当前位置: X-MOL 学术medRxiv. Psychiatry Clin. Psychol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies
medRxiv - Psychiatry and Clinical Psychology Pub Date : 2021-09-02 , DOI: 10.1101/2021.04.01.21254790
Paul J. Thomas , Alex Leow , Heide Klumpp , K. Luan Phan , Olusola Ajilore

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 heterogenous 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, 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 identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

中文翻译:

网络扩散嵌入揭示内化精神病理学中的跨诊断子网破坏和潜在治疗目标

网络扩散模型是研究信息在复杂系统中传播的常用且强大的方法,它们为研究多模态大脑网络数据提供了直接的方法。我们开发了一个分析框架,使用最能映射到静息状态功能连接的结构基础来识别具有扰动信息扩散能力的大脑子网络,并将其应用于内化精神病理学 (IP) 的异质数据集,这是一组类似大脑网络的精神疾病在整个疾病范围内都发现了缺陷,但目前尚不清楚用于跨诊断症状表达的统一神经病理学基础。这项研究提供了跨诊断大脑子网络缺陷的初步证据,其特征是右侧区域 8BM 的信息扩散障碍,这是一个参与组织广泛认知任务的关键大脑区域,这可能是先前报道的 IP 中多个大脑回路功能障碍的基础。我们还证明,涉及针对该大脑区域的神经调节模型使 IP 扩散动力学向健康对照的扩散动力学正常化。这些分析为多模态方法提供了一个框架,这些方法可以识别信息扩散中断的大脑子网络和这些子网络的潜在目标,用于基于先前充分表征的方法进行治疗性神经调节干预。这可能是先前报道的 IP 中多个大脑回路功能障碍的基础。我们还证明,涉及针对该大脑区域的神经调节模型使 IP 扩散动力学向健康对照的扩散动力学正常化。这些分析为多模态方法提供了一个框架,这些方法可以识别信息扩散中断的大脑子网络和这些子网络的潜在目标,用于基于先前充分表征的方法进行治疗性神经调节干预。这可能是先前报道的 IP 中多个大脑回路功能障碍的基础。我们还证明,涉及针对该大脑区域的神经调节模型使 IP 扩散动力学向健康对照的扩散动力学正常化。这些分析为多模态方法提供了一个框架,这些方法可以识别信息扩散中断的大脑子网络和这些子网络的潜在目标,用于基于先前充分表征的方法进行治疗性神经调节干预。
更新日期:2021-09-04
down
wechat
bug