Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior.

Adaptation and learning as strategies to maximize reward in neurofeedback tasks.

Introduction: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.

Discrete field potentials produced by coherent activation of spinal dorsal horn neurons.

In addition to the action potentials generated by the ongoing activation of single dorsal horn neurons in the anesthetized cat, we often recorded small negative field potentials with a fast-rising phase and a slow decay (dIFPs). These potentials could be separated in different classes, each with a specific and rather constant shape and amplitude. They were largest in spinal laminae III-V and gradually faded at deeper locations, without showing the polarity reversal displayed at these depths by the focal potentials produced by stimulation of muscle and cutaneous afferents.

Policies Restricting Overlapping Surgeries Negatively Impact Access to Care, Clinical Efficiency, and Hospital Revenue: A Forecasting Model for Surgical Scheduling.

Objective: To model the financial impact of policies governing the scheduling of overlapping surgeries, and to identify optimal solutions that maximize operating efficiency that satisfy the fiduciary duty to patients.

Background: Hospitals depend on procedural revenue to maintain financial health as the recent pandemic has revealed. Proposed policies governing the scheduling of overlapping surgeries may dramatically impact hospital revenue.

Primate somatosensory cortical neurons are entrained to both spontaneous and peripherally evoked spindle oscillations.

Recurrent thalamocortical circuits produce a number of rhythms critical to brain function. In slow-wave sleep, spindles (7-16 Hz) are a prominent spontaneous oscillation generated by thalamic circuits and triggered by cortical slow waves. In wakefulness and under anesthesia, brief peripheral sensory stimuli can evoke 10-Hz reverberations due potentially to similar thalamic mechanisms.

Supraspinal Shaping of Adaptive Transitions in the State of Functional Connectivity Between Segmentally Distributed Dorsal Horn Neuronal Populations in Response to Nociception and Antinociception.

In the anesthetized cat the correlation between the ongoing cord dorsum potentials (CDPs) recorded from different lumbar spinal segments has a non-random structure, suggesting relatively stable patterns of functional connectivity between the dorsal horn neuronal ensembles involved in the generation of these potentials. During the nociception induced by the intradermic injection of capsaicin, the patterns of segmental correlation between the spontaneous CDPs acquire other non-random configurations that are temporarily reversed to their pre-capsaicin state by the systemic injection of lidocaine, a procedure known to decrease the manifestation of neuropathic pain in both animals and humans. We have now extended these studies and utilized machine learning for the automatic extraction and selection of particular classes of CDPs according to their shapes and amplitudes.

Markovian Analysis of the Sequential Behavior of the Spontaneous Spinal Cord Dorsum Potentials Induced by Acute Nociceptive Stimulation in the Anesthetized Cat.

In a previous study we developed a Machine Learning procedure for the automatic identification and classification of spontaneous cord dorsum potentials (). This study further supported the proposal that in the anesthetized cat, the spontaneous recorded from different lumbar spinal segments are generated by a distributed network of dorsal horn neurons with structured (non-random) patterns of functional connectivity and that these configurations can be changed to other non-random and stable configurations after the noceptive stimulation produced by the intradermic injection of capsaicin in the anesthetized cat. Here we present a study showing that the sequence of identified forms of the spontaneous follows a Markov chain of at least order one.

A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in neuronal connectivity induced by nociceptive stimulation.

Previous studies aimed to disclose the functional organization of the neuronal networks involved in the generation of the spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments used predetermined templates to select specific classes of spontaneous CDPs. Since this procedure was time consuming and required continuous supervision, it was limited to the analysis of two specific types of CDPs (negative CDPs and negative positive CDPs), thus excluding potentials that may reflect activation of other neuronal networks of presumed functional relevance. We now present a novel procedure based in machine learning that allows the efficient and unbiased selection of a variety of spontaneous CDPs with different shapes and amplitudes.