172 research outputs found
Brain structure, IQ, and psychopathology in young offspring of patients with schizophrenia or bipolar disorder
BACKGROUND.: Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology. METHODS.: T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8-19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model. RESULTS.: SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d = -0.56) and Co (d = -0.59), which was largely independent of IQ (respectively, d = -0.54 and d = -0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d = -0.42) and Co (d = -0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings. CONCLUSIONS.: Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder
Early brain magnetic resonance imaging findings and neurodevelopmental outcome in children with congenital heart disease:A systematic review
Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum
Functional connectome alterations, including modular network organization, have been related to the experience of hallucinations. It remains to be determined whether individuals with hallucinations across the psychosis continuum exhibit similar alterations in modular brain network organization. This study assessed functional connectivity matrices of 465 individuals with and without hallucinations, including patients with schizophrenia and bipolar disorder, nonclinical individuals with hallucinations, and healthy controls. Modular brain network organization was examined at different scales of network resolution, including (1) global modularity measured as Qmax and Normalised Mutual Information (NMI) scores, and (2) within- and between-module connectivity. Global modular organization was not significantly altered across groups. However, alterations in within- and between-module connectivity were observed for higher-order cognitive (e.g., central-executive salience, memory, default mode), and sensory modules in patients with schizophrenia and nonclinical individuals with hallucinations relative to controls. Dissimilar patterns of altered within- and between-module connectivity were found bipolar disorder patients with hallucinations relative to controls, including the visual, default mode, and memory network, while connectivity patterns between visual, salience, and cognitive control modules were unaltered. Bipolar disorder patients without hallucinations did not show significant alterations relative to controls. This study provides evidence for alterations in the modular organization of the functional connectome in individuals prone to hallucinations, with schizophrenia patients and nonclinical individuals showing similar alterations in sensory and higher-order cognitive modules. Other higher-order cognitive modules were found to relate to hallucinations in bipolar disorder patients, suggesting differential neural mechanisms may underlie hallucinations across the psychosis continuum.publishedVersio
Functional connectome differences in individuals with hallucinations across the psychosis continuum
Hallucinations may arise from an imbalance between sensory and higher cognitive brain regions, reflected by alterations in functional connectivity. It is unknown whether hallucinations across the psychosis continuum exhibit similar alterations in functional connectivity, suggesting a common neural mechanism, or whether different mechanisms link to hallucinations across phenotypes. We acquired resting-state functional MRI scans of 483 participants, including 40 non-clinical individuals with hallucinations, 99 schizophrenia patients with hallucinations, 74 bipolar-I disorder patients with hallucinations, 42 bipolar-I disorder patients without hallucinations, and 228 healthy controls. The weighted connectivity matrices were compared using network-based statistics. Non-clinical individuals with hallucinations and schizophrenia patients with hallucinations exhibited increased connectivity, mainly among fronto-temporal and fronto-insula/cingulate areas compared to controls (P < 0.001 adjusted). Differential effects were observed for bipolar-I disorder patients with hallucinations versus controls, mainly characterized by decreased connectivity between fronto-temporal and fronto-striatal areas (P = 0.012 adjusted). No connectivity alterations were found between bipolar-I disorder patients without hallucinations and controls. Our results support the notion that hallucinations in non-clinical individuals and schizophrenia patients are related to altered interactions between sensory and higher-order cognitive brain regions. However, a different dysconnectivity pattern was observed for bipolar-I disorder patients with hallucinations, which implies a different neural mechanism across the psychosis continuum.publishedVersio
A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data
The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived
Using machine learning and structural neuroimaging to detect first episode psychosis:reconsidering the evidence
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation
Genetic copy number variants, cognition and psychosis: a meta-analysis and a family study
The burden of large and rare copy number genetic variants (CNVs) as well as certain specific CNVs increase the risk of developing schizophrenia. Several cognitive measures are purported schizophrenia endophenotypes and may represent an intermediate point between genetics and the illness. This paper investigates the influence of CNVs on cognition. We conducted a systematic review and meta-analysis of the literature exploring the effect of CNV burden on general intelligence. We included ten primary studies with a total of 18,847 participants and found no evidence of association. In a new psychosis family study, we investigated the effects of CNVs on specific cognitive abilities. We examined the burden of large and rare CNVs (>200 kb, <1% MAF) as well as known schizophrenia-associated CNVs in patients with psychotic disorders, their unaffected relatives and controls (N = 3428) from the Psychosis Endophenotypes International Consortium (PEIC). The carriers of specific schizophrenia-associated CNVs showed poorer performance than non-carriers in immediate (P = 0.0036) and delayed (P = 0.0115) verbal recall. We found suggestive evidence that carriers of schizophrenia-associated CNVs had poorer block design performance (P = 0.0307). We do not find any association between CNV burden and cognition. Our findings show that the known high-risk CNVs are not only associated with schizophrenia and other neurodevelopmental disorders, but are also a contributing factor to impairment in cognitive domains such as memory and perceptual reasoning, and act as intermediate biomarkers of disease risk.This work was supported by the Medical Research Council (G0901310) and the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z). This study was
supported by the NIHR Biomedical Research Centre at University
College London Hospitals NHS Foundation Trust and University
College London and by the NIHR Biomedical Research Centre for
Mental Health at the South London and Maudsley NHS Foundation
Trust at King’s College London. Further support to EB: Mental Health
Research UK’s John Grace QC award, BMA Margaret Temple grants
2016 and 2006, MRC—Korean Health Industry Development Institute
Partnering Award (MC_PC_16014), MRC New Investigator Award
and a MRC Centenary Award (G0901310), National Institute of
Health Research UK post-doctoral fellowship, the Psychiatry Research
Trust, the Schizophrenia Research Fund, the Brain and Behaviour
Research foundation’s NARSAD Young Investigator Awards 2005,
2008, Wellcome Trust Research Training Fellowship, the NIHR
Biomedical Research Centre at UCLH, and the NIHR Biomedical
Research Centre for Mental Health at the South London and Maudsley
NHS Foundation Trust and Institute of Psychiatry King’s College
London. Further support to co-authors: The Brain and Behaviour
Research foundation’s (NARSAD’s) Young Investigator Award
(Grant 22604, awarded to CI). The BMA Margaret Temple grant 2016
to JT. A 2014 European Research Council Marie Curie award to A
Díez-Revuelta. HI has received funding from the European Union’s
Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie grant agreement No 747429. A Medical Research
Council doctoral studentship to JH-S, IA-Z and AB. A Mental Health
Research UK studentship to RM. VB is supported by a Wellcome
Trust Seed Award in Science (200589/Z/16/Z). FWO Senior Clinical
Fellowship to RvW. The infrastructure for the GROUP consortium is
funded through the Geestkracht programme of the Dutch Health
Research Council (ZON-MW, grant number 10-000-1001), and
matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and
mental health care organisations (Amsterdam: Academic Psychiatric
Centre of the Academic Medical Centre and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen,
Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen:
University Medical Centre Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant,
GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical
centre The Hague. Maastricht: Maastricht University Medical Centre
and the mental health institutions: GGZ Eindhoven en De Kempen,
GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke
Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz,
Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of
Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden,
GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Centre Utrecht and the mental health institutions Altrecht, GGZ Centraal and
Delta). The Santander cohort was supported by Instituto de Salud
Carlos III (PI020499, PI050427, PI060507), SENY Fundació (CI
2005-0308007), Fundacion Ramón Areces and Fundacion Marqués de
Valdecilla (API07/011, API10/13). We thank Valdecilla Biobank for
providing the biological PAFIP samples and associated data included
in this study and for its help in the technical execution of this work; we
also thank IDIVAL Neuroimaging Unit for its help in the acquisition
and processing of imaging PAFIP data
What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group
MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness
Genetic variants for head size share genes and pathways with cancer
The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.</p
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Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?
This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)
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