Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows spatiotemporal recordings of neuronal activity in preclinical models. When applied to the study of sleep, WFCI data are manually scored into sleep states of wakefulness, non-rapid eye movement (NREM) and REM by use of adjunct electroencephalogram (EEG) and electromyogram (EMG) recordings. However, this process is time-consuming, invasive and suffers from low inter- and intra-rater reliability. To overcome these limitations, an automated sleep state classification method that operates on spatiotemporal WFCI recordings is desired. Previous work that classifies sleep states from WFCI data by use of multiplex visibility graphs and deep learning only leverages shared information derived from average time series across parcellated brain regions, and thus fails to fully explore the spatiotemporal calcium dynamics recorded. In this work, a hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to jointly learn spatial and temporal information from the WFCI sleep data. Nineteen transgenic mice expressing GCaMP6f in excitatory neurons were used for network training and testing. The CNN-BiLSTM achieved a weighted F1-score of 0.84 and Cohen’s κ of 0.64, indicating substantial agreement with EEG/EMG-based human scoring. The gradient-weighted class activation maps were computed to provide deeper insights into the brain regions most relevant to the inference of individual sleep state. This work will enable further investigation of sleep neural activity using WFCI.
Exploring functional brain networks (FBNs) from wide-field calcium imaging (WFCI) data is important to understand the functional architecture and organization of the brain. In the study, an unsupervised deep learning method is implemented for identifying FBNs from WFCI data. Specifically, a recurrent autoencoder is adapted to extract spatial-temporal latent embeddings of brain activity followed by use of ordinary least square regression to establish the corresponding function brain networks. Spatial similarities are shared between FBNs estimated from learned embeddings and those derived by seed-based correlation method. The proposed method allows investigations about the effect of spatial-temporal calcium dynamics on FBNs.
Modulation of brain state, e.g., by anesthesia, alters the correlation structure of spontaneous activity, especially in the delta band. This effect has largely been attributed to the ∼1 Hz slow oscillation that is characteristic of anesthesia and nonrapid eye movement (NREM) sleep. However, the effect of the slow oscillation on correlation structures and the spectral content of spontaneous activity across brain states (including NREM) has not been comprehensively examined. Further, discrepancies between activity dynamics observed with hemoglobin versus calcium (GCaMP6) imaging have not been reconciled. Lastly, whether the slow oscillation replaces functional connectivity (FC) patterns typical of the alert state, or superimposes on them, remains unclear. Here, we use wide-field calcium imaging to study spontaneous cortical activity in awake, anesthetized, and naturally sleeping mice. We find modest brain state-dependent changes in infraslow correlations but larger changes in GCaMP6 delta correlations. Principal component analysis of GCaMP6 sleep/anesthesia data in the delta band revealed that the slow oscillation is largely confined to the first three components. Removal of these components revealed a correlation structure strikingly similar to that observed during wake. These results indicate that, during NREM sleep/anesthesia, the slow oscillation superimposes onto a canonical FC architecture.
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