Biology subjects

Kable, J.

Publications and source records attributed to Kable, J..

9 recordsLinked to original sources

bioRxiv2024

A structural MRI marker predicts individual differences in impulsivity and classifies patients with behavioral-variant frontotemporal dementia from matched controls

Impulsive decision-making is a symptom across many neuropsychiatric and neurological disorders. Yet, it is still unknown whether impulsivity can be predicted based on individual differences in brain structure. Here, we used machine-learning to develop a structural MRI signature of impulsivity, tested its validity in several independent samples, and assessed its diagnostic value in patients with behavioral variant frontotemporal dementia (bvFTD)--a neurodegenerative disease characterized by high impulsivity. The resulting whole-brain grey matter density pattern--the Structural Impulsivity Signature (SIS)--showed a good prediction-outcome correlation of r=0.35 (out-of-sample, p=0.0028) in the training sample of healthy adults (N=117) and significantly predicted individual differences in impulsivity in four other, independent studies (total N=626), including healthy and clinical participants with neuropsychiatric and neurological conditions. Further, the SIS separated bvFTD patients from controls with high accuracy (81% correct, p=0.002) and predicted impulsivity-related symptom severity among patients. The spatial distribution of weights in the SIS brain pattern highlights the key role of brain regions associated with affective processing in impulsivity. Together, these results provide evidence for a new structural neuromarker of individual differences in impulsivity that can be easily applied to new samples. Future studies can further assess its predictive value towards better prevention, diagnosis and treatment of mental disorders associated with impulsivity symptoms.

neuroscience↗

bioRxiv2022

A meta-analysis of neural systems underlying delay discounting: implications for transdiagnostic research

Delay discounting is a promising paradigm for transdiagnostic research because both excessive and insufficient tendency to discount future rewards have been reported across diagnoses. Because delay discounting involves multiple neurocognitive functions, researchers have used many strategies to characterize brain activity during delay discounting. However, which of these analytic approaches yield truly robust and replicable findings remains unclear. To this end, we conducted a meta-analysis of 80 fMRI studies of delay discounting, testing which statistical contrasts give rise to reliable effects across studies. Despite being a widely used analytic approach, comparing impulsive and patient choices did not reliably yield the expected effects. Instead, subjective value contrasts reliably engaged the valuation network, and task versus baseline and choice difficulty contrasts reliably engaged regions in the frontoparietal and salience networks. We strongly recommend that future neuroimaging studies of delay discounting use these analytic approaches shown to reliably identify specific networks. In addition, we provide all cluster maps from our meta-analysis for use as a priori regions of interest for future experiments.

neuroscience↗

bioRxiv2022

Resting fMRI-guided TMS evokes subgenual anterior cingulate response in depression

Depression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, it has not yet been confirmed that rsFC-guided TMS coil placement leads to TMS modulation of the sgACC. For each participant (N=115, 34 depressed patients), a peak rsFC cortical hotspot for the sgACC and control targets were prospectively identified. Single pulses of TMS interleaved with fMRI readouts were then administered to these targets and established significant downstream fMRI BOLD responses in the sgACC. We then marked an association between TMS-evoked BOLD responses in the sgACC and rsFC between the stimulation site and sgACC. This effect was qualified by a difference between healthy and patient participants: only in depressed patients, positively connected sites of stimulation led to the strongest evoked responses in the sgACC. Our results highlight rsFC-based targeting as a viable strategy to causally modulate sgACC subcortical targets and further suggest that cortical sites with high positive rsFC to the sgACC might represent an alternative target for the treatment of depression.

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bioRxiv2020

Dissecting the midlife crisis: Disentangling social, personality and demographic determinants in social brain anatomy

In any stage of life, humans crave social connection with other people. In midlife, transitions in social networks can be related to new leadership roles at work or becoming a caregiver for aging parents. Previous neuroimaging studies have reported that during midlife, especially the medial prefrontal cortex (mPFC) undergoes structural remodeling changes. Social behavior, personality predisposition, and demographic profile all bear intimate relation with the mPFC according to separate literature streams. To integrate these three areas commonly studied in isolation, we explicitly modeled their unique links with brain structure using a fully probabilistic framework. We weighed against each other a rich collection of 40 traits with their interindividual variation in social brain morphology in ~10,000 middle-aged UK Biobank participants (40-69 years at recruitment). Across conducted analyses, household size and daily routine schedules showed several of the largest effects in explaining variation in social brain regions. We revealed male-biased effects in the dorsal mPFC and amygdala for job income, and a female-biased effect in the ventral mPFC for health satisfaction. Our population investigation offers a more complete perspective into how adults at the midlife milestone may navigate life depending on their identity and status.

neuroscience↗

bioRxiv2020

Decision value signals in the ventromedial prefrontal cortex and anhedonia across mood and psychotic disorders

Deficits in motivation and pleasure are common across many psychiatric disorders, and manifest as symptoms of amotivation and anhedonia, which are prominent features of both mood and psychotic disorders. Here we provide evidence for a shared transdiagnostic mechanism underlying impairments in motivation and pleasure across major depression, bipolar disorder, and schizophrenia. We found that value signals in the ventromedial prefrontal cortex (vmPFC) during decision-making were dampened in individuals with greater motivational and hedonic deficits, regardless of the primary diagnosis. This relationship remained significant while controlling for diagnosis-specific symptoms of mood and psychosis, such as depression as well as positive and negative symptoms. Our results demonstrate that dysfunction in the vmPFC during value-based decision-making is specifically linked to motivational and hedonic impairments across various psychiatric conditions. These findings provide a quantitative neural target for the potential development of novel treatments for amotivation and anhedonia.

neuroscience↗

bioRxiv2020

Neural Correlates of Value Are Intrinsically History Dependent

The authors have withdrawn their manuscript since they no longer stand by the results reported in this preprint. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

neuroscience↗

bioRxiv2018

Dissociable forms of uncertainty-driven representational change across the human brain.

Environmental change can lead decision makers to shift rapidly among different behavioral regimes. These behavioral shifts can be accompanied by rapid changes in the firing pattern of neural networks. However, it is unknown what the populations of neurons that participate in such \"network reset\" phenomena are representing. Here we examined 1) whether and where rapid changes in multivariate activity patterns are observable with fMRI during periods of rapid behavioral change, and 2) what types of representations give rise to these phenomena. We did so by examining fluctuations in multi-voxel patterns of BOLD activity from human subjects making sequential inferences about the state of a partially observable and discontinuously changing variable. We found that, within the context of this sequential inference task, the multivariate patterns of activity in a number of cortical regions contain representations that change more rapidly during periods of uncertainty following a change in behavioral context. In motor cortex, this phenomenon was indicative of discontinuous change in behavioral outputs, whereas in visual regions the same basic phenomenon was evoked by tracking of salient environmental changes. In most other cortical regions, including dorsolateral prefrontal and anterior cingulate cortex, the phenomenon was most consistent with directly encoding the degree of uncertainty. However, in a few other regions, including orbitofrontal cortex, the phenomenon was best explained by representations of a shifting context that evolve more rapidly during periods of rapid learning. These representations may provide a dynamic substrate for learning that facilitates rapid disengagement from learned responses during periods of change.

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bioRxiv2018

Simple but Robust Improvement in Multivoxel Pattern Classification

Multivoxel pattern analysis (MVPA) typically begins with the estimation of single trial activation levels, and several studies have examined how different procedures for estimating single trial activity affect the ultimate classification accuracy of MVPA. Here we show that the currently preferred estimation procedures impart spurious shifts in run-level means that cause the estimated activities to be misaligned across runs. These shifts are caused by positive correlations between the means of different category activity estimates within the same scanner run. In other words, if the mean of the estimates for one type of trials is high (low) in a given scanner run, then the mean of the other type of trials is also high (low) for that same scanner run, and the mean across all trials therefore shifts from run to run. Simulations show that these correlations are unavoidable whenever there is a need to deconvolve overlapping trial activities in the presence of noise. We show that subtracting the mean across all trials of a run from all the estimates within that run (i.e., run-level mean centering of estimates), by cancelling out these mean shifts, leads to robust and significant improvements in MVPA classification accuracy. These improvements are seen in both simulated and real data across a wide variety of situations and can provide significant direct benefits with no computational cost. However, we also point out that there could be cases when mean activations are expected to shift across runs and that run-level mean centering could be detrimental in some of these cases (e.g., different proportion of trial types between different runs).

neuroscience↗

bioRxiv2017

Diminished Cortical Thickness is Associated with Impulsive Choice in Adolescence

Adolescence is characterized by both maturation of brain structure and increased risk of negative outcomes from behaviors associated with impulsive decision-making. One important index of impulsive choice is delay discounting (DD), which measures the tendency to prefer smaller rewards available soon over larger rewards delivered after a delay. However, it remains largely unknown how individual differences in structural brain development may be associated with impulsive choice during adolescence. Leveraging a unique large sample of 427 human youths (208 males and 219 females) imaged as part of the Philadelphia Neurodevelopmental Cohort, we examined associations between delay discounting and cortical thickness within structural covariance networks. These structural networks were derived using non-negative matrix factorization, an advanced multivariate technique for dimensionality reduction, and analyzed using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. We found that impulsive choice, as measured by greater discounting, was most strongly associated with diminished cortical thickness in structural brain networks that encompassed the ventromedial prefrontal cortex, orbitofrontal cortex, temporal pole, and temporoparietal junction. Furthermore, structural brain networks predicted DD above and beyond cognitive performance. Taken together, these results suggest that reduced cortical thickness in regions known to be involved in value-based decision-making is a marker of impulsive choice during the critical period of adolescence.\n\nSIGNIFICANCERisky behaviors during adolescence, such as initiation of substance use or reckless driving, are a major source of morbidity and mortality. In this study, we present evidence from a large sample of youths that diminished cortical thickness in specific structural brain networks is associated with impulsive choice. Notably, the strongest association between impulsive choice and brain structure was seen in regions implicated in value-based decision-making; namely, the ventromedial prefrontal and orbitofrontal cortices. Moving forward, such neuroanatomical markers of impulsivity may aid in the development of personalized interventions targeted to reduce risk of negative outcomes resulting from impulsivity during adolescence.

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