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, they and offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with impaired information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous internalizing psychopathology (IP) cohort. This research provides preliminary evidence of a transdiagnostic deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, that 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 identity diffusion disrupted subnetworks and potential targets for 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.…”