RESUMEN
The increasing global life expectancy brings forth challenges associated with age-related cognitive and motor declines. To better understand underlying mechanisms, we investigated the connection between markers of biological brain aging based on magnetic resonance imaging (MRI), cognitive and motor performance, as well as modifiable vascular risk factors, using a large-scale neuroimaging analysis in 40,579 individuals of the population-based UK Biobank and Hamburg City Health Study. Employing partial least squares correlation analysis (PLS), we investigated multivariate associative effects between three imaging markers of biological brain aging - relative brain age, white matter hyperintensities of presumed vascular origin, and peak-width of skeletonized mean diffusivity - and multi-domain cognitive test performances and motor test results. The PLS identified a latent dimension linking higher markers of biological brain aging to poorer cognitive and motor performances, accounting for 94.7% of shared variance. Furthermore, a mediation analysis revealed that biological brain aging mediated the relationship of vascular risk factors - including hypertension, glucose, obesity, and smoking - to cognitive and motor function. These results were replicable in both cohorts. By integrating multi-domain data with a comprehensive methodological approach, our study contributes evidence of a direct association between vascular health, biological brain aging, and functional cognitive as well as motor performance, emphasizing the need for early and targeted preventive strategies to maintain cognitive and motor independence in aging populations.
RESUMEN
Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.
Asunto(s)
Conectoma , Imagen por Resonancia Magnética , Películas Cinematográficas , Red Nerviosa , Humanos , Adulto , Conectoma/métodos , Red Nerviosa/fisiología , Red Nerviosa/diagnóstico por imagen , Emociones/fisiología , Individualidad , Femenino , Masculino , Narración , Adulto Joven , Nivel de Alerta/fisiologíaRESUMEN
The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.
RESUMEN
Adolescence is a period of dynamic brain remodeling and susceptibility to psychiatric risk factors, mediated by the protracted consolidation of association cortices. Here, we investigated whether longitudinal variation in adolescents' resilience to psychosocial stressors during this vulnerable period is associated with ongoing myeloarchitectural maturation and consolidation of functional networks. We used repeated myelin-sensitive Magnetic Transfer (MT) and resting-state functional neuroimaging (n = 141), and captured adversity exposure by adverse life events, dysfunctional family settings, and socio-economic status at two timepoints, one to two years apart. Development toward more resilient psychosocial functioning was associated with increasing myelination in the anterolateral prefrontal cortex, which showed stabilized functional connectivity. Studying depth-specific intracortical MT profiles and the cortex-wide synchronization of myeloarchitectural maturation, we further observed wide-spread myeloarchitectural reconfiguration of association cortices paralleled by attenuated functional reorganization with increasingly resilient outcomes. Together, resilient/susceptible psychosocial functioning showed considerable intra-individual change associated with multi-modal cortical refinement processes at the local and system-level.
Asunto(s)
Imagen por Resonancia Magnética , Vaina de Mielina , Funcionamiento Psicosocial , Resiliencia Psicológica , Humanos , Adolescente , Masculino , Femenino , Vaina de Mielina/metabolismo , Estudios Longitudinales , Corteza Prefrontal/fisiología , Corteza Prefrontal/diagnóstico por imagen , Estrés Psicológico/fisiopatología , Corteza Cerebral/fisiología , Corteza Cerebral/diagnóstico por imagenRESUMEN
Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as default or visual. Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.
RESUMEN
INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.
Asunto(s)
Fibrilación Atrial , Encéfalo , Disfunción Cognitiva , Imagen por Resonancia Magnética , Pruebas Neuropsicológicas , Humanos , Fibrilación Atrial/complicaciones , Masculino , Femenino , Disfunción Cognitiva/patología , Anciano , Encéfalo/patología , Encéfalo/diagnóstico por imagen , Pruebas Neuropsicológicas/estadística & datos numéricos , Neuroimagen , Persona de Mediana Edad , Función Ejecutiva/fisiología , Sustancia Blanca/patología , Sustancia Blanca/diagnóstico por imagenRESUMEN
Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.
Asunto(s)
Teorema de Bayes , Mapeo Encefálico , Encéfalo , Imagen por Resonancia Magnética , Humanos , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Masculino , Femenino , Mapeo Encefálico/métodos , Adulto , Adulto Joven , Modelos Neurológicos , Procesamiento de Imagen Asistido por Computador/métodos , Vías Nerviosas/fisiología , Vías Nerviosas/diagnóstico por imagenRESUMEN
STUDY OBJECTIVES: Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network (SN), and central executive network (CEN). METHODS: We selected 320 subjects from the ADNI database and divided by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores. RESULTS: Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterised by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were non-significant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD. CONCLUSIONS: We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.
RESUMEN
In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.
Asunto(s)
Encéfalo , Aprendizaje Automático , Imagen por Resonancia Magnética , Fenotipo , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Femenino , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Persona de Mediana Edad , Adulto , Anciano , Conducta , Descanso/fisiología , Mapeo Encefálico/métodosRESUMEN
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
Asunto(s)
Conectoma , Emociones , Individualidad , Imagen por Resonancia Magnética , Memoria a Corto Plazo , Red Nerviosa , Humanos , Adulto , Red Nerviosa/fisiología , Red Nerviosa/diagnóstico por imagen , Masculino , Femenino , Memoria a Corto Plazo/fisiología , Emociones/fisiología , Teoría de la Mente/fisiología , Adulto Joven , Encéfalo/fisiología , Encéfalo/diagnóstico por imagenRESUMEN
Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.
RESUMEN
Adaptive decision-making, which is often impaired in various psychiatric conditions, is essential for well-being. Recent evidence has indicated that decision-making capacity in multiple tasks could be accounted for by latent dimensions, enlightening the question of whether there is a common disruption of brain networks in economic decision-making across psychiatric conditions. Here, we addressed the issue by combining activation/lesion network mapping analyses with a transdiagnostic brain imaging meta-analysis. Our findings indicate that there were transdiagnostic alterations in the thalamus and ventral striatum during the decision or outcome stage of decision-making. The identified regions represent key nodes in a large-scale network, which is composed of multiple heterogeneous brain regions and plays a causal role in motivational functioning. The findings suggest that disturbances in the network associated with emotion- and reward-related processing play a key role in dysfunctions of decision-making observed in various psychiatric conditions. This study provides the first meta-analytic evidence of common neural alterations linked to deficits in economic decision-making.
Asunto(s)
Toma de Decisiones , Trastornos Mentales , Humanos , Toma de Decisiones/fisiología , Trastornos Mentales/fisiopatología , Imagen por Resonancia Magnética , Recompensa , Mapeo Encefálico/métodos , Estriado Ventral/diagnóstico por imagen , Estriado Ventral/fisiología , Estriado Ventral/fisiopatología , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Tálamo/diagnóstico por imagen , Tálamo/fisiología , AdultoRESUMEN
Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.
RESUMEN
Human neuroimaging studies consistently show multimodal patterns of variability along a key principle of macroscale cortical organization - the sensorimotor-association (S-A) axis. However, little is known about day-to-day fluctuations in functional activity along this axis within an individual, including sex-specific neuroendocrine factors contributing to such transient changes. We leveraged data from two densely sampled healthy young adults, one female and one male, to investigate intra-individual daily variability along the S-A axis, which we computed as our measure of functional cortical organization by reducing the dimensionality of functional connectivity matrices. Daily variability was greatest in temporal limbic and ventral prefrontal regions in both participants, and was more strongly pronounced in the male subject. Next, we probed local- and system-level effects of steroid hormones and self-reported perceived stress on functional organization. Our findings revealed modest effects that differed between participants, hinting at subtle -potentially sex-specific- associations between neuroendocrine fluctuations and intra-individual variability along the S-A axis. In sum, our study points to neuroendocrine factors as possible modulators of intra-individual variability in functional brain organization, highlighting the need for further research in larger samples.
RESUMEN
Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.
Asunto(s)
Encéfalo , Humanos , Adulto , Trastorno del Espectro Autista/terapia , Medicina de Precisión/métodos , Medicina de Precisión/tendencias , Estimulación Magnética Transcraneal/métodos , Trastorno Autístico/terapia , Trastorno Autístico/fisiopatología , Trastorno Autístico/psicología , Estimulación Transcraneal de Corriente Directa/métodosRESUMEN
Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.
RESUMEN
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.
Asunto(s)
Conectoma , Aprendizaje Automático , Imagen por Resonancia Magnética , Humanos , Femenino , Masculino , Adulto , Conectoma/métodos , Caracteres Sexuales , Conjuntos de Datos como Asunto , Adulto Joven , Encéfalo/diagnóstico por imagen , Encéfalo/fisiologíaRESUMEN
BACKGROUND: Human healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood. METHODS: Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson's disease-related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems. RESULTS: We found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson's disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration. CONCLUSIONS: These findings provide new insights into molecular mechanisms underlying age- and Parkinson's-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson's disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson's disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.
RESUMEN
OBJECTIVE: To investigate shared and specific neural correlates of cognitive functions in attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), the authors performed a comprehensive meta-analysis and considered a balanced set of neuropsychological tasks across the two disorders. METHODS: A broad set of electronic databases was searched up to December 4, 2022, for task-based functional MRI studies investigating differences between individuals with ADHD or ASD and typically developing control subjects. Spatial coordinates of brain loci differing significantly between case and control subjects were extracted. To avoid potential diagnosis-driven selection bias of cognitive tasks, the tasks were grouped according to the Research Domain Criteria framework, and stratified sampling was used to match cognitive component profiles. Activation likelihood estimation was used for the meta-analysis. RESULTS: After screening 20,756 potentially relevant references, a meta-analysis of 243 studies was performed, which included 3,084 participants with ADHD (676 females), 2,654 participants with ASD (292 females), and 6,795 control subjects (1,909 females). ASD and ADHD showed shared greater activations in the lingual and rectal gyri and shared lower activations in regions including the middle frontal gyrus, the parahippocampal gyrus, and the insula. By contrast, there were ASD-specific greater and lower activations in regions including the left middle temporal gyrus and the left middle frontal gyrus, respectively, and ADHD-specific greater and lower activations in the amygdala and the global pallidus, respectively. CONCLUSIONS: Although ASD and ADHD showed both shared and disorder-specific standardized neural activations, disorder-specific activations were more prominent than shared ones. Functional brain differences between ADHD and ASD are more likely to reflect diagnosis-related pathophysiology than bias from the selection of specific neuropsychological tasks.
Asunto(s)
Trastorno por Déficit de Atención con Hiperactividad , Trastorno del Espectro Autista , Imagen por Resonancia Magnética , Humanos , Trastorno por Déficit de Atención con Hiperactividad/fisiopatología , Trastorno por Déficit de Atención con Hiperactividad/diagnóstico por imagen , Trastorno por Déficit de Atención con Hiperactividad/psicología , Trastorno del Espectro Autista/fisiopatología , Trastorno del Espectro Autista/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Femenino , Masculino , Pruebas Neuropsicológicas/estadística & datos numéricosRESUMEN
This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.