A method of performing personalized neuromodulation on a subject is provided. The method includes acquiring functional magnetic resonance imaging (fMRI) data of a brain of the subject. The method also includes calculating functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data. The method also includes identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity.

The present invention relates to an apparatus for task determination during brain activity analysis. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with measurement data of the brain of a patient performing a task during brain activity analysis. The processing unit is configured to determine a measure of brain activity based on the measurement data of the brain. The processing unit is configured to determine: that the patient should perform a different task to the task they are currently performing and select the different task; or that the patient should continue performing the task that they are currently performing; or that the patient should stop performing the task; The determination comprises utilization of information relating to the task and the determined measure of brain activity and information relating to a plurality of reference tasks and associated plurality of reference measures of brain activity. The output unit is configured to output an indication that the patient should perform the different task, that the patient should continue performing the task, or that the patient should stop performing the task.

Embodiments may provide self-guided, self-directed diagnostics and treatment of neural conditions. For example, a system may comprise a processor, memory accessible by the processor, and program instructions and data stored in the memory, a plurality of stimulation devices connected to signal output circuitry interfacing the processor with the stimulation devices, program instructions and data 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 to receive sensed signals from the sensing devices, a communication device adapted to wirelessly communicate with a server computer system, and program instructions and data 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.

A laser device includes a seed laser, a plurality of optical amplifiers, and an optical distribution assembly. The seed laser is configured to emit seed laser light. The plurality of optical amplifiers is configured to generate amplified laser light by amplifying the seed laser light. The optical distribution assembly is configured to distribute the seed laser light to an input of each of the optical amplifiers in the plurality and each of the optical amplifiers is configured to direct its respective amplified laser light to a common target.

A method and apparatus for determining spatial distribution of a complex radio frequency (RF) of both transmit field and receive sensitivity a magnetic resonance imaging (MRI) system. The method includes estimation of the absolute phase of transmit field using a reference transmit coil or array coils with minimal absolute phase. The method and apparatus include estimation of complex receive sensitivity of a transceiver coil using the complex transmit field of the transceiver coil or array coils.

Magnetic resonance imaging according to the present invention includes T1, T2, or diffusion mapping with improved image resolution. The improved image resolution is achieved by leveraging the delay in the image acquisition to remove the partial volume effect of fluid in and around the tissue being imaged.

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.