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Yazdi M, Samaee M, Massicotte D. A Review on Automated Sleep Study. Ann Biomed Eng 2024; 52:1463-1491. [PMID: 38493234 DOI: 10.1007/s10439-024-03486-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 02/25/2024] [Indexed: 03/18/2024]
Abstract
In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.
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Affiliation(s)
- Mehran Yazdi
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada.
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Mahdi Samaee
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Daniel Massicotte
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
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Kaheni H, Shiran MB, Kamrava SK, Zare-Sadeghi A. Intra and inter-regional functional connectivity of the human brain due to Task-Evoked fMRI Data classification through CNN & LSTM. J Neuroradiol 2024; 51:101188. [PMID: 38408721 DOI: 10.1016/j.neurad.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/27/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND AND PURPOSE Olfaction is an early marker of neurodegenerative disease. Standard olfactory function is essential due to the importance of olfaction in human life. The psychophysical evaluation assesses the olfactory function commonly. It is patient-reported, and results rely on the patient's answers and collaboration. However, methodological difficulties attributed to the psychophysical evaluation of olfactory-related cerebral areas led to limited assessment of olfactory function in the human brain. MATERIALS AND METHODS The current study utilized clustering approaches to assess olfactory function in fMRI data and used brain activity to parcellate the brain with homogeneous properties. Deep neural network architecture based on ResNet convolutional neural networks (CNN) and Long Short-Term Model (LSTM) designed to classify healthy with olfactory disorders subjects. RESULTS The fMRI result obtained by k-means unsupervised machine learning model was within the expected outcome and similar to those found with the conn toolbox in detecting active areas. There was no significant difference between the means of subjects and every subject. Proposing a CRNN deep learning model to classify fMRI data in two different healthy and with olfactory disorders groups leads to an accuracy score of 97 %. CONCLUSIONS The K-means unsupervised algorithm can detect the active regions in the brain and analyze olfactory function. Classification results prove the CNN-LSTM architecture using ResNet provides the best accuracy score in olfactory fMRI data. It is the first attempt conducted on olfactory fMRI data in detail until now.
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Affiliation(s)
- Haniyeh Kaheni
- Finetech in Medicine Research Center, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mohammad Bagher Shiran
- Finetech in Medicine Research Center, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Seyed Kamran Kamrava
- ENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Zare-Sadeghi
- Finetech in Medicine Research Center, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran.
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Wang Y, Ren Y, Bi Y, Zhao F, Bai X, Wei L, Liu W, Ma H, Bai P. Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder. Quant Imaging Med Surg 2024; 14:3350-3365. [PMID: 38720838 PMCID: PMC11074748 DOI: 10.21037/qims-23-1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/06/2024] [Indexed: 05/12/2024]
Abstract
Background In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.
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Affiliation(s)
- Yulong Wang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuzhen Bi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xingzhen Bai
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
| | - Liangzhou Wei
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wanting Liu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hancheng Ma
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Peirui Bai
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Afshani M, Mahmoudi-Aznaveh A, Noori K, Rostampour M, Zarei M, Spiegelhalder K, Khazaie H, Tahmasian M. Discriminating Paradoxical and Psychophysiological Insomnia Based on Structural and Functional Brain Images: A Preliminary Machine Learning Study. Brain Sci 2023; 13:brainsci13040672. [PMID: 37190637 DOI: 10.3390/brainsci13040672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/02/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Insomnia disorder (ID) is a prevalent mental illness. Several behavioral and neuroimaging studies suggested that ID is a heterogenous condition with various subtypes. However, neurobiological alterations in different subtypes of ID are poorly understood. We aimed to assess whether unimodal and multimodal whole-brain neuroimaging measurements can discriminate two commonly described ID subtypes (i.e., paradoxical and psychophysiological insomnia) from each other and healthy subjects. We obtained T1-weighted images and resting-state fMRI from 34 patients with ID and 48 healthy controls. The outcome measures were grey matter volume, cortical thickness, amplitude of low-frequency fluctuation, degree centrality, and regional homogeneity. Subsequently, we applied support vector machines to classify subjects via unimodal and multimodal measures. The results of the multimodal classification were superior to those of unimodal approaches, i.e., we achieved 81% accuracy in separating psychophysiological vs. control, 87% for paradoxical vs. control, and 89% for paradoxical vs. psychophysiological insomnia. This preliminary study provides evidence that structural and functional brain data can help to distinguish two common subtypes of ID from each other and healthy subjects. These initial findings may stimulate further research to identify the underlying mechanism of each subtype and develop personalized treatments for ID in the future.
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Affiliation(s)
- Mortaza Afshani
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran 1983969411, Iran
| | | | - Khadijeh Noori
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran
| | - Masoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran 1983969411, Iran
- Department of Neurology, Odense University Hospital, 5000 Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark
| | - Kai Spiegelhalder
- Department of Psychiatry and Psychotherapy, Medical Centre-University of Freiburg, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran
| | - Masoud Tahmasian
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Zhao J, Kong Q, Zhou X, Zhang Y, Yu Z, Qu W, Huang H, Luo X. Differences in Gray Matter Volume in Cerebral Small Vessel Disease Patients with and without Sleep Disturbance. Brain Sci 2023; 13:brainsci13020294. [PMID: 36831837 PMCID: PMC9953873 DOI: 10.3390/brainsci13020294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Recently, there has been increased interest in the relationship between cerebral small vessel disease (CSVD) and circadian rhythm disruption, particularly sleep disturbance. However, the neural mechanism of sleep disturbance in CSVD patients remains poorly understood. The purpose of this study is to explore the gray matter alterations in CSVD patients with and without sleep disturbance. 59 patients with CSVD and 40 healthy controls (HC) were recruited for the present study. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. CSVD patients were categorized into either the good sleepers group (CSVD-GS, n = 23) or the poor sleepers group (CSVD-PS, n = 36) based on PSQI score. Voxel-based morphometry (VBM) analysis was used to assess differences in gray matter volume (GMV) between groups. Multivariate regression analyses were performed to investigate the relationships between sleep quality, GMV, and white matter hyperintensities (WMH). We observed GMV differences between the three groups in the bilateral caudate, right thalamus, bilateral calcarine cortex, left precentral gyrus, right orbitofrontal cortex, left cingulate gyrus, and right sub-gyral temporal lobe. Additionally, the CSVD-PS group exhibited decreased GMV in the bilateral calcarine cortex yet increased GMV in the right caudate compared to the CSVD-GS group. In fully adjusted models, GMV of the right caudate and bilateral calcarine cortex was associated with sleep quality in CSVD patients. The present study revealed structural brain alterations in CSVD patients with sleep disturbance. These findings may provide novel insights into the neural mechanisms of sleep disturbance in CSVD.
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Affiliation(s)
| | | | | | | | | | | | | | - Xiang Luo
- Correspondence: ; Tel.: +86-27-83663337
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Gong L, Xu R, Yang D, Wang J, Ding X, Zhang B, Zhang X, Hu Z, Xi C. Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms. Front Psychiatry 2022; 13:907978. [PMID: 35873230 PMCID: PMC9299364 DOI: 10.3389/fpsyt.2022.907978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/16/2022] [Indexed: 11/24/2022] Open
Abstract
Depression is a common comorbid symptom in patients with chronic insomnia disorder (CID). Previous neuroimaging studies found that the orbital frontal cortex (OFC) might be the core brain region linking insomnia and depression. Here, we used a machine learning approach to differentiate CID patients with depressive symptoms from CID patients without depressive symptoms based on OFC functional connectivity. Seventy patients with CID were recruited and subdivided into CID with high depressive symptom (CID-HD) and low depressive symptom (CID-LD) groups. The OFC functional connectivity (FC) network was constructed using the altered structure of the OFC region as a seed. A linear kernel SVM-based machine learning approach was carried out to classify the CID-HD and CID-LD groups based on OFC FC features. The predict model was further verified in a new cohort of CID group (n = 68). The classification model based on the OFC FC pattern showed a total accuracy of 76.92% (p = 0.0009). The area under the receiver operating characteristic curve of the classification model was 0.84. The OFC functional connectivity with reward network, salience network and default mode network contributed the highest weights to the prediction model. These results were further validated in an independent CID group with high and low depressive symptom (accuracy = 67.9%). These findings provide a potential biomarker for early diagnosis and intervention in CID patients comorbid with depression based on an OFC FC-based machine learning approach.
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Affiliation(s)
- Liang Gong
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Ronghua Xu
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Dan Yang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Jian Wang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Xin Ding
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Bei Zhang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Xingping Zhang
- Department of General Practice, Chengdu Second People's Hospital, Chengdu, China
| | - Zhengjun Hu
- The Third People's Hospital of Chengdu, Chengdu, China
| | - Chunhua Xi
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, China
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