Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

A system and related method for determining a motor state of a subject includes a multi-axial measurement system and a processor that measure a signal indicative of an acceleration trend on three axes, limiting the frequency band and compensating the offset of the output signals from the multi-axial measurement system; compute a motor activity; perform a frequency and spectral analysis of the signal with the Fournier transform; computing the power spectral density; compute integrals of the power spectral density calculated by considering a pre-determined frequency interval and the entire frequency range; and compare the determined parameters against a reference value or range.

The present disclosure is directed to methods for treating a neurological disorder of a patient. In some embodiments, the method comprises: acquiring neural activity signals from the patient; calculating a power (P.sub.BF) of a beta frequency band of the acquired neural activity signals; and adjusting a stimulation parameter of a stimulation signal (V.sub.stim) when the power (P.sub.BF) of the beta frequency band meets or exceeds an upper power value threshold (P.sub.BF2). In some embodiments, the method comprises: acquiring neural activity signals from the patient; calculating a power (P.sub.BF) of a low frequency band of the acquired neural activity signals; and adjusting a stimulation parameter of a stimulation signal (V.sub.stim) when the power (P.sub.BF) of the low frequency band (4-10 Hz) meets or exceeds an upper power value threshold (P.sub.BF2).

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

A method includes engaging Parkinson's Disease (PD) subjects in a continuous motor performance task that elicits natural motor variability, quantifying natural motor variability of each PD subject with an array of motor metrics at short timescales, applying a machine-learning classification or regression algorithm to determine weights for each of these metrics to maximally differentiate each patient's motor performance from that of controls performing the same task, and combining the weights to determine a scalar metric of motor performance for each short epoch of motor behavior.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Systems and methods are provided for evaluating a patient for a neurological movement disorder. A three-dimensional medical image of a brain of the patient is captured and provided to an artificial neural network having at least one convolutional layer to provide a set of output values. The set of output values is provided to a machine learning model to provide a clinical parameter representing one of a presence of the neurological movement disorder in the patient and a response of the patient to a specific treatment for the neurological movement disorder.

A method for early diagnosis of Parkinson's disease based on multimodal deep learning is provided. Audio-visual data of a to-be-diagnosed subject while performing a speech task is acquired. The audio-visual data are preprocessed to extract a plurality of audio segments and a plurality of video segments. A face image sequence is extracted from each of the plurality of video segments. A Mel-spectrogram of each of the plurality of audio segments is calculated. The face image sequence and the Mel-spectrogram are input into a multimodal deep learning model to output a classification result for Parkinson's disease early diagnosis of the to-be-diagnosed subject. A system for early diagnosis of Parkinson's disease based on multimodal deep learning is also provided.