Biology subjects

Birman, D.

Publications and source records attributed to Birman, D..

9 recordsLinked to original sources

bioRxiv2024

Interactive data exploration websites for large-scale electrophysiology

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each sites code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

neuroscience↗

bioRxiv2023

Brain-wide topographic coordination of traveling spiral waves

Traveling waves of activity are a prevalent phenomenon within neural networks of diverse brain regions and species, and have been implicated in myriad brain functions including sensory perception, memory, spatial navigation, and motor control. However, their spatial organization, anatomical basis, and whether they are locally confined versus distributed across the brain, remains unclear. Here we used cortex-wide imaging and large-scale electrophysiology in awake mice to reveal the organization of traveling waves across spatial scales. Traveling waves formed spiral patterns predominantly centered on somatosensory cortex, sweeping across somatotopic maps. Strikingly, the local axonal architecture of neurons in sensory cortex exhibited a matching circular arrangement. At the cortex-wide scale, these spiral waves were mirrored between hemispheres and between sensory and motor cortex, reflecting topographic long-range axons. At the brain-wide scale, cortical spiral waves were coordinated with subcortical spiking patterns in the thalamus, striatum, and midbrain. These results establish that traveling waves are shaped by axonal pathways into coordinated spiral patterns that globally impact neural activity across diverse brain systems, revealing a distributed, multi-sensory organizational principle for propagating neural activity.

neuroscience↗

bioRxiv2023

Pinpoint: trajectory planning for multi-probe electrophysiology and injections in an interactive web-based 3D environment

Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often cant be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoints easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Brain-wide representations of prior information in mouse decision-making

The neural representations of prior information about the state of the world are poorly understood. To investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACAd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

neuroscience↗

bioRxiv2022

Behavioral benefits of spatial attention explained by multiplicative gain, not receptive field shifts, in a neural network model

Attention allows us to focus sensory processing on behaviorally relevant aspects of the visual world. One potential mechanism of attention is a change in the gain of sensory responses. However, changing gain at early stages could have multiple downstream consequences for visual processing. Which, if any, of these effects can account for the benefits of attention for detection and discrimination? Using a model of primate visual cortex we document how a Gaussian-shaped gain modulation results in changes to spatial tuning properties. Forcing the model to use only these changes failed to produce any benefit in task performance. Instead, we found that gain alone was both necessary and sufficient to explain category detection and discrimination during attention. Our results show how gain can give rise to changes in receptive fields which are not necessary for enhancing task performance.

neuroscience↗

bioRxiv2021

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Human observers use cues to guide visual attention to the most behaviorally relevant parts of the visual world. Cues are often separated into two forms: those that rely on spatial location and those that use features, such as motion or color. These forms of cueing are known to rely on different populations of neurons. Despite these differences in neural implementation, attention may rely on shared computational principles, enhancing and selecting sensory representations in a similar manner for all types of cues. Here we examine whether evidence for shared computational mechanisms can be obtained from how attentional cues enhance performance in estimation tasks. In our tasks, observers were cued either by spatial location or feature to two of four dot patches. They then estimated the color or motion direction of one of the cued patches, or averaged them. In all cases we found that cueing improved performance. We decomposed the effects of the cues on behavior into model parameters that separated sensitivity enhancement from sensory selection and found that both were important to explain improved performance. We found that a model which shared parameters across forms of cueing was favored by our analysis, suggesting that observers have equal sensitivity and likelihood of making selection errors whether cued by location or feature. Our perceptual data support theories in which a shared computational mechanism is re-used by all forms of attention. Significance StatementCues about important features or locations in visual space are similar from the perspective of visual cortex, both allow relevant sensory representations to be enhanced while irrelevant ones can be ignored. Here we studied these attentional cues in an estimation task designed to separate different computational mechanisms of attention. Despite cueing observers in three different ways, to spatial locations, colors, or motion directions, we found that all cues led to similar perceptual improvements. Our results provide behavioral evidence supporting the idea that all forms of attention can be reconciled as a single repeated computational motif, re-implemented by the brain in different neural architectures for many different visual features.

neuroscience↗

bioRxiv2018

A quantitative framework for motion visibility in human cortex

Despite the central use of motion visibility to reveal the neural basis of perception, perceptual decision making, and sensory inference there exists no comprehensive quantitative framework establishing how motion visibility parameters modulate human cortical response. Random-dot motion stimuli can be made less visible by reducing image contrast or motion coherence, or by shortening the stimulus duration. Because each of these manipulations modulates the strength of sensory neural responses they have all been extensively used to reveal cognitive and other non-sensory phenomenon such as the influence of priors, attention, and choice-history biases. However, each of these manipulations is thought to influence response in different ways across different cortical regions and a comprehensive study is required to interpret this literature. Here, human participants observed random-dot stimuli varying across a large range of contrast, coherence, and stimulus durations as we measured blood-oxygen-level dependent responses. We developed a framework for modeling these responses which quantifies their functional form and sensitivity across areas. Our framework demonstrates the sensitivity of all visual areas to each parameter, with early visual areas V1-V4 showing more parametric sensitivity to changes in contrast and V3A and MT to coherence. Our results suggest that while motion contrast, coherence, and duration share cortical representation, they are encoded with distinct functional forms and sensitivity. Thus, our quantitative framework serves as a reference for interpretation of the vast perceptual literature manipulating these parameters and shows that different manipulations of visibility will have different effects across human visual cortex and need to be interpreted accordingly.

neuroscience↗

bioRxiv2017

MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N=1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%{+/-}13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N=265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

bioinformatics↗