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INTRODUCTION: Parkinson's disease (PD) is a heterogeneous disorder with great variability in motor and non-motor manifestations. It is hypothesized that different motor subtypes are characterized by different neuropsychiatric and cognitive symptoms, but the underlying correlates in cerebral connectivity remain unknown. Our aim is to compare brain network connectivity between the postural instability and gait disorder (PIGD) and tremor-dominant (TD) subtypes, using both a within- and between-network analysis.METHODS: This cross-sectional resting-state fMRI study includes 81 PD patients, 54 belonging to the PIGD and 27 to the TD subgroup. Group-level spatial maps were created using independent component analysis. Differences in functional connectivity were investigated using dual regression analysis and inter-network connectivity analysis. An additional voxel-based morphometry analysis was performed to examine if results were influenced by grey matter atrophy.RESULTS: The PIGD subgroup scored worse than the TD subgroup on all cognitive domains. Resting-state fMRI network analyses suggested that the connection between the visual and sensorimotor network is a potential differentiator between PIGD and TD subgroups. However, after correcting for dopaminergic medication use these results were not significant anymore. There was no between-group difference in grey matter volume.CONCLUSION: Despite clear motor and cognitive differences between the PIGD and TD subtypes, no significant differences were found in network connectivity. Methodological challenges, substantial symptom heterogeneity and many involved variables make analyses and hypothesis building around PD subtypes highly complex. More sensitive visualisation methods combined with machine learning approaches may be required in the search for characteristic underpinnings of PD subtypes.

Rich-club organization is key to efficient global neuronal signaling and integration of information. Alterations interfere with higher-order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in obsessive–compulsive disorder (OCD) suggest lower efficiency of information transfer across the brain. However, it remains unclear whether this is due to alterations in rich-club organization. In the current study, the structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed by means of diffusion-weighted imaging and probabilistic tractography. Topological and weighted measures of rich-club organization and connectivity were computed, alongside global and nodal measures of network integration and segregation. The relationship between clinical scores and network properties was explored. Compared 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. Global clustering coefficient, local efficiency, and clustering of nonrich club nodes were significantly higher in OCD patients. Significant three-group differences emerged, with siblings displaying highest and lowest values in different measures. No significant correlation with any clinical score was found. Our results suggest weaker structural connectivity between rich-club nodes 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.

Contains fulltext : 291574.pdf (Publisher’s version ) (Open Access) Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; 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]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123

PURPOSE: Early detection and intervention of mental health problems in youth are topical given that mental disorders often start early in life. Young people with emerging mental disorders however, often present with non-specific, fluctuating symptoms. Recent reports indicate a decline in social functioning (SF) as an early sign of specific emerging mental disorders such as depression or anxiety, making SF a favorable transdiagnostic approach for earlier detection and intervention. Our aim was to investigate the value of SF in relation to transdiagnostic symptoms, and as a predictor of psychopathology over time, while exploring traditional retrospective versus innovative daily diary measurements of SF in youth. METHOD: Participants (N = 75) were 16-25 years of age and presented early stage psychiatric symptomatology. Psychiatric symptoms, including anxiety and depression, as well as SF -both in retrospect and in daily life- were assessed at two time points and analyzed cross-sectionally and longitudinally. RESULTS: A significant and negative association between SF and all psychiatric symptoms was found, and SF was a significant predictor of change in general psychiatric symptoms over time. Results were only significant when SF was measured traditionally retrospective. CONCLUSION: This study confirms a distinct relation between SF and transdiagnostic psychiatric symptoms in youth, even in a (sub)clinical population, and points towards SF as a predictor of transdiagnostic psychiatric symptoms. Further research is needed to learn more about the added value of daily life versus retrospective measurements. ispartof: COMPREHENSIVE PSYCHIATRY vol:115 ispartof: location:United States status: published

Background The neuroanatomical substrates of Parkinson's disease (PD) with tremor‐dominance (TD) and those with non‐tremor dominance (nTD), postural instability and gait difficulty (PIGD), and akinetic‐rigid (AR) are not fully differentiated. A better understanding of symptom specific pathoanatomical markers of PD subtypes may result in earlier diagnosis and more tailored treatment. Here, we aim to give an overview of the neuroimaging literature that compared PD motor subtypes. Methods A systematic literature review on neuroimaging studies of PD subtypes was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Search terms submitted to the PubMed database included: “Parkinson's disease”, “MRI” and “motor subtypes” (TD, nTD, PIGD, AR). The results are first discussed from macro to micro level of organization (i.e., (1) structural; (2) functional; and (3) molecular) and then by applied imaging methodology. Findings Several neuroimaging methods including diffusion imaging and positron emission tomography (PET) distinguish specific PD motor subtypes well, although findings are mixed. Furthermore, our review demonstrates that nTD‐PD patients have more severe neuroalterations compared to TD‐PD patients. More specifically, nTD‐PD patients have deficits within striato‐thalamo‐cortical (STC) circuitry and other thalamocortical projections related to cognitive and sensorimotor function, while TD‐PD patients tend to have greater cerebello‐thalamo‐cortical (CTC) circuitry dysfunction. Conclusions Based on the literature, STC and CTC circuitry deficits seem to be the key features of PD and the subtypes. Future research should make greater use of multimodal neuroimaging and techniques that have higher sensitivity in delineating subcortical structures involved in motor diseases.

Group comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and network connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as network connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery–Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and network connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and network connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with network efficiency/cohesion, which requires replication and may indicate network alterations in emerging mild psychopathology. Electronic supplementary material The online version of this article (10.1007/s11682-019-00129-0) contains supplementary material, which is available to authorized users.

Extinction learning is assumed to represent a core mechanism underlying exposure therapy. Empirical evaluations of this assumption, however, are largely lacking. The current study investigated whether neural activations and self-report outcomes during extinction learning and extinction recall could specifically predict exposure therapy response in specific phobia. In this double-blind randomized controlled trial, individuals with spider phobia (N = 45; female/male = 41/4) were on group basis randomly allocated to exposure therapy (n = 25; female/male = 24/1) or progressive muscle relaxation (PMR; n = 20; female/male = 17/3). Intervention effects were measured with the Fears of Spiders questionnaire. Participants also underwent a three-day fear conditioning, extinction learning, and extinction recall paradigm during functional magnetic resonance imaging at baseline. Extinction outcomes were self-reported fear and threat expectancy, and neural responses during conditioned stimulus processing and during extinction-related prediction errors (US omissions) in regions of interest (ventromedial prefrontal cortex (vmPFC) and nucleus accumbens). Results showed that exposure therapy resulted in stronger symptom reductions than PMR (Cohen's d = 0.90). Exposure therapy response was specifically predicted by prediction-error related vmPFC activation during early extinction. There were also indications vmPFC activations during conditioned safety stimulus processing at early extinction predicted therapy outcome. Neural activations during extinction recall and self-report data did however not predict therapy outcome. These findings indicate that exposure therapy may rely on neural extinction learning processes. Prediction errors are thought to drive the extinction learning process, and prediction error-related vmPFC activation specifically predicted therapy outcome. The extent to which vmPFC processes safety signals may additionally be predictive of exposure therapy response, but the specificity is less clear.

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.

Previous research in patients with psychotic disorder has shown widespread abnormalities in brain activation during reward anticipation. Research at the level of subclinical psychotic experiences in individuals unexposed to antipsychotic medication is limited with inconclusive results. Therefore, brain activation during reward anticipation was examined in a larger sample of individuals with subclinical psychotic experiences (PE). Participants in the PE-group were included based on CAPE scores. A sample of emerging adults aged 16-26 years (n = 47) with PE and healthy controls (HC) (n = 40) underwent fMRI scanning. The Monetary Incentive Delay task was conducted with cues related to win, loss or neutral conditions. fMRI nonparametric tests were used to examine the reward versus neutral cue contrast. A significant main effect of the large win ((sic)3.00) > neutral contrast was found in both groups showing activation in many brain areas, including classic reward regions. Whole brain analysis on the group comparison regarding the large win > neutral contrast showed significantly decreased activation in the right insula, putamen and supramarginal gyrus in the PE-group compared to controls. There was no group difference in the hypothesized reward-related region. Decreased activation in the right insula, putamen and supramarginal gyrus during reward anticipation in individuals with PE may be consistent with altered processing of sensory information, related to decreased emotional valuing and motivational tendencies and/or altered motor-cognitive processes. The absence of group differences in striatal activation suggests that activation here is intact in the earliest stages of psychosis and may exhibit progressive deterioration in as the disease develops. (C) 2019 Elsevier B.V. and ECNP. All rights reserved.

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.

BACKGROUND: The emerging field of ultra-high field MRI (UHF-MRI, 7 Tesla and higher) provides the opportunity to image human brains at a higher resolution and with higher signal-to-noise ratios compared to the more widely available 1.5 and 3T scanners. Scanning postmortem tissue additionally allows for greatly increased scan times and fewer movement issues leading to improvements in image quality. However, typical postmortem neuroimaging routines involve placing the tissue within plastic bags that leave room for susceptibility artifacts from tissue-air interfaces, inadequate submersion, and leakage issues. To address these challenges in postmortem imaging, a custom-built nonferromagnetic container was developed that allows whole brain hemispheres to be scanned at sub-millimeter resolution within typical head-coils.METHOD: The custom-built polymethylmethacrylaat container consists of a cylinder with a hemispheric side and a lid with valves on the adjacent side. This shape fits within common MR head-coils and allows whole hemispheres to be submerged and vacuum sealed within it reducing imaging artifacts that would otherwise arise at air-tissue boundaries. Two hemisphere samples were scanned on a Siemens 9.4T Magnetom MRI scanner. High resolution T2* weighted data was obtained with a custom 3D gradient echo (GRE) sequence and diffusion-weighted imaging (DWI) scans were obtained with a 3D kT-dSTEAM sequence along 48 directions.RESULTS: The custom-built container proved to submerge and contain tissue samples effectively and showed no interferences with MR scanning acquisition. The 3D GRE sequence provided high resolution isotropic T2* weighted data at 250μm which showed a clear visualization of grey and white matter structures. DWI scans allowed for dense reconstruction of structural white matter connections via tractography.CONCLUSION: Using this custom-built container worked towards achieving high quality MR images of postmortem brain material. This procedure can have advantages over traditional schemes including utilization of a standardized protocol and the reduced likelihood of leakage. This methodology could be adjusted and used to improve typical postmortem imaging routines.

Background: Childhood maltreatment is a transdiagnostic risk factor for later psychopathology and has been associated with altered brain circuitry involved in the processing of threat and safety. Examining threat generalization mechanisms in young adults with childhood maltreatment and psychiatric symptoms may elucidate a pathway linking early-life adversities to the presence of subclinical psychopathology Methods: We recruited youth aged 16–25 years with subclinical psychiatric symptomatology and healthy controls. They were dichotomized into 2 groups: 1 with a high level of childhood maltreatment (n = 58) and 1 with no or a low level of childhood maltreatment (n = 55). Participants underwent a functional MRI threat generalization paradigm, measuring self-reported fear, expectancy of an unconditioned stimulus (US) and neural responses. Results: We observed interactions between childhood maltreatment and threat generalization indices on subclinical symptom load. In individuals reporting high levels of childhood maltreatment, enhanced generalization in self-reported fear and US expectancy was related to higher levels of psychopathology. Imaging results revealed that in the group with high levels of childhood maltreatment, lower activation in the left hippocampus during threat generalization was associated with a higher symptom load. Associations between threat generalization and psychopathology were nonsignificant overall in the group with no or low levels of childhood maltreatment. Limitations: The data were acquired in a cross-sectional manner, precluding definitive insight into the causality of childhood maltreatment, threat generalization and psychopathology. Conclusion: Our results suggest that threat generalization mechanisms may moderate the link between childhood maltreatment and subclinical psychopathology during emerging adulthood. Threat generalization could represent a vulnerability factor for developing later psychopathology in individuals being exposed to childhood maltreatment.

Background The diagnosis of Parkinson’s Disease (PD) remains a challenge and is currently based on the assessment of clinical symptoms. PD is also a heterogeneous disease with great variability in symptoms, disease course, and response to therapy. There is a general need for a better understanding of this heterogeneity and the interlinked long-term changes in brain function and structure in PD. Over the past years there is increasing interest in the value of new paradigms in Magnetic Resonance Imaging (MRI) and the potential of ultra-high field strength imaging in the diagnostic work-up of PD. With this multimodal 7 T MRI study, our objectives are: 1) To identify distinctive MRI characteristics in PD patients and to create a diagnostic tool based on these differences. 2) To correlate MRI characteristics to clinical phenotype, genetics and progression of symptoms. 3) To detect future imaging biomarkers for disease progression that could be valuable for the evaluation of new therapies. Methods The TRACK-PD study is a longitudinal observational study in a cohort of 130 recently diagnosed (≤ 3 years after diagnosis) PD patients and 60 age-matched healthy controls (HC). A 7 T MRI of the brain will be performed at baseline and repeated after 2 and 4 years. Complete assessment of motor, cognitive, neuropsychiatric and autonomic symptoms will be performed at baseline and follow-up visits with wearable sensors, validated questionnaires and rating scales. At baseline a blood DNA sample will also be collected. Discussion This is the first longitudinal, observational, 7 T MRI study in PD patients. With this study, an important contribution can be made to the improvement of the current diagnostic process in PD. Moreover, this study will be able to provide valuable information related to the different clinical phenotypes of PD and their correlating MRI characteristics. The long-term aim of this study is to better understand PD and develop new biomarkers for disease progression which may help new therapy development. Eventually, this may lead to predictive models for individual PD patients and towards personalized medicine in the future. Trial registration Dutch Trial Register, NL7558. Registered March 11, 2019.

In this study, the feasibility and efficacy of Acceptance and Commitment Therapy in Daily Life (ACT-DL), ACT augmented with a daily life application, was investigated in 55 emerging adults (age 16 to 25) with subthreshold depressive and/or psychotic complaints. Participants were randomized to ACT-DL (n = 27) or to active control (n = 28), with assessments completed at pre- and post-measurement and 6- and 12-months follow-up. It took up to five (ACT-DL) and 11 (control) months to start group-based interventions. Participants attended on average 4.32 out of 5 ACT-DL sessions. On the app, they filled in on average 69 (48%) of signal-contingent beep-questionnaires, agreed to 15 (41%) of offered beep-exercises, initiated 19 on-demand exercises, and rated ACT-DL metaphors moderately useful. Relative to active control, interviewer-rated depression scores decreased significantly in ACT-DL participants (p = .027). Decreases in self-reported depression, psychotic-related distress, anxiety, and general psychopathology did not differ between conditions. ACT-DL participants reported increased mean NA (p = .011), relative to active controls. Mean PA did not change in either group, nor did psychological flexibility. ACT-DL is a feasible intervention, although adaptations in future research may improve delivery of and compliance with the intervention. There were mixed findings for its efficacy in reducing subthreshold psychopathology in emerging adults. Dutch Trial Register no.: NTR3808. ispartof: BEHAVIOUR RESEARCH AND THERAPY vol:128 ispartof: location:England status: published

Background: Diffusion tensor imaging (DTI) studies in psychotic disorder have shown reduced FA, often interpreted as disturbed white matter integrity. The observed 'dysintegrity' may be of multifactorial origin, as changes in FA are thought to reflect a combination of changes in myelination, fiber organization and number of axons. Examining the structural substrate of the diffusion tensor in individuals with (risk for) psychotic disorder may provide better understanding of the underlying structural changes.Methods: DTI scans were acquired from 85 patients with psychotic disorder, 93 siblings of patients with psychotic disorder and 80 controls. Cross-sectional group comparisons were performed using Tract -Based Spatial Statistics (TBSS) on six DTI measures: axial diffusivity (AXD), radial diffusivity (RD), mean diffusivity (MD), and the case linear (CL), case planar (CP) and case spherical (CS) tensor shape measures.Results: AXD did not differ between the groups. RD and CS values were significantly increased in patients compared to controls and siblings, with no significant differences between the latter two groups. MD was higher in patients compared to controls (but not siblings), with no difference between siblings and controls. CL was smaller in patients than in siblings and controls, and CP was smaller in both patients and siblings as compared to controls.Conclusion: The differences between individuals with psychotic disorder and healthy controls, derived from detailed diffusion data analyses, suggest less fiber orientation and increased free water movement in the patients. There was some evidence for association with familial risk expressed by decreased fiber orientation. (C) 2017 Elsevier B.V. All rights reserved.

Subclinical symptoms of depression are common in emerging adults. Anhedonia is one such symptom that specifically puts one at risk for developing clinical depression. Recently, important progress has been made in elucidating the underlying neurobiology of anhedonia. This progress rests on many experimental studies examining how subjects with depressive symptoms respond to anticipating and consuming rewarding stimuli. Translating these findings to real-life reward processing dynamics is an important next step in order to guide fine-tuning of preventive treatments. We propose that the Experience Sampling Methodology (ESM) represents a useful tool in addressing this issue. ESM requires individuals to carry a device that beeps at semirandom moments, inviting them to fill out a short questionnaire on mood, context, and behavior. Using this methodology, we aimed to decompose the construct of reward processing into its daily life dynamics, by investigating how positive affect (PA), reward anticipation and active behavior influence each other over time. A group of emerging adults (aged 16-25) was included, of which two-thirds presented with subclinical depressive symptoms. Associations between PA, reward anticipation and active behavior manifested in the flow of daily life. Depressive symptoms were significantly associated with reduced time-lagged associations between reward anticipation and active behavior (beta = -.005, p = .006) and active behavior and reward anticipation (beta = -.002, p = .027). The moderating effect of depressive symptoms on the time-lagged association between reward anticipation and PA approached significance (beta = -.002, p = .051). These findings represent an important step in translating experimental knowledge on reward processing into daily life processes.General Scientific SummaryThe current study suggests that findings from carefully controlled laboratory studies regarding the experience of positive emotions can be translated into day-to-day experiences and behaviors. This was investigated in a group of young people experiencing mild depressive symptoms. We suggest that this information can help the development of treatments that prevent the worsening of these symptoms.

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.

Background: Theoretical models have implicated classical fear conditioning, fear generalization, and extinction learning in the development of anxiety disorders. To date, it is largely unknown to what extent these mechanisms and the underlying neurobiology may be altered in specific phobia, a disorder characterized by focal fears. The current study systematically examined fear conditioning, fear generalization, extinction learning, and extinction recall in a sample of individuals with a specific phobia.Methods: Participants with a specific phobia (SP) of spiders (n = 46) and healthy controls (HC) (n = 48) underwent a 3-day fMRI cue-conditioning protocol, including a fear acquisition and a fear generalization phase (day 1), an extinction learning phase (day 2), and an extinction recall phase (day 3). Stimuli were phobia-irrelevant, as geometrical shapes served as conditioned threat (CS + ) and safety stimuli (CS-), and an electrical shock as the unconditioned stimulus (US). Self-reported fear, US expectancy, and blood-oxygen-level dependent responses were measured.Results: Behavioral results only revealed enhanced CS + /CS-differentiation in fear scores during acquisition retention in SP. Some neural differences were observed during other task phases. During early fear acquisition, SP showed enhanced differential activation in the angular gyrus and lateral occipital cortex, and during extinction recall, more precuneus deactivation was found in SP compared to HC. There were no clear indications of altered neural fear generalization or extinction learning mechanisms in the SP group.Conclusions: Results indicate that spider phobia may be characterized by enhanced differential fear retention and altered brain activation patterns during fear acquisition and extinction recall. The findings provide insight into the nature of fear learning alterations in specific phobia, and how these may differ from those found in disorders characterized by broad anxious distress.

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.

Fear generalization is a prominent feature of anxiety disorders and post-traumatic stress disorder (PTSD). It is defined as enhanced fear responding to a stimulus that bears similarities, but is not identical to a threatening stimulus. Pattern separation, a hippocampal-dependent process, is critical for stimulus discrimination; it transforms similar experiences or events into non-overlapping representations. This study is the first in humans to investigate the extent to which fear generalization relies on behavioral pattern separation abilities. Participants (N = 46) completed a behavioral task taxing pattern separation, and a neuroimaging fear conditioning and generalization paradigm. Results show an association between lower behavioral pattern separation performance and increased generalization in shock expectancy scores, but not in fear ratings. Furthermore, lower behavioral pattern separation was associated with diminished recruitment of the subcallosal cortex during presentation of generalization stimuli. This region showed functional connectivity with the orbitofrontal cortex and ventromedial prefrontal cortex. Together, the data provide novel experimental evidence that pattern separation is related to generalization of threat expectancies, and reduced fear inhibition processes in frontal regions. Deficient pattern separation may be critical in overgeneralization and therefore may contribute to the pathophysiology of anxiety disorders and PTSD. ispartof: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE vol:12 issue:11 pages:1720-1729 ispartof: location:England status: published

Background Although widespread reduced white matter (WM) integrity is a consistent finding in cross-sectional diffusion tensor imaging (DTI) studies of schizophrenia, little is known about the course of these alterations. This study examined to what degree microstructural WM alterations display differential trajectories over time as a function of level of psychosis liability. Methods Two DTI scans with a 3-year time interval were acquired from 159 participants (55 patients with a psychotic disorder, 55 nonpsychotic siblings and 49 healthy controls) and processed with tract-based spatial statistics. The mean fractional anisotropy (FA) change over time was calculated. Main effects of group, as well as group × region interactions in the model of FA change were examined with multilevel (mixed-effects) models. Results Siblings revealed a significant mean FA decrease over time compared to controls (B = −0.004, P = .04), resulting in a significant sibling-control difference at follow-up (B = −0.007, P = .03). Patients did not show a significant change over time, but their mean FA was lower than controls both at baseline and at follow-up. A significant group × region interaction (χ2 = 105.4, P = .01) revealed group differences in FA change in the right cingulum, left posterior thalamic radiation, right retrolenticular part of the internal capsule, and the right posterior corona radiata. Conclusion Whole brain mean FA remained stable over a 3-year period in patients with psychotic disorder and declined over time in nonaffected siblings, so that at follow-up both groups had lower FA with respect to controls. The results suggest that liability for psychosis may involve a process of WM alterations.

S100B is a protein with dose-dependent neurotrophic and neurotoxic effects. Whether S100B in psychotic disorder mirrors pathophysiological mechanisms (which elicit exacerbation of disease) or compensatory action is unclear, as is its validity as a proxy marker for brain status. Insight may be gained by examining associations between serum S100B and indices of grey (cortical thickness (CT)) and white matter (fractional anisotropy (FA)), in relation to the absence or presence of (increased risk of) psychotic disorder. Blood samples and cerebral magnetic resonance imaging (MRI) scans were acquired in 32 patients with psychotic disorder, 44 non-psychotic siblings of patients with psychotic disorder and 26 controls. Interactions between S100B and group were examined in separate models of CT and FA measures with multilevel regression analyses weighted for number of vertices and voxels (i.e. units of volume) respectively. All analyses were adjusted for sex, age, body mass index (BMI), scan sequence, handedness and highest level of education. Neither CT nor FA was associated with S100B. There were no significant S100B x group interactions (CT: chi(2) = 0.044, p = 0.978; FA: chi(2) = 3.672, p = 0.159). No evidence was present for S100B as a proxy marker of grey or white matter status. The association between S100B and brain measures was not moderated by psychosis risk.

BackgroundDecreased white matter (WM) integrity in patients with psychotic disorder has been a consistent finding in diffusion tensor imaging (DTI) studies. However, the contribution of environmental risk factors to these WM alterations is rarely investigated. The current study examines whether individuals with (increased risk for) psychotic disorder will show increased WM integrity change over time with increasing levels of childhood trauma and cannabis exposure.MethodsDTI scans were obtained from 85 patients with a psychotic disorder, 93 non-psychotic siblings and 80 healthy controls, of which 60% were rescanned 3 years later. In a whole-brain voxel-based analysis, associations between change in fractional anisotropy (ΔFA) and environmental exposures as well as interactions between group and environmental exposure in the model of FA and ΔFA were investigated. Analyses were adjusted for a priori hypothesized confounding variables: age, sex, and level of education.ResultsAt baseline, no significant associations were found between FA and both environmental risk factors. At follow-up as well as over a 3-year interval, significant interactions between group and, respectively, cannabis exposure and childhood trauma exposure in the model of FA and ΔFA were found. Patients showed more FA decrease over time compared with both controls and siblings when exposed to higher levels of cannabis or childhood trauma.ConclusionsHigher levels of cannabis or childhood trauma may compromise connectivity over the course of the illness in patients, but not in individuals at low or higher than average genetic risk for psychotic disorder, suggesting interactions between the environment and illness-related factors.

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.

article i nfo Background: Bone mineral density (BMD), as an indicator of cumulative estrogen exposure, may be reduced in female patients with psychotic disorder (van der Leeuw et al., 2013), possibly reflecting reduced cerebral exposure to estrogen and alterations in neuroprotective effects. To the degree that BMD is a marker of cu- mulative (endogenous) estrogen exposure, we hypothesized that BMD would be positively associated with cerebral gray and white matter indices. Methods: Dual X-ray absorptiometry (DEXA) and magnetic resonance (MRI) scans were acquired in fourteen fe- male patients diagnosed with a psychotic disorder. BMD was expressed in total BMD (g/cm 2 ), Z- and T-scores. Cerebral cortical thickness (CT) (as indicator of gray matter status) and fractional anisotropy (FA) (as indicator of white matter integrity) were measured and served as the dependent variables in multilevel random regres- sion models. BMD measures were the independent variables. Results: Femoral BMD measures were positively associated with CT at trend significance (total BMD: B = 0.266, 95%CI:�0.019-0.552,p = 0.067;Z-score:B = 0.034,95%CI:0.001-0.067,p = 0.046;T-score:B = 0.034,95% CI: 0.000-0.068, p = 0.052). There were no significant associations between femoral BMD measures and FA. Conclusions: The data suggest that in women with psychotic disorder, alterations in the neuroprotective effect of estrogen (as measuredbyBMD)impact corticalgraymatter, but not white matter integrity.These findingsmerit further investigation and, if replicated, would lend support to the estrogen hypothesis of schizophrenia.

Background There is evidence for microstructural white matter alterations in patients with psychotic disorder, suggesting altered interregional connectivity. Less is known about the presence and role of white matter alterations in well individuals at higher than average genetic risk for psychotic disorder. Methods 85 patients with psychotic disorder, 93 non-psychotic siblings of patients with psychotic disorder and 80 healthy controls underwent a diffusion tensor imaging (DTI) scanning protocol. In a whole brain voxel-based analysis using Tract Based Spatial Statistics (TBSS), fractional anisotropy (FA) values were compared between the three groups. Effects of antipsychotic medication and drug use were examined. Results The patients displayed significantly lower mean FA than the controls in the following regions: corpus callosum (genu, body, splenium), forceps major and minor, external capsule bilaterally, corona radiata (anterior, posterior) bilaterally, left superior corona radiata and posterior thalamic radiation bilaterally. Similar FA differences existed between the patients and siblings; the siblings did not differ from the controls. Conclusion Profound microstructural white matter alterations were found in the corpus callosum and other tracti and fasciculi in the patients with psychotic disorder, but not in siblings and the controls. These alterations may reflect brain pathology associated with the illness, illness-related environmental risk factors, or its treatment, rather than genetic risk.

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.

Available online 22 August 2021. White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. This work was conducted in part using the resources of the Ad- vanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the Na- tional Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universitéde Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk ł odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Cen- ter from the National Institute of Child Health and Human Develop- ment (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the sup- port of the Cluster of Excellence Matters of Activity. Image Space Mate- rial funded by the Deutsche Forschungsgemeinschaft (DFG, German Re- search Foundation) under Germany´s Excellence Strategy –EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group’s processing was performed using the University of Nottingham’s Augusta HPC service and the Pre- cision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CIN- TESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellow- ship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Sci- ence Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Re- search Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32- MH103213 to William Hetrick (Indiana University). CL is partly sup- ported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children’s Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children’s Hospital Foundation, Murdoch Children’s Research Institute, The Uni- versity of Melbourne Department of Paediatrics, and the Victorian Gov- ernment’s Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222- E-182-001-MY3) for the support. LC acknowledges support from CONA- CYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Pro- gramme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID- FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions.