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Azarias FR, Almeida GHDR, de Melo LF, Rici REG, Maria DA. The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health. BIOLOGY 2025; 14:395. [PMID: 40282260 PMCID: PMC12025022 DOI: 10.3390/biology14040395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2025] [Revised: 03/24/2025] [Accepted: 03/25/2025] [Indexed: 04/29/2025]
Abstract
The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally, the DMN plays a crucial role in brain development during childhood and adolescence, influencing fundamental cognitive and emotional processes. This literature review aims to provide a comprehensive overview of the DMN, addressing its structural, functional, and evolutionary aspects, as well as its impact from infancy to adulthood. By gaining a deeper understanding of the organization and function of the DMN, we can advance our knowledge of the neural mechanisms that underlie cognition, behavior, and mental health. This, in turn, can lead to more effective therapeutic strategies for a range of neuropsychiatric conditions.
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Affiliation(s)
- Felipe Rici Azarias
- Graduate Program in Medical Sciences, School of Medicine, University of São Paulo, São Paulo 05508-220, SP, Brazil;
| | - Gustavo Henrique Doná Rodrigues Almeida
- Graduate Program in Anatomy of Domestic and Wild Animals, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-220, SP, Brazil; (G.H.D.R.A.); (L.F.d.M.); (R.E.G.R.)
| | - Luana Félix de Melo
- Graduate Program in Anatomy of Domestic and Wild Animals, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-220, SP, Brazil; (G.H.D.R.A.); (L.F.d.M.); (R.E.G.R.)
| | - Rose Eli Grassi Rici
- Graduate Program in Anatomy of Domestic and Wild Animals, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-220, SP, Brazil; (G.H.D.R.A.); (L.F.d.M.); (R.E.G.R.)
- Graduate Program in Structural and Functional Interactions in Rehabilitation, School of Medicine, University of Marília (UNIMAR), Marília 17525-902, SP, Brazil
| | - Durvanei Augusto Maria
- Graduate Program in Anatomy of Domestic and Wild Animals, College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo 05508-220, SP, Brazil; (G.H.D.R.A.); (L.F.d.M.); (R.E.G.R.)
- Graduate Program in Structural and Functional Interactions in Rehabilitation, School of Medicine, University of Marília (UNIMAR), Marília 17525-902, SP, Brazil
- Development and Innovation Laboratory, Butantan Institute, São Paulo 05585-000, SP, Brazil
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Ye L, Ba L, Yan D. A study of dynamic functional connectivity changes in flight trainees based on a triple network model. Sci Rep 2025; 15:7828. [PMID: 40050304 PMCID: PMC11885617 DOI: 10.1038/s41598-025-89023-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Accepted: 02/03/2025] [Indexed: 03/09/2025] Open
Abstract
The time-varying functional connectivity of the Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) in flight trainees during a resting state was investigated using dynamic functional network connectivity (dFNC). The study included 39 flight trainees and 37 age- and sex-matched healthy controls. Resting-state fMRI data and behavioral test outcomes were obtained from both groups. Independent component analysis (ICA), sliding window, and K-means clustering approaches were utilized for evaluating functional network connectivity (FNC) and temporal metrics based on the triple networks. Correlation analyses were performed on the behavioral assessments and these metrics. The flight trainees demonstrated a significantly enhanced functional connection linking the CEN and DMN in state 2 (P < 0.05, FDR corrected). Additionally, flight trainees spent less time in state 5, while they exhibited a protracted mean dwell time and fractional windows in state 2, which were significantly correlated with accuracy on the Berg Card Sorting Test (BCST) and Change Detection Test (all P < 0.05). The improved connectivity of flight trainees between the CEN and DMN following the completion of rigorous flight training resulted in increased stability. This enhancement may be relevant to cognitive abilities such as decision-making, memory, and information integration. When multitasking, flight trainees displayed superior visual processing skills and enhanced cognitive flexibility. dFNC research provides a unique perspective on the sophisticated cognitive capabilities that are required in high-demand, high-stress occupations such as piloting, thereby providing significant insights into the intricate brain mechanisms that are inherent in these domains.
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Affiliation(s)
- Lu Ye
- ¹Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, 618307, China
| | - Liya Ba
- ¹Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, 618307, China
| | - Dongfeng Yan
- ¹Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan, 618307, China.
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3
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Antos Z, Żukow X, Bursztynowicz L, Jakubów P. Beyond NMDA Receptors: A Narrative Review of Ketamine's Rapid and Multifaceted Mechanisms in Depression Treatment. Int J Mol Sci 2024; 25:13658. [PMID: 39769420 PMCID: PMC11728282 DOI: 10.3390/ijms252413658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2024] [Revised: 12/12/2024] [Accepted: 12/17/2024] [Indexed: 01/14/2025] Open
Abstract
The rising prevalence of depression, with its associated suicide risk, demands effective fast-acting treatments. Ketamine has emerged as promising, demonstrating rapid antidepressant effects. While early studies show swift mood improvements, its precise mechanisms remain unclear. This article aims to compile and synthesize the literature on ketamine's molecular actions. Ketamine primarily works by antagonizing NMDA receptors, reducing GABAergic inhibition, and increasing glutamate release. This enhanced glutamate activates AMPA receptors, triggering crucial downstream cascades, including BDNF-TrkB and mTOR pathways, promoting synaptic proliferation and regeneration. Moreover, neuroimaging studies have demonstrated alterations in brain networks involved in emotional regulation, including the Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN), which are frequently disrupted in depression. Despite the promising findings, the literature reveals significant inaccuracies and gaps in understanding the full scope of ketamine's therapeutic potential. For instance, ketamine engages with opioid receptors, insinuating a permissive role of the opioid system in amplifying ketamine's antidepressant effects, albeit ketamine does not operate as a direct opioid agonist. Further exploration is requisite to comprehensively ascertain its safety profile, long-term efficacy, and the impact of genetic determinants, such as BDNF polymorphisms, on treatment responsiveness.
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Affiliation(s)
| | | | | | - Piotr Jakubów
- Department of Paediatric Anaesthesiology and Intensive Therapy with Pain Division, Faculty of Medicine, Medical University of Bialystok, 15-089 Bialystok, Poland; (Z.A.); (X.Ż.); (L.B.)
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4
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Ganesan S, Misaki M, Zalesky A, Tsuchiyagaito A. Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.05.24306889. [PMID: 38766116 PMCID: PMC11100839 DOI: 10.1101/2024.05.05.24306889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD. Methods We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B). Results Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037). Limitations The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback. Conclusion We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.
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Affiliation(s)
- Saampras Ganesan
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
- Contemplative Studies Centre, Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
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Beckmann FE, Gruber H, Seidenbecher S, Schirmer ST, Metzger CD, Tozzi L, Frodl T. Specific alterations of resting-state functional connectivity in the triple network related to comorbid anxiety in major depressive disorder. Eur J Neurosci 2024; 59:1819-1832. [PMID: 38217400 DOI: 10.1111/ejn.16249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 12/17/2023] [Indexed: 01/15/2024]
Abstract
The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting-state functional connectivity (RSFC) within this so-called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients. Sixty-eight participants (52.9% male, mean age 35.3 years), consisting of 25 patients with comorbid MDD and ADs (MDD + AD), 20 patients with MDD only (MDD) and 23 healthy controls (HCs) were investigated clinically and with 3T resting-state fMRI. RSFC utilizing a seed-based approach within the three networks belonging to the triple network was compared between the groups. Compared with HC, MDD + AD showed significantly reduced RSFC between the ECN and the VAN, the DMN and the VAN and within the ECN. No differences could be found for the MDD group compared with both other groups. Furthermore, symptom severity and medication status did not affect RSFC values. The results of this study show a distinct set of alterations of RSFC for patients with comorbid MDD and AD compared with HCs. This set of dysfunctions might be related to less adequate switching between the DMN and the ECN as well as poorer functioning of the ECN. This might contribute to additional difficulties in engaging and utilizing consciously controlled emotional regulation strategies.
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Affiliation(s)
- Fienne-Elisa Beckmann
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Hanna Gruber
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie Seidenbecher
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Saskia Thérèse Schirmer
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Coraline D Metzger
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital RWTH, Aachen, Germany
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6
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Andreev AV, Kurkin SA, Stoyanov D, Badarin AA, Paunova R, Hramov AE. Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures. CHAOS (WOODBURY, N.Y.) 2023; 33:063140. [PMID: 37318340 DOI: 10.1063/5.0155567] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/24/2023] [Indexed: 06/16/2023]
Abstract
We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.
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Affiliation(s)
- Andrey V Andreev
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia
| | - Semen A Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 15A Vassil Aprilov Blvd., Plovdiv 4002, Bulgaria
| | - Artem A Badarin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia
| | - Rossitsa Paunova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 15A Vassil Aprilov Blvd., Plovdiv 4002, Bulgaria
| | - Alexander E Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia
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Martin JC, Clark SR, Schubert KO. Towards a Neurophenomenological Understanding of Self-Disorder in Schizophrenia Spectrum Disorders: A Systematic Review and Synthesis of Anatomical, Physiological, and Neurocognitive Findings. Brain Sci 2023; 13:845. [PMID: 37371325 DOI: 10.3390/brainsci13060845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/18/2023] [Accepted: 05/18/2023] [Indexed: 06/29/2023] Open
Abstract
The concept of anomalous self-experience, also termed Self-Disorder, has attracted both clinical and research interest, as empirical studies suggest such experiences specifically aggregate in and are a core feature of schizophrenia spectrum disorders. A comprehensive neurophenomenological understanding of Self-Disorder may improve diagnostic and therapeutic practice. This systematic review aims to evaluate anatomical, physiological, and neurocognitive correlates of Self-Disorder (SD), considered a core feature of Schizophrenia Spectrum Disorders (SSDs), towards developing a neurophenomenological understanding. A search of the PubMed database retrieved 285 articles, which were evaluated for inclusion using PRISMA guidelines. Non-experimental studies, studies with no validated measure of Self-Disorder, or those with no physiological variable were excluded. In total, 21 articles were included in the review. Findings may be interpreted in the context of triple-network theory and support a core dysfunction of signal integration within two anatomical components of the Salience Network (SN), the anterior insula and dorsal anterior cingulate cortex, which may mediate connectivity across both the Default Mode Network (DMN) and Fronto-Parietal Network (FPN). We propose a theoretical Triple-Network Model of Self-Disorder characterized by increased connectivity between the Salience Network (SN) and the DMN, increased connectivity between the SN and FPN, decreased connectivity between the DMN and FPN, and increased connectivity within both the DMN and FPN. We go on to describe translational opportunities for clinical practice and provide suggestions for future research.
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Affiliation(s)
- James C Martin
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Scott R Clark
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
- Basil Hetzel Institute, Woodville, SA 5011, Australia
| | - K Oliver Schubert
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
- Division of Mental Health, Northern Adelaide Local Health Network, SA Health, Adelaide, SA 5000, Australia
- Headspace Early Psychosis, Sonder, Adelaide, SA 5000, Australia
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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: 8] [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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9
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Zhang L, Cui X, Ou Y, Liu F, Li H, Xie G, Li P, Zhao J, Xie G, Guo W. Abnormal long- and short-range functional connectivity in patients with first-episode drug-naïve melancholic and non-melancholic major depressive disorder. J Affect Disord 2023; 320:360-369. [PMID: 36206876 DOI: 10.1016/j.jad.2022.09.161] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND We attempted to explore the common and distinct long- and short-range functional connectivity (FC) patterns of melancholic and non-melancholic major depressive disorder (MDD) and their associations with clinical characteristics. METHODS Fifty-nine patients with first-episode drug-naïve MDD, including 31 patients with melancholic features and 28 patients with non-melancholic features, underwent resting-state functional magnetic resonance imaging (fMRI) scanning to examine long- and short-range FC. Thirty-two healthy volunteers were recruited as controls. The support vector machines (SVM) was applied to distinguish the melancholic patients from the non-melancholic patients by using the FC of abnormal brain regions. RESULTS Compared to healthy volunteers, patients with MDD showed increased long-range positive FC (lpFC) in the right insula/inferior frontal gyrus and left insula. Relative to non-melancholic patients, melancholic patients displayed decreased lpFC in the right lingual gyrus, decreased short-range positive FC (spFC) in the right middle temporal gyrus and right superior parietal lobule, increased lpFC in the left inferior parietal lobule, and increased spFC in the left middle occipital gyrus/inferior occipital gyrus, left cerebellum VII/IX, and bilateral cerebellum CrusII. Increased lpFC in the left inferior parietal lobule in melancholic patients was correlated with the TEPS abstract anticipatory scores. SVM results showed that FCs of five combinations within different brain regions could distinguish melancholic patients from non-melancholic patients. CONCLUSIONS FC abnormalities in the default mode network and parietal-occipital brain regions may underlie the neurobiology of melancholic MDD. An increased lpFC in the left inferior parietal lobule correlated with anhedonia may be a distinctive neurobiological feature of melancholic MDD.
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Affiliation(s)
- Lulu Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China; Department of Psychiatry, Guangzhou First People's Hospital, Guangzhou 510180, Guangdong, China
| | - Xilong Cui
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300000, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang 161006, China
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Guangrong Xie
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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10
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Stoyanov D, Khorev V, Paunova R, Kandilarova S, Simeonova D, Badarin A, Hramov A, Kurkin S. Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14045. [PMID: 36360924 PMCID: PMC9656256 DOI: 10.3390/ijerph192114045] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/14/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
AIM This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression. METHOD AND SUBJECTS We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups. RESULTS AND DISCUSSION Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects. CONCLUSION The study provides supportive evidence for impaired functional connectivity networks in MDE patients.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Denitsa Simeonova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
| | - Artem Badarin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
| | - Alexander Hramov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia
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