Data-driven filtration and segmentation of mesoscale neural dynamics

Abstract

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

An analog of psychedelics restores functional neural circuits disrupted by unpredictable stress

“…Wide-field Ca imaging data were first processed with pySEAS, an independent component analysis (ICA) filtering method to remove components corresponding to hemodynamic changes; other components were recombined for subsequent data analysis 52 . A mask for the regions with strong virus expression was generating by thresholding the autocorrelation of dF/F 0 of each pixel (threshold = 0.95).…”

Perinatal Penicillin Exposure Affects Cortical Development and Sensory Processing

“…Therefore, neurons in superficial layers, as well as apical dendrites of deeper layer neurons, together contribute most significantly to the recorded signal. We used an independent component analysis pipeline (pySEAS) ( Weiser et al, 2021 ) to isolate neuronal activities and identify distinct functional domains. Domain maps generated by pySEAS have been shown to be very similar across imaging sessions within the same animal, and share structural similarities across animals ( Weiser et al, 2021 ).…”

“…We used an independent component analysis pipeline (pySEAS) ( Weiser et al, 2021 ) to isolate neuronal activities and identify distinct functional domains. Domain maps generated by pySEAS have been shown to be very similar across imaging sessions within the same animal, and share structural similarities across animals ( Weiser et al, 2021 ). We further assigned each functional domain to one of the ten cortical regions defined by the Allen Brain Atlas ( Figure 2A ).…”

To be and not to be: Wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility

“…ICs were sorted anterior to posterior based on the IC centroid position and assigned a unique color value. Pairwise comparisons of ICs in the template map and timescale maps were performed using the Jaccard index, a common and robust statistic to determine similarity between data sets, including images and more recently graphs and segmentations (Leung et al 2011; Frigo et al 2021; Pérez-Ortega et al 2021; Weiser et al 2021). The threshold for ICs to be defined as spatially matching was 0.5, that is 50% or more pixels overlapped between a pair of binary ICs (see Figure 2B).…”

“…To date, a few wide-field Ca 2+ imaging studies have used ICA to segment the dorsal cerebral cortex into functional domains (Reidl et al 2007; Makino et al 2017; Weiser et al 2021; West et al 2021). In most cases, ICA was performed across the entire dataset, generating a singular set of ICs.…”