Biology subjects

Frankot, M.

Publications and source records attributed to Frankot, M..

2 recordsLinked to original sources

bioRxiv2023

Cognitive rehabilitation can improve brain injury-induced deficits in behavioral flexibility and impulsivity linked to impaired reward-feedback activity.

Traumatic brain injury (TBI) affects a large population, resulting in severe cognitive impairments. Although cognitive rehabilitation is an accepted treatment for some deficits, studies in patients are limited in ability to probe physiological and behavioral mechanisms. Therefore, animal models are needed to optimize strategies. Frontal TBI in a rat model results in robust and replicable cognitive deficits, making this an ideal candidate for investigating cognitive rehabilitation. In this study, we report three distinct frontal TBI experiments assessing behavior well into the chronic post-injury period using male Long-Evans rats. First, we evaluated the impact of frontal injury on local field potentials recorded simultaneously from 12 brain regions during a probabilistic reversal learning task (PbR). Next, rats were tested on reversal learning (PbR) or impulsivity (differential reinforcement of low-rate behavior: DRL) and half received salient cues associated with reinforcement contingencies as a form of "cognitive rehabilitation". After rehabilitation on the PbR task, brains were stained for markers of activity. On the DRL, cues were devalued to determine if beneficial effects persisted on impulsive behavior. TBI resulted in outcome salience deficits evident in task performance and reward-feedback signals occurring at beta frequencies in orbitofrontal cortex (OFC) and associated frontostriatal regions. Cognitive rehabilitation improved flexibility and increased OFC activity. Rehabilitation also reduced impulsivity, even after cues were degraded, which was partially mediated by improvements in timing behavior. The current study established a robust platform for investigating cognitive rehabilitation in animals and identified a strong role for dysfunctional OFC signaling after frontal TBI.

neuroscience↗

bioRxiv2023

Statistical power and false positive rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience

Statistical errors in preclinical science are a barrier to reproducibility and translation. For instance, linear models (e.g., ANOVA, linear regression) may be misapplied to data that violate assumptions. In behavioral neuroscience and psychopharmacology, linear models are frequently applied to interdependent or compositional data, which includes behavioral assessments where animals concurrently choose between chambers, objects, outcomes, or types of behavior (e.g., forced swim, novel object, place/social preference). The current study simulated behavioral data for a task with four interdependent choices (i.e., increased choice of a given outcome decreases others) using Monte Carlo methods. 16,000 datasets were simulated (1,000 each of 4 effect sizes by 4 sample sizes) and statistical approaches evaluated for accuracy. Linear regression and linear mixed effects regression (LMER) with a single random intercept resulted in high false positives (>60%). Elevated false positives were attenuated in an LMER with random effects for all choice-levels and a binomial logistic mixed effects regression. However, these models were underpowered to reliably detect effects at common preclinical sample sizes. A Bayesian method using prior knowledge for control subjects increased power by up to 30%. These results were confirmed in a second simulation (8,000 datasets). These data suggest that statistical analyses may often be misapplied in preclinical paradigms, with common linear methods increasing false positives, but potential alternatives lacking power. Ultimately, using informed priors may balance statistical requirements with ethical imperatives to minimize the number of animals used. These findings highlight the importance of considering statistical assumptions and limitations when designing research studies.

neuroscience↗