Abstract: An example system includes a first lead configured to be positioned in or beside a left internal jugular vein (IJV) of a patient to deliver a first stimulation signal to a first vagus nerve, the first lead including one or more first segmented electrodes positioned on a distal portion of the first lead and a first anchoring mechanism; a second lead configured to be positioned in or beside a right IJV of the patient to deliver a second stimulation signal to a second vagus nerve, the second lead including one or more second segmented electrodes positioned on a distal portion of the second lead and a second anchoring mechanism; and circuitry configured to deliver electrical energy to the first lead to deliver the first stimulation signal and the second lead to deliver the second stimulation signal to provide bilateral stimulation to the first vagus nerve and the second vagus nerve.

Abstract: A method for classifying tissue in a magnetic resonance image, and particularly for measuring a volume of pathological tissue such as white tissue hyperintensity (leukoaraiosis) in the brain based on the segmentation of the intensity histogram of fluid attenuated inversion recovery (FLAIR) images is described. A magnetic resonance image of the brain of a subject is acquired, and a pixel intensity histogram is constructed from the image. A statistically-based regression analysis is applied to the histogram to determine upper and lower threshold values, which define different types of brain tissue, particularly normal brain, cerebral spinal fluid (CSF), or lesion.

Abstract: A method for classifying tissue in a magnetic resonance image. and particularly for measuring a volume of pathological tissue such as white tissue hyperintensity (leukoaraiosis) in the brain based on the segmentation of the intensity histogram of fluid attenuated inversion recovery (FLAIR) images is described. A magnetic resonance image of the brain of a subject is acquired, and a pixel intensity histogram is constructed from the image. A statistically-based regression analysis is applied to the histogram to determine upper and lower threshold values, which define different types of brain tissue, particularly normal brain, cerebral spinal fluid (CSF), or lesion.