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Siarkos K, Karavasilis E, Velonakis G, Papageorgiou C, Smyrnis N, Kelekis N, Politis A. Brain multi-contrast, multi-atlas segmentation of diffusion tensor imaging and ensemble learning automatically diagnose late-life depression. Sci Rep 2023; 13:22743. [PMID: 38123613 PMCID: PMC10733280 DOI: 10.1038/s41598-023-49935-z] [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: 08/01/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
We investigated the potential of machine learning for diagnostic classification in late-life major depression based on an advanced whole brain white matter segmentation framework. Twenty-six late-life depression and 12 never depressed individuals aged > 55 years, matched for age, MMSE, and education underwent brain diffusion tensor imaging and a multi-contrast, multi-atlas segmentation in MRIcloud. Fractional anisotropy volume, mean fractional anisotropy, trace, axial and radial diffusivity (RD) extracted from 146 white matter parcels for each subject were used to train and test the AdaBoost classifier using stratified 12-fold cross validation. Performance was evaluated using various measures. The statistical power of the classifier was assessed using label permutation test. Statistical analysis did not yield significant differences in DTI measures between the groups. The classifier achieved a balanced accuracy of 71% and an Area Under the Receiver Operator Characteristic Curve (ROC-AUC) of 0.81 by trace, and a balanced accuracy of 70% and a ROC-AUC of 0.80 by RD, in limbic, cortico-basal ganglia-thalamo-cortical loop, brainstem, external and internal capsules, callosal and cerebellar structures. Both indices shared important structures for classification, while fornix was the most important structure for classification by both indices. The classifier proved statistically significant, as trace and RD ROC-AUC scores after permutation were lower than those obtained with the actual data (P = 0.022 and P = 0.024, respectively). Similar results were obtained with the Gradient Boosting classifier, whereas the RBF-kernel Support Vector Machine with k-best feature selection did not exceed the chance level. Finally, AdaBoost significantly predicted the class using all features together. Limitations are discussed. The results encourage further investigation of the implemented methods for computer aided diagnostics and anatomically informed therapeutics.
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Affiliation(s)
- Kostas Siarkos
- Division of Geriatric Psychiatry, First Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece.
| | - Efstratios Karavasilis
- Medical School, Democritus University of Thrace, Alexandroupolis, Greece
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios Velonakis
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Charalabos Papageorgiou
- University Mental Health, Neurosciences and Precision Medicine Research Institute "Costas Stefanis", Athens, Greece
| | - Nikolaos Smyrnis
- Second Department of Psychiatry, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos Kelekis
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Antonios Politis
- Division of Geriatric Psychiatry, First Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins Medical School, Baltimore, USA
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2
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Hinault T, D'Argembeau A, Bowler DM, La Corte V, Desaunay P, Provasi J, Platel H, Tran The J, Charretier L, Giersch A, Droit-Volet S. Time processing in neurological and psychiatric conditions. Neurosci Biobehav Rev 2023; 154:105430. [PMID: 37871780 DOI: 10.1016/j.neubiorev.2023.105430] [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: 03/24/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/25/2023]
Abstract
A central question in understanding cognition and pathology-related cognitive changes is how we process time. However, time processing difficulties across several neurological and psychiatric conditions remain seldom investigated. The aim of this review is to develop a unifying taxonomy of time processing, and a neuropsychological perspective on temporal difficulties. Four main temporal judgments are discussed: duration processing, simultaneity and synchrony, passage of time, and mental time travel. We present an integrated theoretical framework of timing difficulties across psychiatric and neurological conditions based on selected patient populations. This framework provides new mechanistic insights on both (a) the processes involved in each temporal judgement, and (b) temporal difficulties across pathologies. By identifying underlying transdiagnostic time-processing mechanisms, this framework opens fruitful avenues for future research.
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Affiliation(s)
- Thomas Hinault
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France.
| | - Arnaud D'Argembeau
- Psychology and Neuroscience of Cognition Research Unit, University of Liège, F.R.S-FNRS, 4000 Liège, Belgium
| | - Dermot M Bowler
- Autism Research Group, City, University of London, EC1V 0HB London, United Kingdom
| | - Valentina La Corte
- Laboratoire Mémoire, Cerveau et Cognition (MC2Lab), UR 7536, Université de Paris cité, 92774 Boulogne-Billancourt, France; Institut Universitaire de France, 75231 Paris, France
| | - Pierre Desaunay
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France; Service de Psychiatrie de l'enfant et de l'adolescent, CHU de Caen, 14000 Caen, France
| | - Joelle Provasi
- CHArt laboratory (Human and Artificial Cognition), EPHE-PSL, 75014 Paris, France
| | - Hervé Platel
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Jessica Tran The
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Laura Charretier
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Anne Giersch
- Cognitive Neuropsychology and Pathophysiology of Schizophrenia Laboratory, National Institute of Health and Medical Research, University of Strasbourg, 67081 Strasbourg, France
| | - Sylvie Droit-Volet
- Université Clermont Auvergne, LAPSCO, CNRS, UMR 6024, 60032 Clermont-Ferrand, France
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3
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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4
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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5
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A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures. Neurosci Biobehav Rev 2023; 144:104972. [PMID: 36436736 DOI: 10.1016/j.neubiorev.2022.104972] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/08/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. AIMS In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. DESIGN The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. RESULTS The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. LIMITATIONS The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. CONCLUSIONS In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.
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6
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Carrier M, Dolhan K, Bobotis BC, Desjardins M, Tremblay MÈ. The implication of a diversity of non-neuronal cells in disorders affecting brain networks. Front Cell Neurosci 2022; 16:1015556. [PMID: 36439206 PMCID: PMC9693782 DOI: 10.3389/fncel.2022.1015556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
Abstract
In the central nervous system (CNS) neurons are classically considered the functional unit of the brain. Analysis of the physical connections and co-activation of neurons, referred to as structural and functional connectivity, respectively, is a metric used to understand their interplay at a higher level. A myriad of glial cell types throughout the brain composed of microglia, astrocytes and oligodendrocytes are key players in the maintenance and regulation of neuronal network dynamics. Microglia are the central immune cells of the CNS, able to affect neuronal populations in number and connectivity, allowing for maturation and plasticity of the CNS. Microglia and astrocytes are part of the neurovascular unit, and together they are essential to protect and supply nutrients to the CNS. Oligodendrocytes are known for their canonical role in axonal myelination, but also contribute, with microglia and astrocytes, to CNS energy metabolism. Glial cells can achieve this variety of roles because of their heterogeneous populations comprised of different states. The neuroglial relationship can be compromised in various manners in case of pathologies affecting development and plasticity of the CNS, but also consciousness and mood. This review covers structural and functional connectivity alterations in schizophrenia, major depressive disorder, and disorder of consciousness, as well as their correlation with vascular connectivity. These networks are further explored at the cellular scale by integrating the role of glial cell diversity across the CNS to explain how these networks are affected in pathology.
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Affiliation(s)
- Micaël Carrier
- Neurosciences Axis, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Kira Dolhan
- Department of Psychology, University of Victoria, Victoria, BC, Canada
- Department of Biology, University of Victoria, Victoria, BC, Canada
| | | | - Michèle Desjardins
- Department of Physics, Physical Engineering and Optics, Université Laval, Québec City, QC, Canada
- Oncology Axis, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Marie-Ève Tremblay
- Neurosciences Axis, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Molecular Medicine, Université Laval, Québec City, QC, Canada
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada
- *Correspondence: Marie-Ève Tremblay,
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7
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Yang J, Lei D, Suo X, Tallman MJ, Qin K, Li W, Bruns KM, Blom TJ, Duran LRP, Cotton S, Sweeney JA, Gong Q, DelBello MP. A preliminary study of the effects of mindfulness-based cognitive therapy on structural brain networks in mood-dysregulated youth with a familial risk for bipolar disorder. Early Interv Psychiatry 2022; 16:1011-1019. [PMID: 34808702 DOI: 10.1111/eip.13245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mindfulness-based cognitive therapy for children (MBCT-C), as a psychotherapeutic intervention, has been shown to be effective for treating mood dysregulation (MD). While previous neuroimaging studies of MD have reported both pre-treatment structural and functional alterations, the effects of MBCT-C on brain morphological network organisation has not been investigated. METHODS We investigated brain morphological network organisation in 10 mood-dysregulated youth with familial risk for bipolar disorder and 15 matched healthy comparison youth (HC). Effects of 12 weeks of MBCT-C were examined in the mood-dysregulated youth. Topological properties of brain networks used for analyses were constructed based on morphological similarities in regional grey matter using a graph-theory approach using MRI data. RESULTS At baseline, compared with the HC group, the mood-dysregulated group exhibited increased global efficiency (Eglob ), decreased path length (Lp ), and abnormal nodal properties, mainly in the limbic system. Right temporal pole alterations at baseline predicted change in Child and Adolescent Mindfulness Measure scores after treatment. The mood-dysregulated group showed significant decreases in both the Eglob and Lp metrics after MBCT-C, suggesting an improved capacity for optimal information processing. Changes in Lp were correlated with changes in Emotion Regulation Checklist scores. Our results show significant topological alterations in the mood-dysregulated group as compared to controls at baseline. After MBCT-C, disrupted topological properties in the mood-dysregulated group were significantly reduced. CONCLUSION MBCT-C may facilitate clinically meaningful changes in the brain structural network in mood-dysregulated individuals.
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Affiliation(s)
- Jing Yang
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kaitlyn M Bruns
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Thomas J Blom
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Luis Rodrigo Patino Duran
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Sian Cotton
- Department of Family and Community Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi Xiamen Hospital of Sichuan University, Xiamen, China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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8
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Kaczanowska J, Ganglberger F, Chernomor O, Kargl D, Galik B, Hess A, Moodley Y, von Haeseler A, Bühler K, Haubensak W. Molecular archaeology of human cognitive traits. Cell Rep 2022; 40:111287. [PMID: 36044840 DOI: 10.1016/j.celrep.2022.111287] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 05/20/2022] [Accepted: 08/05/2022] [Indexed: 01/06/2023] Open
Abstract
The brains and minds of our human ancestors remain inaccessible for experimental exploration. Therefore, we reconstructed human cognitive evolution by projecting nonsynonymous/synonymous rate ratios (ω values) in mammalian phylogeny onto the anatomically modern human (AMH) brain. This atlas retraces human neurogenetic selection and allows imputation of ancestral evolution in task-related functional networks (FNs). Adaptive evolution (high ω values) is associated with excitatory neurons and synaptic function. It shifted from FNs for motor control in anthropoid ancestry (60-41 mya) to attention in ancient hominoids (26-19 mya) and hominids (19-7.4 mya). Selection in FNs for language emerged with an early hominin ancestor (7.4-1.7 mya) and was later accompanied by adaptive evolution in FNs for strategic thinking during recent (0.8 mya-present) speciation of AMHs. This pattern mirrors increasingly complex cognitive demands and suggests that co-selection for language alongside strategic thinking may have separated AMHs from their archaic Denisovan and Neanderthal relatives.
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Affiliation(s)
- Joanna Kaczanowska
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | | | - Olga Chernomor
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna, Medical University of Vienna, Dr. Bohr Gasse 9, 1030 Vienna, Austria
| | - Dominic Kargl
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria; Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Bence Galik
- Bioinformatics and Scientific Computing, Vienna Biocenter Core Facilities (VBCF), Dr. Bohr Gasse 3, 1030 Vienna, Austria
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nuremberg, Fahrstrasse 17, 91054 Erlangen, Germany
| | - Yoshan Moodley
- Department of Zoology, University of Venda, Private Bag X5050, Thohoyandou, Republic of South Africa
| | - Arndt von Haeseler
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna, Medical University of Vienna, Dr. Bohr Gasse 9, 1030 Vienna, Austria; Faculty of Computer Science, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
| | - Katja Bühler
- VRVis Research Center, Donau-City Strasse 11, 1220 Vienna, Austria
| | - Wulf Haubensak
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria; Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria.
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9
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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10
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Wang YM, Cai XL, Zhang RT, Zhang YJ, Zhou HY, Wang Y, Wang Y, Huang J, Wang YY, Cheung EFC, Chan RCK. Altered brain structural and functional connectivity in schizotypy. Psychol Med 2022; 52:834-843. [PMID: 32677599 DOI: 10.1017/s0033291720002445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of 'brain disconnection' and 'brain connectivity compensation' to 'brain connectivity decompensation'. METHODS In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated. RESULTS We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network. CONCLUSIONS These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.
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Affiliation(s)
- Yong-Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Xin-Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Rui-Ting Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi-Jing Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Han-Yu Zhou
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Ya Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Jia Huang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yan-Yu Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, Weifang Medical University, Shandong Province, PR China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region, PR China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
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11
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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12
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Miller JG, Chahal R, Gotlib IH. Early Life Stress and Neurodevelopment in Adolescence: Implications for Risk and Adaptation. Curr Top Behav Neurosci 2022; 54:313-339. [PMID: 35290658 DOI: 10.1007/7854_2022_302] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An alarming high proportion of youth experience at least one kind of stressor in childhood and/or adolescence. Exposure to early life stress is associated with increased risk for psychopathology, accelerated biological aging, and poor physical health; however, it is important to recognize that not all youth who experience such stress go on to develop difficulties. In fact, resilience, or positive adaptation in the face of adversity, is relatively common. Individual differences in vulnerability or resilience to the effects of early stress may be represented in the brain as specific patterns, profiles, or signatures of neural activation, structure, and connectivity (i.e., neurophenotypes). Whereas neurophenotypes of risk that reflect the deleterious effects of early stress on the developing brain are likely to exacerbate negative outcomes in youth, neurophenotypes of resilience may reduce the risk of experiencing these negative outcomes and instead promote positive functioning. In this chapter we describe our perspective concerning the neurobiological mechanisms and moderators of risk and resilience in adolescence following early life stress and integrate our own work into this framework. We present findings suggesting that exposure to stress in childhood and adolescence is associated with functional and structural alterations in neurobiological systems that are important for social-affective processing and for cognitive control. While some of these neurobiological alterations increase risk for psychopathology, they may also help to limit adolescents' sensitivity to subsequent negative experiences. We also discuss person-centered strategies that we believe can advance our understanding of risk and resilience to early stress in adolescents. Finally, we describe ways in which the field can broaden its focus to include a consideration of other types of environmental factors, such as environmental pollutants, in affecting both risk and resilience to stress-related health difficulties in youth.
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Affiliation(s)
- Jonas G Miller
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Rajpreet Chahal
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA.
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13
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Xiong S, Li W, Zhou Y, Ren H, Lin G, Zhang S, Xiang X. Vortioxetine Modulates the Regional Signal in First-Episode Drug-Free Major Depressive Disorder at Rest. Front Psychiatry 2022; 13:950885. [PMID: 35845440 PMCID: PMC9277001 DOI: 10.3389/fpsyt.2022.950885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/08/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Previous studies on brain functional alterations associated with antidepressants for major depressive disorder (MDD) have produced conflicting results because they involved short treatment periods and a variety of compounds. METHODS Resting-state functional magnetic resonance imaging scans were obtained from 25 first-episode drug-free patients with MDD and 25 healthy controls. The patients, who were treated with vortioxetine for 8 weeks, were scanned at two-time points (baseline and week 8 of treatment). The amplitude of low-frequency fluctuation (ALFF) in the imaging data was used to analyze local brain signal alterations associated with antidepressant treatment. RESULTS Compared with the controls, the patients at baseline showed decreased ALFF values in the right inferior temporal gyrus and increased ALFF values in the left inferior cerebellum, right cingulate gyrus and postcentral gyrus. After 8 weeks of vortioxetine treatment, patients showed increased ALFF values in the bilateral cingulate gyrus, middle temporal gyrus, medial superior frontal gyrus, and inferior cerebellum. CONCLUSION This study provided evidence that vortioxetine modulates brain signals in MDD sufferers. These findings contribute to the understanding of how antidepressants effect brain function.
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Affiliation(s)
- Shihong Xiong
- Department of Nephrology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Wei Li
- Department of Otolaryngology-Head and Neck Surgery, Wuhan Asia General Hospital, Wuhan, China
| | - Yang Zhou
- Wuhan Mental Health Center, Wuhan, China
| | - Hongwei Ren
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | | | - Sheng Zhang
- Liyuan Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Xiang
- Department of Spine and Orthopedics, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
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14
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Fang F, Gao Y, Schulz PE, Selvaraj S, Zhang Y. Brain controllability distinctiveness between depression and cognitive impairment. J Affect Disord 2021; 294:847-856. [PMID: 34375212 DOI: 10.1016/j.jad.2021.07.106] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 01/14/2023]
Abstract
Alzheimer's disease (AD) is a progressive form of dementia marked by cognitive and memory deficits, estimated to affect ∼5.7 million Americans and account for ∼$277 billion in medical costs in 2018. Depression is one of the most common neuropsychiatric disorders that accompanies AD, appearing in up to 50% of patients. AD and Depression commonly occur together with overlapped symptoms (depressed mood, anxiety, apathy, and cognitive deficits.) and pose diagnostic challenges early in the clinical presentation. Understanding their relationship is critical for advancing treatment strategies, but the interaction remains poorly studied and thus often leads to a rapid decline in functioning. Modern systems and control theory offer a wealth of novel methods and concepts to assess the important property of a complex control system, such as the brain. In particular, the brain controllability analysis captures the ability to guide the brain behavior from an initial state (healthy or diseased) to a desired state in finite time, with suitable choice of inputs such as external or internal stimuli. The controllability property of the brain's dynamic processes will advance our understanding of the emergence and progression of brain diseases and thus helpful in the early diagnosis and novel treatment approaches. This study aims to assess the brain controllability differences between mild cognitive impairment (MCI), as prodromal AD, and Depression. This study used diffusion tensor imaging (DTI) data from 60 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 15 cognitively normal subjects and 45 patients with MCI, including 15 early MCI (EMCI) patients without depression, 15 EMCI patients with mild depression (EMCID), and 15 late MCI (LMCI) patients without depression. The structural brain network was firstly constructed and the brain controllability was characterized for each participant. The controllability of default mode network (DMN) and its sub-regions were then compared across groups in a structural basis. Results indicated that the brain average controllability of DMN in EMCI, LMCI, and EMCID were significantly decreased compared to healthy subjects (P < 0.05). The EMCI and LMCI groups also showed significantly greater average controllability of DMN versus the EMCID group. Furthermore, compared to healthy subjects, the regional controllability of the left/right superior prefrontal cortex and the left/right cingulate gyrus in the EMCID group showed a significant decrease (P < 0.01). Among these regions, the left superior prefrontal region's controllability was significantly decreased (P < 0.05) in the EMCID group compared with EMCI and LMCI groups. Our results provide a new perspective in understanding depressive symptoms in MCI patients and provide potential biomarkers for diagnosing depression from MCI and AD.
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Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Yunyuan Gao
- Department of Intelligent Control & Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Paul E Schulz
- Department of Neurology, The McGovern Medical School of UT Health Houston, Houston, TX, USA
| | - Sudhakar Selvaraj
- Department of Psychiatry and Behavioral Sciences, The McGovern Medical School of UT Health Houston, Houston, TX, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
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15
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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome. Diagnostics (Basel) 2021; 11:diagnostics11091631. [PMID: 34573974 PMCID: PMC8468112 DOI: 10.3390/diagnostics11091631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/30/2022] Open
Abstract
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.
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16
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Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis. Sci Rep 2021; 11:16930. [PMID: 34417487 PMCID: PMC8379217 DOI: 10.1038/s41598-021-96319-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Symptomatic overlap of depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) is a major diagnostic and therapeutic problem. Mania in medical history remains the only reliable distinguishing marker which is problematic given that episodes of depression compared to episodes of mania are more frequent and predominantly present at the beginning of BD. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive, task-free, and well-tolerated method that may provide diagnostic markers acquired from spontaneous neural activity. Previous rs-fMRI studies focused on differentiating BD from MDD depression were inconsistent in their findings due to low sample power, heterogeneity of compared samples, and diversity of analytical methods. This meta-analysis investigated resting-state activity differences in BD and MDD depression using activation likelihood estimation. PubMed, Web of Science, Scopus and Google Scholar databases were searched for whole-brain rs-fMRI studies which compared MDD and BD currently depressed patients between Jan 2000 and August 2020. Ten studies were included, representing 234 BD and 296 MDD patients. The meta-analysis found increased activity in the left insula and adjacent area in MDD compared to BD. The finding suggests that the insula is involved in neural activity patterns during resting-state that can be potentially used as a biomarker differentiating both disorders.
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17
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Sharma RK, Chern A, Golub JS. Age-Related Hearing Loss and the Development of Cognitive Impairment and Late-Life Depression: A Scoping Overview. Semin Hear 2021; 42:10-25. [PMID: 33883788 DOI: 10.1055/s-0041-1725997] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Age-related hearing loss (ARHL) has been connected to both cognitive decline and late-life depression. Several mechanisms have been offered to explain both individual links. Causal and common mechanisms have been theorized for the relationship between ARHL and impaired cognition, including dementia. The causal mechanisms include increased cognitive load, social isolation, and structural brain changes. Common mechanisms include neurovascular disease as well as other known or as-yet undiscovered neuropathologic processes. Behavioral mechanisms have been used to explain the potentially causal association of ARHL with depression. Behavioral mechanisms include social isolation, loneliness, as well as decreased mobility and impairments of activities of daily living, all of which can increase the risk of depression. The mechanisms underlying the associations between hearing loss and impaired cognition, as well as hearing loss and depression, are likely not mutually exclusive. ARHL may contribute to both impaired cognition and depression through overlapping mechanisms. Furthermore, ARHL may contribute to impaired cognition which may, in turn, contribute to depression. Because ARHL is highly prevalent and greatly undertreated, targeting this condition is an appealing and potentially influential strategy to reduce the risk of developing two potentially devastating diseases of later life. However, further studies are necessary to elucidate the mechanistic relationship between ARHL, depression, and impaired cognition.
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Affiliation(s)
- Rahul K Sharma
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York.,Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Alexander Chern
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York
| | - Justin S Golub
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York
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18
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Hyung WSW, Kang J, Kim J, Lee S, Youn H, Ham BJ, Han C, Suh S, Han CE, Jeong HG. Cerebral amyloid accumulation is associated with distinct structural and functional alterations in the brain of depressed elders with mild cognitive impairment. J Affect Disord 2021; 281:459-466. [PMID: 33360748 DOI: 10.1016/j.jad.2020.12.049] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/03/2020] [Accepted: 12/11/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Elderly patients with late-life depression (LLD) often report mild cognitive impairment (MCI), so Alzheimer's disease (AD) is hard to identify in these patients. We aimed to identify the structural and functional differences between prodromal AD and LLD-related MCI. METHODS We performed voxel-based morphometry and functional connectivity (FC) analyses in elderly patients with both LLD and MCI to compare alterations between those with cerebral amyloidopathy and those without. We subdivided patients into subthreshold depression (STD) and major depressive disorder (MDD) groups. Using florbetaben positron emission tomography (PET), we compared volume and connectivity between healthy controls and four STD and MDD groups with or without amyloid deposition(A): STD-MCI-A(+), MDD-MCI-A(+), STD-MCI-A(-), and MDD-MCI-A(-). RESULTS Subjects with MDD or amyloid deposition showed greater volume reduction in the left middle temporal gyrus. MDD groups had lower FC than STD groups in the frontal, cortical, and limbic areas. The STD-MCI-A(+) group showed greater FC reduction than the MDD-MCI-A(-) and STD-MCI-A(-) groups, particularly in the hippocampus, parahippocampus, and frontal and temporal cortices. The functional differences associated with amyloid plaques were more evident in the STD group than in the MDD group. LIMITATIONS Limitations include disproportional sex ratios, inability to determine the longitudinal effects of amyloidopathy in large populations. CONCLUSIONS Regional gray matter loss and alterations in brain networks may reflect impairments caused by amyloid deposition and depression. Such changes may facilitate the detection of prodromal AD in elderly patients with both depression and cognitive dysfunction, allowing earlier intervention and more appropriate treatment.
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Affiliation(s)
- Won Seok William Hyung
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - June Kang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Junhyung Kim
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Suji Lee
- Department of Biomedical Sciences, Korea University Graduate School, Seoul, Republic of Korea
| | - HyunChul Youn
- Department of Psychiatry, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Changsu Han
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sangil Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Cheol E Han
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea
| | - Hyun-Ghang Jeong
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea; Department of Biomedical Sciences, Korea University Graduate School, Seoul, Republic of Korea.
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Ienca M, Ignatiadis K. Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges. AJOB Neurosci 2020; 11:77-87. [PMID: 32228387 DOI: 10.1080/21507740.2020.1740352] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Clinical neuroscience is increasingly relying on the collection of large volumes of differently structured data and the use of intelligent algorithms for data analytics. In parallel, the ubiquitous collection of unconventional data sources (e.g. mobile health, digital phenotyping, consumer neurotechnology) is increasing the variety of data points. Big data analytics and approaches to Artificial Intelligence (AI) such as advanced machine learning are showing great potential to make sense of these larger and heterogeneous data flows. AI provides great opportunities for making new discoveries about the brain, improving current preventative and diagnostic models in both neurology and psychiatry and developing more effective assistive neurotechnologies. Concurrently, it raises many new methodological and ethical challenges. Given their transformative nature, it is still largely unclear how AI-driven approaches to the study of the human brain will meet adequate standards of scientific validity and affect normative instruments in neuroethics and research ethics. This manuscript provides an overview of current AI-driven approaches to clinical neuroscience and an assessment of the associated key methodological and ethical challenges. In particular, it will discuss what ethical principles are primarily affected by AI approaches to human neuroscience, and what normative safeguards should be enforced in this domain.
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Affiliation(s)
- Marcello Ienca
- Swiss Federal Institute of Technology, ETH Zurich, Department of Health Sciences and Technology
| | - Karolina Ignatiadis
- Swiss Federal Institute of Technology, ETH Zurich, Department of Health Sciences and Technology
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20
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Gray matter changes in chronic heavy cannabis users: a voxel-level study using multivariate pattern analysis approach. Neuroreport 2020; 31:1236-1241. [PMID: 33044327 DOI: 10.1097/wnr.0000000000001532] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent structural MRI studies on gray matter (GM) volumes using group-level mass-univariate statistical analysis suggest that chronic and heavy cannabis exposure may affect brain region-based morphology. In this prospective study, we use a multivariate pattern analysis approach to investigate the voxel-level change of GM densities in chronic heavy cannabis users. Principal component analysis and linear support vector machine are used in this study, resulting in an 88.1% separation between chronic heavy cannabis users (N = 20) and non-cannabis healthy controls (HCs, N = 22) through leave-one-out cross-validation. The model's discriminative pattern showed that GM density decreases in the part of middle frontal gyrus, inferior frontal gyrus, middle temporal gyrus, inferior temporal gyrus and left occipital lobe in heavy cannabis users with respect to HCs and increases in the part of lentiform nucleus, left cerebellum and right parietal lobe. These results suggest that GM densities alteration has taken place on chronic heavy cannabis users compared with HCs at voxel level.
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21
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Grier MD, Zimmermann J, Heilbronner SR. Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:846-854. [PMID: 32513555 PMCID: PMC7483308 DOI: 10.1016/j.bpsc.2020.04.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/20/2020] [Accepted: 04/18/2020] [Indexed: 01/22/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is a popular tool for noninvasively assessing properties of white matter in the brain. Among other uses, dMRI data can be used to produce estimates of anatomical connectivity on the basis of tractography. However, direct comparisons of anatomical connectivity as estimated through invasive neural tract-tracing experiments and dMRI-derived connectivity have shown only a moderate relationship in nonhuman primate (particularly macaque) studies. Tractography is plagued by known problems associated with resolution, crossing fibers, and curving fibers, among others. These problems lead to deficits in both sensitivity and specificity, which trade off with each other in multiple datasets. Although not yet examined quantitatively, there is reason to believe that some large white matter bundles, those with more topographic organization, may produce more accurate results than others. Moving forward, sophisticated analytical approaches and anatomical constraints may improve tractography accuracy. However, broadly speaking, dMRI-derived estimates of brain connectivity should be approached with caution.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota.
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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23
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Chronic stress induced depressive-like behaviors in a classical murine model of Parkinson's disease. Behav Brain Res 2020; 399:112816. [PMID: 32783904 DOI: 10.1016/j.bbr.2020.112816] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 07/16/2020] [Accepted: 07/18/2020] [Indexed: 11/23/2022]
Abstract
Depression occurs in around 40 % of patients with Parkinson's disease (PD) and contributes to severe disability and a poor quality of life. The underlying mechanisms and pathophysiology of depression in PD (PDD) remain obscure, due to a lack of stable animal models of PDD. In this study, we established a PDD model by inducing exposure to chronic mild (CMS) and strong stress (CSS) using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) in PD mice. We detected changes in motor and non-motor symptoms, brain structure, neurotransmitters, levels of 5-HT related genes and inflammation. CMS exposed PD (PDMS) mice exhibited obviously decreased levels of neuromuscular strength and enhanced levels of inflammation, compared with that of control mice. CSS exposed MPTP (PDSS) mice exhibited the highest level of motor impairment and depression states along with the highest levels of inflammation enhancement and a decrease in the expression levels of 5-hydroxytryptamine (5-HT) related genes in all groups. Our results suggested that CSS can successfully induce stable depression like symptoms in sub-chronic MPTP PD mice and appears to be a valuable tool for investigating PDD. Furthermore, it was found that 5-HT system dysfunction may contribute to depression like symptoms in PD.
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Validation of Chronic Restraint Stress Model in Young Adult Rats for the Study of Depression Using Longitudinal Multimodal MR Imaging. eNeuro 2020; 7:ENEURO.0113-20.2020. [PMID: 32669346 PMCID: PMC7396811 DOI: 10.1523/eneuro.0113-20.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/15/2020] [Accepted: 07/03/2020] [Indexed: 12/25/2022] Open
Abstract
Prior research suggests that the neurobiological underpinnings of depression include aberrant brain functional connectivity, neurometabolite levels, and hippocampal volume. Chronic restraint stress (CRS) depression model in rats has been shown to elicit behavioral, gene expression, protein, functional connectivity, and hippocampal volume changes similar to those in human depression. However, no study to date has examined the association between behavioral changes and brain changes within the same animals. This study specifically addressed the correlation between the outcomes of behavioral tests and multiple 9.4 T magnetic resonance imaging (MRI) modalities in the CRS model using data collected longitudinally in the same animals. CRS involved placing young adult male Sprague Dawley rats in individual transparent tubes for 2.5 h daily over 13 d. Elevated plus maze (EPM) and forced swim tests (FSTs) confirmed the presence of anxiety-like and depression-like behaviors, respectively, postrestraint. Resting-state functional MRI (rs-fMRI) data revealed hypoconnectivity within the salience and interoceptive networks and hyperconnectivity of several brain regions to the cingulate cortex. Proton magnetic resonance spectroscopy revealed decreased sensorimotor cortical glutamate (Glu), glutamine (Gln), and combined Glu-Gln (Glx) levels. Volumetric analysis of T2-weighted images revealed decreased hippocampal volume. Importantly, these changes parallel those found in human depression, suggesting that the CRS rodent model has utility for translational studies and novel intervention development for depression.
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Zheng Y, Chen X, Li D, Liu Y, Tan X, Liang Y, Zhang H, Qiu S, Shen D. Treatment-naïve first episode depression classification based on high-order brain functional network. J Affect Disord 2019; 256:33-41. [PMID: 31158714 PMCID: PMC6750956 DOI: 10.1016/j.jad.2019.05.067] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 05/22/2019] [Accepted: 05/28/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Recent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging. METHODS We enrolled 82 treatment-naïve first episode depression (FED) adults and 72 matched normal control (NC). A computer-aided diagnosis framework was utilized to classify the FEDs from the NCs based on the features extracted from not only traditional "low-order" FC networks (LON) based on temporal synchronization of original rs-fMRI signals, but also "high-order" FC networks (HON) that characterize more complex functional interactions via correlation of the dynamic (time-varying) FCs. We contrasted a classifier using HON feature (CHON) and compared its performance with using LON only (CLON). Finally, an integrated classification model with both features was proposed to further enhance FED classification. RESULTS The CHON had significantly improved diagnostic accuracy compared to the CLON (82.47% vs. 67.53%). Joint classification further improved the performance (83.77%). The brain regions with potential diagnostic values mainly encompass the high-order cognitive function-related networks. Importantly, we found previously less-reported potential imaging biomarkers that involve the vermis and the crus II in the cerebellum. LIMITATIONS We only used one imaging modality and did not examine data from different subtypes of depression. CONCLUSIONS Depression classification could be significantly improved by using HON features that better capture the higher-level brain functional interactions. The findings suggest the importance of higher-level cerebro-cerebellar interactions in the pathophysiology of MDD.
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Affiliation(s)
- Yanting Zheng
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Danian Li
- Cerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China
| | - Yujie Liu
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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Leng X, Fang P, Lin H, Qin C, Tan X, Liang Y, Zhang C, Wang H, An J, Wu D, Liu Q, Qiu S. Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma. Cancer Imaging 2019; 19:19. [PMID: 30909974 PMCID: PMC6434635 DOI: 10.1186/s40644-019-0203-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/13/2019] [Indexed: 12/16/2022] Open
Abstract
Background The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic. Methods A DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject’s DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier. Results For FA, the accuracy of classification between the 0–6, 6–12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0–6, 6–12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced. Conclusions RBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.
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Affiliation(s)
- Xi Leng
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Peng Fang
- Department of Psychology, The Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Huan Lin
- Department of Radiology, Zhujiang Hospital of Southern Medical University, No. 253, Gong Ye Da Dao Zhong, Guangzhou, Guangdong, 510280, People's Republic of China
| | - Chunhong Qin
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Xin Tan
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Yi Liang
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Chi Zhang
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Hongzhuo Wang
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Jie An
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Donglin Wu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Qihui Liu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China
| | - Shijun Qiu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China.
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Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018; 24:1037-1052. [PMID: 30136381 DOI: 10.1111/cns.13048] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/19/2018] [Accepted: 07/21/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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Affiliation(s)
- Shuang Gao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Chu SH, Lenglet C, Schreiner MW, Klimes-Dougan B, Cullen K, Parhi KK. Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2740-2743. [PMID: 30440968 DOI: 10.1109/embc.2018.8512852] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks.
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Chu SH, Lenglet C, Schreiner MW, Klimes-Dougan B, Cullen K, Parhi KK. Biomarkers for Adolescent MDD from Anatomical Connectivity and Network Topology Using Diffusion MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1152-1155. [PMID: 30440595 DOI: 10.1109/embc.2018.8512505] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Due to the high resistance (35%) to the current treatment methods in adolescent Major Depressive Disorder (MDD) and its tragic outcomes, the discovery of treatmentrelated responders is critical to developing effective treatments. In this paper, the permutation test is performed to identify statistically significant changes in anatomical characteristics during pairwise comparisons among the control group (n=27), treated MDD group (n=37), and untreated MDD group (n=15). The anatomical characteristics include: 1) anatomical connectivity defined using DTI metrics between a pair of brain regions, and 2) topological measurements of anatomical networks. With the Bonferroni correction for multiple-comparison, significant alterations in community structure and local topology were identified as the p-value < 5%, which include: 1) a reduced nodal centrality (degree and strength) on right hippocampus for treated compared to untreated group, 2) an elevated clustering coefficient and local efficiency on right lateral orbitofrontal cortex for untreated compared to the combination of control and treated groups, 3) an increased participation coefficient for untreated patients on left insula cortex in the meandiffusivity network compared to the combination of control and treated groups, and 4) a degraded module degree z-score on right caudate nucleus for all the patients compared to the control group. Two connections, hippocampus-insula in the right hemisphere and parahippocampal-insula in the left hemisphere, were found significantly altered in TR, AD, and FA due to MDD.
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30
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Tae WS, Ham BJ, Pyun SB, Kang SH, Kim BJ. Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders. J Clin Neurol 2018; 14:129-140. [PMID: 29504292 PMCID: PMC5897194 DOI: 10.3988/jcn.2018.14.2.129] [Citation(s) in RCA: 151] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/02/2017] [Accepted: 11/02/2017] [Indexed: 12/11/2022] Open
Abstract
Diffusion-tensor imaging (DTI) is a noninvasive medical imaging tool used to investigate the structure of white matter. The signal contrast in DTI is generated by differences in the Brownian motion of the water molecules in brain tissue. Postprocessed DTI scalars can be used to evaluate changes in the brain tissue caused by disease, disease progression, and treatment responses, which has led to an enormous amount of interest in DTI in clinical research. This review article provides insights into DTI scalars and the biological background of DTI as a relatively new neuroimaging modality. Further, it summarizes the clinical role of DTI in various disease processes such as amyotrophic lateral sclerosis, multiple sclerosis, Parkinson's disease, Alzheimer's dementia, epilepsy, ischemic stroke, stroke with motor or language impairment, traumatic brain injury, spinal cord injury, and depression. Valuable DTI postprocessing tools for clinical research are also introduced.
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Affiliation(s)
- Woo Suk Tae
- Brain Convergence Research Center, Korea University, Seoul, Korea
| | - Byung Joo Ham
- Brain Convergence Research Center, Korea University, Seoul, Korea
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Sung Bom Pyun
- Brain Convergence Research Center, Korea University, Seoul, Korea
- Department of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Korea
| | - Shin Hyuk Kang
- Brain Convergence Research Center, Korea University, Seoul, Korea
- Department of Neurosurgery, Korea University College of Medicine, Seoul, Korea
| | - Byung Jo Kim
- Brain Convergence Research Center, Korea University, Seoul, Korea
- Department of Neurology, Korea University College of Medicine, Seoul, Korea.
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Functional connectivity between salience, default mode and frontoparietal networks in post-stroke depression. J Affect Disord 2018; 227:554-562. [PMID: 29169125 DOI: 10.1016/j.jad.2017.11.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 09/12/2017] [Accepted: 11/11/2017] [Indexed: 11/21/2022]
Abstract
BACKGROUND Previous studies have demonstrated altered resting state functional connectivity (rsFC) in patients with post-stroke depression (PSD). It remains unclear whether rsFC is changed at the network level as was shown for major depressive disorder (MDD). To address this question, we investigated rsFC of resting sate networks (RSNs) in PSD. METHODS Eleven subjects with PSD underwent fMRI scanning at rest before and after treatment. The severity of depression was assessed using the aphasic depression rating scale (ADRS). We performed functional network connectivity (FNC) analysis for RSNs, region of interest - FC analysis (ROI-FC) and calculation of brain matter volumes in ROIs overlapping with RSNs and in other brain regions associated with mood maintenance. RESULTS We found positive correlation of FNC between anterior default mode network (aDMN) and salience network (SAL) with depression severity before treatment, the latter accompanied by the increase of white matter in the middle frontal and left angular gyri. FNC of aDMN and left frontoparietal network (LFP) decreased after treatment. ROI-FC and the brain matter volumes of several regions of DMN, LFP and SAL also showed a correlation with ADRS or significant change after treatment. LIMITATIONS Limitations include small sample size and methodological issues concerning altered hemodynamics in stroke. However, we took complex preprocessing steps to overcome these issues. CONCLUSION Present results of altered rsFC in PSD are consistent with previous findings in MDD. The convergence of results obtained in PSD and MDD supports the validity of rsFC approach for investigation of brain network dysfunctions underling these psychiatric symptoms.
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Helm K, Viol K, Weiger TM, Tass PA, Grefkes C, Del Monte D, Schiepek G. Neuronal connectivity in major depressive disorder: a systematic review. Neuropsychiatr Dis Treat 2018; 14:2715-2737. [PMID: 30425491 PMCID: PMC6200438 DOI: 10.2147/ndt.s170989] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The causes of major depressive disorder (MDD), as one of the most common psychiatric disorders, still remain unclear. Neuroimaging has substantially contributed to understanding the putative neuronal mechanisms underlying depressed mood and motivational as well as cognitive impairments in depressed individuals. In particular, analyses addressing changes in interregional connectivity seem to be a promising approach to capture the effects of MDD at a systems level. However, a plethora of different, sometimes contradicting results have been published so far, making general conclusions difficult. Here we provide a systematic overview about connectivity studies published in the field over the last decade considering different methodological as well as clinical issues. METHODS A systematic review was conducted extracting neuronal connectivity results from studies published between 2002 and 2015. The findings were summarized in tables and were graphically visualized. RESULTS The review supports and summarizes the notion of an altered frontolimbic mood regulation circuitry in MDD patients, but also stresses the heterogeneity of the findings. The brain regions that are most consistently affected across studies are the orbitomedial prefrontal cortex, anterior cingulate cortex, amygdala, hippocampus, cerebellum and the basal ganglia. CONCLUSION The results on connectivity in MDD are very heterogeneous, partly due to different methods and study designs, but also due to the temporal dynamics of connectivity. While connectivity research is an important step toward a complex systems approach to brain functioning, future research should focus on the dynamics of functional and effective connectivity.
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Affiliation(s)
- Katharina Helm
- Institute of Physiology and Pathophysiology, Paracelsus Medical University Salzburg, Salzburg, Austria.,Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Kathrin Viol
- Institute of Synergetics and Psychotherapy Research, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University Salzburg, Salzburg, Austria,
| | - Thomas M Weiger
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Peter A Tass
- Department of Neurosurgery, Stanford University, Stanford CA, USA
| | - Christian Grefkes
- Department of Neurology, Cologne University Hospital, Cologne, Germany.,Institute of Medicine and Neurosciences - Cognitive Neurology (INM-3), Research Center Juelich, Juelich, Germany
| | - Damir Del Monte
- Institute of Synergetics and Psychotherapy Research, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University Salzburg, Salzburg, Austria,
| | - Günter Schiepek
- Institute of Synergetics and Psychotherapy Research, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University Salzburg, Salzburg, Austria, .,Ludwig Maximilians University, Department for Psychology, Munich, Germany,
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Abstract
OBJECTIVE Brain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles. METHODS Forty women supplied fecal samples for 16S rRNA profiling. Microbial clusters were identified using Partitioning Around Medoids. Functional magnetic resonance imaging was acquired. Microbiota-based group differences were analyzed in response to affective images. Structural and diffusion tensor imaging provided gray matter metrics (volume, cortical thickness, mean curvature, surface area) as well as fiber density between regions. A sparse Partial Least Square-Discrimination Analysis was applied to discriminate microbiota clusters using white and gray matter metrics. RESULTS Two bacterial genus-based clusters were identified, one with greater Bacteroides abundance (n = 33) and one with greater Prevotella abundance (n = 7). The Prevotella group showed less hippocampal activity viewing negative valences images. White and gray matter imaging discriminated the two clusters, with accuracy of 66.7% and 87.2%, respectively. The Prevotella cluster was associated with differences in emotional, attentional, and sensory processing regions. For gray matter, the Bacteroides cluster showed greater prominence in the cerebellum, frontal regions, and the hippocampus. CONCLUSIONS These results support the concept of brain-gut-microbiota interactions in healthy humans. Further examination of the interaction between gut microbes, brain, and affect in humans is needed to inform preclinical reports that microbial modulation may affect mood and behavior.
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Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, Walter M, Falkai P, Koutsouleris N. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol Psychiatry 2017; 82:330-338. [PMID: 28110823 DOI: 10.1016/j.biopsych.2016.10.028] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 09/27/2016] [Accepted: 10/20/2016] [Indexed: 12/24/2022]
Abstract
BACKGROUND Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. METHODS We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs. RESULTS Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity). CONCLUSIONS Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
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Affiliation(s)
- Joseph Kambeitz
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
| | - Carlos Cabral
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich
| | - Matthew D Sacchet
- Neurosciences Program and Department of Psychology, Stanford University, Stanford, California
| | - Ian H Gotlib
- Neurosciences Program and Department of Psychology, Stanford University, Stanford, California
| | - Roland Zahn
- Institute of Psychiatry, King's College London, London, United Kingdom
| | - Mauricio H Serpa
- Laboratory of Psychiatric Neuroimaging, Institute and Department of Psychiatry, Sao Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of Sao Paulo, Sao Paulo, Brazil
| | - Martin Walter
- Clinical Affective Neuroimaging Laboratory, Department of Behavioural Neurology, Leibniz Institute for Neurobiology, Magdeburg; Department of Psychiatry and Psychotherapy, Eberhard Karls University, Tubingen, Germany
| | - Peter Falkai
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich
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Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging 2017; 264:1-9. [PMID: 28388468 PMCID: PMC5486995 DOI: 10.1016/j.pscychresns.2017.03.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Revised: 11/02/2016] [Accepted: 03/08/2017] [Indexed: 02/07/2023]
Abstract
Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.
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Affiliation(s)
- David M Schnyer
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.
| | - Peter C Clasen
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Christopher Gonzalez
- Department of Psychology, University of California, San Diego, San Diego, CA, USA
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McIntosh AL, Gormley S, Tozzi L, Frodl T, Harkin A. Recent Advances in Translational Magnetic Resonance Imaging in Animal Models of Stress and Depression. Front Cell Neurosci 2017; 11:150. [PMID: 28596724 PMCID: PMC5442179 DOI: 10.3389/fncel.2017.00150] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 05/09/2017] [Indexed: 12/28/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a valuable translational tool that can be used to investigate alterations in brain structure and function in both patients and animal models of disease. Regional changes in brain structure, functional connectivity, and metabolite concentrations have been reported in depressed patients, giving insight into the networks and brain regions involved, however preclinical models are less well characterized. The development of more effective treatments depends upon animal models that best translate to the human condition and animal models may be exploited to assess the molecular and cellular alterations that accompany neuroimaging changes. Recent advances in preclinical imaging have facilitated significant developments within the field, particularly relating to high resolution structural imaging and resting-state functional imaging which are emerging techniques in clinical research. This review aims to bring together the current literature on preclinical neuroimaging in animal models of stress and depression, highlighting promising avenues of research toward understanding the pathological basis of this hugely prevalent disorder.
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Affiliation(s)
| | - Shane Gormley
- Institute of Neuroscience, Trinity College DublinDublin, Ireland
| | - Leonardo Tozzi
- Institute of Neuroscience, Trinity College DublinDublin, Ireland
| | - Thomas Frodl
- Institute of Neuroscience, Trinity College DublinDublin, Ireland.,Universitätsklinikum A.ö.R, Universitätsklinik für Psychiatrie und Psychotherapie, Medizinische Fakultät, Otto von Guericke UniversitätMagdeburg, Germany
| | - Andrew Harkin
- Institute of Neuroscience, Trinity College DublinDublin, Ireland.,School of Pharmacy and Pharmaceutical sciences, Trinity College DublinDublin, Ireland
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Tylee DS, Kikinis Z, Quinn TP, Antshel KM, Fremont W, Tahir MA, Zhu A, Gong X, Glatt SJ, Coman IL, Shenton ME, Kates WR, Makris N. Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study. NEUROIMAGE-CLINICAL 2017; 15:832-842. [PMID: 28761808 PMCID: PMC5522376 DOI: 10.1016/j.nicl.2017.04.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 03/27/2017] [Accepted: 04/04/2017] [Indexed: 11/27/2022]
Abstract
Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental syndrome that has been studied intensively in order to understand relationships between the genetic microdeletion, brain development, cognitive function, and the emergence of psychiatric symptoms. White matter microstructural abnormalities identified using diffusion tensor imaging methods have been reported to affect a variety of neuroanatomical tracts in 22q11.2DS. In the present study, we sought to combine two discovery-based approaches: (1) white matter query language was used to parcellate the brain's white matter into tracts connecting pairs of 34, bilateral cortical regions and (2) the diffusion imaging characteristics of the resulting tracts were analyzed using a machine-learning method called support vector machine in order to optimize the selection of a set of imaging features that maximally discriminated 22q11.2DS and comparison subjects. With this unique approach, we both confirmed previously-recognized 22q11.2DS-related abnormalities in the inferior longitudinal fasciculus (ILF), and identified, for the first time, 22q11.2DS-related anomalies in the middle longitudinal fascicle and the extreme capsule, which may have been overlooked in previous, hypothesis-guided studies. We further observed that, in participants with 22q11.2DS, ILF metrics were significantly associated with positive prodromal symptoms of psychosis.
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Key Words
- (-fp), fronto-parietal aspect
- (-to), temporo-occipital aspect
- (-tp), temporo-parietal aspect
- (22q11.2DS), 22q11.2 deletion syndrome
- (AD), axial diffusivity
- (DTI), diffusion tensor imaging
- (DWI), diffusion weighted image
- (EmC), extreme capsule
- (FA), fractional anisotropy
- (FOV), field of view
- (GDS), Gordon Diagnostic Systems
- (ILF), inferior longitudinal fasciculus
- (MdLF), middle longitudinal fascicle
- (RD), radial diffusivity
- (ROI), region of interest
- (SIPS), Structured Interview for Prodromal Syndromes
- (SRS), Social Responsiveness Scale
- (STG), superior temporal gyrus
- (SVM), support vector machine
- (UKF), Unscented Kalman Filter
- (WAIS-III), Wechsler Adult Intelligence Scale – 3rd edition
- (WMQL), white matter query language
- (dTP), dorsal temporal pole
- 22q11.2 deletion syndrome
- Callosal asymmetry
- Diffusion tensor imaging
- Extreme capsule
- Inferior longitudinal fasciculus
- Machine-learning
- Middle longitudinal fascicle
- Support vector machine
- Velocardiofacial syndrome
- White matter query language
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Affiliation(s)
- Daniel S Tylee
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA
| | - Zora Kikinis
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Thomas P Quinn
- Bioinformatics Core Research Group, Deakin University, Geelong, Victoria, Australia
| | | | - Wanda Fremont
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Muhammad A Tahir
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA
| | - Anni Zhu
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Xue Gong
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen J Glatt
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Ioana L Coman
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA.
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Nikos Makris
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Fang P, An J, Tan X, Zeng LL, Shen H, Qiu S, Hu D. Changes in the cerebellar and cerebro-cerebellar circuit in type 2 diabetes. Brain Res Bull 2017; 130:95-100. [DOI: 10.1016/j.brainresbull.2017.01.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 12/21/2016] [Accepted: 01/09/2017] [Indexed: 02/07/2023]
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Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety. Neuroimage 2017; 150:50-59. [PMID: 28213111 DOI: 10.1016/j.neuroimage.2017.02.037] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/31/2016] [Accepted: 02/13/2017] [Indexed: 11/21/2022] Open
Abstract
Childhood maltreatment is a major risk factor for psychopathology. It is also associated with alterations in the network architecture of the brain, which we hypothesized may play a significant role in the development of psychopathology. In this study, we analyzed the global network architecture of physically healthy unmedicated 18-25 year old subjects (n=262) using diffusion tensor imaging (DTI) MRI and tractography. Anatomical networks were constructed from fiber streams interconnecting 90 cortical or subcortical regions for subjects with no-to-low (n=122) versus moderate-to-high (n=140) exposure to maltreatment. Graph theory analysis revealed lower degree, strength, global efficiency, and maximum Laplacian spectra, higher pathlength, small-worldness and Laplacian skewness, and less deviation from artificial networks in subjects with moderate-to-high exposure to maltreatment. On balance, local clustering was similar in both groups, but the different clusters were more strongly interconnected in the no-to-low exposure group. History of major depression, anxiety and attention deficit hyperactivity disorder did not have a significant impact on global network measures over and above the effect of maltreatment. Maltreatment is an important factor that needs to be taken into account in studies examining the relationship between network differences and psychopathology.
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 518] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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Fang P, An J, Zeng LL, Shen H, Qiu S, Hu D. Mapping the convergent temporal epileptic network in left and right temporal lobe epilepsy. Neurosci Lett 2016; 639:179-184. [PMID: 27989571 DOI: 10.1016/j.neulet.2016.12.029] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 12/06/2016] [Accepted: 12/14/2016] [Indexed: 12/26/2022]
Abstract
Left and right mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS) exhibits similar functional and clinical dysfunctions, such as depressive mood and emotional dysregulation, implying that the left and right mTLE may share a common network substrate. However, the convergent anatomical network disruption between the left and right HS remains largely uncharacterized. This study aimed to investigate whether the left and right mTLE share a similar anatomical network. We examined 43 (22 left, 21 right) mTLE patients with HS and 39 healthy controls using diffusion tensor imaging. Machine learning approaches were applied to extract the abnormal anatomical connectivity patterns in both the left and right mTLE. The left and right mTLE showed that 28 discriminating connections were exactly the same when compared to the controls. The same 28 connections showed high discriminating power in comparisons of the left mTLE versus controls (91.7%) and the right mTLE versus controls (90.0%); however, these connections failed to discriminate the left from the right mTLE. These discriminating connections, which were diminished both in the left and right mTLE, were primarily located in the limbic-frontal network, partially agreeing with the limbic-frontal dysregulation model of depression. These findings suggest that left and right mTLE share a convergent circuit, which may account for the mood and emotional deficits in mTLE and may suggest the neuropathological mechanisms underlying the comorbidity of depression and mTLE.
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Affiliation(s)
- Peng Fang
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Jie An
- Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China
| | - Ling-Li Zeng
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hui Shen
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Shijun Qiu
- Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China.
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.
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Nugent AC, Robinson SE, Coppola R, Zarate CA. Preliminary differences in resting state MEG functional connectivity pre- and post-ketamine in major depressive disorder. Psychiatry Res 2016; 254:56-66. [PMID: 27362845 PMCID: PMC4992587 DOI: 10.1016/j.pscychresns.2016.06.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 06/13/2016] [Accepted: 06/14/2016] [Indexed: 01/06/2023]
Abstract
Functional neuroimaging techniques including magnetoencephalography (MEG) have demonstrated that the brain is organized into networks displaying correlated activity. Group connectivity differences between healthy controls and participants with major depressive disorder (MDD) can be detected using temporal independent components analysis (ICA) on beta-bandpass filtered Hilbert envelope MEG data. However, the response of these networks to treatment is unknown. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, exerts rapid antidepressant effects. We obtained MEG recordings before and after open-label infusion of 0.5mg/kg ketamine in MDD subjects (N=13) and examined networks previously shown to differ between healthy individuals and those with MDD. Connectivity between the amygdala and an insulo-temporal component decreased post-ketamine in MDD subjects towards that observed in control subjects at baseline. Decreased baseline connectivity of the subgenual anterior cingulate cortex (sgACC) with a bilateral precentral network had previously been observed in MDD compared to healthy controls, and the change in connectivity post-ketamine was proportional to the change in sgACC glucose metabolism in a subset (N=8) of subjects receiving [11F]FDG-PET imaging. Ketamine appeared to reduce connectivity, regardless of whether connectivity was abnormally high or low compared to controls at baseline. These preliminary findings suggest that sgACC connectivity may be directly related to glutamate levels.
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Affiliation(s)
- Allison C Nugent
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Stephen E Robinson
- NIMH Magnetoencephalography Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Richard Coppola
- NIMH Magnetoencephalography Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Zarate
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Duggento A, Valenza G, Passamonti L, Guerrisi M, Barbieri R, Toschi N. Reconstructing multivariate causal structure between functional brain networks through a Laguerre-Volterra based Granger causality approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:5477-5480. [PMID: 28269497 DOI: 10.1109/embc.2016.7591966] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity. We validate our method in synthetic noisy oscillator networks, and analyze experimental fMRI data from 30 healthy subjects publicly available within the Human Connectome Project (HCP). Synthetic results demonstrate that our Laguerre-Volterra based GC estimates outperform classical approaches in terms of accuracy in detecting true causal links while rejecting false causal links in complex nonlinear networks. Human data analysis shows for the first time that the default mode network modulates both the salience network as well as fronto-temporal circuits in a causal fashion.
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Mitra J, Shen KK, Ghose S, Bourgeat P, Fripp J, Salvado O, Pannek K, Taylor DJ, Mathias JL, Rose S. Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. Neuroimage 2016; 129:247-259. [DOI: 10.1016/j.neuroimage.2016.01.056] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 12/21/2015] [Accepted: 01/24/2016] [Indexed: 12/13/2022] Open
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Changes in the regional cerebral blood flow detected by arterial spin labeling after 6-week escitalopram treatment for major depressive disorder. J Affect Disord 2016; 194:135-43. [PMID: 26826533 DOI: 10.1016/j.jad.2015.12.062] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 12/25/2015] [Accepted: 12/26/2015] [Indexed: 11/22/2022]
Abstract
BACKGROUND A few studies have used pseudo-continuous arterial spin labeling (pCASL) to assess the regional cerebral blood flow (rCBF) in patients with major depressive disorder (MDD). However, rCBF changes during treatment with escitalopram have not been studied in detail. We used pCASL to investigate the effect of 6-week escitalopram treatment on the rCBF in MDD patients. METHODS We subjected 53 MDD patients and 36 controls to pCASL (T1, baseline). The patients then received treatment with escitalopram for 6 weeks and 27 were scanned again (T2). We used selected regions of interest that exhibited differences between the controls and patients at T1 and compared the T2 rCBF in the patients with the T1 rCBF of the controls. We also compared the T1 and T2 rCBF in the patients to assess their response to escitalopram. RESULTS After 6-week treatment with escitalopram, the rCBF in the patients' left inferior temporal gyri, the middle- and inferior frontal gyri, and the subgenual anterior cingulate, which had been higher at T1 than in the controls, was decreased. Their rCBF in the right lingual gyrus remained significantly lower at T2. LIMITATION We did not have a placebo-control group and the number of patients available at T2 was small. CONCLUSION In MDD patients, 6-week escitalopram treatment elicited significant rCBF changes toward normalization in most of the areas that had shown significant differences between the patients and the controls at T1. The persistence of rCBF anomalies in the right lingual gyrus may be a trait marker of MDD.
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Lai CH, Wu YT. The White Matter Microintegrity Alterations of Neocortical and Limbic Association Fibers in Major Depressive Disorder and Panic Disorder: The Comparison. Medicine (Baltimore) 2016; 95:e2982. [PMID: 26945417 PMCID: PMC4782901 DOI: 10.1097/md.0000000000002982] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The studies regarding to the comparisons between major depressive disorder (MDD) and panic disorder (PD) in the microintegrity of white matter (WM) are uncommon. Therefore, we tried to a way to classify the MDD and PD. Fifty-three patients with 1st-episode medication-naive PD, 54 healthy controls, and 53 patients with 1st-episode medication-naive MDD were enrolled in this study. The controls and patients were matched for age, gender, education, and handedness. The diffusion tensor imaging scanning was also performed. The WM microintegrity was analyzed and compared between 3 groups of participants (ANOVA analysis) with age and gender as covariates. The MDD group had lower WM microintegrity than the PD group in the left anterior thalamic radiation, left uncinate fasciculus, left inferior fronto-occipital fasciculus, and bilateral corpus callosum. The MDD group had reductions in the microintegrity when compared to controls in the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, inferior fronto-occipital fasciculi, and corpus callosum. The PD group had lower microintegrity in bilateral superior longitudinal fasciculi and left inferior fronto-occipital fasciculus when compared to controls. The widespread pattern of microintegrity alterations in fronto-limbic WM circuit for MDD was different from restrictive pattern of alterations for PD.
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Affiliation(s)
- Chien-Han Lai
- From the Department of Psychiatry, Cheng Hsin General Hospital, Taipei City (C-HL); Department of Biomedical Imaging and Radiological Sciences (C-HL, Y-TW); Brain Research Center (Y-TW); and Institute of Biophotonics, National Yang-Ming University (C-HL, Y-TW), Taipei, Taiwan, ROC
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Bi K, Hua L, Wei M, Qin J, Lu Q, Yao Z. Dynamic functional-structural coupling within acute functional state change phases: Evidence from a depression recognition study. J Affect Disord 2016; 191:145-55. [PMID: 26655124 DOI: 10.1016/j.jad.2015.11.041] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 11/18/2015] [Accepted: 11/23/2015] [Indexed: 01/13/2023]
Abstract
BACKGROUND Dynamic functional-structural connectivity (FC-SC) coupling might reflect the flexibility by which SC relates to functional connectivity (FC). However, during the dynamic acute state change phases of FC, the relationship between FC and SC may be distinctive and embody the abnormality inherent in depression. This study investigated the depression-related inter-network FC-SC coupling within particular dynamic acute state change phases of FC. METHODS Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data were collected from 26 depressive patients (13 women) and 26 age-matched controls (13 women). We constructed functional brain networks based on MEG data and structural networks from DTI data. The dynamic connectivity regression algorithm was used to identify the state change points of a time series of inter-network FC. The time period of FC that contained change points were partitioned into types of dynamic phases (acute rising phase, acute falling phase,acute rising and falling phase and abrupt FC variation phase) to explore the inter-network FC-SC coupling. The selected FC-SC couplings were then fed into the support vector machine (SVM) for depression recognition. RESULTS The best discrimination accuracy was 82.7% (P=0.0069) with FC-SC couplings, particularly in the acute rising phase of FC. Within the FC phases of interest, the significant discriminative network pair was related to the salience network vs ventral attention network (SN-VAN) (P=0.0126) during the early rising phase (70-170ms). LIMITATIONS This study suffers from a small sample size, and the individual acute length of the state change phases was not considered. CONCLUSIONS The increased values of significant discriminative vectors of FC-SC coupling in depression suggested that the capacity to process negative emotion might be more directly related to the SC abnormally and be indicative of more stringent and less dynamic brain function in SN-VAN, especially in the acute rising phase of FC. We demonstrated that depressive brain dysfunctions could be better characterized by reduced FC-SC coupling flexibility in this particular phase.
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Affiliation(s)
- Kun Bi
- Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China
| | - Lingling Hua
- Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Maobin Wei
- Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China
| | - Jiaolong Qin
- Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China; Suzhou Research Institute of Southeast University, 399 Linquan Street, Suzhou 215123, China.
| | - Zhijian Yao
- Medical School, Nanjing University, 22 Hankou Road, Nanjing 210093, China; Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
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48
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Reorganization of Anatomical Connectome following Electroconvulsive Therapy in Major Depressive Disorder. Neural Plast 2015; 2015:271674. [PMID: 26770836 PMCID: PMC4684875 DOI: 10.1155/2015/271674] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 07/01/2015] [Accepted: 07/13/2015] [Indexed: 01/25/2023] Open
Abstract
Objective. Electroconvulsive therapy (ECT) is considered one of the most effective and fast-acting treatment options for depressive episodes. Little is known, however, about ECT's enabling brain (neuro)plasticity effects, particular for plasticity of white matter pathway. Materials and Methods. We collected longitudinal diffusion tensor imaging in the first-episode, drug-naïve major depressive disorder (MDD) patients (n = 24) before and after a predefined time window ECT treatment. We constructed large-scale anatomical networks derived from white matter fiber tractography and evaluated the topological reorganization using graph theoretical analysis. We also assessed the relationship between topological reorganization with improvements in depressive symptoms. Results. Our investigation revealed three main findings: (1) the small-worldness was persistent after ECT series; (2) anatomical connections changes were found in limbic structure, temporal and frontal lobes, in which the connection changes between amygdala and parahippocampus correlate with depressive symptom reduction; (3) significant nodal strength changes were found in right paralimbic network. Conclusions. ECT elicits neuroplastic processes associated with improvements in depressive symptoms that act to specific local ventral frontolimbic circuits, but not small-world property. Overall, ECT induced topological reorganization in large-scale brain structural network, opening up new avenues to better understand the mode of ECT action in MDD.
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49
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Cognition-related brain networks underpin the symptoms of unipolar depression: Evidence from a systematic review. Neurosci Biobehav Rev 2015; 61:53-65. [PMID: 26562681 DOI: 10.1016/j.neubiorev.2015.09.022] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/16/2015] [Accepted: 09/21/2015] [Indexed: 01/23/2023]
Abstract
This systematic review sources the latest neuroimaging evidence for the role of cognition-related brain networks in depression, and relates their abnormal functioning to symptoms of the disorder. Using theoretically informed and rigorous inclusion criteria, we integrate findings from 59 functional neuroimaging studies of adults with unipolar depression using a narrative approach. Results demonstrate that two distinct neurocognitive networks, the autobiographic memory network (AMN) and the cognitive control network (CCN), are central to the symptomatology of depression. Specifically, hyperactivity of the introspective AMN is linked to pathological brooding, self-blame, rumination. Anticorrelated under-engagement of the CCN is associated with indecisiveness, negative automatic thoughts, poor concentration, distorted cognitive processing. Downstream effects of this imbalance include reduced regulation of networks linked to the vegetative and affective symptoms of depression. The configurations of these networks can change between individuals and over time, plausibly accounting for both the variable presentation of depressive disorders and their fluctuating course. Framing depression as a disorder of neurocognitive networks directly links neurobiology to psychiatric practice, aiding researchers and clinicians alike.
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50
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Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 2015; 57:328-49. [PMID: 26254595 DOI: 10.1016/j.neubiorev.2015.08.001] [Citation(s) in RCA: 183] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 07/29/2015] [Accepted: 08/02/2015] [Indexed: 12/19/2022]
Abstract
Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.
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Affiliation(s)
- Thomas Wolfers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, LondonUnited Kingdom
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