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Geffen T, Hardikar S, Smallwood J, Kaliuzhna M, Carruzzo F, Böge K, Zierhut MM, Gutwinski S, Katthagen T, Kaiser S, Schlagenhauf F. Striatal Functional Hypoconnectivity in Patients With Schizophrenia Suffering From Negative Symptoms, Longitudinal Findings. Schizophr Bull 2024; 50:1337-1348. [PMID: 38687874 PMCID: PMC11548920 DOI: 10.1093/schbul/sbae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
BACKGROUND Negative symptoms in schizophrenia (SZ), such as apathy and diminished expression, have limited treatments and significantly impact daily life. Our study focuses on the functional division of the striatum: limbic-motivation and reward, associative-cognition, and sensorimotor-sensory and motor processing, aiming to identify potential biomarkers for negative symptoms. STUDY DESIGN This longitudinal, 2-center resting-state-fMRI (rsfMRI) study examines striatal seeds-to-whole-brain functional connectivity. We examined connectivity aberrations in patients with schizophrenia (PwSZ), focusing on stable group differences across 2-time points using intra-class-correlation and associated these with negative symptoms and measures of cognition. Additionally, in PwSZ, we used negative symptoms to predict striatal connectivity aberrations at the baseline and used the striatal aberration to predict symptoms 9 months later. STUDY RESULTS A total of 143 participants (77 PwSZ, 66 controls) from 2 centers (Berlin/Geneva) participated. We found sensorimotor-striatum and associative-striatum hypoconnectivity. We identified 4 stable hypoconnectivity findings over 3 months, revealing striatal-fronto-parietal-cerebellar hypoconnectivity in PwSZ. From those findings, we found hypoconnectivity in the bilateral associative striatum with the bilateral paracingulate-gyrus and the anterior cingulate cortex in PwSZ. Additionally, hypoconnectivity between the associative striatum and the superior frontal gyrus was associated with lower cognition scores in PwSZ, and weaker sensorimotor striatum connectivity with the superior parietal lobule correlated negatively with diminished expression and could predict symptom severity 9 months later. CONCLUSIONS Importantly, patterns of weaker sensorimotor striatum and superior parietal lobule connectivity fulfilled the biomarker criteria: clinical significance, reflecting underlying pathophysiology, and stability across time and centers.
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Affiliation(s)
- Tal Geffen
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, NeuroCure Clinical Research Center (NCRC), Campus Mitte, Berlin, Germany
| | - Samyogita Hardikar
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Mariia Kaliuzhna
- Clinical and Experimental Psychopathology Laboratory, University of Geneva, Faculty of Medicine, Geneva, Switzerland
| | - Fabien Carruzzo
- Clinical and Experimental Psychopathology Laboratory, University of Geneva, Faculty of Medicine, Geneva, Switzerland
| | - Kerem Böge
- Department of Psychiatry and Neuroscience, Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- German Center for Mental Health (DZPG), Partner Site, Berlin, Germany
| | - Marco Matthäus Zierhut
- Department of Psychiatry and Neuroscience, Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- German Center for Mental Health (DZPG), Partner Site, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Clinician Scientist Program, Berlin, Germany
| | - Stefan Gutwinski
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, NeuroCure Clinical Research Center (NCRC), Campus Mitte, Berlin, Germany
| | - Teresa Katthagen
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, NeuroCure Clinical Research Center (NCRC), Campus Mitte, Berlin, Germany
| | - Stephan Kaiser
- Adult Psychiatry Division, Department of Psychiatry, Geneva University Hospital, Geneva, Switzerland
| | - Florian Schlagenhauf
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, NeuroCure Clinical Research Center (NCRC), Campus Mitte, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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Cattarinussi G, Gugliotta AA, Sambataro F. The Risk for Schizophrenia-Bipolar Spectrum: Does the Apple Fall Close to the Tree? A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6540. [PMID: 37569080 PMCID: PMC10418911 DOI: 10.3390/ijerph20156540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric disorders that share clinical features and several risk genes. Important information about their genetic underpinnings arises from intermediate phenotypes (IPs), quantifiable biological traits that are more prevalent in unaffected relatives (RELs) of patients compared to the general population and co-segregate with the disorders. Within IPs, neuropsychological functions and neuroimaging measures have the potential to provide useful insight into the pathophysiology of SCZ and BD. In this context, the present narrative review provides a comprehensive overview of the available evidence on deficits in neuropsychological functions and neuroimaging alterations in unaffected relatives of SCZ (SCZ-RELs) and BD (BD-RELs). Overall, deficits in cognitive functions including intelligence, memory, attention, executive functions, and social cognition could be considered IPs for SCZ. Although the picture for cognitive alterations in BD-RELs is less defined, BD-RELs seem to present worse performances compared to controls in executive functioning, including adaptable thinking, planning, self-monitoring, self-control, and working memory. Among neuroimaging markers, SCZ-RELs appear to be characterized by structural and functional alterations in the cortico-striatal-thalamic network, while BD risk seems to be associated with abnormalities in the prefrontal, temporal, thalamic, and limbic regions. In conclusion, SCZ-RELs and BD-RELs present a pattern of cognitive and neuroimaging alterations that lie between patients and healthy individuals. Similar abnormalities in SCZ-RELs and BD-RELs may be the phenotypic expression of the shared genetic mechanisms underlying both disorders, while the specificities in neuropsychological and neuroimaging profiles may be associated with the differential symptom expression in the two disorders.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Alessio A. Gugliotta
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
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Ionescu TM, Grohs-Metz G, Hengerer B. Functional ultrasound detects frequency-specific acute and delayed S-ketamine effects in the healthy mouse brain. Front Neurosci 2023; 17:1177428. [PMID: 37266546 PMCID: PMC10229773 DOI: 10.3389/fnins.2023.1177428] [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: 03/01/2023] [Accepted: 04/21/2023] [Indexed: 06/03/2023] Open
Abstract
Introduction S-ketamine has received great interest due to both its antidepressant effects and its potential to induce psychosis when administered subchronically. However, no studies have investigated both its acute and delayed effects using in vivo small-animal imaging. Recently, functional ultrasound (fUS) has emerged as a powerful alternative to functional magnetic resonance imaging (fMRI), outperforming it in sensitivity and in spatiotemporal resolution. In this study, we employed fUS to thoroughly characterize acute and delayed S-ketamine effects on functional connectivity (FC) within the same cohort at slow frequency bands ranging from 0.01 to 1.25 Hz, previously reported to exhibit FC. Methods We acquired fUS in a total of 16 healthy C57/Bl6 mice split in two cohorts (n = 8 received saline, n = 8 S-ketamine). One day after the first scans, performed at rest, the mice received the first dose of S-ketamine during the second measurement, followed by four further doses administered every 2 days. First, we assessed FC reproducibility and reliability at baseline in six frequency bands. Then, we investigated the acute and delayed effects at day 1 after the first dose and at day 9, 1 day after the last dose, for all bands, resulting in a total of four fUS measurements for every mouse. Results We found reproducible (r > 0.9) and reliable (r > 0.9) group-average readouts in all frequency bands, only the 0.01-0.27 Hz band performing slightly worse. Acutely, S-ketamine induced strong FC increases in five of the six bands, peaking in the 0.073-0.2 Hz band. These increases comprised both cortical and subcortical brain areas, yet were of a transient nature, FC almost returning to baseline levels towards the end of the scan. Intriguingly, we observed robust corticostriatal FC decreases in the fastest band acquired (0.75 Hz-1.25 Hz). These changes persisted to a weaker extent after 1 day and at this timepoint they were accompanied by decreases in the other five bands as well. After 9 days, the decreases in the 0.75-1.25 Hz band were maintained, however no changes between cohorts could be detected in any other bands. Discussion In summary, the study reports that acute and delayed ketamine effects in mice are not only dissimilar but have different directionalities in most frequency bands. The complementary readouts of the employed frequency bands recommend the use of fUS for frequency-specific investigation of pharmacological effects on FC.
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Zouraraki C, Karamaouna P, Giakoumaki SG. Cognitive Processes and Resting-State Functional Neuroimaging Findings in High Schizotypal Individuals and Schizotypal Personality Disorder Patients: A Systematic Review. Brain Sci 2023; 13:615. [PMID: 37190580 PMCID: PMC10137138 DOI: 10.3390/brainsci13040615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 04/07/2023] Open
Abstract
Ample research findings indicate that there is altered brain functioning in the schizophrenia spectrum. Nevertheless, functional neuroimaging findings remain ambiguous for healthy individuals expressing high schizotypal traits and patients with schizotypal personality disorder (SPD). The purpose of this systematic review was to identify patterns of task-related and resting-state neural abnormalities across these conditions. MEDLINE-PubMed and PsycINFO were systematically searched and forty-eight studies were selected. Forty studies assessed healthy individuals with high schizotypal traits and eight studies examined SPD patients with functional neuroimaging techniques (fNIRS; fMRI; Resting-state fMRI). Functional alterations in striatal, frontal and temporal regions were found in healthy individuals with high schizotypal traits. Schizotypal personality disorder was associated with default mode network abnormalities but further research is required in order to better conceive its neural correlates. There was also evidence for functional compensatory mechanisms associated with both conditions. To conclude, the findings suggest that brain dysfunctions are evident in individuals who lie along the subclinical part of the spectrum, further supporting the continuum model for schizophrenia susceptibility. Additional research is required in order to delineate the counterbalancing processes implicated in the schizophrenia spectrum, as this approach will provide promising insights for both conversion and protection from conversion into schizophrenia.
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Affiliation(s)
- Chrysoula Zouraraki
- Laboratory of Neuropsychology, Department of Psychology, University of Crete, 74100 Rethymno, Greece; (C.Z.); (P.K.)
- University of Crete Research Center for the Humanities, The Social and Education Sciences (UCRC), University of Crete, Gallos University Campus, 74100 Rethymno, Greece
| | - Penny Karamaouna
- Laboratory of Neuropsychology, Department of Psychology, University of Crete, 74100 Rethymno, Greece; (C.Z.); (P.K.)
- University of Crete Research Center for the Humanities, The Social and Education Sciences (UCRC), University of Crete, Gallos University Campus, 74100 Rethymno, Greece
| | - Stella G. Giakoumaki
- Laboratory of Neuropsychology, Department of Psychology, University of Crete, 74100 Rethymno, Greece; (C.Z.); (P.K.)
- University of Crete Research Center for the Humanities, The Social and Education Sciences (UCRC), University of Crete, Gallos University Campus, 74100 Rethymno, Greece
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Wang R, Bashyam V, Yang Z, Yu F, Tassopoulou V, Chintapalli SS, Skampardoni I, Sreepada LP, Sahoo D, Nikita K, Abdulkadir A, Wen J, Davatzikos C. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage 2023; 269:119898. [PMID: 36702211 PMCID: PMC9992336 DOI: 10.1016/j.neuroimage.2023.119898] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/16/2022] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
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Affiliation(s)
- Rongguang Wang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
| | - Vishnu Bashyam
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Fanyang Yu
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Vasiliki Tassopoulou
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Sai Spandana Chintapalli
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Lasya P Sreepada
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Dushyant Sahoo
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Ahmed Abdulkadir
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
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Sabaroedin K, Tiego J, Fornito A. Circuit-Based Approaches to Understanding Corticostriatothalamic Dysfunction Across the Psychosis Continuum. Biol Psychiatry 2023; 93:113-124. [PMID: 36253195 DOI: 10.1016/j.biopsych.2022.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 06/14/2022] [Accepted: 07/17/2022] [Indexed: 11/28/2022]
Abstract
Dopamine is known to play a role in the pathogenesis of psychotic symptoms, but the mechanisms driving dopaminergic dysfunction in psychosis remain unclear. Considerable attention has focused on the role of corticostriatothalamic (CST) circuits, given that they regulate and are modulated by the activity of dopaminergic cells in the midbrain. Preclinical studies have proposed multiple models of CST dysfunction in psychosis, each prioritizing different brain regions and pathophysiological mechanisms. A particular challenge is that CST circuits have undergone considerable evolutionary modification across mammals, complicating comparisons across species. Here, we consider preclinical models of CST dysfunction in psychosis and evaluate the degree to which they are supported by evidence from human resting-state functional magnetic resonance imaging studies conducted across the psychosis continuum, ranging from subclinical schizotypy to established schizophrenia. In partial support of some preclinical models, human studies indicate that dorsal CST and hippocampal-striatal functional dysconnectivity are apparent across the psychosis spectrum and may represent a vulnerability marker for psychosis. In contrast, midbrain dysfunction may emerge when symptoms warrant clinical assistance and may thus be a trigger for illness onset. The major difference between clinical and preclinical findings is the strong involvement of the dorsal CST in the former, consistent with an increasing prominence of this circuitry in the primate brain. We close by underscoring the need for high-resolution characterization of phenotypic heterogeneity in psychosis to develop a refined understanding of how the dysfunction of specific circuit elements gives rise to distinct symptom profiles.
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Affiliation(s)
- Kristina Sabaroedin
- Departments of Radiology and Paediatrics, Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada.
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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Janz P, Bainier M, Marashli S, Schoenenberger P, Valencia M, Redondo RL. Neurexin1α knockout rats display oscillatory abnormalities and sensory processing deficits back-translating key endophenotypes of psychiatric disorders. Transl Psychiatry 2022; 12:455. [PMID: 36307390 PMCID: PMC9616904 DOI: 10.1038/s41398-022-02224-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
Neurexins are presynaptic transmembrane proteins crucial for synapse development and organization. Deletion and missense mutations in all three Neurexin genes have been identified in psychiatric disorders, with mutations in the NRXN1 gene most strongly linked to schizophrenia (SZ) and autism spectrum disorder (ASD). While the consequences of NRXN1 deletion have been extensively studied on the synaptic and behavioral levels, circuit endophenotypes that translate to the human condition have not been characterized yet. Therefore, we investigated the electrophysiology of cortico-striatal-thalamic circuits in Nrxn1α-/- rats and wildtype littermates focusing on a set of translational readouts, including spontaneous oscillatory activity, auditory-evoked oscillations and potentials, as well as mismatch negativity-like (MMN) responses and responses to social stimuli. On the behavioral level Nrxn1α-/- rats showed locomotor hyperactivity. In vivo freely moving electrophysiology revealed pronounced increases of spontaneous oscillatory power within the gamma band in all studied brain areas and elevation of gamma coherence in cortico-striatal and thalamocortical circuits of Nrxn1α-/- rats. In contrast, auditory-evoked oscillations driven by chirp-modulated tones showed reduced power in cortical areas confined to slower oscillations. Finally, Nrxn1α-/- rats exhibited altered auditory evoked-potentials and profound deficits in MMN-like responses, explained by reduced prediction error. Despite deficits for auditory stimuli, responses to social stimuli appeared intact. A central hypothesis for psychiatric and neurodevelopmental disorders is that a disbalance of excitation-to-inhibition is underlying oscillatory and sensory deficits. In a first attempt to explore the impact of inhibitory circuit modulation, we assessed the effects of enhancing tonic inhibition via δ-containing GABAA receptors (using Gaboxadol) on endophenotypes possibly associated with network hyperexcitability. Pharmacological experiments applying Gaboxadol showed genotype-specific differences, but failed to normalize oscillatory or sensory processing abnormalities. In conclusion, our study revealed endophenotypes in Nrxn1α-/- rats that could be used as translational biomarkers for drug development in psychiatric disorders.
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Affiliation(s)
- Philipp Janz
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases Discovery & Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Marie Bainier
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases Discovery & Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Samuel Marashli
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases Discovery & Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Philipp Schoenenberger
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases Discovery & Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Miguel Valencia
- Universidad de Navarra, CIMA, Program of Neuroscience, 31080, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31080, Pamplona, Spain
- Institute of Data Science and Artificial Intelligence, Universidad de Navarra, 31080, Pamplona, Spain
| | - Roger L Redondo
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases Discovery & Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
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Xie S, Zhuo J, Song M, Chu C, Cui Y, Chen Y, Wang H, Li L, Jiang T. Tract-specific white matter microstructural alterations in subjects with schizophrenia and unaffected first-degree relatives. Brain Imaging Behav 2022; 16:2110-2119. [PMID: 35732912 DOI: 10.1007/s11682-022-00681-2] [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] [Accepted: 04/26/2022] [Indexed: 11/26/2022]
Abstract
White matter tracts alterations have been reported in schizophrenia (SZ), but whether such abnormalities are associated with the effects of the disorder itself and/or genetic vulnerability remains unclear. Moreover, the specific patterns of different parts of these altered tracts have been less well studied. Thus, diffusion-weighted images were acquired from 38 healthy controls (HCs), 48 schizophrenia patients, and 33 unaffected first-degree relatives of SZs (FDRs). Diffusion properties of the 25 major tracts automatically extracted with probabilistic tractography were calculated and compared among groups. Regarding the peripheral regions of the tracts, significantly higher diffusivity values in the left superior longitudinal fasciculus (SLF) and the left anterior thalamic radiation (ATR) were observed in SZs than in HCs and unaffected FDRs. However, there were no significant differences between HCs and FDRs in these two tracts. While no main effects of group with respect to the core regions of the 25 tracts survived multiple comparisons correction, FDRs had significantly higher diffusivity values in the left medial lemniscus and lower diffusivity values in the middle cerebellar peduncle than HCs and SZs. These findings enhance the understanding of the abnormal patterns in the peripheral and core regions of the tracts in SZs and those at high genetic risk for schizophrenia. Our results suggest that alterations in the peripheral regions of the left SLF and ATR are features of established illness rather than genetic predisposition, which may serve as critical neural substrates for the psychopathology of schizophrenia.
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Affiliation(s)
- Sangma Xie
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, 310018, Hangzhou, China
| | - Junjie Zhuo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, 570228, Haikou, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, 710032, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, 710032, Xi'an, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, 310018, Hangzhou, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- University of Chinese Academy of Sciences, 100190, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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Walther S, Lefebvre S, Conring F, Gangl N, Nadesalingam N, Alexaki D, Wüthrich F, Rüter M, Viher PV, Federspiel A, Wiest R, Stegmayer K. Limbic links to paranoia: increased resting-state functional connectivity between amygdala, hippocampus and orbitofrontal cortex in schizophrenia patients with paranoia. Eur Arch Psychiatry Clin Neurosci 2022; 272:1021-1032. [PMID: 34636951 PMCID: PMC9388427 DOI: 10.1007/s00406-021-01337-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/22/2021] [Indexed: 11/24/2022]
Abstract
Paranoia is a frequent and highly distressing experience in psychosis. Models of paranoia suggest limbic circuit pathology. Here, we tested whether resting-state functional connectivity (rs-fc) in the limbic circuit was altered in schizophrenia patients with current paranoia. We collected MRI scans in 165 subjects including 89 patients with schizophrenia spectrum disorders (schizophrenia, schizoaffective disorder, brief psychotic disorder, schizophreniform disorder) and 76 healthy controls. Paranoia was assessed using a Positive And Negative Syndrome Scale composite score. We tested rs-fc between bilateral nucleus accumbens, hippocampus, amygdala and orbitofrontal cortex between groups and as a function of paranoia severity. Patients with paranoia had increased connectivity between hippocampus and amygdala compared to patients without paranoia. Likewise, paranoia severity was linked to increased connectivity between hippocampus and amygdala. Furthermore, paranoia was associated with increased connectivity between orbitofrontal and medial prefrontal cortex. In addition, patients with paranoia had increased functional connectivity within the frontal hubs of the default mode network compared to healthy controls. These results demonstrate that current paranoia is linked to aberrant connectivity within the core limbic circuit and prefrontal cortex reflecting amplified threat processing and impaired emotion regulation. Future studies will need to explore the association between limbic hyperactivity, paranoid ideation and perceived stress.
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Affiliation(s)
- Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stephanie Lefebvre
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Frauke Conring
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Nicole Gangl
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Niluja Nadesalingam
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Danai Alexaki
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Florian Wüthrich
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Maximilian Rüter
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Petra V. Viher
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Katharina Stegmayer
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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Heller C, Steinmann S, Levitt JJ, Makris N, Antshel KM, Fremont W, Coman IL, Schweinberger SR, Weiß T, Bouix S, Kubicki MR, Kates WR, Kikinis Z. Abnormalities in white matter tracts in the fronto-striatal-thalamic circuit are associated with verbal performance in 22q11.2DS. Schizophr Res 2020; 224:141-150. [PMID: 33268158 PMCID: PMC7727455 DOI: 10.1016/j.schres.2020.09.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/13/2020] [Accepted: 09/14/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Abnormalities in fronto-striatal-thalamic (FST) sub-circuits are present in schizophrenia and are associated with cognitive impairments. However, it remains unknown whether abnormalities in FST sub-circuits are present before psychosis onset. This may be elucidated by investigating 22q11.2 deletion syndrome (22q11DS), a genetic syndrome associated with a 30% risk for developing schizophrenia in adulthood and a decline in Verbal IQ (VIQ) preceding psychosis onset. Here, we examined white matter (WM) tracts in FST sub-circuits, especially those in the dorsolateral (DLPFC) and ventrolateral prefrontal cortex (VLPFC) sub-circuits, and their associations with VIQ in young adults with 22q11DS. METHODS Diffusion MRI scans were acquired from 21 individuals with 22q11DS with prodromal symptoms of schizophrenia, 30 individuals with 22q11DS without prodromal symptoms, and 30 healthy controls (mean age: 21 ± 2 years). WM tracts were reconstructed between striatum and thalamus with rostral middle frontal gyrus (rMFG) and inferior frontal gyrus (IFG), representing DLPFC and VLPFC respectively. Fractional anisotropy (FA) and radial diffusivity (RD) were used for group comparisons. VIQ was assessed and associations with the diffusion measures were evaluated. RESULTS FA was significantly increased and RD decreased in most tracts of the DLPFC and VLPFC sub-circuits in 22q11DS. Verbal IQ scores correlated negatively with FA and, at trend level, positively with RD in the right thalamus-IFG tract in 22q11DS with prodromal symptoms. CONCLUSIONS While abnormalities in FST sub-circuits are associated with schizophrenia, we observed that these abnormalities are also present in 22q11DS individuals with prodromal symptoms and are associated with verbal performance in the right thalamus-IFG tract.
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Affiliation(s)
- Carina Heller
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; Department of Clinical Psychology, Friedrich-Schiller-University Jena, Germany.
| | - Saskia Steinmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - James J. Levitt
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin M. Antshel
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA,Department of Psychology, Syracuse University, Syracuse, NY, USA
| | - Wanda Fremont
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Ioana L. Coman
- Department of Computer Science, SUNY Oswego, Oswego, NY, USA
| | | | - Thomas Weiß
- Department of Clinical Psychology, Friedrich Schiller University Jena, Germany
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek R. Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Wendy R. Kates
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Zora Kikinis
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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