A MRS (magnetic resonance spectroscopy or nuclear magnetic resonance NMR)-based PTSD (post-traumatic stress disorder) and mTBI (mild traumatic brain injury) diagnostic system and method uses MRS signals, already pre-processed by the MRS scanner software. The signals are collected in vivo from specific regions of the brain. A wavelet decomposition is applied to the MRS signals, and the amplitude of the wavelet coefficients and their location in the MRS signals are used as features for training diagnostic classifiers of disease states. These classifiers are identified through analysis of features of individuals whose health status is known. Once the classifiers are trained, patients can be diagnosed by using the same wavelet features extracted from in vivo MRS scans of their brain regions.

A subject's Default Mode Network is accessed through corresponding measurements of the Micro-Coherence Oximetry Network Strength (MCO-S). An associated MCO-S system (100) includes a wearable (102), a user device (112) and a processing platform (123). The wearable (102) collects subject information sufficient to enable monitoring and optimization of the subject's Default Mode Network include sensors such as pulse oximetry instrumentation and EEG electrodes to obtain brainwave data, oxygen saturation data, heart rate variability data, and galvanic skin conductance data. Information from the sensors may be communicated to a user device (112), such as a cell phone or VR headset. The user device (112) communicates with a remote processing platform (123) that may execute artificial intelligence functionality and other logic in connection with assessing the patient's micro-coherence network strength and optimizing behavior modification protocols in relation to attributes and objectives of the subject.

A cannula with a proximally mounted camera and proximally mounted light sources. The lighting sources have beam axes directed distally, toward a workspace at the distal end of the cannula. The light sources are coupled with focusing lenses, to reduce the beam angle of the lighting sources and reduce glare within the cannula tube.

A method of localizing brain regions for the purpose of guiding placement of electrodes and related implants is disclosed. The inventive method involves effecting a pulse in a patient's brain, temporally aligning readings taken from an electrode at various depths, measuring local field potentials at each depth during interstimulus intervals, performing a coherence analysis comparing the local field potential measurements of the different depths, and determining a corresponding brain region for the depths compared.

Techniques for measuring metabolic tissue state and oxygenation in human or animal models, through optical techniques capable of simultaneous measurement at single region of interest. Simultaneously measuring CCO, oxygenated hemoglobin (HbO), and deoxygenated (HbR) hemoglobin is performed and metabolic activity of the tissue is determined. The methods employ a super-continuum light source and a probe to deliver light to the individual, and reflected light from the individual is analyzed to determine the metabolic function of the individual.

A biomarker predictive of a survival outcome of a brain tumor patient is disclosed. The biomarker includes a functional connectivity matrix that includes a plurality of matrix elements. Each matrix element includes a correlation of resting-state fMRI activities of a first and second region of interest from a plurality of regions of interest within the patient's brain. Computing device and systems are disclosed to transform a resting-state fMRI dataset obtained from the patient into the biomarker and to transform the biomarker into a predicted survival outcome using a machine learning model.

The present disclosure concerns methods and systems for improving cognitive performance. More specifically, the disclosure concerns methods and systems for cognitive training under hyperbaric conditions.

A device for locating target points on a magnetic resonance image of the brain of a subject includes a trained neural network configured to receive as input a 3D MR image of the brain of a subject, and to output the location, on the image, of at least one determined brain target point. The neural network includes a plurality of processing stages. Each processing stage processes an image at a respective resolution, and the processing stage of lowest resolution outputs an estimate of the location of each target point. Each other processing stage is configured to receive, from a lower resolution processing stage, an estimate of the locations of the target points, crop the input image to a smaller region surrounding each estimated target point, determine an updated estimate of the location of each target point, and provide the updated estimation to the processing stage of the next higher resolution.

The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

A system and method for detecting brain concussion includes detecting and measuring of acceleration at one or more points on a subject's head. Sensors, which can be accelerometers placed against the head, detect and measure natural motions of the patient's head due to blood flow in the brain and resultant movement of tissue in the brain. An observation is then made, as compared with data corresponding to non-concussion, of a change in frequency response pattern exhibited when accelerations are plotted as a function of time or frequency, to identify probable concussion. Preferably the observation and comparison are made by a computer using an algorithm.