Biology subjects

Escola, S.

Publications and source records attributed to Escola, S..

7 recordsLinked to original sources

bioRxiv2022

Specific connectivity optimizes learning in thalamocortical loops

1Cortico-thalamo-cortical loops have a central role in cognition and motor control, but precisely how thalamus contributes to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for thalamus in shaping cortical dynamics - and thus behavior - through learning. Since corticothalamic projections compress cortical activity into a lower-dimensional thalamic activity space, we hypothesized that the computational role of thalamus would depend critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic structures specialized to carry an efference copy of the cortical output benefit motor control, while corticothalamic connections that communicate the directions of highest variance in cortical activity are optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination, and found corticothalamic interactions consistent with these predictions. These results suggest that thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

neuroscience↗

bioRxiv2022

Similar striatal activity exerts different control over automatic and flexible motor sequences

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. Such flexibility, however, is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast, and effortless, i.e., automatic. The basal ganglia are thought to underlie both modes of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence in response to cues and in an overtrained, or automatic, condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of execution mode. While lesions affected the detailed kinematics similarly across modes, they disrupted high-level sequence structure for automatic, but not visually-guided, behaviors. These results suggest that the basal ganglia contribute to learned movement kinematics and are essential for automatic motor skills but can be dispensable for sensory-guided motor sequences.

neuroscience↗

bioRxiv2021

Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

bioinformatics↗

bioRxiv2021

Hierarchical confounder discovery in the experiment-machine learning cycle

The promise of using machine learning (ML) to extract scientific insights from high dimensional datasets is tempered by the frequent presence of confounding variables, and it behooves scientists to determine whether or not a model has extracted the desired information or instead may have fallen prey to bias. Due both to features of many natural phenomena and to practical constraints of experimental design, complex bioscience datasets tend to be organized in nested hierarchies which can obfuscate the origin of a confounding effect and obviate traditional methods of confounder amelioration. We propose a simple non-parametric statistical method called the Rank-to-Group (RTG) score that can identify hierarchical confounder effects in raw data and ML-derived data embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders in cases where linear methods such as regression fail. In a large public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover cross-modal correlated variability in a complex multi-phenotypic biological dataset. This approach should be of general use in experiment-analysis cycles and to ensure confounder robustness in ML models.

bioinformatics↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗

bioRxiv2019

A model of flexible motor sequencing through thalamic control of cortical dynamics

The mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise robust way. If the thalamic units associated with each motif are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.

neuroscience↗

bioRxiv2019

Remembrance of things practiced: A two-pathway circuit for sequential learning

The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support such learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning, and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

neuroscience↗