Abstract: A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MM data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MM image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MM image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Abstract: A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MRI data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MRI image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MRI image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Abstract: A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MRI data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MRI image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MRI image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Abstract: Methods for deriving an indication of truth or deception are disclosed. Some methods include (a) monitoring the activation of a plurality of regions of a subject's brain, while the subject responds to questions and (b) measuring one or more physiological parameters while the subject responds to questions and combining the results of (a) and (b) to form a composite evaluation indicative of truth or deception in the subject's response. Another method further includes (c) measuring one or more behavioral components while the subject responds to questions and then combining the results of (a), (b), and (c) to form a composite evaluation indicative of truth or deception in the subject's response. Methods for scoring, weighting, and combining the measurements for (a), (b), and (c), and combinations thereof, are also disclosed.

Abstract: Methods for deriving an indication of truth or deception are disclosed. Some methods include (a) monitoring the activation of a plurality of regions of a subject's brain, while the subject responds to questions and (b) measuring one or more physiological parameters while the subject responds to questions and combining the results of (a) and (b) to form a composite evaluation indicative of truth or deception in the subject's response. Another method further includes (c) measuring one or more behavioral components while the subject responds to questions and then combining the results of (a), (b), and (c) to form a composite evaluation indicative of truth or deception in the subject's response. Methods for scoring, weighting, and combining the measurements for (a), (b), and (c), and combinations thereof, are also disclosed.