Biology subjects

Westwater, M. L.

Publications and source records attributed to Westwater, M. L..

10 recordsLinked to original sources

bioRxiv2026

Mapping the genetic architecture of human cortical expansion and its links to neuropsychiatric disorders

Cortical expansion is a defining feature of human evolution and neurodevelopment, involving the tangential growth and gyrification of the cerebral cortex. Although disruptions to the expansion of the cortex are implicated in diverse brain-based disorders, the genetic architecture underlying this process remains undercharacterized. Here, we integrated GWAS data for five neuroanatomical phenotypes measured in up to 73,800 individuals to model a latent genomic factor capturing the shared genetic basis of cortical surface area, folding, curvature, gyrification, and intracranial volume. Using a multivariate framework, we mapped this pleiotropic architecture across biological scales, identifying novel effector genes, neural cell types, and developmental pathways involved in cortical expansion. Functional genomic evidence indicated that a diverse cellular ensemble contributes to the tangential growth and gyrification of the cortex during prenatal neurodevelopment, with progenitor cell lineages prominently involved. Regional analyses further revealed that genetic influences are spatially heterogeneous and organized along canonical anatomical, functional, and developmental gradients. Finally, cortical expansion exhibited substantial genetic overlap with neurodevelopmental, psychiatric, and neurological disorders, with shared genetic influences often concentrated in prefrontal cortical regions. Together, these findings map the genetic architecture of human cortical expansion and implicate it as a common etiological axis linking evolution, neurodevelopment, and health and disease.

genomics↗

bioRxiv2025

Expectation modulates the hedonic experiences of and midbrain responses to sweet flavour

Non-nutritive sweeteners are sugar substitutes that may promote weight management by reducing an individuals calorie intake. It is, however, unclear whether (i) sugar and non-nutritive sweetener elicit distinct orosensory responses in the human brain, and (ii) whether the neural responses to these flavours are modulated by expectancy. Addressing these questions has direct relevance to our understanding of food choice behaviour and how it may be modified in dietary interventions. We screened N=99 healthy adults to select a sample (N=27, M[SD]age = 24.25[2.94] years) who reported similar perceptual experiences of sugar and sweetener, thus removing a potential confound of sensory differences, for fMRI scanning. While scanning, they received sugar- and artificially-sweetened beverages in two conditioning paradigms, which both manipulated participants expectation of flavour delivery: first in a probabilistic and second in a deterministic way. Participants ability to accurately distinguish sugar from non-nutritive sweetener depended largely on their expectations, which also significantly affected the perceived pleasantness of each flavour. Expectation altered brain responses to flavour delivery during the deterministic task only, where the (mistaken) expectation of sugar significantly increased midbrain responses to sweetener compared to when sweetener was expected. Trial-wise confidence and pleasantness ratings differentially augmented brain responses to sugar and sweetener delivery. These results highlight the importance of expectancy in both the behavioural and neural encoding of sweet flavour, particularly in the context of unreliable sensory information. The expectation of sugar appears to increase the subjective value of noncaloric sweetener, which may result from flavour-nutrient conditioning that preferentially reinforcers sugar.

neuroscience↗

bioRxiv2025

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on brain connectivity data are increasingly used as tools for predicting behavioral phenotypes 1,2, enhancing precision medicine 3,4, and improving generalizability compared to traditional MRI studies 5. However, the high dimensionality of brain connectivity data makes model interpretation challenging 6. Prevailing practices within the field rely on sparsely selected brain connectivity features, implicitly interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Here, we show that commonly overlooked brain connectivity features can achieve similar prediction accuracies while yielding markedly different neurobiological interpretations. Using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that this phenomenon is widespread across cognitive, developmental, and psychiatric phenotypes. It extends to both functional connectivity (fMRI) and structural (DTI) connectomes and remains evident even in external validation. These findings suggest that common practices may lead to feature overinterpretation and a misrepresentation of the neurobiological bases of brain-behavior associations. Such interpretations present only the "tip of the iceberg" when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results point to the possibility that multiple neurobiologically distinct models may exist for the same phenotype, with implications for identifying meaningful subtypes within clinical and research populations.

neuroscience↗

bioRxiv2024

Brain-handedness associations depend on how and when handedness is measured

Hand preference is ubiquitous, intuitive, and often simplified to right- or left-handed. Accordingly, differences between right- and left-handed individuals in the brain have been established. Nevertheless, considering handedness as a binarized construct fails to capture the variability of brain-handedness associations across different domains or activities. Further, many cultures, environments, and generations impose right-handed norms, and handedness preferences can change over the lifespan. As a result, brain-handedness associations may depend on how and when handedness is measured. We used two large datasets, the Human Connectome Project-Development (HCP-D; n=465; age=5-21 years) and Human Connectome Project-Aging (HCP-A; n=368; age=36-100 years), to explore handedness preferences and brain-handedness associations. Nine items from the Edinburgh Handedness Inventory were associated with resting-state functional connectomes. We show that brain-handedness associations differed across the two cohorts. Moreover, these differences depended on the way handedness was measured. Given that brain-handedness associations differ across handedness measures and datasets, we caution against a one-size-fits-all approach to neuroimaging studies of this complex trait.

neuroscience↗

bioRxiv2023

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

neuroscience↗

bioRxiv2022

Trends in self-citation rates in neuroscience literature

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates--or how much authors cite themselves--have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020. In analyzing over five million citations, we demonstrate four key findings: 1) increasing self-citation rates of Last Authors relative to First Authors, 2) lower self-citation rates in low- and middle-income countries, 3) gender differences in self-citation stemming from differences in the number of previously published papers, and 4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

neuroscience↗

bioRxiv2020

Prefrontal responses during proactive and reactive inhibition are differentially impacted by stress in anorexia and bulimia nervosa

BackgroundBinge-eating is a distressing, transdiagnostic eating disorder symptom associated with impulsivity, particularly in negative mood states. Neuroimaging studies of bulimia nervosa (BN) report reduced activity in fronto-striatal regions implicated in self-regulatory control. However, it remains unknown if negative affective states, including stress, impair self-regulation, and, if so, whether such self-regulatory deficits generalize to binge-eating in underweight individuals (i.e., the bingeing/purging subtype of anorexia nervosa; AN-BP). MethodsWe determined the effect of acute stress on inhibitory control in 85 women (33 BN, 22 AN-BP, 30 matched controls). Participants underwent repeated functional MRI scanning, during performance of the stop-signal anticipation task, a validated measure of proactive (i.e., anticipation of stopping) and reactive (outright stopping) inhibition. Neural and behavioral responses to induced, psychological stress and a control task were evaluated on two separate days. ResultsWomen with BN had reduced proactive inhibition while prefrontal responses were increased in both AN-BP and BN. Reactive inhibition was neurally and behaviorally intact in both diagnostic groups. Both AN-BP and BN groups showed distinct, stress-induced changes in prefrontal activity during both proactive and reactive inhibition. However, task performance was not significantly affected by stress. ConclusionsThese findings offer novel evidence of reduced proactive inhibition in BN, yet inhibitory control deficits did not generalize to AN-BP. While both groups showed altered neural responses during inhibition following stress, neither group demonstrated stress-induced performance deficits. As such, our findings counsel against a simplistic, stress-induced failure of regulation as a holistic explanation for binge-eating in these conditions.

neuroscience↗

bioRxiv2019

Combined effects of age and BMI are related to altered cortical thickness in adolescence and adulthood

Overweight and obesity are associated with functional and structural alterations in the brain, but how these associations change across critical developmental periods remains unknown. Here, we examined the relationship between age, body mass index (BMI) and cortical thickness (CT) in healthy adolescents (n=70; 14 - 19 y) and adults (n=75; 25 - 45 y). We also examined the relationship between adiposity, impulsivity, measured by delay discounting (DD), and CT of the inferior frontal gyrus (IFG), a region key to impulse control. A significant age-by-BMI interaction was observed in both adolescents and adults; however, the direction of this relationship differed between age groups. In adolescents, increased age-adjusted BMI Z-score attenuated age-related thinning globally and in the right superior frontal gyrus. In adults, increased BMI augmented age-related CT reductions, both globally and in bilateral parietal cortex. Although DD was unrelated to adiposity in both groups, increased DD and BMI were both associated with reduced IFG thickness in adults. Our findings suggest that the known age-related changes in CT in adolescence and adulthood are altered by adiposity. The impact of weight on cortical development and its functional implications would suggest that future studies of adolescent and adult brain development take adiposity into account.

neuroscience↗

bioRxiv2017

Alpha power reflects suppression of Pavlovian bias during social reinforcement learning

Socially appropriate behavior involves learning actions that are valued by others and those that have a social cost. Facial expressions are one way that others can signal the social value of our actions. The rewarding or aversive properties of signals such as smiles or frowns also evoke automatic approach or avoidance behaviors in receivers, and a Pavlovian system learns cues that predict rewarding or aversive outcomes. In this study, we examined the computational and neural mechanisms underlying interactions between Pavlovian and Instrumental systems during social reinforcement learning. We found that Pavlovian biases to approach cues predicting social reward and avoid cues predicting social punishment interfered with Instrumental learning from social feedback. While the computations underlying Pavlovian and Instrumental interactions remained the same as when learning from monetary feedback, Pavlovian biases from social outcomes to approach or withdraw were not significantly correlated with biases from money. Trial-by-trial measures of alpha (8-14Hz) EEG power was associated with suppression of Pavlovian bias to social outcomes, while suppression of bias from money was associated with theta (4-7Hz) EEG power. Our findings demonstrate how emotional reactions to feedback from others are balanced with the instrumental value of that feedback to guide social behavior.\n\nSignificance statementA smile from another can be a signal to continue what we are doing, while an angry scowl is a sure sign to stop. Feedback from others such as this plays an important role in shapeing social behavior. The rewarding nature of a smile (or the aversive nature of a scowl) can also lead to automatic tendencies to approach (or avoid), and we can learn situations that predict positive or negative social outcomes. In this study, we examined the brain mechanisms that come into play when the instrumental demands of a situation are in conflict with our automatic biases to approach or withdraw, such as when we have to approach someone who is scowling at us or withdraw from someone who is smiling.

neuroscience↗

bioRxiv2017

Alterations in cortical thickness and structural connectivity are associated with symptom severity in bulimia nervosa

Bulimia nervosa (BN) is a serious psychiatric illness defined by preoccupation with weight and shape, episodic binge-eating and compensatory behaviors. Although diagnosed BN has been associated with diffuse grey matter volume reductions, characterization of brain structure alterations in women with a range of BN symptoms has yet to be made. This study examined whether changes in cortical thickness (CT) scaled with BN symptom severity in a sample of 33 adult women (n = 10 BN; n = 5 EDNOS-BN). Our second objective was to assess global structural connectivity (SC) of CT and to determine if individual differences in global SC relate to BN symptom severity. We used the validated Eating Disorder Examination Questionnaire (EDE-Q; Fairburn & Beglin, 1994) as a continuous measure of BN symptom severity. Increased EDE-Q score was negatively related to global CT and local CT in the left middle frontal gyrus, right superior frontal gyrus and bilateral orbitofrontal cortex (OFC) and temporoparietal regions. Moreover, analysis of global SC indicated that BN-related cortical thinning preferentially occurred in regions with high global connectivity. Finally, we showed that individuals contribution to global SC at the group level were significantly related to EDE-Q score, where increased EDE-Q score correlated with reduced connectivity of the left OFC and middle temporal cortex and increased connectivity of the right superior parietal lobule. Our findings offer novel insight into CT alterations in BN and further suggest that the combination of CT and structural connectivity measures may be sensitive to individual differences in BN symptom severity.

neuroscience↗