Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study.

Background: Machine learning-based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage.

Objective: This study aimed to determine the accuracy of machine learning-based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones.

Remote Digital Measurement of Facial and Vocal Markers of Major Depressive Disorder Severity and Treatment Response: A Pilot Study.

Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine uptake inhibitors (SNRIs). Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT ( = 18).

Technology for Measuring and Monitoring Treatment Compliance Remotely.

Medication non-adherence during clinical trials is an ongoing challenge that can result in insufficient safety and efficacy data. For patients with Parkinson's disease and other neurological disorders, symptomatology such as forgetfulness compounds traditional obstacles to adherence. Today, sponsors and clinical study sites can call upon various technology tools that improve adherence by monitoring and confirming dosage in near real-time.

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology.

Introduction: Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote "digital phenotyping" of disease symptomatology.

Accuracy of machine learning-based prediction of medication adherence in clinical research.

Medication non-adherence represents a significant barrier to treatment efficacy. Remote, real-time measurement of medication dosing can facilitate dynamic prediction of risk for medication non-adherence, which in-turn allows for proactive clinical intervention to optimize health outcomes. We examine the accuracy of dynamic prediction of non-adherence using data from remote real-time measurements of medication dosing.

Resting Brain Fluctuations Are Intrinsically Coupled to Visual Response Dynamics.

How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation.

Author Correction: Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Quasi-periodic patterns contribute to functional connectivity in the brain.

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood.

Quasi-periodic patterns of brain activity in individuals with attention-deficit/hyperactivity disorder.

Individuals with attention-deficit/hyperactivity disorder have disrupted functional connectivity in the default mode and task positive networks. Traditional fMRI analysis techniques that focus on 'static' changes in functional connectivity have been successful in identifying differences between healthy controls and individuals with ADHD. However, such analyses are unable to explain the mechanisms behind the functional connectivity differences observed.

Resting State fMRI in Rodents.

Resting state functional MRI (fMRI) and functional connectivity are widely applied in humans to examine the role of brain networks in normal function and dysfunction. A similar approach can be taken in rodents, either to obtain translational measures in models of brain disorders or to more carefully examine the neurophysiological underpinnings of the networks. A protocol for resting state functional connectivity in the anesthetized rat, from animal setup to data acquisition to possible pipelines for data analysis, is described.

Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice.

Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis.

Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal.

Time-resolved 'dynamic' over whole-period 'static' analysis of low frequency (LF) blood-oxygen level dependent (BOLD) fluctuations provides many additional insights into the macroscale organization and dynamics of neural activity. Although there has been considerable advancement in the development of mouse resting state fMRI (rsfMRI), very little remains known about its dynamic repertoire. Here, we report for the first time the detection of a set of recurring spatiotemporal Quasi-Periodic Patterns (QPPs) in mice, which show spatial similarity with known resting state networks.

Luria-Delbrück, revisited: the classic experiment does not rule out Lamarckian evolution.

We re-examined data from the classic Luria-Delbrück fluctuation experiment, which is often credited with establishing a Darwinian basis for evolution. We argue that, for the Lamarckian model of evolution to be ruled out by the experiment, the experiment must favor pure Darwinian evolution over both the Lamarckian model and a model that allows both Darwinian and Lamarckian mechanisms (as would happen for bacteria with CRISPR-Cas immunity). Analysis of the combined model was not performed in the original 1943 paper.

Instantaneous brain dynamics mapped to a continuous state space.

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains.

Multiscale network activity in resting state fMRI.

The brain is inherently multiscalar in both space and time. We argue that this multiscalar nature is reflected in the blood oxygenation level dependent (BOLD) fluctuations used to map functional connectivity. We present evidence that global fluctuations in activity, quasiperiodic spatiotemporal patterns, and aperiodic time-varying activity coexist within the BOLD signal.