# US 11,896,390 B2 · Efficacy and/or therapeutic parameter recommendation using individual patient data and therapeutic brain network maps

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- Number: US 11,896,390 B2
- Document ID: US11896390B2
- Status: Granted
- Kind: B2
- Application number: 16/186,374
- Filed: 2018-11-09
- Published: 2019-05-16
- Granted: 2024-02-13
- Earliest claimed: 2017-11-10
- Assignees: LVIS Corporation
- Applicants: Lvis Corporation
- Inventors: Zhongnan Fang; Jin Hyung Lee
- Web page: https://patentgrep.com/patent/US11896390B2

## CPC

- A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication}
- A61B 5/0035: {adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography}
- A61B 5/0036: {including treatment, e.g., using an implantable medical device, ablating, ventilating}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/245: specially adapted for magnetoencephalographic [MEG] signals
- A61B 5/372: Analysis of electroencephalograms
- A61B 5/4094: {Diagnosing or monitoring seizure diseases, e.g. epilepsy}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 6/5247: {combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound}
- G01R 33/4808: {Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/048: Activation functions
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 6/037: {Emission tomography}
- G01R 33/4806: {Functional imaging of brain activation}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Examples described herein may predict therapy efficacy and/or therapeutic parameters using a comparison of individual patient status data and brain network response maps for the therapy. For example, VNS parameters may be predicted using a comparison of patient EEG data and brain network response maps of VNS therapy at various parameters.

## Claims

### Claim 1

A method comprising:
collecting individual patient data from a patient using an electroencephalogram (EEG) system or magnetoencephalogram (MEG) system, wherein the individual patient data includes data of a neural status of a patient, wherein the individual patient data of the neural status indicates brain regions affected by a neural event;
collecting additional data from the patient using functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), or a combination thereof;
generating from the additional data, a therapeutic brain network response map of a treatment, wherein the therapeutic brain network response map indicates brain responses to the treatment, wherein generating the therapeutic brain network response map comprises aligning the additional data to a template brain, and the therapeutic brain network response map indicates a frequency of activation of brain regions affected by the treatment, average signal values of the brain regions affected by the treatment, or a combination thereof; and
predicting an efficacy of the treatment for the patient based on a comparison of the individual patient data of the neural status and the therapeutic brain network response map by a statistical prediction model for the treatment implemented by a processor executing computer-readable instructions encoded on a non-transitory medium.

### Claim 2 (depends on 1)

The method of claim 1, further comprising predicting parameters for the treatment using the statistical prediction model.

### Claim 3 (depends on 1)

The method of claim 1, wherein the therapeutic brain network response map corresponds to a brain response to the treatment using a certain set of parameters.

### Claim 4 (depends on 1)

The method of claim 1, wherein the statistical prediction model comprises feature extraction techniques configured to extract features from the individual patient data regarding neural status and the therapeutic brain network response map.

### Claim 5 (depends on 4)

The method of claim 4, wherein the features comprise an overlap area between the individual patient data and the therapeutic brain network response map, wherein the efficacy predicted is based on a size of the overlap area.

### Claim 6 (depends on 1)

The method of claim 1, wherein the treatment comprises vagus nerve stimulation and the neural status comprises a seizure.

### Claim 7 (depends on 6)

The method of claim 6, wherein the individual patient data of the neural status comprises data of epileptiform spike or seizure network image indicative of brain regions in a seizure generation and propagation and pathways between these regions.

### Claim 8 (depends on 1)

The method of claim 1, further comprising analyzing the individual patient data of the neural status using group averaged spikes and source localization, using spike ICA analysis and source localization, using seizure network analysis, or combinations thereof.

### Claim 9 (depends on 8)

The method of claim 8, further comprising based on the analyzing of the individual patient data, projecting the individual patient data onto a brain space, identifying brain regions that are involved in the neural status, calculating pathways between seizure brain regions, or combinations thereof.

### Claim 10 (depends on 1)

The method of claim 1, wherein the statistical prediction model comprises:
convolution kernels configured to extract features from the individual patient data of the neural status and the therapeutic brain network response map.

### Claim 11 (depends on 10)

The method of claim 10, wherein values in the convolution kernels are learned during a training of the statistical prediction model for the treatment.

### Claim 12 (depends on 10)

The method of claim 10, further comprising repeatedly multiplying each convolution kernel with the individual patient data and the therapeutic brain network response map, and storing results as pixels in a plurality of feature maps.

### Claim 13 (depends on 12)

The method of claim 12, wherein the statistical prediction model further comprises a deep convolutional neural network comprises stacked convolution, rectified linear unit, and pooling layers configured to extract further features from the feature maps.

### Claim 14 (depends on 13)

The method of claim 13, further comprising predicting the efficacy based on the further features from the feature maps.

### Claim 15

A system comprising:
at least one processor; and
computer readable media encoded with instructions that, when executed by the at least one processor, cause the system to:
provide image data of a neural status of a patient, wherein the image data indicates brain regions affected by a neural event and a therapeutic brain network response map, wherein the therapeutic brain network response map indicates brain responses to a treatment, as inputs to a statistical prediction model for the treatment, wherein the therapeutic brain network response map indicates a frequency of activation of brain regions affected by the treatment, average signal values of the brain regions affected by the treatment, or a combination thereof; and
predict an efficacy of the treatment using the statistical prediction model, wherein the efficacy predicted is based, at least in part, on a comparison of the image data and the therapeutic brain network response map.

### Claim 16 (depends on 15)

The system of claim 15, wherein the instructions further cause the system to predict parameters for the treatment using the statistical prediction model.

### Claim 17 (depends on 15)

The system of claim 15, further comprising an electroencephalogram (EEG) system coupled to the at least one processor or a magnetoencephalogram (MEG) system coupled to the at least one processor and configured to provide the image data, and a functional magnetic resonance imaging (fMRI) system coupled to the at least one processor and configured to provide the therapeutic brain network map.

### Claim 18 (depends on 15)

The system of claim 15, wherein the treatment comprises vagus nerve stimulation and the neural status comprises a seizure.

### Claim 19 (depends on 18)

The system of claim 18, wherein the image data of the neural status comprises data of an epilepsy source image indicative of a seizure origin.

### Claim 20 (depends on 15)

The system of claim 15, wherein the statistical prediction model comprises:
convolution kernels configured to extract features from the image data of the neural status and the therapeutic brain network response map.

### Claim 21 (depends on 20)

The system of claim 20, wherein values in the convolution kernels are learned during a training of the statistical prediction model for the treatment.

### Claim 22 (depends on 20)

The system of claim 20, wherein the instructions further cause the system to repeatedly multiply each convolution kernel with the image data and the therapeutic brain network response map, and store results as pixels in a plurality of feature maps.

### Claim 23 (depends on 22)

The system of claim 22, wherein the statistical prediction model further comprises a deep convolutional neural network comprises stacked convolution, rectified linear, and pool layers configured to extract further features from the feature maps.

### Claim 24 (depends on 23)

The system of claim 23, wherein the instructions further cause the system to predict the efficacy based on the further features from the feature maps.