Biology subjects

Aponte, E. A.

Publications and source records attributed to Aponte, E. A..

5 recordsLinked to original sources

bioRxiv2021

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience↗

bioRxiv2019

Computational dissociation of dopaminergic and cholinergic effects on voluntary behavior and inhibitory control

BackgroundPatients with schizophrenia make more errors than healthy subjects on the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation. MethodsTo study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200mg/galantamine 8mg) to healthy volunteers (N=100) performing the antisaccade task. A computational model (SERIA) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates. ResultsModeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy. ConclusionsOur results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather levodopa improved the ability to select correct actions. Instead, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia.

pharmacology and toxicology↗

bioRxiv2018

Thermodynamic integration for dynamic causal models

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In DCM, the log evidence is usually approximated using variational Bayes (VB) under the Laplace approximation (VBL). Although this approach is highly efficient, it makes distributional assumptions and can be vulnerable to local extrema. An alternative to VBL is Markov Chain Monte Carlo (MCMC) sampling, which is asymptotically exact but orders of magnitude slower than VB. This has so far prevented its routine use for DCM.\n\nThis paper makes four contributions. First, we introduce a powerful MCMC scheme - thermodynamic integration (TI) - to neuroimaging and present a derivation that establishes a theoretical link to VB. Second, this derivation is based on a tutorial-like introduction to concepts of free energy in physics and statistics. Third, we present an implementation of TI for DCM that rests on population MCMC. Fourth, using simulations and empirical functional magnetic resonance imaging (fMRI) data, we compare log evidence estimates obtained by TI, VBL, and other MCMC-based estimators (prior arithmetic mean and posterior harmonic mean). We find that model comparison based on VBL gives reliable results in most cases, justifying its use in standard DCM for fMRI. Furthermore, we demonstrate that for complex and/or nonlinear models, TI may provide more robust estimates of the log evidence. Importantly, accurate estimates of the model evidence can be obtained with TI in acceptable computation time. This paves the way for using DCM in scenarios where the robustness of single-subject inference and model selection becomes paramount, such as differential diagnosis in clinical applications.

neuroscience↗

bioRxiv2018

Switch costs in inhibitory control and goal-directed behavior: A computational study of the antisaccade task

An integral aspect of human cognition is the ability to inhibit habitual responses in order to initiate complex, rule-guided actions. Moreover, humans have also the ability to alternate between different sets of rules or tasks, at the cost of degraded performance when compared to repeating the same task, a phenomenon called the task switch cost. While it is recognized that switching between tasks requires often to inhibit habitual responses, the interaction between these two forms of cognitive control has been much less studied than each of them separately. Here, we use a computational model to draw a bridge between inhibitory control and voluntary action generation and thereby provide a novel account of seemingly paradoxical findings in the task switch literature. We investigated task switching in the mixed antisaccade task, in which participants are cued to saccade either in the same or in the opposite direction to a peripheral stimulus. Our model demonstrates that stopping a habitual action leads to increased inhibitory control that persists on the next trial. However, enhanced inhibition affects only the probability of generating habitual responses, and, contrary to previous accounts, cannot be characterized as proactive task interference. In addition, our model demonstrates that voluntary actions (but not habitual responses) are slower and more prompt to errors on switch trials compared to repeat trials. We conclude that precisely the interaction between these two effects explains a variety of contradictory findings reported in the literature.

animal behavior and cognition↗

bioRxiv2018

Inhibitory and late errors in the antisaccade task: Influence of task design

In the antisaccade task, subjects are instructed to saccade in the opposite direction of a peripheral visual cue (PVC). Importantly, several psychiatric disorders are associated with increased error rates in this paradigm. Despite this observation, there is no consensus about the mechanism behind antisaccade errors: while often explained as inhibition failures, some studies have suggested that errors are caused by deficits in the ability to initiate voluntary saccades. Using a computational model, we recently showed that under some conditions high latency or late errors can be explained by a race process between voluntary pro- and antisaccades. A limitation of our findings is that in our previous experiment the PVC signaled the trial type, whereas in most studies, subjects are informed about the trial type before the PVC is presented. We refer to these task designs as asynchronous (AC) and synchronous cues (SC) conditions. Here, we investigated to which extent differences in design affect the type and frequency of errors in the antisaccade task. Twenty-four subjects participated in mixed blocks of pro- and antisaccade trials in both conditions. Our results demonstrate that error rates were highly correlated across task designs and a non-negligible fraction of them were classified as late errors in both conditions. In summary, our findings indicate that errors in the AC task are the result of both inhibition failures and inaccurate voluntary action initiation.

neuroscience↗