Resting-state neural signal variability in women with depressive disorders.
Resting-state neural signal variability in women with depressive disorders.
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DOI：
10.1016/j.bbr.2022.113999
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发表时间：
2022-09-05
影响因子：
2.7
通讯作者：
Philippi, Carissa L.
中科院分区：
文献类型：
--
作者：
Pessin, Sally;Walsh, Erin C.;Hoks, Roxanne M.;Birn, Rasmus M.;Abercrombie, Heather C.;Philippi, Carissa L.
关键词：
在抑郁症中，默认模式（DMN）、额顶叶（FPN）和显著性（SN）网络区域的异常活动和连通性得到了很好的证明。最近的神经影像学研究表明，血氧水平依赖性（BOLD）信号的变化可能会破坏正常的网络整合，并成为精神病理学的一个重要的新预测因子。然而，目前还没有研究确定静息状态BOLD信号变异性与抑郁症之间的关系，也没有研究将BOLD信号变异性特征应用于使用机器学习（ML）对抑郁症病史进行分类。我们收集了79名具有不同抑郁症病史的女性的静息状态fMRI数据：无病史、既往病史和当前抑郁症。我们测试了与抑郁症组和严重程度相关的BOLD信号变异性的体素差异。我们还研究了DMN，FPN和SN区域的BOLD信号变异性是否可以使用监督随机森林ML模型预测抑郁症历史组。结果表明，具有任何抑郁症病史的个体在左右小脑和右顶叶皮层中的BOLD信号变异性显著降低（pFWE < .05）。此外，更严重的抑郁症也与小脑中BOLD信号变异性降低相关。一个随机森林模型对参与者的抑郁史进行了分类，准确率为74%，DMN的腹侧前扣带皮层是模型中最重要的变量。这些研究结果提供了新的支持休息状态BOLD信号变异性作为抑郁症的神经功能障碍的标志，并牵连抑郁症的病理生理学神经信号变异性降低。
Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (pFWE < .05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression.
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影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A
DOI：
10.1016/j.bpsc.2017.11.005
发表时间：
2018-03
期刊：
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子：
--
作者：
Abercrombie HC;Frost CP;Walsh EC;Hoks RM;Cornejo MD;Sampe MC;Gaffey AE;Plante DT;Ladd CO;Birn RM
通讯作者：
Birn RM
影响因子：
4.7
作者：
Easson, Amanda K.;McIntos, Anthony R.
通讯作者：
McIntos, Anthony R.
影响因子：
6.6
作者：
Depping, Malte S.;Wolf, Nadine D.;Wolf, Robert C.
通讯作者：
Wolf, Robert C.
影响因子：
4.2
作者：
Berman, Marc G.;Peltier, Scott;Jonides, John
通讯作者：
Jonides, John