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Background: Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods: We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results: The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions: The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.
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Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ('latent indices') and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.
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Encéfalo , Cognición , Imagen por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Femenino , Cognición/fisiología , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Adulto , Conectoma/métodos , Mapeo Encefálico/métodos , Persona de Mediana Edad , Conducta/fisiologíaRESUMEN
Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.
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Emociones , Imagen por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Femenino , Masculino , Emociones/fisiología , Adulto , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Mapeo Encefálico/métodos , Adulto Joven , Persona de Mediana Edad , Expresión Facial , Reconocimiento Facial/fisiologíaRESUMEN
Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.
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Neuroimaging studies have identified the anterior cingulate cortex (ACC) as one of the major targets of ketamine in the human brain, which may be related to ketamine's antidepressant (AD) mechanisms of action. However, due to different methodological approaches, different investigated populations, and varying measurement timepoints, results are not consistent, and the functional significance of the observed brain changes remains a matter of open debate. Inhibition of glutamate release during acute ketamine administration by lamotrigine provides the opportunity to gain additional insight into the functional significance of ketamine-induced brain changes. Furthermore, the assessment of trait negative emotionality holds promise to link findings in healthy participants to potential AD mechanisms of ketamine. In this double-blind, placebo-controlled, randomized, single dose, parallel-group study, we collected resting-state fMRI data before, during, and 24 h after ketamine administration in a sample of 75 healthy male and female participants who were randomly allocated to one of three treatment conditions (ketamine, ketamine with lamotrigine pre- treatment, placebo). Spontaneous brain activity was extracted from two ventral and one dorsal subregions of the ACC. Our results showed activity decreases during the administration of ketamine in all three ACC subregions. However, only in the ventral subregions of the ACC this effect was attenuated by lamotrigine. 24 h after administration, ACC activity returned to baseline levels, but group differences were observed between the lamotrigine and the ketamine group. Trait negative emotionality was closely linked to activity changes in the subgenual ACC after ketamine administration. These results contribute to an understanding of the functional significance of ketamine effects in different subregions of the ACC by combining an approach to modulate glutamate release with the assessment of multiple timepoints and associations with trait negative emotionality in healthy participants.
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Emociones , Giro del Cíngulo , Ketamina , Lamotrigina , Imagen por Resonancia Magnética , Humanos , Ketamina/farmacología , Ketamina/administración & dosificación , Lamotrigina/farmacología , Lamotrigina/administración & dosificación , Giro del Cíngulo/efectos de los fármacos , Giro del Cíngulo/diagnóstico por imagen , Giro del Cíngulo/metabolismo , Masculino , Femenino , Método Doble Ciego , Adulto , Emociones/efectos de los fármacos , Adulto Joven , Antidepresivos/farmacología , Antidepresivos/administración & dosificación , Antagonistas de Aminoácidos Excitadores/farmacología , Antagonistas de Aminoácidos Excitadores/administración & dosificaciónRESUMEN
Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.
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Modular artificial hip joints are a clinical standard today. However, the release of wear products from the head-taper interface, which includes wear particles in the nm size range, as well as metal ions, have raised concerns. Depending on the loading of such taper joints, a wide variety of different mechanisms have been found by retrieval analyses. From these, this paper concentrates on analyzing the contribution of gross slip fretting corrosion at ultra-mild wear rates using a bovine calf serum solution (BCS) as the lubricant. The parameters were chosen based on biomechanical considerations, producing wear rates of some ng/m wear path. In parallel, the evolution of tribomaterial (third bodies) was analyzed as to its constituents and generation rates. It has already been shown earlier that, by an advantageous combination of wear mechanisms and submechanisms, certain constituents of the tribomaterial remain inside the contact area and act like extreme-pressure lubricant additives. For the known wear and corrosion resistance of austenitic high-nitrogen steels (AHNSs), which outperform CoCrMo alloys even under inflammatory conditions, we hypothesized that such steels will generate ultra-mild wear rates under gross slip fretting. While testing AHNSs against commercially available biomedical-grade materials of CoCrMo and TiAlV alloys, as well as zirconia-toughened alumina (ZTA) and against itself, it was found that AHNSs in combination with a Ti6Al4V alloy generated the smallest wear rate under gross slip fretting corrosion. This paper then discusses the wear behavior on the basis of ex situ analyses of the worn surfaces as to the acting wear mechanisms and submechanisms, as well as to the tribological reaction products.
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While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.
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The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz - max| = 0.33, |rraw - max| = 0.30; ICs: |rz - max| = 0.23, |rraw - max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.
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BACKGROUND: Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group. METHODS: We studied 781 autistic and 927 non-autistic individuals (6-30 years, IQ ≥ 50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants' loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group. RESULTS: The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (ß = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (ß = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085). LIMITATIONS: The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample. CONCLUSIONS: The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.
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Trastorno del Espectro Autista , Trastorno Autístico , Humanos , Sustancia Gris/diagnóstico por imagen , Trastorno Autístico/diagnóstico por imagen , Trastorno del Espectro Autista/diagnóstico por imagen , Estudios Retrospectivos , Imagen por Resonancia Magnética/métodos , Encéfalo/diagnóstico por imagenRESUMEN
BACKGROUND: Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. METHODS: We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. RESULTS: Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. DISCUSSION: Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.
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Trastorno Depresivo Mayor , Fenotipo , Teléfono Inteligente , Humanos , Trastorno Depresivo Mayor/diagnósticoRESUMEN
The current neuropsychiatric nosological categories underlie pragmatic treatment choice, regulation and clinical research but does not encompass biological rationale. However, subgroups of patients suffering from schizophrenia or Alzheimer's disease have more in common than the neuropsychiatric nature of their condition, such as the expression of social dysfunction. The PRISM project presents here initial quantitative biological insights allowing the first steps toward a novel trans-diagnostic classification of psychiatric and neurological symptomatology intended to reinvigorate drug discovery in this area. In this study, we applied spectral clustering on digital behavioural endpoints derived from passive smartphone monitoring data in a subgroup of Schizophrenia and Alzheimer's disease patients, as well as age matched healthy controls, as part of the PRISM clinical study. This analysis provided an objective social functioning characterization with three differential clusters that transcended initial diagnostic classification and was shown to be linked to quantitative neurobiological parameters assessed. This emerging quantitative framework will both offer new ways to classify individuals in biologically homogenous clusters irrespective of their initial diagnosis, and also offer insights into the pathophysiological mechanisms underlying these clusters.
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Enfermedad de Alzheimer , Esquizofrenia , Humanos , Esquizofrenia/diagnóstico , Enfermedad de Alzheimer/diagnósticoRESUMEN
BACKGROUND: Autism is a heterogeneous neurodevelopmental condition accompanied by differences in brain connectivity. Structural connectivity in autism has mainly been investigated within the white matter. However, many genetic variants associated with autism highlight genes related to synaptogenesis and axonal guidance, thus also implicating differences in intrinsic (i.e., gray matter) connections in autism. Intrinsic connections may be assessed in vivo via so-called intrinsic global and local wiring costs. METHODS: Here, we examined intrinsic global and local wiring costs in the brain of 359 individuals with autism and 279 healthy control participants ages 6 to 30 years from the EU-AIMS LEAP (Longitudinal European Autism Project). FreeSurfer was used to derive surface mesh representations to compute the estimated length of connections required to wire the brain within the gray matter. Vertexwise between-group differences were assessed using a general linear model. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to link neuroanatomical differences to putative underpinnings. RESULTS: Group differences in global and local wiring costs were predominantly observed in medial and lateral prefrontal brain regions, in inferior temporal regions, and at the left temporoparietal junction. The resulting neuroanatomical patterns were enriched for genes that had been previously implicated in the etiology of autism at genetic and transcriptomic levels. CONCLUSIONS: Based on intrinsic gray matter connectivity, the current study investigated the complex neuroanatomy of autism and linked between-group differences to putative genomic and/or molecular mechanisms to parse the heterogeneity of autism and provide targets for future subgrouping approaches.
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Trastorno del Espectro Autista , Sustancia Blanca , Humanos , Sustancia Gris/diagnóstico por imagen , Trastorno del Espectro Autista/diagnóstico por imagen , Trastorno del Espectro Autista/genética , Imagen por Resonancia Magnética/métodos , Corteza Cerebral , Encéfalo/diagnóstico por imagen , Sustancia Blanca/diagnóstico por imagen , GenómicaRESUMEN
While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.
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BACKGROUND: Anhedonia and other deficits in reward- and motivation-related processing in psychiatric patients, including patients with major depressive disorder (MDD), represent a high unmet medical need. Neurobiologically, these deficits in MDD patients are mainly associated with low dopamine function in a frontostriatal network. In this study, alterations in brain activation changes during reward processing and at rest in MDD patients compared with healthy subjects are explored and the effects of a single low dose of the dopamine D2 receptor antagonist amisulpride are investigated. METHODS: This is a randomized, controlled, double-blind, single-dose, single-center parallel-group clinical trial to assess the effects of a single dose of amisulpride (100 mg) on blood-oxygenation-level-dependent (BOLD) responses during reward- and motivation-related processing in healthy subjects (n = 60) and MDD patients (n = 60). Using functional magnetic resonance imaging (fMRI), BOLD responses are assessed during the monetary incentive delay (MID) task (primary outcome). Exploratory outcomes include BOLD responses and behavioral measures during the MID task, instrumental learning task, effort-based decision-making task, social incentive delay task, and probabilistic reward task as well as changes in resting state functional connectivity and cerebral blood flow. DISCUSSION: This study broadly covers all aspects of reward- and motivation-related processing as categorized by the National Institute of Mental Health Research Domain Criteria and is thereby an important step towards precision psychiatry. Results regarding the immediate effects of a dopaminergic drug on deficits in reward- and motivation-related processing not only have the potential to significantly broaden our understanding of underlying neurobiological processes but might eventually also pave the way for new treatment options. TRIAL REGISTRATION: ClinicalTrials.gov NCT05347199. April 12, 2022.
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Trastorno Depresivo Mayor , Humanos , Trastorno Depresivo Mayor/diagnóstico por imagen , Trastorno Depresivo Mayor/tratamiento farmacológico , Motivación , Amisulprida/efectos adversos , Imagen por Resonancia Magnética/métodos , Voluntarios Sanos , Encéfalo/diagnóstico por imagen , Recompensa , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
An increasing number of patients with type 2 diabetes (T2DM) will require total joint replacement (TJR) in the next decade. T2DM patients are at increased risk for TJR failure, but the mechanisms are not well understood. The current study used the Zucker Diabetic-Sprague Dawley (ZDSD) rat model of T2DM with Sprague Dawley (SPD) controls to investigate the effects of intramedullary implant placement on osseointegration, peri-implant bone structure and matrix composition, and fixation strength at 2 and 10 weeks post-implant placement. Postoperative inflammation was assessed with circulating MCP-1 and IL-10 2 days post-implant placement. In addition to comparing the two groups, stepwise linear regression modeling was performed to determine the relative contribution of glucose, cytokines, bone formation, bone structure, and bone matrix composition on osseointegration and implant fixation strength. ZDSD rats had decreased peri-implant bone formation and reduced trabecular bone volume per total volume compared with SPD controls. The osseointegrated bone matrix of ZDSD rats had decreased mineral-to-matrix and increased crystallinity compared with SPD controls. Osseointegrated bone volume per total volume was not different between the groups, whereas implant fixation was significantly decreased in ZDSD at 2 weeks but not at 10 weeks. A combination of trabecular mineral apposition rate and postoperative MCP-1 levels explained 55.6% of the variance in osseointegration, whereas cortical thickness, osseointegration mineral apposition rate, and matrix compositional parameters explained 69.2% of the variance in implant fixation strength. The results support the growing recognition that both peri-implant structure and matrix composition affect implant fixation and suggest that postoperative inflammation may contribute to poor outcomes after TJR surgeries in T2DM patients. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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BACKGROUND: Autism spectrum disorders (ASD) are neurodevelopmental conditions accompanied by differences in brain development. Neuroanatomical differences in autism are variable across individuals and likely underpin distinct clinical phenotypes. To parse heterogeneity, it is essential to establish how the neurobiology of ASD is modulated by differences associated with co-occurring conditions, such as attention-deficit/hyperactivity disorder (ADHD). This study aimed to (1) investigate between-group differences in autistic individuals with and without co-occurring ADHD, and to (2) link these variances to putative genomic underpinnings. METHODS: We examined differences in cortical thickness (CT) and surface area (SA) and their genomic associations in a sample of 533 individuals from the Longitudinal European Autism Project. Using a general linear model including main effects of autism and ADHD, and an ASD-by-ADHD interaction, we examined to which degree ADHD modulates the autism-related neuroanatomy. Further, leveraging the spatial gene expression data of the Allen Human Brain Atlas, we identified genes whose spatial expression patterns resemble our neuroimaging findings. RESULTS: In addition to significant main effects for ASD and ADHD in fronto-temporal, limbic, and occipital regions, we observed a significant ASD-by-ADHD interaction in the left precentral gyrus and the right frontal gyrus for measures of CT and SA, respectively. Moreover, individuals with ASD + ADHD differed in CT to those without. Both main effects and the interaction were enriched for ASD-but not for ADHD-related genes. LIMITATIONS: Although we employed a multicenter design to overcome single-site recruitment limitations, our sample size of N = 25 individuals in the ADHD only group is relatively small compared to the other subgroups, which limits the generalizability of the results. Also, we assigned subjects into ADHD positive groupings according to the DSM-5 rating scale. While this is sufficient for obtaining a research diagnosis of ADHD, our approach did not take into account for how long the symptoms have been present, which is typically considered when assessing ADHD in the clinical setting. CONCLUSION: Thus, our findings suggest that the neuroanatomy of ASD is significantly modulated by ADHD, and that autistic individuals with co-occurring ADHD may have specific neuroanatomical underpinnings potentially mediated by atypical gene expression.
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Trastorno por Déficit de Atención con Hiperactividad , Trastorno del Espectro Autista , Trastorno Autístico , Humanos , Trastorno Autístico/diagnóstico por imagen , Trastorno Autístico/genética , Trastorno Autístico/complicaciones , Trastorno por Déficit de Atención con Hiperactividad/diagnóstico por imagen , Trastorno por Déficit de Atención con Hiperactividad/genética , Trastorno por Déficit de Atención con Hiperactividad/complicaciones , Neuroanatomía , Encéfalo/diagnóstico por imagen , Trastorno del Espectro Autista/diagnóstico por imagen , Trastorno del Espectro Autista/genética , Trastorno del Espectro Autista/complicaciones , GenómicaRESUMEN
Background: The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop. Methods: To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset. Results: At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively. Conclusions: This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.
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Environmental adversities constitute potent risk factors for psychiatric disorders. Evidence suggests the brain adapts to adversity, possibly in an adversity-type and region-specific manner. However, the long-term effects of adversity on brain structure and the association of individual neurobiological heterogeneity with behavior have yet to be elucidated. Here we estimated normative models of structural brain development based on a lifespan adversity profile in a longitudinal at-risk cohort aged 25 years (n = 169). This revealed widespread morphometric changes in the brain, with partially adversity-specific features. This pattern was replicated at the age of 33 years (n = 114) and in an independent sample at 22 years (n = 115). At the individual level, greater volume contractions relative to the model were predictive of future anxiety. We show a stable neurobiological signature of adversity that persists into adulthood and emphasize the importance of considering individual-level rather than group-level predictions to explain emerging psychopathology.
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Longevidad , Trastornos Mentales , Adulto , Humanos , Encéfalo , Ansiedad , NeurobiologíaRESUMEN
The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.