Abstract: A system which can be used for carrying out a method for determining an efficacy of a transcranial magnetic stimulation (TMS) treatment, including: delivering TMS to a patient using a TMS protocol in a TMS treatment session; recording EEG signals before and after the delivering during the treatment session; recording EEG signals after completion of the treatment session; analyzing the recorded EEG signals to generate values of one or more EEG parameters; determining an acute effect and an intermediate effect of the TMS protocol on a brain state of the patient based on a change in values of the one or more EEG parameters; determining an efficacy of the TMS protocol based on the determined acute effect and the intermediate effect.

Abstract: Methods and systems for predicting the onset and progression of neurodegenerative diseases using blood test data and machine learning models are disclosed. The method involves receiving blood test values for a target subject over a previous time period, extracting features from the blood test data, and applying a trained predictive model to compute a risk score indicating the probability of developing a neurodegenerative disease. The system includes a processor that executes code to perform the method and can be implemented as a standalone application, cloud-based service, or integrated with healthcare systems. The predictive models are trained using supervised learning on labeled data and can include statistical models or machine learning algorithms. The systems and methods enable early detection of neurodegenerative diseases, personalized risk prediction, and integration with existing healthcare infrastructure. By leveraging blood test data and machine learning techniques, patient outcomes may be improved.

Abstract: Using microstate data to evaluate Alzheimer's disease including dementia and/or mild cognitive impairment and/or depression and optionally provide treatment based on the diagnosis and/or modify treatment and/or diagnosis based on response to treatment, optionally as measured using microstate data. In one example, the method includes analyzing EEG data to generate one or more EEG parameter; and generating a risk for one or both of depression and MCI (mild cognitive impairment) based on said one or more EEG parameters. Optionally, the disease comprises MCI. Optionally, the disease comprises depression. Optionally, the one or more EEG parameter comprises a microstate-derived parameter. Optionally, the one or more EEG parameter comprises a microstate duration derived parameter.

Abstract: A method for evaluating a treatment for a brain condition, including: extracting one or more microstate parameter values from at least one EEG signal that was measured after the treatment; and evaluating at least one parameter of the treatment based on the one or more microstate parameter values.

Abstract: Using microstate data to evaluate Alzheimer's disease including dementia and/or mild cognitive impairment and/or depression and optionally provide treatment based on the diagnosis and/or modify treatment and/or diagnosis based on response to treatment, optionally as measured using microstate data. In one example, the method includes analyzing EEG data to generate one or more EEG parameter; and generating a risk for one or both of depression and MCI (mild cognitive impairment) based on said one or more EEG parameters. Optionally, the disease comprises MCI. Optionally, the disease comprises depression. Optionally, the one or more EEG parameter comprises a microstate-derived parameter. Optionally, the one or more EEG parameter comprises a microstate duration derived parameter.

Abstract: A method for evaluating a treatment for a brain condition, including: extracting one or more microstate parameter values from at least one EEG signal that was measured after the treatment; evaluating at least one parameter of the treatment based on the one or more microstate parameter values.