Investigator Scientist, University of Cambridge
Investigating individual differences in brain structure and how they relate to thinking skills
Why do some children struggle in school while others sail through? In this research project, I'm investigating differences in brain structure and thinking skills that may explain why some children find it hard to keep up in school. One aspect of the project is to find brain differences that distinguish subgroups of children. Typically, children are grouped according to observable behaviours, e.g. hyperactivity in Attention Deficit Hyperactivity Disorder (ADHD). However, there is a lot of heterogeneity within these groups meaning that children with the same diagnosis might have very different problems, e.g. a difficulty to focus versus a difficulty to keep things in mind. This also creates problems for researchers trying to understand the mechanism leading to difficulties in school, because we have to rely on groupings that mash apples and oranges together. So, I tried a different approach of grouping children by similarities in their brain structure using neuroimaging and machine learning. I found that different "brain types" can be distinguished that show clear differences in thinking skills. This approach helps us to understand the neurobiological mechanisms that lead to difficulties in school, which can be used to devise more targeted interventions in the future that will enable more children to reach their full potential.
Abstract: A challenge facing clinical neuroscientists is how best to synthesize diverse and sometimes inconsistent evidence for neuropsychological deficits and brain system dysfunction found in psychiatric disorders into models that guide etiological and treatment research. Multiple-pathway models suggest that psychiatric symptoms might arise from pathophysiology in different neural systems. This study tested dual-pathway model predictions for attention-deficit/hyperactivity disorder (ADHD) that reward and executive function cognitive deficits should be related to abnormalities in corresponding functionally specialized neural systems.
Pub.: 01 Sep '17, Pinned: 01 Feb '18
Abstract: Psychiatric nosology is limited by behavioral and biological heterogeneity within existing disorder categories. The imprecise nature of current nosologic distinctions limits both mechanistic understanding and clinical prediction. We demonstrate an approach consistent with the National Institute of Mental Health Research Domain Criteria initiative to identify superior, neurobiologically valid subgroups with better predictive capacity than existing psychiatric categories for childhood attention-deficit/hyperactivity disorder (ADHD).To refine subtyping of childhood ADHD by using biologically based behavioral dimensions (i.e., temperament), novel classification algorithms, and multiple external validators.A total of 437 clinically well-characterized, community-recruited children, with and without ADHD, participated in an ongoing longitudinal study. Baseline data were used to classify children into subgroups based on temperament dimensions and examine external validators including physiological and magnetic resonance imaging measures. One-year longitudinal follow-up data are reported for a subgroup of the ADHD sample to address stability and clinical prediction.Parent/guardian ratings of children on a measure of temperament were used as input features in novel community detection analyses to identify subgroups within the sample. Groups were validated using 3 widely accepted external validators: peripheral physiological characteristics (cardiac measures of respiratory sinus arrhythmia and pre-ejection period), central nervous system functioning (via resting-state functional connectivity magnetic resonance imaging), and clinical outcomes (at 1-year longitudinal follow-up).The community detection algorithm suggested 3 novel types of ADHD, labeled as mild (normative emotion regulation), surgent (extreme levels of positive approach-motivation), and irritable (extreme levels of negative emotionality, anger, and poor soothability). Types were independent of existing clinical demarcations including DSM-5 presentations or symptom severity. These types showed stability over time and were distinguished by unique patterns of cardiac physiological response, resting-state functional brain connectivity, and clinical outcomes 1 year later.Results suggest that a biologically informed temperament-based typology, developed with a discovery-based community detection algorithm, provides a superior description of heterogeneity in the ADHD population than does any current clinical nosologic criteria. This demonstration sets the stage for more aggressive attempts at a tractable, biologically based nosology.
Pub.: 10 Jul '14, Pinned: 01 Feb '18
Abstract: Research and clinical investigations in psychiatry largely rely on the de facto assumption that the diagnostic categories identified in the Diagnostic and Statistical Manual (DSM) represent homogeneous syndromes. However, the mechanistic heterogeneity that potentially underlies the existing classification scheme might limit discovery of etiology for most developmental psychiatric disorders. Another, perhaps less palpable, reality may also be interfering with progress-heterogeneity in typically developing populations. In this report we attempt to clarify neuropsychological heterogeneity in a large dataset of typically developing youth and youth with attention deficit/hyperactivity disorder (ADHD), using graph theory and community detection. We sought to determine whether data-driven neuropsychological subtypes could be discerned in children with and without the disorder. Because individual classification is the sine qua non for eventual clinical translation, we also apply support vector machine-based multivariate pattern analysis to identify how well ADHD status in individual children can be identified as defined by the community detection delineated subtypes. The analysis yielded several unique, but similar subtypes across both populations. Just as importantly, comparing typically developing children with ADHD children within each of these distinct subgroups increased diagnostic accuracy. Two important principles were identified that have the potential to advance our understanding of typical development and developmental neuropsychiatric disorders. The first tenet suggests that typically developing children can be classified into distinct neuropsychological subgroups with high precision. The second tenet proposes that some of the heterogeneity in individuals with ADHD might be "nested" in this normal variation.
Pub.: 05 Apr '12, Pinned: 01 Feb '18
Abstract: Executive functions are a collection of cognitive abilities necessary for behavioural control and regulation, and are important for school success. Executive deficits are common across acquired and developmental disorders in childhood and beyond. This review aims to summarize how studies using event-related potential (ERP) can provide insight into mechanisms underpinning how executive functions develop in children from preschool to adolescence. We specifically focus on ERP components that are considered to be well-established markers of executive functioning, including the ability to resist distraction (inhibition, N200), hold scenes in mind (visuospatial working memory, contralateral delay activity), attend to specific stimuli (information processing, P300), follow rules (response monitoring, error-related negativity [ERN], and error-related positivity [Pe]), and adjust to feedback (outcome monitoring, feedback-related negativity). All of these components show developmental changes from preschool to adolescence, in line with behavioural and neuroimaging findings. These ERP markers also show altered developmental trajectories in the context of atypical executive functions. As an example, deficits in executive function are prominently implicated in attention-deficit-hyperactivity disorder. Therefore, this review highlights ERP studies that have investigated the above ERP components in this population. Overall, ERPs provide a useful marker for the development and dysfunction of executive skills, and provide insight into their neurophysiological basis.
Pub.: 07 Feb '17, Pinned: 01 Feb '18
Abstract: Working memory (WM) skills are closely associated with learning progress in key areas such as reading and mathematics across childhood. As yet, however, little is known about how the brain systems underpinning WM develop over this critical developmental period. The current study investigated whether and how structural brain correlates of components of the working memory system change over development. Verbal and visuospatial short-term and working memory were assessed in 153 children between 5.58 and 15.92 years, and latent components of the working memory system were derived. Fractional anisotropy and cortical thickness maps were derived from T1-weighted and diffusion-weighted MRI and processed using eigenanatomy decomposition. There was a greater involvement of the corpus callosum and posterior temporal white matter in younger children for performance associated with the executive part of the working memory system. For older children, this was more closely linked with the thickness of the occipitotemporal cortex. These findings suggest that increasing specialization leads to shifts in the contribution of neural substrates over childhood, moving from an early dependence on a distributed system supported by long-range connections to later reliance on specialized local circuitry. Our findings demonstrate that despite the component factor structure being stable across childhood, the underlying brain systems supporting working memory change. Taking the age of the child into account, and not just their overall score, is likely to be critical for understanding the nature of the limitations on their working memory capacity.
Pub.: 26 Jul '17, Pinned: 01 Feb '18