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Ozubko JD, Campbell M, Verhayden A, Demetri B, Brady M, Thorp J, Brunec I. Stereotypical Hippocampal Clustering Predicts Navigational Success in Virtualized Real-World Environments. J Neurosci 2024; 44:e1057232024. [PMID: 38641405 PMCID: PMC11170676 DOI: 10.1523/jneurosci.1057-23.2024] [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: 07/07/2023] [Revised: 02/23/2024] [Accepted: 04/08/2024] [Indexed: 04/21/2024] Open
Abstract
Structural differences along the hippocampal long axis are believed to underlie meaningful functional differences. Yet, recent data-driven parcellations of the hippocampus subdivide the hippocampus into a 10-cluster map with anterior-medial, anterior-lateral, and posteroanterior-lateral, middle, and posterior components. We tested whether task and experience could modulate this clustering using a spatial learning experiment where male and female participants were trained to virtually navigate a novel neighborhood in a Google Street View-like environment. Participants were scanned while navigating routes early in training and after a 2 week training period. Using the 10-cluster map as the ideal template, we found that participants who eventually learn the neighborhood well have hippocampal cluster maps consistent with the ideal-even on their second day of learning-and their cluster mappings do not deviate over the 2 week training period. However, participants who eventually learn the neighborhood poorly begin with hippocampal cluster maps inconsistent with the ideal template, though their cluster mappings may become more stereotypical after the 2 week training. Interestingly this improvement seems to be route specific: after some early improvement, when a new route is navigated, participants' hippocampal maps revert back to less stereotypical organization. We conclude that hippocampal clustering is not dependent solely on anatomical structure and instead is driven by a combination of anatomy, task, and, importantly, experience. Nonetheless, while hippocampal clustering can change with experience, efficient navigation depends on functional hippocampal activity clustering in a stereotypical manner, highlighting optimal divisions of processing along the hippocampal anterior-posterior and medial-lateral axes.
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Affiliation(s)
- Jason D Ozubko
- Psychology Department, SUNY Geneseo, Geneseo, New York 14454
| | | | | | - Brooke Demetri
- Psychology Department, SUNY Geneseo, Geneseo, New York 14454
| | - Molly Brady
- Psychology Department, SUNY Geneseo, Geneseo, New York 14454
| | - John Thorp
- Psychology Department, Columbia University, New York, New York 10027
| | - Iva Brunec
- Psychology Department, University of Pennsylvania & Temple University, Philadelphia, Pennsylvania 19104
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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024:10.1007/s12264-024-01214-1. [PMID: 38703276 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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Skouras S, Kleinert ML, Lee EHM, Hui CLM, Suen YN, Camchong J, Chong CSY, Chang WC, Chan SKW, Lo WTL, Lim KO, Chen EYH. Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping. Brain Commun 2024; 6:fcae094. [PMID: 38707706 PMCID: PMC11069118 DOI: 10.1093/braincomms/fcae094] [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: 03/05/2023] [Revised: 12/04/2023] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.
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Affiliation(s)
- Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
- Department of Neurology, Inselspital University Hospital Bern, CH3010 Bern, Switzerland
| | | | - Edwin H M Lee
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Christy L M Hui
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Jazmin Camchong
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | | | - Wing Chung Chang
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Sherry K W Chan
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - William T L Lo
- Department of Psychiatry, Kwai Chung Hospital, Hong Kong, China
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | - Eric Y H Chen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
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Rocca P, Brasso C, Montemagni C, Del Favero E, Bellino S, Bozzatello P, Giordano GM, Caporusso E, Fazio L, Pergola G, Blasi G, Amore M, Calcagno P, Rossi R, Rossi A, Bertolino A, Galderisi S, Maj M. The relationship between the resting state functional connectivity and social cognition in schizophrenia: Results from the Italian Network for Research on Psychoses. Schizophr Res 2024; 267:330-340. [PMID: 38613864 DOI: 10.1016/j.schres.2024.04.009] [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: 08/07/2023] [Revised: 03/24/2024] [Accepted: 04/04/2024] [Indexed: 04/15/2024]
Abstract
Deficits in social cognition (SC) interfere with recovery in schizophrenia (SZ) and may be related to resting state brain connectivity. This study aimed at assessing the alterations in the relationship between resting state functional connectivity and the social-cognitive abilities of patients with SZ compared to healthy subjects. We divided the brain into 246 regions of interest (ROI) following the Human Healthy Volunteers Brainnetome Atlas. For each participant, we calculated the resting-state functional connectivity (rsFC) in terms of degree centrality (DC), which evaluates the total strength of the most powerful coactivations of every ROI with all other ROIs during rest. The rs-DC of the ROIs was correlated with five measures of SC assessing emotion processing and mentalizing in 45 healthy volunteers (HVs) chosen as a normative sample. Then, controlling for symptoms severity, we verified whether these significant associations were altered, i.e., absent or of opposite sign, in 55 patients with SZ. We found five significant differences between SZ patients and HVs: in the patients' group, the correlations between emotion recognition tasks and rsFC of the right entorhinal cortex (R-EC), left superior parietal lobule (L-SPL), right caudal hippocampus (R-c-Hipp), and the right caudal (R-c) and left rostral (L-r) middle temporal gyri (MTG) were lost. An altered resting state functional connectivity of the L-SPL, R-EC, R-c-Hipp, and bilateral MTG in patients with SZ may be associated with impaired emotion recognition. If confirmed, these results may enhance the development of non-invasive brain stimulation interventions targeting those cerebral regions to reduce SC deficit in SZ.
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Affiliation(s)
- Paola Rocca
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy.
| | - Cristiana Montemagni
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Elisa Del Favero
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Silvio Bellino
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Paola Bozzatello
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Giulia Maria Giordano
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Edoardo Caporusso
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Leonardo Fazio
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy; Department of Medicine and Surgery, LUM University, Strada Statale 100, 70010 Casamassima (BA), Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Mario Amore
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, Section of Psychiatry, University of Genoa, Largo Paolo Daneo, 3, 16132 Genoa, Italy
| | - Pietro Calcagno
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, Section of Psychiatry, University of Genoa, Largo Paolo Daneo, 3, 16132 Genoa, Italy
| | - Rodolfo Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, via Vetoio - Coppito, 67100 L'Aquila, Italy; Policlinico Tor Vergata, Viale Oxford, 81, 00133 Rome, Italy
| | - Alessandro Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, via Vetoio - Coppito, 67100 L'Aquila, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
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Zhou Z, Jones K, Ivleva EI, Colon-Perez L. Macro- and Micro-Structural Alterations in the Midbrain in Early Psychosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588901. [PMID: 38645197 PMCID: PMC11030414 DOI: 10.1101/2024.04.10.588901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Introduction Early psychosis (EP) is a critical period in the course of psychotic disorders during which the brain is thought to undergo rapid and significant functional and structural changes 1 . Growing evidence suggests that the advent of psychotic disorders is early alterations in the brain's functional connectivity and structure, leading to aberrant neural network organization. The Human Connectome Project (HCP) is a global effort to map the human brain's connectivity in healthy and disease populations; within HCP, there is a specific dataset that focuses on the EP subjects (i.e., those within five years of the initial psychotic episode) (HCP-EP), which is the focus of our study. Given the critically important role of the midbrain function and structure in psychotic disorders (cite), and EP in particular (cite), we specifically focused on the midbrain macro- and micro-structural alterations and their association with clinical outcomes in HCP-EP. Methods We examined macro- and micro-structural brain alterations in the HCP-EP sample (n=179: EP, n=123, Controls, n=56) as well as their associations with behavioral measures (i.e., symptoms severity) using a stepwise approach, incorporating a multimodal MRI analysis procedure. First, Deformation Based Morphometry (DBM) was carried out on the whole brain 3 Tesla T1w images to examine gross brain anatomy (i.e., seed-based and voxel-based volumes). Second, we extracted Fractional Anisotropy (FA), Axial Diffusivity (AD), and Mean Diffusivity (MD) indices from the Diffusion Tensor Imaging (DTI) data; a midbrain mask was created based on FreeSurfer v.6.0 atlas. Third, we employed Tract-Based Spatial Statistics (TBSS) to determine microstructural alterations in white matter tracts within the midbrain and broader regions. Finally, we conducted correlation analyses to examine associations between the DBM-, DTI- and TBSS-based outcomes and the Positive and Negative Syndrome Scale (PANSS) scores. Results DBM analysis showed alterations in the hippocampus, midbrain, and caudate/putamen. A DTI voxel-based analysis shows midbrain reductions in FA and AD and increases in MD; meanwhile, the hippocampus shows an increase in FA and a decrease in AD and MD. Several key brain regions also show alterations in DTI indices (e.g., insula, caudate, prefrontal cortex). A seed-based analysis centered around a midbrain region of interest obtained from freesurfer segmentation confirms the voxel-based analysis of DTI indices. TBSS successfully captured structural differences within the midbrain and complementary alterations in other main white matter tracts, such as the corticospinal tract and cingulum, suggesting early altered brain connectivity in EP. Correlations between these quantities in the EP group and behavioral scores (i.e., PANSS and CAINS tests) were explored. It was found that midbrain volume noticeably correlates with the Cognitive score of PA and all DTI metrics. FA correlates with the several dimensions of the PANSS, while AD and MD do not show many associations with PANSS or CAINS. Conclusions Our findings contribute to understanding the midbrain-focused circuitry involvement in EP and complimentary alteration in EP. Our work provides a path for future investigations to inform specific brain-based biomarkers of EP and their relationships to clinical manifestations of the psychosis course.
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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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Dominicus LS, van Rijn L, van der A J, van der Spek R, Podzimek D, Begemann M, de Haan L, van der Pluijm M, Otte WM, Cahn W, Röder CH, Schnack HG, van Dellen E. fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review. Neuroimage Clin 2023; 40:103515. [PMID: 37797435 PMCID: PMC10568423 DOI: 10.1016/j.nicl.2023.103515] [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: 07/17/2023] [Revised: 08/31/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis. METHOD A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R? RESULTS In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions. CONCLUSION The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.
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Affiliation(s)
- L S Dominicus
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - L van Rijn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J van der A
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R van der Spek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D Podzimek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Begemann
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - L de Haan
- Department Early Psychosis, Academical Medical Centre of the University of Amsterdam, Amsterdam, Amsterdam, The Netherlands
| | - M van der Pluijm
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - W M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, and Utrecht University, Utrecht, The Netherlands
| | - W Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C H Röder
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H G Schnack
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Xue K, Chen J, Wei Y, Chen Y, Han S, Wang C, Zhang Y, Song X, Cheng J. Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia. Front Neurosci 2023; 17:1167942. [PMID: 37342466 PMCID: PMC10277613 DOI: 10.3389/fnins.2023.1167942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 06/23/2023] Open
Abstract
Background and objective The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. Methods A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Results Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Conclusion Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.
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Affiliation(s)
- Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
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9
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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10
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Nelson EA, Kraguljac NV, Maximo JO, Armstrong W, Lahti AC. Hippocampal Hyperconnectivity to the Visual Cortex Predicts Treatment Response. Schizophr Bull 2023; 49:605-613. [PMID: 36752830 PMCID: PMC10154738 DOI: 10.1093/schbul/sbac213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
BACKGROUND Converging lines of evidence point to hippocampal dysfunction in psychosis spectrum disorders, including altered functional connectivity. Evidence also suggests that antipsychotic medications can modulate hippocampal dysfunction. The goal of this project was to identify patterns of hippocampal connectivity predictive of response to antipsychotic treatment in 2 cohorts of patients with a psychosis spectrum disorder, one medication-naïve and the other one unmedicated. HYPOTHESIS We hypothesized that we would identify reliable patterns of hippocampal connectivity in the 2 cohorts that were predictive of treatment response and that medications would modulate abnormal hippocampal connectivity after 6 weeks of treatment. STUDY DESIGN We used a prospective design to collect resting-state fMRI scans prior to antipsychotic treatment and after 6 weeks of treatment with risperidone, a commonly used antipsychotic medication, in both cohorts. We enrolled 44 medication-naïve first-episode psychosis patients (FEP) and 39 unmedicated patients with schizophrenia (SZ). STUDY RESULTS In both patient cohorts, we observed a similar pattern where greater hippocampal connectivity to regions of the occipital cortex was predictive of treatment response. Lower hippocampal connectivity of the frontal pole, orbitofrontal cortex, subcallosal area, and medial prefrontal cortex was predictive of treatment response in unmedicated SZ, but not in the medication-naïve cohort. Furthermore, greater reduction in hippocampal connectivity to the visual cortex with treatment was associated with better clinical response. CONCLUSIONS Our results suggest that greater connectivity between the hippocampus and occipital cortex is not only predictive of better treatment response, but that antipsychotic medications have a modulatory effect by reducing hyperconnectivity.
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Affiliation(s)
- Eric A Nelson
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - William Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
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11
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Lee EE, Adamowicz DH, Frangou S. An NIMH Workshop on Non-Affective Psychosis in Midlife and Beyond: Research Agenda on Phenomenology, Clinical Trajectories, Underlying Mechanisms, and Intervention Targets. Am J Geriatr Psychiatry 2023; 31:353-365. [PMID: 36858928 PMCID: PMC10990076 DOI: 10.1016/j.jagp.2023.01.019] [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: 11/15/2022] [Revised: 01/05/2023] [Accepted: 01/23/2023] [Indexed: 02/05/2023]
Abstract
We present a review of the state of the research in the phenomenology, clinical trajectories, biological mechanisms, aging biomarkers, and treatments for middle-aged and older people with schizophrenia (PwS) discussed at the NIMH sponsored workshop "Non-affective Psychosis in Midlife and Beyond." The growing population of PwS has specific clinical needs that require tailored and mechanistically derived interventions. Differentiating between the effects of aging and disease progression is a key challenge of studying older PwS. This review of the workshop highlights the recent findings in this understudied clinical population and the critical gaps in knowledge and consensus for research priorities. This review showcases the major challenges and opportunities for research to advance clinical care for this growing and understudied population.
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Affiliation(s)
- Ellen E Lee
- Department of Psychiatry (EEL, DA), University of California San Diego, La Jolla, CA; Sam and Rose Stein Institute for Research on Aging (EEL, DA), University of California San Diego, La Jolla, CA; Desert-Pacific Mental Illness Research Education and Clinical Center, Veterans Affairs San Diego Healthcare System (EEL), San Diego, CA.
| | - David H Adamowicz
- Department of Psychiatry (EEL, DA), University of California San Diego, La Jolla, CA; Sam and Rose Stein Institute for Research on Aging (EEL, DA), University of California San Diego, La Jolla, CA
| | - Sophia Frangou
- Department of Psychiatry (SF), University of British Columbia, Vancouver, British Columbia, Canada; Icahn School of Medicine at Mount Sinai (SF), New York, NY
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12
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Ozubko JD, Campbell M, Verhayden A, Demetri B, Brady M, Brunec I. Stereotypical hippocampal clustering predicts navigational success in virtualized real-world environments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.23.533994. [PMID: 36993464 PMCID: PMC10055426 DOI: 10.1101/2023.03.23.533994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Structural differences along the long-axis of the hippocampus have long been believed to underlie meaningful functional differences, such as the granularity of information processing. Recent findings show that data-driven parcellations of the hippocampus sub-divide the hippocampus into a 10-cluster map with anterior-medial, anterior-lateral, and posteroanterior-lateral, middle, and posterior components. We tested whether task and experience could modulate this clustering using a spatial learning experiment where subjects were trained to virtually navigate a novel neighborhood in a Google Street View-like environment over a two-week period. Subjects were scanned while navigating routes early in training and at the end of their two-week training. Using the 10-cluster map as the ideal template, we find that subjects who eventually learn the neighborhood well have hippocampal cluster-maps consistent with the ideal-even on their second day of learning-and their cluster mappings do not change over the two week training period. However, subjects who eventually learn the neighborhood poorly begin with hippocampal cluster-maps inconsistent with the ideal, though their cluster mappings become more stereotypical by the end of the two week training. Interestingly this improvement seems to be route specific as even after some early improvement, when a new route is navigated participants' hippocampal maps revert back to less stereotypical organization. We conclude that hippocampal clustering is not dependent solely on anatomical structure, and instead is driven by a combination of anatomy, task, and importantly, experience. Nonetheless, while hippocampal clustering can change with experience, efficient navigation depends on functional hippocampal activity clustering in a stereotypical manner, highlighting optimal divisions of processing along the hippocampal anterior-posterior and medial-lateral-axes.
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13
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Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023; 13:75. [PMID: 36864017 PMCID: PMC9981732 DOI: 10.1038/s41398-023-02371-z] [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: 10/14/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
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14
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McHugo M, Avery S, Armstrong K, Rogers BP, Vandekar SN, Woodward ND, Blackford JU, Heckers S. Anterior hippocampal dysfunction in early psychosis: a 2-year follow-up study. Psychol Med 2023; 53:160-169. [PMID: 33875028 PMCID: PMC8919704 DOI: 10.1017/s0033291721001318] [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] [Indexed: 11/08/2022]
Abstract
BACKGROUND Cross-sectional studies indicate that hippocampal function is abnormal across stages of psychosis. Neural theories of psychosis pathophysiology suggest that dysfunction worsens with illness stage. Here, we test the hypothesis that hippocampal function is impaired in the early stage of psychosis and declines further over the next 2 years. METHODS We measured hippocampal function over 2 years using a scene processing task in 147 participants (76 individuals in the early stage of a non-affective psychotic disorder and 71 demographically similar healthy control individuals). Two-year follow-up was completed in 97 individuals (50 early psychosis, 47 healthy control). Voxelwise longitudinal analysis of activation in response to scenes was carried out within a hippocampal region of interest to test for group differences at baseline and a group by time interaction. RESULTS At baseline, we observed lower anterior hippocampal activation in the early psychosis group relative to the healthy control group. Contrary to our hypothesis, hippocampal activation remained consistent and did not show the predicted decline over 2 years in the early psychosis group. Healthy controls showed a modest reduction in hippocampal activation after 2 years. CONCLUSIONS The results of this study suggest that hippocampal dysfunction in early psychosis does not worsen over 2 years and highlight the need for longer-term longitudinal studies.
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Affiliation(s)
- Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Suzanne Avery
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kristan Armstrong
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P. Rogers
- Vanderbilt University Institute of Imaging Sciences, Nashville, TN, USA
| | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil D. Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Research and Development, Tennessee Valley Healthcare System, United States Department of Veteran Affairs
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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15
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Thorp JN, Gasser C, Blessing E, Davachi L. Data-Driven Clustering of Functional Signals Reveals Gradients in Processing Both within the Anterior Hippocampus and across Its Long Axis. J Neurosci 2022; 42:7431-7441. [PMID: 36002264 PMCID: PMC9525160 DOI: 10.1523/jneurosci.0269-22.2022] [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: 02/01/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 11/21/2022] Open
Abstract
A particularly elusive puzzle concerning the hippocampus is how the structural differences along its long anteroposterior axis might beget meaningful functional differences, particularly in terms of the granularity of information processing. One measure posits to quantify this granularity by calculating the average statistical independence of the BOLD signal across neighboring voxels, or intervoxel similarity (IVS), and has shown the anterior hippocampus to process coarser-grained information than the posterior hippocampus. This measure, however, has yielded opposing results in studies of developmental and healthy aging samples, which also varied in fMRI acquisition parameters and hippocampal parcellation methods. To reconcile these findings, we measured IVS across two separate resting-state fMRI acquisitions and compared the results across many of the most widely used parcellation methods in a large young-adult sample of male and female humans (Acquisition 1, N = 233; Acquisition 2, N = 176). Finding conflicting results across acquisitions and parcellations, we reasoned that a data-driven approach to hippocampal parcellation is necessary. To this end, we implemented a group masked independent components analysis to identify functional subunits of the hippocampus, most notably separating the anterior hippocampus into separate anterior-medial, anterior-lateral, and posteroanterior-lateral components. Measuring IVS across these components revealed a decrease in IVS along the medial-lateral axis of the anterior hippocampus but an increase from anterior to posterior. We conclude that intervoxel similarity is deeply affected by parcellation and that grounding one's parcellation in a functionally informed approach might allow for a more complex and reliable characterization of the hippocampus.SIGNIFICANCE STATEMENT Processing information along hierarchical scales of granularity is critical for many of the feats of cognition considered most human. Recently, the changes in structure, cortical connectivity, and apparent functional properties across parcels of the hippocampal long axis have been hypothesized to underlie this hierarchical gradient in information processing. We show here, however, that the choice of parcellation method itself drastically affects one particular measure of granularity across the hippocampus and that a functionally informed approach to parcellation reveals gradients both within the anterior hippocampus and in nonlinear form across the long axis. These results point to the issue of parcellation as a critical one in the study of the hippocampus and reorient interpretation of existing results.
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Affiliation(s)
- John N Thorp
- Department of Psychology, Columbia University, New York, New York 10027
| | - Camille Gasser
- Department of Psychology, Columbia University, New York, New York 10027
| | - Esther Blessing
- Department of Psychiatry, New York University Langone Medical Center, New York University Grossman School of Medicine, New York, New York 10016
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, New York 10027
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 10962
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16
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Su W, Zhao Z, Li G, Tang X, Xu L, Tang Y, Wei Y, Cui H, Zhang T, Zhang J, Liu X, Guo Q, Wang J. Thalamo-hippocampal dysconnectivity is associated with serum cholesterol level in drug-naïve patients with first-episode schizophrenia. J Psychiatr Res 2022; 151:497-506. [PMID: 35623125 DOI: 10.1016/j.jpsychires.2022.05.013] [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: 12/26/2021] [Revised: 03/25/2022] [Accepted: 05/09/2022] [Indexed: 11/30/2022]
Abstract
Hippocampal deficits and metabolic dysregulations such as dyslipidemia have been frequently reported in schizophrenia and are suggested to contribute to the pathophysiology of schizophrenia. Hippocampus is particularly susceptible to environmental challenges including metabolism and inflammation. However, evidence linking hippocampal alterations and metabolic dysregulations are quite sparse in drug-naïve schizophrenia. A total of 166 drug-naïve patients with first-episode schizophrenia (FES) and 78 healthy controls (HC) underwent measures for several serum metabolic markers, structural and resting-state functional magnetic resonance imaging (rs-fMRI), as well as diffusion tensor imaging (DTI). Seed-to-voxel functional connectivity (FC) and probabilistic tractography were performed to assess the functional and microstructural connectivity of the bilateral hippocampi. Clinical symptoms were evaluated with Positive and Negative Syndrome Scale (PANSS). Patients with FES showed significantly decreased total cholesterol (Chol) level. Patients showed elevated FC between the left hippocampus and bilateral thalami while showing decreased microstructural connectivity between the left hippocampus and bilateral thalami. Multiple regression analyses showed that FC from the left hippocampus to the right superior frontal gyrus (SFG), bilateral frontal pole (FP), and right thalamus were negatively associated with the Chol level, while no association was observed in the HC group. Our study validated alterations in both functional and microstructural thalamo-hippocampal connectivities, and abnormal cholesterol level in FES. Moreover, decreased cholesterol level is associated with elevated thalamo-hippocampal functional connectivity in patients with FES, suggesting that dyslipidemia may interact with the hippocampal dysfunction in FES.
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Affiliation(s)
- Wenjun Su
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Zexin Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Guanjun Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Department of Early Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiaochen Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Lihua Xu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yanyan Wei
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Huiru Cui
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Xiaohua Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Department of Early Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Qian Guo
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Department of Early Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, 200031, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, 200240, China.
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17
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Kim A, Zisman CR, Holingue C. Influences of the Immune System and Microbiome on the Etiology of ASD and GI Symptomology of Autistic Individuals. Curr Top Behav Neurosci 2022; 61:141-161. [PMID: 35711026 DOI: 10.1007/7854_2022_371] [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: 10/18/2022]
Abstract
Autism Spectrum Disorder is a developmental condition associated with impairments in communication and social interactions, and repetitive and restricted behavior or interests. Autistic individuals are more likely to experience gastrointestinal (GI) symptoms than neurotypical individuals. This may be partially due to dysbiosis of the gut microbiome. In this article, we describe the interaction of the microbiome and immune system on autism etiology. We also summarize the links between the microbiome and gastrointestinal and related symptoms among autistic individuals. We report that microbial interventions, including diet, probiotics, antibiotics, and fecal transplants, and immune-modulating therapies such as cytokine blockade during the preconception, pregnancy, and postnatal period may impact the neurodevelopment, behavior, and gastrointestinal health of autistic individuals.
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Affiliation(s)
- Amanda Kim
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Corina R Zisman
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
| | - Calliope Holingue
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. .,Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, USA.
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18
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Structural and Functional Deviations of the Hippocampus in Schizophrenia and Schizophrenia Animal Models. Int J Mol Sci 2022; 23:ijms23105482. [PMID: 35628292 PMCID: PMC9143100 DOI: 10.3390/ijms23105482] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 01/04/2023] Open
Abstract
Schizophrenia is a grave neuropsychiatric disease which frequently onsets between the end of adolescence and the beginning of adulthood. It is characterized by a variety of neuropsychiatric abnormalities which are categorized into positive, negative and cognitive symptoms. Most therapeutical strategies address the positive symptoms by antagonizing D2-dopamine-receptors (DR). However, negative and cognitive symptoms persist and highly impair the life quality of patients due to their disabling effects. Interestingly, hippocampal deviations are a hallmark of schizophrenia and can be observed in early as well as advanced phases of the disease progression. These alterations are commonly accompanied by a rise in neuronal activity. Therefore, hippocampal formation plays an important role in the manifestation of schizophrenia. Furthermore, studies with animal models revealed a link between environmental risk factors and morphological as well as electrophysiological abnormalities in the hippocampus. Here, we review recent findings on structural and functional hippocampal abnormalities in schizophrenic patients and in schizophrenia animal models, and we give an overview on current experimental approaches that especially target the hippocampus. A better understanding of hippocampal aberrations in schizophrenia might clarify their impact on the manifestation and on the outcome of this severe disease.
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Reyes-Madrigal F, Guma E, León-Ortiz P, Gómez-Cruz G, Mora-Durán R, Graff-Guerrero A, Kegeles LS, Chakravarty MM, de la Fuente-Sandoval C. Striatal glutamate, subcortical structure and clinical response to first-line treatment in first-episode psychosis patients. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110473. [PMID: 34748864 PMCID: PMC8643337 DOI: 10.1016/j.pnpbp.2021.110473] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Recent studies have observed that patients with treatment-resistant schizophrenia as well as patients with schizophrenia who do not respond within a medication trial exhibit excess activity of the glutamate system. In this study we sought to replicate the within-trial glutamate abnormality and to investigate the potential for structural differences and treatment-induced changes to improve identification of medication responders and non-responders. METHODS We enrolled 48 medication-naïve patients in a 4-week trial of risperidone and classified them retrospectively into responders and non-responders using clinical criteria. Proton magnetic resonance spectroscopy and T1-weighted structural MRI were acquired pre- and post-treatment to quantify striatal glutamate levels and several measures of subcortical brain structure. RESULTS Patients were classified as 29 responders and 19 non-responders. Striatal glutamate was higher in the non-responders than responders both pre- and post-treatment (F1,39 = 7.15, p = .01). Volumetric measures showed a significant group x time interaction (t = 5.163, <1%FDR), and group x time x glutamate interaction (t = 4.23, <15%FDR) were seen in several brain regions. Striatal volumes increased at trend level with treatment in both groups, and a positive association of striatal volumes with glutamate levels was seen in the non-responders. CONCLUSIONS Combining anatomic measures with glutamate levels offers the potential to enhance classification of responders and non-responders to antipsychotic medications as well as to provide mechanistic understanding of the interplay between neuroanatomical and neurochemical changes induced by these medications. Ethical statement The study was approved by the Ethics and Scientific committees of the Instituto Nacional de Neurología y Neurocirugía in Mexico City. All participants over 18 years fully understood and signed the informed consent; in case the patient was under 18 years, informed consent was obtained from both parents. Participants did not receive a stipend.
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Affiliation(s)
- Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Elisa Guma
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy (CoBrA) Lab, Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | - Pablo León-Ortiz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Gladys Gómez-Cruz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Ricardo Mora-Durán
- Emergency Department, Hospital Fray Bernardino Álvarez, Mexico City, Mexico
| | - Ariel Graff-Guerrero
- Multimodal Neuroimaging Schizophrenia Group, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Lawrence S Kegeles
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, USA
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy (CoBrA) Lab, Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico; Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico.
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20
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A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance. Mol Psychiatry 2022; 27:1184-1191. [PMID: 34642460 PMCID: PMC9001745 DOI: 10.1038/s41380-021-01331-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 09/17/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022]
Abstract
Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.
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21
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Nelson EA, Kraguljac NV, Maximo JO, Briend F, Armstrong W, Ver Hoef LW, Johnson V, Lahti AC. Hippocampal Dysconnectivity and Altered Glutamatergic Modulation of the Default Mode Network: A Combined Resting-State Connectivity and Magnetic Resonance Spectroscopy Study in Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:108-118. [PMID: 32684484 PMCID: PMC7904096 DOI: 10.1016/j.bpsc.2020.04.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/06/2020] [Accepted: 04/21/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Converging lines of evidence point to hippocampal dysfunction in schizophrenia. It is thought that hippocampal dysfunction spreads across hippocampal subfields and to cortical regions by way of long-range efferent projections. Importantly, abnormalities in the excitation/inhibition balance could impair the long-range modulation of neural networks. The goal of this project was twofold. First, we sought to identify replicable patterns of hippocampal dysconnectivity in patients with a psychosis spectrum disorder. Second, we aimed to investigate a putative link between glutamatergic metabolism and hippocampal connectivity alterations. METHODS We evaluated resting-state hippocampal functional connectivity alterations in two cohorts of patients with a psychosis spectrum disorder. The first cohort consisted of 55 medication-naïve patients with first-episode psychosis and 41 matched healthy control subjects, and the second cohort consisted of 42 unmedicated patients with schizophrenia and 41 matched control subjects. We also acquired measurements of glutamate + glutamine in the left hippocampus using magnetic resonance spectroscopy for 42 patients with first-episode psychosis and 37 healthy control subjects from our first cohort. RESULTS We observed a pattern of hippocampal functional hypoconnectivity to regions of the default mode network and hyperconnectivity to the lateral occipital cortex in both cohorts. We also show that in healthy control subjects, greater hippocampal glutamate + glutamine levels predicted greater hippocampal functional connectivity to the anterior default mode network. Furthermore, this relationship was reversed in medication-naïve subjects with first-episode psychosis. CONCLUSIONS These results suggest that an alteration in the relationship between glutamate and functional connectivity may disrupt the dynamic of major neural networks.
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Affiliation(s)
- Eric A. Nelson
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina V. Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frederic Briend
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - William Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lawrence W. Ver Hoef
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Victoria Johnson
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne C. Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Correspondence: Adrienne C. Lahti, MD, University of Alabama at Birmingham, Sparks Center, Room 501, 1720 7 Ave. S, Birmingham, Al 35233, Telephone: 205-996-6776, Fax: 205-975-4879,
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22
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Schizophrenia Outside the Brain. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1400:53-63. [DOI: 10.1007/978-3-030-97182-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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23
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Smucny J, Shi G, Lesh TA, Carter CS, Davidson I. Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis. Neuroimage Clin 2022; 36:103214. [PMID: 36183611 PMCID: PMC9668611 DOI: 10.1016/j.nicl.2022.103214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/18/2022] [Accepted: 09/28/2022] [Indexed: 12/14/2022]
Abstract
Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resources necessary to obtain them. A recently developed self-supervised learning method called Mixup may help overcome this challenge. In Mixup, the learner combines pairs of training instances to produce a virtual third instance that is a linear combination of the two instances and their labels. This procedure is also well-suited to the coregistered images typically found in fMRI datasets. Here we compared performance of a task fMRI-based deep learner with Mixup vs without Mixup on predicting response to treatment in recent onset psychosis. Whole brain fMRI time series data were extracted from a cognitive control task in 82 patients with recent onset psychosis and used to predict "Improver" (n = 47) vs "Non-Improver" (n = 35) status, with Improver defined as showing a 20 % reduction in total Brief Psychiatric Rating Scale score after 1 year of treatment. Mixup significantly improved performance (accuracy without Mixup: 76.5 % [95 % CI: 75.9-77.1 %]; accuracy with Mixup: 80.1 % [95 % CI: 79.4-80.8 %]). Ablation showed the improvement was due to improvement in both Improvers and Non-Improvers. These results suggest that using Mixup may significantly improve performance and reduce overfitting of fMRI-based prognostic deep learners and may also help overcome the small sample size challenge inherent to many neuroimaging datasets.
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Affiliation(s)
- Jason Smucny
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, United States.
| | - Ge Shi
- Department of Computer Sciences, University of California, Davis, United States
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, United States
| | - Cameron S Carter
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, United States
| | - Ian Davidson
- Department of Computer Sciences, University of California, Davis, United States
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24
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Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis. Schizophr Res 2021; 237:153-165. [PMID: 34534947 DOI: 10.1016/j.schres.2021.09.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/18/2021] [Accepted: 09/06/2021] [Indexed: 11/21/2022]
Abstract
We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.
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25
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Chopra S, Francey SM, O’Donoghue B, Sabaroedin K, Arnatkeviciute A, Cropley V, Nelson B, Graham J, Baldwin L, Tahtalian S, Yuen HP, Allott K, Alvarez-Jimenez M, Harrigan S, Pantelis C, Wood SJ, McGorry P, Fornito A. Functional Connectivity in Antipsychotic-Treated and Antipsychotic-Naive Patients With First-Episode Psychosis and Low Risk of Self-harm or Aggression: A Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2021; 78:994-1004. [PMID: 34160595 PMCID: PMC8223142 DOI: 10.1001/jamapsychiatry.2021.1422] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
IMPORTANCE Altered functional connectivity (FC) is a common finding in resting-state functional magnetic resonance imaging (rs-fMRI) studies of people with psychosis, yet how FC disturbances evolve in the early stages of illness, and how antipsychotic treatment influences these disturbances, remains unknown. OBJECTIVE To investigate longitudinal FC changes in antipsychotic-naive and antipsychotic-treated patients with first-episode psychosis (FEP). DESIGN, SETTING, AND PARTICIPANTS This secondary analysis of a triple-blind, randomized clinical trial was conducted over a 5-year recruitment period between April 2008 and December 2016 with 59 antipsychotic-naive patients with FEP receiving either a second-generation antipsychotic or a placebo pill over a treatment period of 6 months. Participants were required to have low suicidality and aggression, to have a duration of untreated psychosis of less than 6 months, and to be living in stable accommodations with social support. Both FEP groups received intensive psychosocial therapy. A healthy control group was also recruited. Participants completed rs-fMRI scans at baseline, 3 months, and 12 months. Data were analyzed from May 2019 to August 2020. INTERVENTIONS Resting-state functional MRI was used to probe brain FC. Patients received either a second-generation antipsychotic or a matched placebo tablet. Both patient groups received a manualized psychosocial intervention. MAIN OUTCOMES AND MEASURES The primary outcomes of this analysis were to investigate (1) FC differences between patients and controls at baseline; (2) FC changes in medicated and unmedicated patients between baseline and 3 months; and (3) associations between longitudinal FC changes and clinical outcomes. An additional aim was to investigate long-term FC changes at 12 months after baseline. These outcomes were not preregistered. RESULTS Data were analyzed for 59 patients (antipsychotic medication plus psychosocial treatment: 28 [47.5%]; mean [SD] age, 19.5 [3.0] years; 15 men [53.6%]; placebo plus psychosocial treatment: 31 [52.5%]; mean [SD] age, 18.8 [2.7]; 16 men [51.6%]) and 27 control individuals (mean [SD] age, 21.9 [1.9] years). At baseline, patients showed widespread functional dysconnectivity compared with controls, with reductions predominantly affecting interactions between the default mode network, limbic systems, and the rest of the brain. From baseline to 3 months, patients receiving placebo showed increased FC principally within the same systems; some of these changes correlated with improved clinical outcomes (canonical correlation analysis R = 0.901; familywise error-corrected P = .005). Antipsychotic exposure was associated with increased FC primarily between the thalamus and the rest of the brain. CONCLUSIONS AND RELEVANCE In this secondary analysis of a clinical trial, antipsychotic-naive patients with FEP showed widespread functional dysconnectivity at baseline, followed by an early normalization of default mode network and cortical limbic dysfunction in patients receiving placebo and psychosocial intervention. Antipsychotic exposure was associated with FC changes concentrated on thalamocortical networks. TRIAL REGISTRATION ACTRN12607000608460.
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Affiliation(s)
- Sidhant Chopra
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Shona M. Francey
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Brian O’Donoghue
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristina Sabaroedin
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica Graham
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lara Baldwin
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Steven Tahtalian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mario Alvarez-Jimenez
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susy Harrigan
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia,Department of Social Work, Monash University, Caulfield, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Stephen J. Wood
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia,University of Birmingham School of Psychology, Edgbaston, United Kingdom
| | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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26
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Functional connectivity abnormalities of the long-axis hippocampal subregions in schizophrenia during episodic memory. NPJ SCHIZOPHRENIA 2021; 7:19. [PMID: 33658524 PMCID: PMC7930183 DOI: 10.1038/s41537-021-00147-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/19/2021] [Indexed: 01/05/2023]
Abstract
Past evidence suggests that hippocampal subregions, namely the anterior and posterior parts, may be engaged in distinct networks underlying the memory functions which may be altered in patients with schizophrenia. However, of the very few studies that have investigated the hippocampal longitudinal axis subdivisions functional connectivity in patients with schizophrenia, the majority was based on resting-state data, and yet, none aimed to examine these during an episodic memory task. A total of 41 patients with schizophrenia and 45 healthy controls were recruited for a magnetic resonance imaging protocol in which they performed an explicit memory task. Seed-based functional connectivity analysis was employed to assess connectivity abnormalities between hippocampal subregions and voxel-wise connectivity targets in patients with schizophrenia. We observed a significantly reduced connectivity between the posterior hippocampus and regions from the default mode network, but increased connectivity with the primary visual cortex, in patients with schizophrenia compared to healthy subjects. Increased connectivity between the anterior hippocampus and anterior temporal regions also characterized patients with schizophrenia. In the current study, we provided evidence and support for studying hippocampal subdivisions along the longitudinal axis in schizophrenia. Our results suggest that the abnormalities in hippocampal subregions functional connectivity reflect deficits in episodic memory that may be implicated in the pathophysiology of schizophrenia.
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27
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Korda AI, Andreou C, Borgwardt S. Pattern classification as decision support tool in antipsychotic treatment algorithms. Exp Neurol 2021; 339:113635. [PMID: 33548218 DOI: 10.1016/j.expneurol.2021.113635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.
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Affiliation(s)
- Alexandra I Korda
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany.
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28
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Comer AL, Carrier M, Tremblay MÈ, Cruz-Martín A. The Inflamed Brain in Schizophrenia: The Convergence of Genetic and Environmental Risk Factors That Lead to Uncontrolled Neuroinflammation. Front Cell Neurosci 2020; 14:274. [PMID: 33061891 PMCID: PMC7518314 DOI: 10.3389/fncel.2020.00274] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/03/2020] [Indexed: 12/12/2022] Open
Abstract
Schizophrenia is a disorder with a heterogeneous etiology involving complex interplay between genetic and environmental risk factors. The immune system is now known to play vital roles in nervous system function and pathology through regulating neuronal and glial development, synaptic plasticity, and behavior. In this regard, the immune system is positioned as a common link between the seemingly diverse genetic and environmental risk factors for schizophrenia. Synthesizing information about how the immune-brain axis is affected by multiple factors and how these factors might interact in schizophrenia is necessary to better understand the pathogenesis of this disease. Such knowledge will aid in the development of more translatable animal models that may lead to effective therapeutic interventions. Here, we provide an overview of the genetic risk factors for schizophrenia that modulate immune function. We also explore environmental factors for schizophrenia including exposure to pollution, gut dysbiosis, maternal immune activation and early-life stress, and how the consequences of these risk factors are linked to microglial function and dysfunction. We also propose that morphological and signaling deficits of the blood-brain barrier, as observed in some individuals with schizophrenia, can act as a gateway between peripheral and central nervous system inflammation, thus affecting microglia in their essential functions. Finally, we describe the diverse roles that microglia play in response to neuroinflammation and their impact on brain development and homeostasis, as well as schizophrenia pathophysiology.
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Affiliation(s)
- Ashley L. Comer
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Department of Biology, Boston University, Boston, MA, United States
- Neurophotonics Center, Boston University, Boston, MA, United States
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
| | - Micaël Carrier
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Marie-Ève Tremblay
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada
| | - Alberto Cruz-Martín
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Department of Biology, Boston University, Boston, MA, United States
- Neurophotonics Center, Boston University, Boston, MA, United States
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
- Department of Pharmacology and Experimental Therapeutics, Boston University, Boston, MA, United States
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29
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Adams RA, Bush D, Zheng F, Meyer SS, Kaplan R, Orfanos S, Marques TR, Howes OD, Burgess N. Impaired theta phase coupling underlies frontotemporal dysconnectivity in schizophrenia. Brain 2020; 143:1261-1277. [PMID: 32236540 PMCID: PMC7174039 DOI: 10.1093/brain/awaa035] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/21/2019] [Accepted: 12/16/2019] [Indexed: 12/17/2022] Open
Abstract
Frontotemporal dysconnectivity is a key pathology in schizophrenia. The specific nature of this dysconnectivity is unknown, but animal models imply dysfunctional theta phase coupling between hippocampus and medial prefrontal cortex (mPFC). We tested this hypothesis by examining neural dynamics in 18 participants with a schizophrenia diagnosis, both medicated and unmedicated; and 26 age, sex and IQ matched control subjects. All participants completed two tasks known to elicit hippocampal-prefrontal theta coupling: a spatial memory task (during magnetoencephalography) and a memory integration task. In addition, an overlapping group of 33 schizophrenia and 29 control subjects underwent PET to measure the availability of GABAARs expressing the α5 subunit (concentrated on hippocampal somatostatin interneurons). We demonstrate-in the spatial memory task, during memory recall-that theta power increases in left medial temporal lobe (mTL) are impaired in schizophrenia, as is theta phase coupling between mPFC and mTL. Importantly, the latter cannot be explained by theta power changes, head movement, antipsychotics, cannabis use, or IQ, and is not found in other frequency bands. Moreover, mPFC-mTL theta coupling correlated strongly with performance in controls, but not in subjects with schizophrenia, who were mildly impaired at the spatial memory task and no better than chance on the memory integration task. Finally, mTL regions showing reduced phase coupling in schizophrenia magnetoencephalography participants overlapped substantially with areas of diminished α5-GABAAR availability in the wider schizophrenia PET sample. These results indicate that mPFC-mTL dysconnectivity in schizophrenia is due to a loss of theta phase coupling, and imply α5-GABAARs (and the cells that express them) have a role in this process.
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Affiliation(s)
- Rick A Adams
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK.,Division of Psychiatry, University College London, 149 Tottenham Court Road, London, W1T 7NF, UK.,Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, 10-12 Russell Square, London, WC1B 5EH, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, Malet Place, London, WC1E 7JE, UK.,Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK
| | - Daniel Bush
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK.,Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Fanfan Zheng
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190 Beijing, China
| | - Sofie S Meyer
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK.,Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Raphael Kaplan
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK.,Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stelios Orfanos
- South West London and St George's Mental Health NHS Trust, Springfield University Hospital, 61 Glenburnie Rd, London SW17 7DJ, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Tiago Reis Marques
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK.,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK
| | - Oliver D Howes
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK.,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK
| | - Neil Burgess
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK.,Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK.,Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
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Martel JC, Gatti McArthur S. Dopamine Receptor Subtypes, Physiology and Pharmacology: New Ligands and Concepts in Schizophrenia. Front Pharmacol 2020; 11:1003. [PMID: 32765257 PMCID: PMC7379027 DOI: 10.3389/fphar.2020.01003] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022] Open
Abstract
Dopamine receptors are widely distributed within the brain where they play critical modulator roles on motor functions, motivation and drive, as well as cognition. The identification of five genes coding for different dopamine receptor subtypes, pharmacologically grouped as D1- (D1 and D5) or D2-like (D2S, D2L, D3, and D4) has allowed the demonstration of differential receptor function in specific neurocircuits. Recent observation on dopamine receptor signaling point at dopamine-glutamate-NMDA neurobiology as the most relevant in schizophrenia and for the development of new therapies. Progress in the chemistry of D1- and D2-like receptor ligands (agonists, antagonists, and partial agonists) has provided more selective compounds possibly able to target the dopamine receptors homo and heterodimers and address different schizophrenia symptoms. Moreover, an extensive evaluation of the functional effect of these agents on dopamine receptor coupling and intracellular signaling highlights important differences that could also result in highly differentiated clinical pharmacology. The review summarizes the recent advances in the field, addressing the relevance of emerging new targets in schizophrenia in particular in relation to the dopamine - glutamate NMDA systems interactions.
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Hinney B, Walter A, Aghlmandi S, Andreou C, Borgwardt S. Does Hippocampal Volume Predict Transition to Psychosis in a High-Risk Group? A Meta-Analysis. Front Psychiatry 2020; 11:614659. [PMID: 33519555 PMCID: PMC7840882 DOI: 10.3389/fpsyt.2020.614659] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/14/2020] [Indexed: 01/11/2023] Open
Abstract
Schizophrenia has a prodromal phase of several years in most patients, making it possible to identify patients at clinical high risk (CHR) for developing the disorder. So far, these individuals are identified based on clinical criteria alone, and there is no reliable biomarker for predicting the transition to psychosis. It is well-established that reductions in brain volume, especially in the hippocampus, are associated with schizophrenia. Therefore, hippocampal volume may serve as a biomarker for psychosis. Several studies have already investigated hippocampal volume in CHR groups. Based on these studies, the present meta-analysis compares the baseline left and right hippocampal volume of CHR patients who developed a psychosis with that of CHR patients without such a transition. Our results show no statistically significant effect of the hippocampal volume on the transition risk for psychosis.
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Affiliation(s)
- Bernd Hinney
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Anna Walter
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Soheila Aghlmandi
- Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
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