The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data

Dai, Peishan; Xiong, Tong; Zhou, Xiaoyan; Ou, Yilin; Li, Yang; Kui, Xiaoyan; Chen, Zailiang; Zou, Beiji; Li, Weihui; Huang, Zijian; Consortium, The REST-meta-MDD

doi:10.1016/j.bbr.2022.114058

Cited by 15 publications

(8 citation statements)

References 66 publications

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“…Hence, compared with the activity of local brain regions, FC analysis can better reflect the actual operation mode of the whole brain. The FC approach has obtained good results in fMRI data classification studies (Wang et al, 2019;Dai et al, 2022). Recently, an increasing number of rs-fMRI studies have reported that resting-state FC before surgery has a primary role in predicting cognitive dysfunction after surgery.…”

Section: Discussionmentioning

confidence: 99%

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Jiang

Cai

Liu

et al. 2023

Front. Aging Neurosci.

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ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

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Section: Discussionmentioning

confidence: 99%

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Jiang

Cai

Liu

et al. 2023

Front. Aging Neurosci.

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“…Linear SVM recursive feature elimination (SVM-RFE): An SVM-RFE fits linear SVM models on all elements in the feature vector and iteratively drops the least class-separable element. It has been previously been found to show high performance with a linear SVM in MDD diagnosis (63).…”

Section: Hdrs =mentioning

confidence: 97%

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task

Pilmeyer,

Lamerichs,

Ramsaransing

et al. 2024

Front. Psychiatry

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IntroductionApproximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features.MethodsFor our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups.ResultsThe number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%.DiscussionThe dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

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“…The C parameter for SVM and β of L 1 sparsity term in Edge Mask were chosen from {1e −1 , 1e −2 , 1e −3 }. For RFE-SVM, we predefined the number of features following the previous research [13] for ABIDE and [14] for REST-meta-MDD. We conducted five-fold cross-validation and repeated it five times for the proposed and all competing methods.…”

Section: B Experimental Settingsmentioning

confidence: 99%

“…However, independent implementation of feature selection and classifier may result in a suboptimal problem, which fails to provide the best predictive features for the classification model [12]. To address this concern in brain disease diagnosis, a method called recursive feature elimination (RFE) with SVM has been employed for disease diagnosis [13], [14]. This technique iteratively eliminates a subset of features from the input data until the optimal set of features is obtained based on their importance evaluated by SVM weights.…”

Section: Introductionmentioning

confidence: 99%

Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

Kang¹,

Heo²,

Suk³

2022

Lecture Notes in Computer Science

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To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover diseaserelated biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individualspecific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the internetwork relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

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The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data

Cited by 15 publications

References 66 publications

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task

Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

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