Abstract: Systems and methods for generating thin slice images from thick slice images are disclosed herein. In some examples, a deep learning system may calculate a residual from a thick slice image and add the residual to the thick slice image to generate a thin slice image. In some examples, the deep learning system includes a neural network. In some examples, the neural network may include one or more levels, where one or more of the levels include one or more blocks. In some examples, each level includes a convolution block and a non-linear activation function block. The levels of the neural network may be in a cascaded arrangement in some examples.

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.

Abstract: Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.

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.

Abstract: A method for dynamic cash flow modeling may include a cash flow computer program executed by a computer processor: (1) identifying a plurality of cash flow forecasting models, each cash flow forecasting model having a different cash flow forecasting approach; (2) receiving a cash flow forecast request comprising a plurality of cash flow forecast parameters; (3) training each of the plurality of cash flow forecasting models using training data; (4) tuning each of the plurality of cash flow forecasting models to identify optimal hyperparameters for each cash flow forecasting model using a validation data set; (5) selecting one of the plurality of tuned cash flow forecasting models with a lowest error; (6) forecasting, using the selected tuned cash flow forecasting model, a cash flow forecast using the training data set and the validation data set; and (7) outputting the forecast from the selected tuned cash flow forecasting model.

Abstract: The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

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.

Abstract: Examples described herein may provide for pre-triggering imaging scans (e.g. fMRI scans) using an electronic timer synchronized to a stimulation system.

Abstract: Systems and methods for generating thin slice images from thick slice images are disclosed herein. In some examples, a deep learning system may calculate a residual from a thick slice image and add the residual to the thick slice image to generate a thin slice image. In some examples, the deep learning system includes a neural network. In some examples, the neural network may include one or more levels, where one or more of the levels include one or more blocks. In some examples, each level includes a convolution block and a non-linear activation function block. The levels of the neural network may be in a cascaded arrangement in some examples.

Abstract: The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

Abstract: Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.

Abstract: The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

Abstract: Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.

Abstract: Examples described herein may provide for pre-triggering imaging scans (e.g. fMRI scans) using an electronic timer synchronized to a stimulation system.

Abstract: The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

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.

Abstract: Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.