Search

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.64 years (95% CI: 3.01, 4.26; I2 = 55.28%) compared to controls, after adjusting for age and sex (Cohen’s d = 0.50). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

ObjectivesAlthough the exact neurobiology of obsessive-compulsive disorder (OCD) remains elusive, it has long been recognized that looking at brain networks is key. Graph theoretical analyses have proven valuable to understanding how information is integrated and communicated throughout a complex network. Key players in this process are network hubs, regions densely interconnected with each other forming a ‘rich-club’ within the brain, necessary for efficient communication between distant and/or segregated regions of the network.1Alterations in rich-club organization interfere with higher-order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in OCD suggest lower efficiency of information transfer across the brain.2–4The present study adds to the discussion and investigates rich-club organization and rich-club connectivity as potential markers of OCD, while including the preliminary analyses of a sample of unaffected first-degree relatives, with the aim of prompting further research into familial vulnerability.MethodsThe structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed using diffusion-weighted imaging and probabilistic tractography. Following established methods,1topological and weighted measures of rich-club organization and connectivity were computed. Rich-club regions were selected as the top 16% most highly connected regions displaying rich-club organization at the group level. Global and local measures of network integration (efficiency) and segregation (clustering coefficient) were calculated alongside.ResultsCompared to healthy controls, OCD patients displayed significantly lower topological and weighted rich-club organization, allocating a smaller fraction of all connection weights to the rich-club core. Consistent with previous reports, regions selected at the top 16% rich-club level included the bilateral precuneus, inferior parietal lobule, insula, cingulate gyrus, superior temporal lobe, fusiform gyrus, occipital cortex and, subcortically, bilateral hippocampus, thalamus, caudate and putamen. Global clustering coefficient, local efficiency and clustering of non-rich club nodes were significantly higher in OCD patients. Significant three-group differences emerged, with siblings displaying highest and lowest (never intermediate) values in different measures.ConclusionsOur results suggest weaker structural connectivity between rich-club regions in OCD patients, possibly resulting in lower network integration in favor of higher network segregation. We highlight the need of looking at network-based alterations in brain organization and function when investigating the neurobiological basis of this disorder, and stimulate further research into potential familial protective factors against the development of OCD.Referencesvan den Heuvel MP, Kahn RS, Goni J, Sporns O. High-cost, high-capacity backbone for global brain communication.Proceedings of the National Academy of Sciences2012;109:11372–11377. doi:10.1073/pnas.1203593109Zhong Z, Zhao T, Luo J, Guo Z, Guo M, Li P, Sun J, He Y, Li Z. Abnormal topological organization in white matter structural networks revealed by diffusion tensor tractography in unmedicated patients with obsessive-compulsive disorder.Progress in Neuro-Psychopharmacology and Biological Psychiatry2014;51:39–50. doi:10.1016/j.pnpbp.2014.01.005Reess TJ, Rus OG, Schmidt R, de Reus MA, Zaudig M, Wagner G, Zimmer C, van den Heuvel MP, Koch K. Connectomics-based structural network alterations in obsessive-compulsive disorder.Translational Psychiatry2016;6. doi:10.1038/tp.2016.163Peng Z, Yang X, Xu C, Wu X, Yang Q, Wei Z, Zhou Z, Verguts T, Chen Q. Aberrant rich club organization in patients with obsessive-compulsive disorder and their unaffected first-degree relatives.NeuroImage: Clinical2021;32:102808.

WOS: 000485217300010, PubMed ID: 31443932, BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +10.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct., Research Council of NorwayResearch Council of Norway [223273]; National Institutes of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 MH117601, R01 MH116147, R01 MH111671, P41 EB015922, 5T32MH073526, U54EB020403, R03 MH105808]; National Institute on Aging (NIA)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 341717]; National Centre for Mental Health; 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award [17319]; Dokuz Eylul University Department of Scientific Research Projects FundingDokuz Eylul University [2012.KB.SAG.062]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London; Ege University School of Medicine Research Foundation [2009-D-00017]; Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Geestkracht program of the Netherlands Organisation for Health Research and Development [10-000-1002]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH 085667]; Nova Scotia Health Research Foundation; Dalhousie Clinical Research Scholarship; 2007 Brain and Behavior Research Foundation Young Investigator Award; Ministry of Health of the Czech RepublicMinistry of Health, Czech Republic [NR8786, NT13891]; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Stockholm County CouncilStockholm County Council; Karolinska InstitutetKarolinska Institutet; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; HUBIN project; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [PI070066, PI1100683, PI1500467]; Fundacio Marato TV3 [091630]; ERDF Funds from the European Commission A Way of Making Europe"); Brain and Behaviour Research Foundation (NARSAD Young Investigator Award); Alicia Koplowitz Foundation; Stanley Medical Research Institute; NARSADNARSAD [20244]; Wellcome TrustWellcome Trust [064971, 085475/B/08/Z, 085475/Z/08/Z]; NARSAD Young Investigator AwardNARSAD; European CommunityEuropean Community (EC); NIMH Intramural Research ProgramUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH); NIHR Biomedical Research Centre at University College London Hospital; British Medical Association Margaret Temple Fellowship 2016; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF) [O1ZX1314B, O1ZX1314G]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; NIMHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH116147, R01 MH113619]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [U01 MH097435, R01 MH084803, R01 EB020062, R01 MH080912, R01 MH052857]; National Science FoundationNational Science Foundation (NSF) [1636893, 1734853]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [510135, 1037196, 1063960, 1066177]; ZonMwNetherlands Organization for Health Research and Development [908-02-123]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Janssen PharmaceuticaJohnson & Johnson USAJanssen Biotech Inc; Otsuka PharmaceuticalOtsuka Pharmaceutical; Bipolar Disorder Research Network, The researchers and studies included in this article were supported by the Research Council of Norway (Grant No. 223273), National Institutes of Health (NIH) (Grant No. R01 MH117601 [to NJ], Grant Nos. R01 MH116147, R01 MH111671, and P41 EB015922 [to PMT], Grant Nos. 5T32MH073526 and U54EB020403 [to CRKC], and Grant No. R03 MH105808 [to CEB and SCF]) and National Institute on Aging (NIA) (Grant No. T32AG058507 [to CRKC]).; C-SFS: This work was supported by Canadian Institutes of Health Research.; Cardiff: This work was supported by the National Centre for Mental Health, Bipolar Disorder Research Network, 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (Grant No. 17319).; DEU: This work was supported by Dokuz Eylul University Department of Scientific Research Projects Funding (Grant No. 2012. KB. SAG. 062). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.; EGEU: This work was supported by the Ege University School of Medicine Research Foundation (Grant No. 2009-D-00017).; EHRS: The Edinburgh High Risk Study was supported by the Medical Research Council.; GROUP: The infrastructure for the GROUP study was supported by the Geestkracht program of the Netherlands Organisation for Health Research and Development (Grant No. 10-000-1002).; ENBD_UT/BPO_FLB: This work was supported by the National Institute of Mental Health (Grant No. R01 MH 085667).; HHR/PHHR: This work was supported by the Canadian Institutes of Health Research (Grant Nos. 103703, 106469, and 341717), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship (to TH), 2007 Brain and Behavior Research Foundation Young Investigator Award (to TH), and Ministry of Health of the Czech Republic (Grant Nos. NR8786 and NT13891).; HUBIN: This work was supported by the Swedish Research Council (Grant Nos. K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3), regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet, Knut and Alice Wallenberg Foundation, and HUBIN project.; IDIBAPS: This work was supported by the Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI070066, PI1100683, and PI1500467) and Fundacio Marato TV3 (Grant No. 091630), co-financed by ERDF Funds from the European Commission A Way of Making Europe"), Brain and Behaviour Research Foundation (NARSAD Young Investigator Award), and Alicia Koplowitz Foundation.; IoP-BD: The Maudsley Bipolar Twin Study was supported by the Stanley Medical Research Institute and NARSAD.; IoP-SZ: This work was supported by a Wellcome Trust Research Training Fellowship (Grant No. 064971 to MMP), NARSAD Young Investigator Award (to TT), and European Community's Sixth Framework Programme through a Marie Curie Training Network called the European Twin Study Network on Schizophrenia.; Lieber Institute for Brain Development (LIBD): This work was supported by the NIMH Intramural Research Program (to DRW's laboratory). LIBD is a nonprofit research institute located in Baltimore, MD. The work performed at LIBD was performed in accordance with an NIMH material transfer agreement with LIBD.; MFS: The Maudsley Family Study cohort collection was supported by the Wellcome Trust (Grant Nos. 085475/B/08/Z and 085475/Z/08/Z), NIHR Biomedical Research Centre at University College London Hospital, Medical Research Council (Grant No. G0901310), and British Medical Association Margaret Temple Fellowship 2016.; MooDS: This work was supported by the German Federal Ministry for Education and Research grants NGFNplus MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia) and Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e: Med program (Grant Nos. O1ZX1314B and O1ZX1314G) and Deutsche Forschungsgemeinschaft (Grant No. 1617 [to AH]).; MSSM: This work was supported by NIMH (Grant Nos. R01 MH116147 and R01 MH113619).; NU: This work was supported by NIH (Grant Nos. U01 MH097435, R01 MH084803, and R01 EB020062) and National Science Foundation (Grant Nos. 1636893 and 1734853).; OLIN: This work was supported by NIH (Grant No. R01 MH080912).; STAR: This work was supported by NIH (Grant No. R01 MH052857).; SydneyBipolarGroup: The Australian cohort collection was supported by the Australian National Health and Medical Research Council Program Grants (Grant No. 510135 [to PBM] and Grant No. 1037196 [to PBM and PRS]) and Project Grants (Grant No. 1063960 [to JMF and PRS] and Grant No. 1066177 [to JMF]).; UMCU: This work was supported by NARSAD (Grant No. 20244 [to MHJH]), ZonMw (Grant No. 908-02-123 [to HEHP]), VIDI (Grant No. 452-11-014 [to NEMvH] and Grant No. 917-46-370 [to HEHP]), and Stanley Medical Research Institute.; CliNG: We thank Anna Fanelli, Kathrin Jakob, and Maria Keil for help with data acquisition.; All authors have contributed to and approved the contents of this manuscript.; GS has received research and travel support from Janssen Pharmaceutica and Otsuka Pharmaceutical and honoraria from Adamed Pharma. NY has been an investigator in clinical studies conducted together with Janssen-Cilag, Corcept Therapeutics, and COMPASS Pathways in the last 3 years. AM-L has received consultant fees from Boehringer Ingelheim, BrainsWay, Elsevier, Lundbeck International Neuroscience Foundation, and Science Advances. CRKC has received partial research support from Biogen, Inc. (Boston, MA) for work unrelated to the topic of this manuscript. The remaining authors report no biomedical financial interests or potential conflicts of interest.

BackgroundDepression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed – but non-help-seeking and unmedicated – individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.MethodsIndividuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.ResultsSignificant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = −0.017, p = 0.006) and putamen (b = −0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).ConclusionsStriatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.

Background: Studying young adults in the prodromal phase of psychotic disorder could provide information on the development of psychosis and contribute to early interventions. Previous research in patients with psychotic disorder shows alterations in the brain’s reward system (ventral medial prefrontal cortex (VMPFC), ventral striatum (VS), nucleus accumbens, caudate, putamen, ventral tegmental area (VTA)) using the monetary incentive delay task (MIDt). Reward anticipation is the expectation of reward and therefore a positive reinforcement of an action. It is hypothesized in a prodromal psychotic experiences (PE) group that reward anticipation is reduced.

We examined age-related changes in the cerebral white matter. Structural magnetic resonance images (MRIs) and diffusion tensor images (DTIs) were acquired from 69 healthy subjects aged 22-84 years. Quantitative DTI tractography was performed for nine different white matter tracts to determine tract volume, fractional anisotropy (FA), mean diffusivity (MD), axial, and radial diffusivities. We used automated and manual segmentation to determine volumes of gray matter (GM), white mater (WM), cerebrospinal fluid (CSF), and intracranial space. The results showed significant effects of aging on WM, GM, CSF volumes, and selective effects of aging on structural integrity of different white matter tracts. WM of the prefrontal region was the most vulnerable to aging, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. This study was cross-sectional, and therefore, additional longitudinal studies are needed to confirm our findings.

Previous diffusion tensor imaging (DTI) studies confirmed the vulnerability of frontal callosal fibers to normal aging. The present study extended this examination systematically to other prefrontal white matter regions. Structural magnetic resonance imaging and DTI datasets were acquired from 69 healthy subjects aged 22–84 years. The prefrontal white matter was parcellated into several anatomical sub-regions: medial and lateral orbitofrontal white matter, dorsolateral prefrontal white matter, and medial prefrontal white matter, using reliable DTI-tractography protocols. Tract-specific characteristics were calculated using Matlab. Regression models were used to determine the relationship between age and structural integrity of white matter tracts. The results of our study demonstrate regional age-related changes in the prefrontal white matter tracts of the human brain. This study was cross-sectional and therefore additional longitudinal studies are needed to confirm our findings.