ORCID Profile
0000-0002-4720-8867
Current Organisation
University of Southern California
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Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NN.4228
Publisher: Cold Spring Harbor Laboratory
Date: 02-08-2022
DOI: 10.1101/2022.07.31.502227
Abstract: Whole brain tractography is commonly used to study the brain’s white matter fiber pathways, but the large number of streamlines generated - up to one million per brain - can be challenging for large-scale population studies. We propose a robust dimensionality reduction framework for tractography, using a Convolutional Variational Autoencoder (ConvVAE) to learn low-dimensional embeddings from white matter bundles. The resulting embeddings can be used to facilitate downstream tasks such as outlier and abnormality detection, and mapping of disease effects on white matter tracts in in iduals or groups. We design experiments to evaluate how well embeddings of different dimensions preserve distances from the original high-dimensional dataset, using distance correlation methods. We find that streamline distances and inter-bundle distances are well preserved in the latent space, with a 6-dimensional optimal embedding space. The generative ConvVAE model allows fast inference on new data, and the smooth latent space enables meaningful decodings that can be used for downstream tasks. We demonstrate the use of a ConvVAE model trained on control subjects’ data to detect structural anomalies in white matter tracts in patients with Alzheimer’s disease (AD). Using ConvVAEs to facilitate population analyses, we identified 6 tracts with statistically significant differences between AD and controls after controlling for age and sex effect, visualizing specific locations along the tracts with high anomalies despite large inter-subject variations in fiber bundle geometry.
Publisher: Elsevier BV
Date: 07-2014
Publisher: Cold Spring Harbor Laboratory
Date: 21-04-2022
DOI: 10.1101/2022.04.20.22273878
Abstract: Spinal cord damage is a hallmark of Friedreich ataxia (FRDA), but its progression and clinical correlates remain unclear. Here we performed a characterization of cervical spinal cord structural abnormalities in a large multisite FRDA cohort. We performed a cross-sectional analysis of cervical spinal cord (C1 to C4) cross-sectional area (CSA) and eccentricity using MRI data from eight sites within the ENIGMA-Ataxia initiative, including 256 in iduals with FRDA and 223 age- and sex-matched controls. Correlations and subgroup analyses within the FRDA cohort were undertaken based on disease duration, ataxia severity, and onset age. In iduals with FRDA, relative to controls, had significantly reduced CSA at all examined levels, with large effect sizes ( d .1) and significant correlations with disease severity ( r -0.4). Similarly, we found significantly increased eccentricity ( d .2), but without significant clinical correlations. Subgroup analyses showed that CSA and eccentricity are abnormal at all disease stages. However, while CSA appears to decrease progressively, eccentricity remains stable over time. Previous research has shown that increased eccentricity reflects dorsal column (DC) damage, while decreased CSA reflects either DC or corticospinal tract (CST) damage or both. Hence, our data support the hypothesis that damage to DC and CST follow distinct courses in FRDA: developmental abnormalities likely define the DC, whereas CST alterations may be both developmental and degenerative. These results provide new insights about FRDA pathogenesis and indicate that CSA of the cervical spinal cord should be investigated further as a potential biomarker of disease progression.
Publisher: Center for Open Science
Date: 14-10-2019
Abstract: A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates common to in iduals with major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of in iduals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across 6 continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA-MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data-sharing for mental health research.
Publisher: Elsevier BV
Date: 15-08-2010
Publisher: Elsevier BV
Date: 2015
Publisher: Wiley
Date: 08-10-2014
DOI: 10.1111/GBB.12177
Publisher: Springer Science and Business Media LLC
Date: 08-01-2014
Publisher: Cold Spring Harbor Laboratory
Date: 20-05-2022
DOI: 10.1101/2022.05.19.492753
Abstract: We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9-14 years session 2: N = 304, 10-16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological s les for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.
Publisher: Proceedings of the National Academy of Sciences
Date: 05-11-2013
Abstract: Beta-amyloid plaque accumulation, glucose hypometabolism, and neuronal atrophy are hallmarks of Alzheimer’s disease. However, the regional ordering of these biomarkers prior to dementia remains untested. In a cohort with Alzheimer’s disease mutations, we performed an integrated whole-brain analysis of three major imaging techniques: amyloid PET, [ 18 F]fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism followed by atrophy. Critically, however, not all regions lose metabolic function, and not all regions atrophy, even when there is significant amyloid deposition. These regional disparities have important implications for clinical trials of disease-modifying therapies.
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 09-2008
Publisher: Elsevier BV
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 02-05-2017
DOI: 10.1038/TP.2017.84
Publisher: Cold Spring Harbor Laboratory
Date: 21-01-2022
DOI: 10.1101/2022.01.21.476409
Abstract: Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection ersity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.844434
Publisher: Springer Science and Business Media LLC
Date: 21-01-2015
DOI: 10.1038/NATURE14101
Publisher: Elsevier BV
Date: 09-2007
Publisher: Elsevier BV
Date: 11-2014
Publisher: Society for Neuroscience
Date: 18-02-2009
DOI: 10.1523/JNEUROSCI.4184-08.2009
Abstract: The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal ( a 2 = 0.55, p = 0.04, left a 2 = 0.74, p = 0.006, right), bilateral parietal ( a 2 = 0.85, p 0.001, left a 2 = 0.84, p 0.001, right), and left occipital ( a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata ( p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.
Publisher: Cold Spring Harbor Laboratory
Date: 22-07-2022
DOI: 10.1101/2022.07.20.22277727
Abstract: Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and in iduals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a erse population of 50,699 in iduals (12 studies, 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are novel, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate new genetic and biological underpinnings that influence structural covariance patterns in the human brain. The coordinated patterns of changes in the human brain throughout life, driven by brain development, aging, and diseases, remain largely unexplored regarding their underlying genetic determinants. This study delineates 2003 multi-scale patterns of structural covariance (PSCs) and identifies 617 novel genomic loci, with the mapped genes enriched in biological pathways implicated in reelin signaling, apoptosis, neurogenesis, and appendage development. Overall, the 2003 PSCs provide new genetic insights into understanding human brain morphological changes and demonstrate great potential in predicting various neurologic conditions.
Publisher: Elsevier BV
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 16-10-2014
Publisher: IEEE
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 09-02-2016
DOI: 10.1038/MP.2015.227
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-11-2020
Abstract: Brain atrophy in human epilepsy syndromes is explainable by network architecture and strongest in hub regions.
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: 05-2017
Publisher: Society for Neuroscience
Date: 04-05-2011
DOI: 10.1523/JNEUROSCI.5794-10.2011
Abstract: There is a strong genetic risk for late-onset Alzheimer's disease (AD), but so far few gene variants have been identified that reliably contribute to that risk. A newly confirmed genetic risk allele C of the clusterin ( CLU ) gene variant rs11136000 is carried by ∼88% of Caucasians. The C allele confers a 1.16 greater odds of developing late-onset AD than the T allele. AD patients have reductions in regional white matter integrity. We evaluated whether the CLU risk variant was similarly associated with lower white matter integrity in healthy young humans. Evidence of early brain differences would offer a target for intervention decades before symptom onset. We scanned 398 healthy young adults (mean age, 23.6 ± 2.2 years) with diffusion tensor imaging, a variation of magnetic resonance imaging sensitive to white matter integrity in the living brain. We assessed genetic associations using mixed-model regression at each point in the brain to map the profile of these associations with white matter integrity. Each C allele copy of the CLU variant was associated with lower fractional anisotropy—a widely accepted measure of white matter integrity—in multiple brain regions, including several known to degenerate in AD. These regions included the splenium of the corpus callosum, the fornix, cingulum, and superior and inferior longitudinal fasciculi in both brain hemispheres. Young healthy carriers of the CLU gene risk variant showed a distinct profile of lower white matter integrity that may increase vulnerability to developing AD later in life.
Publisher: SAGE Publications
Date: 19-02-2014
Abstract: To describe the development, design and function of an innovative international clinical research network for neuroimaging research, based in Australia, within a joint state health service/medical school. This Australian, US, Scandinavian Imaging Exchange (AUSSIE) network focuses upon identifying neuroimaging biomarkers for neuropsychiatric and neurodegenerative disease. We describe a case study of the iterative development of the network, identifying characteristic features and methods which may serve as potential models for virtual clinical research networks. This network was established to analyse clinically-derived neuroimaging data relevant to neuropsychiatric and neurodegenerative disease, specifically in relation to subcortical brain structures. The AUSSIE network has harnessed synergies from the in idual expertise of the component groups, primarily clinical neuroscience researchers, to analyse a variety of clinical data. AUSSIE is an active virtual clinical research network, analogous to a connectome, which is embedded in health care and has produced significant research, advancing our understanding of neuropsychiatric and neurodegenerative disease through the lens of neuroimaging.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2012
DOI: 10.1038/NG.2237
Publisher: Wiley
Date: 15-03-2013
DOI: 10.1002/HBM.22022
Publisher: Public Library of Science (PLoS)
Date: 24-03-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 06-2009
Publisher: Proceedings of the National Academy of Sciences
Date: 07-02-2013
Abstract: Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subs le. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.
Publisher: Elsevier BV
Date: 08-2010
Publisher: IEEE
Date: 2010
Publisher: Elsevier BV
Date: 02-2008
Publisher: IEEE
Date: 06-2009
Publisher: Elsevier BV
Date: 05-2015
Publisher: Oxford University Press (OUP)
Date: 04-2012
Publisher: Springer Science and Business Media LLC
Date: 18-01-2017
DOI: 10.1038/NCOMMS13624
Abstract: The hippoc al formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippoc al volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippoc al structure here we perform a genome-wide association study (GWAS) of 33,536 in iduals and discover six independent loci significantly associated with hippoc al volume, four of them novel. Of the novel loci, three lie within genes ( ASTN2 , DPP4 and MAST4 ) and one is found 200 kb upstream of SHH . A hippoc al subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippoc al volume are also associated with increased risk for Alzheimer’s disease ( r g =−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippoc al volume and risk for neuropsychiatric illness.
Publisher: Cold Spring Harbor Laboratory
Date: 21-09-2021
DOI: 10.1101/2021.09.15.21263562
Abstract: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behaviour. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. Here, we examine this issue through two approaches: (i) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (ii) by pooling data (N∼6,000 participants) from cohorts from the ENIGMA-Major Depressive Disorder (ENIGMA-MDD) and ENIGMA-Suicidal Thoughts and Behaviour (ENIGMA-STB) working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behaviour. Our results suggested a pattern of moderate-to-high correlations between instruments, consistent with the wide range of correlations, r=0.22-0.97, reported in the literature. Two common complex instruments, the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Scale for Suicidal Ideation (SSI), were highly correlated with each other (r=0.83), as were suicidal ideation items from common depression severity questionnaires. Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behaviour, but share a core factor with single suicidal ideation items found in depression severity questionnaires. Multi-site collaborations including cohorts that used distinct instruments for suicide risk assessment should be feasible provided that they harmonise across instruments or focus on specific constructs of suicidal thoughts or behaviours. Question: To inform future suicide research in multi-site international consortia, it is important to examine how different suicide measures relate to each other and whether they can be used interchangeably. Findings: Findings suggest detailed instruments (such as the Columbia Suicide Severity Rating Scale and Beck Scale for Suicidal Ideation) provide valuable information on suicidal thoughts and behaviour, and share a core factor with items on suicidal ideation from depression severity rating scale (such as the Hamilton Depression Rating Scale or the Beck Depression Inventory). Importance: Results from international collaborations can mitigate biases by harmonising distinct suicide risk assessment instruments. Next steps: Pooling data within international suicide research consortia may reveal novel clinical, biological and cognitive correlates of suicidal thoughts and/or behaviour.
Publisher: IEEE
Date: 06-2009
Publisher: Cold Spring Harbor Laboratory
Date: 27-12-2022
DOI: 10.1101/2022.12.24.22283926
Abstract: While traditionally ignored as a region purely responsible for motor function, the cerebellum is increasingly being appreciated for its contributions to higher order functions through various cerebro-cerebellar networks. Traumatic brain injury (TBI) research generally focuses on the cerebrum, in part because acute pathology is not found in the cerebellum as often. Acute pathology is an important predictor of outcome, but neural disruption also evolves over time in ways that have implications for daily-life functioning. Here we examine these changes in a multi-modal, multi-cohort study. Combining 12 datasets from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Pediatric msTBI (moderate-severe TBI) working group, we measured volume of the total cerebellum and 17 subregions using a state-of-the-art, deep learning-based approach for automated parcellation in 598 children and adolescents with or without TBI (msTBI n = 314 | non-TBI n = 284 age M = 14.0 ± 3.1 years). Further, we investigated brain-behavior relations between cerebellar volumes and a measure of executive functioning (i.e., Behavioral Rating Inventory of Executive Function [BRIEF]). In a subs le with longitudinal data, we then assessed whether late changes in cerebellar volume were associated with early white matter microstructural organization using diffusion tensor imaging (DTI). Significantly smaller total cerebellar volume was observed in the msTBI group (Cohen’s d = −0.37). In addition, lower regional cerebellar volume was found in posterior lobe regions including crus II, lobule VIIB, lobule VIIIB, vermis VII, and IX (Cohen’s d range = −0.22 to −0.43). Smaller cerebellum volumes were associated with more parent-reported executive function problems. These alterations were primarily driven by participants in the chronic phase of injury ( 6 months). In a subset of participants with longitudinal data (n = 80), we found evidence of altered growth in total cerebellum volume, with younger msTBI participants showing secondary degeneration in the form of volume reductions, and older participants showing disrupted development reflected in slower growth rates. Changes in total cerebellum volume over time were also associated with white matter microstructural organization in the first weeks and months post-injury, such that poorer white matter organization in the first months post-injury was associated with decreases in volume longitudinally. Pediatric msTBI was characterized by smaller cerebellar volumes, primarily in the posterior lobe and vermis. The course of these alterations, along with group differences in longitudinal volume changes as well as injury-specific associations between DTI measures and volume changes, is suggestive of secondary cerebellar atrophy, possibly related to supra-tentorial lesions, and/or disruption in cerebellar structural and functional circuits. Moreover, evidence for robust brain-behavior relationships underscore the potential cognitive and behavioral consequences of cerebellar disruption during a critical period of brain development.
Publisher: SPIE
Date: 29-03-2016
DOI: 10.1117/12.2217370
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 2014
Publisher: Elsevier BV
Date: 15-02-2009
Publisher: Elsevier BV
Date: 02-2017
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2022
DOI: 10.1101/2022.10.13.512111
Abstract: The cerebellum critically contributes to higher-order cognitive and emotional functions such fear learning and memory. Prior research on cerebellar volume in PTSD is scant and has neglected neuroanatomical sub isions of the cerebellum that differentially map on to motor, cognitive, and affective functions. We quantified cerebellar lobule volumes using structural magnetic resonance imaging in 4,215 adults (PTSD n= 1640 Control n=2575) across 40 sites from the from the ENIGMA-PGC PTSD working group. Using a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation, we obtained volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum total and subregional volume in PTSD compared to healthy controls. The Benjamini-Hochberg procedure was used to control the false discovery rate ( p -FDR .05). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume. In addition, people with PTSD showed reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), but also the vermis (VI, VIII), flocculonodular lobe (lobule X), and cerebellar white matter (all p -FDR 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in high-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.
Publisher: Elsevier BV
Date: 03-2017
Publisher: Wiley
Date: 08-07-2014
DOI: 10.1002/HBM.22292
Publisher: Springer Science and Business Media LLC
Date: 12-04-2016
DOI: 10.1038/NPP.2016.48
Publisher: SPIE
Date: 21-03-2016
DOI: 10.1117/12.2217376
Publisher: Cold Spring Harbor Laboratory
Date: 28-10-2021
DOI: 10.1101/2021.10.26.465924
Abstract: Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippoc us is involved in sensorimotor behavior but has not been widely studied within the context of post-stroke upper limb sensorimotor impairment. The hippoc us is vulnerable to secondary degeneration after stroke, and damage to this region could further weaken sensorimotor circuits, leading to greater chronic sensorimotor impairment. The purpose of this study was to investigate the cross-sectional association between non-lesioned hippoc al volume and upper limb sensorimotor impairment in people with chronic stroke. We hypothesized that smaller ipsilesional hippoc al volumes would be associated with worse upper-limb sensorimotor impairment. Cross-sectional T1-weighted brain MRIs were pooled from 357 participants at the chronic stage after stroke ( days post-stroke) compiled from 18 research cohorts worldwide in the ENIGMA Stroke Recovery Working Group (age: median = 61 years, interquartile range = 18, range = 23-93 135 women and 222 men). Sensorimotor impairment was estimated from the Fugl-Meyer Assessment of Upper Extremity scores. Robust mixed-effects linear models were used to test associations between post-stroke sensorimotor impairment and hippoc al volumes (ipsilesional and contralesional separately Bonferroni-corrected, p - value 0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. We also performed an exploratory analysis to test whether sex differences influence the relationship between sensorimotor impairment and hippoc al volume. Upper limb sensorimotor impairment was positively associated with ipsilesional ( p = 0.005 d = 0.33) but not contralesional ( p = 0.96 d = 0.01) hippoc al volume, such that impairment was worse for participants with smaller ipsilesional hippoc al volume. This association remained significant independent of lesion volume or other covariates ( p = 0.001 d = 0.36). Evidence indicates an interaction between sensorimotor impairment and sex for both ipsilesional ( p = 0.008 d = −0.29) and contralesional ( p = 0.006 d = −0.30) hippoc al volumes, whereby women showed progressively worsening sensorimotor impairment with smaller hippoc al volumes compared to men. The present study has identified a novel association between chronic post-stroke sensorimotor impairment and ipsilesional, but not contralesional, hippoc al volume. This finding was not due to lesion size and may be stronger in women. We also provide supporting evidence that smaller hippoc al volume post-stroke is likely a consequence of ipsilesional damage, which could provide a link between vascular disease and other disorders, such as dementia.
Publisher: Springer Science and Business Media LLC
Date: 09-04-2013
DOI: 10.1038/MP.2013.37
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 22-08-2012
Publisher: SAGE Publications
Date: 20-11-2014
Abstract: The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test–retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test–retest reliability of estimating HRF parameters using data from block design fMRI studies.
Publisher: Wiley
Date: 2001
DOI: 10.1002/1096-911X(20010101)36:1<32::AID-MPO1009>3.0.CO;2-0
Publisher: Elsevier BV
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 2014
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 05-2008
Publisher: Wiley
Date: 02-2007
Publisher: IEEE
Date: 06-2009
Publisher: Springer Science and Business Media LLC
Date: 15-12-2016
DOI: 10.1038/NCOMMS13738
Abstract: The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to in idual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippoc us, pallidum, putamen and thalamus. In 3,686 unrelated in iduals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent s le of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.
Publisher: Wiley
Date: 19-03-2022
DOI: 10.1002/HBM.25829
Abstract: The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe ( H armonized A na l ysis of F unctional MRI pipe line), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the in idual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional litude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at github.com/HALFpipe/HALFpipe .
Publisher: American Psychological Association (APA)
Date: 08-2022
DOI: 10.1037/ABN0000738
Abstract: Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite s le of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-s le split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in in iduals with low educational attainment, but less likely to predict MDD in in iduals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: Elsevier BV
Date: 02-2011
Publisher: Wiley
Date: 18-02-2016
DOI: 10.1002/HBM.23136
Publisher: Elsevier BV
Date: 2004
DOI: 10.1016/J.NEUROIMAGE.2004.07.071
Abstract: This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in meth hetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in in idual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative ex les are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.
Publisher: IEEE
Date: 04-2010
Publisher: IEEE
Date: 10-2007
Publisher: SAGE Publications
Date: 04-10-2013
Publisher: Cold Spring Harbor Laboratory
Date: 18-01-2023
DOI: 10.1101/2023.01.17.523348
Abstract: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of in iduals at psychosis risk may be nested within the range observed in healthy in iduals. To quantify deviations from the normative range of neuroanatomical variation in in iduals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Clinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P in iduals [47.09% female mean age: 20.75 (4.74) years] and 1,237 healthy in iduals [44.70% female mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. For each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of in iduals with infranormal (z -1.96) or supranormal (z .96) scores. CHR-P and healthy in iduals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P in iduals with infranormal or supranormal values in any metric was low ( %) and similar to that of healthy in iduals. CHR-P in iduals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADS SA showed significant but weak associations (|β| .09 P FDR .05) with positive symptoms and IQ. The study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk. Is the risk of psychosis associated with brain morphometric changes that deviate significantly from healthy variation? In this study of 1340 in iduals high-risk for psychosis (CHR-P) and 1237 healthy participants, in idual-level variation in macroscale neuromorphometric measures of the CHR-P group was largely nested within healthy variation and was not associated with the severity of positive psychotic symptoms or conversion to a psychotic disorder. The findings suggest the macroscale neuromorphometric measures have limited utility as diagnostic biomarkers of psychosis risk.
Publisher: Public Library of Science (PLoS)
Date: 20-06-2014
Publisher: Elsevier BV
Date: 02-2015
Publisher: SPIE
Date: 26-01-2017
DOI: 10.1117/12.2256935
Publisher: Elsevier BV
Date: 10-2009
Publisher: Elsevier BV
Date: 11-2014
Publisher: Proceedings of the National Academy of Sciences
Date: 09-01-2012
Abstract: Control of iron homeostasis is essential for healthy central nervous system function: iron deficiency is associated with cognitive impairment, yet iron overload is thought to promote neurodegenerative diseases. Specific genetic markers have been previously identified that influence levels of transferrin, the protein that transports iron throughout the body, in the blood and brain. Here, we discovered that transferrin levels are related to detectable differences in the macro- and microstructure of the living brain. We collected brain MRI scans from 615 healthy young adult twins and siblings, of whom 574 were also scanned with diffusion tensor imaging at 4 Tesla. Fiber integrity was assessed by using the diffusion tensor imaging-based measure of fractional anisotropy. In bivariate genetic models based on monozygotic and dizygotic twins, we discovered that partially overlapping additive genetic factors influenced transferrin levels and brain microstructure. We also examined common variants in genes associated with transferrin levels, TF and HFE , and found that a commonly carried polymorphism (H63D at rs1799945) in the hemochromatotic HFE gene was associated with white matter fiber integrity. This gene has a well documented association with iron overload. Our statistical maps reveal previously unknown influences of the same gene on brain microstructure and transferrin levels. This discovery may shed light on the neural mechanisms by which iron affects cognition, neurodevelopment, and neurodegeneration.
Publisher: Springer Science and Business Media LLC
Date: 30-06-2015
DOI: 10.1038/MP.2015.69
Publisher: Cold Spring Harbor Laboratory
Date: 28-11-2022
DOI: 10.1101/2022.11.22.22282598
Abstract: Schizotypy represents an index of psychosis-proneness in the general population often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. We addressed this question using data from a total of 1,182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical grey matter volume and cortical thickness were determined. A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of thicker bilateral medial orbitofrontal gyri, right rostral anterior cingulate gyrus, left temporal pole, left insula, and thinner left paracentral lobule directly associated with increasing levels of schizotypy. In addition, thinner left postcentral, superior parietal and lingual gyri, as well as thicker left caudal middle frontal gyrus and smaller left thalamus and right caudate were associated with increasing levels of childhood trauma exposure. These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in in iduals exposed to high levels of trauma.
Publisher: Center for Open Science
Date: 05-09-2020
Abstract: Here we review the motivation for creating the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings and future directions of the Genetics Working Group. A major goal of the working group is tackling the reproducibility crisis affecting ‘candidate gene’ and genome-wide association analyses in neuroimaging. To address this, we developed harmonised analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We showed common and distinct genetic loci to be associated with different brain structures, as well as genetic correlations with psychiatric and neurological diseases.
Publisher: Cold Spring Harbor Laboratory
Date: 29-04-2022
DOI: 10.1101/2022.04.27.489791
Abstract: Sensorimotor performance after stroke is strongly related to focal injury measures such as corticospinal tract lesion load. However, the role of global brain health is less clear. Here, we examined the impact of brain age, a measure of neurobiological aging derived from whole brain structural neuroimaging, on sensorimotor outcomes. We hypothesized that stroke lesion damage would result in older brain age, which would in turn be associated with poorer sensorimotor outcomes. We also expected that brain age would mediate the impact of lesion damage on sensorimotor outcomes and that these relationships would be driven by post-stroke secondary atrophy (e.g., strongest in the ipsilesional hemisphere in chronic stroke). We further hypothesized that structural brain resilience, which we define in the context of stroke as the brain’s ability to maintain its global integrity despite focal lesion damage, would differentiate people with better versus worse outcomes. We analyzed cross-sectional high-resolution brain MRI and outcomes data from 963 people with stroke from 38 cohorts worldwide using robust linear mixed-effects regressions to examine the relationship between sensorimotor behavior, lesion damage, and brain age. We used a mediation analysis to examine whether brain age mediates the impact of lesion damage on stroke outcomes and if associations are driven by ipsilesional measures in chronic (≥180 days) stroke. We assessed the impact of brain resilience on sensorimotor outcome using logistic regression with propensity score matching on lesion damage. Stroke lesion damage was associated with older brain age, which in turn was associated with poorer sensorimotor outcomes. Brain age mediated the impact of corticospinal tract lesion load on sensorimotor outcomes most strongly in the ipsilesional hemisphere in chronic stroke. Greater brain resilience, as indexed by younger brain age, explained why people have better versus worse sensorimotor outcomes when lesion damage was fixed. We present novel evidence that global brain health is associated with superior post-stroke sensorimotor outcomes and modifies the impact of focal damage. This relationship appears to be due to post-stroke secondary degeneration. Brain resilience provides insight into why some people have better outcomes after stroke, despite similar amounts of focal injury. Inclusion of imaging-based assessments of global brain health may improve prediction of post-stroke sensorimotor outcomes compared to focal injury measures alone. This investigation is important because it introduces the potential to apply novel therapeutic interventions to prevent or slow brain aging from other fields (e.g., Alzheimer’s disease) to stroke.
Publisher: Springer Science and Business Media LLC
Date: 08-07-2008
DOI: 10.1038/MP.2008.34
Publisher: Public Library of Science (PLoS)
Date: 08-01-2016
Publisher: Elsevier BV
Date: 08-2014
Publisher: Cold Spring Harbor Laboratory
Date: 23-10-2023
Publisher: Elsevier BV
Date: 10-2014
Publisher: American Medical Association (AMA)
Date: 09-2014
Publisher: Elsevier BV
Date: 03-2016
Publisher: Elsevier BV
Date: 03-2017
Publisher: Oxford University Press (OUP)
Date: 26-06-2011
Publisher: Elsevier BV
Date: 04-2008
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 20-07-2021
DOI: 10.1212/WNL.0000000000012222
Abstract: Our study addressed aims (1) to test the hypothesis that moderate-severe traumatic brain injury (TBI) in pediatric patients is associated with widespread white matter (WM) disruption, (2) to test the hypothesis that age and sex affect WM organization after injury, and (3) to examine associations between WM organization and neurobehavioral outcomes. Data from 10 previously enrolled, existing cohorts recruited from local hospitals and clinics were shared with the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Pediatric Moderate/Severe TBI (msTBI) working group. We conducted a coordinated analysis of diffusion MRI (dMRI) data using the ENIGMA dMRI processing pipeline. Five hundred seven children and adolescents (244 with complicated msTBI and 263 controls) were included. Patients were clustered into 3 postinjury intervals: acute/subacute, months postacute, 2 to 6 months and chronic, ≥6 months. Outcomes were dMRI metrics and postinjury behavioral problems as indexed by the Child Behavior Checklist. Our analyses revealed altered WM diffusion metrics across multiple tracts and all postinjury intervals (effect sizes range d = −0.5 to −1.3). Injury severity is a significant contributor to the extent of WM alterations but explained less variance in dMRI measures with increasing time after injury. We observed a sex-by-group interaction: female patients with TBI had significantly lower fractional anisotropy in the uncinate fasciculus than controls (β = 0.043), which coincided with more parent-reported behavioral problems (β = −0.0027). WM disruption after msTBI is widespread, persistent, and influenced by demographic and clinical variables. Future work will test techniques for harmonizing neurocognitive data, enabling more advanced analyses to identify symptom clusters and clinically meaningful patient subtypes.
Publisher: Springer Science and Business Media LLC
Date: 19-04-2011
DOI: 10.1038/MP.2011.32
Publisher: Cold Spring Harbor Laboratory
Date: 19-10-2021
DOI: 10.1101/2021.10.18.464713
Abstract: Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem co-expression patterns of epilepsy risk genes. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1,328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippoc al sclerosis in TLE and for generalized epilepsy in IGE. These imaging-genetic signatures could guide diagnosis, and ultimately, tailor therapeutic approaches to specific epilepsy syndromes.
Publisher: IEEE
Date: 04-2010
Publisher: Springer Science and Business Media LLC
Date: 07-11-2017
DOI: 10.1038/S41467-017-01285-X
Abstract: Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Publisher: Cold Spring Harbor Laboratory
Date: 22-03-2022
DOI: 10.1101/2022.03.21.484899
Abstract: Mechanisms underpinning age-related variations in cortical thickness in the human brain remain poorly understood. We investigated whether inter-regional age-related variations in cortical thinning (in a multicohort neuroimaging dataset from the ENIGMA Lifespan Working Group totalling 14,248 in iduals, aged 4-89 years) depended on cell-specific marker gene expression levels. We found differences amidst early-life ( years), mid-life (20-60 years), and late-life ( years) in the patterns of association between inter-regional profiles of cortical thickness and expression profiles of marker genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated that each of the three life-stages was associated with different biological processes and cellular components: synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights into age-related cortical thinning during typical development and aging.
Publisher: Elsevier BV
Date: 08-2004
Publisher: Springer Science and Business Media LLC
Date: 03-10-2016
DOI: 10.1038/NN.4398
Publisher: Elsevier BV
Date: 02-2012
Publisher: Springer Science and Business Media LLC
Date: 03-05-2016
DOI: 10.1038/MP.2016.60
Publisher: Springer Science and Business Media LLC
Date: 13-10-2016
Publisher: Elsevier BV
Date: 03-2009
Publisher: Cold Spring Harbor Laboratory
Date: 13-12-2022
DOI: 10.1101/2022.12.12.519838
Abstract: Current clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify in iduals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. We analyzed brain MRI data from 3,527 structural-MRI 2,502 resting state-fMRI and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish in iduals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Our findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. ⍰ Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC). ⍰ Using deep learning for feature reduction (denoising variational auto-encoder DVAE) dramatically reduced the number of features with no concomitant performance degradation. ⍰ Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 18-09-2013
Publisher: IEEE
Date: 06-2009
Publisher: Society for Neuroscience
Date: 25-04-2012
DOI: 10.1523/JNEUROSCI.5561-11.2012
Abstract: The NTRK1 gene (also known as TRKA ) encodes a high-affinity receptor for NGF , a neurotrophin involved in nervous system development and myelination. NTRK1 has been implicated in neurological function via links between the T allele at rs6336 ( NTRK1 -T) and schizophrenia risk. A variant in the neurotrophin gene, BDNF , was previously associated with white matter integrity in young adults, highlighting the importance of neurotrophins to white matter development. We hypothesized that NTRK1 -T would relate to lower fractional anisotropy in healthy adults. We scanned 391 healthy adult human twins and their siblings (mean age: 23.6 ± 2.2 years 31 NTRK1 -T carriers, 360 non-carriers) using 105-gradient diffusion tensor imaging at 4 tesla. We evaluated in brain white matter how NTRK1 -T and NTRK1 rs4661063 allele A (rs4661063-A, which is in moderate linkage disequilibrium with rs6336) related to voxelwise fractional anisotropy—a common diffusion tensor imaging measure of white matter microstructure. We used mixed-model regression to control for family relatedness, age, and sex. The s le was split in half to test reproducibility of results. The false discovery rate method corrected for voxelwise multiple comparisons. NTRK1 -T and rs4661063-A correlated with lower white matter fractional anisotropy, independent of age and sex (multiple-comparisons corrected: false discovery rate critical p = 0.038 for NTRK1 -T and 0.013 for rs4661063-A). In each half-s le, the NTRK1 -T effect was replicated in the cingulum, corpus callosum, superior and inferior longitudinal fasciculi, inferior fronto-occipital fasciculus, superior corona radiata, and uncinate fasciculus. Our results suggest that NTRK1 -T is important for developing white matter microstructure.
Publisher: IEEE
Date: 05-2009
Publisher: Elsevier BV
Date: 2017
Publisher: Cold Spring Harbor Laboratory
Date: 16-07-2019
DOI: 10.1101/703793
Abstract: Structural brain changes along the lineage that led to modern Homo sapiens have contributed to our unique cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at erse timescales with common variant associations from large-scale neuroimaging genetic screens in living humans, to reveal how selective pressures have shaped neocortical surface area. We show that variation within human gained enhancers active in the developing brain is associated with global surface area as well as that of specific regions. Moreover, we find evidence of recent polygenic selection over the past 2,000 years influencing surface area of multiple cortical regions, including those involved in spoken language and visual processing.
Publisher: IEEE
Date: 2007
Publisher: Society for Neuroscience
Date: 27-07-2011
Publisher: Elsevier BV
Date: 11-2013
Publisher: Elsevier BV
Date: 08-2009
Publisher: Wiley
Date: 05-06-2017
DOI: 10.1002/HBM.23672
Publisher: Cold Spring Harbor Laboratory
Date: 23-06-2023
DOI: 10.1101/2023.06.19.545638
Abstract: Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years time since stroke 12.2±0.2 months normalised motor score 0.7±0.5 (range [0,1]). The out-of-s le prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, p 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, p 0.001) and of multiple descending motor tracts (R 2 = 0.180, p 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 =0.200, p 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 =0.167, p 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, p 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.
Publisher: Cold Spring Harbor Laboratory
Date: 09-05-2021
DOI: 10.1101/2021.05.07.442790
Abstract: The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe ( H armonized A na L ysis of F unctional MRI p i p e line), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the in idual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional litude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at github.com/HALFpipe/HALFpipe .
Publisher: Elsevier BV
Date: 02-2018
Publisher: Elsevier BV
Date: 03-2011
Publisher: Public Library of Science (PLoS)
Date: 13-09-2012
Publisher: Cambridge University Press (CUP)
Date: 06-2012
DOI: 10.1017/THG.2012.15
Abstract: The development of late-onset Alzheimer's disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer's disease risk gene, growth factor receptor bound protein 2-associated protein ( GAB2 ), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young adult twins (469 females) ( M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer's disease.
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2023
Publisher: Springer Science and Business Media LLC
Date: 27-05-2014
DOI: 10.1038/NN.3718
Publisher: Elsevier BV
Date: 2010
Publisher: American Psychiatric Association Publishing
Date: 2017
Publisher: SPIE
Date: 20-03-2015
DOI: 10.1117/12.2081677
Publisher: Springer Science and Business Media LLC
Date: 23-09-2008
Publisher: Cold Spring Harbor Laboratory
Date: 15-10-2021
DOI: 10.1101/2021.10.13.463489
Abstract: The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored. Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 in iduals in the UK Biobank. Combining both genotyping and whole-exome sequencing data (∼12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p ×10 −8 , which replicated at p .05. We found genes in associated loci to be independently enriched for expression in the cerebral cortex, neuronal development processes and differential regulation in early brain development. We integrated coding associations and brain eQTLs to refine genes for various loci and demonstrated shared signal in the pleiotropic KCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes. We provide an interactive 3D visualisation of our summary associations, making complex association patterns easier to interpret, and emphasising the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.843642
Publisher: Elsevier BV
Date: 11-2013
Publisher: Frontiers Media SA
Date: 2012
Publisher: Elsevier BV
Date: 04-2016
Publisher: SPIE
Date: 17-03-2015
DOI: 10.1117/12.2082241
Publisher: Society for Neuroscience
Date: 20-06-2012
Publisher: Proceedings of the National Academy of Sciences
Date: 03-01-2023
Abstract: The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how in idual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent s le. BA estimation errors are notably lower than those of previous studies. At both in idual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In in iduals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN in iduals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen in iduals according to their AD risk.
Publisher: Springer Science and Business Media LLC
Date: 18-04-2012
DOI: 10.1038/NPP.2012.49
Publisher: Elsevier BV
Date: 12-2010
DOI: 10.1016/J.NEUROIMAGE.2010.07.018
Abstract: Cerebellar dysfunction has been proposed to lead to "cognitive dysmetria" in schizophrenia via the cortico-cerebellar-thalamic-cortical circuit, contributing to a range of cognitive and clinical symptoms of the disorder. Here we investigated total cerebellar grey and white matter volumes and cerebellar regional grey matter abnormalities in 13 remitted first-episode schizophrenia patients with less than 2 years' duration of illness. Patient data were compared to 13 pair-wise age, gender, and handedness-matched healthy volunteers using cortical pattern averaging on high-resolution magnetic resonance images. Total cerebellar volume and total grey matter volumes in first-episode schizophrenia patients did not differ from healthy control subjects, but total cerebellar white matter was increased and total grey to white matter ratios were reduced in patients. Four clusters of cerebellar grey matter reduction were identified: (i) in superior vermis (ii) in the left lobuli VI (iii) in right-inferior lobule IX, extending into left lobule IX and (iv) bilaterally in the areas of lobuli III, peduncle and left flocculus. Grey matter deficits were particularly prominent in right lobuli III and IX, left flocculus and bilateral pedunculi. These cerebellar areas have been implicated in attention control, emotional regulation, social functioning, initiation of smooth pursuit eye movements, eye-blink conditioning, language processing, verbal memory, executive function and the processing of spatial and emotional information. Consistent with common clinical, cognitive, and pathophysiological signs of established illness, our findings demonstrate cerebellar pathology as early as in first-episode schizophrenia.
Publisher: IEEE
Date: 05-2008
Publisher: Elsevier BV
Date: 12-2014
Publisher: Cambridge University Press (CUP)
Date: 20-10-2023
Publisher: Springer Science and Business Media LLC
Date: 11-10-2016
DOI: 10.1038/MP.2016.164
Publisher: Elsevier BV
Date: 2015
Publisher: SAGE Publications
Date: 18-09-2012
Abstract: Direct neuronal loss or deafferentation of the putamen, a critical hub in corticostriatal circuits, may result in erse and distinct cognitive and motoric dysfunction in neurodegenerative disease. Differential putaminal morphology, as a quantitative measure of corticostriatal integrity, may thus be evident in Huntington’s disease (HD), Alzheimer’s disease (AD) and frontotemporal dementia (FTD), diseases with differential clinical dysfunction. HD ( n = 17), FTD ( n = 33) and AD ( n = 13) patients were diagnosed according to international consensus criteria and, with healthy controls ( n = 17), were scanned on the same MRI scanner. Patients underwent brief cognitive testing using the Neuropsychiatry Unit Cognitive Assessment Tool (NUCOG). Ten MRI scans from this dataset were manually segmented as a training set for the Adaboost algorithm, which automatically segmented all remaining scans for the putamen, yielding the following subset of the data: 9 left and 12 right putamen segmentations for AD 25 left and 26 right putamina for FTD 16 left and 15 right putamina for HD 12 left and 12 right putamina for controls. Shape analysis was performed at each point on the surface of each structure using a multiple regression controlling for age and sex to compare radial distance across diagnostic groups. Age, but not sex and intracranial volume (ICV), were significantly different in the segmentation subgroups by diagnosis. The AD group showed significantly poorer performance on cognitive testing than FTD. Mean putaminal volumes were HD FTD AD ≤ controls, controlling for age and ICV. The greatest putaminal shape deflation was evident in HD, followed by FTD, in regions corresponding to the interconnections to motoric cortex. Differential patterns of putaminal atrophy in HD, FTD and AD, with relevance to corticostriatal circuits, suggest the putamen may be a suitable clinical biomarker in neurodegenerative disease.
Publisher: Springer Science and Business Media LLC
Date: 05-08-2008
Publisher: Center for Open Science
Date: 28-04-2021
Abstract: On average, men and women differ in brain structure and behaviour, raising the possibility of a link between sex differences in brain and behaviour. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (N=1,040) and Human Connectome Project (N=1,113), we obtained data-driven measures of in idual differences along a male-female dimension for brain and behaviour based on average sex differences in brain structure and behaviour, respectively. We found a weak association between these brain and behavioural differences, driven by brain size. These brain and behavioural differences were moderately heritable. Our findings suggest that behavioural sex differences are to some extent related to sex differences in brain structure, but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously.
Publisher: Elsevier BV
Date: 04-2017
Publisher: Springer Science and Business Media LLC
Date: 15-04-2012
DOI: 10.1038/NG.2250
Publisher: Wiley
Date: 26-02-2022
DOI: 10.1111/PCN.13337
Abstract: This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder‐related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ BD MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
Publisher: Springer Science and Business Media LLC
Date: 18-08-2013
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 16-05-2023
DOI: 10.1212/WNL.0000000000207219
Abstract: Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21 95% CI 0.04–0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β = −0.28 95% CI −0.41 to −0.15, p 0.001) and across multiple domains of function (β = −0.14 95% CI −0.22 to −0.06, p 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%–58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01–1.08, p = 0.004). We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 16-02-2011
Publisher: Wiley
Date: 20-08-2012
DOI: 10.1111/J.1468-1331.2012.03859.X
Abstract: The European Federation of the Neurological Societies (EFNS) guidelines on the use of neuroimaging in the diagnosis and management of dementia are designed to revise and expand previous EFNS recommendations for the diagnosis and management of patients with Alzheimer's disease (AD) and to provide an overview of the evidence for the use of neuroimaging techniques in non-AD dementias, as well as general recommendations that apply to all types of dementia in clinical practice. The task force working group reviewed evidence from original research articles, meta-analyses and systematic reviews, published before April 2012. The evidence was classified, and consensus recommendations were given and graded according to the EFNS guidance regulations. Structural imaging, which should be performed at least once in the diagnostic work-up of patients with cognitive impairment, serves to exclude other potentially treatable diseases, to recognize vascular lesions and to identify specific findings to help distinguish different forms of neurodegenerative types of dementia. Although typical cases of dementia may not benefit from routine functional imaging, these tools are recommended in those cases where diagnosis remains in doubt after clinical and structural imaging work-up and in particular clinical settings. Amyloid imaging is likely to find clinical utility in several fields, including the stratification of patients with mild cognitive impairment into those with and without underlying AD and the evaluation of atypical AD presentations. A number of recommendations and good practice points are made to improve the diagnosis of AD and other dementias.
Publisher: Wiley
Date: 23-03-2016
DOI: 10.1002/HBM.23177
Publisher: Acoustical Society of America (ASA)
Date: 29-05-2003
DOI: 10.1121/1.1568943
Abstract: Comparative analyses of the roar vocalization of male harbor seals from ten sites throughout their distribution showed that vocal variation occurs at the oceanic, regional, population, and subpopulation level. Genetic barriers based on the physical distance between harbor seal populations present a likely explanation for some of the observed vocal variation. However, site-specific vocal variations were present between genetically mixed subpopulations in California. A tree-based classification analysis grouped Scottish populations together with eastern Pacific sites, rather than amongst Atlantic sites as would be expected if variation was based purely on genetics. Lastly, within the classification tree no in idual vocal parameter was consistently responsible for consecutive splits between geographic sites. Combined, these factors suggest that site-specific variation influences the development of vocal structure in harbor seals and these factors may provide evidence for the occurrence of vocal dialects.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Start Date: 07-2005
End Date: 09-2007
Amount: $95,254.00
Funder: Australian Research Council
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