1
|
Huang H, Chen Z, Fan B, Huang D, Qiu Z, Luo C, Zheng J. Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study. Brain Res 2024; 1837:148985. [PMID: 38714228 DOI: 10.1016/j.brainres.2024.148985] [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: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
OBJECTIVE We decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA). METHODS Functional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected. RESULTS The MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia. CONCLUSION Our findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.
Collapse
Affiliation(s)
- Huachun Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zexiang Chen
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Binglin Fan
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongying Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhuoyan Qiu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cuimi Luo
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| |
Collapse
|
2
|
Liang X, Tang K, Ke X, Jiang J, Li S, Xue C, Deng J, Liu X, Yan C, Gao M, Zhou J, Zhao L. Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor. J Magn Reson Imaging 2024; 60:523-533. [PMID: 37897302 DOI: 10.1002/jmri.29098] [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: 02/20/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE Retrospective. POPULATION Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Xiaohong Liang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kaiqiang Tang
- Department of Orthopedics, The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Cheng Yan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingzi Gao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Liqin Zhao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
3
|
Wen C, Zeng Q, Zhou R, Xie L, Yu J, Zhang C, Wang J, Yu Y, Gu Y, Cao G, Feng Y, Wang M. Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study. Comput Biol Med 2024; 179:108750. [PMID: 38996551 DOI: 10.1016/j.compbiomed.2024.108750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 05/24/2024] [Accepted: 06/08/2024] [Indexed: 07/14/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD. Diffusion tensor imaging data from a public dataset (53 patients with AD and 70 healthy controls [HCs]) and a clinical dataset (27 patients with AD and 19 HCs) were included for mutual validation. Whole-brain tractograms were automatically subdivided into 800 clusters through the automatic fiber clustering approach. Then, 100 segments were divided along the clusters, and the diffusion properties of each segment were calculated. Results showed that patients with AD had significantly lower fraction anisotropy (FA) and significantly higher mean diffusivity (MD) in some regions of the fiber clusters in the cingulum bundle, uncinate fasciculus, external capsule, and corpus callosum than HCs. Importantly, these changes were reproducible across the two datasets. Correlation analysis revealed a positive correlation between FA and Mini-Mental State Examination (MMSE) scores and a negative correlation between MD and MMSE in these clusters. The accuracy of the constructed classifier reached 89.76% with an area under the curve of 0.93. This finding indicates that this study can effectively identify local WM microstructural changes in AD and provides new insight into the analysis and diagnosis of WM abnormalities in patients with AD.
Collapse
Affiliation(s)
- Caiyun Wen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ronghui Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiangli Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Chengzhe Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jingqiang Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yan Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yixin Gu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guoquan Cao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| |
Collapse
|
4
|
Wang G, Datta A, Lindquist MA. Improved fMRI-based pain prediction using Bayesian group-wise functional registration. Biostatistics 2024; 25:885-903. [PMID: 37805937 DOI: 10.1093/biostatistics/kxad026] [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: 03/13/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 10/10/2023] Open
Abstract
In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.
Collapse
Affiliation(s)
- Guoqing Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| |
Collapse
|
5
|
Ghosh S, Burger P, Simeunovic-Ostojic M, Maas J, Petković M. Review of machine learning solutions for eating disorders. Int J Med Inform 2024; 189:105526. [PMID: 38935998 DOI: 10.1016/j.ijmedinf.2024.105526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/10/2024] [Accepted: 06/14/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Eating Disorders (EDs) are one of the most complex psychiatric disorders, with significant impairment of psychological and physical health, and psychosocial functioning, and are associated with low rates of early detection, low recovery and high relapse rates. This underscores the need for better diagnostic and treatment methods. OBJECTIVE This narrative review explores current Machine Learning (ML) and Artificial Intelligence (AI) applications in the domain of EDs, with a specific emphasis on clinical management in treatment settings. The primary objective are to (i) decrease the knowledge gap between ED researchers and AI-practitioners, by presenting the current state-of-the-art AI applications (including models for causality) in different ED use-cases; (ii) identify limitations of these existing AI interventions and how to address them. RESULTS AI/ML methods have been applied in different ED use-cases, including ED risk factor identification and incidence prediction (including the analysis of social media content in the general population), diagnosis, monitoring patients and treatment response and prognosis in clinical populations. A comparative analysis of AI-techniques deployed in these use-cases have been performed, considering factors such as complexity, flexibility, functionality, explainability and adaptability to healthcare constraints. CONCLUSION Multiple restrictions have been identified in the existing methods in ML and Causality in terms of achieving actionable healthcare for ED, like lack of good quality and quantity of data for models to train on, while requiring models to be flexible, high-performing, yet being explainable and producing counterfactual explanations, for ensuring the fairness and trustworthiness of its decisions. We conclude that to overcome these limitations and for future AI research and application in clinical management of ED, (i) careful considerations are required with regards to AI-model selection, and (ii) joint efforts from ED researcher and patient community are essential in building better quality and quantity of dedicated ED datasets and secure AI-solution framework.
Collapse
Affiliation(s)
- Sreejita Ghosh
- Dept. M & CS, Technical University of Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, the Netherlands.
| | - Pia Burger
- Center of Eating Disorders, GGZ Oost-Brabant, Wesselmanlaan 25a, 5707 HA Helmond, the Netherlands.
| | | | - Joyce Maas
- Center of Eating Disorders, GGZ Oost-Brabant, Wesselmanlaan 25a, 5707 HA Helmond, the Netherlands; Dept. Medical and Clinical Psychology, Tilburg University, Prof. Cobbenhagenlaan, 5037 AB Tilburg, the Netherlands
| | - Milan Petković
- Dept. M & CS, Technical University of Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, the Netherlands; Philips Hospital Patient Monitoring, High Tech Campus 34, 5656 AE Eindhoven, the Netherlands
| |
Collapse
|
6
|
Sundermann B, Pfleiderer B, McLeod A, Mathys C. Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain. Clin Neuroradiol 2024:10.1007/s00062-024-01422-2. [PMID: 38842737 DOI: 10.1007/s00062-024-01422-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/05/2024] [Indexed: 06/07/2024]
Abstract
Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.
Collapse
Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany.
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany.
| | - Bettina Pfleiderer
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany
| | - Anke McLeod
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| |
Collapse
|
7
|
Vieira S, Bolton TAW, Schöttner M, Baecker L, Marquand A, Mechelli A, Hagmann P. Multivariate brain-behaviour associations in psychiatric disorders. Transl Psychiatry 2024; 14:231. [PMID: 38824172 PMCID: PMC11144193 DOI: 10.1038/s41398-024-02954-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/03/2024] Open
Abstract
Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.
Collapse
Affiliation(s)
- S Vieira
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
| | - T A W Bolton
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital, Lausanne, Switzerland
| | - M Schöttner
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - L Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, UK
| | - A Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - P Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
8
|
Atzil-Slonim D, Penedo JMG, Lutz W. Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2024; 51:306-317. [PMID: 37880473 DOI: 10.1007/s10488-023-01309-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/29/2023] [Indexed: 10/27/2023]
Abstract
Mental health services are experiencing notable transformations as innovative technologies and artificial intelligence (AI) are increasingly utilized in a growing number of studies and services.These cutting-edge technologies carry the promise of substantial improvements in the field of mental health. Nevertheless, questions emerge about the alignment of novel technologies and AI systems with human needs, especially in the context of vulnerable populations receiving mental healthcare. The practice-oriented research (POR) model is pivotal in seamlessly integrating these emerging technologies into clinical research and practice. It underscores the importance of tight collaboration between clinicians and researchers, all driven by the central goal of ensuring and elevating client well-being. This paper focuses on how novel technologies can enhance the POR model and highlights its pivotal role in integrating these technologies into clinical research and practice. We discuss two key phases: pre-treatment, and during treatment. For each phase, we describe the challenges, present the major technological innovations, describe recent studies exemplifying technology use, and suggest future directions. Ethical concerns and the importance of aligning humans and technology are also considered, in addition to implications for practice and training.
Collapse
Affiliation(s)
| | | | - Wolfgang Lutz
- Department of Psychology, University of Trier, Trier, Germany
| |
Collapse
|
9
|
Long JY, Qin K, Pan N, Fan WL, Li Y. Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set. Br J Psychiatry 2024; 224:170-178. [PMID: 38602159 PMCID: PMC11039554 DOI: 10.1192/bjp.2024.41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/20/2024] [Accepted: 02/11/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.
Collapse
Affiliation(s)
- Jing-Yi Long
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Nanfang Pan
- Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Wen-Liang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Radiology, Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yi Li
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| |
Collapse
|
10
|
Chang C, Shi W, Wang Y, Zhang Z, Huang X, Jiao Y. The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis. Comput Biol Med 2024; 172:108258. [PMID: 38467093 DOI: 10.1016/j.compbiomed.2024.108258] [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: 10/04/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/13/2024]
Abstract
Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.
Collapse
Affiliation(s)
- Chuheng Chang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Wen Shi
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Youyang Wang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Zhan Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.
| | - Xiaoming Huang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Yang Jiao
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| |
Collapse
|
11
|
Parlatini V, Bellato A, Gabellone A, Margari L, Marzulli L, Matera E, Petruzzelli MG, Solmi M, Correll CU, Cortese S. A state-of-the-art overview of candidate diagnostic biomarkers for Attention-deficit/hyperactivity disorder (ADHD). Expert Rev Mol Diagn 2024; 24:259-271. [PMID: 38506617 DOI: 10.1080/14737159.2024.2333277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/18/2024] [Indexed: 03/21/2024]
Abstract
INTRODUCTION Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental conditions and is highly heterogeneous in terms of symptom profile, associated cognitive deficits, comorbidities, and outcomes. Heterogeneity may also affect the ability to recognize and diagnose this condition. The diagnosis of ADHD is primarily clinical but there are increasing research efforts aiming at identifying biomarkers that can aid the diagnosis. AREAS COVERED We first discuss the definition of biomarkers and the necessary research steps from discovery to implementation. We then provide a broad overview of research studies on candidate diagnostic biomarkers in ADHD encompassing genetic/epigenetic, biochemical, neuroimaging, neurophysiological and neuropsychological techniques. Finally, we critically appraise current limitations in the field and suggest possible ways forward. EXPERT OPINION Despite the large number of studies and variety of techniques used, no promising biomarkers have been identified so far. Clinical and biological heterogeneity as well as methodological limitations, including small sample size, lack of standardization, confounding factors, and poor replicability, have hampered progress in the field. Going forward, increased international collaborative efforts are warranted to support larger and more robustly designed studies, develop multimodal datasets to combine biomarkers and improve diagnostic accuracy, and ensure reproducibility and meaningful clinical translation.
Collapse
Affiliation(s)
- Valeria Parlatini
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alessio Bellato
- School of Psychology, University of Southampton, Southampton, UK
- School of Psychology, University of Nottingham Malaysia, Semenyih, Malaysia
- Mind and Neurodevelopment (MiND) Research Cluster, University of Nottingham Malaysia, Semenyih, Malaysia
- Centre for Innovation in Mental Health, University of Southampton, Southampton, UK
| | - Alessandra Gabellone
- DiBraiN-Department of Translational Biomedicine Neurosciences, University "Aldo Moro", Bari, Italy
| | - Lucia Margari
- DiMePRe-J-Department of Precision and Regenerative Medicine-Jonic Area, University "Aldo Moro", Bari, Italy
| | - Lucia Marzulli
- DiBraiN-Department of Translational Biomedicine Neurosciences, University "Aldo Moro", Bari, Italy
| | - Emilia Matera
- DiBraiN-Department of Translational Biomedicine Neurosciences, University "Aldo Moro", Bari, Italy
| | | | - Marco Solmi
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
- The Ottawa Hospital, Mental Health Department, Ottawa, Ontario, Canada
- Department of Psychiatry, Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
- Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Samuele Cortese
- Centre for Innovation in Mental Health, University of Southampton, Southampton, UK
- DiMePRe-J-Department of Precision and Regenerative Medicine-Jonic Area, University "Aldo Moro", Bari, Italy
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Child and Adolescent Mental Health Services, Solent NHS Trust, Southampton, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA
| |
Collapse
|
12
|
Chen M, Xia X, Kang Z, Li Z, Dai J, Wu J, Chen C, Qiu Y, Liu T, Liu Y, Zhang Z, Shen Q, Tao S, Deng Z, Lin Y, Wei Q. Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data. J Psychiatr Res 2024; 172:119-128. [PMID: 38377667 DOI: 10.1016/j.jpsychires.2024.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.
Collapse
Affiliation(s)
- Ming Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Mental Health Institute, Guangdong ProvincialPeople's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaowei Xia
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhinan Li
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiamin Dai
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junyan Wu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Cai Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Qiu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, Mindfront Caring Medical, Guangzhou, China
| | - Tong Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | - Yanxi Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ziyi Zhang
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Division, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qingni Shen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sichu Tao
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zixin Deng
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| | - Qinling Wei
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
13
|
Hui P, Jiang Y, Wang J, Wang C, Li Y, Fang B, Wang H, Wang Y, Qie S. Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease. Front Aging Neurosci 2024; 16:1354147. [PMID: 38524116 PMCID: PMC10957543 DOI: 10.3389/fnagi.2024.1354147] [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: 12/12/2023] [Accepted: 02/22/2024] [Indexed: 03/26/2024] Open
Abstract
Background Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention. Objective The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice. Methods Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability. Results The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08. Conclusion The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.
Collapse
Affiliation(s)
- Pengsheng Hui
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yu Jiang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Congxiao Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yingqi Li
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Boyan Fang
- Department of Neurological Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Hujun Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yingpeng Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Shuyan Qie
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
14
|
Tang QY, Zhong YL, Wang XM, Huang BL, Qin WG, Huang X. Machine Learning Analysis Classifies Patients with Primary Angle-Closure Glaucoma Using Abnormal Brain White Matter Function. Clin Ophthalmol 2024; 18:659-670. [PMID: 38468914 PMCID: PMC10926922 DOI: 10.2147/opth.s451872] [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: 11/26/2023] [Accepted: 02/05/2024] [Indexed: 03/13/2024] Open
Abstract
Objective Primary angle-closure glaucoma (PACG) is a globally prevalent, irreversible eye disease leading to blindness. Previous neuroimaging studies demonstrated that PACG patients were associated with gray matter function changes. However, whether the white matter(WM) function changes in PACG patients remains unknown. The purpose of the study is to investigate WM function changes in the PACG patients. Methods In total, 40 PACG patients and 40 well-matched HCs participated in our study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. We compared between-group differences between PACG patients and HC in the WM function using amplitude of low-frequency fluctuations (ALFF). In addition, the SVM method was applied to the construction of the PACG classification model. Results Compared with the HC group, ALFF was attenuated in right posterior thalamic radiation (include optic radiation), splenium of corpus callosum, and left tapetum in the PACG group, the results are statistically significant (GRF correction, voxel-level P < 0.001, cluster-level P < 0.05). Furthermore, the SVM classification had an accuracy of 80.0% and an area under the curve (AUC) of 0.86 for distinguishing patients with PACG from HC. Conclusion The findings of our study uncover abnormal WM functional alterations in PACG patients and mainly involves vision-related regions. These findings provide new insights into widespread brain damage in PACG from an alternative WM functional perspective.
Collapse
Affiliation(s)
- Qiu-Yu Tang
- College of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang City, Jiangxi, 330004, People’s Republic of China
| | - Yu-Lin Zhong
- Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330006, People’s Republic of China
| | - Xin-Miao Wang
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330000, People’s Republic of China
| | - Bing-Lin Huang
- College of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang City, Jiangxi, 330004, People’s Republic of China
| | - Wei-Guo Qin
- Department of Cardiothoracic Surgery, The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force’, Nanchang, People’s Republic of China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330006, People’s Republic of China
| |
Collapse
|
15
|
Minissi ME, Altozano A, Marín-Morales J, Chicchi Giglioli IA, Mantovani F, Alcañiz M. Biosignal comparison for autism assessment using machine learning models and virtual reality. Comput Biol Med 2024; 171:108194. [PMID: 38428095 DOI: 10.1016/j.compbiomed.2024.108194] [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: 09/05/2023] [Revised: 02/08/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.
Collapse
Affiliation(s)
- Maria Eleonora Minissi
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain.
| | - Alberto Altozano
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Javier Marín-Morales
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Irene Alice Chicchi Giglioli
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Fabrizia Mantovani
- Centre for Studies in Communication Sciences "Luigi Anolli" (CESCOM), Department of Human Sciences for Education ''Riccardo Massa'', University of Milano - Bicocca, Building U16, Via Tomas Mann, 20162, Milan, Italy
| | - Mariano Alcañiz
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| |
Collapse
|
16
|
Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, Caspers S, Jockwitz C. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience 2024; 46:283-308. [PMID: 37308769 PMCID: PMC10828156 DOI: 10.1007/s11357-023-00831-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023] Open
Abstract
Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.
Collapse
Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
17
|
Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
Collapse
Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
| |
Collapse
|
18
|
Shen S, Wei R, Gao Y, Yang X, Zhang G, Yan B, Xiao Z, Li J. Cortical atrophy in early-stage patients with anti-NMDA receptor encephalitis: a machine-learning MRI study with various feature extraction. Cereb Cortex 2024; 34:bhad499. [PMID: 38185983 DOI: 10.1093/cercor/bhad499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
Conventional brain magnetic resonance imaging (MRI) of anti-N-methyl-D-aspartate-receptor encephalitis (NMDARE) is non-specific, thus showing little differential diagnostic value, especially for MRI-negative patients. To characterize patterns of structural alterations and facilitate the diagnosis of MRI-negative NMDARE patients, we build two support vector machine models (NMDARE versus healthy controls [HC] model and NMDARE versus viral encephalitis [VE] model) based on radiomics features extracted from brain MRI. A total of 109 MRI-negative NMDARE patients in the acute phase, 108 HCs and 84 acute MRI-negative VE cases were included for training. Another 29 NMDARE patients, 28 HCs and 26 VE cases were included for validation. Eighty features discriminated NMDARE patients from HCs, with area under the receiver operating characteristic curve (AUC) of 0.963 in validation set. NMDARE patients presented with significantly lower thickness, area, and volume and higher mean curvature than HCs. Potential atrophy predominately presented in the frontal lobe (cumulative weight = 4.3725, contribution rate of 29.86%), and temporal lobe (cumulative weight = 2.573, contribution rate of 17.57%). The NMDARE versus VE model achieved certain diagnostic power, with AUC of 0.879 in validation set. Our research shows potential atrophy across the entire cerebral cortex in acute NMDARE patients, and MRI machine learning model has a potential to facilitate the diagnosis MRI-negative NMDARE.
Collapse
Affiliation(s)
- Sisi Shen
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
| | - Ran Wei
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Yu Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Xinyuan Yang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Guoning Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Bo Yan
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Zhuoling Xiao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Jinmei Li
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
| |
Collapse
|
19
|
Baba A. Neural networks from biological to artificial and vice versa. Biosystems 2024; 235:105110. [PMID: 38176518 DOI: 10.1016/j.biosystems.2023.105110] [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: 08/08/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024]
Abstract
In this paper, we examine how deep learning can be utilized to investigate neural health and the difficulties in interpreting neurological analyses within algorithmic models. The key contribution of this paper is the investigation of the impact of a dead neuron on the performance of artificial neural networks (ANNs). Therefore, we conduct several tests using different training algorithms and activation functions to identify the precise influence of the training process on neighboring neurons and the overall performance of the ANN in such cases. The aim is to assess the potential application of the findings in the biological domain, the expected results may have significant implications for the development of effective treatment strategies for neurological disorders. Successive training phases that incorporate visual and acoustic data derived from past social and familial experiences could be suggested to achieve this goal. Finally, we explore the conceptual analogy between the Adam optimizer and the learning process of the brain by delving into the specifics of both systems while acknowledging their fundamental differences.
Collapse
Affiliation(s)
- Abdullatif Baba
- Kuwait College of Science and Technology, Computer Science and Engineering Department, Kuwait; University of Turkish Aeronautical Association, Computer Engineering Department, Ankara, Turkey.
| |
Collapse
|
20
|
Sun X, Zhao C, Chen SY, Chang Y, Han YL, Li K, Sun HM, Wang ZF, Liang Y, Jia JJ. Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease. J Magn Reson Imaging 2023. [PMID: 38100518 DOI: 10.1002/jmri.29189] [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: 04/13/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD). PURPOSE To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes. STUDY TYPE Retrospective. POPULATION Thirty-eight Aβ-negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ-negative MCI patients (MCI-n) (68.87 ± 8.83 years old, 60% female), 29 Aβ-positive MCI patients (MCI-p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ-positive AD patients (72.93 ± 9.11 years old, 55% female). FIELD STRENGTH/SEQUENCE 3.0T; DTI, T1 -weighted, T2 -weighted, T2 star-weighted angiography, and Aβ PET (18 F-florbetaben or 11 C-PIB). ASSESSMENT FW-corrected and standard diffusion indices were analyzed using trace-based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM). STATISTICAL TESTS Chi-squared test, one-way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant. RESULTS Compared with CH/MCI-n/MCI-p, AD showed significant change in tissue compartment indices of FW-DTI. No difference was found in the FW index among pair-wise group comparisons (the minimum FWE-corrected P = 0.114). There was a significant association between FW-DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW-DTI was all over 89.89%. DATA CONCLUSION FW-DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW-DTI indices has good accuracy and could help early diagnosis. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Xuan Sun
- Medical School of Chinese PLA, Beijing, China
- Department of Geriatric Neurology, The Second Medical Centre, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Cui Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Si-Yu Chen
- Medical School of Chinese PLA, Beijing, China
- Department of Geriatric Neurology, The Second Medical Centre, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yan Chang
- Department of Nuclear Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yu-Liang Han
- Department of Neurology, The 305 Hospital of PLA, Beijing, China
| | - Ke Li
- Department of Geriatric Neurology, The Second Medical Centre, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hong-Mei Sun
- Medical School of Chinese PLA, Beijing, China
- Institute of Geriatrics, Chinese PLA General Hospital, Beijing, China
| | - Zhen-Fu Wang
- Department of Geriatric Neurology, The Second Medical Centre, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Jian-Jun Jia
- National Clinical Research Center of Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- Institute of Geriatrics, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
21
|
Rezaei S, Gharepapagh E, Rashidi F, Cattarinussi G, Sanjari Moghaddam H, Di Camillo F, Schiena G, Sambataro F, Brambilla P, Delvecchio G. Machine learning applied to functional magnetic resonance imaging in anxiety disorders. J Affect Disord 2023; 342:54-62. [PMID: 37683943 DOI: 10.1016/j.jad.2023.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/21/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders. METHODS We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria. RESULTS Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %. LIMITATIONS The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review. CONCLUSIONS ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.
Collapse
Affiliation(s)
- Sahar Rezaei
- Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Nuclear Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Esmaeil Gharepapagh
- Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Nuclear Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fatemeh Rashidi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy
| | | | - Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Giandomenico Schiena
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| |
Collapse
|
22
|
Abdoli N, Zhang K, Gilley P, Chen X, Sadri Y, Thai T, Dockery L, Moore K, Mannel R, Qiu Y. Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy. Bioengineering (Basel) 2023; 10:1334. [PMID: 38002458 PMCID: PMC10669238 DOI: 10.3390/bioengineering10111334] [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: 09/26/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.
Collapse
Affiliation(s)
- Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
| | - Ke Zhang
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Patrik Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
| | - Youkabed Sadri
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
| | - Theresa Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA;
| | - Lauren Dockery
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Kathleen Moore
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Robert Mannel
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (N.A.); (K.Z.); (Y.S.)
| |
Collapse
|
23
|
Hu H, Zhou J, Fang W, Chen HH, Jiang WH, Pu XY, Xu XQ, Gu WH, Wu FY. Increased brain iron in patients with thyroid-associated ophthalmopathy: a whole-brain analysis. Front Endocrinol (Lausanne) 2023; 14:1268279. [PMID: 38034014 PMCID: PMC10687634 DOI: 10.3389/fendo.2023.1268279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Background To investigate the whole-brain iron deposition alternations in patients with thyroid-associated ophthalmopathy (TAO) using quantitative susceptibility mapping (QSM). Methods Forty-eight patients with TAO and 33 healthy controls (HCs) were enrolled. All participants underwent brain magnetic resonance imaging scans and clinical scale assessments. QSM values were calculated and compared between TAO and HCs groups using a voxel-based analysis. A support vector machine (SVM) analysis was performed to evaluate the performance of QSM values in differentiating patients with TAO from HCs. Results Compared with HCs, patients with TAO showed significantly increased QSM values in the bilateral caudate nucleus (CN), left thalamus (TH), left cuneus, left precuneus, right insula and right middle frontal gyrus. In TAO group, QSM values in left TH were positively correlated with Hamilton Depression Rating Scale (HDRS) scores (r = 0.414, p = 0.005). The QSM values in right CN were negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r = -0.342, p = 0.021). Besides that, a nearly negative correlation was found between QSM values in left CN and MoCA scores (r = -0.286, p = 0.057). The SVM model showed a good performance in distinguishing patients with TAO from the HCs (area under the curve, 0.958; average accuracy, 90.1%). Conclusion Patients with TAO had significantly increased iron deposition in brain regions corresponding to known visual, emotional and cognitive deficits. QSM values could serve as potential neuroimaging markers of TAO.
Collapse
Affiliation(s)
- Hao Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Fang
- Department of Radiology, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of Taicang, Taicang, China
| | - Huan-Huan Chen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wen-Hao Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiong-Ying Pu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wen-Hao Gu
- Department of Radiology, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of Taicang, Taicang, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
24
|
Plank IS, Koehler JC, Nelson AM, Koutsouleris N, Falter-Wagner CM. Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model. Front Psychiatry 2023; 14:1257569. [PMID: 38025455 PMCID: PMC10658003 DOI: 10.3389/fpsyt.2023.1257569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous.
Collapse
Affiliation(s)
- I. S. Plank
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - J. C. Koehler
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - A. M. Nelson
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - N. Koutsouleris
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom
| | - C. M. Falter-Wagner
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
25
|
Abdul Baki S, Zakeri Z, Chari G, Fenton A, Omurtag A. Relaxed Alert Electroencephalography Screening for Mild Traumatic Brain Injury in Athletes. Int J Sports Med 2023; 44:896-905. [PMID: 37164326 DOI: 10.1055/a-2091-4860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Due to the mildness of initial injury, many athletes with recurrent mild traumatic brain injury (mTBI) are misdiagnosed with other neuropsychiatric illnesses. This study was designed as a proof-of-principle feasibility trial for athletic trainers at a sports facility to generate electroencephalograms (EEGs) from student athletes for discriminating (mTBI) associated EEGs from uninjured ones. A total of 47 EEGs were generated, with 30 athletes recruited at baseline (BL) pre-season, after a concussive injury (IN), and post-season (PS). Outcomes included: 1) visual analyses of EEGs by a neurologist; 2) support vector machine (SVM) classification for inferences about whether particular groups belonged to the three subgroups of BL, IN, or PS; and 3) analyses of EEG synchronies including phase locking value (PLV) computed between pairs of distinct electrodes. All EEGs were visually interpreted as normal. SVM classification showed that BL and IN could be discriminated with 81% accuracy using features of EEG synchronies combined. Frontal inter-hemispheric phase synchronization measured by PLV was significantly lower in the IN group. It is feasible for athletic trainers to record high quality EEGs from student athletes. Also, spatially localized metrics of EEG synchrony can discriminate mTBI associated EEGs from control EEGs.
Collapse
Affiliation(s)
- Samah Abdul Baki
- Clinical BioSignal Group Corp., Acton, Massachusetts, United States
| | - Zohreh Zakeri
- Department of Engineering, Nottingham Trent University School of Science and Technology, Nottingham, United Kingdom of Great Britain and Northern Ireland
| | - Geetha Chari
- Pediatric Neurology, SUNY Downstate Medical Center, New York City, United States
| | - André Fenton
- Center for Neural Science, NYU, New York, United States
| | - Ahmet Omurtag
- Department of Engineering, Nottingham Trent University School of Science and Technology, Nottingham, United Kingdom of Great Britain and Northern Ireland
| |
Collapse
|
26
|
Huang FF, Yang XY, Luo J, Yang XJ, Meng FQ, Wang PC, Li ZJ. Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods. BMC Psychiatry 2023; 23:792. [PMID: 37904114 PMCID: PMC10617132 DOI: 10.1186/s12888-023-05299-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/23/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. METHODS Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. RESULTS SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. CONCLUSION SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.
Collapse
Affiliation(s)
- Fang-Fang Huang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Henan, China
| | - Xiang-Yun Yang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jia Luo
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiao-Jie Yang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Fan-Qiang Meng
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Peng-Chong Wang
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zhan-Jiang Li
- Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
27
|
Kawaji K, Nakajo M, Shinden Y, Jinguji M, Tani A, Hirahara D, Kitazono I, Ohtsuka T, Yoshiura T. Application of Machine Learning Analyses Using Clinical and [ 18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Mol Imaging Biol 2023; 25:923-934. [PMID: 37193804 DOI: 10.1007/s11307-023-01823-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/20/2023] [Accepted: 04/28/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery. PROCEDURES This retrospective study included 112 patients with 118 breast cancer lesions who underwent [18F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [18F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances. RESULTS In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892-0.999), radiomic (range, 0.809-0.984), and combined (range, 0.897-0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM_ZLNU and AJCC stage. CONCLUSIONS ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.
Collapse
Affiliation(s)
- Kodai Kawaji
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Masatoyo Nakajo
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Yoshiaki Shinden
- Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Megumi Jinguji
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atsushi Tani
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Daisuke Hirahara
- Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan
| | - Ikumi Kitazono
- Department of Pathology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takao Ohtsuka
- Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| |
Collapse
|
28
|
Bruin WB, Abe Y, Alonso P, Anticevic A, Backhausen LL, Balachander S, Bargallo N, Batistuzzo MC, Benedetti F, Bertolin Triquell S, Brem S, Calesella F, Couto B, Denys DAJP, Echevarria MAN, Eng GK, Ferreira S, Feusner JD, Grazioplene RG, Gruner P, Guo JY, Hagen K, Hansen B, Hirano Y, Hoexter MQ, Jahanshad N, Jaspers-Fayer F, Kasprzak S, Kim M, Koch K, Bin Kwak Y, Kwon JS, Lazaro L, Li CSR, Lochner C, Marsh R, Martínez-Zalacaín I, Menchon JM, Moreira PS, Morgado P, Nakagawa A, Nakao T, Narayanaswamy JC, Nurmi EL, Zorrilla JCP, Piacentini J, Picó-Pérez M, Piras F, Piras F, Pittenger C, Reddy JYC, Rodriguez-Manrique D, Sakai Y, Shimizu E, Shivakumar V, Simpson BH, Soriano-Mas C, Sousa N, Spalletta G, Stern ER, Evelyn Stewart S, Szeszko PR, Tang J, Thomopoulos SI, Thorsen AL, Yoshida T, Tomiyama H, Vai B, Veer IM, Venkatasubramanian G, Vetter NC, Vriend C, Walitza S, Waller L, Wang Z, Watanabe A, Wolff N, Yun JY, Zhao Q, van Leeuwen WA, van Marle HJF, van de Mortel LA, van der Straten A, van der Werf YD, Thompson PM, Stein DJ, van den Heuvel OA, van Wingen GA. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium. Mol Psychiatry 2023; 28:4307-4319. [PMID: 37131072 PMCID: PMC10827654 DOI: 10.1038/s41380-023-02077-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/04/2023]
Abstract
Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
Collapse
Grants
- R01 AG058854 NIA NIH HHS
- R01 MH126213 NIMH NIH HHS
- R21 MH101441 NIMH NIH HHS
- R01 MH121520 NIMH NIH HHS
- R21 MH093889 NIMH NIH HHS
- R01 MH116147 NIMH NIH HHS
- R01 MH111794 NIMH NIH HHS
- R01 MH085900 NIMH NIH HHS
- P41 EB015922 NIBIB NIH HHS
- IA/CPHE/18/1/503956 DBT-Wellcome Trust India Alliance
- UL1 TR001863 NCATS NIH HHS
- R01 MH081864 NIMH NIH HHS
- R01 MH104648 NIMH NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- R01 MH116038 NIMH NIH HHS
- R01 MH126981 NIMH NIH HHS
- R01 NS107513 NINDS NIH HHS
- RF1 MH123163 NIMH NIH HHS
- R33 MH107589 NIMH NIH HHS
- K24 MH121571 NIMH NIH HHS
- R01 MH121246 NIMH NIH HHS
- Wellcome Trust
- K23 MH115206 NIMH NIH HHS
- R01 AG059874 NIA NIH HHS
- Funding from Japan Society for the Promotion of Science (KAKENHI Grant No. 18K15523)
- Carlos III Health Institute PI18/00856
- NIMH: 5R01MH116038
- Sara Bertolin was supported by Instituto de Salud Carlos III through the grant CM21/00278 (Co-funded by European Social Fund. ESF investing in your future).
- Hartmann Müller Foundation (no. 1460, principal investigator: S.Brem)
- NIHM: R01MH085900, R01MH121520
- NIH: K23 MH115206 & IOCDF Annual Research Award
- AMED Brain/MINDS Beyond program Grant No. JP22dm0307002, JSPS KAKENHI Grants No. 22H01090, 21K03084, 19K03309, 16K04344
- NIH: R01MH117601, R01AG059874, P41EB015922, R01MH126213, R01MH121246
- Michael Smith Health Research BC
- the Deutsche Forschungsgemeinschaf (KO 3744/11-1)
- This work was supported by the Medical Research Council of South Africa (SAMRC), and the National Research Foundation of South Africa (Christine Lochner), and we acknowledge the contribution of our research assistants.
- NIMH: R21MH093889, R21MH101441 and R01MH104648
- IM-Z was supported by a PFIS grant (FI17/00294) from the Carlos III Health Institute
- This work was supported by National funds, through the Foundation for Science and Technology (project UIDB/50026/2020 and UIDP/50026/2020); by the Norte Portugal Regional Operational Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) (projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-000023), and by the FLAD Science Award Mental Health 2021.
- JSPS KAKENHI (C)21K07547, 22K07598 and 22K15766
- Government of India grants from Department of Science and Technology (DST INSPIRE faculty grant -IFA12-LSBM-26) & Department of Biotechnology (BT/06/IYBA/2012)
- NIMH: R01MH081864
- MPP was supported by the Spanish Ministry of Universities, with funds from the European Union - NextGenerationEU (MAZ/2021/11).
- Italian Ministry of Health, Ricerca Corrente 2022, 2023
- NIMH: K24MH121571
- Government of India grants to: Prof. Reddy [(SR/S0/HS/0016/2011) & (BT/PR13334/Med/30/259/2009)], Dr. Janardhanan Narayanaswamy (DST INSPIRE faculty grant -IFA12-LSBM-26) & (BT/06/IYBA/2012) and the Wellcome-DBT India Alliance grant to Dr. Ganesan Venkatasubramanian (500236/Z/11/Z)
- the Japan Agency for Medical Research and Development: JP22dm0307008
- DBT-Wellcome Trust India Alliance Early Career Fellowship grant (IA/CPHE/18/1/503956)
- NIMH: R21MH093889 and R01MH104648
- Grant #PI19/01171 from the Carlos III Health Institute, and 2017SGR 1247 from AGAUR-Generalitat de Catalunya.
- Italian Ministry of Health grant RC19-20-21-22/A
- Grants R01MH126981, R01MH111794, and R33MH107589 from the National Institute of Mental Health/National Institute of Health awarded to ERS.
- National Natural Science Foundation of China (Nos. 81871057, 82171495), and Key Technologies Research and Development Program of China (Nos.2022YFE0103700)
- Helse Vest Health Authority (Grant ID 911754 and 911880)
- JSPS KAKENHI (C) JP21K07547, 22K07598 and 22K15766.
- Ganesan Venkatasubramanian acknowledges the support of Department of Biotechnology (DBT) - Wellcome Trust India Alliance CRC grant (IA/CRC/19/1/610005) & senior fellowship grant (500236/Z/11/Z)
- Supported by an grant from Amsterdam Neuroscience CIA-2019-03-A
- Swiss National Science Foundation (no. 320030_130237, principal investigator: S.Walitza)
- The National Natural Science Foundation of China (82071518)
- Else Kröner Fresenius Stiftung (2017_A101)
- ENIGMA World Aging Center, NIA Award No. R01AG058854; ENIGMA Parkinson's Initiative: A Global Initiative for Parkinson's Disease, NINDS award RO1NS107513
- the Obsessive-Compulsive Foundation to Dan J. Stein
- Dutch Organization for Scientific Research (NWO/ZonMW) VENI grant (916-86-038) and Brain & Behavior Research Foundation (NARSAD grant), Netherlands Brain Foundation (2010(1)-50)
- Netherlands Organization for Scientific Research (NWO/ZonMW Vidi Grant No. 165.610.002, 016.156.318, and 917.15.318 G.A. van Wingen)
Collapse
Affiliation(s)
- Willem B Bruin
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Pino Alonso
- Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Science, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Lea L Backhausen
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Srinivas Balachander
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Nuria Bargallo
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Radiology Service, Diagnosis Image Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marcelo C Batistuzzo
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
- Department of Methods and Techniques in Psychology, Pontifical Catholic University, Sao Paulo, Brazil
| | - Francesco Benedetti
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Sara Bertolin Triquell
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Federico Calesella
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Beatriz Couto
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Damiaan A J P Denys
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Marco A N Echevarria
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Goi Khia Eng
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Sónia Ferreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Jamie D Feusner
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- General Adult Psychiatry & Health Systems, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | | | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Joyce Y Guo
- University of California, San Diego, CA, USA
| | - Kristen Hagen
- Molde Hospital, Møre og Romsdal Hospital Trust, Molde, Norway
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjarne Hansen
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Center for Crisis Psychology, University of Bergen, Bergen, Norway
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Marcelo Q Hoexter
- Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fern Jaspers-Fayer
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Selina Kasprzak
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kathrin Koch
- Department of Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Yoo Bin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Luisa Lazaro
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic of Barcelona, Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Rachel Marsh
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Ignacio Martínez-Zalacaín
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Jose M Menchon
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Pedro S Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Akiko Nakagawa
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Tomohiro Nakao
- Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan
| | - Janardhanan C Narayanaswamy
- National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
- GVAMHS, Goulburn Valley Health, Shepparton, VIC, Australia
| | - Erika L Nurmi
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
| | - Jose C Pariente Zorrilla
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - John Piacentini
- Division of Child and Adolescent Psychiatry, UCLA Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Maria Picó-Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castelló de la Plana, Spain
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | | | - Janardhan Y C Reddy
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Daniela Rodriguez-Manrique
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Ludwig-Maximilians-Universität, Munich, Germany
| | - Yuki Sakai
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Eiji Shimizu
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Japan
- Department of Cognitive Behavioral Physiology Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Venkataram Shivakumar
- Department of Integrative Medicine, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Blair H Simpson
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Carles Soriano-Mas
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Nuno Sousa
- ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Emily R Stern
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - S Evelyn Stewart
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Philip R Szeszko
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anders L Thorsen
- Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
- Center for Crisis Psychology, University of Bergen, Bergen, Norway
| | - Tokiko Yoshida
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Hirofumi Tomiyama
- Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan
| | - Benedetta Vai
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Ilya M Veer
- Department of Developmental Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ganesan Venkatasubramanian
- Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
| | - Nora C Vetter
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Department of Psychology, Faculty of Natural Sciences, MSB Medical School Berlin, Berlin, Germany
| | - Chris Vriend
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging program, Amsterdam, The Netherlands
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Lea Waller
- Department of Psychiatry and Neurosciences CCM, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao, China
| | - Anri Watanabe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nicole Wolff
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Qing Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao, China
| | - Wieke A van Leeuwen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hein J F van Marle
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood Anxiety Psychosis Stress Sleep, Amsterdam, The Netherlands
| | - Laurens A van de Mortel
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Anouk van der Straten
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ysbrand D van der Werf
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging program, Amsterdam, The Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Odile A van den Heuvel
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention program, Amsterdam, The Netherlands
| | - Guido A van Wingen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| |
Collapse
|
29
|
Shim M, Hwang HJ, Lee SH. Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach. J Affect Disord 2023; 338:199-206. [PMID: 37302509 DOI: 10.1016/j.jad.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 03/30/2023] [Accepted: 06/04/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach. METHODS Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance. RESULTS The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy). CONCLUSION We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach.
Collapse
Affiliation(s)
- Miseon Shim
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Industry Development Institute, Korea University, Sejong, Republic of Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, Republic of Korea.
| | - Seung-Hwan Lee
- Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; BWAVE Inc., Goyang, Republic of Korea.
| |
Collapse
|
30
|
Orrù G, De Marchi B, Sartori G, Gemignani A, Scarpazza C, Monaro M, Mazza C, Roma P. Machine learning item selection for short scale construction: A proof-of-concept using the SIMS. Clin Neuropsychol 2023; 37:1371-1388. [PMID: 36017966 DOI: 10.1080/13854046.2022.2114548] [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: 04/05/2022] [Accepted: 08/12/2022] [Indexed: 11/03/2022]
Abstract
ObjectiveThis proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. MethodsIn the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. Results State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. ConclusionsThe results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value.
Collapse
Affiliation(s)
- Graziella Orrù
- Department of Surgical, Medical, Molecular & Critical Area Pathology, University of Pisa, Pisa, Italy
| | - Barbara De Marchi
- Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Giuseppe Sartori
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Gemignani
- Department of Surgical, Medical, Molecular & Critical Area Pathology, University of Pisa, Pisa, Italy
| | | | - Merylin Monaro
- Department of General Psychology, University of Padua, Padua, Italy
| | - Cristina Mazza
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Paolo Roma
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
31
|
Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
Collapse
Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
| |
Collapse
|
32
|
Chen Q, Bi Y, Yan W, Wu S, Xia T, Wang Y, Huang S, Zhou C, Xie S, Kuang S, Kong W, Lv Z. Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach. Front Psychiatry 2023; 14:1241670. [PMID: 37766927 PMCID: PMC10520785 DOI: 10.3389/fpsyt.2023.1241670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Objective To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approach. Methods 52 healthy controls and 48 first-episode MDD patients were recruited in the study. We performed neuropsychological tests and resting-state fMRI scanning on all subjects. The VMHC values of the symmetrical interhemispheric voxels in the whole brain were calculated. The VMHC alterations were compared between two groups, and the relationship between VMHC values and clinical variables was analyzed. Then, abnormal brain regions were selected as features to conduct the classification model by using the support vector machine (SVM) approach. Results Compared to the healthy controls, MDD patients exhibited decreased VMHC values in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus. Furthermore, the VMHC value of the bilateral fusiform gyrus was positively correlated with the total Hamilton Depression Scale (HAMD). Moreover, SVM analysis displayed that a combination of all clusters demonstrated the highest area under the curve (AUC) of 0.87 with accuracy, sensitivity, and specificity values of 86.17%, 76.74%, and 94.12%, respectively. Conclusion MDD patients had reduced functional connectivity in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus, which may be related to depressive symptoms. The abnormality in these brain regions could represent potential imaging markers to distinguish MDD patients from healthy controls.
Collapse
Affiliation(s)
- Qing Chen
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Yanmeng Bi
- College of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, China
| | - Weixin Yan
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shuhui Wu
- The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Ting Xia
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Yuhua Wang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Sha Huang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Chuying Zhou
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Shuwen Xie
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Shanshan Kuang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Wen Kong
- Guangzhou Hospital of Integrated Chinese and Western Medicine, Guangzhou, China
| | - Zhiping Lv
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| |
Collapse
|
33
|
Matéos M, Hacein-Bey L, Hanafi R, Mathys L, Amad A, Pruvo JP, Krystal S. Advanced imaging in first episode psychosis: a systematic review. J Neuroradiol 2023; 50:464-469. [PMID: 37028754 DOI: 10.1016/j.neurad.2023.04.001] [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: 03/14/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023]
Abstract
First-episode psychosis (FEP) is defined as the first occurrence of delusions, hallucinations, or psychic disorganization of significant magnitude, lasting more than 7 days. Evolution is difficult to predict since the first episode remains isolated in one third of cases, while recurrence occurs in another third, and the last third progresses to a schizo-affective disorder. It has been suggested that the longer psychosis goes unnoticed and untreated, the more severe the probability of relapse and recovery. MRI has become the gold standard for imaging psychiatric disorders, especially first episode psychosis. Besides ruling out some neurological conditions that may have psychiatric manifestations, advanced imaging techniques allow for identifying imaging biomarkers of psychiatric disorders. We performed a systematic review of the literature to determine how advanced imaging in FEP may have high diagnostic specificity and predictive value regarding the evolution of disease.
Collapse
Affiliation(s)
- Marjorie Matéos
- Lille University Hospital Center, Department of Neuroradiology, Lille, France.
| | - Lotfi Hacein-Bey
- Neuroradiology, Radiology Department, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
| | - Riyad Hanafi
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Luc Mathys
- Lille University Hospital Center, Department of Neuroradiology, Lille, France
| | - Ali Amad
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; Department of neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jean-Pierre Pruvo
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Sidney Krystal
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Radiology Department, A. de Rothschild Foundation Hospital, Paris, France; Neurospin, CEA, Université Paris-Saclay, Gif-Sur-Yvette, Paris, France
| |
Collapse
|
34
|
Zhou Q, Chen Z, Wu B, Lin D, Hu Y, Zhang X, Liu J. A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep Learning. Urology 2023; 179:188-195. [PMID: 37315592 DOI: 10.1016/j.urology.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To develop two intelligent diagnosis models of detrusor overactivity (DO) based on deep learning to assist doctors no longer heavily rely on visual observation of urodynamic study (UDS) curves. METHODS UDS curves of 92 patients were collected during 2019. We constructed two DO event recognition models based on convolutional neural network (CNN) with 44 samples, and tested the model performance with the remaining 48 samples by comparing other four classical machine learning models. During the testing phase, we developed a threshold screening strategy to quickly filter out suspected DO event segments in each patient's UDS curve. If two or more DO event fragments are determined to be DO by the diagnostic model, the patient is diagnosed as having DO. RESULTS We extracted 146 DO event samples and 1863 non-DO event samples from the UDS curves of 44 patients to train CNN models. Through 10-fold cross-validation, the training accuracy and validation accuracy of our models achieved the highest accuracy. In the model testing phase, we used a threshold screening strategy to quickly screen out the suspected DO event samples in the UDS curve of another 48 patients, and then input them into the trained models. Finally, the diagnostic accuracy of patients without DO and patients with DO was 78.12% and 100%, respectively. CONCLUSION Under the available data, the accuracy of the DO diagnostic model based on CNN is satisfactory. With the increase of the amount of data, the deep learning model is likely to have better performance. CLINICAL TRIAL REGISTRATION This experiment was certified by the Chinese Clinical Trial Registry (ChiCTR2200063467).
Collapse
Affiliation(s)
- Quan Zhou
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Zhong Chen
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Urology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Bo Wu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Dongxu Lin
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youmin Hu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Xin Zhang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Liu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
35
|
Lv Q, Zeljic K, Zhao S, Zhang J, Zhang J, Wang Z. Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning. Neurosci Bull 2023; 39:1309-1326. [PMID: 37093448 PMCID: PMC10387015 DOI: 10.1007/s12264-023-01057-2] [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: 09/02/2022] [Accepted: 02/17/2023] [Indexed: 04/25/2023] Open
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Collapse
Affiliation(s)
- Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Kristina Zeljic
- School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Shaoling Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Jiangtao Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Jianmin Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
| |
Collapse
|
36
|
Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput Biol Med 2023; 162:107051. [PMID: 37271113 DOI: 10.1016/j.compbiomed.2023.107051] [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: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
Collapse
Affiliation(s)
- Nancy Sanjay Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
| |
Collapse
|
37
|
Huth F, Tozzi L, Marxen M, Riedel P, Bröckel K, Martini J, Berndt C, Sauer C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Thomas-Odenthal F, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Biere S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M, Pfennig A, Mikolas P. Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes. Brain Sci 2023; 13:870. [PMID: 37371350 DOI: 10.3390/brainsci13060870] [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: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.
Collapse
Affiliation(s)
- Fabian Huth
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Marxen
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Julia Martini
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christina Berndt
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Cathrin Sauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christoph Vogelbacher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, 35037 Marburg, Germany
- Translational Clinical Psychology, Philipps-University Marburg, 35037 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
| | - Andreas Jansen
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, 35037 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Tilo Kircher
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Irina Falkenberg
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Florian Thomas-Odenthal
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Vivien Kraft
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Christoph Mulert
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig University Giessen, 35390 Gießen, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Thomas Ethofer
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Anne Rau
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Silvia Biere
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
| | - Jana Fiebig
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy, Brandenburg Medical School Theodor Fontane, 16816 Neuruppin, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany
- Department of Psychiatry, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, New York, NY 11004, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Georg Juckel
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44791 Bochum, Germany
| | - Vera Flasbeck
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44791 Bochum, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| |
Collapse
|
38
|
Orrù G, Piarulli A, Conversano C, Gemignani A. Human-like problem-solving abilities in large language models using ChatGPT. Front Artif Intell 2023; 6:1199350. [PMID: 37293238 PMCID: PMC10244637 DOI: 10.3389/frai.2023.1199350] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Backgrounds The field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT). GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations. Aim The aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants. Materials and methods A total of 30 problems labeled as "practice problems" and "transfer problems" were administered to ChatGPT. ChatGPT's answers received a score of "0" for each incorrectly answered problem and a score of "1" for each correct response. The highest possible score for both the practice and transfer problems was 15 out of 15. The solution rate for each problem (based on a sample of 20 subjects) was used to assess and compare the performance of ChatGPT with that of human subjects. Results The study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well. Conclusions The use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI into psychological research. However, it is acknowledged that there are still open challenges. Indeed, further research is required to fully understand AI's capabilities and limitations in verbal problem-solving.
Collapse
|
39
|
Zhao K, Fonzo GA, Xie H, Oathes DJ, Keller CJ, Carlisle N, Etkin A, Garza-Villarreal EA, Zhang Y. A generalizable functional connectivity signature characterizes brain dysfunction and links to rTMS treatment response in cocaine use disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.21.23288948. [PMID: 37162878 PMCID: PMC10168499 DOI: 10.1101/2023.04.21.23288948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Cocaine use disorder (CUD) is a prevalent substance abuse disorder, and repetitive transcranial magnetic stimulation (rTMS) has shown potential in reducing cocaine cravings. However, a robust and replicable biomarker for CUD phenotyping is lacking, and the association between CUD brain phenotypes and treatment response remains unclear. Our study successfully established a cross-validated functional connectivity signature for accurate CUD phenotyping, using resting-state functional magnetic resonance imaging from a discovery cohort, and demonstrated its generalizability in an independent replication cohort. We identified phenotyping FCs involving increased connectivity between the visual network and dorsal attention network, and between the frontoparietal control network and ventral attention network, as well as decreased connectivity between the default mode network and limbic network in CUD patients compared to healthy controls. These abnormal connections correlated significantly with other drug use history and cognitive dysfunctions, e.g., non-planning impulsivity. We further confirmed the prognostic potential of the identified discriminative FCs for rTMS treatment response in CUD patients and found that the treatment-predictive FCs mainly involved the frontoparietal control and default mode networks. Our findings provide new insights into the neurobiological mechanisms of CUD and the association between CUD phenotypes and rTMS treatment response, offering promising targets for future therapeutic development.
Collapse
Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Desmond J. Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Corey J. Keller
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Mexico
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
| |
Collapse
|
40
|
Sato Y, Okada G, Yokoyama S, Ichikawa N, Takamura M, Mitsuyama Y, Shimizu A, Itai E, Shinzato H, Kawato M, Yahata N, Okamoto Y. Resting-state functional connectivity disruption between the left and right pallidum as a biomarker for subthreshold depression. Sci Rep 2023; 13:6349. [PMID: 37072448 PMCID: PMC10113366 DOI: 10.1038/s41598-023-33077-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 04/06/2023] [Indexed: 05/03/2023] Open
Abstract
Although the identification of late adolescents with subthreshold depression (StD) may provide a basis for developing effective interventions that could lead to a reduction in the prevalence of StD and prevent the development of major depressive disorder, knowledge about the neural basis of StD remains limited. The purpose of this study was to develop a generalizable classifier for StD and to shed light on the underlying neural mechanisms of StD in late adolescents. Resting-state functional magnetic resonance imaging data of 91 individuals (30 StD subjects, 61 healthy controls) were included to build an StD classifier, and eight functional connections were selected by using the combination of two machine learning algorithms. We applied this biomarker to an independent cohort (n = 43) and confirmed that it showed generalization performance (area under the curve = 0.84/0.75 for the training/test datasets). Moreover, the most important functional connection was between the left and right pallidum, which may be related to clinically important dysfunctions in subjects with StD such as anhedonia and hyposensitivity to rewards. Investigation of whether modulation of the identified functional connections can be an effective treatment for StD may be an important topic of future research.
Collapse
Affiliation(s)
- Yosuke Sato
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
- Deloitte Analytics R&D, Deloitte Touche Tohmatsu LLC, Tokyo, Japan
| | - Masahiro Takamura
- Department of Neurology, Shimane University, Matsue, Japan
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Mitsuyama
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Ayaka Shimizu
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Eri Itai
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Hotaka Shinzato
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Mitsuo Kawato
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Noriaki Yahata
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| |
Collapse
|
41
|
He M, Kolesar TA, Goertzen AL, Ng MC, Ko JH. Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism? Biomedicines 2023; 11:biomedicines11041108. [PMID: 37189726 DOI: 10.3390/biomedicines11041108] [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: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ2 = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.
Collapse
Affiliation(s)
- Michael He
- Undergraduate Medical Education, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
| | - Tiffany A Kolesar
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
| | - Andrew L Goertzen
- Section of Nuclear Medicine, Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Marcus C Ng
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
- Section of Neurology, Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W2, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| |
Collapse
|
42
|
Lin S, Wang D, Sang H, Xiao H, Yan K, Wang D, Zhang Y, Yi L, Shao G, Shao Z, Yang A, Zhang L, Sun J. Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network. NEUROPHOTONICS 2023; 10:025001. [PMID: 37025568 PMCID: PMC10072005 DOI: 10.1117/1.nph.10.2.025001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients. AIM We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction. APPROACH Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics. RESULTS The small-world properties of the motor network were significantly different between groups: (1) clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2) global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%. CONCLUSIONS Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.
Collapse
Affiliation(s)
- Shuoshu Lin
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Dan Wang
- Beijing Rehabilitation Hospital of Capital Medical University, Department of Traditional Chinese Medicine, Beijing, China
| | - Haojun Sang
- Chinese Institute for Brain Research, Beijing, China
| | - Hongjun Xiao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Kecheng Yan
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Dongyang Wang
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Yizheng Zhang
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Li Yi
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Guangjian Shao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Zhiyong Shao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Aoran Yang
- Beijing Rehabilitation Hospital of Capital Medical University, Department of Traditional Chinese Medicine, Beijing, China
| | - Lei Zhang
- Chinese Institute for Brain Research, Beijing, China
- Capital Medical University, School of Biomedical Engineering, Beijing, China
| | - Jinyan Sun
- Foshan University, School of Medicine, Foshan, China
| |
Collapse
|
43
|
Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA, Pittman B, Scheinost D, Constable RT, Blumberg HP. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med 2023; 53:1-10. [PMID: 36891769 PMCID: PMC10491744 DOI: 10.1017/s003329172300003x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
Collapse
Affiliation(s)
- Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lejla Colic
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, Halle-Jena-Magdeburg, Magdeburg, Germany
| | - Danielle A. Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jihoon A. Kim
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| |
Collapse
|
44
|
Moinian S, Vegh V, Reutens D. Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals. Cereb Cortex 2023; 33:1550-1565. [PMID: 35483706 PMCID: PMC9977388 DOI: 10.1093/cercor/bhac155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
Collapse
Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| |
Collapse
|
45
|
Bajaj S, Blair KS, Dobbertin M, Patil KR, Tyler PM, Ringle JL, Bashford-Largo J, Mathur A, Elowsky J, Dominguez A, Schmaal L, Blair RJR. Machine learning based identification of structural brain alterations underlying suicide risk in adolescents. DISCOVER MENTAL HEALTH 2023; 3:6. [PMID: 37861863 PMCID: PMC10501026 DOI: 10.1007/s44192-023-00033-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 10/21/2023]
Abstract
Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.
Collapse
Affiliation(s)
- Sahil Bajaj
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA.
| | - Karina S Blair
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Matthew Dobbertin
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Child and Adolescent Psychiatric Inpatient Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick M Tyler
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jay L Ringle
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Johannah Bashford-Largo
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Avantika Mathur
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Jaimie Elowsky
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Ahria Dominguez
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Lianne Schmaal
- Center for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, Australia
| | - R James R Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| |
Collapse
|
46
|
Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
Collapse
Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
| |
Collapse
|
47
|
Wang ZL, Potenza MN, Song KR, Dong GH, Fang XY, Zhang JT. Subgroups of internet gaming disorder based on addiction-related resting-state functional connectivity. Addiction 2023; 118:327-339. [PMID: 36089824 DOI: 10.1111/add.16047] [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: 03/31/2022] [Accepted: 08/31/2022] [Indexed: 01/05/2023]
Abstract
AIMS To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. DESIGN Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. SETTING Zhejiang province and Beijing, China. PARTICIPANTS One hundred and sixty-nine IGD and 147 control subjects. MEASUREMENTS k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures. FINDINGS Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks. CONCLUSIONS There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.
Collapse
Affiliation(s)
- Zi-Liang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Marc N Potenza
- Department of Psychiatry and Child Study Center, Yale University School of Medicine, New Haven, CT, USA.,Connecticut Council on Problem Gambling, Wethersfield, CT, USA.,Connecticut Mental Health Center, New Haven, CT, USA.,Department of Neuroscience and Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Kun-Ru Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Xiao-Yi Fang
- Institute of Developmental Psychology, Beijing Normal University, Beijing, China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| |
Collapse
|
48
|
Goenka MK, Afzalpurkar S, Jejurikar S, Rodge GA, Tiwari A. Role of artificial intelligence-guided esophagogastroduodenoscopy in assessing the procedural completeness and quality. Indian J Gastroenterol 2023; 42:128-135. [PMID: 36715841 DOI: 10.1007/s12664-022-01294-9] [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: 03/22/2022] [Accepted: 08/12/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS The quality of esophagogastroduodenoscopy (EGD) can have great impact on the detection of esophageal and gastric lesions, including malignancies. The aim of the study is to investigate the use of artificial intelligence (AI) during EGD by the endoscopists-in-training so that a real-time feedback can be provided, ensuring compliance to a pre-decided protocol for examination. METHODS This is an observational pilot study. The videos of the EGD procedure performed between August 1, 2021, and September 30, 2021, were prospectively analyzed using AI system. The assessment of completeness of the procedure was done based on the visualizsation of pre-defined 29 locations. Endoscopists were divided into two categories - whether they are in the training period (category A) or have competed their endoscopy training (category B). RESULTS A total of 277 procedures, which included 114 category-A and 163 category-B endoscopists, respectively, were included. Most commonly covered areas by the endoscopists were greater curvature of antrum (97.47%), second part of duodenum (96.75%), other parts of antrum such as the anterior, lesser curvature and the posterior aspect (96.75%, 94.95%, and 94.22%, respectively). Commonly missed or inadequately seen areas were vocal cords (99.28%), epiglottis (93.14%) and posterior, anterior, and lateral aspect of incisura (78.70%, 73.65%, and 73.53%, respectively). The good quality procedures were done predominantly by categoryB endoscopists (88.68% vs. 11.32%, p < 0.00001). CONCLUSION AI can play an important role in assessing the quality and completeness of EGD and can be a part of training of endoscopy in future.
Collapse
Affiliation(s)
- Mahesh Kumar Goenka
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India.
| | - Shivaraj Afzalpurkar
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | | | - Gajanan Ashokrao Rodge
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | - Awanish Tiwari
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| |
Collapse
|
49
|
Cîrstian R, Pilmeyer J, Bernas A, Jansen JFA, Breeuwer M, Aldenkamp AP, Zinger S. Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI. J Neuroimaging 2023; 33:404-414. [PMID: 36710075 DOI: 10.1111/jon.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND PURPOSE The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.
Collapse
Affiliation(s)
- Ramona Cîrstian
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Antoine Bernas
- Department of Biophysics, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| |
Collapse
|
50
|
Gorenflo MP, Davis PB, Kendall EK, Olaker VR, Kaelber DC, Xu R. Association of Aspirin Use with Reduced Risk of Developing Alzheimer's Disease in Elderly Ischemic Stroke Patients: A Retrospective Cohort Study. J Alzheimers Dis 2023; 91:697-704. [PMID: 36502331 DOI: 10.3233/jad-220901] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Currently there are no effective therapies to prevent or halt the development of Alzheimer's disease (AD). Multiple risk factors are involved in AD, including ischemic stroke (IS). Aspirin is often prescribed following IS to prevent blood clot formation. Observational studies have shown inconsistent findings with respect to the relationship between aspirin use and the risk of AD. OBJECTIVE To investigate the relationship between aspirin therapy after IS and the new diagnosis of AD in elderly patients. METHODS This retrospective cohort study leveraged a large database that contains over 90 million electronic health records to compare the hazard rates of AD after IS in elderly patients prescribed aspirin versus those not prescribed aspirin after propensity-score matching for relevant confounders. RESULTS At 1, 3, and 5 years after first IS, elderly patients prescribed aspirin were less likely to develop AD than those not prescribed aspirin: Hazard Ratio = 0.78 [0.65,0.94], 0.81 [0.70,0.94], and 0.76 [0.70,0.92]. CONCLUSION Our findings suggest that aspirin use may prevent AD in patients with IS, a subpopulation at high risk of developing the disease.
Collapse
Affiliation(s)
- Maria P Gorenflo
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Pamela B Davis
- Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Ellen K Kendall
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Veronica R Olaker
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - David C Kaelber
- The Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| |
Collapse
|