Vike Nicole L, Bari Sumra, Kim Byoung Woo, Katsaggelos Aggelos K, Blood Anne J, Breiter Hans C
Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America.
Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, United States of America.
PLoS One. 2024 Mar 11;19(3):e0299528. doi: 10.1371/journal.pone.0299528. eCollection 2024.
Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD.
Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups.
This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants.
These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.
在过去十年中，抑郁症和成瘾率急剧上升，但缺乏综合技术仍然是准确诊断这些精神疾病的障碍。在患有重度抑郁症（MDD）和可卡因依赖多物质滥用障碍（CD）的患者中，已经发现了奖励/厌恶行为以及相应脑结构的变化。评估计算行为与脑结构之间的统计相互作用可能会对MDD和CD进行定量区分。
在此，111名参与者[40名对照（CTRL），25名MDD，46名CD]接受了脑部结构MRI检查，并完成了一项操作性按键任务以生成计算判断指标。进行了三项分析：（1）线性回归以评估结构 - 行为关联中的组间（CTRL对MDD对CD）差异；（2）对结构 - 行为关联模式进行定性和定量热图评估；（3）使用脑结构和按键变量输入的k近邻机器学习方法来区分组。
本研究产生了三个主要发现。首先，CTRL、MDD和CD参与者具有不同的结构 - 行为线性关系，任意两组之间只有7.8%的关联重叠。其次，三组在统计学上具有不同的斜率和定性不同的关联模式。第三，机器学习方法可以区分CTRL和CD参与者，但不能区分MDD参与者。
这些发现表明，计算行为与脑结构之间的可变相互作用以及这些相互作用的模式可以区分MDD和CD。这项工作提出了一个假设，即对操作性任务与结构神经影像学之间相互作用的分析可能有助于对MDD、CD和其他心理健康状况进行客观分类。
