Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Abstract

Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination

“…At the time of enrollment (Pre) and after 12 weeks of treatment (Post), severity of IP symptoms was assessed in all subjects using the Inventory of Depression and Anxiety Symptoms (IDAS-II) [46] and the Rumination Responses Scale (RRS) [44] . The IDAS-II Depression subscale is used for assessing the 'distress/dysphoria' symptom domain as in [32,46,27,43]. To quantify rumination, we use average of the 10 RRS items from the brooding and reflection as the structure of these factors was not found to be distinct in currently depressed patients [48].…”

“…Subjects were instructed to keep their eyes open and focused on the cross, and to try not to think of anything in particular for the duration of the scan. Functional MRI (fMRI) data preprocessing and analysis were performed using the CONN Toolbox (www.nitrc.org/projects/conn) [47], which employs procedures from the Statistical Parametric Mapping software (SPM12; Wellcome Trust Center for Neuroimaging, London, UK), using the standard preprocessing and denoising pipelines as done previously [43], detailed in [26]. Breifly, fMRI images were co-registered to the T1w structural imaging data using an affine tranformation and T1w images were warped non-linearly to native space.…”

Understanding mental health through computers: An introduction to computational psychiatry

“…Furthermore, they could integrate with data and knowledge taken from the connectomics fields. In doing so, these approaches are helpful in investigating alterations in the brain connections in specific diseases ( 77 ) or arrive at transdiagnostic alterations ( 78 ).…”