Shionogi has filed a patent for an estimation system that uses brain wave measurement data and functional magnetic resonance imaging (fMRI) measurement data to estimate disorder-likelihood. The system calculates functional connectivity between channels in the brain wave data and between regions of interest in the fMRI data. It then determines an estimation model for disorder-likelihood based on machine learning using the calculated functional connectivity and a disorder-likelihood label. GlobalData’s report on Shionogi gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Estimation system for assessing disorder-likelihood using brain wave and mri data
A recently filed patent (Publication Number: US20230293036A1) describes an estimation system and method for assessing disorder-likelihood in a subject based on brain wave measurement data and functional magnetic resonance imaging (fMRI) measurement data. The system includes one or more processors that obtain simultaneous measurements of brain wave and fMRI data from a subject. The brain wave data includes time waveforms from multiple sensors in the subject's head. The system calculates first functional connectivity for each channel combination based on the correlation between channels in the brain wave data. It also calculates second functional connectivity for each brain network based on the correlation between regions of interest in the fMRI data.
The system further calculates a disorder-likelihood label by determining a score representing the estimated disorder-likelihood based on multiple second functional connectivities. This score is used to determine an estimation model for estimating the disorder-likelihood using machine learning and the first functional connectivity for each channel combination. The estimation model can be prepared for each specific disorder and applied to the subject based on the disorder that manifests in them.
The system can estimate the disorder-likelihood of the subject by inputting the brain wave measurement data into the estimation model. It can also calculate a second score based on the estimated disorder-likelihood and provide information to the subject accordingly. Additionally, the system can assess changes in the subject's symptoms based on the estimated disorder-likelihood.
The calculation of the disorder-likelihood label involves multiplying the second functional connectivities with corresponding weight parameters and summing the results. The score representing the disorder-likelihood is then normalized and subjected to threshold processing.
The estimation system includes an estimation model that includes information for selecting the appropriate first functional connectivity and weight parameter for estimation. The first functional connectivity is calculated based on the correlation value between time waveforms in a section of the brain wave data.
The patent also describes a brain activity training apparatus and method for conducting neurofeedback training. The apparatus includes a storage device for storing the estimation model, an electroencephalograph for measuring brain wave data during training, a presentation apparatus, and a processing apparatus. The processing apparatus calculates the disorder-likelihood of the subject using the estimation model and outputs a signal for representation to the presentation apparatus.
Overall, this patent presents a comprehensive system and method for estimating disorder-likelihood in a subject based on brain wave and fMRI data, providing valuable insights for diagnosis and treatment planning.
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