Biology subjects

Kane, G.

Publications and source records attributed to Kane, G..

3 recordsLinked to original sources

bioRxiv2026

Diurnal rhythms of choice: a novel state-dependent drift diffusion model uncovers time-dependent changes in rat decision making

2Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light-dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses. 1 HIGHLIGHTSO_LI24-hour live-in operant system allows autonomous training in cognitive tasks across months C_LIO_LI24-hour measurements reveal that rat performance fluctuates with time of day C_LIO_LINovel DDM-HMM framework identifies reaction time and accuracy shifts across multiple timescales C_LIO_LIDDM-HMM captures serial dependence in decisions that classic models ignore C_LI

animal behavior and cognition↗

bioRxiv2023

RETINOID ORPHAN RECEPTOR GAMMA T (RORgT) PROMOTES INFLAMMATORY EOSINOPHILIA BUT IS DISPENSABLE FOR INNATE IMMUNE-MEDIATED COLITIS

Inflammatory bowel diseases (IBD) result from uncontrolled inflammation in the intestinal mucosa leading to damage and loss of function. Both innate and adaptive immunity contribute to the inflammation of IBD and innate and adaptive immune cells reciprocally activate each other in a forward feedback loop. In order to better understand innate immune contributions to IBD, we developed a model of spontaneous 100% penetrant, early onset colitis that occurs in the absence of adaptive immunity by crossing villin-TNFAIP3 mice to RAG1-/- mice (TRAG mice). This model is driven by microbes and features increased levels of innate lymphoid cells in the intestinal mucosa. To investigate the role of type 3 innate lymphoid cells (ILC3) in the innate colitis of TRAG mice, we crossed them to retinoid orphan receptor gamma t deficient (Ror{gamma}t-/-) mice. Ror{gamma}t-/- x TRAG mice exhibited markedly reduced eosinophilia in the colonic mucosa, but colitis persisted in these mice. Colitis in Ror{gamma}t-/- x TRAG mice was characterized by increased infiltration of the intestinal mucosa by neutrophils, inflammatory monocytes, macrophages and other innate cells. RNA and cellular profiles of Ror{gamma}t-/- x TRAG mice were consistent with a lack of ILC3 and ILC3 derived cytokines, reduced antimicrobial factors, increased activation oof epithelial repair processes and reduced activation of epithelial cell STAT3. The colitis in Ror{gamma}t-/- x TRAG mice was ameliorated by antibiotic treatment indicating that microbes contribute to the ILC3-independent colitis of these mice. Thus, Ror{gamma}t promotes eosinophilia but Ror{gamma}t and Ror{gamma}t-dependent ILC3 are dispensable for the innate colitis in TRAG mice.

immunology↗

bioRxiv2022

Neural population dynamics in dorsal premotor cortex underlying a reach decision

We investigated if a dynamical systems approach could help understand the link between decision-related neural activity and decision-making behavior, a fundamentally unresolved problem. The dynamical systems approach posits that neural dynamics can be parameterized by a state equation that has different initial conditions and evolves in time by combining at each time step, recurrent dynamics and inputs. For decisions, the two key predictions of the dynamical systems approach are that 1) initial conditions substantially predict subsequent dynamics and behavior and 2) inputs should combine with initial conditions to lead to different choice-related dynamics. We tested these predictions by investigating neural population dynamics in the dorsal premotor cortex (PMd) of monkeys performing a red-green reaction time (RT) checkerboard discrimination task where we varied the sensory evidence (i.e., the inputs). Prestimulus neural state, a proxy for the initial condition, predicted poststimulus neural trajectories and showed organized covariation with RT. Furthermore, faster RTs were associated with faster pre- and poststimulus dynamics as compared to slower RTs, with these effects observed within a stimulus difficulty. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and "fast" initial conditions leading to the fastest choice-related dynamics whereas harder stimuli and "slow" initial conditions led to the slowest dynamics. Finally, changes in initial condition were related to the outcome of the previous trial, with slower pre- and poststimulus population dynamics and RTs on trials following an error as compared to trials following a correct response. Together these results suggest that decision-related activity in PMd is well described by a dynamical system where inputs combine with initial conditions that covary with eventual RT and previous outcome, to induce decision-related dynamics.

neuroscience↗