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Thompson, P. M.

Publications and source records attributed to Thompson, P. M..

At least 109 records · Page 6Linked to original sources

Time Perception through the Processing of Verb Tenses: An ERP study regarding Mental Time Travel

Humans are equipped with the so-called Mental Time Travel (MTT) ability, which allows them to consciously construct and elaborate past or future scenes. The mechanisms underlying MTT remain elusive. This study focused on the late positive potential (LPP) and alpha oscillations, considering that LPP covaries with the temporal continuity whereas the alpha oscillations index the temporal organization of perception. To that end, subjects were asked to focus on performing two mental functions engaging working memory, which involved mental self-projection into either the present-past (PP) border or the present-future (PF) border. To evaluate underlying mechanisms, the evoked frontal late positive potentials (LPP) as well as their cortical sources were analyzed via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The LPP amplitudes - in the left lateral prefrontal areas that were elicited during PF tasks - were significantly higher than those associated with PP, whereas opposite patterns were observed in the central and right prefrontal areas. Crucially, the LPP activations of both the PP and PF self-projections overlapped with the brains default mode network and related interacting areas. Finally, we found enhanced alpha-related activation with respect to PP in comparison to PF, predominantly over the right hemisphere central brain regions (specifically, the precentral gyrus). These findings confirm that the two types of self-projection, as reflected by the frontally-distributed LPP, share common cortical resources that recruit different brain regions in a balanced way. This balanced distribution of brain activation might signify that biological time tends to behave in a homeostatic way.

neuroscience↗

The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

Among big data initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium--a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups--has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing 100+ ENIGMA datasets, facilitating cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs.io. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/423838v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@183a3f3org.highwire.dtl.DTLVardef@180d368org.highwire.dtl.DTLVardef@b752a3org.highwire.dtl.DTLVardef@1928c53_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,125 Individuals from the ADNI and OASIS Databases

Biomarker-assisted preclinical/early detection and intervention in Alzheimers disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (A{beta}) plaques in the human brain. However, current methods to detect A{beta} pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain A{beta} burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each subject. Then we apply these individual representations and a binary random forest classifier to predict brain A{beta} positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate A{beta} positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.81 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM), and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

bioinformatics↗

Sex-Dependent Age Trajectories of Subcortical Brain Structures: Analysis of Large-Scale Percentile Models and Shape Morphometry

Modeling of structural brain variation over the lifespan is important to better understand factors contributing to healthy aging and risk for neurological conditions such as Alzheimers disease. Even so, we lack normative data on brain morphometry across the adult lifespan in large, well-powered samples. Here, in a large population-based sample of 26,440 adults from the UK Biobank (age: 44-81 yrs.), we created normative percentile charts for MRI-derived subcortical volumes. Next, we investigated associations between these morphometric measures and the strongest known genetic risk factor for late-onset Alzheimers disease (APOE genotype) and mapped the spatial distribution of age-by-sex interactions using computational surface mesh modeling and shape analysis. Vertex-wise shape mapping supplements traditional gross volumetric approaches to reveal finer-grained variations across functionally important brain subcompartments. Normative curves revealed volumetric loss with age, as expected, for all subcortical brain structures except for the lateral ventricles, which expanded with age. Surprisingly, no volumetric associations with APOE genotype were detected, despite the very large sample size. Age-related trajectories for volumes differed in women versus men, and surface-based statistical maps revealed the spatial distribution of the age-by-sex interaction. Subcortical volumes declined faster in men than women over the full age range, but after age 60, fewer structures showed sex-dependent trajectories, indicating similar volumetric changes in older men and women. Large-scale statistical modeling of age effects on brain structures may drive new insights into individual differences in brain aging and help to identify factors that promote healthy brain aging and risk for disease.

neuroscience↗

Exogenous sex hormone effects on brain microstructure in women: a diffusion MRI study in the UK Biobank

Changes in estrogen levels in women have been associated with increased risk for age-related neurodegenerative diseases, including Alzheimers disease, but the impact of exogenous estrogen exposure on the brain is poorly understood. Oral contraceptives (OC) and hormone therapy (HT) and are both common sources of exogenous estrogen for women in reproductive and post-menopausal years, respectively. Here we examined the association of exogenous sex hormone exposure with the brains white matter (WM) aging trajectories in postmenopausal women using and not using OC and HT (HT users: n=3,033, non-users n=5,093; OC users: n=6,964; non-users n=1,156), while also investigating multiple dMRI models. Cross-sectional brain dMRI data was analyzed from the UK Biobank using conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted across the whole brain, and fractional polynomial regressions were used to characterize age-related trajectories for WM microstructural measures. Advanced dMRI model NODDI revealed a steeper WM aging trajectory in HT users relative to non-users, and for those using unopposed estrogens relative to combined estrogens treatment. By contrast, no interaction was detected between OC status and age effects on the diffusivity measures we examined. Exogenous sex hormone exposure may negatively impact WM microstructure aging in postmenopausal women. We also present normative reference curves for white matter microarchitectural parameters in women, to help identify individuals with microstructural anomalies.

neuroscience↗

Advanced diffusion-weighted MRI metrics detect sex differences in aging among 15,000 adults in the UK Biobank

A comprehensive characterization of the brains white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45-80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.

neuroscience↗

White Matter Disruption in Pediatric Traumatic Brain Injury: Results from ENIGMA Pediatric msTBI

Annually, approximately 3 million children around the world experience traumatic brain injuries (TBIs), of which up to 20% are characterized as moderate to severe (msTBI) and/or have abnormal imaging findings. Affected children are vulnerable to long-term cognitive and behavioral dysfunction, as injury can disrupt or alter ongoing brain maturation. Post-injury outcomes are highly variable, and there is only limited understanding of how inter-individual differences in outcomes arise. Small sample sizes have also complicated efforts to better understand factors influencing the impact of TBI on the developing brain. White matter (WM) disruption is a critical aspect of TBI neuropathology and diffusion MRI (dMRI) is particularly sensitive to microstructural abnormalities. Here we present the results of a coordinated analysis of dMRI data across ten cohorts from three countries. We had three primary aims: (1) to characterize the nature and extent of WM disruption across key post-injury intervals (acute/subacute - within 2 months, post-acute - 2-6 months, chronic - 6+ months); (2) evaluate the impact of age and sex on WM in the context of injury; and (3) to examine associations between WM and neurobehavioral outcomes. Based on data from 507 children and adolescents (244 with complicated mild to severe TBI and 263 control children), we report widespread WM disruption across all post-injury intervals. As expected, injury severity was a significant contributor to the pattern and extent of WM degradation, but explained less variance in dMRI measures with increasing time since injury, supporting other research indicating that other factors contribute increasingly to outcomes over time. The corpus callosum appears to be particularly vulnerable to injury, an effect that persists years post-TBI. We also report sex differences in the effect of TBI on the uncinate fasciculus (UNC), a structure with a key role in emotion regulation. Females with a TBI had significantly lower fractional anisotropy (FA) in the UNC than those with no TBI, and this phenomenon was further associated with more frequent parent-reported behavioral problems as measured by the Child Behavior Checklist (CBCL). These effects were not detected in males. With future harmonization of imaging and neurocognitive data, more complex modeling of factors influencing outcomes will be possible and help to identify clinically-meaningful patient subtypes.

neuroscience↗

Accurate brain age prediction using recurrent slice-based networks

BrainAge (a subjects apparent age predicted from neuroimaging data) is an important biomarker of brain aging. The deviation of BrainAge from true age has been associated with psychiatric and neurological disease, and has proven effective in predicting conversion from mild cognitive impairment (MCI) to dementia. Conventionally, 3D convolutional neural networks and their variants are used for brain age prediction. However, these networks have a larger number of parameters and take longer to train than their 2D counterparts. Here we propose a 2D slice-based recurrent neural network model, which takes in an ordered sequence of sagittal slices as input to predict the brain age. The model consists of two components: a 2D convolutional neural network (CNN), which encodes the relevant features from the slices, and a recurrent neural network (RNN) that learns the relationship between slices. We compare our method to other recently proposed methods, including 3D deep convolutional regression networks, information theoretic models, and bag-of-features (BoF) models (such as BagNet) - where the classification is based on the occurrences of local features, without taking into consideration their global spatial ordering. In our experiments, our proposed model performs comparably to, or better than, the current state of the art models, with nearly half the number of parameters and a lower convergence time.

neuroscience↗

Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional MRI data from three cohorts of healthy older adults (total N=563; age=55-95years) and a younger-adult cohort (N=128; age=18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (p=2.3x10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.

neuroscience↗

Elevated plasma cortisol associated with larger ventricles and smaller hippocampal volumes, a study in 2 independent elderly cohorts

Cortisol is considered the most fundamental stress hormone and is elevated in stress and multiple neuropsychiatric conditions. Prior studies have shown associations of plasma cortisol levels with total cerebral and hippocampal volumes and less consistently with the amygdala. Here, we extend our hypothesis to test associations of plasma cortisol with 1) ventricular 2) hippocampal and 3) amygdalar volumes, in two independent elderly cohorts across a broad cognitive spectrum ranging from normal cognition to Alzheimers disease. We demonstrate elevated cortisol to be associated with larger lateral ventricular volumes and smaller hippocampal volumes, predominantly in the right cerebral hemisphere, regardless of age, sex or cognitive status. We noted a non-significant trend of smaller amygdalar volumes with elevated cortisol. Our findings support smaller brain parenchyma volumes seen with elevated cortisol and may encourage effective strategies reducing cortisol and stress. They may also serve as imaging biomarkers for assessing therapeutic benefits of stress and cortisol lowering interventions aiming to halt or reverse the brain volume alterations and theoretically improve cognition and quality of life. HighlightsO_LIElevated cortisol associated with larger ventricular volumes and smaller hippocampal volumes C_LIO_LIAssociations are predominantly noted in the right cerebral hemisphere. C_LIO_LISimilar non-significant trends noted in amygdalar volumes C_LIO_LICortisol and stress reducing strategies may halt brain changes and improve quality of life C_LIO_LIImaging biomarkers may help assess efficacy of cortisol-lowering therapeutic interventions C_LI

neuroscience↗

Analysis of structural brain asymmetries in Attention-Deficit/Hyperactivity Disorder in 39 datasets

ObjectiveSome studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here we performed the largest-ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. MethodsWe analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,978 people with ADHD and unaffected 1,917 controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modelling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. ResultsThere was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t=2.4, P=0.019). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t=2.4, P=0.007) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohens d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. ConclusionPrior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

neuroscience↗

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

As stroke mortality rates decrease, there has been a surge of effort to study post-stroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in post-stroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric and spatial comparisons to the manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control and spatial accuracy. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

neuroscience↗

Cross-sectional and longitudinal associations of family income-to-needs ratio with cortical and subcortical brain volume in adolescent boys and girls

Deviations in neurodevelopment may underlie the association between lower childhood socioeconomic status and difficulties in cognitive and socioemotional domains. Most previous investigations of the association between childhood socioeconomic status and brain morphology have used cross-sectional designs with samples that span wide age ranges, occluding effects specific to adolescence. Sex differences in the association between childhood socioeconomic status and neurodevelopment may emerge or intensify during adolescence. We used tensor-based morphometry, a whole brain approach, to examine sex differences in the cross-sectional association between normative variation in family income-to-needs ratio (INR) and cortical and subcortical gray and white matter volume during early adolescence (ages 9-13 years, N=147), as well as in the longitudinal association between in INR and change in volume from early to later adolescence (ages 11-16 years, N=109). Biological sex interacted with INR to explain variation in volume in several areas cross-sectionally and longitudinally. Effects were primarily in cortical gray matter areas, including regions of the association cortex and sensorimotor processing areas. Effect sizes tended to be larger in boys than in girls. Biological sex may be an important variable to consider in analyses of the effects of family income on structural neurodevelopment during adolescence. HighlightsO_LISex-specific associations of SES with neurodevelopment may emerge in adolescence. C_LIO_LIWe used a whole-brain approach to examine gray and white matter volume. C_LIO_LISex interacted with SES to explain variation in volume across adolescence. C_LIO_LISex is an important variable to consider in analyses of SES and brain volume. C_LI

developmental biology↗

The reliability and heritability of cortical folds and their genetic correlations across hemispheres.

The structure of the brains cortical folds varies considerably in human populations. Specific patterns of cortical variation arise with development and aging, and cortical traits are partially influenced by genetic factors. The degree to which genetic factors affect cortical folding patterning remains unknown, yet may be estimated with large-scale in-vivo brain MRI. Using multiple MRI datasets from around the world, we estimated the reliability and heritability of sulcal morphometric characteristics including length, depth, width, and surface area, for 61 sulci per hemisphere of the human brain. Reliability was assessed across four distinct test-retest datasets. We meta-analyzed the heritability across three independent family-based cohorts (N > 3,000), and one cohort of largely unrelated individuals (N~9,000) to examine the robustness of our findings. Reliability was high (interquartile range for ICC: 0.65-0.85) for sulcal metrics. Most sulcal measures were moderately to highly heritable (heritability estimates = 0.3-0.7). These genetic influences vary regionally, with the earlier forming sulci having higher heritability estimates. The central sulcus, the subcallosal and the collateral fissure were the most highly heritable regions. For some frontal and temporal sulci, left and right genetic influences did not completely overlap, suggesting some lateralization of genetic effects on the cortex.

genetics↗

Smaller limbic structures are associated with greater immunosuppression in over 1000 HIV-infected adults across five continents: Findings from the ENIGMA-HIV Working Group

BackgroundHuman immunodeficiency virus type-1 (HIV) infection can be controlled with combination antiretroviral therapy (cART), but neurocognitive impairment remains common even in chronic and treated HIV-infected (HIV+) cohorts. Identifying the neuroanatomical pathways associated with infection has the potential to delineate novel neuropathological processes underlying persisting deficits, yet individual neuroimaging studies have yielded inconsistent findings. The ENIGMA-HIV Working Group was established to harmonize data from diverse studies to identify the common effects of HIV-infection on brain structure.\n\nMethodsData were pooled from 12 independent neuroHIV studies from Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1-weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72.4% on cART; 70.3% male; 41.6% with detectable viral load (dVL)), to identify associations with plasma markers reflecting current immunosuppression (CD4+ T-cell count) or dVL. Follow-up analyses stratified data by cART status and sex. Bonferroni correction was used to determine statistical significance.\n\nFindingsLower current CD4+ count was associated with smaller hippocampal ({beta} = 20.3 mm3 per 100 cells/mm3; p = 0.0001) and thalamic volumes ({beta} = 29.3; p = 0.003); in the subset of participants not on cART, it was associated with smaller putamen volumes ({beta} = 65.1; p = 0.0009). On average, a dVL was associated with smaller hippocampal (Cohens d = 0.24; p = 0.0003) and amygdala volumes (d = 0.18; p = 0.0058).\n\nInterpretationIn HIV+ individuals across five continents, smaller limbic volumes were consistently associated with current plasma markers. As we assessed cohorts with different inclusion/exclusion criteria and demographic distributions, these deficits may represent a generalizable brain-signature of HIV infection in the cART era. Our findings support the importance of achieving viral suppression and immune restoration for maintaining brain health.\n\nFundingThis work was supported, in part, by NIH grant U54 EB020403.\n\nResearch in ContextO_ST_ABSEvidence before this studyC_ST_ABSHIV type-1 infection can be managed with antiretroviral therapy, however neurocognitive impairment persists even in treated HIV+ individuals. Given the challenges associated with standardized cognitive testing, there is a need to identify quantitative markers of central nervous system impairment. A number of neuroimaging studies have reported brain abnormalities in HIV-infected patients; however, prior studies investigating associations between CD4+ T-cell count or HIV viral load and subcortical brain volume report variable effect sizes and regional distributions of effects, limiting the generalizability of the conclusions drawn to date. We have conducted a literature search for reports in English language journals published until June 2019, using the following search terms: HIV AND subcortical AND neuroimaging AND brain AND viral load AND RNA AND CD4. After removing studies that were not applicable, there were 30 studies investigating CD4+ T-cell count and viral load associations with subcortical brain structure.\n\nAdded value of the studyThe aim of the current study was to investigate structural brain associations with two biomarkers universally used to monitor immune function and treatment response, namely plasma RNA viral load and CD4+ T-cell counts. Prior analyses have been performed in smaller, heterogeneous cohorts, but by combining data across cohorts, we can identify consistent associations between brain volume and indicators of HIV infection across cohorts. The ENIGMA-HIV Working Group was established to identify common neurobiological signatures of the HIV-infected brain by harmonizing data analysis from HIV neuroimaging studies worldwide. The value of this dataset is that it is well-powered and representative of many HIV+ people living in the cART era.\n\nImplications of all the available evidenceOur results provide robust evidence that despite demographic and clinical heterogeneity among HIV-infected individuals, brain abnormalities are consistently linked to HIV viral load and immunosuppression. This supports the importance of achieving viral suppression and immune system restoration in maintaining brain health in people living with HIV. The vulnerability of limbic regions, found in this study, extends beyond the classically implicated regions of the basal ganglia; this suggests that these regions remain an important target of cART era HIV research, especially given their heightened vulnerability to age-associated atrophy and neurodegeneration.

neuroscience↗

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area

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 diverse 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.

neuroscience↗

Subcortical brain volume, regional cortical thickness and cortical surface area across attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD): findings from the ENIGMA-ADHD, -ASD, and -OCD working groups

ObjectiveAttention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders.\n\nMethodsStructural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures).\n\nResultsWe found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed.\n\nConclusionOur findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders.

neuroscience↗

Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group

Alterations in regional subcortical brain volumes have been widely investigated as part of the efforts of an international consortium, ENIGMA, to determine reliable structural brain signatures for Major Depressive Disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work to precisely map localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with MDD had lower surface area in the subiculum of the hippocampus, the basolateral amygdala, and the nucleus accumbens shell. Relative to CTL, patients with adolescent-onset MDD ([≤] 21 years) had lower thickness and surface area of the subiculum of the hippocampus and the basolateral amygdala. Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala. Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

neuroscience↗