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Meram ED, Baajour S, Chowdury A, Kopchick J, Thomas P, Rajan U, Khatib D, Zajac-Benitez C, Haddad L, Amirsadri A, Stanley JA, Diwadkar VA. The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia. Netw Neurosci 2023; 7:184-212. [PMID: 37333998 PMCID: PMC10270714 DOI: 10.1162/netn_a_00278] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/05/2022] [Indexed: 07/21/2023] Open
Abstract
There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.
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Affiliation(s)
- Emmanuel D. Meram
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Shahira Baajour
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - John Kopchick
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Patricia Thomas
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Usha Rajan
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Dalal Khatib
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Caroline Zajac-Benitez
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Luay Haddad
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Alireza Amirsadri
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jeffrey A. Stanley
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vaibhav A. Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
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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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Zhao Z, Li J, Niu Y, Wang C, Zhao J, Yuan Q, Ren Q, Xu Y, Yu Y. Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity. Front Neurosci 2021; 15:651439. [PMID: 34149345 PMCID: PMC8209471 DOI: 10.3389/fnins.2021.651439] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Jun Li
- School of International Education, Xinxiang Medical University, Xinxiang, China
| | - Yanxiang Niu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Junqiang Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Qingli Yuan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Qiongqiong Ren
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
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Perrottelli A, Giordano GM, Brando F, Giuliani L, Mucci A. EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review. Front Psychiatry 2021; 12:653642. [PMID: 34017273 PMCID: PMC8129021 DOI: 10.3389/fpsyt.2021.653642] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/06/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.
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Affiliation(s)
- Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Francesco Brando
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Luigi Giuliani
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L. Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study. Schizophr Bull 2020; 46:916-926. [PMID: 32016430 PMCID: PMC7345823 DOI: 10.1093/schbul/sbz137] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. METHODS We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. RESULTS Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. CONCLUSIONS We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.
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Affiliation(s)
- Jie Yang
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, P.R. China
- Medical Psychological Institute of Central South University, Changsha, P.R. China
| | - Guowei Wu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Eric Chen
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Edwin Lee
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Zhening Liu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Lena Palaniyappan
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
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