Robert Whelan

YNIMG Journal 2024 Journal Article

Brain health in diverse settings: How age, demographics and cognition shape brain function

• Hernan Hernandez
• Sandra Baez
• Vicente Medel
• Sebastian Moguilner
• Jhosmary Cuadros
• Hernando Santamaria-Garcia
• Enzo Tagliazucchi
• Pedro A. Valdes-Sosa

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

YNICL Journal 2022 Journal Article

Neuroanatomical markers of psychotic experiences in adolescents: A machine-learning approach in a longitudinal population-based sample

• Joanne P.M. Kenney
• Laura Milena Rueda-Delgado
• Erik O. Hanlon
• Lee Jollans
• Ian Kelleher
• Colm Healy
• Niamh Dooley
• Conor McCandless

It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1: 11–13 years, n = 77; Time 2: 14–16 years, n = 56; Time 3: 18–20 years, n = 40). Neuroimaging data classified adolescents aged 11–13 years with current PEs vs. controls returning an AROC of 0. 62, significantly better than a null model, p = 1. 73e-29. Neuroimaging data also classified those with PEs at 18–20 years (AROC = 0. 59; P = 7. 19e-10) but performance was at chance level at 14–16 years (AROC = 0. 50). Left hemisphere frontal regions were top discriminant classifiers for 11–13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18–20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.

YNICL Journal 2022 Journal Article

White matter microstructure in children and adolescents with ADHD

• Michael Connaughton
• Robert Whelan
• Erik O'Hanlon
• Jane McGrath

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Advances in diffusion magnetic resonance imaging (MRI) acquisition sequences and analytic techniques have led to growing body of evidence that abnormal white matter microstructure is a core pathophysiological feature of ADHD. This systematic review provides a qualitative assessment of research investigating microstructural organisation of white matter amongst children and adolescents with ADHD. This review included 46 studies in total, encompassing multiple diffusion MRI imaging techniques and analytic approaches, including whole-brain, region of interest and connectomic analyses. Whole-brain and region of interest analyses described atypical organisation of white matter microstructure in several white matter tracts: most notably in frontostriatal tracts, corpus callosum, superior longitudinal fasciculus, cingulum bundle, thalamic radiations, internal capsule and corona radiata. Connectomic analyses, including graph theory approaches, demonstrated global underconnectivity in connections between functionally specialised networks. Although some studies reported significant correlations between atypical white matter microstructure and ADHD symptoms or other behavioural measures there was no clear pattern of results. Interestingly however, many of the findings of disrupted white matter microstructure were in neural networks associated with key neuropsychological functions that are atypical in ADHD. Limitations to the extant research are outlined in this review and future studies in this area should carefully consider factors such as sample size, sex balance, head motion and medication status.

YNIMG Journal 2021 Journal Article

Similarity and stability of face network across populations and throughout adolescence and adulthood

• Zhijie Liao
• Tobias Banaschewski
• Arun L.W. Bokde
• Sylvane Desrivières
• Herta Flor
• Antoine Grigis
• Hugh Garavan
• Penny Gowland

The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN (N = 534) and ALSPAC (N = 465), to investigate - both at group and individual levels - the consistency of the regional profile of functional connectivity across populations (IMAGEN, ALSPAC) and time (Visits 1 to 3 in IMAGEN; age 14 to 22 years). At the group level, we found a robust canonical profile of connectivity both across populations and time. At the individual level, connectivity profiles deviated from the canonical profile, and the magnitude of this deviation related to the presence of psychopathology. These findings suggest that the brain processes faces in a highly stereotypical manner, and that the deviations from this normative pattern may be related to the risk of mental illness.

YNIMG Journal 2020 Journal Article

Brain structure and habitat: Do the brains of our children tell us where they have been brought up?

• Simone Kühn
• Tobias Banaschewski
• Arun L.W. Bokde
• Christian Büchel
• Erin Burke Quinlan
• Sylvane Desrivières
• Herta Flor
• Antoine Grigis

Recently many lifestyle factors have been shown to be associated with brain structural alterations. At present we are facing increasing population shifts from rural to urban areas, which considerably change the living environments of human beings. To investigate the association between rural vs. urban upbringing and brain structure we selected 106 14-year old adolescents of whom half were exclusively raised in rural areas and the other half who exclusively lived in cities. Voxel-based morphometry revealed a group difference in left hippocampal formation (Rural > City), which was positively associated with cognitive performance in a spatial processing task. Moreover, significant group differences were observed in spatial processing (Rural > City). A mediation analysis revealed that hippocampal formation accounted for more than half of the association between upbringing and spatial processing. The results are compatible with studies reporting earlier and more intense opportunities for spatial exploration in children brought up in rural areas. The results are interesting in the light of urban planning where spaces enabling spatial exploration for children may deserve more attention.

YNIMG Journal 2020 Journal Article

The empirical replicability of task-based fMRI as a function of sample size

• Han Bossier
• Sanne P. Roels
• Ruth Seurinck
• Tobias Banaschewski
• Gareth J. Barker
• Arun L.W. Bokde
• Erin Burke Quinlan
• Sylvane Desrivières

Replicating results (i. e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.

YNICL Journal 2019 Journal Article

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation

• Hongtao Ruan
• Yunyi Zhou
• Qiang Luo
• Gabriel H. Robert
• Sylvane Desrivières
• Erin Burke Quinlan
• ZhaoWen Liu
• Tobias Banaschewski

Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; therefore, certain behavior, such as alcohol consumption, can have disruptive effects on both brain development and personality maturation. However, these effects remain unclear due to the scarcity of longitudinal studies. In the current study, we used multivariate approaches to explore discriminative features in brain functional architecture, personality traits, and genetic variants in 19-year-old individuals (n = 212). Taking advantage of a longitudinal design, we selected features that were more drastically altered in drinkers with an earlier onset of binge drinking. With the selected features, we trained a hierarchical model of support vector machines using a training sample (n = 139). Using an independent sample (n = 73), we tested the model and achieved a classification accuracy of 71.2%. We demonstrated longitudinally that after the onset of binge drinking the developmental trajectory of improvement in impulsivity slowed down. This study identified the disrupting effects of adolescent binge drinking on the developmental trajectories of both brain and personality.

YNIMG Journal 2019 Journal Article

Quantifying performance of machine learning methods for neuroimaging data

• Lee Jollans
• Rory Boyle
• Eric Artiges
• Tobias Banaschewski
• Sylvane Desrivières
• Antoine Grigis
• Jean-Luc Martinot
• Tomáš Paus

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.

YNIMG Journal 2018 Journal Article

Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology

• Laura O'Halloran
• Zhipeng Cao
• Kathy Ruddy
• Lee Jollans
• Matthew D. Albaugh
• Andrea Aleni
• Alexandra S. Potter
• Nigel Vahey

Moment-to-moment reaction time variability on tasks of attention, often quantified by intra-individual response variability (IRV), provides a good indication of the degree to which an individual is vulnerable to lapses in sustained attention. Increased IRV is a hallmark of several disorders of attention, including Attention-Deficit/Hyperactivity Disorder (ADHD). Here, task-based fMRI was used to provide the first examination of how average brain activation and functional connectivity patterns in adolescents are related to individual differences in sustained attention as measured by IRV. We computed IRV in a large sample of adolescents (n = 758) across 'Go' trials of a Stop Signal Task (SST). A data-driven, multi-step analysis approach was used to identify networks associated with low IRV (i. e. , good sustained attention) and high IRV (i. e. , poorer sustained attention). Low IRV was associated with greater functional segregation (i. e. , stronger negative connectivity) amongst an array of brain networks, particularly between cerebellum and motor, cerebellum and prefrontal, and occipital and motor networks. In contrast, high IRV was associated with stronger positive connectivity within the motor network bilaterally and between motor and parietal, prefrontal, and limbic networks. Consistent with these observations, a separate sample of adolescents exhibiting elevated ADHD symptoms had increased fMRI activation and stronger positive connectivity within the same motor network denoting poorer sustained attention, compared to a matched asymptomatic control sample. With respect to the functional connectivity signature of low IRV, there were no statistically significant differences in networks denoting good sustained attention between the ADHD symptom group and asymptomatic control group. We propose that sustained attentional processes are facilitated by an array of neural networks working together, and provide an empirical account of how the functional role of the cerebellum extends to cognition in adolescents. This work highlights the involvement of motor cortex in the integrity of sustained attention, and suggests that atypically strong connectivity within motor networks characterizes poor attentional capacity in both typically developing and ADHD symptomatic adolescents.

YNIMG Journal 2014 Journal Article

Towards obtaining spatiotemporally precise responses to continuous sensory stimuli in humans: A general linear modeling approach to EEG

• Nuno R. Gonçalves
• Robert Whelan
• John J. Foxe
• Edmund C. Lalor

Noninvasive investigation of human sensory processing with high temporal resolution typically involves repeatedly presenting discrete stimuli and extracting an average event-related response from scalp recorded neuroelectric or neuromagnetic signals. While this approach is and has been extremely useful, it suffers from two drawbacks: a lack of naturalness in terms of the stimulus and a lack of precision in terms of the cortical response generators. Here we show that a linear modeling approach that exploits functional specialization in sensory systems can be used to rapidly obtain spatiotemporally precise responses to complex sensory stimuli using electroencephalography (EEG). We demonstrate the method by example through the controlled modulation of the contrast and coherent motion of visual stimuli. Regressing the data against these modulation signals produces spatially focal, highly temporally resolved response measures that are suggestive of specific activation of visual areas V1 and V6, respectively, based on their onset latency, their topographic distribution and the estimated location of their sources. We discuss our approach by comparing it with fMRI/MRI informed source analysis methods and, in doing so, we provide novel information on the timing of coherent motion processing in human V6. Generalizing such an approach has the potential to facilitate the rapid, inexpensive spatiotemporal localization of higher perceptual functions in behaving humans.

YNIMG Journal 2012 Journal Article

Very large fMRI study using the IMAGEN database: Sensitivity–specificity and population effect modeling in relation to the underlying anatomy

• Benjamin Thyreau
• Yannick Schwartz
• Bertrand Thirion
• Vincent Frouin
• Eva Loth
• Sabine Vollstädt-Klein
• Tomas Paus
• Eric Artiges