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Bai Reddy, C.

Publications and source records attributed to Bai Reddy, C..

4 recordsLinked to original sources

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed the exquisite diversity of cortical inhibitory neurons1-7, but it is not known whether these fine molecular subtypes have correspondingly diverse activity patterns in the living brain. Here, we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We used transcriptomic clusters (t-types)4 to classify inhibitory neurons imaged in layers 1-3 using a three-level hierarchy of 5 Families, 11 Classes, and 35 t-types. Visual responses differed significantly only across Families, but modulation by brain state differed at all three hierarchical levels. Nevertheless, this diversity could be predicted from the first transcriptomic principal component, which predicted a cell types brain state modulation and correlations with simultaneously recorded cells. Inhibitory t-types with narrower spikes, lower input resistance, weaker adaptation, and less axon in layer 1 as determined in vitro8 fired more in resting, oscillatory brain states. Transcriptomic types with the opposite properties fired more during arousal. The former cells had more inhibitory cholinergic receptors, and the latter more excitatory receptors. Thus, despite the diversity of V1 inhibitory neurons, a simple principle determines how their joint activity shapes state-dependent cortical processing.

neuroscience↗

Neural basis of functional ultrasound signals

Functional ultrasound imaging (fUSI) is a popular method for measuring blood flow and thus infer brain activity, but it relies on the physiology of neurovascular coupling and requires extensive signal processing. To establish to what degree its trial-by-trial signals reflect neural activity, we performed simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in local firing rate, and were closely predicted by the smoothed firing rate of local neurons, particularly putative inhibitory neurons. The optimal smoothing filter had width ~3 s, matched the hemodynamic response function of awake mouse, was invariant across mice and stimulus conditions, and similar in cortex and hippocampus. fUSI signals also matched neural firing spatially: firing rates were as highly correlated across hemispheres as fUSI signals. Thus, hemodynamic signals measured by ultrasound bear a simple and accurate relationship to neuronal firing.

neuroscience↗

Spatial encoding in the visual pathway arises in cortex and depends on active navigation

During navigation, the visual responses of neurons in primary visual cortex (V1) are modulated by the animals spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but largely absent in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and requires engagement in active behavior. Active navigation in a familiar environment, therefore, determines spatial modulation of visual signals starting in the cortex.

neuroscience↗

Spontaneous behaviors drive multidimensional, brain-wide population activity

Cortical responses to sensory stimuli are highly variable, and sensory cortex exhibits intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Here, by recording over 10,000 neurons in mouse visual cortex, we show that spontaneous activity reliably encodes a high-dimensional latent state, which is partially related to the mouses ongoing behavior and is represented not just in visual cortex but across the forebrain. Sensory inputs do not interrupt this ongoing signal, but add onto it a representation of visual stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

neuroscience↗