神经影像学特征和深度学习模型对大学生亚临床抑郁共病睡眠障碍的早期诊断和预测非药物治疗成功。

IF 5.3 1区 心理学 Q1 PSYCHOLOGY, CLINICAL International Journal of Clinical and Health Psychology Pub Date : 2024-10-01 DOI:10.1016/j.ijchp.2024.100526
Xinyu Liang , Yunan Guo , Hanyue Zhang , Xiaotong Wang , Danian Li , Yujie Liu , Jianjia Zhang , Luping Zhou , Shijun Qiu
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引用次数: 0

摘要

目的:大学生亚临床抑郁症患者经常出现睡眠障碍,如果不进行早期干预,其发展为重度抑郁症的风险很高。临床指南推荐非药物治疗作为亚临床抑郁症伴伴睡眠障碍(sdsd)的主要选择。然而,与这些治疗相关的神经影像学机制和治疗反应尚不清楚。此外,缺乏早期诊断和治疗效果预测模型阻碍了临床推广和接受非药物干预亚临床抑郁症。方法:本研究采用多中心、单盲、随机临床试验的静息状态功能磁共振成像(rs-fMRI)和临床数据。该试验包括114名首发,drug-naïve大学生亚临床抑郁症和共病睡眠障碍(sDSDs;平均年龄=22.8±2.3岁;73.7%女性)和93名健康对照(hc;平均年龄=22.2±1.7岁;63.4%的女性)。在非药物抗抑郁治疗前后6周,我们使用种子到体素分析检查了与默认模式网络(sub-DMN)子区域相关的功能连接(FC)和脑网络连接模式的改变。此外,我们开发了一个个性化的诊断和治疗效果预测模型,以实现亚临床抑郁症的早期识别,并在变压器框架内使用新提出的分层功能脑网络(HFBN)和先进的深度学习算法,为选择非药物治疗提供客观建议。结果:对非药物治疗的神经影像学反应的特征是功能连接(FC)的改变和大脑网络连接模式的转变,特别是在亚dmn内。在基线时,亚dmn与执行控制网络(ECN)和背侧注意网络(DAN)之间的FC显著增加。在六周的非药物干预后,亚dmn和ECN内的连接模式主要发生了变化,FCs明显减少。HFBN模型在准确预测治疗结果和诊断亚临床抑郁症方面表现出优于传统深度学习模型的性能,在睡眠质量预测和抑郁预测方面的累积得分分别为80.47%和84.67%,总体诊断准确率为82.34%。结论:与亚dmn相关的双尺度神经影像学特征可能是亚临床抑郁症非药物治疗的抗抑郁机制。HFBN模型在亚临床抑郁症的早期诊断和预测非药物治疗结果方面表现出卓越的能力,从而促进客观的临床心理治疗决策。
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Neuroimaging signatures and a deep learning modeling for early diagnosing and predicting non-pharmacological therapy success for subclinical depression comorbid sleep disorders in college students

Objective

College students with subclinical depression often experience sleep disturbances and are at high risk of developing major depressive disorder without early intervention. Clinical guidelines recommend non-pharmacotherapy as the primary option for subclinical depression with comorbid sleep disorders (sDSDs). However, the neuroimaging mechanisms and therapeutic responses associated with these treatments are poorly understood. Additionally, the lack of an early diagnosis and therapeutic effectiveness prediction model hampers the clinical promotion and acceptance of non-pharmacological interventions for subclinical depression.

Methods

This study involved pre- and post-treatment resting-state functional Magnetic Resonance Imaging (rs-fMRI) and clinical data from a multicenter, single-blind, randomized clinical trial. The trial included 114 first-episode, drug-naïve university students with subclinical depression and comorbid sleep disorders (sDSDs; Mean age=22.8±2.3 years; 73.7% female) and 93 healthy controls (HCs; Mean age=22.2±1.7 years; 63.4% female). We examined altered functional connectivity (FC) and brain network connective mode related to subregions of Default Mode Network (sub-DMN) using seed-to-voxel analysis before and after six weeks of non-pharmacological antidepressant treatment. Additionally, we developed an individualized diagnosing and therapeutic effect predicting model to realize early recognition of subclinical depression and provide objective suggestions to select non-pharmacological therapy by using the newly proposed Hierarchical Functional Brain Network (HFBN) with advanced deep learning algorithms within the transformer framework.

Results

Neuroimaging responses to non-pharmacologic treatments are characterized by alterations in functional connectivity (FC) and shifts in brain network connectivity patterns, particularly within the sub-DMN. At baseline, significantly increased FC was observed between the sub-DMN and both Executive Control Network (ECN) and Dorsal Attention Network (DAN). Following six weeks of non-pharmacologic intervention, connectivity patterns primarily shifted within the sub-DMN and ECN, with a predominant decrease in FCs. The HFBN model demonstrated superior performance over traditional deep learning models, accurately predicting therapeutic outcomes and diagnosing subclinical depression, achieving cumulative scores of 80.47% for sleep quality prediction and 84.67% for depression prediction, along with an overall diagnostic accuracy of 82.34%.

Conclusions

Two-scale neuroimaging signatures related to the sub-DMN underlying the antidepressant mechanisms of non-pharmacological treatments for subclinical depression. The HFBN model exhibited supreme capability in early diagnosing and predicting non-pharmacological treatment outcomes for subclinical depression, thereby promoting objective clinical psychological treatment decision-making.
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来源期刊
CiteScore
10.70
自引率
5.70%
发文量
38
审稿时长
33 days
期刊介绍: The International Journal of Clinical and Health Psychology is dedicated to publishing manuscripts with a strong emphasis on both basic and applied research, encompassing experimental, clinical, and theoretical contributions that advance the fields of Clinical and Health Psychology. With a focus on four core domains—clinical psychology and psychotherapy, psychopathology, health psychology, and clinical neurosciences—the IJCHP seeks to provide a comprehensive platform for scholarly discourse and innovation. The journal accepts Original Articles (empirical studies) and Review Articles. Manuscripts submitted to IJCHP should be original and not previously published or under consideration elsewhere. All signing authors must unanimously agree on the submitted version of the manuscript. By submitting their work, authors agree to transfer their copyrights to the Journal for the duration of the editorial process.
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