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Prantik Kundu

Publications and source records attributed to Prantik Kundu.

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Oxytocin enhances intrinsic corticostriatal functional connectivity in women

BackgroundOxytocin may influence various human behaviors and the connectivity across subcortical and cortical networks. Previous oxytocin studies are male-biased and often constrained by task-based inferences. Here we investigate the impact of oxytocin on resting state connectivity between subcortical and cortical networks women.\n\nMethodsWe collected resting state fMRI data on 26 typically-developing women following intranasal oxytocin administration using a double-blind placebo-controlled crossover design. Independent components analysis (ICA) was applied to examine connectivity between networks. An independent analysis of oxytocin receptor (OXTR) gene expression in human subcortical and cortical areas was carried out to determine plausibility of direct oxytocin effects on OXTR.\n\nResultsIn women, OXTR was highly expressed in striatal and other subcortical regions, but showed modest expression in cortical areas. Oxytocin increased connectivity between corticostriatal circuitry typically involved in reward, emotion, social-communication, language, and pain processing. This effect was 1.39 standard deviations above the null effect of no difference between oxytocin and placebo. This oxytocin-related effect on corticostriatal connectivity covaried with autistic traits, such that oxytocin-related increase in connectivity was stronger in individuals with higher autistic traits.\n\nDiscussionOxytocin strengthens corticostriatal connectivity in women, particularly with cortical networks that are involved in social-communicative, motivational, and affective processes. This effect may be important for future work on neurological and psychiatric conditions (e.g., autism, chronic pain), particularly through highlighting how oxytocin may operate differently for subsets of individuals.\n\nSignificance statementThis is the first study to examine how oxytocin affects corticostriatal connectivity in humans, and specifically in women. This is important given oxytocin could impact neural circuitry differently in males and females. Here, we show that oxytocin increases connectivity between corticostriatal circuitry involved in a number of social-communicative, motivational, and affective processes that are implicated in certain neuropsychiatric conditions (e.g., autism). This effect is bolstered by independent evidence that oxytocin receptor gene expression is high in striatal regions and modest in cortical areas. These results may have potential implications for personalized treatment, as oxytocin-related effects on connectivity may vary substantially between individuals.

Neuroscience

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. This effect size boosting is primarily a consequence of reduction of non-BOLD noise at the subject-level, which then translates into consequent reductions in between-subject variance at the group-level. Power simulations demonstrate that enhanced effect size enables highly-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could help issues regarding statistical power and non-BOLD noise and enable potential for novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

Neuroscience