Sacchet Matthew D, Valenti Joseph L, Keshava Poorvi, Walsh Shane W, Smoski Moria J, Krystal Andrew D, Pizzagalli Diego A
Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA.
J Mood Anxiety Disord. 2025 May 9;11:100126. doi: 10.1016/j.xjmad.2025.100126. eCollection 2025 Sep.
Anhedonia remains a difficult-to-treat symptom and has been associated with poor clinical course transdiagnostically. Here, we applied machine learning models to individualized neural patches derived from fMRI data during the Monetary Incentive Delay Task in anhedonic participants (N = 67) recruited for a clinical trial examining K-opioid receptor (KOR) antagonism in the treatment of anhedonia.
Nine ensemble models were estimated using cortical, subcortical, and combined cortical subcortical features from individualized functional topographies to predict changes in symptoms of overall psychopathology (anhedonia, depression, anxiety). Analyses were performed on the KOR (N = 33) and placebo (N = 34) group.
Initial models showed that only subcortical data predicting depression and anxiety symptom change had a significant Spearman correlation between veridical and predicted data ( = 0.480 and = 0.415 respectively). Next, leave-one-out-cross-validation (LOOCV) showed that the best-performing models comprised only the subcortical individualized systems data, which correlated with clinical change for depression and anxiety scores for the KOR group with significantly higher accuracy ( = 0.634 and = 0.562, respectively) compared to the placebo group ( = 0.294 and = 0.034, respectively). Further, 25 subcortical neural features were identified based on correlation and ensemble determined importance in driving prediction. Final models for both depression and anxiety showed an overall higher representation of the dorsal attention network. Cortical and combined cortical-subcortical feature data showed no significant improvement in prediction of clinical change between the two groups.
Using an ensemble of machine learning approaches, we identified individual differences in subcortical individualized systems data that predicted clinical change that was specific to KOR antagonism.
快感缺失仍然是一种难以治疗的症状，并且在跨诊断中与不良临床病程相关。在此，我们将机器学习模型应用于从参加一项研究κ-阿片受体（KOR）拮抗剂治疗快感缺失的临床试验的快感缺失参与者（N = 67）在金钱激励延迟任务期间的功能磁共振成像（fMRI）数据中得出的个体化神经图谱。
使用来自个体化功能地形图的皮质、皮质下和皮质-皮质下联合特征估计九个集成模型，以预测总体精神病理学症状（快感缺失、抑郁、焦虑）的变化。对KOR组（N = 33）和安慰剂组（N = 34）进行了分析。
初始模型显示，只有预测抑郁和焦虑症状变化的皮质下数据在真实数据和预测数据之间具有显著的斯皮尔曼相关性（分别为ρ = 0.480和ρ = 0.415）。接下来，留一法交叉验证（LOOCV）表明，表现最佳的模型仅包括皮质下个体化系统数据，与安慰剂组（分别为ρ = 0.294和ρ = 0.034）相比，该数据与KOR组抑郁和焦虑评分的临床变化相关性更高（分别为ρ = 0.634和ρ = 0.562）。此外，基于相关性和集成确定的驱动预测的重要性，确定了25个皮质下神经特征。抑郁和焦虑的最终模型显示背侧注意网络的总体代表性更高。皮质和皮质-皮质下联合特征数据在两组临床变化预测方面未显示出显著改善。
使用机器学习方法的集成，我们在皮质下个体化系统数据中识别出个体差异，这些差异预测了特定于KOR拮抗作用的临床变化。
