Columbia University

Scott A Small

Jia Guo

Frank A Provenzano

Usman A Khan

Eric R Kandel

Xinyang Feng

Ilias Pavlopoulos

Frank Provenzano

A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.

Classifying neurological disease status using deep learning

An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium ('Gd') enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging.

System, method and computer-accessible medium for detecting functional disorder(s) or aging progression in magnetic resonance imaging

An exemplary system, method, and computer-accessible medium for detection of structural disorder(s) of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium ('Gd') enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the structural disorder(s) of the patient(s) based on a GD contrast of the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map. The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), or (ii) a Gd-free MRI scan(s). The Gd contrast can be generated in the Gd enhanced map(s) using a T2-weighted MRI image of the portion(s). Structural disorder(s) can include Stroke, tumor, trauma, infection, Multiple sclerosis and/or other inflammatory disease.

System, method and computer-accessible medium for detecting structural disorder(s) using magnetic resonance imaging

A technology which enables identifying, via a computer, a vessel in a third image. The third image is obtained from a subtraction of a second image from a first image. The second image and the first image are aligned within an imaging space. The first image is post-contrast. The second image is pre-contrast. The technology enables determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image. The technology enables obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value. The technology enables applying, via the computer, a filter onto the fourth image. The technology enables generating, via the computer, a filter mask based on the fourth image.

Technologies for diagnosing neurological or psychiatric illnesses

A technology which enables identifying, via a computer, a vessel in a third image. The third image is obtained from a subtraction of a second image from a first image. The second image and the first image are aligned on an imaging space. The first image is post-contrast. The second image is pre-contrast. The technology enables determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image. The technology enables obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value. The technology enables applying, via the computer, a filter onto the fourth image. The technology enables generating, via the computer, a filter mask based on the fourth image.

Described is a transgenic mouse with two transgenes, each of which transgene comprises a DNA sequence encoding a dominant negative form of RbAp48 protein, wherein the expression of the dominant negative form of RbAp48 is spatially restricted to the forebrain by a CaM Kinase IIa promoter and wherein the expression of the dominant negative form of RbAp48 is controlled by tetracycline-controlled transcriptional activation. Also provided are methods for evaluating in the transgenic mouse the potential therapeutic effect of an agent for slowing, inhibiting or preventing age-related memory decline in a mammalian subject.

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Millerton, NY, United States of America

Scott A Small