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Wang B, Pan T, Guo M, Li Z, Yu X, Li D, Niu Y, Cui X, Xiang J. Abnormal dynamic reconfiguration of the large-scale functional network in schizophrenia during the episodic memory task. Cereb Cortex 2022; 33:4135-4144. [PMID: 36030383 DOI: 10.1093/cercor/bhac331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
Episodic memory deficits are the core feature in schizophrenia (SCZ). Numerous studies have revealed abnormal brain activity associated with this disorder during episodic memory, however previous work has only relied on static analysis methods that treat the brain as a static monolithic structure, ignoring the dynamic features at different time scales. Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data during episodic memory and quantify integration and recruitment metrics to reveal abnormal dynamic reconfiguration of brain networks in SCZ. In the specific frequency band of 0.06-0.125 Hz, SCZ showed significantly higher integration during encoding and retrieval, and the abnormalities were mainly in the default mode, frontoparietal, and cingulo-opercular modules. Recruitment of SCZ was significantly higher during retrieval, mainly in the visual module. Interestingly, interactions between groups and task status in recruitment were found in the dorsal attention, visual modules. Finally, we observed that integration was significantly associated with memory performance in frontoparietal regions. Our findings revealed the time-varying evolution of brain networks in SCZ, while improving our understanding of cognitive decline and other pathophysiologies in brain diseases.
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Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tingting Pan
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Min Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhifeng Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xuexue Yu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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2
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Gao Y, Tang Y, Zhang H, Yang Y, Dong T, Jia Q. Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network. Interdiscip Sci 2022; 14:532-544. [PMID: 35103919 DOI: 10.1007/s12539-021-00498-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 12/21/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
This work aims to exploit a novel graph neural network to predict the sex of the brain topological network, and to find the sex differences in the cerebrum and cerebellum. A two-branch multi-scale graph convolutional network (TMGCN) is designed to analyze the sex differences of the brain. Two complementary templates are used to construct cerebrum and cerebellum networks, respectively, followed by a two-branch sub-network with multi-scale filters and a trainable weighted fusion strategy for the final prediction. Finally, a trainable graph topk-pooling layer is utilized in our model to visualize key brain regions relevant to the prediction. The proposed TMGCN achieves a prediction accuracy of 84.48%. In the cerebellum, the bilateral Crus I-II, lobule VI and VIIb, and the posterior vermis (VI-X) are discriminative for this task. As for the cerebrum, the discriminative brain regions consist of the bilateral inferior temporal gyrus, the bilateral fusiform gyrus, the bilateral parahippocampal gyrus, the bilateral cingulate gyrus, the bilateral medial ventral occipital cortex, the bilateral lateral occipital cortex, the bilateral amygdala, and the bilateral hippocampus. This study tackles the sex prediction problem from a more comprehensive view, and may provide the resting-state fMRI evidence for further study of sex differences in the cerebellum and cerebrum.
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Affiliation(s)
- Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yuan Yang
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Tulsa, USA
| | - Tingting Dong
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiaolan Jia
- School of Computer Science and Engineering, Central South University, Changsha, China
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3
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Wu EQ, Peng XY, Chen SD, Zhao XY, Tang ZR. Detecting Alzheimer’s Dementia Degree. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2020.3015131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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4
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Zarnowski O, Ziton S, Holmberg R, Musto S, Riegle S, Van Antwerp E, Santos-Nunez G. Functional MRI findings in personality disorders: A review. J Neuroimaging 2021; 31:1049-1066. [PMID: 34468063 DOI: 10.1111/jon.12924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 11/28/2022] Open
Abstract
Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.
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Affiliation(s)
- Oskar Zarnowski
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Shirley Ziton
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Rylan Holmberg
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sarafina Musto
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sean Riegle
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Emily Van Antwerp
- West Virginia School of Osteopathic Medicine, Lewisburg, West Virginia, USA
| | - Gabriela Santos-Nunez
- University of Massachusetts Memorial Medical Center, Radiology Department, Worcester, Massachusetts, USA
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5
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Lai X, Huang Q, Xin J, Yu H, Wen J, Huang S, Zhang H, Shen H, Tang Y. Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform. Front Psychol 2021; 12:684001. [PMID: 34456796 PMCID: PMC8385271 DOI: 10.3389/fpsyg.2021.684001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 07/12/2021] [Indexed: 11/13/2022] Open
Abstract
Few studies have investigated the functional patterns of methamphetamine abstainers. A better understanding of the underlying neurobiological mechanism in the brains of methamphetamine abstainers will help to explain their abnormal behaviors. Forty-two male methamphetamine abstainers, currently in a long-term abstinence status (for at least 14 months), and 32 male healthy controls were recruited. All subjects underwent functional MRI while responding to drug-associated cues. This study proposes to combine a convolutional neural network with a short-time Fourier transform to identify different brain patterns between methamphetamine abstainers and controls. The short-time Fourier transformation provides time-localized frequency information, while the convolutional neural network extracts the structural features of the time-frequency spectrograms. The results showed that the classifier achieved a satisfactory performance (98.9% accuracy) and could extract robust brain voxel information. The highly discriminative power voxels were mainly concentrated in the left inferior orbital frontal gyrus, the bilateral postcentral gyri, and the bilateral paracentral lobules. This study provides a novel insight into the different functional patterns between methamphetamine abstainers and healthy controls. It also elucidates the pathological mechanism of methamphetamine abstainers from the view of time-frequency spectrograms.
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Affiliation(s)
- Xin Lai
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiuping Huang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hufei Yu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jingxi Wen
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Shucai Huang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China.,The Fourth People's Hospital of Wuhu, Wuhu, China
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hongxian Shen
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
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6
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Werhahn JE, Mohl S, Willinger D, Smigielski L, Roth A, Hofstetter C, Stämpfli P, Naaijen J, Mulder LM, Glennon JC, Hoekstra PJ, Dietrich A, Kleine Deters R, Aggensteiner PM, Holz NE, Baumeister S, Banaschewski T, Saam MC, Schulze UME, Lythgoe DJ, Sethi A, Craig MC, Mastroianni M, Sagar-Ouriaghli I, Santosh PJ, Rosa M, Bargallo N, Castro-Fornieles J, Arango C, Penzol MJ, Zwiers MP, Franke B, Buitelaar JK, Walitza S, Brandeis D. Aggression subtypes relate to distinct resting state functional connectivity in children and adolescents with disruptive behavior. Eur Child Adolesc Psychiatry 2021; 30:1237-1249. [PMID: 32789793 PMCID: PMC8310860 DOI: 10.1007/s00787-020-01601-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 07/08/2020] [Indexed: 12/11/2022]
Abstract
There is increasing evidence for altered brain resting state functional connectivity in adolescents with disruptive behavior. While a considerable body of behavioral research points to differences between reactive and proactive aggression, it remains unknown whether these two subtypes have dissociable effects on connectivity. Additionally, callous-unemotional traits are important specifiers in subtyping aggressive behavior along the affective dimension. Accordingly, we examined associations between two aggression subtypes along with callous-unemotional traits using a seed-to-voxel approach. Six functionally relevant seeds were selected to probe the salience and the default mode network, based on their presumed role in aggression. The resting state sequence was acquired from 207 children and adolescents of both sexes [mean age (standard deviation) = 13.30 (2.60); range = 8.02-18.35] as part of a Europe-based multi-center study. One hundred eighteen individuals exhibiting disruptive behavior (conduct disorder/oppositional defiant disorder) with varying comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms were studied, together with 89 healthy controls. Proactive aggression was associated with increased left amygdala-precuneus coupling, while reactive aggression related to hyper-connectivities of the posterior cingulate cortex (PCC) to the parahippocampus, the left amygdala to the precuneus and to hypo-connectivity between the right anterior insula and the nucleus caudate. Callous-unemotional traits were linked to distinct hyper-connectivities to frontal, parietal, and cingulate areas. Additionally, compared to controls, cases demonstrated reduced connectivity of the PCC and left anterior insula to left frontal areas, the latter only when controlling for ADHD scores. Taken together, this study revealed aggression-subtype-specific patterns involving areas associated with emotion, empathy, morality, and cognitive control.
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Affiliation(s)
- Julia E Werhahn
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Susanna Mohl
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
| | - David Willinger
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
| | - Alexander Roth
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Christoph Hofstetter
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
| | - Philipp Stämpfli
- Department of Psychiatry, Psychotherapy and Psychosomatics and Department of Child and Adolescent Psychiatry, Psychiatric Hospital, MR-Center, University of Zurich, Zurich, Switzerland
| | - Jilly Naaijen
- Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Leandra M Mulder
- Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Jeffrey C Glennon
- Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pieter J Hoekstra
- Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Andrea Dietrich
- Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Renee Kleine Deters
- Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pascal M Aggensteiner
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany
| | - Nathalie E Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany
| | - Melanie C Saam
- Department of Child and Adolescent Psychiatry/Psychotherapy, University Hospital, University of Ulm, Ulm, Germany
| | - Ulrike M E Schulze
- Department of Child and Adolescent Psychiatry/Psychotherapy, University Hospital, University of Ulm, Ulm, Germany
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Arjun Sethi
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Michael C Craig
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mathilde Mastroianni
- Department of Child Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ilyas Sagar-Ouriaghli
- Department of Child Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paramala J Santosh
- Department of Child Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mireia Rosa
- Child and Adolescent Psychiatry Department, Hospital Clinic of Barcelona, IDIBAPS, Barcelona, Spain
| | - Nuria Bargallo
- Clinic Image Diagnostic Center (CDIC), Hospital Clinic of Barcelona, Magnetic Resonance Image Core Facility, IDIBAPS, Barcelona, Spain
| | - Josefina Castro-Fornieles
- Child and Adolescent Psychiatry and Psychology Department, Institute Clinic of Neurosciences, Hospital Clinic of Barcelona, CIBERSAM, IDIBAPS, Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Celso Arango
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón School of Medicine, IiSGM, CIBERSAM, Universidad Complutense, Madrid, Spain
| | - Maria J Penzol
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón School of Medicine, IiSGM, CIBERSAM, Universidad Complutense, Madrid, Spain
| | - Marcel P Zwiers
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan K Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Neumünsterallee 9, 8032, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany.
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7
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Localization of epileptic seizure focus by computerized analysis of fMRI recordings. Brain Inform 2020; 7:13. [PMID: 33128629 PMCID: PMC7603444 DOI: 10.1186/s40708-020-00114-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/19/2020] [Indexed: 01/04/2023] Open
Abstract
By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.
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8
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Functional connectivity of reflective and brooding rumination in depressed and healthy women. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2019; 18:884-901. [PMID: 29949111 DOI: 10.3758/s13415-018-0611-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Ruminative thinking is related to an increased risk for major depressive disorder (MDD) and perpetuates negative mood states. Rumination, uncontrollable negative thoughts about the self, may comprise both reflective and brooding components. However, only brooding rumination is consistently associated with increased negativity bias and negative coping styles, while reflective rumination has a less clear relationship with negative outcomes in healthy and depressed participants. The current study examined seed-to-voxel (S2.V) resting-state functional connectivity (FC) in a sample of healthy (HC) and depressed (MDD) adult women (HC: n=50, MDD: n=33). The S2V FC of six key brain regions, including the left and right amygdala, anterior and posterior cingulate cortex (ACC, PCC), and medial and dorsolateral prefrontal cortices (mPFC, dlPFC), was correlated with self-reported reflective and brooding rumination. Results indicate that HC and MDD participants had increased brooding rumination associated with decreased FC between the left amygdala and the right temporal pole. Moreover, reflective rumination was associated with distinct FC of the mPFC, PCC, and ACC with parietal, occipital, and cingulate regions. Depressed participants, compared with HC, exhibited decreased FC between the PCC and a region in the right middle frontal gyrus. The results of the current study add to the understanding of the neural underpinnings of different forms of self-related cognition-brooding and reflective rumination-in healthy and depressed women.
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Pujol J, Harrison BJ, Contreras-Rodriguez O, Cardoner N. The contribution of brain imaging to the understanding of psychopathy. Psychol Med 2019; 49:20-31. [PMID: 30207255 DOI: 10.1017/s0033291718002507] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Psychopathy is a personality type characterized by both callous emotional dysfunction and deviant behavior that affects society in the form of actions that harm others. Historically, researchers have been concerned with seeking data and arguments to support a neurobiological foundation of psychopathy. In the past few years, increasing research has begun to reveal brain alterations putatively underlying the enigmatic psychopathic personality. In this review, we describe the brain anatomical and functional features that characterize psychopathy from a synthesis of available neuroimaging research and discuss how such brain anomalies may account for psychopathic behavior. The results are consistent in showing anatomical alterations involving primarily a ventral system connecting the anterior temporal lobe to anterior and ventral frontal areas, and a dorsal system connecting the medial frontal lobe to the posterior cingulate cortex/precuneus complex and, in turn, to medial structures of the temporal lobe. Functional imaging data indicate that relevant emotional flow breakdown may occur in both these brain systems and suggest specific mechanisms via which emotion is anomalously integrated into cognition in psychopathic individuals during moral challenge. Directions for future research are delineated emphasizing, for instance, the relevance of further establishing the contribution of early life stress to a learned blockage of emotional self-exposure, and the potential role of androgenic hormones in the development of cortical anomalies.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, Department of Radiology,Hospital del Mar,CIBERSAM G21, Barcelona,Spain
| | - Ben J Harrison
- Department of Psychiatry,Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health,Melbourne,Australia
| | - Oren Contreras-Rodriguez
- Psychiatry Department,Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL,CIBERSAM G17, Barcelona,Spain
| | - Narcis Cardoner
- Department of Mental Health, Corporació Sanitaria Parc Taulí, Sabadell, and Department of Psychiatry,Autonomous University of Barcelona,Barcelona,Spain
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10
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Tang Y, Xiao X, Xie H, Wan CM, Meng L, Liu ZH, Liao WH, Tang BS, Guo JF. Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients. Front Neuroanat 2017; 11:99. [PMID: 29163072 PMCID: PMC5681528 DOI: 10.3389/fnana.2017.00099] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 10/19/2017] [Indexed: 11/30/2022] Open
Abstract
Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.
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Affiliation(s)
- Yan Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,School of Information Science and Engineering, Central South University, Changsha, China
| | - Xue Xiao
- School of Basic Medical Science, Central South University, Changsha, China
| | - Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States
| | - Chang-Min Wan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhen-Hua Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Bei-Sha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,School of Basic Medical Science, Central South University, Changsha, China.,Parkinson's Disease Center of Beijing Institute for Brain Disorders, Beijing, China.,Collaborative Innovation Center for Brain Science, Shanghai, China.,State Key Laboratory of Medical Genetics, Changsha, China
| | - Ji-Feng Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,State Key Laboratory of Medical Genetics, Changsha, China
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Wen H, Liu Y, Rekik I, Wang S, Chen Z, Zhang J, Zhang Y, Peng Y, He H. Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children. Mol Neurobiol 2017; 55:3251-3269. [PMID: 28478510 DOI: 10.1007/s12035-017-0519-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Accepted: 04/06/2017] [Indexed: 01/18/2023]
Abstract
Tourette syndrome (TS) is a childhood-onset neurological disorder. To date, accurate TS diagnosis remains challenging due to its varied clinical expressions and dependency on qualitative description of symptoms. Therefore, identifying accurate and objective neuroimaging biomarkers may help improve early TS diagnosis. As resting-state functional MRI (rs-fMRI) has been demonstrated as a promising neuroimaging tool for TS diagnosis, previous rs-fMRI studies on TS revealed functional connectivity (FC) changes in a few local brain networks or circuits. However, no study explored the disrupted topological organization of whole-brain FC networks in TS children. Meanwhile, very few studies have examined brain functional networks using machine-learning methods for diagnostics. In this study, we construct individual whole-brain, ROI-level FC networks for 29 drug-naive TS children and 37 healthy children. Then, we use graph theory analysis to investigate the topological disruptions between groups. The identified disrupted regions in FC networks not only involved the sensorimotor association regions but also the visual, default-mode and language areas, all highly related to TS. Furthermore, we propose a novel classification framework based on similarity network fusion (SNF) algorithm, to both diagnose an individual subject and explore the discriminative power of FC network topological properties in distinguishing between TS children and controls. We achieved a high accuracy of 88.79%, and the involved discriminative regions for classification were also highly related to TS. Together, both the disrupted topological properties between groups and the discriminative topological features for classification may be considered as comprehensive and helpful neuroimaging biomarkers for assisting the clinical TS diagnosis.
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Affiliation(s)
- Hongwei Wen
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yue Liu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China
| | - Islem Rekik
- CVIP, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Shengpei Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhiqiang Chen
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jishui Zhang
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Yue Zhang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China.
| | - Huiguang He
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China. .,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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