This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.

中文翻译：

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基于子图熵的网络方法从静息状态功能性MRI对青少年强迫症进行分类。

本文提出了一种从静息状态fMRI数据分类青少年强迫症（OCD）的新方法。当前，最先进的诊断青年强迫症的方法包括由经验丰富的临床医生与青春期患者及其父母进行访谈，基于精神障碍诊断和统计手册（DSM）的症状评定量表以及行为观察。从患者的功能磁共振成像（fMRI）扫描中发现信号处理和基于网络的生物标记物，有可能帮助临床医生对患有强迫症的青少年进行诊断评估。本文研究了一组单变量的临床诊断效用，使用信息论方法从静息状态功能磁共振成像中提取的双变量和多变量特征用于15名患有强迫症的青少年和13名匹配的健康对照者。结果表明，基于子图熵的信息理论方法能够对OCD与健康受试者进行高精度分类。从85个脑区提取平均时间序列，并用于计算Shannon小波熵，Pearson相关矩阵，网络特征和子图熵。此外，还研究了两个子图熵的特殊情况，即节点和边缘熵，以识别强迫症患者的重要大脑区域和边缘。留一法交叉验证方法用于最终预测指标性能。所提出的使用微分子图（边缘）熵的方法实现了0的精度。89种，特异性为1，灵敏度为0.80，采用留一法交叉验证以及折叠特征分级和选择。高分类精度表明子网的预测能力以及边缘熵度量。