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Whelan, R.

Publications and source records attributed to Whelan, R..

33 records · Page 2Linked to original sources

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

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.

neuroscience↗

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use. TeaserA robust brain network for sustained attention at age 14 predicts cigarette and cannabis use from ages 14 to 23.

neuroscience↗

Human cortex development is shaped by molecular and cellular brain systems

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

neuroscience↗

Connectome-based predictive modeling of cognitive reserve using task-based functional connectivity

Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modeling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). Three network-strength predicted cognitive reserve measures were generated that accurately predicted the residual measures of unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.

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Anatomical connectivity profile development constrains medial-lateral topography in the dorsal prefrontal cortex

The prefrontal cortex (PFC) is a highly variable, evolutionarily expanded brain region that is engaged in multiple cognitive processes. The subregions of the PFC mature relatively late compared with other brain regions, and the maturation times vary between these subregions. Among these, the dorsomedial and dorsolateral prefrontal cortex (dmPFC and dlPFC) share a parallel topographic pattern of functional connectivity, while participating in different types of complex behaviors. However, the developmental trajectories of the two areas remain obscure. In this study, we uncovered differences in the developmental trends of the dmPFC and dlPFC. These differences were mainly caused by structural and functional changes in the medial area of the superior frontal gyrus (SFG). The developmentally different arealization patterns were verified using multiple parcellation approaches with multimodal data, including structural magnetic resonance imaging (sMRI), diffusion MRI (dMRI), resting state functional MRI (rfMRI), and a publicly available transcriptomic dataset. Human brain gene expression data was also used to perform downstream analyses, which could inform us about the potential biological mechanisms underlying the developmentally different arealizations. Furthermore, behavioral analyses hinted at the effects of regionalization on ontogeny. In brief, this study revealed a tendency toward a medial-lateral prefrontal division and can provide a fuller understanding of the potential underlying genetic underpinnings as well as of the potential effects on developmental behavior.

neuroscience↗

Self-regulation of the brain's Beta rhythm using a brain-computer interface improves inhibitory control

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using non-invasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n=44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz) over the right inferior frontal cortex (rIFC). The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n=38) who underwent training of the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brains inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change. Thus, this form of neurofeedback training of the tonic Beta rhythm would not serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis.

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Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.

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Genetic variation in CSMD1 affects amygdala connectivity and prosocial behavior

The amygdala is one of the most widely connected structures in the primate brain and plays a key role in social and emotional behavior. Here, we present the first genome-wide association study (GWAS) of whole-brain resting-state amygdala networks to discern whether connectivity in these networks could serve as an endophenotype for social behavior. Leveraging published resting-state amygdala networks as a priori endophenotypes in a GWAS meta-analysis of two adolescent cohorts, we identified a common polymorphism on chr.8p23.2 (rs10105357 A/G, MAF (G)=0.35) associated with stronger connectivity in the medial amygdala network (beta=0.20, p=2.97x10-8). This network contains regions that support reward processes and affiliative behavior. People carrying two copies of the minor allele for rs10105357 participate in more prosocial behaviors (t=2.644, p=0.008) and have higher CSMD1 expression in the temporal cortex (t=3.281, p=0.002) than people with one or no copy of the allele. In post-mortem brains across the lifespan, we found that CSMD1 expression is relatively high in the amygdala (2.79 fold higher than white matter, p=1.80x10-29), particularly so for nuclei in the medial amygdala, reaching a maximum in later stages of development. Amygdala network endophenotyping has the potential to accelerate genetic discovery in disorders of social function, such as autism, in which CSMD1 may serve as a diagnostic and therapeutic target.

neuroscience↗

Early life glucocorticoid exposure modulates immune function in zebrafish (Danio rerio) larvae

In this study we have assessed the effects of increased cortisol levels during early embryonic development on immune function in zebrafish (Danio rerio) larvae. Fertilized eggs were exposed to either a cortisol-containing, a dexamethasone-containing (to stimulate the glucocorticoid receptor selectively) or a control medium for 6 hours post-fertilisation (0-6 hpf). First, we measured baseline expression of a number of immune-related genes (socs3a, mpeg1.1, mpeg1.2 and irg1l) 5 days post-fertilisation (dpf) in larvae of the AB and TL strain to assess the effectiveness of our exposure procedure and potential strain differences. Cortisol and dexamethasone strongly up-regulated baseline expression of these genes independent of strain. The next series of experiments were therefore carried out in larvae of the AB strain only. We measured neutrophil/macrophage recruitment following tail fin amputation (performed at 3 dpf) and phenotypical changes as well as survival following LPS-induced sepsis (150 g/ml; 4-5 dpf). Dexamethasone, but not cortisol, exposure at 0-6 hpf enhanced neutrophil recruitment 4 hours post tail fin amputation. Cortisol and dexamethasone exposure at 0-6 hpf led to a milder phenotype (e.g. less tail fin damage) and enhanced survival following LPS challenge compared to control exposure. Gene-expression analysis showed accompanying differences in transcript abundance of tlr4bb, cxcr4a, myd88, il1{beta} and il10. These data show that early-life exposure to cortisol, which may be considered to be a model or proxy of maternal stress, induces an adaptive response to immune challenges, which seems mediated via the glucocorticoid receptor.

immunology↗

Independent effects of socioeconomic status and genetics on adolescent cognition and brain development.

Genetic factors and socioeconomic (SES) inequalities play a large role in educational attainment, and both have been associated with variations in brain structure and cognition. However, genetics and SES are correlated, and no prior study has assessed their neural associations independently. Here we used polygenic score for educational attainment (EduYears-PGS) as well as SES, in a longitudinal study of 551 adolescents, to tease apart genetic and environmental associations with brain development and cognition. Subjects received a structural MRI scan at ages 14 and 19. At both time-points, they performed three working memory (WM) tasks. SES and EduYears-PGS were correlated (r = 0.27) and had both common and independent associations with brain structure and cognition. Specifically, lower SES was related to less total cortical surface area and lower WM. EduYears-PGS was also related to total cortical surface area, but in addition had a regional association with surface area in the right parietal lobe, a region related to non-verbal cognitive functions, including mathematics, spatial cognition, and WM. SES, but not EduYears-PGS, was related to a change in total cortical surface area from age 14 to 19. This is the first study demonstrating a regional association of EduYears-PGS and the independent prediction of SES on cognitive function and brain development. It suggests that the SES inequalities, in particular parental education, are related to global aspects of cortical development, and exert a persistent influence on brain development during adolescence. Significance statementThe influence of socioeconomic (SES) inequalities on brain and cognitive development is a hotly debated topic. However, previous studies have not considered that genetic factors overlap with SES. Here we showed, for the first time, that SES and EduYears-PGS (a score from thousands of genetic markers for educational attainment) have independent associations with both cognition and global cortical surface area in adolescents. EduYears-PGS also had a localized association in the brain: the intraparietal sulcus, a region related to non-verbal intelligence. In contrast, SES had global, but not regional, associations, and these persisted throughout adolescence. This suggests that the influence of SES inequalities is widespread - a result that opposes the current paradigm and can help inform policies in education.

neuroscience↗

Neurobehavioural characterisation and stratification of reinforcement-related behaviour

AbstractReinforcement-related cognitive processes, such as reward processing, impulsiveness and emotional processing are critical components of externalising and internalising behaviours. It is unclear to what extent each of these processes contributes to individual behavioural symptoms, how their neural substrates give rise to distinct behavioural outcomes, and if neural profiles across different reinforcement-related processes might differentiate individual behaviours. We created a statistical framework that enabled us to directly compare functional brain activation during reward anticipation, motor inhibition and viewing of emotional faces in the European IMAGEN cohort of 2000 14-year-old adolescents. We observe significant correlations and modulation of reward anticipation and motor inhibition networks in hyperactivity, impulsivity, inattention and conduct symptoms, and describe neural signatures across neuroimaging tasks that differentiate these behaviours. We thus characterise shared and distinct functional brain activation patterns that underlie different externalising symptoms and identify neural stratification markers, while accounting for clinically observed co-morbidity.

neuroscience↗

Contralateral Delay Activity is not sensitive to cognitive decline in older adults at risk of Mild Cognitive Impairment.

Contralateral delay activity (CDA) has been proposed as a pre-clinical marker for Mild Cognitive Impairment (MCI). However, existing evidence is limited to one study with a small sample size (n=12 per group). Our aim was to compare CDA amplitudes in a larger sample of low- and high-risk older adult groups (n=35 per group). As expected, behavioural performance decreased as the number of memory items increased, and the low-risk group out-performed the high-risk group. However, we found no differences in CDA amplitudes across groups, indicating that WM capacity increased irrespective of risk-level. These findings suggest that the CDA is not a sensitive marker of MCI risk. More broadly, our results highlight the difficulty in identifying at-risk individuals, particularly as MCI is a heterogeneous, unstable condition. Future research should prioritise longitudinal approaches in order to track the progression of the CDA and its association with cognitive decline in later life.

neuroscience↗

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Alzheimers disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

neuroscience↗

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

BackgroundAdults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behavior (mediational endophenotypes).\n\nMethodsResting-state eyes-open and eyes-closed EEG were measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis.\n\nResultsEyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = .71-.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = .68-.72), suggesting that these features may be a mediational endophenotype for adult ADHD.\n\nConclusionsResting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

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Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

Brain-predicted age difference scores are calculated by subtracting chronological age from brain age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylul University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

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