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Yang C, Jung B, Lee SH. Transcranial Electrical Stimulation: Clinical Implication and Practice for Treatment of Psychiatric Illness. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:391-404. [PMID: 39069679 PMCID: PMC11289600 DOI: 10.9758/cpn.23.1118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 07/30/2024]
Abstract
Brain electrical stimulation, particularly non-invasive brain stimulation (NIBS) techniques such as transcranial electrical stimulation (tES), have emerged as a promising treatment for various psychiatric disorders, including depression, anxiety, and post-traumatic stress disorder. tES techniques, such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS), are cost-effective and safe interventions that are designed to affect neuronal circuits in the brain using various modalities. Although tES has shown effectiveness in the treatment of psychiatric disorders, there is a lack of comprehensive papers that consider its clinical implications. Therefore, this review aims to evaluate the clinical implications of tES and provide practical guidance for the treatment of psychiatric illnesses. Moreover, this review provides an overview of tES techniques and their mechanisms of action and summarizes recent clinical studies that have examined the use of tES for psychiatric disorders.
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Affiliation(s)
- Chaeyeon Yang
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
| | - Bori Jung
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Psychology, Sogang University, Seoul, Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
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Dimitriadis SI. ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns. Comput Biol Med 2024; 180:108862. [PMID: 39068901 DOI: 10.1016/j.compbiomed.2024.108862] [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: 02/11/2024] [Revised: 06/30/2024] [Accepted: 07/06/2024] [Indexed: 07/30/2024]
Abstract
Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Passeig Vall D'Hebron 171, 08035, Barcelona, Spain; Institut de Neurociencies, University of Barcelona, Municipality of Horta-Guinardó, 08035, Barcelona, Spain; Integrative Neuroimaging Lab, Thessaloniki, 55133, Makedonia, Greece; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Rd, CF24 4HQ, Cardiff, Wales, United Kingdom.
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3
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Liharska L, Charney A. Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain. Curr Top Behav Neurosci 2024. [PMID: 38972894 DOI: 10.1007/7854_2024_466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.
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Affiliation(s)
- Lora Liharska
- Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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4
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Bang M, Park K, Choi SH, Ahn SS, Kim J, Lee SK, Park YW, Lee SH. Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum. Psychiatry Clin Neurosci 2024. [PMID: 38953397 DOI: 10.1111/pcn.13707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 05/26/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024]
Abstract
AIMS The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. METHODS A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. RESULTS We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. CONCLUSION The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.
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Affiliation(s)
- Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kisung Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Seoung-Ho Choi
- National Program Excellence in Software at Kwangwoon University, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
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Mana L, Schwartz-Pallejà M, Vila-Vidal M, Deco G. Overview on cognitive impairment in psychotic disorders: From impaired microcircuits to dysconnectivity. Schizophr Res 2024; 269:132-143. [PMID: 38788432 DOI: 10.1016/j.schres.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Schizophrenia's cognitive deficits, often overshadowed by positive symptoms, significantly contribute to the disorder's morbidity. Increasing attention highlights these deficits as reflections of neural circuit dysfunction across various cortical regions. Numerous connectivity alterations linked to cognitive symptoms in psychotic disorders have been reported, both at the macroscopic and microscopic level, emphasizing the potential role of plasticity and microcircuits impairment during development and later stages. However, the heterogeneous clinical presentation of cognitive impairment and diverse connectivity findings pose challenges in summarizing them into a cohesive picture. This review aims to synthesize major cognitive alterations, recent insights into network structural and functional connectivity changes and proposed mechanisms and microcircuit alterations underpinning these symptoms, particularly focusing on neurodevelopmental impairment, E/I balance, and sleep disturbances. Finally, we will also comment on some of the most recent and promising therapeutic approaches that aim to target these mechanisms to address cognitive symptoms. Through this comprehensive exploration, we strive to provide an updated and nuanced overview of the multiscale connectivity impairment underlying cognitive impairment in psychotic disorders.
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Affiliation(s)
- L Mana
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.
| | - M Schwartz-Pallejà
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Department of Experimental and Health Science, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Eurecat, Technology Center of Catalonia, Multimedia Technologies, Barcelona, Spain.
| | - M Vila-Vidal
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Computational Biology and Complex Systems Group, Department of Physics, Universitat Politècnica de Catalunya, Barcelona, Spain.
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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6
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Gautam D, Shields A, Krepps E, Ummear Raza M, Sivarao DV. Click train elicited local gamma synchrony: differing performance and pharmacological responsivity of primary auditory and prefrontal cortices. Brain Res 2024; 1841:149091. [PMID: 38897535 DOI: 10.1016/j.brainres.2024.149091] [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: 02/22/2024] [Revised: 06/05/2024] [Accepted: 06/16/2024] [Indexed: 06/21/2024]
Abstract
Auditory neural networks in the brain naturally entrain to rhythmic stimuli. Such synchronization is an accessible index of local network performance as captured by EEG. Across species, click trains delivered ∼ 40 Hz show strong entrainment with primary auditory cortex (Actx) being a principal source. Imaging studies have revealed additional cortical sources, but it is unclear if they are functionally distinct. Since auditory processing evolves hierarchically, we hypothesized that local synchrony would differ between between primary and association cortices. In female SD rats (N = 12), we recorded 40 Hz click train-elicited gamma oscillations using epidural electrodes situated at two distinct sites; one above the prefrontal cortex (PFC) and another above the Actx, after dosing with saline (1 ml/kg, sc) or the NMDA antagonist, MK801 (0.025, 0.05 or 0.1 mpk), in a blocked crossover design. Post-saline, both regions showed a strong 40 Hz auditory steady state response (ASSR). The latencies for the N1 response were ∼ 16 ms (Actx) and ∼ 34 ms (PFC). Narrow band (38-42 Hz) gamma oscillations appeared rapidly (<40 ms from stim onset at Actx but in a more delayed fashion (∼200 ms) at PFC. MK801 augmented gamma synchrony at Actx while dose-dependently disrupting at the PFC. Event-related gamma (but not beta) coherence, an index of long-distance connectivity, was disrupted by MK801. In conclusion, local network gamma synchrony in a higher order association cortex performs differently from that of the primary auditory cortex. We discuss these findings in the context of evolving sound processing across the cortical hierarchy.
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Affiliation(s)
- Deepshila Gautam
- Department of Pharmaceutical Sciences, Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, TN, United States
| | - Abby Shields
- Department of Pharmaceutical Sciences, Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, TN, United States
| | - Emily Krepps
- Department of Pharmaceutical Sciences, Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, TN, United States
| | - Muhammad Ummear Raza
- Department of Pharmaceutical Sciences, Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, TN, United States
| | - Digavalli V Sivarao
- Department of Pharmaceutical Sciences, Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, TN, United States.
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7
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Keane BP, Abrham Y, Cole MW, Johnson BA, Hu B, Cocuzza CV. Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308836. [PMID: 38946974 PMCID: PMC11213076 DOI: 10.1101/2024.06.14.24308836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients-both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged the visual2 and somatomotor network connections and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges' g=1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, or anxiety. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.
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Affiliation(s)
- Brian P Keane
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, 430 Elmwood Ave, Rochester, NY 14642, USA
- Center for Visual Science, University of Rochester, 601 Elmwood Ave, P.O. Box 319, Rochester, NY 14642, USA
- Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall P.O. Box 270268, Rochester, NY 14627-0268, USA
| | - Yonatan Abrham
- Center for Visual Science, University of Rochester, 601 Elmwood Ave, P.O. Box 319, Rochester, NY 14642, USA
- Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall P.O. Box 270268, Rochester, NY 14627-0268, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, 197 University Ave, 07102, USA
| | - Brent A Johnson
- Department of Biostatistics, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA
| | - Boyang Hu
- Department of Brain & Cognitive Science, University of Rochester, 358 Meliora Hall P.O. Box 270268, Rochester, NY 14627-0268, USA
| | - Carrisa V Cocuzza
- Department of Psychology, Yale University, 100 College St, New Haven, CT 06510, USA
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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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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Lin P, Zhu G, Xu X, Wang Z, Li X, Li B. Brain network analysis of working memory in schizophrenia based on multi graph attention network. Brain Res 2024; 1831:148816. [PMID: 38387716 DOI: 10.1016/j.brainres.2024.148816] [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: 11/02/2023] [Revised: 01/25/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
The cognitive impairment in schizophrenia (SZ) is characterized by significant deficits in working memory task. In order to explore the brain changes of SZ during a working memory task, we performed time-domain and time-frequency analysis of event related potentials (ERP) of SZ during a 0-back task. The P3 wave amplitude was found to be significantly lower in SZ patients than in healthy controls (HC) (p < 0.05). The power in the θ and α bands was significantly enhanced in the SZ group 200 ms after stimulation, while the θ band was significantly enhanced and the β band was weakened in the HC group. Furthermore, phase lag index (PLI) based brain functional connectivity maps showed differences in the connections between parietal and frontotemporal lobes between SZ and HC (p < 0.05). Due to the natural similarity between brain networks and graph data, and the fact that graph attention network can aggregate the features of adjacent nodes, it has more advantages in learning the features of brain regions. We propose a multi graph attention network model combined with adaptive initial residual (AIR) for SZ classification, which achieves an accuracy of 90.90 % and 78.57 % on an open dataset (Zenodo) and our 0-back dataset, respectively. Overall, the proposed methodology offers promising potential for understanding the brain functional connections of schizophrenia.
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Affiliation(s)
- Ping Lin
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China; College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
| | - Xinyi Xu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China; College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zhen Wang
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China; College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Shanghai Yangpu Mental Health Center, Shanghai 200093, China.
| | - Bin Li
- Shanghai Yangpu Mental Health Center, Shanghai 200093, China.
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Wynn JK, Green MF. An EEG-Based Neuroplastic Approach to Predictive Coding in People With Schizophrenia or Traumatic Brain Injury. Clin EEG Neurosci 2024:15500594241252897. [PMID: 38711326 DOI: 10.1177/15500594241252897] [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] [Indexed: 05/08/2024]
Abstract
Despite different etiologies, people with schizophrenia (SCZ) or with traumatic brain injury (TBI) both show aberrant neuroplasticity. One neuroplastic mechanism that may be affected is prediction error coding. We used a roving mismatch negativity (rMMN) paradigm which uses different lengths of standard tone trains and is optimized to assess predictive coding. Twenty-five SCZ, 22 TBI (mild to moderate), and 25 healthy controls were assessed. We used a frequency-deviant rMMN in which the number of standards preceding the deviant was either 2, 6, or 36. We evaluated repetition positivity to the standard tone immediately preceding a deviant tone (repetition positivity [RP], to assess formation of the memory trace), deviant negativity to the deviant stimulus (deviant negativity [DN], which reflects signaling of a prediction error), and the difference wave between the 2 (the MMN). We found that SCZ showed reduced DN and MMN compared with healthy controls and with people with mild to moderate TBI. We did not detect impairments in any index (RP, DN, or MMN) in people with TBI compared to controls. Our findings suggest that prediction error coding assessed with rMMN is aberrant in SCZ but intact in TBI, though there is a suggestion that severity of head injury results in poorer prediction error coding.
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Affiliation(s)
- Jonathan K Wynn
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Michael F Green
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
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12
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Cattarinussi G, Di Giorgio A, Sambataro F. Cerebellar dysconnectivity in schizophrenia and bipolar disorder is associated with cognitive and clinical variables. Schizophr Res 2024; 267:497-506. [PMID: 38582653 DOI: 10.1016/j.schres.2024.03.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/11/2024] [Accepted: 03/27/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Abnormal cerebellar functional connectivity (FC) has been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). However, the patterns of cerebellar dysconnectivity in these two disorders and their association with cognitive functioning and clinical symptoms have not been fully clarified. In this study, we examined cerebellar FC alterations in SCZ and BD-I and their association with cognition and psychotic symptoms. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data of 39 SCZ, 43 BD-I, and 61 healthy controls from the Consortium for Neuropsychiatric Phenomics dataset were examined. The cerebellum was parcellated into ten functional networks, and seed-based FC was calculated for each cerebellar system. Principal component analyses were used to reduce the dimensionality of the diagnosis-related FC and cognitive variables. Multiple regression analyses were used to assess the relationship between FC and cognitive and clinical data. RESULTS We observed decreased cerebellar FC with the frontal, temporal, occipital, and thalamic areas in individuals with SCZ, and a more widespread decrease in cerebellar FC in individuals with BD-I, involving the frontal, cingulate, parietal, temporal, occipital, and thalamic regions. SCZ had increased within-cerebellum and cerebellar frontal FC compared to BD-I. In BD-I, memory and verbal learning performances, which were higher compared to SCZ, showed a greater interaction with cerebellar FC patterns. Additionally, patterns of increased cortico-cerebellar FC were marginally associated with positive symptoms in patients. CONCLUSIONS Our findings suggest that shared and distinct patterns of cortico-cerebellar dysconnectivity in SCZ and BD-I could underlie cognitive impairments and psychotic symptoms in these disorders.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy.
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Takai Y, Tamura S, Hoaki N, Kitajima K, Nakamura I, Hirano S, Ueno T, Nakao T, Onitsuka T, Hirano Y. Aberrant thalamocortical connectivity and shifts between the resting state and task state in patients with schizophrenia. Eur J Neurosci 2024; 59:1961-1976. [PMID: 38440952 DOI: 10.1111/ejn.16298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/16/2024] [Accepted: 02/12/2024] [Indexed: 03/06/2024]
Abstract
Prominent pathological hypotheses for schizophrenia include auditory processing deficits and dysconnectivity within cerebral networks. However, most neuroimaging studies have focused on impairments in either resting-state or task-related functional connectivity in patients with schizophrenia. The aims of our study were to examine (1) blood oxygen level-dependent (BOLD) signals during auditory steady-state response (ASSR) tasks, (2) functional connectivity during the resting-state and ASSR tasks and (3) state shifts between the resting-state and ASSR tasks in patients with schizophrenia. To reduce the functional consequences of scanner noise, we employed resting-state and sparse sampling auditory fMRI paradigms in 25 schizophrenia patients and 25 healthy controls. Auditory stimuli were binaural click trains at frequencies of 20, 30, 40 and 80 Hz. Based on the detected ASSR-evoked BOLD signals, we examined the functional connectivity between the thalamus and bilateral auditory cortex during both the resting state and ASSR task state, as well as their alterations. The schizophrenia group exhibited significantly diminished BOLD signals in the bilateral auditory cortex and thalamus during the 80 Hz ASSR task (corrected p < 0.05). We observed a significant inverse relationship between the resting state and ASSR task state in altered functional connectivity within the thalamo-auditory network in schizophrenia patients. Specifically, our findings demonstrated stronger functional connectivity in the resting state (p < 0.004) and reduced functional connectivity during the ASSR task (p = 0.048), which was mediated by abnormal state shifts, within the schizophrenia group. These results highlight the presence of abnormal thalamocortical connectivity associated with deficits in the shift between resting and task states in patients with schizophrenia.
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Affiliation(s)
- Yoshifumi Takai
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shunsuke Tamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Nobuhiko Hoaki
- Psychiatry Neuroimaging Center, Hoaki Hospital, Oita, Japan
| | - Kazutoshi Kitajima
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Itta Nakamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takefumi Ueno
- Division of Clinical Research, National Hospital Organization, Hizen Psychiatric Center, Saga, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- National Hospital Organization Sakakibara Hospital, Tsu, Mie, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
- Institute of Industrial Science, University of Tokyo, Tokyo, Japan
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14
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López-Caballero F, Auksztulewicz R, Howard Z, Rosch RE, Todd J, Salisbury DF. Computational Synaptic Modeling of Pitch and Duration Mismatch Negativity in First-Episode Psychosis Reveals Selective Dysfunction of the N-Methyl-D-Aspartate Receptor. Clin EEG Neurosci 2024:15500594241238294. [PMID: 38533562 DOI: 10.1177/15500594241238294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Mismatch negativity (MMN) to pitch (pMMN) and to duration (dMMN) deviant stimuli is significantly more attenuated in long-term psychotic illness compared to first-episode psychosis (FEP). It was recently shown that source-modeling of magnetically recorded MMN increases the detection of left auditory cortex MMN deficits in FEP, and that computational circuit modeling of electrically recorded MMN also reveals left-hemisphere auditory cortex abnormalities. Computational modeling using dynamic causal modeling (DCM) can also be used to infer synaptic activity from EEG-based scalp recordings. We measured pMMN and dMMN with EEG from 26 FEP and 26 matched healthy controls (HCs) and used a DCM conductance-based neural mass model including α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid, N-methyl-D-Aspartate (NMDA), and Gamma-aminobutyric acid receptors to identify any changes in effective connectivity and receptor rate constants in FEP. We modeled MMN sources in bilateral A1, superior temporal gyrus, and inferior frontal gyrus (IFG). No model parameters distinguished groups for pMMN. For dMMN, reduced NMDA receptor activity in right IFG in FEP was detected. This finding is in line with literature of prefrontal NMDA receptor hypofunction in chronic schizophrenia and suggests impaired NMDA-induced synaptic plasticity may be present at psychosis onset where scalp dMMN is only moderately reduced. To the best of our knowledge, this is the first report of impaired NMDA receptor activity in FEP found through computational modeling of dMMN and shows the potential of DCM to non-invasively reveal synaptic-level abnormalities that underly subtle functional auditory processing deficits in early psychosis.
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Affiliation(s)
- F López-Caballero
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - R Auksztulewicz
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Z Howard
- School of Psychological Science, University of Western Australia, Perth, Australia
| | - R E Rosch
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - J Todd
- School of Psychological Sciences, University of Newcastle, Callaghan, Australia
| | - D F Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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15
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Feng S, Zheng S, Dong L, Li Z, Zhu H, Liu S, Li X, Ning Y, Jia H. Effects of aripiprazole on resting-state functional connectivity of large-scale brain networks in first-episode drug-naïve schizophrenia patients. J Psychiatr Res 2024; 171:215-221. [PMID: 38309211 DOI: 10.1016/j.jpsychires.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/04/2024] [Accepted: 01/15/2024] [Indexed: 02/05/2024]
Abstract
Aripiprazole modulates functional connectivity (FC) between several brain regions in first-episode schizophrenia patients, contributing to improvement in clinical symptoms. However, the effects of aripiprazole on abnormal connections among extensive brain networks in schizophrenia patients remain unclear. We aimed to investigate the effects of 12 weeks of aripiprazole treatment on the FC of large-scale brain networks. Forty-five first-episode drug-naïve schizophrenia patients and 45 healthy controls were recruited for this longitudinal study. Resting-state functional magnetic resonance imaging (fMRI) data were collected at baseline and after 12 weeks of aripiprazole treatment. The patients were classified into those in response (SCHr group) and non-response (SCHnr group) according to the improvement of clinical symptoms after 12-weeks treatment. The FC were evaluated for seven large-scale brain networks. In addition, correlation analysis was performed to investigate associations between changes FC of large-scale brain networks and clinical symptoms. Before aripiprazole treatment, schizophrenia patients showed decreased FC of extensive brain networks compared to healthy controls. The 12-week aripiprazole treatment significantly prevented the constantly decreased FC of subcortical network, default mode network and other brain networks in patients with SCHr, in association with the improvement of clinical symptoms. Taken together, these findings have revealed the effects of aripiprazole on FC in large-scale networks in schizophrenia patients, which could provide new insight on interpreting symptom improvement in SCH.
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Affiliation(s)
- Sitong Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sisi Zheng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Linrui Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ziyan Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hong Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shanshan Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xue Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanzhe Ning
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Hongxiao Jia
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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16
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Wolff A, Northoff G. Temporal imprecision of phase coherence in schizophrenia and psychosis-dynamic mechanisms and diagnostic marker. Mol Psychiatry 2024; 29:425-438. [PMID: 38228893 DOI: 10.1038/s41380-023-02337-z] [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: 04/27/2023] [Revised: 11/03/2023] [Accepted: 11/23/2023] [Indexed: 01/18/2024]
Abstract
Schizophrenia (SCZ) is a complex disorder in which various pathophysiological models have been postulated. Brain imaging studies using EEG/MEG and fMRI show altered amplitude and, more recently, decrease in phase coherence in response to external stimuli. What are the dynamic mechanisms of such phase incoherence, and can it serve as a differential-diagnostic marker? Addressing this gap in our knowledge, we uniquely combine a review of previous findings, novel empirical data, and computational-dynamic simulation. The main findings are: (i) the review shows decreased phase coherence in SCZ across a variety of different tasks and frequencies, e.g., task- and frequency-unspecific, which is further supported by our own novel data; (ii) our own data demonstrate diagnostic specificity of decreased phase coherence for SCZ as distinguished from major depressive disorder; (iii) simulation data exhibit increased phase offset in SCZ leading to a precision index, in the millisecond range, of the phase coherence relative to the timing of the external stimulus. Together, we demonstrate the key role of temporal imprecision in phase coherence of SCZ, including its mechanisms (phase offsets, precision index) on the basis of which we propose a phase-based temporal imprecision model of psychosis (PTP). The PTP targets a deeper dynamic layer of a basic disturbance. This converges well with other models of psychosis like the basic self-disturbance and time-space experience changes, as discussed in phenomenological and spatiotemporal psychopathology, as well as with the models of aberrant predictive coding and disconnection as in computational psychiatry. Finally, our results show that temporal imprecision as manifest in decreased phase coherence is a promising candidate biomarker for clinical differential diagnosis of SCZ, and more broadly, psychosis.
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Affiliation(s)
- Annemarie Wolff
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada.
| | - Georg Northoff
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada.
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17
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Krcmar M, Wannan CMJ, Lavoie S, Allott K, Davey CG, Yuen HP, Whitford T, Formica M, Youn S, Shetty J, Beedham R, Rayner V, Murray G, Polari A, Gawęda Ł, Koren D, Sass L, Parnas J, Rasmussen AR, McGorry P, Hartmann JA, Nelson B. The self, neuroscience and psychosis study: Testing a neurophenomenological model of the onset of psychosis. Early Interv Psychiatry 2024; 18:153-164. [PMID: 37394278 DOI: 10.1111/eip.13448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/04/2023]
Abstract
AIM Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up. METHODS SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months. RESULTS This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures. CONCLUSIONS The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.
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Affiliation(s)
- Marija Krcmar
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Cassandra M J Wannan
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Suzie Lavoie
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher G Davey
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Thomas Whitford
- School of Psychology, University of New South Wales (UNSW), Kensington, New South Wales, Australia
| | - Melanie Formica
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Youn
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Jashmina Shetty
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Rebecca Beedham
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Victoria Rayner
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Andrea Polari
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Łukasz Gawęda
- Experimental Psychopathology Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - Dan Koren
- Psychology Department, University of Haifa, Haifa, Israel
| | - Louis Sass
- Department of Clinical Psychology, GSAPP-Rutgers University, Piscataway, New Jersey, USA
| | - Josef Parnas
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Center for Subjectivity Research, University of Copenhagen, Copenhagen, Denmark
| | - Andreas R Rasmussen
- Orygen, Parkville, Parkville, Victoria, Australia
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Patrick McGorry
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica A Hartmann
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Barnaby Nelson
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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18
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Chan YLE, Tsai SJ, Chern Y, Yang AC. Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging. Front Psychiatry 2024; 14:1305359. [PMID: 38260783 PMCID: PMC10800602 DOI: 10.3389/fpsyt.2023.1305359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Pathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia. Methods Two hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction). Results The right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub. Discussion The thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.
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Affiliation(s)
- Yee-Lam E. Chan
- Doctoral Degree Program of Translational Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei, Taiwan
- Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yijuang Chern
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C. Yang
- Institute of Brain Science/Digital Medicine Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
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19
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Cattarinussi G, Di Giorgio A, Moretti F, Bondi E, Sambataro F. Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110827. [PMID: 37473954 DOI: 10.1016/j.pnpbp.2023.110827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/05/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Federica Moretti
- Department of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
| | - Emi Bondi
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy.
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20
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Rządeczka M, Wodziński M, Moskalewicz M. Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity. Front Psychiatry 2023; 14:1291854. [PMID: 38116384 PMCID: PMC10729319 DOI: 10.3389/fpsyt.2023.1291854] [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: 09/10/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.
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Affiliation(s)
- Marcin Rządeczka
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
| | | | - Marcin Moskalewicz
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
- Philosophy of Mental Health Unit, Department of Social Sciences and the Humanities, Poznan University of Medical Sciences, Poznań, Poland
- Phenomenological Psychopathology and Psychotherapy, Psychiatric Clinic, University of Heidelberg, Heidelberg, Germany
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21
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Hauke DJ, Charlton CE, Schmidt A, Griffiths JD, Woods SW, Ford JM, Srihari VH, Roth V, Diaconescu AO, Mathalon DH. Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1176-1185. [PMID: 37536567 DOI: 10.1016/j.bpsc.2023.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. METHODS Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. RESULTS Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. CONCLUSIONS Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.
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Affiliation(s)
- Daniel J Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Colleen E Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - André Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - John D Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Judith M Ford
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Daniel H Mathalon
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
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22
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Harikumar A, Solovyeva KP, Misiura M, Iraji A, Plis SM, Pearlson GD, Turner JA, Calhoun VD. Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Curr Neurol Neurosci Rep 2023; 23:937-946. [PMID: 37999830 PMCID: PMC11126894 DOI: 10.1007/s11910-023-01325-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE OF REVIEW Over the last decade, evidence suggests that a combination of behavioral and neuroimaging findings can help illuminate changes in functional dysconnectivity in schizophrenia. We review the recent connectivity literature considering several vital models, considering connectivity findings, and relationships with clinical symptoms. We reviewed resting state fMRI studies from 2017 to 2023. We summarized the role of two sets of brain networks (cerebello-thalamo-cortical (CTCC) and the triple network set) across three hypothesized models of schizophrenia etiology (neurodevelopmental, vulnerability-stress, and neurotransmitter hypotheses). RECENT FINDINGS The neurotransmitter and neurodevelopmental models best explained CTCC-subcortical dysfunction, which was consistently connected to symptom severity and motor symptoms. Triple network dysconnectivity was linked to deficits in executive functioning, and the salience network (SN)-default mode network dysconnectivity was tied to disordered thought and attentional deficits. This paper links behavioral symptoms of schizophrenia (symptom severity, motor, executive functioning, and attentional deficits) to various hypothesized mechanisms.
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Affiliation(s)
- Amritha Harikumar
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA
| | - Kseniya P Solovyeva
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA
| | - Maria Misiura
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA
| | - Armin Iraji
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA
| | - Sergey M Plis
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Jessica A Turner
- The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Vince D Calhoun
- The Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), 55 Park Pl NE, Atlanta, GA, 30303, USA.
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23
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Kolar D, Krajcovic B, Kleteckova L, Kuncicka D, Vales K, Brozka H. Review: Genes Involved in Mitochondrial Physiology Within 22q11.2 Deleted Region and Their Relevance to Schizophrenia. Schizophr Bull 2023; 49:1637-1653. [PMID: 37379469 PMCID: PMC10686339 DOI: 10.1093/schbul/sbad066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is associated with altered energy metabolism, but the cause and potential impact of these metabolic changes remain unknown. 22q11.2 deletion syndrome (22q11.2DS) represents a genetic risk factor for schizophrenia, which is associated with the loss of several genes involved in mitochondrial physiology. Here we examine how the haploinsufficiency of these genes could contribute to the emergence of schizophrenia in 22q11.2DS. STUDY DESIGN We characterize changes in neuronal mitochondrial function caused by haploinsufficiency of mitochondria-associated genes within the 22q11.2 region (PRODH, MRPL40, TANGO2, ZDHHC8, SLC25A1, TXNRD2, UFD1, and DGCR8). For that purpose, we combine data from 22q11.2DS carriers and schizophrenia patients, in vivo (animal models) and in vitro (induced pluripotent stem cells, IPSCs) studies. We also review the current knowledge about seven non-coding microRNA molecules located in the 22q11.2 region that may be indirectly involved in energy metabolism by acting as regulatory factors. STUDY RESULTS We found that the haploinsufficiency of genes of interest is mainly associated with increased oxidative stress, altered energy metabolism, and calcium homeostasis in animal models. Studies on IPSCs from 22q11.2DS carriers corroborate findings of deficits in the brain energy metabolism, implying a causal role between impaired mitochondrial function and the development of schizophrenia in 22q11.2DS. CONCLUSIONS The haploinsufficiency of genes within the 22q11.2 region leads to multifaceted mitochondrial dysfunction with consequences to neuronal function, viability, and wiring. Overlap between in vitro and in vivo studies implies a causal role between impaired mitochondrial function and the development of schizophrenia in 22q11.2DS.
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Affiliation(s)
- David Kolar
- National Institute of Mental Health, Klecany, Czech Republic
| | - Branislav Krajcovic
- Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | - Daniela Kuncicka
- Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic
| | - Karel Vales
- National Institute of Mental Health, Klecany, Czech Republic
| | - Hana Brozka
- Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic
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24
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Ghaneirad E, Borgolte A, Sinke C, Čuš A, Bleich S, Szycik GR. The effect of multisensory semantic congruency on unisensory object recognition in schizophrenia. Front Psychiatry 2023; 14:1246879. [PMID: 38025441 PMCID: PMC10646423 DOI: 10.3389/fpsyt.2023.1246879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Multisensory, as opposed to unisensory processing of stimuli, has been found to enhance the performance (e.g., reaction time, accuracy, and discrimination) of healthy individuals across various tasks. However, this enhancement is not as pronounced in patients with schizophrenia (SZ), indicating impaired multisensory integration (MSI) in these individuals. To the best of our knowledge, no study has yet investigated the impact of MSI deficits in the context of working memory, a domain highly reliant on multisensory processing and substantially impaired in schizophrenia. To address this research gap, we employed two adopted versions of the continuous object recognition task to investigate the effect of single-trail multisensory encoding on subsequent object recognition in 21 schizophrenia patients and 21 healthy controls (HC). Participants were tasked with discriminating between initial and repeated presentations. For the initial presentations, half of the stimuli were audiovisual pairings, while the other half were presented unimodal. The task-relevant stimuli were then presented a second time in a unisensory manner (either auditory stimuli in the auditory task or visual stimuli in the visual task). To explore the impact of semantic context on multisensory encoding, half of the audiovisual pairings were selected to be semantically congruent, while the remaining pairs were not semantically related to each other. Consistent with prior studies, our findings demonstrated that the impact of single-trial multisensory presentation during encoding remains discernible during subsequent object recognition. This influence could be distinguished based on the semantic congruity between the auditory and visual stimuli presented during the encoding. This effect was more robust in the auditory task. In the auditory task, when congruent multisensory pairings were encoded, both participant groups demonstrated a multisensory facilitation effect. This effect resulted in improved accuracy and RT performance. Regarding incongruent audiovisual encoding, as expected, HC did not demonstrate an evident multisensory facilitation effect on memory performance. In contrast, SZs exhibited an atypically accelerated reaction time during the subsequent auditory object recognition. Based on the predictive coding model we propose that this observed deviations indicate a reduced semantic modulatory effect and anomalous predictive errors signaling, particularly in the context of conflicting cross-modal sensory inputs in SZ.
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Affiliation(s)
- Erfan Ghaneirad
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
| | - Anna Borgolte
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
| | - Christopher Sinke
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Division of Clinical Psychology and Sexual Medicine, Hannover Medical School, Hannover, Germany
| | - Anja Čuš
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
| | - Stefan Bleich
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
- Center for Systems Neuroscience, University of Veterinary Medicine, Hanover, Germany
| | - Gregor R. Szycik
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
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25
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Avery SN, Rogers BP, McHugo M, Armstrong K, Blackford JU, Vandekar SN, Woodward ND, Heckers S. Hippocampal Network Dysfunction in Early Psychosis: A 2-Year Longitudinal Study. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:979-989. [PMID: 37881573 PMCID: PMC10593896 DOI: 10.1016/j.bpsgos.2022.10.002] [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: 06/20/2022] [Revised: 08/17/2022] [Accepted: 10/05/2022] [Indexed: 11/07/2022] Open
Abstract
Background Hippocampal abnormalities are among the most consistent findings in schizophrenia. Numerous studies have reported deficits in hippocampal volume, function, and connectivity in the chronic stage of illness. While hippocampal volume and function deficits are also present in the early stage of illness, there is mixed evidence of both higher and lower functional connectivity. Here, we use graph theory to test the hypothesis that hippocampal network connectivity is broadly lowered in early psychosis and progressively worsens over 2 years. Methods We examined longitudinal resting-state functional connectivity in 140 participants (68 individuals in the early stage of psychosis, 72 demographically similar healthy control individuals). We used an anatomically driven approach to quantify hippocampal network connectivity at 2 levels: 1) a core hippocampal-medial temporal lobe cortex (MTLC) network; and 2) an extended hippocampal-cortical network. Group and time effects were tested in a linear mixed effects model. Results Early psychosis patients showed elevated functional connectivity in the core hippocampal-MTLC network, but contrary to our hypothesis, did not show alterations within the broader hippocampal-cortical network. Hippocampal-MTLC network hyperconnectivity normalized longitudinally and predicted improvement in positive symptoms but was not associated with increasing illness duration. Conclusions These results show abnormally elevated functional connectivity in a core hippocampal-MTLC network in early psychosis, suggesting that selectively increased hippocampal signaling within a localized cortical circuit may be a marker of the early stage of psychosis. Hippocampal-MTLC hyperconnectivity could have prognostic and therapeutic implications.
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Affiliation(s)
- Suzanne N. Avery
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Baxter P. Rogers
- Vanderbilt University Institute of Imaging Sciences, Nashville, Tennessee
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kristan Armstrong
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neil D. Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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26
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Li F, Wang G, Jiang L, Yao D, Xu P, Ma X, Dong D, He B. Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning. Brain Res Bull 2023; 202:110744. [PMID: 37591404 DOI: 10.1016/j.brainresbull.2023.110744] [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: 06/27/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Guangying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China; Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, China.
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, China.
| | - Debo Dong
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany.
| | - Baoming He
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China; Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China.
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27
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von Gal A, Boccia M, Nori R, Verde P, Giannini AM, Piccardi L. Neural networks underlying visual illusions: An activation likelihood estimation meta-analysis. Neuroimage 2023; 279:120335. [PMID: 37591478 DOI: 10.1016/j.neuroimage.2023.120335] [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: 04/13/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023] Open
Abstract
Visual illusions have long been used to study visual perception and contextual integration. Neuroimaging studies employ illusions to identify the brain regions involved in visual perception and how they interact. We conducted an Activation Likelihood Estimation (ALE) meta-analysis and meta-analytic connectivity modeling on fMRI studies using static and motion illusions to reveal the neural signatures of illusory processing and to investigate the degree to which different areas are commonly recruited in perceptual inference. The resulting networks encompass ventral and dorsal regions, including the inferior and middle occipital cortices bilaterally in both types of illusions. The static and motion illusion networks selectively included the right posterior parietal cortex and the ventral premotor cortex respectively. Overall, these results describe a network of areas crucially involved in perceptual inference relying on feed-back and feed-forward interactions between areas of the ventral and dorsal visual pathways. The same network is proposed to be involved in hallucinogenic symptoms characteristic of schizophrenia and other disorders, with crucial implications in the use of illusions as biomarkers.
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Affiliation(s)
| | - Maddalena Boccia
- Department of Psychology, Sapienza University of Rome, Rome, Italy; Cognitive and Motor Rehabilitation and Neuroimaging Unit, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Raffaella Nori
- Department of Psychology, University of Bologna, Bologna, Italy
| | - Paola Verde
- Italian Air Force Experimental Flight Center, Aerospace Medicine Department, Pratica di Mare, Rome, Italy
| | | | - Laura Piccardi
- Department of Psychology, Sapienza University of Rome, Rome, Italy; San Raffaele Cassino Hospital, Cassino, FR, Italy
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28
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Palaniyappan L, Benrimoh D, Voppel A, Rocca R. Studying Psychosis Using Natural Language Generation: A Review of Emerging Opportunities. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:994-1004. [PMID: 38441079 DOI: 10.1016/j.bpsc.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/16/2023] [Accepted: 04/19/2023] [Indexed: 03/07/2024]
Abstract
Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in silico tools to study linguistic features of psychosis. We argue that a program of in silico experimental research on the network architecture, function, learning rules, and training of NLG systems can help us understand better why thought disorder occurs in patients. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.
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Affiliation(s)
- Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada.
| | - David Benrimoh
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Stanford University, Palo Alto, California
| | - Alban Voppel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, University of Groningen, Groningen, the Netherlands
| | - Roberta Rocca
- Interacting Minds Centre, Department of Culture, Cognition and Computation, Aarhus University, Aarhus, Denmark
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29
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Raven EP, Veraart J, Kievit RA, Genc S, Ward IL, Hall J, Cunningham A, Doherty J, van den Bree MBM, Jones DK. In vivo evidence of microstructural hypo-connectivity of brain white matter in 22q11.2 deletion syndrome. Mol Psychiatry 2023; 28:4342-4352. [PMID: 37495890 PMCID: PMC7615578 DOI: 10.1038/s41380-023-02178-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rogier A Kievit
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, VIC, Australia
| | - Isobel L Ward
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Jessica Hall
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Adam Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Joanne Doherty
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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30
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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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31
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Fan YS, Xu Y, Bayrak Ş, Shine JM, Wan B, Li H, Li L, Yang S, Meng Y, Valk SL, Chen H. Macroscale Thalamic Functional Organization Disturbances and Underlying Core Cytoarchitecture in Early-Onset Schizophrenia. Schizophr Bull 2023; 49:1375-1386. [PMID: 37078906 PMCID: PMC10483446 DOI: 10.1093/schbul/sbad048] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is a polygenetic mental disorder with heterogeneous positive and negative symptom constellations, and is associated with abnormal cortical connectivity. The thalamus has a coordinative role in cortical function and is key to the development of the cerebral cortex. Conversely, altered functional organization of the thalamus might relate to overarching cortical disruptions in schizophrenia, anchored in development. STUDY DESIGN Here, we contrasted resting-state fMRI in 86 antipsychotic-naive first-episode early-onset schizophrenia (EOS) patients and 91 typically developing controls to study whether macroscale thalamic organization is altered in EOS. Employing dimensional reduction techniques on thalamocortical functional connectome (FC), we derived lateral-medial and anterior-posterior thalamic functional axes. STUDY RESULTS We observed increased segregation of macroscale thalamic functional organization in EOS patients, which was related to altered thalamocortical interactions both in unimodal and transmodal networks. Using an ex vivo approximation of core-matrix cell distribution, we found that core cells particularly underlie the macroscale abnormalities in EOS patients. Moreover, the disruptions were associated with schizophrenia-related gene expression maps. Behavioral and disorder decoding analyses indicated that the macroscale hierarchy disturbances might perturb both perceptual and abstract cognitive functions and contribute to negative syndromes in patients. CONCLUSIONS These findings provide mechanistic evidence for disrupted thalamocortical system in schizophrenia, suggesting a unitary pathophysiological framework.
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Affiliation(s)
- Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Bin Wan
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
- International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom), Leipzig, Germany
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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Odkhuu S, Kim WS, Tsogt U, Shen J, Cheraghi S, Li L, Rami FZ, Le TH, Lee KH, Kang NI, Kim SW, Chung YC. Network biomarkers in recovered psychosis patients who discontinued antipsychotics. Mol Psychiatry 2023; 28:3717-3726. [PMID: 37773447 PMCID: PMC10730417 DOI: 10.1038/s41380-023-02279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/08/2023] [Accepted: 09/20/2023] [Indexed: 10/01/2023]
Abstract
There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.
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Affiliation(s)
- Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Woo-Sung Kim
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Ling Li
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Thi-Hung Le
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Keon-Hak Lee
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Korea
| | - Nam-In Kang
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea.
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Wu D, Jiang L, He R, Chen B, Yao D, Wang K, Xu P, Li F. Brain rhythmic abnormalities in convalescent patients with anti-NMDA receptor encephalitis: a resting-state EEG study. Front Neurol 2023; 14:1163772. [PMID: 37545720 PMCID: PMC10398954 DOI: 10.3389/fneur.2023.1163772] [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: 02/24/2023] [Accepted: 07/10/2023] [Indexed: 08/08/2023] Open
Abstract
Objective Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is autoimmune encephalitis with a characteristic neuropsychiatric syndrome and persistent cognition deficits even after clinical remission. The objective of this study was to uncover the potential noninvasive and quantified biomarkers related to residual brain distortions in convalescent anti-NMDARE patients. Methods Based on resting-state electroencephalograms (EEG), both power spectral density (PSD) and brain network analysis were performed to disclose the persistent distortions of brain rhythms in these patients. Potential biomarkers were then established to distinguish convalescent patients from healthy controls. Results Oppositely configured spatial patterns in PSD and network architecture within specific rhythms were identified, as the hyperactivated PSD spanning the middle and posterior regions obstructs the inter-regional information interactions in patients and thereby leads to attenuated frontoparietal and frontotemporal connectivity. Additionally, the EEG indexes within delta and theta rhythms were further clarified to be objective biomarkers that facilitated the noninvasive recognition of convalescent anti-NMDARE patients from healthy populations. Conclusion Current findings contributed to understanding the persistent and residual pathological states in convalescent anti-NMDARE patients, as well as informing clinical decisions of prognosis evaluation.
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Affiliation(s)
- Dengchang Wu
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Kang Wang
- Department of Neurology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
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Francisco AA, Foxe JJ, Molholm S. Event-related potential (ERP) markers of 22q11.2 deletion syndrome and associated psychosis. J Neurodev Disord 2023; 15:19. [PMID: 37328766 PMCID: PMC10273715 DOI: 10.1186/s11689-023-09487-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/07/2023] [Indexed: 06/18/2023] Open
Abstract
22q11.2 deletion syndrome (22q11.2DS) is a multisystemic disorder characterized by a wide range of clinical features, ranging from life-threatening to less severe conditions. One-third of individuals with the deletion live with mild to moderate intellectual disability; approximately 60% meet criteria for at least one psychiatric condition.22q11.2DS has become an important model for several medical, developmental, and psychiatric disorders. We have been particularly interested in understanding the risk for psychosis in this population: Approximately 30% of the individuals with the deletion go on to develop schizophrenia. The characterization of cognitive and neural differences between those individuals who develop schizophrenia and those who do not, despite being at genetic risk, holds important promise in what pertains to the clarification of paths to disease and to the development of tools for early identification and intervention.Here, we review our previous event-related potential (ERP) findings as potential markers for 22q11.2DS and the associated risk for psychosis, while discussing others' work. We focus on auditory processing (auditory-evoked potentials, auditory adaptation, and auditory sensory memory), visual processing (visual-evoked potentials and visual adaptation), and inhibition and error monitoring.The findings discussed suggest basic mechanistic and disease process effects on neural processing in 22q11.2DS that are present in both early sensory and later cognitive processing, with possible implications for phenotype. In early sensory processes, both during auditory and visual processing, two mechanisms that impact neural responses in opposite ways seem to coexist-one related to the deletion, which increases brain responses; another linked to psychosis, decreasing neural activity. Later, higher-order cognitive processes may be equally relevant as markers for psychosis. More specifically, we argue that components related to error monitoring may hold particular promise in the study of risk for schizophrenia in the general population.
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Affiliation(s)
- Ana A Francisco
- Department of Pediatrics, The Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - John J Foxe
- Department of Pediatrics, The Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neuroscience, Rose F. Kennedy Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neuroscience, The Frederick J. and Marion A, Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monde Institute for Neuroscience, University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA
| | - Sophie Molholm
- Department of Pediatrics, The Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Neuroscience, Rose F. Kennedy Center, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Neuroscience, The Frederick J. and Marion A, Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monde Institute for Neuroscience, University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA.
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Wang J, Dong W, Li Y, Wydell TN, Quan W, Tian J, Song Y, Jiang L, Li F, Yi C, Zhang Y, Yao D, Xu P. Discrimination of auditory verbal hallucination in schizophrenia based on EEG brain networks. Psychiatry Res Neuroimaging 2023; 331:111632. [PMID: 36958075 DOI: 10.1016/j.pscychresns.2023.111632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/23/2023] [Accepted: 03/15/2023] [Indexed: 03/25/2023]
Abstract
Auditory verbal hallucinations (AVH) are a core positive symptom of schizophrenia and are regarded as a consequence of the functional breakdown in the related sensory process. Yet, the potential mechanism of AVH is still lacking. In the present study, we explored the difference between AVHs (n = 23) and non-AVHs (n = 19) in schizophrenia and healthy controls (n = 29) by using multidimensional electroencephalograms data during an auditory oddball task. Compared to healthy controls, both AVH and non-AVH groups showed reduced P300 amplitudes. Additionally, the results from brain networks analysis revealed that AVH patients showed reduced left frontal to posterior parietal/temporal connectivity compared to non-AVH patients. Moreover, using the fused network properties of both delta and theta bands as features for in-depth learning made it possible to identify the AVH from non-AVH patients at an accuracy of 80.95%. The left frontal-parietal/temporal networks seen in the auditory oddball paradigm might be underlying biomarkers of AVH in schizophrenia. This study demonstrated for the first time the functional breakdown of the auditory processing pathway in the AVH patients, leading to a better understanding of the atypical brain network of the AVH patients.
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Affiliation(s)
- Jiuju Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Wentian Dong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Taeko N Wydell
- Centre for Cognitive Neuroscience, Brunel University London, Uxbridge, UK
| | - Wenxiang Quan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Ju Tian
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Yanping Song
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China.
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yangsong Zhang
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China.
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Qian H, Liu X, Guo Z, Wang G, Chen X, Liu J. Alterations in Resting-State Interhemispheric Coordination With Refractory Auditory Verbal Hallucinations in Schizophrenia. J Neuropsychiatry Clin Neurosci 2023; 35:385-392. [PMID: 37259546 DOI: 10.1176/appi.neuropsych.20220054] [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] [Indexed: 06/02/2023]
Abstract
OBJECTIVE The purpose of this study was to investigate resting-state interhemispheric functional connectivity in patients with schizophrenia and refractory auditory verbal hallucinations (RAVHs) by using voxel-mirrored homotopic connectivity (VMHC). METHODS Thirty-four patients with schizophrenia and RAVHs (RAVH group), 23 patients with schizophrenia but no auditory verbal hallucinations (non-AVH group), and 28 matched healthy volunteers (healthy control group) were recruited in China. VMHC analyses were used to identify brain areas with significant differences in functional connectivity among the three groups, and correlations between symptom scores and neurological measures were examined. RESULTS VMHC analyses showed aberrant bilateral connectivity between several homotopic brain regions: the RAVH and non-AVH groups showed differences in bilateral connectivity of the superior and middle temporal gyri, and the RAVH and healthy control groups showed differences in bilateral connectivity of the gyrus rectus, inferior frontal gyrus, and putamen. In addition, interhemispheric connectivity of the superior and middle temporal gyri correlated with patients' positive symptom scores. CONCLUSIONS These findings may help to elucidate the pathophysiological mechanisms underlying auditory verbal hallucinations. The results revealed interhemispheric functional dysconnectivity among patients with schizophrenia and suggest that the dysconnectivity of homotopic brain regions may play an important role in the development of auditory verbal hallucinations.
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Affiliation(s)
- Huichang Qian
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Xiaozheng Liu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Zhongwei Guo
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Guanjun Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Xiuhong Chen
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Jian Liu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (Qian, J. Liu); Department of Radiology, Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China (X. Liu); Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China (Guo); and Departments of Radiology (Wang), Psychogeriatrics (Chen), and Science and Education (J. Liu), Hangzhou Seventh People's Hospital, Hangzhou, China
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37
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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38
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Rong B, Huang H, Gao G, Sun L, Zhou Y, Xiao L, Wang H, Wang G. Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia. J Clin Med 2023; 12:jcm12093176. [PMID: 37176617 PMCID: PMC10179370 DOI: 10.3390/jcm12093176] [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: 01/27/2023] [Revised: 03/08/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023] Open
Abstract
Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.
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Affiliation(s)
- Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Guoqing Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Limin Sun
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Beijing 100101, China
| | - Ling Xiao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430071, China
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39
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Tuovinen N, Hofer A. Resting-state functional MRI in treatment-resistant schizophrenia. FRONTIERS IN NEUROIMAGING 2023; 2:1127508. [PMID: 37554635 PMCID: PMC10406237 DOI: 10.3389/fnimg.2023.1127508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/17/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Abnormalities in brain regions involved in the pathophysiology of schizophrenia (SCZ) may present insight into individual clinical symptoms. Specifically, functional connectivity irregularities may provide potential biomarkers for treatment response or treatment resistance, as such changes can occur before any structural changes are visible. We reviewed resting-state functional magnetic resonance imaging (rs-fMRI) findings from the last decade to provide an overview of the current knowledge on brain functional connectivity abnormalities and their associations to symptoms in treatment-resistant schizophrenia (TRS) and ultra-treatment-resistant schizophrenia (UTRS) and to look for support for the dysconnection hypothesis. METHODS PubMed database was searched for articles published in the last 10 years applying rs-fMRI in TRS patients, i.e., who had not responded to at least two adequate treatment trials with different antipsychotic drugs. RESULTS Eighteen articles were selected for this review involving 648 participants (TRS and control cohorts). The studies showed frontal hypoconnectivity before the initiation of treatment with CLZ or riluzole, an increase in frontal connectivity after riluzole treatment, fronto-temporal hypoconnectivity that may be specific for non-responders, widespread abnormal connectivity during mixed treatments, and ECT-induced effects on the limbic system. CONCLUSION Probably due to the heterogeneity in the patient cohorts concerning antipsychotic treatment and other clinical variables (e.g., treatment response, lifetime antipsychotic drug exposure, duration of illness, treatment adherence), widespread abnormalities in connectivity were noted. However, irregularities in frontal brain regions, especially in the prefrontal cortex, were noted which are consistent with previous SCZ literature and the dysconnectivity hypothesis. There were major limitations, as most studies did not differentiate between TRS and UTRS (i.e., CLZ-resistant schizophrenia) and investigated heterogeneous cohorts treated with mixed treatments (with or without CLZ). This is critical as in different subtypes of the disorder an interplay between dopaminergic and glutamatergic pathways involving frontal, striatal, and hippocampal brain regions in separate ways is likely. Better definitions of TRS and UTRS are necessary in future longitudinal studies to correctly differentiate brain regions underlying the pathophysiology of SCZ, which could serve as potential functional biomarkers for treatment resistance.
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Affiliation(s)
- Noora Tuovinen
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University of Innsbruck, Innsbruck, Austria
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40
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Roberts D, Rösler L, Wijnen JP, Thakkar KN. Associations between N-Acetylaspartate and white matter integrity in individuals with schizophrenia and unaffected relatives. Psychiatry Res Neuroimaging 2023; 330:111612. [PMID: 36805928 PMCID: PMC10023491 DOI: 10.1016/j.pscychresns.2023.111612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/27/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
Compromised white matter has been reported in schizophrenia; however, few studies have investigated neurochemical abnormalities underlying microstructural differences. N-acetylaspartate (NAA) is used to synthesize myelin and is often reduced in persons with schizophrenia (PSZ) and their unaffected first-degree relatives (REL). Low levels of NAA could affect white matter by preventing the synthesis or repair of myelin. We used magnetic resonance spectroscopy and diffusion tensor imaging to investigate the relationship between NAA and white matter integrity in PSZ. REL were included to examine whether putative relationships are associated with symptom expression or illness liability. 52 controls, 23 REL and 25 PSZ underwent 7T proton magnetic resonance spectroscopy and/or 3T diffusion tensor imaging. NAA in the visual cortex and basal ganglia were measured and compared across groups. Diffusivity measures were compared across groups using tract-based spatial statistics and related to NAA concentrations. Visual cortex NAA was significantly reduced in PSZ compared to controls. White matter integrity did not differ between groups. Reduced cortical and subcortical NAA were associated with diffusivity measures of poor white matter microstructure. These data suggest that levels of neural NAA may be related to white matter integrity similarly across individuals with schizophrenia, those at genetic risk, and controls.
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Affiliation(s)
- Dominic Roberts
- Department of Psychology, Michigan State University, East Lansing, MI, United States
| | - Lara Rösler
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Jannie P Wijnen
- Department of Radiology, High Field MR Research, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Katharine N Thakkar
- Department of Psychology, Michigan State University, East Lansing, MI, United States; Department of Psychiatry and Behavioral Medicine, Michigan State University, East Lansing, Michigan, United States.
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41
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de Bartolomeis A, Ciccarelli M, De Simone G, Mazza B, Barone A, Vellucci L. Canonical and Non-Canonical Antipsychotics' Dopamine-Related Mechanisms of Present and Next Generation Molecules: A Systematic Review on Translational Highlights for Treatment Response and Treatment-Resistant Schizophrenia. Int J Mol Sci 2023; 24:ijms24065945. [PMID: 36983018 PMCID: PMC10051989 DOI: 10.3390/ijms24065945] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Schizophrenia is a severe psychiatric illness affecting almost 25 million people worldwide and is conceptualized as a disorder of synaptic plasticity and brain connectivity. Antipsychotics are the primary pharmacological treatment after more than sixty years after their introduction in therapy. Two findings hold true for all presently available antipsychotics. First, all antipsychotics occupy the dopamine D2 receptor (D2R) as an antagonist or partial agonist, even if with different affinity; second, D2R occupancy is the necessary and probably the sufficient mechanism for antipsychotic effect despite the complexity of antipsychotics' receptor profile. D2R occupancy is followed by coincident or divergent intracellular mechanisms, implying the contribution of cAMP regulation, β-arrestin recruitment, and phospholipase A activation, to quote some of the mechanisms considered canonical. However, in recent years, novel mechanisms related to dopamine function beyond or together with D2R occupancy have emerged. Among these potentially non-canonical mechanisms, the role of Na2+ channels at the dopamine at the presynaptic site, dopamine transporter (DAT) involvement as the main regulator of dopamine concentration at synaptic clefts, and the putative role of antipsychotics as chaperones for intracellular D2R sequestration, should be included. These mechanisms expand the fundamental role of dopamine in schizophrenia therapy and may have relevance to considering putatively new strategies for treatment-resistant schizophrenia (TRS), an extremely severe condition epidemiologically relevant and affecting almost 30% of schizophrenia patients. Here, we performed a critical evaluation of the role of antipsychotics in synaptic plasticity, focusing on their canonical and non-canonical mechanisms of action relevant to the treatment of schizophrenia and their subsequent implication for the pathophysiology and potential therapy of TRS.
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Affiliation(s)
- Andrea de Bartolomeis
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
| | - Mariateresa Ciccarelli
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
| | - Giuseppe De Simone
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
| | - Benedetta Mazza
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
| | - Annarita Barone
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
| | - Licia Vellucci
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Dentistry, University Medical School of Naples "Federico II", 80131 Naples, Italy
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42
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Kai J, Mackinley M, Khan AR, Palaniyappan L. Aberrant frontal lobe "U"-shaped association fibers in first-episode schizophrenia: A 7-Tesla Diffusion Imaging Study. Neuroimage Clin 2023; 38:103367. [PMID: 36913907 PMCID: PMC10011060 DOI: 10.1016/j.nicl.2023.103367] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 02/08/2023] [Accepted: 03/01/2023] [Indexed: 03/07/2023]
Abstract
Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been extensively studied (e.g. arcuate fasciculus), studies of short-ranged, "U"-shaped tracts have been limited in patients with schizophrenia, in part due to the sheer abundance of tracts present and due to the spatial variations across individuals that defy probabilistic characterization in the absence of reliable templates. In this study, we use diffusion magnetic resonance imaging (dMRI) to investigate frontal lobe superficial white matter that are present in the majority of study participants, comparing healthy controls and minimally treated patients with first-episode schizophrenia (<3 median days of lifetime treatment). Through group comparisons, 3 out of 63 frontal lobe "U"-shaped tracts were found to demonstrate localized aberrations affecting the microstructural tissue properties (via diffusion tensor metrics) in this early stage of disease. No associations were found in patients between aberrant segments of affected tracts and clinical or cognitive variables. Aberrations in the frontal lobe "U"-shaped tracts in early untreated stages of psychosis occur irrespective of symptom burden, and are distributed across critical functional networks associated with executive function and salience processing. While we limited the investigation to the frontal lobe, a framework has been developed to study such connections in other brain regions, enabling further extensive investigations jointly with the major deep white matter pathways.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Michael Mackinley
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Lena Palaniyappan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
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43
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Mitochondria play an essential role in the trajectory of adolescent neurodevelopment and behavior in adulthood: evidence from a schizophrenia rat model. Mol Psychiatry 2023; 28:1170-1181. [PMID: 36380234 PMCID: PMC10005953 DOI: 10.1038/s41380-022-01865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022]
Abstract
Ample evidence implicate mitochondria in early brain development. However, to the best of our knowledge, there is only circumstantial data for mitochondria involvement in late brain development occurring through adolescence, a critical period in the pathogenesis of various psychiatric disorders, specifically schizophrenia. In schizophrenia, neurodevelopmental abnormalities and mitochondrial dysfunction has been repeatedly reported. Here we show a causal link between mitochondrial transplantation in adolescence and brain functioning in adulthood. We show that transplantation of allogenic healthy mitochondria into the medial prefrontal cortex of adolescent rats was beneficial in a rat model of schizophrenia, while detrimental in healthy control rats. Specifically, disparate initial changes in mitochondrial function and inflammatory response were associated with opposite long-lasting changes in proteome, neurotransmitter turnover, neuronal sprouting and behavior in adulthood. A similar inverse shift in mitochondrial function was also observed in human lymphoblastoid cells deived from schizophrenia patients and healthy subjects due to the interference of the transplanted mitochondria with their intrinsic mitochondrial state. This study provides fundamental insights into the essential role of adolescent mitochondrial homeostasis in the development of normal functioning adult brain. In addition, it supports a therapeutic potential for mitochondria manipulation in adolescence in disorders with neurodevelopmental and bioenergetic deficits, such as schizophrenia, yet emphasizes the need to monitor individuals' state including their mitochondrial function and immune response, prior to intervention.
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44
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Collins MA, Ji JL, Chung Y, Lympus CA, Afriyie-Agyemang Y, Addington JM, Goodyear BG, Bearden CE, Cadenhead KS, Mirzakhanian H, Tsuang MT, Cornblatt BA, Carrión RE, Keshavan M, Stone WS, Mathalon DH, Perkins DO, Walker EF, Woods SW, Powers AR, Anticevic A, Cannon TD. Accelerated cortical thinning precedes and predicts conversion to psychosis: The NAPLS3 longitudinal study of youth at clinical high-risk. Mol Psychiatry 2023; 28:1182-1189. [PMID: 36434057 PMCID: PMC10005940 DOI: 10.1038/s41380-022-01870-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022]
Abstract
Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.
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Affiliation(s)
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Yoonho Chung
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Cole A Lympus
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Jean M Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Bradley G Goodyear
- Department of Radiology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
| | | | | | - Ming T Tsuang
- Department of Psychiatry, UCSD, San Diego, CA, USA
- Institute of Genomic Medicine, UCSD, La Jolla, CA, USA
| | | | - Ricardo E Carrión
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Institute of Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston, MA, USA
| | - Wiliam S Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston, MA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, UCSF, and SFVA Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Elaine F Walker
- Departments of Psychology and Psychiatry, Emory University, Atlanta, GA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Albert R Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Alan Anticevic
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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45
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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46
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Arnatkeviciute A, Markello RD, Fulcher BD, Misic B, Fornito A. Toward Best Practices for Imaging Transcriptomics of the Human Brain. Biol Psychiatry 2023; 93:391-404. [PMID: 36725139 DOI: 10.1016/j.biopsych.2022.10.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/03/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia.
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Sydney, New South Wales, Australia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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Yoon JH, Zhang Z, Mormino E, Davidzon G, Minzenberg MJ, Ballon J, Kalinowski A, Hardy K, Naganawa M, Carson RE, Khalighi M, Park JH, Levinson DF, Chin FT. Reductions in synaptic marker SV2A in early-course Schizophrenia. J Psychiatr Res 2023; 161:213-217. [PMID: 36934603 DOI: 10.1016/j.jpsychires.2023.02.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/14/2023] [Accepted: 02/22/2023] [Indexed: 03/21/2023]
Abstract
Excess synaptic pruning during neurodevelopment has emerged as one of the leading hypotheses on the causal mechanism for schizophrenia. It proposes that excess synaptic elimination occurs during development before the formal onset of illness. Accordingly, synaptic deficits may be observable at all stages of illnesses, including in the early phases. The availability of [11C]UCB-J, the first-in-human in vivo synaptic marker, represents an opportunity for testing this hypothesis with a relatively high level of precision. The first two published [11C]UCB-J schizophrenia studies have documented significant, widespread reductions in binding in chronic patients. The present study tested the hypothesis that reductions are present in early-course patients. 18 subjects completed [11C]UCB-J PET scans, (nine with schizophrenia, average duration of illness of 3.36 years, and nine demographically-matched healthy individuals). We compared binding levels, quantified as non-displaceable specific binding (BPND), in a set of a priori-specified brain regions of interest (ROIs). Eight ROIs (left and right hippocampus, right superior temporal and Heschl's gyrus, left and right putamen, and right caudal and rostral middle frontal gyrus) showed large reductions meeting Bonferroni corrected significant levels, p < 0.0036. Exploratory, atlas-wide analyses confirmed widespread reductions in schizophrenia. We also observed significant positive correlations between binding levels and cognitive performance and a negative correlation with the severity of delusions. These results largely replicate findings from chronic patients, indicating that extensive [11C]UCB-J binding deficits are reliable and reproducible. Moreover, these results add to the growing evidence that excess synaptic pruning is a major disease mechanism for schizophrenia.
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Affiliation(s)
- Jong H Yoon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine and VA Palo Alto Health Care System, Palo Alto, CA, USA.
| | - Zhener Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine and VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Elizabeth Mormino
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Guido Davidzon
- Department of Radiology - Nuclear Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Michael J Minzenberg
- Department of Psychiatry, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Jacob Ballon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Agnieszka Kalinowski
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kate Hardy
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Mika Naganawa
- Department of Radiology, Yale University, New Haven, CT, USA
| | | | - Mehdi Khalighi
- Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jun Hyung Park
- Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Douglas F Levinson
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Frederick T Chin
- Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA
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Functional connectivity signatures of NMDAR dysfunction in schizophrenia-integrating findings from imaging genetics and pharmaco-fMRI. Transl Psychiatry 2023; 13:59. [PMID: 36797233 PMCID: PMC9935542 DOI: 10.1038/s41398-023-02344-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/26/2023] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry.
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49
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Bearden CE. Editorial: New Clues Into Cortical Changes That Converge on Psychosis. J Am Acad Child Adolesc Psychiatry 2023; 62:515-517. [PMID: 36773697 DOI: 10.1016/j.jaac.2023.02.003] [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: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023]
Abstract
Adults living with schizophrenia have prominent and widespread alterations in brain structure and function, but until recently little was known about the developmental timing and course of such changes. Prospective studies of individuals at elevated risk for developing psychosis, termed clinical high-risk (CHR) or psychosis risk syndrome patients, can address these questions, thus providing clues into neurobiological mechanisms that occur prior to illness onset. In this issue, Fortea et al.1 present the results of a prospective longitudinal brain imaging investigation of 107 adolescents at CHR for developing a psychotic disorder (23% of whom developed psychosis over the follow-up period) and 102 typically developing controls. Participants were scanned at baseline and at 18-month follow-up or at the time of conversion to psychosis. Using linear mixed-effects models to measure cortical surface area over time, the authors found that youth who developed a psychotic disorder during the follow-up period experienced greater loss of cortical surface area in bilateral parietal and right frontal regions compared to CHR youth who did not develop psychosis, and in left parietal and occipital regions compared to healthy controls. Findings were not accounted for by antipsychotic medication use, cannabis use, or general intelligence. Thus, these observations suggest that emerging psychosis may have an impact on typical neuromaturational changes that occur during adolescence.
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Affiliation(s)
- Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, and the University of California, Los Angeles, California.
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Yu Z, Ueno K, Funayama R, Sakai M, Nariai N, Kojima K, Kikuchi Y, Li X, Ono C, Kanatani J, Ono J, Iwamoto K, Hashimoto K, Kinoshita K, Nakayama K, Nagasaki M, Tomita H. Sex-Specific Differences in the Transcriptome of the Human Dorsolateral Prefrontal Cortex in Schizophrenia. Mol Neurobiol 2023; 60:1083-1098. [PMID: 36414910 DOI: 10.1007/s12035-022-03109-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/20/2022] [Indexed: 11/24/2022]
Abstract
Schizophrenia presents clinical and biological differences between males and females. This study investigated transcriptional profiles in the dorsolateral prefrontal cortex (DLPFC) using postmortem data from the largest RNA-sequencing (RNA-seq) database on schizophrenic cases and controls. Data for 154 male and 113 female controls and 160 male and 93 female schizophrenic cases were obtained from the CommonMind Consortium. In the RNA-seq database, the principal component analysis showed that sex effects were small in schizophrenia. After we analyzed the impact of sex-specific differences on gene expression, the female group showed more significantly changed genes compared with the male group. Based on the gene ontology analysis, the female sex-specific genes that changed were overrepresented in the mitochondrion, ATP (phosphocreatine and adenosine triphosphate)-, and metal ion-binding relevant biological processes. An ingenuity pathway analysis revealed that the differentially expressed genes related to schizophrenia in the female group were involved in midbrain dopaminergic and γ-aminobutyric acid (GABA)-ergic neurons and microglia. We used methylated DNA-binding domain-sequencing analyses and microarray to investigate the DNA methylation that potentially impacts the sex differences in gene transcription using a maternal immune activation (MIA) murine model. Among the sex-specific positional genes related to schizophrenia in the PFC of female offspring from MIA, the changes in the methylation and transcriptional expression of loci ACSBG1 were validated in the females with schizophrenia in independent postmortem samples by real-time PCR and pyrosequencing. Our results reveal potential genetic risks in the DLPFC for the sex-dependent prevalence and symptomology of schizophrenia.
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Affiliation(s)
- Zhiqian Yu
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
| | - Kazuko Ueno
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Ryo Funayama
- Division of Cell Proliferation, United Centers for Advanced Research and Translational Medicine, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Mai Sakai
- Department of Psychiatric Nursing, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Naoki Nariai
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kaname Kojima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yoshie Kikuchi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
| | - Xue Li
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
| | - Chiaki Ono
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
| | - Junpei Kanatani
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
| | - Jiro Ono
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
| | - Kazuya Iwamoto
- Department of Molecular Brain Science, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Kenji Hashimoto
- Division of Clinical Neuroscience, Center for Forensic Mental Health, Chiba University, Chiba, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Keiko Nakayama
- Division of Cell Proliferation, United Centers for Advanced Research and Translational Medicine, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Masao Nagasaki
- Human Biosciences Unit for the Top Global Course Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute for Disaster Science, Tohoku University, Sendai, Japan
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