortical networks. This immaturity in connectivity may contribute to immaturity in memory formation in the first years of life.“New and Noteworthy”While animal data, and anatomical and behavioral human data from young children suggest that the hippocampus is immature at birth, to date, there are no direct assessments of human hippocampal functional connectivity (FC) very early in life. Our study explores the FC of the hippocampus to the cortex at birth, allowing insight into the development of human memory systems.