ORCID Profile
0000-0003-3870-3388
Current Organisations
The Ohio State University
,
Charles University
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Publisher: Elsevier BV
Date: 04-2009
DOI: 10.1016/J.NEUROIMAGE.2008.12.019
Abstract: Imaging studies of epilepsy patients with comorbid affective disturbance demonstrate morphometric changes in limbic brain regions implicated in psychiatric disease. Genetic Absence Epilepsy Rats from Strasbourg (GAERS), specifically bred for their epilepsy phenotype, also exhibit elevated anxiety-like behaviors suggesting a common causality. Here we examined whether relevant cerebral morphological alterations exist in this rat strain using volumetric measurements and large deformation high dimensional mapping (HDM-LD), a tool recently validated to produce accurate three-dimensional surface representations of the hippoc us. Volumetric MRI and the Open Field test of anxiety were performed in adult female GAERS (n=12) and Non-Epileptic Controls (NEC n=11). The volumes of selected brain regions, including cortex, hippoc us, amygdala, thalamus, hypothalamus and lateral ventricles, were measured using Region-Of-Interest analysis from the MRI data and total volumes compared between the two strains. GAERS had increased amygdala (right: p=0.003 left p<0.001), cortices (right: p=0.006 left p=0.012) and ventricular volumes (p=0.002) when compared with NEC rats. Further, HDM-LD showed GAERS to have hippoc al volume loss in two regions: the medial hippoc al surface immediately caudal to the hippoc al commissure, and the lateral hippoc al surface over the mid-portion of the septotemporal axis. GAERS exhibited increased anxiety in the Open Field compared with NEC rats: reduced distance traveled (p<0.001) and reduced time in the centre area (p=0.042). Morphometric brain changes in GAERS could be relevant to their hyperanxious and epileptic phenotypes. This model may be useful in illuminating the pathogenesis of affective disorders generally, as well as modeling psychiatric comorbidities of epilepsy.
Publisher: Wiley
Date: 08-09-2021
DOI: 10.1002/HBM.25625
Abstract: Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta‐analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between in iduals with schizophrenia and healthy control participants. The study analyzed data from 2,833 in iduals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippoc us, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1‐weighted structural MRI scans. Mass univariate meta‐analyses revealed more‐concave‐than‐convex shape differences in the hippoc us, amygdala, accumbens, and thalamus in in iduals with schizophrenia compared with control participants, more‐convex‐than‐concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippoc us, amygdala, and thalamus in in iduals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta‐analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
Publisher: Center for Open Science
Date: 04-07-2019
Abstract: This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has ersified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across erse s les and associated genetic, environmental, demographic, cognitive and psychosocial factors.
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 03-2021
Publisher: Proceedings of the National Academy of Sciences
Date: 07-09-2021
Abstract: The contribution of midlife vascular risk factors to brain structure and cognition in late life is well established. In this large cohort of midlife in iduals, we replicated and extended previous findings of relationships between functional brain network connectivity and executive function in the context of white matter hyperintensities (WMH) and extended them to our measure of cumulative blood pressure exposure over 30 y from young adulthood. We report mediation of the relationship between WMH and executive function via functional connectivity. Our results support conclusions that neuroplastic reorganization of network connectivity may follow disruption to white matter tracks and furthermore suggest that this reorganization occurs early in the preclinical stage, prior to the onset of overt disease.
Publisher: Elsevier BV
Date: 2020
Publisher: Wiley
Date: 22-04-2009
DOI: 10.1002/JMRI.21766
Abstract: To report the detection of structural and functional biological changes in living animals using small animal in vivo MRI that complements traditional ex vivo histological techniques. We report the development and validation of the application of large deformation high dimensional mapping (HDM-LD) segmentation for the hippoc us in the rat. High resolution volumetric T2 weighted MRI images were acquired at 4.7 Tesla from six male in-breed nonepileptic Wistar rats. Two HDM-LD segmentations of the hippoc us (automated 1 and automated 2) were compared with the manual segmentations of two investigators who independently segmented the hippoc i (manual 1 and manual 2). The mean overlap for the hippoc i between automated 1 and automated 2 for the right hippoc i was 94.4% (SD 1.0) and for the left hippoc i was 94.3% (SD 2.5), while the mean overlap between automated 1 and manual 1 for the right hippoc i was 91.4% (SD 1.3) and for the left hippoc i was 91.9% (SD 1.4). Mean values for absolute differences for comparisons of all the segmentations were the following: automated 1 versus automated 2, 3.2% (SD 1.0) manual 1 versus manual 2 6.82% (SD 5.22) automated 1 versus manual 1 13.0% (SD 1.8). HDM-LD can be applied to obtain accurate and reproducible three-dimensional segmentations of the hippoc us from rat MR images. HDM-LD will be a useful tool for investigations of hippoc al structural changes in vivo in rat models of human disease.
Publisher: American Chemical Society (ACS)
Date: 08-08-2023
Publisher: Elsevier BV
Date: 2012
DOI: 10.1016/J.NEUROIMAGE.2011.08.013
Abstract: Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a s le of community-dwelling adults aged 70-90 years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS+LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09% sensitivity, 51.96% specificity, 78.40% and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of in iduals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults.
Location: United States of America
Location: Czechia
No related grants have been discovered for Lei Wang.