The invention provides dosage regimes for treatment of synucleinopathies. In one regime, a subject receives 3000-5000 mg of an antibody intravenously every 3-5 weeks. In another regime, a subject receives 1300-1700 mg of an antibody intravenously every 3-5 weeks.

The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing one or more Parkinson's disease symptoms. The exemplary embodiments may include collecting data of a user's motion, extracting one or more features from the collected data, and assessing one or more Parkinson's disease symptoms of the user based on applying one or more models to the data.

The application relates to assessing cognitive impairment and/or speech motor impairment. The method comprises analysing a voice recording from a word-reading test by identifying a plurality of segments of the voice recording that correspond to single words or syllables and determining the number of correctly read words in the voice recording and/or the speech rate. Determining the correct number of words in the recording may comprise computing one or more Mel-frequency cepstral coefficients for the segments, clustering the resulting vectors of values into n clusters, wherein each cluster has n possible labels, predicting a sequence of words in the voice recording using the labels associated with the clustered vectors of values, performing a sequence alignment between the predicted sequence of words and the sequence of words used in the word reading test, selecting the labels that result in the best alignment and counting the number of matches in the alignment.

The present disclosure relates to systems and methods for assessing severity of involuntary movement associated with tardive dyskinesia (TD). The method includes receiving video data of the patient. The video data may include facial movements of the patient. The method includes processing the video data to identify facial landmark data of the patient, which may include applying an image processing technique to the video data to identify facial landmarks on one or more frames of the video data to produce labeled frames. The method may include applying a plurality of trained machine learning models to the facial landmark data to determine one or more movement severity scores based on changes in the facial landmark data of the patient, where each movement severity score is representative of a category of facial movement, and a total severity score may be generated based on multiple movement severity scores.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

A method of diagnosing a neurodegenerative disorder (ND) in a patient comprising: (a) obtaining MRI image(s) of the patient's brain, (b) using the MRI image(s) of the patient's brain to segment sub-cortical structures associated with the ND into sub-regions, based on structural connectivity to cortical sub-regions, (c) extracting one or more MRI features from each of the sub-regions generated by the segmentation, and (d) using one or more machine learning techniques to classify the patient as being ND positive or ND negative based on comparisons of the one or more MRI features to at least one training data set that includes MRI features of each of the sub-regions generated by the segmentation of known ND positive controls and MRI features of each of the sub-regions generated by the segmentation of ND negative controls, thereby diagnosing ND. Also computer-based or cloud-based systems to diagnose a ND in a subject.

Provided is a method of digital therapeutics for modulating one or more Parkinson's disease-related markers, symptoms, or disease progression in an individual, on a standalone basis or by combining them with medicinal agents, particularly dopaminergic agents. Also provided are corresponding systems for implementing, delivering, and adapting such therapeutic interventions.

The present invention relates to systems and methods for analyzing data from motion activity monitoring technology. It is particularly, but not exclusively, concerned with a method for determining motion activity patterns during sleep.