An example of a system for delivering neurostimulation energy to a patient using a plurality of electrodes may include a stimulation circuit and a sensing circuit. The stimulation circuit may be configured to deliver the neurostimulation energy using stimulation electrodes selected from the plurality of electrodes and to control the delivery of the neurostimulation energy. The sensing circuit may be configured to receive one or more neural signals from sensing electrodes selected from the plurality of electrodes and may include a signal processing circuit. The signal processing circuit may include a detection circuit and an analysis circuit. The detection circuit may be configured to detect one or more attributes of neural responses from the received one or more neural signals. The analysis circuit may be configured to analyze the detected one or more attributes of the neural responses for one or more indications of a neurodegenerative disease.

Methods, systems, and devices, including computer programs encoded on a computer storage medium are provided for combined dynamic causal modeling and biophysics modeling of brain function. In particular, the disclosed methods of modeling brain function can be used to integrate brain function measurements by two or more methods, such as functional neuroimaging and electrophysiology. Sequential model fitting is used to improve modeling accuracy to generate a more comprehensive model of brain neuronal circuitry.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

It is an object to provide a device for estimating a plurality of mental/nervous system diseases by the voice analysis, the device being capable of estimating either major depression or bipolar disorder. Furthermore, there are provided an estimation device including an extraction means for extracting an acoustic feature amount that is not affected by a location where voices are acquired, and a method for operating the estimation device.

The disclosed systems and methods include displaying disease trigger icons within a disease trigger map in a GUI, where the disease trigger map corresponds to a particular disease symptom, and where the position and size of a particular trigger icon within the trigger map based one or more of (i) the degree or strength of a statistical association between the trigger icon's corresponding disease trigger and the disease symptom, and (ii) a cumulative frequency and/or amount of exposure to the trigger icon's corresponding disease trigger. Some embodiments also include displaying a patient population disease trigger map one or more relationships between (i) one or more disease triggers and (ii) one or more patients of the patient population. Some embodiments may further include facilitating communication and/or disease trigger information sharing among patients.

Disclosed herein are implementations of a method and apparatus for stroke self-detection. The method and apparatus may include a mobile platform for stroke detection. The method may include receiving sensor data. The method may include comparing the sensor data with a baseline test result to determine a test score. The method may include determining a passing test result based on a threshold. The method may include transmitting the results or an alert to one or more of an emergency contact, emergency medical services, a physician, or a telemedicine provider.

Disclosed herein are systems and methods of analyzing 3D structure of a portion of the CNS. An analytics module may be used to calculate one or more metrics the describe changes in the 3D structure of a CNS structure over time. The one or more metrics may be used to identify patterns of structural change prior to progressive symptom development. Healthcare providers may use the one or more metrics and or patterns of structural change to diagnose neurological conditions, track the progress of neurological conditions in the patient, and determine the patient's risk of progressive disease development. The 3D structure analytics techniques described herein may also be used to develop treatments and create a care delivery that is individualized for each patient.

An example of a system for delivering neurostimulation to a patient and controlling the delivery of neurostimulation using sensors may include a stimulation output circuit, a sensing circuit, and a control circuit. The stimulation output circuit may be configured to deliver the neurostimulation. The sensing circuit may be configured to receive sensed signals from the sensors and to process the sensed signals. The sensing circuit has adjustable settings controlling the processing of the sensed signals. The control circuit may be configured to control the delivery of the neurostimulation using the processed sensed signals and to control the settings of the sensing circuit according to a sequence of sensing blocks each including a set of sensing parameters.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

The disclosure relates to methods of slowing brain volume loss. In certain aspects, methods of slowing brain volume loss in a patient with multiple sclerosis are disclosed.