Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task

功能近红外光谱 神经影像学 口语流利性测试 重性抑郁障碍 心理学 背外侧前额叶皮质 人工智能 听力学 临床心理学 计算机科学 认知心理学 精神科 神经心理学 医学 认知 前额叶皮质
作者
Lingyun Mao,Xin Hong,Maorong Hu
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:365: 9-20
标识
DOI:10.1016/j.jad.2024.08.082
摘要

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
量子星尘发布了新的文献求助10
1秒前
1秒前
Kayla发布了新的文献求助10
1秒前
1秒前
张雯思发布了新的文献求助10
3秒前
HuSP完成签到,获得积分10
3秒前
3秒前
4秒前
zzcres完成签到,获得积分10
4秒前
anna发布了新的文献求助10
6秒前
勤奋梨愁发布了新的文献求助10
7秒前
7秒前
潘善若发布了新的文献求助10
7秒前
momo发布了新的文献求助10
8秒前
8秒前
12秒前
14秒前
诺奇完成签到,获得积分10
16秒前
潘善若发布了新的文献求助10
18秒前
乖猫要努力应助猪猪hero采纳,获得10
18秒前
21秒前
21秒前
科目三应助不学无术采纳,获得10
21秒前
MrSong完成签到,获得积分10
25秒前
25秒前
momo发布了新的文献求助10
25秒前
小曾应助安静的万声采纳,获得10
26秒前
高贵的飞阳完成签到,获得积分10
26秒前
小梦发布了新的文献求助10
27秒前
27秒前
28秒前
科研通AI5应助GooJohn采纳,获得10
29秒前
Ava应助yyy采纳,获得10
33秒前
善学以致用应助yyy采纳,获得10
33秒前
共享精神应助yyy采纳,获得10
33秒前
李健的小迷弟应助yyy采纳,获得10
33秒前
JamesPei应助yyy采纳,获得10
33秒前
共享精神应助yyy采纳,获得10
33秒前
潘善若发布了新的文献求助10
33秒前
LL完成签到,获得积分10
34秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136