Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory University, Georgia State University, Atlanta, Georgia, USA.
Hum Brain Mapp. 2023 Jun 1;44(8):3180-3195. doi: 10.1002/hbm.26273. Epub 2023 Mar 15.
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.
精神病学中的诊断有效性和可靠性是心理健康领域一个具有挑战性的话题。目前的心理健康分类主要基于症状和临床过程，而没有生物学验证。在多项正在进行的努力中，神经学观察与临床评估被认为是解决诊断问题的潜在解决方案。双相情感障碍-精神分裂症网络中间表型（B-SNIP）已经发表了多篇论文，试图根据生物学而不是症状措施重新分类精神病。然而，调查这种新的分类方法与其他神经影像学技术（包括静息态 fMRI 数据）之间关系的努力仍然有限。本研究专注于使用最先进的人工智能（AI）方法研究不同精神病分类方法与基于静息态 fMRI 的动态功能网络连接（dFNC）之间的关系。我们将我们的方法应用于 613 名受试者，包括精神病患者和健康对照者，这些受试者分别使用精神障碍诊断与统计手册（DSM-IV）和 B-SNIP 基于生物标志物（Biotype）的方法进行分类。在每个框架内进行了统计组差异和交叉验证分类器，以评估不同的分类方法。结果突出了与健康个体相比，DSM-IV 和 Biotype 分类中有趣的占位差异，这些差异分布在特定的瞬态连接状态中。Biotype 分类在占位水平上的独特性较小，包括较少的细胞差异。DSM-IV 和 Biotype 分类的分类准确性都明显高于机会水平。结果提供了新的见解，强调了 DSM-IV 和基于生物学的分类的优势，同时也强调了在这一方向上开展进一步工作的重要性，包括采用进一步的数据类型。
