Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD: 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 classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS: Sleep states were classified with an accuracy of 84% and Cohen's kappa of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD: On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep.

Determinants of gait dystonia severity in cerebral palsy.

AIM: To determine the movement features governing expert assessment of gait dystonia severity in individuals with cerebral palsy (CP). METHOD: In this prospective cohort study, three movement disorder neurologists graded lower extremity dystonia severity in gait videos of individuals with CP using a 10-point Likert-like scale. Using conventional content analysis, we determined the features experts cited when grading dystonia severity. Then, using open-source pose estimation techniques, we determined gait variable analogs of these expert-cited features correlating with their assessments of dystonia severity. RESULTS: Experts assessed videos from 116 participants (46 with dystonia aged 15 years [SD 3] and 70 without dystonia aged 15 years [SD 2], both groups ranging 10-20 years old and 50% male). Variable limb adduction was most commonly cited by experts when identifying dystonia, comprising 60% of expert statements. Effect on gait (regularity, stability, trajectory, speed) and dystonia amplitude were common features experts used to determine dystonia severity, comprising 19% and 13% of statements respectively. Gait variables assessing adduction variability and amplitude (inter-ankle distance variance and foot adduction amplitude) were significantly correlated with expert assessment of dystonia severity (multiple linear regression, p < 0.001). INTERPRETATION: Adduction variability and amplitude are quantifiable gait features that correlate with expert-determined gait dystonia severity in individuals with CP. Consideration of these features could help optimize and standardize the clinical assessment of gait dystonia severity in individuals with CP.

Arousal as a universal embedding for spatiotemporal brain dynamics.

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

Validation of Deep Learning-based Sleep State Classification.

Deep learning methods have been developed to classify sleep states of mouse electroencephalogram (EEG) and electromyogram (EMG) recordings with accuracy reported as high as 97%. However, when applied to independent datasets, with a variety of experimental and recording conditions, sleep state classification accuracy often drops due to distributional shift. Mixture z-scoring, a pre-processing standardization of EEG/EMG signals, has been suggested to account for these variations. This study sought to validate mixture z-scoring in combination with a deep learning method on an independent dataset. The open-source software Accusleep, which implements mixture z-scoring in combination with deep learning via a convolutional neural network, was used to classify sleep states in 12, three-hour EEG/EMG recordings from mice sleeping in a head-fixed position. Mixture z-scoring with deep learning classified sleep states on two independent recordings with 85-92% accuracy and a Cohen's κ of 0.66-0.71. These results validate mixture z-scoring in combination with deep learning to classify sleep states with the potential for widespread use.

Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity.

Normal aging is associated with a variety of neurologic changes including declines in cognition, memory, and motor activity. These declines correlate with neuronal changes in synaptic structure and function. Degradation of brain network activity and connectivity represents a likely mediator of age-related functional deterioration resulting from these neuronal changes. Human studies have demonstrated both general decreases in spontaneous cortical activity and disruption of cortical networks with aging. Current techniques used to study cerebral network activity are hampered either by limited spatial resolution (e.g. electroencephalography, EEG) or limited temporal resolution (e.g., functional magnetic resonance imaging, fMRI). Here we utilize mesoscale imaging of neuronal activity in Thy1-GCaMP6f mice to characterize neuronal network changes in aging with high spatial resolution across a wide frequency range. We show that while evoked activity is unchanged with aging, spontaneous neuronal activity decreases across a wide frequency range (0.01-4 Hz) involving all regions of the cortex. In contrast to this global reduction in cortical power, we found that aging is associated with functional connectivity (FC) deterioration of select networks including somatomotor, cingulate, and retrosplenial nodes. These changes are corroborated by reductions in homotopic FC and node degree within somatomotor and visual cortices. Finally, we found that whole-cortex delta power and delta band node degree correlate with exploratory activity in young but not aged animals. Together these data suggest that aging is associated with global declines in spontaneous cortical activity and focal deterioration of network connectivity, and that these reductions may be associated with age-related behavioral declines.

Spasticity and Dystonia are Underidentified in Young Children at High Risk for Cerebral Palsy.

BACKGROUND: Early spasticity and dystonia identification in cerebral palsy is critical for guiding diagnostic workup and prompting targeted treatment early when it is most efficacious. However, differentiating spasticity from dystonia is difficult in young children with cerebral palsy. METHODS: We sought to determine spasticity and dystonia underidentification rates in children at high risk for cerebral palsy (following neonatal hypoxic-ischemic encephalopathy) by assessing how often child neurologists identified hypertonia alone versus specifying the hypertonia type as spasticity and/or dystonia by age 5 years. RESULTS: Of 168 children, 63 developed cerebral palsy and hypertonia but only 19 (30%) had their hypertonia type specified as spasticity and/or dystonia by age 5 years. CONCLUSIONS: Child neurologists did not specify the type of hypertonia in a majority of children at high risk of cerebral palsy. Because early tone identification critically guides diagnostic workup and treatment of cerebral palsy, these results highlight an important gap in current cerebral palsy care.

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.

BACKGROUND: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. NEW METHOD: A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM. RESULTS: Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered. COMPARISON WITH EXISTING METHOD: On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

Objective and Clinically Feasible Analysis of Diffusion MRI Data can Help Predict Dystonia After Neonatal Brain Injury.

BACKGROUND: Dystonia in cerebral palsy is debilitating but underdiagnosed precluding targeted treatment that is most effective if instituted early. Deep gray matter injury is associated with dystonic cerebral palsy but is difficult to quantify. Objective and clinically feasible identification of injury preceding dystonia could help determine the children at the highest risk for developing dystonia and thus facilitate early dystonia detection. METHODS: We examined brain magnetic resonance images from four- to five-day-old neonates after therapeutic hypothermia for hypoxic-ischemic encephalopathy at a tertiary care center. Apparent diffusion coefficient values in the striatum and thalamus were determined using a web-based viewer integrated with the electronic medical record (IBM iConnect Access). The notes of specialists in neonatal neurology, pediatric movement disorders, and pediatric cerebral palsy (physicians most familiar with motor phenotyping after neonatal brain injury) were screened for all subjects through age of five years for motor phenotype documentation. RESULTS: Striatal and thalamic apparent diffusion coefficient values significantly predicted dystonia with receiver operator characteristic areas under the curve of 0.862 (P = 0.0004) and 0.838 (P = 0.001), respectively (n = 50 subjects). Striatal apparent diffusion coefficient values less than 1.014 × 10-3 mm2/s provided 100% specificity and 70% sensitivity for dystonia. Thalamic apparent diffusion coefficient values less than 0.973 × 10-3 mm2/s provided 100% specificity and 80% sensitivity for dystonia. CONCLUSIONS: Lower striatal and thalamic apparent diffusion coefficient values predicted dystonia in four- to five-day-old neonates who underwent therapeutic hypothermia for hypoxic ischemic encephalopathy. Objective and clinically feasible neonatal brain imaging assessment could help increase vigilance for dystonia in cerebral palsy.

Gait features of dystonia in cerebral palsy.

AIM: To determine the features cited by motor phenotyping experts when identifying dystonia in people with cerebral palsy (CP). METHOD: Dystonia identification in CP, particularly when comorbid with spasticity, can be difficult. The dystonia diagnostic criterion standard remains subjective visual identification by expert consensus. For this qualitative study, we conducted an inductive thematic analysis of consensus-building discussions between three pediatric movement disorder physicians as they identified the presence or absence of dystonia in gait videos of 40 participants with spastic CP and periventricular leukomalacia. RESULTS: Unanimous consensus about the presence or absence of dystonia was achieved for 34 out of 40 videos. Two main themes were present during consensus-building discussions as videos were evaluated for dystonia: (1) unilateral leg or foot adduction that was variable over time, and (2) difficulty in identifying dystonia. Codes contributing to the first theme were more likely to be cited by a discussant when they felt dystonia was present (as opposed to absent) in a video (χ2 test, p=0.004). DISCUSSION: These results describe the gait features cited by experts during consensus-building discussion as they identify dystonia in ambulatory people with CP. Qualitative thematic analysis of these discussions could help codify the subjective process of dystonia diagnosis.