Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

A target location for a therapeutic intervention is determined in a subject with a neurological disorder. The target location is selected within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. The at least one RSN map includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. Instructions are transmitted to a treatment system that cause operation to be performed on the selected target location.

A universal implantable integrated circuit medical device platform having integral and monolithic circuit traces. The platform allows for implanting into a mammalian body single and multi-functional interface devices for sensing, monitoring stimulating and/or modulating physiological conditions within the body. Microelectronic circuitry may be integrated onto the platform or may be joined as modular components to the platform.

A method for assessing a cancer status of biological tissue includes the steps of: obtaining a Raman spectrum indicating a Raman spectroscopy response of the biological tissue, the Raman spectrum captured using a fiber-optic probe of a fiber-optic Raman spectroscopy system; inputting the Raman spectrum into a boosted tree classification algorithm of a computer program, and using the boosted tree classification algorithm for comparing, in real-time, the captured Raman spectrum to reference data and assessing the cancer status of the biological tissue based on said comparison, the reference data being previously determined based on a set of reference Raman spectra indicating Raman spectroscopy responses of reference biological tissues wherein each of the reference biological tissues is associated with a known cancer status; and generating a real-time output indicating the assessed cancer status of the biological tissue,

In accordance with one embodiment of the present disclosure, a method includes generating a plurality of icons, wherein each icon has a target frequency unique from each other, receiving brain activity data based on an epoch, generating a reference signal based on the epoch, calculating correlation coefficients between the brain activity data and the reference signal, wherein the correlation coefficients are calculated in a window that is within ±0.5 Hz of the target frequencies, including endpoints, determining a confidence score based on the correlation coefficients and the epoch, and determining a selected icon among the plurality of icons based on the correlation coefficients in response to the confidence score surpassing a threshold confidence score.

An MEMS airborne ultrasonic transducer system operating on a thermoacoustic principle to determine brain haemorrhage, includes: an RF transmitter and ultrasound receiver systems to transmit RF energy and receive ultrasound wave, respectively, an RF transmitter system having an RF signal generator, an RF amplifier and a horn antenna, and an ultrasound receiver system having a lock-in amplifier, a DC supply and two ultrasonic transducer arrays wirebonded to low noise amplifier (LNA) chips. The MEMS airborne ultrasonic transducer system determines brain haemorrhage based on detecting RF-induced, blood-originating, thermoacoustic ultrasound wave at the pulse modulation frequency.

Disclosed is a magnetoencephalography apparatus (100) and a method. The apparatus comprises a plurality of magnetic sensors, one or more processors and one or more memories. The method comprises obtaining a reference data, calculating from the reference data a reference basis, obtaining a source basis, obtain a source data, adding together the source basis and the reference basis to form a joint basis and determine an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

Automated detection of head and/or head-affecting body impact events in data collected is performed using instrumented mouthguard devices. For example, in some embodiments the present disclosure relates to training and operation of an impact classifier system, which is configured to identify head affecting impacts from time-series data collected by an instrumented mouthguard device. Some embodiments relate to a two-stage method for processing impacts, including a first stage in which a set of data is classified by such an impact classifier system, and a second stage whereby impacts classified as head affecting impacts are designated a numerical value based on a predefined scale.

The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.

The present disclosure relates to materials and methods for evaluating acoustic startle response (ASR) and pre-pulse inhibition (PPI) in a subject. In particular, the present disclosure relates to a set of acoustic signals and their use in methods for evaluation and/or treatment of mental disorder in a subject. The methods comprise delivering a set of acoustic signals as described herein to the subject, and measuring the startle response in the subject. The startle response may be the blink reflex, pupil dilation, skin conductive response, and/or brain activity in fMRI. For example, measuring the blink reflex may involve measuring the speed, magnitude, and/or duration of the blink reflex in the subject.