Joint dynamic causal modeling and biophysics modeling to enable multi-scale brain network function modeling
AssigneeThe Board of Trustees of the Leland Stanford Junior University
InventorsJin Hyung Lee, Qin Liu, Onur Cezmi Mutlu
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Abstract
Methods, systems, and devices, including computer programs encoded on a computer storage medium are provided for combined dynamic causal modeling and biophysics modeling of brain function. In particular, the disclosed methods of modeling brain function can be used to integrate brain function measurements by two or more methods, such as functional neuroimaging and electrophysiology. Sequential model fitting is used to improve modeling accuracy to generate a more comprehensive model of brain neuronal circuitry.
Claims10
A method of detecting a brain response to a treatment of a neurological disease or disorder in a subject, the method comprising:
- a) treating the neurological disease or disorder in the subject by administering a therapeutic for treatment of the neurological disease or disorder to the subject, delivering stimulation with an electrical current or magnetic field to the brain of the subject, or optogenetically stimulating the brain of the subject using light;
- b) acquiring functional neuroimaging data of neural activity in one or more regions of interest in a brain of the subject before and after said treating the neurological disease or disorder in the subject;
- c) acquiring experimental electrophysiology data for one or more neurons in said one or more regions of interest in the brain of the subject before and after said treating the neurological disease or disorder in the subject;
- d) fitting the functional neuroimaging data of the neural activity acquired before said treating the neurological disease or disorder in the subject to a first dynamic causal model (DCM), and fitting the functional neuroimaging data of the neural activity acquired after said treating the neurological disease or disorder in the subject to a second DCM;
- e) calculating neural inter-regional and intra-regional connectivity strength estimates from the first DCM and the second DCM;
- f) fitting the experimental electrophysiology data acquired before said treating the neurological disease or disorder in the subject to a first biophysics model, wherein the first biophysics model uses the inter-regional and intra-regional connectivity strength estimates calculated from the first DCM to estimate effects of inter-regional and intra-regional neural activities on rates of change of neural activities of neuronal populations included in the first biophysics model, and wherein the first biophysics model simulates synthetic electrophysiology data, and fitting the experimental electrophysiology data acquired after said treating the neurological disease or disorder in the subject to a second biophysics model, wherein the second biophysics model uses the inter-regional and intra-regional connectivity strength estimates calculated from the second DCM to estimate effects of inter-regional and intra-regional neural activities on rates of change of neural activities of neuronal populations included in the second biophysics model, and wherein the second biophysics model simulates synthetic electrophysiology data;
- wherein said fitting the experimental electrophysiology data acquired before said treating the neurological disease or disorder in the subject further comprises comparing the synthetic electrophysiology data simulated from the first biophysics model to the experimental electrophysiology data acquired before said treating the neurological disease or disorder in the subject and adjusting parameters of the first biophysical model iteratively until model convergence is reached with the experimental electrophysiology data acquired before said treating the neurological disease or disorder in the subject;
- and wherein said fitting the experimental electrophysiology data acquired after said treating the neurological disease or disorder in the subject further comprises comparing the synthetic electrophysiology data simulated from the second biophysics model to the experimental electrophysiology data acquired after said treating the neurological disease or disorder in the subject and adjusting parameters of the second biophysical model iteratively until model convergence is reached with the experimental electrophysiology data acquired after said treating the neurological disease or disorder in the subject;
- g) detecting the brain response to the treatment based on comparing the first biophysical model to the second biophysical model to detect a difference between the first biophysical model and the second biophysical model to identify a change in the neural activity in the subject after administering the treatment for the neurological disease or disorder compared to the neural activity in the subject before the treatment for the neurological disease or disorder;
- h) displaying a depiction of the brain including the one or more regions of interest wherein each region of interest of the one or more regions of interest in the depiction of the brain are associated with a corresponding chart illustrating the synthetic electrophysiology data generated by the first biophysics model for the region of interest and the synthetic electrophysiology data generated by the second biophysics model for the region of interest.
Dependencies are inferred from the claim text.