The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward

作者：Elizabeth L. Martin, Asadur Chowdury, John J. Kopchick, Patricia L. Thomas, Dalal Khatib, Usha Rajan, Caroline Zajac‐Benitez, Luay Haddad, Alireza Amirsadri, Alfred J. Robison, Katharine N. Thakkar, Jeffrey A. Stanley, Vaibhav A. Diwadkar · 发表于：Frontiers in Psychiatry · 年份：2024 · DOI：10.3389/fpsyt.2024.1337882 · 被引用次数：7 · 研究领域：Schizophrenia research and treatment、Functional Brain Connectivity Studies、Mental Health Research Topics

Introduction Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward. Methods fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2 nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2 nd orde...