spatial exploration

Dosing and Deployment Trial: A Home-based Optokinetic Treatment for Ipsilesional Gaze Deviation

Stroke can have devastating consequences including ipsilesional gaze deviation (IGD), which directly impacts mobility and falls. IGD, a hallmark sign of spatial neglect (SN), is a major predictor of poor recovery and can persist after inpatient rehabilitation with targeted treatments. Our preliminary data show that more than half of stroke survivors who have SN at the time of admission to inpatient rehabilitation still have SN at time of discharge, even after treatment. Therefore, because of the challenges of the traditional rehabilitation paradigm we need to bring treatments into the home setting. We plan to examine the feasibility and deployment of Eyemove, an optokinetic stimulation treatment, which induces brain neural plasticity and improves spatial exploration, in turn reducing SN symptoms, including IGD. We hypothesize that by treating IGD, improvements in mobility and fall risk scores will occur, as participants can now interact with the space that was previously “neglected”. Here, we propose to test the following aims with 50 community-dwelling individuals with SN, by identifying the practical dosage associated with mobility improvement: Aim 1 will determine feasibility and acceptability of home deployment of Eyemove. We will collect qualitative information from stroke survivors and their care partners, to determine their pre-treatment and post-treatment perspectives of this home treatment. Aim 2 will determine whether Eyemove in the home is associated with improved mobility-related outcomes (including risk of falls) and to evaluate sufficient dosing. We will randomize participants into either 3 or 5 sessions of a 40-minute treatment given over a week-long intervention period. The primary outcome will be the Mobility Assessment Course and secondary outcomes will be the Stroke Assessment of Fall Risk and the Life Space Assessment. For Aim 1, we expect to learn practical suggestions for home implementation and obtain reports of post-experience enthusiasm and acceptability for specific aspects of the intervention. Our hypotheses for Aim 2 are: 1a-- After controlling for pre-treatment score changes (T2-T1), the intervention (T3) will lead to improved mobility/ fall risk compared to baseline (T1), regardless of treatment group; 1b-- The amount of mobility/ fall risk improvement (T3-T1) in the 3- session and 5-session groups will be different. The expected findings will provide critical insight into the use of Eyemove for spatial neglect remediation. Results from this research will be used to develop a subsequent R01 proposal that uses pragmatic, randomized clinical trial methods to determine the efficacy of Eyemove, in order to provide an effective, accessible treatment to remediate SN at home and improve individuals’ ability to move without spatial bias or risk of falls.

Structures in space and time - Hierarchical network dynamics in the amygdala

In addition to its role in the learning and expression of conditioned behavior, the amygdala has long been implicated in the regulation of persistent states, such as anxiety and drive. Yet, it is not evident what projections of the neuronal activity capture the functional role of the network across such different timescales, specifically when behavior and neuronal space are complex and high-dimensional. We applied a data-driven dynamical approach for the analysis of calcium imaging data from the basolateral amygdala, collected while mice performed complex, self-paced behaviors, including spatial exploration, free social interaction, and goal directed actions. The seemingly complex network dynamics was effectively described by a hierarchical, modular structure, that corresponded to behavior on multiple timescales. Our results describe the response of the network activity to perturbations along different dimensions and the interplay between slow, state-like representation and the fast processing of specific events and actions schemes. We suggest hierarchical dynamical models offer a unified framework to capture the involvement of the amygdala in transitions between persistent states underlying such different functions as sensory associative learning, action selection and emotional processing. * Work done in collaboration with Jan Gründemann, Sol Fustinana, Alejandro Tsai and Julien Courtin (@theLüthiLab)

Emergence of long time scales in data-driven network models of zebrafish activity

How can neural networks exhibit persistent activity on time scales much larger than allowed by cellular properties? We address this question in the context of larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies. We study in particular the dynamics of a bilaterally distributed circuit, the so-called ARTR, including hundreds neurons. ARTR exhibits slow antiphasic alternations between its left and right subpopulations, which can be modulated by the water temperature, and drive the coordinated orientation of swim bouts, thus organizing the fish spatial exploration. To elucidate the mechanism leading to the slow self-oscillation, we train a network graphical model (Ising) on neural recordings. Sampling the inferred model allows us to generate synthetic oscillatory activity, whose features correctly capture the observed dynamics. A mean-field analysis of the inferred model reveals the existence several phases; activated crossing of the barriers in between those phases controls the long time scales present in the network oscillations. We show in particular how the barrier heights and the nature of the phases vary with the water temperature.

Linking neural representations of space by multiple attractor networks in the entorhinal cortex and the hippocampus

In the past decade evidence has accumulated in favor of the hypothesis that multiple sub-networks in the medial entorhinal cortex (MEC) are characterized by low-dimensional, continuous attractor dynamics. Much has been learned about the joint activity of grid cells within a module (a module consists of grid cells that share a common grid spacing), but little is known about the interactions between them. Under typical conditions of spatial exploration in which sensory cues are abundant, all grid-cells in the MEC represent the animal’s position in space and their joint activity lies on a two-dimensional manifold. However, if the grid cells in a single module mechanistically constitute independent attractor networks, then under conditions in which salient sensory cues are absent, errors could accumulate in the different modules in an uncoordinated manner. Such uncoordinated errors would give rise to catastrophic readout errors when attempting to decode position from the joint grid-cell activity. I will discuss recent theoretical works from our group, in which we explored different mechanisms that could impose coordination in the different modules. One of these mechanisms involves coordination with the hippocampus and must be set up such that it operates across multiple spatial maps that represent different environments. The other mechanism is internal to the entorhinal cortex and independent of the hippocampus.

Chronic functional ultrasound imaging combined with behavior tracking on freely moving rats performing spatial exploration