Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing brain data using autoencoder neural networks. One of the methods includes obtaining brain data captured by one or more sensors characterizing brain activity of a patient; processing the brain data to generate modified brain data that characterizes a predicted local effect of a future treatment on the brain of the patient; processing the modified brain data using an autoencoder neural network to generate reconstructed brain data; and determining, using the reconstructed brain data, a predicted global effect of the future treatment on the brain of the patient.

A system for classifying an activity and connectivity of a brain into at least one neuropsychiatric disorder from magnetic resonance imaging (MRI) images. The system includes an imaging device, a network, and a brain activity analyzing server. The system (i) generate a three-dimensional (3D) structural MRI image and a 4D functional MRI images of the brain, (ii) extracts one or more features associated with one or more regions of the brain using a parcellation scheme, (iii) analyses, using a machine learning model, an intensity of at least one voxel in the one or more regions, and (iv) classifies the activity and the connectivity of the brain into at least one neuro-psychiatric disorder based on a percentage of variation of intensity of the at least one voxel in the one or more regions of the brain over the one or more features from a predefined threshold value.

A brain function evaluation system includes a receiving unit, and a processing unit. The receiving unit is configured to receive examinee information on a brain function. The processing unit is configured to calculate an evaluation result by evaluating a brain function based on the examinee information and additional examinee information.

A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

fMRI data of a subject brain is accessed and may include a plurality of time-sequenced volumetric images of activity in a subject brain. A plurality of emotion vectors are accessed, each emotion vector tagged with a specified emotional state. From the fMRI data and using the emotion vectors, a plurality of fMRI state vectors are determined at various points in time, where each fMRI state vector is a combination of the emotion vectors and represents the state of the subject brain at a particular point in time. Flow data is determined to identify a trajectory, over time, of the subject brain as reflected by the fMRI data, through a state space defined by the emotion vectors, where the flow data is based at least in part on the fMRI vectors at various points in time. From the flow data, data is generated that shows changes, through time, in at least one emotional state of the brain.

System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting state functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges EEG data and rsfMRI data to map the patient's epilepsy/seizure network. Correlated sections of the brain and inversely correlated sections of the brain are identified to determine which sections of the brain work in conjunction with each other and which sections work oppositely to each other during epileptic episodes.

The present disclosure is directed to instruments and methods that provide one or more stimulations with multiple fibers and multiple imaging implants inserted in a subject for capturing images from one or more regions of the subject's brain. The microendoscope can include a single spatial light modulator or multiple spatial light modulators, a furcated imaging fiber bundle with multiple fibers, multi-implants coupled to a subject for multi-implant patterned brain imaging and stimulation. Both the single spatial light modulator and multiple spatial light modulators are capable to project optical patterns to different imaging fibers and thereby stimulate multiple regions of the brain.

Embodiments may provide techniques that may provide the capability to provide self-guided, self-directed diagnostics and treatment of neural conditions. For example, in an embodiment, a system may comprise program instructions and data stored in the memory to configure the processor to control the stimulation devices to generate and transmit stimulation signals, a plurality of sensing devices connected to signal input circuitry interfacing the processor with the sensing devices, program instructions and data stored in the memory to configure the processor to receive sensed signals from the sensing devices, and program instructions and data stored in the memory to configure the processor to perform dynamic closed loop feedback of the stimulation signals based on the received sensed signals to provide self-guided, self-directed diagnostics and treatment of neural conditions using at least one recipe for a treatment strategy guided by artificial intelligence.

Systems and methods for generating neuroprotective and cardioprotective nutrition programs are described herein. These neuroprotective and cardioprotective nutrition programs are especially applicable for patients at risk of cardiac arrest (e.g. due to hypoxia or ischemia of the brain or other body parts). The programs may feature caloric restriction, for example, short-term caloric restriction. The programs may be generated or iteratively modified based on the hemodynamic and metabolic state of the patient's brain, limbs, or other tissues or organs. Dynamic feedback about the patient's hemodynamic and metabolic state may be provided by techniques including, but not limited to, optical technology for quantitatively and noninvasively measuring blood flow, oxygenation, metabolic rate of oxygen, and perfusion/metabolism ratio in the brain, limbs, or other tissues or organs. The systems described herein may also induce spreading depolarization and repolarization at specific times during or after cardiac arrest based on the patient's cerebral metabolic state.

Mechanisms including: receiving a first set of observed spike counts (FSoOSCs) for the spiking cells; determining a set of probabilities (SoPs) by: retrieving the SoPs from stored information (SI); or calculating the SopS based on the SI, wherein the SI regards possible biological states (BSs) of a subject, wherein each of the possible BSs belongs to at least one of a plurality of time sequences (PoTSs) of BSs, wherein each of the PoTSs of BSs corresponds to a possible action of the subject, and wherein each probability in the set of probabilities indicates a likelihood of observing a possible spike count for one of the plurality of spiking cells; identifying using a hardware processor a first identified BS of the subject from the possible BSs based on the FSoOSCs and the set of probabilities; and determining an action to be performed based on the first identified BS.