Adam Liska

Functional connectivity hubs of the mouse brain.

Recent advances in functional connectivity methods have made it possible to identify brain hubs - a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states.

Group-Wise Functional Community Detection through Joint Laplacian Diagonalization

There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature.

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos

Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted impaired or aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological underpinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to aberrant connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. Despite some obvious limitations in reliably modeling the full phenotypic spectrum of a complex developmental disorder like ASD, mouse models have played a central role in advancing our basic mechanistic and molecular understanding of this syndrome. Recent progress in mouse brain connectivity mapping via resting-state fMRI (rsfMRI) offers the opportunity to generate and test mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism. Here we discuss recent progress toward this goal, and illustrate initial examples of how the approach can be employed to establish causal links between ASD-related mutations, developmental processes, and brain connectional architecture. As the spectrum of genetic and pathophysiological components of ASD modeled in the mouse is rapidly expanding, the use of rsfMRI can advance our mechanistic understanding of the origin and significance of the connectional alterations associated with autism, and their heterogeneous expression across patient cohorts.

Autism-associated 16p11.2 microdeletion impairs prefrontal functional connectivity in mouse and human

Human genetic studies are rapidly identifying variants that increase risk for neurodevelopmental disorders. However, it remains unclear how specific mutations impact brain function and contribute to neuropsychiatric risk. Chromosome 16p11.2 deletion is one of the most common copy number variations in autism and related neurodevelopmental disorders. Using resting state functional MRI data from the Simons Variation in Individuals Project (VIP) database, we show that 16p11.2 deletion carriers exhibit impaired prefrontal connectivity, resulting in weaker long-range functional coupling with temporal-parietal regions. These functional changes are associated with socio-cognitive impairments. We also document that a mouse with the same genetic deficiency exhibits similarly diminished prefrontal connectivity, together with thalamo-prefrontal miswiring and reduced long-range functional synchronization. These results reveal a mechanistic link between specific genetic risk for neurodevelopmental disorders and long-range functional coupling, and suggest that deletion in 16p11.2 may lead to impaired socio-cognitive function via dysregulation of prefrontal connectivity.

Deletion of autism risk gene Shank3 disrupts prefrontal connectivity

Mutations in the synaptic scaffolding protein Shank3 are a major cause of autism, and are associated with prominent intellectual and language deficits. However, the neural mechanisms whereby SHANK3 deficiency affects higher order socio-communicative functions remain unclear. Using high-resolution functional and structural MRI in mice, here we show that loss of Shank3 (Shank3B-/-) results in disrupted local and long-range prefrontal functional connectivity, as well as fronto-striatal decoupling. We document that prefrontal hypo-connectivity is associated with reduced short-range cortical projections density, and reduced gray matter volume. Finally, we show that prefrontal disconnectivity is predictive of social communication deficits, as assessed with ultrasound vocalization recordings. Collectively, our results reveal a critical role of SHANK3 in the development of prefrontal anatomy and function, and suggest that SHANK3 deficiency may predispose to intellectual disability and socio-communicative impairments via dysregulation of higher-order cortical connectivity.

Automated brain state identification using graph embedding

The functional activation pattern within the human brain is known to change at varying time-scales. This existence of and dynamics between inherently different brain functional states are found to be related to human learning, behavior, and development, and, are therefore of high importance. Yet, tools to automatically identify such cognitive states are limited. In this study, we consider high-dimensional functional connectome data constructed from BOLD fMRI over short time-intervals as a graph, each time-point as a node, and the similarity between two time-points as the edge between those two nodes. We apply graph embedding techniques to automatically extract clusters of time-points, which represent canonical brain states. Application of graph embedding technique to BOLD fMRI time-series of a population comprised of autistic and neurotypical subjects demonstrates that two-layer embedding by preserving the higherorder similarity between different time-points is crucial toward successful identification of low-dimensional brain functional states. Finally, the present study reveals inherent existence of two brain meta-states within human brain.