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Moya Quiros V, Adham A, Convers P, Lesca G, Mauguiere F, Soulier H, Arzimanoglou A, Bayat A, Braakman H, Camdessanche JP, Casenave P, Chaton L, Chaix Y, Chochoi M, Depienne C, Desportes V, De Ridder J, Dinkelacker V, Gardella E, Kluger GJ, Jung J, Lemesle Martin M, Mancardi MM, Mueller M, Poulat AL, Platzer K, Roubertie A, Stokman MF, Vulto-van Silfhout AT, Wiegand G, Mazzola L. Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy. Ann Neurol 2024. [PMID: 39177219 DOI: 10.1002/ana.27063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 08/24/2024]
Abstract
OBJECTIVE There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern. METHODS In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro-clinical data from 10 epileptic seizures in 5 patients, using prolonged video-EEG monitoring recordings. Inter-ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age- and sex-matched controls. RESULTS The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro-clinical semiology of seizures was mainly temporal or temporo-insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo-perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions. INTERPRETATION A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo-insular involvement. ANN NEUROL 2024.
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Affiliation(s)
| | - Ahmed Adham
- Physical Medicine and Rehabilitation Department, University Hospital of Saint-Étienne, Saint-Étienne, France
- CEA, LETI, CLINATEC, University Grenoble Alpes, Grenoble, France
| | - Philippe Convers
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
| | - Gaetan Lesca
- Department of Genetics, Member of the ERN EpiCARE, Hospices Civils de Lyon, Bron, France
- Institute NeuroMyoGène, Laboratoire Physiopathologie et Génétique du Neurone et du Muscle, CNRS UMR 5261-INSERM U1315, Université de Lyon-Université Claude Bernard Lyon 1, Lyon, France
| | - François Mauguiere
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
| | - Hugo Soulier
- Neurology Department, University Hospital, Saint-Etienne, France
| | - Alexis Arzimanoglou
- Department of Clinical Epileptology, Sleep Disorders and Functional Pediatric Neurology, coordinating member of the ERN EpiCARE, University Hospitals of Lyon (HCL), Lyon, France
- Sección Epilepsia, Sueño y Neurofisiología, Department of Neurology, coordinating member of the ERN EpiCARE, Hospital Sant Joan de Déu Barcelona, Barcelona, Spain
| | - Allan Bayat
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Hilde Braakman
- Department of Paediatric Neurology, Radboud University Medical Centre, Amalia Children's Hospital, Nijmegen, The Netherlands
| | | | | | - Laurence Chaton
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Yves Chaix
- Toulouse NeuroImaging Center, University of Toulouse, INSERM, Université Paul Sabatier, Toulouse, France
- Pediatric Neurology Unit, Children's Hospital, Toulouse-Purpan University Hospital, Toulouse, France
| | - Maxime Chochoi
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Christel Depienne
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Vincent Desportes
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Jessie De Ridder
- Department of Neurology, Academic Center for Epileptology, Kempenhaeghe, Heeze, The Netherlands
| | - Vera Dinkelacker
- Department of Neurology, University Hospital Strasbourg, Strasbourg, France
| | - Elena Gardella
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
| | - Gerhard J Kluger
- Schön Klinik Vogtareuth, Center for Pediatric Neurology, Neurorehabilitation and Epileptology, Collaborating Partner of the ERN EpiCARE, PMU, Vogtareuth, Salzburg, Germany
| | - Julien Jung
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
- Department of Neurology, University Hospital, Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, Lyon, France
| | | | - Maria Margherita Mancardi
- Unit of Child Neuropsychiatry, Epilepsy Center, Member of the ERN EpiCARE, Istituto Giannina Gaslini, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Markus Mueller
- Department of Epileptology, Krankenhaus Mara, Bethel Epilepsy Center, Bielefeld University, Bielefeld, Germany
| | - Anne-Lise Poulat
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Agathe Roubertie
- Department of Pediatric Neurology, INSERM, University Hospital Montpellier, Montpellier, France
| | - Marijn F Stokman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Gert Wiegand
- Division of Pediatric Neurology, Department of Pediatrics, Asklepios Klinik Nord-Heidberg, Hamburg, Germany
- Department of Pediatric and Adolescent Medicine II (Neuropediatrics, Social Pediatrics), University Medical Centre Schleswig-Holstein, Kiel, Germany
| | - Laure Mazzola
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
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Zhang W, Zhai X, Zhang C, Cheng S, Zhang C, Bai J, Deng X, Ji J, Li T, Wang Y, Tong HHY, Li J, Li K. Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder. Psychiatry Res Neuroimaging 2024; 344:111862. [PMID: 39153232 DOI: 10.1016/j.pscychresns.2024.111862] [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: 10/31/2023] [Revised: 03/22/2024] [Accepted: 07/31/2024] [Indexed: 08/19/2024]
Abstract
Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.
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Affiliation(s)
- Wenjie Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xiaobing Zhai
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Chan Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Song Cheng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Chaoqing Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Jinji Bai
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xuan Deng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Junjun Ji
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Ting Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Yu Wang
- Department of Psychiatry, Changzhi Mental Health Center, Changzhi, Shanxi, China
| | - Henry H Y Tong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Junfeng Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Kefeng Li
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
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Yu P, Dong R, Wang X, Tang Y, Liu Y, Wang C, Zhao L. Neuroimaging of motor recovery after ischemic stroke - functional reorganization of motor network. Neuroimage Clin 2024; 43:103636. [PMID: 38950504 PMCID: PMC11267109 DOI: 10.1016/j.nicl.2024.103636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/03/2024]
Abstract
The long-term motor outcome of acute stroke patients may be correlated to the reorganization of brain motor network. Abundant neuroimaging studies contribute to understand the pathological changes and recovery of motor networks after stroke. In this review, we summarized how current neuroimaging studies have increased understanding of reorganization and plasticity in post stroke motor recovery. Firstly, we discussed the changes in the motor network over time during the motor-activation and resting states, as well as the overall functional integration trend of the motor network. These studies indicate that the motor network undergoes dynamic bilateral hemispheric functional reorganization, as well as a trend towards network randomization. In the second part, we summarized the current study progress in the application of neuroimaging technology to early predict the post-stroke motor outcome. In the third part, we discuss the neuroimaging techniques commonly used in the post-stroke recovery. These methods provide direct or indirect visualization patterns to understand the neural mechanisms of post-stroke motor recovery, opening up new avenues for studying spontaneous and treatment-induced recovery and plasticity after stroke.
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Affiliation(s)
- Pei Yu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ruoyu Dong
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Xiao Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yuqi Tang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yaning Liu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Can Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ling Zhao
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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Sapienza J, Pacchioni F, Spangaro M, Bosia M. Dysconnection in schizophrenia: Filling the dots from old to new evidence. Clin Neurophysiol 2024; 162:226-228. [PMID: 38555237 DOI: 10.1016/j.clinph.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/02/2024]
Affiliation(s)
- Jacopo Sapienza
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy; Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy.
| | - Federico Pacchioni
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marco Spangaro
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marta Bosia
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy; School of Medicine, Vita -Salute San Raffaele University, Milan, Italy
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Gonuguntla V, Adebisi AT, Veluvolu KC. Identification of Optimal and Most Significant Event Related Brain Functional Network. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1906-1915. [PMID: 38722721 DOI: 10.1109/tnsre.2024.3399308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.
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Sunil G, Gowtham S, Bose A, Harish S, Srinivasa G. Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia. BMC Neurosci 2024; 25:2. [PMID: 38166747 PMCID: PMC10759601 DOI: 10.1186/s12868-023-00841-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites. RESULTS The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84. CONCLUSIONS This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.
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Affiliation(s)
- Gayathri Sunil
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India
| | - Smruthi Gowtham
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India
| | - Anurita Bose
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India
| | - Samhitha Harish
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India
| | - Gowri Srinivasa
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India.
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Chen Y, Zhao P, Pan C, Chang M, Zhang X, Duan J, Wei Y, Tang Y, Wang F. State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study. J Psychiatry Neurosci 2024; 49:E11-E22. [PMID: 38238036 PMCID: PMC10803102 DOI: 10.1503/jpn.230069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/10/2023] [Accepted: 09/14/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.
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Affiliation(s)
- Yifan Chen
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Pengfei Zhao
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Chunyu Pan
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Miao Chang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Xizhe Zhang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Jia Duan
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Yange Wei
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Yanqing Tang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Fei Wang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
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9
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [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: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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10
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Vecchio D, Piras F, Ciullo V, Piras F, Natalizi F, Ducci G, Ambrogi S, Spalletta G, Banaj N. Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices. J Pers Med 2023; 13:jpm13050799. [PMID: 37240969 DOI: 10.3390/jpm13050799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Patients with deficit schizophrenia (SZD) suffer from primary and enduring negative symptoms. Limited pieces of evidence and neuroimaging studies indicate they differ from patients with non-deficit schizophrenia (SZND) in neurobiological aspects, but the results are far from conclusive. We applied for the first time, graph theory analyses to discriminate local and global indices of brain network topology in SZD and SZND patients compared with healthy controls (HC). High-resolution T1-weighted images were acquired for 21 SZD patients, 21 SZND patients, and 21 HC to measure cortical thickness from 68 brain regions. Graph-based metrics (i.e., centrality, segregation, and integration) were computed and compared among groups, at both global and regional networks. When compared to HC, at the regional level, SZND were characterized by temporoparietal segregation and integration differences, while SZD showed widespread alterations in all network measures. SZD also showed less segregated network topology at the global level in comparison to HC. SZD and SZND differed in terms of centrality and integration measures in nodes belonging to the left temporoparietal cortex and to the limbic system. SZD is characterized by topological features in the network architecture of brain regions involved in negative symptomatology. Such results help to better define the neurobiology of SZD (SZD: Deficit Schizophrenia; SZND: Non-Deficit Schizophrenia; SZ: Schizophrenia; HC: healthy controls; CC: clustering coefficient; L: characteristic path length; E: efficiency; D: degree; CCnode: CC of a node; CCglob: the global CC of the network; Eloc: efficiency of the information transfer flow either within segregated subgraphs or neighborhoods nodes; Eglob: efficiency of the information transfer flow among the global network; FDA: Functional Data Analysis; and Dmin: estimated minimum densities).
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Affiliation(s)
- Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Federica Natalizi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- Department of Psychology, "Sapienza" University of Rome, Via dei Marsi 78, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, 00161 Rome, Italy
| | - Giuseppe Ducci
- Department of Mental Health, ASL Roma 1, 00135 Rome, Italy
| | - Sonia Ambrogi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
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11
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Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering (Basel) 2023; 10:bioengineering10030372. [PMID: 36978763 PMCID: PMC10044923 DOI: 10.3390/bioengineering10030372] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
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Affiliation(s)
- Giovanni Chiarion
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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12
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Jia Y, Jariwala N, Hinkley LBN, Nagarajan S, Subramaniam K. Abnormal resting-state functional connectivity underlies cognitive and clinical symptoms in patients with schizophrenia. Front Hum Neurosci 2023; 17:1077923. [PMID: 36875232 PMCID: PMC9976937 DOI: 10.3389/fnhum.2023.1077923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/31/2023] [Indexed: 02/17/2023] Open
Abstract
Introduction The cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity. Methods We capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC). Results We found that SZ showed significant global disrupted functional connectivity in delta-theta (2-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition. Discussion The multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.
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Affiliation(s)
- Yingxin Jia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Namasvi Jariwala
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Leighton B. N. Hinkley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Karuna Subramaniam
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
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13
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Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:5. [PMID: 36690632 PMCID: PMC9870995 DOI: 10.1038/s41537-022-00329-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/23/2022] [Indexed: 01/24/2023]
Abstract
Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4-6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication.
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14
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Galdino LB, Fernandes T, Schmidt KE, Santos NA. Altered brain connectivity during visual stimulation in schizophrenia. Exp Brain Res 2022; 240:3327-3337. [PMID: 36322165 DOI: 10.1007/s00221-022-06495-4] [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: 02/28/2022] [Accepted: 10/20/2022] [Indexed: 11/23/2022]
Abstract
Schizophrenia (SCZ) can be described as a functional dysconnectivity syndrome that affects brain connectivity and circuitry. However, little is known about how sensory stimulation modulates network parameters in schizophrenia, such as their small-worldness (SW) during visual processing. To address this question, we applied graph theory algorithms to multi-electrode EEG recordings obtained during visual stimulation with a checkerboard pattern-reversal stimulus. Twenty-six volunteers participated in the study, 13 diagnosed with schizophrenia (SCZ; mean age = 38.3 years; SD = 9.61 years) and 13 healthy controls (HC; mean age = 28.92 years; SD = 12.92 years). The visually evoked potential (VEP) showed a global amplitude decrease (p < 0.05) for SCZ patients as opposed to HC but no differences in latency (p > 0.05). As a signature of functional connectivity, graph measures were obtained from the Magnitude-Squared Coherence between signals from pairs of occipital electrodes, separately for the alpha (8-13 Hz) and low-gamma (36-55 Hz) bands. For the alpha band, there was a significant effect of the visual stimulus on all measures (p < 0.05) but no group interaction between SCZ and HZ (p > 0.05). For the low-gamma spectrum, both groups showed a decrease of Characteristic Path Length (L) during visual stimulation (p < 0.05), but, contrary to the HC group, only SCZ significantly lowered their small-world (SW) connectivity index during visual stimulation (SCZ p < 0.05; HC p > 0.05). This indicates dysconnectivity of the functional network in the low-gamma band of SCZ during stimulation, which might indirectly reflect an altered ability to react to new sensory input in patients. These results provide novel evidence about a possible electrophysiological signature of the global deficits revealed by the application of graph theory onto electroencephalography in schizophrenia.
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Affiliation(s)
- Lucas B Galdino
- Laboratory of Perception, Neurosciences and Behaviour, Department of Psychology, Federal University of Paraiba, João Pessoa, Brazil. .,Neurobiology of Vision Lab, Brain Institute (ICe), Federal University of Rio Grande do Norte, Natal, Brazil.
| | - Thiago Fernandes
- Laboratory of Perception, Neurosciences and Behaviour, Department of Psychology, Federal University of Paraiba, João Pessoa, Brazil
| | - Kerstin E Schmidt
- Neurobiology of Vision Lab, Brain Institute (ICe), Federal University of Rio Grande do Norte, Natal, Brazil
| | - Natanael A Santos
- Laboratory of Perception, Neurosciences and Behaviour, Department of Psychology, Federal University of Paraiba, João Pessoa, Brazil
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15
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Tagawa M, Takei Y, Kato Y, Suto T, Hironaga N, Ohki T, Takahashi Y, Fujihara K, Sakurai N, Ujita K, Tsushima Y, Fukuda M. Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study. Psychiatry Clin Neurosci 2022; 76:309-320. [PMID: 35397141 DOI: 10.1111/pcn.13362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/14/2022] [Accepted: 03/25/2022] [Indexed: 11/30/2022]
Abstract
AIMS Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ. METHODS We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms. RESULTS SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms. CONCLUSIONS The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.
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Affiliation(s)
- Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Tsutsuji Mental Hospital, Gunma, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Takefumi Ohki
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan
| | - Yumiko Takahashi
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Kazuyuki Fujihara
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Noriko Sakurai
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Koichi Ujita
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
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Oyefiade A, Moxon-Emre I, Beera K, Bouffet E, Taylor M, Ramaswamy V, Laughlin S, Skocic J, Mabbott D. Structural connectivity and intelligence in brain-injured children. Neuropsychologia 2022; 173:108285. [PMID: 35690116 DOI: 10.1016/j.neuropsychologia.2022.108285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 05/28/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022]
Abstract
In children, higher general intelligence corresponds with better processing speed ability. However, the relationship between structural brain connectivity and processing speed in the context of intelligence is unclear. Furthermore, the impact of brain injury on this relationship is also unknown. Structural networks were constructed for 36 brain tumor patients (mean age: 13.45 ± 2.73, 58% males) and 35 typically developing children (13.30 ± 2.86, 51% males). Processing speed and general intelligence scores were acquired using standard batteries. The relationship between network properties, processing speed, and intelligence was assessed using a partial least squares analysis. Results indicated that structural networks in brain-injured children were less integrated (β = -.38, p = 0.001) and more segregated (β = 0.4, p = 0.0005) compared to typically developing children. There was an indirect effect of network segregation on general intelligence via processing speed, where greater network segregation predicted slower processing speed which in turn predicted worse general intelligence (GoF = 0.37). These findings provide the first evidence of relations between structural connectivity, processing speed, and intelligence in children. Injury-related disruption to the structural network may result in worse intelligence through impacts on information processing. Our findings are discussed in the context of a network approach to understanding brain-behavior relationships.
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Affiliation(s)
- Adeoye Oyefiade
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA
| | - Iska Moxon-Emre
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Kiran Beera
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Eric Bouffet
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Michael Taylor
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Vijay Ramaswamy
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Suzanne Laughlin
- Division of Radiology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Jovanka Skocic
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA.
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17
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Oyefiade A, Moxon-Emre I, Beera K, Bouffet E, Taylor M, Ramaswamy V, Laughlin S, Skocic J, Mabbott D. Abnormalities of Structural Brain Connectivity in Pediatric Brain Tumor Survivors. Neurooncol Adv 2022; 4:vdac064. [PMID: 35875689 PMCID: PMC9297943 DOI: 10.1093/noajnl/vdac064] [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] [Indexed: 11/28/2022] Open
Abstract
Background Pediatric brain tumor survivors are at an increased risk for white matter (WM) injury. However, damage to whole-brain structural connectivity is unelucidated. The impact of treatment on WM connectivity was investigated. Methods Whole-brain WM networks were derived from diffusion tensor imaging data acquired for 28 irradiated patients (radiotherapy, RT) (mean age = 13.74 ± 3.32 years), 13 patients not irradiated (No RT) (mean age = 12.57 ± 2.87), and 41 typically developing children (TDC) (mean age = 13.32 ± 2.92 years). Differences in network properties were analyzed using robust regressions. Results Participation coefficient was lower in both patient groups (RT: adj. P = .015; No RT: adj. P = .042). Compared to TDC, RT had greater clustering (adj. P = .015), local efficiency (adj. P = .003), and modularity (adj. P = .000003). WM traced from hubs was damaged in patients: left hemisphere pericallosal sulcus (FA [F = 4.97; q < 0.01]; MD [F = 11.02; q < 0.0001]; AD [F = 10.00; q < 0.0001]; RD [F = 8.53; q < 0.0001]), right hemisphere pericallosal sulcus (FA [F = 8.87; q < 0.0001]; RD [F = 8.27; q < 0.001]), and right hemisphere parietooccipital sulcus (MD [F = 5.78; q < 0.05]; RD [F = 5.12; q < 0.05]). Conclusions Findings indicate greater segregation of WM networks after RT. Intermodular connectivity was lower after treatment with and without RT. No significant network differences were observed between patient groups. Our results are discussed in the context of a network approach that emphasizes interactions between brain regions.
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Affiliation(s)
- Adeoye Oyefiade
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
- Department of Psychology, University of Toronto, Toronto, Ontario
| | - Iska Moxon-Emre
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Kiran Beera
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
| | - Eric Bouffet
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
| | - Michael Taylor
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario
| | - Vijay Ramaswamy
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
| | - Suzanne Laughlin
- Division of Radiology, The Hospital for Sick Children, Toronto, Ontario
| | - Jovanka Skocic
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
- Department of Psychology, University of Toronto, Toronto, Ontario
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18
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Trepl J, Dahlmanns M, Kornhuber J, Groemer TW, Dahlmanns JK. Common network effect-patterns after monoamine reuptake inhibition in dissociated hippocampus cultures. J Neural Transm (Vienna) 2022; 129:261-275. [PMID: 35211818 PMCID: PMC8930948 DOI: 10.1007/s00702-022-02477-6] [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: 04/01/2021] [Accepted: 02/11/2022] [Indexed: 12/04/2022]
Abstract
The pharmacological treatment of major depressive disorder with currently available antidepressant drugs is still unsatisfying as response to medication is delayed and in some patients even non-existent. To understand complex psychiatric diseases such as major depressive disorder and their treatment, research focus is shifting from investigating single neurons towards a view of the entire functional and effective neuronal network, because alterations on single synapses through antidepressant drugs may translate to alterations in the entire network. Here, we examined the effects of monoamine reuptake inhibitors on in vitro hippocampal network dynamics using calcium fluorescence imaging and analyzing the data with means of graph theoretical parameters. Hypothesizing that monoamine reuptake inhibitors operate through changes of effective connectivity on micro-scale neuronal networks, we measured the effects of the selective monoamine reuptake inhibitors GBR-12783, Sertraline, Venlafaxine, and Amitriptyline on neuronal networks. We identified a common pattern of effects of the different tested monoamine reuptake inhibitors. After treatment with GBR-12783, Sertraline, and Venlafaxine, the connectivity degree, measuring the number of existing connections in the network, was significantly decreased. All tested substances led to networks with more submodules and a reduced global efficiency. No monoamine reuptake inhibitor did affect network-wide firing rate, the characteristic path length, or the network strength. In our study, we found that monoamine reuptake inhibition in neuronal networks in vitro results in a sharpening of the network structure. These alterations could be the basis for the reorganization of a large-scale miswired network in major depressive disorder.
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Affiliation(s)
- Julia Trepl
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Marc Dahlmanns
- Institute for Physiology and Pathophysiology, Friedrich-Alexander University Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Teja Wolfgang Groemer
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jana Katharina Dahlmanns
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany.
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19
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Liu S, Chen S, Huang Z, Liu X, Li M, Su F, Hao X, Ming D. Hypofunction of directed brain network within alpha frequency band in depressive patients: a graph-theoretic analysis. Cogn Neurodyn 2022; 16:1059-1071. [PMID: 36237415 PMCID: PMC9508312 DOI: 10.1007/s11571-022-09782-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 12/04/2021] [Accepted: 01/08/2022] [Indexed: 02/08/2023] Open
Abstract
Directed brain networks may provide new insights into exploring physiological mechanism and neuromarkers for depression. This study aims to investigate the abnormalities of directed brain networks in depressive patients. We constructed the directed brain network based on resting electroencephalogram for 19 depressive patients and 20 healthy controls with eyes closed and eyes open. The weighted directed brain connectivity was measured by partial directed coherence for α, β, γ frequency band. Furthermore, topological parameters (clustering coefficient, characteristic path length, and et al.) were computed based on graph theory. The correlation between network metrics and clinical symptom was also examined. Depressive patients had a significantly weaker value of partial directed coherence at alpha frequency band in eyes-closed state. Clustering coefficient and characteristic path length were significantly lower in depressive patients (both p < .01). More importantly, in depressive patients, disruption of directed connectivity was noted in left-to-left (p < .05), right-to-left (p < .01) hemispheres and frontal-to-central (p < .01), parietal-to-central (p < .05), occipital-to-central (p < .05) regions. Furthermore, connectivity in LL and RL hemispheres was negatively correlated with depression scale scores (both p < .05). Depressive patients showed a more randomized network structure, disturbed directed interaction of left-to-left, right-to-left hemispheric information and between different cerebral regions. Specifically, left-to-left, right-to-left hemispheric connectivity was negatively correlated with the severity of depression. Our analysis may serve as a potential neuromarker of depression.
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20
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Sun Y, Zhang Z, Kakkos I, Matsopoulos GK, Yuan J, Suckling J, Xu L, Cao S, Chen W, Hu X, Li T, Sim K, Qi P, Sun Y. Inferring the Individual Psychopathologic Deficits with Structural Connectivity in a Longitudinal Cohort of Schizophrenia. IEEE J Biomed Health Inform 2022; 26:2536-2546. [PMID: 34982705 DOI: 10.1109/jbhi.2021.3139701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.
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21
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Wang Y, Hu X, Li Y. Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity. Front Psychiatry 2022; 13:1069036. [PMID: 36479558 PMCID: PMC9719952 DOI: 10.3389/fpsyt.2022.1069036] [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: 10/13/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear. METHOD We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls. RESULTS The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing. CONCLUSION These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.
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Affiliation(s)
- Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueping Hu
- School of Linguistic Science and Art, Jiangsu Normal University, Xuzhou, China.,Key Laboratory of Language and Cognitive Neuroscience of Jiangsu Province, Collaborative Innovation Center for Language Ability, Xuzhou, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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22
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Schettler L, Thomasius R, Paschke K. Neural correlates of problematic gaming in adolescents: A systematic review of structural and functional magnetic resonance imaging studies. Addict Biol 2022; 27:e13093. [PMID: 34496459 DOI: 10.1111/adb.13093] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/19/2022]
Abstract
Problematic gaming in adolescents is associated with neural alterations in structural and functional imaging studies. Especially frontal regions, associated with cognitive control functions, as well as temporoparietal areas, responsible for attention processes and self-concepts, and frontolimbic and subcortical regions, connected to emotion regulation and reward processing, are affected. The differences provide a further explanation for addictive disorders and emphasize the importance of interventions that address executive and cognitive-affective deficits.
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Affiliation(s)
- Leonie Schettler
- German Center for Addiction Research in Childhood and Adolescence (DZSKJ) University Medical Center Hamburg‐Eppendorf (UKE) Hamburg Germany
| | - Rainer Thomasius
- German Center for Addiction Research in Childhood and Adolescence (DZSKJ) University Medical Center Hamburg‐Eppendorf (UKE) Hamburg Germany
| | - Kerstin Paschke
- German Center for Addiction Research in Childhood and Adolescence (DZSKJ) University Medical Center Hamburg‐Eppendorf (UKE) Hamburg Germany
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23
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Dupuy M, Abdallah M, Swendsen J, N’Kaoua B, Chanraud S, Schweitzer P, Fatseas M, Serre F, Barse E, Auriacombe M, Misdrahi D. Real-time cognitive performance and positive symptom expression in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 272:415-425. [PMID: 34287696 PMCID: PMC8938338 DOI: 10.1007/s00406-021-01296-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 07/04/2021] [Indexed: 11/28/2022]
Abstract
Deficits in cognitive functions are frequent in schizophrenia and are often conceptualized as stable characteristics of this disorder. However, cognitive capacities may fluctuate over the course of a day and it is unknown if such variation may be linked to the dynamic expression of psychotic symptoms. This investigation used Ecological Momentary Assessment (EMA) to provide mobile tests of cognitive functions and positive symptoms in real time. Thirty-three individuals with schizophrenia completed five EMA assessments per day for a one-week period that included real-time assessments of cognitive performance and psychotic symptoms. A subsample of patients and 31 healthy controls also completed a functional MRI examination. Relative to each individual's average score, moments of worsened cognitive performance on the mobile tests were associated with an increased probability of positive symptom occurrence over subsequent hours of the day (coef = 0.06, p < 0.05), adjusting for the presence of psychotic symptoms at the moment of mobile test administration. These prospective associations varied as a function of graph theory indices in MRI analyses. These findings demonstrate that cognitive performance is prospectively linked to psychotic symptom expression in daily life, and that underlying brain markers may be observed in the Executive Control Network. While the potential causal nature of this association remains to be investigated, our results offer promising prospects for a better understanding of the underlying mechanisms of symptom expression in schizophrenia.
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Affiliation(s)
- Maud Dupuy
- Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France.
| | - Majd Abdallah
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France
| | - Joel Swendsen
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Bernard N’Kaoua
- Handicap, Activity, Cognition, Health, Inserm/University of Bordeaux, Talence, France
| | - Sandra Chanraud
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Pierre Schweitzer
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France
| | - Melina Fatseas
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,CHU Bordeaux, Bordeaux, France
| | - Fuschia Serre
- Addiction and Neuropsychiatry (SANPSY), University of Bordeaux, CNRS USR 3413 – Sleep, Bordeaux, France
| | - Elodie Barse
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Marc Auriacombe
- Addiction and Neuropsychiatry (SANPSY), University of Bordeaux, CNRS USR 3413 – Sleep, Bordeaux, France ,CH Charles Perrens, Bordeaux, France ,CHU Bordeaux, Bordeaux, France
| | - David Misdrahi
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,CH Charles Perrens, Bordeaux, France
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24
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Gallos IK, Mantonakis L, Spilioti E, Kattoulas E, Savvidou E, Anyfandi E, Karavasilis E, Kelekis N, Smyrnis N, Siettos CI. The relation of integrated psychological therapy to resting state functional brain connectivity networks in patients with schizophrenia. Psychiatry Res 2021; 306:114270. [PMID: 34775295 DOI: 10.1016/j.psychres.2021.114270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/22/2021] [Accepted: 10/31/2021] [Indexed: 01/05/2023]
Abstract
Functional brain dysconnectivity measured with resting state functional magnetic resonance imaging (rsfMRI) has been linked to cognitive impairment in schizophrenia. This study investigated the effects on functional brain connectivity of Integrated Psychological Therapy (IPT), a cognitive behavioral oriented group intervention program, in 31 patients with schizophrenia. Patients received IPT or an equal intensity non-specific psychological treatment in a non-randomized design. Evidence of improvement in executive and social functions, psychopathology and overall level of functioning was observed after treatment completion at six months only in the IPT treatment group and was partially sustained at one-year follow up. Independent Component Analysis and Isometric Mapping (ISOMAP), a non-linear manifold learning algorithm, were used to construct functional connectivity networks from the rsfMRI data. Functional brain dysconnectivity was observed in patients compared to a group of 17 healthy controls, both globally and specifically including the default mode (DMN) and frontoparietal network (FPN). DMN and FPN connectivity were reversed towards healthy control patterns only in the IPT treatment group and these effects were sustained at follow up for DMN but not FPN. These data suggest the use of rsfMRI as a biomarker for accessing and monitoring the therapeutic effects of cognitive remediation therapy in schizophrenia.
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Affiliation(s)
- I K Gallos
- School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - L Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Spilioti
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Kattoulas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Savvidou
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Anyfandi
- First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Karavasilis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Kelekis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; Second Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece.
| | - C I Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Università degli Studi di Napoli Federico II, Naples, Italy
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25
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Luo WY, Liu H, Feng Y, Hao JX, Zhang YJ, Peng WF, Zhang PM, Ding J, Wang X. Efficacy of cathodal transcranial direct current stimulation on electroencephalographic functional networks in patients with focal epilepsy: Preliminary findings. Epilepsy Res 2021; 178:106791. [PMID: 34837824 DOI: 10.1016/j.eplepsyres.2021.106791] [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: 04/25/2021] [Revised: 09/13/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Neuromodulation is a promising therapeutic alternative for epilepsy. We aimed to explore the efficacy and safety of cathodal transcranial current direct stimulation (ctDCS) on electroencephalographic functional networks in focal epilepsy. METHODS A sham-controlled, double-blinded, randomized study was conducted on 25 participants with focal epilepsy who underwent a 5-day, -1.0 mA, 20 min ctDCS, which targeted at the most active interictal epileptiform discharge (IED) region. We examined the electroencephalograms (EEGs) at baseline, immediately and at 4 weeks following ctDCS. The graph theory-based brain networks were established through time-variant partial directed coherence (TVPDC), and were calculated between each pair of EEG signals. The functional networks were characterized using average clustering coefficient, characteristic path length, and small-worldness index. The seizure frequencies, IEDs, graph-theory metrics and cognitive tests were compared. RESULTS Preliminary findings indicated an IED reduction of 30.2% at the end of 5-day active ctDCS compared to baseline (p < 0.10) and a significant IED reduction of 33.4% 4 weeks later (p < 0.05). In terms of the EEG functional network, the small-worldness index significantly reduced by 3.5% (p < 0.05) and the characteristic path length increased by 1.8% (p < 0.10) at the end of the session compared to the baseline. No obvious change was found in the seizure frequency during follow-up (p > 0.05). The Mini-Mental State Examination (MMSE) showed no difference between the active and sham groups (p > 0.05). No severe adverse reactions were observed. CONCLUSIONS In focal epilepsy, the 5-day consecutive ctDCS may potentially decrease the IEDs and ameliorate the EEG functional network, proposing a novel personalized therapeutic scenario for epilepsy.
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Affiliation(s)
- Wen-Yi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hao Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jia-Xin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yi-Jun Zhang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei-Feng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Pu-Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of The State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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26
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Franciotti R, Moretti DV, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili FM, Babiloni C, Borroni B, Padovani A, Onofrj M, Bonanni L. Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases. Neurobiol Aging 2021; 110:37-46. [PMID: 34847523 DOI: 10.1016/j.neurobiolaging.2021.10.016] [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: 06/18/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 12/14/2022]
Abstract
Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Davide V Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola W Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | | | - Flavio M Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer," Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino (FR), Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.
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27
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Yang C, Wang P, Tan J, Liu Q, Li X. Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med 2021; 139:104963. [PMID: 34700253 DOI: 10.1016/j.compbiomed.2021.104963] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/13/2021] [Accepted: 10/17/2021] [Indexed: 11/16/2022]
Abstract
The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained. Moreover, it remains unclear whether and to what extent the non-Euclidean structure of different FBNs affect the performance of GNN-based disease classification. In this paper, we proposed a new method named Pearson's correlation-based Spatial Constraints Representation (PSCR) to estimate the FBN structures that were transformed to brain graphs and then fed into a graph attention network (GAT) to diagnose ASD. Extensive experiments on comparing different FBN construction methods and classification frameworks were conducted on the ABIDE I dataset (n = 871). The results demonstrated the superiority of our PSCR method and the influence of different FBNs on the GNN-based classification results. The proposed PSCR and GAT framework achieved promising classification results for ASD (accuracy: 72.40%), which significantly outperformed competing methods. This will help facilitate patient-control separation, and provide a promising solution for future disease diagnosis based on the FBN and GNN framework.
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Affiliation(s)
- Chunde Yang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China; School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Panyu Wang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jia Tan
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qingshui Liu
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinwei Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.
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28
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Zhang Y, Liao Y, Zhang Y, Huang L. Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective. Brain Sci 2021; 11:brainsci11111424. [PMID: 34827420 PMCID: PMC8615999 DOI: 10.3390/brainsci11111424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/18/2021] [Accepted: 10/21/2021] [Indexed: 11/24/2022] Open
Abstract
In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS) is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.
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Affiliation(s)
- Yuhong Zhang
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (Y.Z.); (Y.L.); (Y.Z.)
| | - Yuan Liao
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (Y.Z.); (Y.L.); (Y.Z.)
| | - Yudi Zhang
- College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (Y.Z.); (Y.L.); (Y.Z.)
| | - Liya Huang
- College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China
- Correspondence:
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29
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Averna A, Hayley P, Murphy MD, Barban F, Nguyen J, Buccelli S, Nudo RJ, Chiappalone M, Guggenmos DJ. Entrainment of Network Activity by Closed-Loop Microstimulation in Healthy Ambulatory Rats. Cereb Cortex 2021; 31:5042-5055. [PMID: 34165137 PMCID: PMC8491688 DOI: 10.1093/cercor/bhab140] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 11/13/2022] Open
Abstract
As our understanding of volitional motor function increases, it is clear that complex movements are the result of the interactions of multiple cortical regions rather than just the output properties of primary motor cortex. However, our understanding of the interactions among these regions is limited. In this study, we used the activity-dependent stimulation (ADS) technique to determine the short/long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses the intrinsic neural activity of one region to trigger stimulations in a separate region of the brain and can manipulate neuronal connectivity in vivo. Our aim was to compare single-unit neuronal activity within premotor cortex (rostral forelimb area, [RFA] in rats) in response to ADS (triggered from RFA) and randomly-generated stimulation in the somatosensory area (S1) within single sessions and across 21 consecutive days of stimulation. We examined firing rate and correlation between spikes and stimuli in chronically-implanted healthy ambulatory rats during spontaneous and evoked activity. At the end of the treatment, we evaluated changes of synaptophysin expression. Our results demonstrated the ability of ADS to modulate RFA firing properties and to promote synaptogenesis in S1, strengthening the idea that this Hebbian-inspired protocol can be used to modulate cortical connectivity.
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Affiliation(s)
- Alberto Averna
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova 16163, Italy.,CRC Aldo Ravelli, Dipartimento di Scienze della Salute, Università degli Studi di Milano, 20122, Milano, Italy
| | - Page Hayley
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City 66160, USA.,Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Maxwell D Murphy
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City 66160, USA.,Bioengineering Graduate Program, University of Kansas, Kansas 66045, USA
| | - Federico Barban
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova 16163, Italy.,Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova 16145, Italy
| | - Jimmy Nguyen
- University of Kansas School of Medicine, Kansas 66160, USA
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova 16163, Italy
| | - Randolph J Nudo
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City 66160, USA.,Landon Center on Aging, University of Kansas Medical Center, Kansas 66160, USA
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova 16163, Italy.,Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova 16145, Italy
| | - David J Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City 66160, USA
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30
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Zhu M, Wang H, Li S, Liang X, Zhang M, Dai X, Zhang Y. Flexible Electrodes for In Vivo and In Vitro Electrophysiological Signal Recording. Adv Healthc Mater 2021; 10:e2100646. [PMID: 34050635 DOI: 10.1002/adhm.202100646] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 05/10/2021] [Indexed: 12/19/2022]
Abstract
A variety of electrophysiological signals (electrocardiography, electromyography, electroencephalography, etc.) are generated during the physiological activities of human bodies, which can be collected by electrodes and thus provide critical insights into health status or facilitate fundamental scientific research. The long-term stable and high-quality recording of electrophysiological signals is the premise for their further applications, leading to demands for flexible electrodes with similar mechanical modulus and minimized irritation to human bodies. This review summarizes the latest advances in flexible electrodes for the acquisition of various electrophysiological signals. First, the concept of electrophysiological signals and the characteristics of different subcategory signals are introduced. Second, the invasive and noninvasive methods are reviewed for electrophysiological signal recording with a highlight on the design of flexible electrodes, followed by a discussion on their material selection. Subsequently, the applications of the electrophysiological signal acquisition in pathological diagnosis and restoration of body functions are discussed, showing the advantages of flexible electrodes. Finally, the main challenges and opportunities in this field are discussed. It is believed that the further exploration of materials for flexible electrodes and the combination of multidisciplinary technologies will boost the applications of flexible electrodes for medical diagnosis and human-machine interface.
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Affiliation(s)
- Mengjia Zhu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Huimin Wang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Shuo Li
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Xiaoping Liang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Mingchao Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
| | - Xiaochuan Dai
- Department of Biomedical Engineering School of Medicine Tsinghua University Beijing 100084 P. R. China
| | - Yingying Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China
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31
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Leng X, Qin C, Lin H, Li M, Zhao K, Wang H, Duan F, An J, Wu D, Liu Q, Qiu S. Altered Topological Properties of Static/Dynamic Functional Networks and Cognitive Function After Radiotherapy for Nasopharyngeal Carcinoma Using Resting-State fMRI. Front Neurosci 2021; 15:690743. [PMID: 34335167 PMCID: PMC8316765 DOI: 10.3389/fnins.2021.690743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 06/21/2021] [Indexed: 12/17/2022] Open
Abstract
Objectives The purpose of this study was to (1) explore the changes in topological properties of static and dynamic brain functional networks after nasopharyngeal carcinoma (NPC) radiotherapy (RT) using rs-fMRI and graph theoretical analysis, (2) explore the correlation between cognitive function and changes in brain function, and (3) add to the understanding of the pathogenesis of radiation brain injury (RBI). Methods Fifty-four patients were divided into 3 groups according to time after RT: PT1 (0–6 months); PT2 (>6 to ≤12 months); and PT3 (>12 months). 29 normal controls (NCs) were included. The subjects’ topological properties were evaluated by graph-theoretic network analysis, the functional connectivity of static functional networks was calculated using network-based statistics, and the dynamic functional network matrix was subjected to cluster analysis. Finally, correlation analyses were conducted to explore the relationship between the altered network parameters and cognitive function. Results Assortativity, hierarchy, and network efficiency were significantly abnormal in the PT1 group compared with the NC or PT3 group. The small-world variance in the PT3 group was smaller than that in NCs. The Nodal ClustCoeff of Postcentral_R in the PT2 group was significantly smaller than that in PT3 and NC groups. Functional connectivities were significantly reduced in the patient groups. Most of the functional connectivities of the middle temporal gyrus (MTG) were shown to be significantly reduced in all three patient groups. Most of the functional connectivities of the insula showed significantly reduced in the PT1 and PT3 groups, and most of the functional connectivities in brain regions such as frontal and parietal lobes showed significantly reduced in the PT2 and PT3 groups. These abnormal functional connectivities were correlated with scores on multiple scales that primarily assessed memory, executive ability, and overall cognitive function. The frequency F of occurrence of various states in each subject differed significantly, and the interaction effect of group and state was significant. Conclusion The disruption of static and dynamic functional network stability, reduced network efficiency and reduced functional connectivity may be potential biomarkers of RBI. Our findings may provide new insights into the pathogenesis of RBI from the perspective of functional networks.
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Affiliation(s)
- Xi Leng
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chunhong Qin
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Mingrui Li
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kui Zhao
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hongzhuo Wang
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fuhong Duan
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jie An
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Donglin Wu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Liu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shijun Qiu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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32
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Llano DA, Ma C, Di Fabrizio U, Taheri A, Stebbings KA, Yudintsev G, Xiao G, Kenyon RV, Berger-Wolf TY. A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse. Netw Neurosci 2021; 5:569-590. [PMID: 34189378 PMCID: PMC8233117 DOI: 10.1162/netn_a_00191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 03/11/2021] [Indexed: 12/12/2022] Open
Abstract
Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices by using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.
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Affiliation(s)
- Daniel A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Chihua Ma
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Umberto Di Fabrizio
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Aynaz Taheri
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Kevin A. Stebbings
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Georgiy Yudintsev
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Gang Xiao
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Robert V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Tanya Y. Berger-Wolf
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Current affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
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33
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Vázquez MA, Maghsoudi A, Mariño IP. An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals. Front Syst Neurosci 2021; 15:652662. [PMID: 34122021 PMCID: PMC8194273 DOI: 10.3389/fnsys.2021.652662] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/30/2021] [Indexed: 01/01/2023] Open
Abstract
In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
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Affiliation(s)
- Manuel A Vázquez
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, Spain
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Inés P Mariño
- Department of Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Móstoles, Spain.,Research Laboratory Systemic Medicine of Healthy Ageing, Institute of Biology and Medicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Institute for Women's Health, University College London, London, United Kingdom
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34
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Wang Q, Wang Y, Wang P, Peng M, Zhang M, Zhu Y, Wei S, Chen C, Chen X, Luo S, Bai X. Neural representations of the amount and the delay time of reward in intertemporal decision making. Hum Brain Mapp 2021; 42:3450-3469. [PMID: 33934449 PMCID: PMC8249888 DOI: 10.1002/hbm.25445] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/28/2021] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.
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Affiliation(s)
- Qiang Wang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Pinchun Wang
- Faculty of Education, Tianjin Normal University, Tianjin, China
| | - Maomiao Peng
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
| | - Manman Zhang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yuxuan Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, California, USA
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shan Luo
- Department of Internal Medicine, Division of Endocrinology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.,Department of Psychology, University of Southern California, Los Angeles, California, USA
| | - Xuejun Bai
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
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35
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Northoff G, Gomez-Pilar J. Overcoming Rest-Task Divide-Abnormal Temporospatial Dynamics and Its Cognition in Schizophrenia. Schizophr Bull 2021; 47:751-765. [PMID: 33305324 PMCID: PMC8661394 DOI: 10.1093/schbul/sbaa178] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a complex psychiatric disorder exhibiting alterations in spontaneous and task-related cerebral activity whose relation (termed "state dependence") remains unclear. For unraveling their relationship, we review recent electroencephalographic (and a few functional magnetic resonance imaging) studies in schizophrenia that assess and compare both rest/prestimulus and task states, ie, rest/prestimulus-task modulation. Results report reduced neural differentiation of task-related activity from rest/prestimulus activity across different regions, neural measures, cognitive domains, and imaging modalities. Together, the findings show reduced rest/prestimulus-task modulation, which is mediated by abnormal temporospatial dynamics of the spontaneous activity. Abnormal temporospatial dynamics, in turn, may lead to abnormal prediction, ie, predictive coding, which mediates cognitive changes and psychopathological symptoms, including confusion of internally and externally oriented cognition. In conclusion, reduced rest/prestimulus-task modulation in schizophrenia provides novel insight into the neuronal mechanisms that connect task-related changes to cognitive abnormalities and psychopathological symptoms.
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Affiliation(s)
- Georg Northoff
- Mental Health Center/7th Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, Royal Ottawa Healthcare Group, University of Ottawa, Ottawa ON, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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36
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Decreased modulation of segregated SEEKING and selective attention systems in chronic insomnia. Brain Imaging Behav 2021; 15:430-443. [PMID: 32367486 DOI: 10.1007/s11682-020-00271-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Sleep-related attentional bias and instinctual craving-sleep status may be associated with value-driven selective attention network and SEEKING system. We hypothesized that the two networks might be important components and underlie etiology of inability to initiate or/and maintain sleep in patients with chronic insomnia (PIs). Our aim is to investigate whether frequency-frequency couplings(temporal and spatial coupling, and differences of a set of imaging parameters) could elevate the sensibility to characterize the two insomnia-related networks in studying their relationships with sleep parameters and post-insomnia emotions. Forty-eight PIs and 48 status-matched good sleepers were requested to complete sleep and emotion-related questionnaires. Receiver operating characteristic curve was used to calculate the discriminatory power of a set of parameters. Granger causality and mediating causality analysis were used to address the causal relationships between the two networks and sleep/emotion-related parameters. Frequency-frequency couplings could characterize the two networks with high discriminatory power (AUC, 0.951; sensitivity, 87.5%; specificity, 95.8%), which suggested that the frequency-frequency couplings could be served as a useful biomarker to address the insomnia-related brain networks. Functional deficits of the SEEKING system played decreased mediator acting in post-insomnia negative emotions (decreased frequency-frequency coupling). Functional hyperarousal of the value-driven attention network played decreased mediator acting in sleep regulation (increased frequency-frequency coupling). Granger causality analysis showed decreased causal effect connectivity between and within the two networks. The between-network causal effect connectivity segregation played decreased mediator acting in sleep regulation (decreased connectivity). These findings suggest that the functional deficits and segregation of the two systems may underlie etiology of PIs.
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37
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Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients. Transl Psychiatry 2021; 11:243. [PMID: 33895787 PMCID: PMC8068724 DOI: 10.1038/s41398-021-01353-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/08/2021] [Accepted: 04/01/2021] [Indexed: 02/08/2023] Open
Abstract
Suicide ideation (SI) is a most high-risk clinical sign for major depressive disorder (MDD). However, whether the rich-club network organization as a core structural network is associated with SI and how the related neural circuits are distributed in MDD patients remain unknown. Total 177 participants including 69 MDD patients with SI (MDDSI), 58 MDD without SI (MDDNSI) and 50 cognitively normal (CN) subjects were recruited and completed neuropsychological tests and diffusion-tensor imaging scan. The rich-club organization was identified and the global and regional topological properties of structural networks, together with the brain connectivity of specific neural circuit architectures, were analyzed. Further, the support vector machine (SVM) learning was applied in classifying MDDSI or MDDNSI from CN subjects. MDDSI and MDDNSI patients both exhibited disrupted rich-club organizations. However, MDDSI patients showed that the differential network was concentrated on the non-core low-level network and significantly destroyed betweeness centrality was primarily located in the regional non-hub regions relative to MDDNSI patients. The differential structural network connections involved the superior longitudinal fasciculus and the corpus callosum were incorporated in the cognitive control circuit and default mode network. Finally, the feeder serves as a potentially powerful indicator for distinguishing MDDSI patients from MDDNSI or CN subjects. The altered rich-club organization provides new clues to understand the underlying pathogenesis of MDD patients, and the feeder was useful as a diagnostic neuroimaging biomarker for differentiating MDD patients with or without SI.
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Kim BH, Kim HE, Lee JS, Kim JJ. Anhedonia Relates to the Altered Global and Local Grey Matter Network Properties in Schizophrenia. J Clin Med 2021; 10:jcm10071395. [PMID: 33807226 PMCID: PMC8038049 DOI: 10.3390/jcm10071395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/22/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
Anhedonia is one of the major negative symptoms in schizophrenia and defined as the loss of hedonic experience to various stimuli in real life. Although structural magnetic resonance imaging has provided a deeper understanding of anhedonia-related abnormalities in schizophrenia, network analysis of the grey matter focusing on this symptom is lacking. In this study, single-subject grey matter networks were constructed in 123 patients with schizophrenia and 160 healthy controls. The small-world property of the grey matter network and its correlations with the level of physical and social anhedonia were evaluated using graph theory analysis. In the global scale whole-brain analysis, the patients showed reduced small-world property of the grey matter network. The local-scale analysis further revealed reduced small-world property in the default mode network, salience/ventral attention network, and visual network. The regional-level analysis showed an altered relationship between the small-world properties and the social anhedonia scale scores in the cerebellar lobule in patients with schizophrenia. These results indicate that anhedonia in schizophrenia may be related to abnormalities in the grey matter network at both the global whole-brain scale and local-regional scale.
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Affiliation(s)
- Byung-Hoon Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Korea;
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; (H.E.K.); (J.S.L.)
| | - Hesun Erin Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; (H.E.K.); (J.S.L.)
| | - Jung Suk Lee
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; (H.E.K.); (J.S.L.)
- Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang, Gyeonggi-do 10444, Korea
| | - Jae-Jin Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Korea;
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; (H.E.K.); (J.S.L.)
- Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea
- Correspondence:
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39
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Tooley UA, Mackey AP, Ciric R, Ruparel K, Moore TM, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Associations between Neighborhood SES and Functional Brain Network Development. Cereb Cortex 2021; 30:1-19. [PMID: 31220218 PMCID: PMC7029704 DOI: 10.1093/cercor/bhz066] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.
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Affiliation(s)
- Ursula A Tooley
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
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40
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Klein PC, Ettinger U, Schirner M, Ritter P, Rujescu D, Falkai P, Koutsouleris N, Kambeitz-Ilankovic L, Kambeitz J. Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics. Cereb Cortex 2021; 31:2013-2025. [PMID: 33279967 DOI: 10.1093/cercor/bhaa339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/01/2020] [Accepted: 10/11/2020] [Indexed: 11/14/2022] Open
Abstract
Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.
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Affiliation(s)
- Pedro Costa Klein
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
| | - Ulrich Ettinger
- Department of Psychology, University of Bonn, Bonn, 53111, Germany
| | - Michael Schirner
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Petra Ritter
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Dan Rujescu
- University Clinic for Psychiatry, Psychotherapy and Psychosomatic, Martin-Luther-University, Halle-Wittenberg, 06112, Germany
| | - Peter Falkai
- Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | | | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany.,Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | - Joseph Kambeitz
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
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41
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Chen Z, Liu P, Zhang C, Yu Z, Feng T. Neural markers of procrastination in white matter microstructures and networks. Psychophysiology 2021; 58:e13782. [PMID: 33586198 DOI: 10.1111/psyp.13782] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/13/2021] [Accepted: 01/13/2021] [Indexed: 01/20/2023]
Abstract
More than 15% of adults suffer from pathological procrastination, which leads to substantial harm to their mental and psychiatric health. Our previous work demonstrated the role of three neuroanatomical networks as neural substrates of procrastination, but their potential interaction remains unknown. Three large-scale independent samples (total n = 901) were recruited. In sample A, tract-based spatial statistics (TBSS) and connectome-based graph-theoretical analysis was conducted to probe association between topological properties of white matter (WM) network and procrastination. In sample B, the above analysis was reproduced to demonstrate replicability. In sample C, machine learning models were built to predict individual procrastination. TBSS results showed a negative association between procrastination and WM integrity of limbic-prefrontal connection, and a positive relationship between intra-connection within the limbic system and procrastination. Also, both the efficiency and integrity of limbic WM network were found to be linked to procrastination. The above findings were all confirmed to replicate in an independent sample; prediction models demonstrated that these WM features can predict procrastination accurately in sample C. In conclusion, this study moves forward our understanding of procrastination by clarifying the role of interplay of self-control and emotional regulation with it.
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Affiliation(s)
- Zhiyi Chen
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
| | - Peiwei Liu
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Chenyan Zhang
- Cognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Zeyuan Yu
- Teacher College, Southwest University, Chongqing, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
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42
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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43
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Li Z, Zhang L, Zhang F, Gu R, Peng W, Hu L. Demystifying signal processing techniques to extract resting-state EEG features for psychologists. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2020.9050019] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.
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Affiliation(s)
- Zhenjiang Li
- School of Psychology, Jiangxi Normal University, Nanchang 330022,Jiangxi, China
- These authors contributed equally to this work
| | - Libo Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- These authors contributed equally to this work
| | - Fengrui Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
| | - Weiwei Peng
- College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China
| | - Li Hu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
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44
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Tumulty JS, Royster M, Cruz L. Columnar grouping preserves synchronization in neuronal networks with distance-dependent time delays. Phys Rev E 2021; 101:022408. [PMID: 32168702 DOI: 10.1103/physreve.101.022408] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 01/10/2020] [Indexed: 11/07/2022]
Abstract
Neuronal connectivity at the cellular level in the cerebral cortex is far from random, with characteristics that point to a hierarchical design with intricately connected neuronal clusters. Here we investigate computationally the effects of varying neuronal cluster connectivity on network synchronization for two different spatial distributions of clusters: one where clusters are arranged in columns in a grid and the other where neurons from different clusters are spatially intermixed. We characterize each case by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of intercluster connectivity. We find that in both cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent only on intrinsic parameters of the biophysical model. We also observe in both cases that with very low intercluster connectivity clusters have independent firing dynamics yielding a low degree of synchrony. More importantly, we find that for a high number of connections per neuron but intermediate intercluster connectivity, the two spatial distributions of clusters differ in their response where the clusters in a grid have a higher degree of synchrony than the clusters that are intermixed.
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Affiliation(s)
- Joseph S Tumulty
- Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, United States
| | - Michael Royster
- Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, United States
| | - Luis Cruz
- Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, United States
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45
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Dong SY, Choi J, Park Y, Baik SY, Jung M, Kim Y, Lee SH. Prefrontal Functional Connectivity During the Verbal Fluency Task in Patients With Major Depressive Disorder: A Functional Near-Infrared Spectroscopy Study. Front Psychiatry 2021; 12:659814. [PMID: 34093276 PMCID: PMC8175962 DOI: 10.3389/fpsyt.2021.659814] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/14/2021] [Indexed: 11/13/2022] Open
Abstract
Deviations in activation patterns and functional connectivity have been observed in patients with major depressive disorder (MDD) with prefrontal hemodynamics of patients compared with healthy individuals. The graph-theoretical approach provides useful network metrics for evaluating functional connectivity. The evaluation of functional connectivity during a cognitive task can be used to explain the neurocognitive mechanism underlying the cognitive impairments caused by depression. Overall, 31 patients with MDD and 43 healthy individuals completed a verbal fluency task (VFT) while wearing a head-mounted functional near-infrared spectroscopy (fNIRS) devices. Hemodynamics and functional connectivity across eight prefrontal subregions in the two groups were analyzed and compared. We observed a reduction in prefrontal activation and weaker overall and interhemispheric subregion-wise correlations in the patient group compared with corresponding values in the control group. Moreover, efficiency, the network measure related to the effectiveness of information transfer, showed a significant between-group difference [t (71.64) = 3.66, corrected p < 0.001] along with a strong negative correlation with depression severity (rho = -0.30, p = 0.009). The patterns of prefrontal functional connectivity differed significantly between the patient and control groups during the VFT. Network measures can quantitatively characterize the reduction in functional connectivity caused by depression. The efficiency of the functional network may play an important role in the understanding of depressive symptoms.
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Affiliation(s)
- Suh-Yeon Dong
- Department of Information Technology Engineering, Sookmyung Women's University, Seoul, South Korea
| | | | - Yeonsoo Park
- Department of Psychology, University of Notre Dame, Dame, RI, United States
| | - Seung Yeon Baik
- Department of Psychology, Penn State University, State College, PA, United States
| | - Minjee Jung
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea
| | - Yourim Kim
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea.,Department of Psychiatry, Inje University Ilsan Paik Hospital, Goyang, South Korea
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46
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Frusque G, Borgnat P, Gonçalves P, Jung J. Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures. Front Neurol 2020; 11:579725. [PMID: 33362688 PMCID: PMC7759641 DOI: 10.3389/fneur.2020.579725] [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: 07/03/2020] [Accepted: 09/08/2020] [Indexed: 11/24/2022] Open
Abstract
Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.
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Affiliation(s)
- Gaëtan Frusque
- Univ Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, Lyon, France
| | - Pierre Borgnat
- Univ Lyon, CNRS, ENS de Lyon, UCB Lyon 1, Laboratoire de Physique, UMR 5672, Lyon, France
| | - Paulo Gonçalves
- Univ Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, Lyon, France
| | - Julien Jung
- National Institute of Health and Medical Research U1028/National Center for Scientific Research, Mixed Unit of Research 5292, Lyon Neuroscience Research Center, Lyon, France.,Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE Lyon University Hospital and Lyon 1 University, Lyon, France
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47
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Zhang B, Yan G, Yang Z, Su Y, Wang J, Lei T. Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification. IEEE Trans Neural Syst Rehabil Eng 2020; 29:215-229. [PMID: 33296307 DOI: 10.1109/tnsre.2020.3043426] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.
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48
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EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach. J Clin Med 2020; 9:jcm9123934. [PMID: 33291657 PMCID: PMC7761931 DOI: 10.3390/jcm9123934] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/26/2020] [Accepted: 12/02/2020] [Indexed: 12/12/2022] Open
Abstract
A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.
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49
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Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia. Neuroscience 2020; 453:187-205. [PMID: 33249224 DOI: 10.1016/j.neuroscience.2020.11.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022]
Abstract
Electroencephalograph (EEG) signals and graph theory measures have been widely used to characterize the brain functional networks of healthy individuals and patients by calculating the correlations between different electrodes over an entire time series. Although EEG signals have a high temporal resolution and can provide relatively stable results, the process of constructing and analyzing brain functional networks is inevitably complicated by high time complexity. Our goal in this research was to distinguish the brain function networks of schizophrenia patients from those of healthy participants during working memory tasks. Consequently, we utilized a method involving microstates, which are each characterized by a unique topography of electric potentials over an entire channel array, to reduce the dimension of the EEG signals during working memory tasks and then compared and analyzed the brain functional networks using the microstates time series (MTS) and original time series (OTS) of the schizophrenia patients and healthy individuals. We found that the right frontal and parietal-occipital regions neurons of the schizophrenia patients were less active than those of the healthy participants during working memory tasks. Notably, compared with OTS, the time needed to construct the brain functional networks was significantly reduced by using MTS. In conclusion, our results show that, like OTS, MTS can well distinguish the brain functional network of schizophrenia patients from those of healthy individuals during working memory tasks while greatly decreasing time complexity. MTS can thus provide a method for characterizing the original time series for the construction and analysis of EEG brain functional networks.
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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