Abstract: A computer implemented method includes supplying stimuli to an organism, measuring brain activity of the organism responsive to the stimuli and producing a brain feature activity map characterizing the brain activity. The operations of supplying, measuring and producing are repeated for different stimuli to form a brain feature activity map database. New stimuli are received. New stimuli features are mapped to a projected brain activity map. The projected brain activity map is compared to the brain feature activity map database to identify similarities and dissimilarities between the projected brain activity map and entries in the brain feature activity map database to designate a match. The new stimuli are characterized based upon the match.

Abstract: A method for content delivery includes dividing a reference group of human subjects into multiple segments according to one or more segmentation criteria. Subjective responses of the human subjects to a reference set of data items are collected, and neurophysiological responses of the human subjects to the data items in the reference set are measured. The human subjects are classified into multiple brain types according to the measured neurophysiological responses. Based on the collected subjective responses, a mapping is defined between the segmentation criteria and the brain types and is applied in predicting a brain type of a human subject outside the reference group. A content offering is selected for presentation to the human subject responsively to the predicted brain type.

Abstract: A content classification method includes receiving a set of content items from categories of a specific discipline, and extracting respective features from each content item. A labeling of the content items of the specific discipline is received, performed by human viewers, the labeling indicating a respective category assigned to the content item by the human viewers. A general-content brain-response model is uploaded, the model estimated using measurements of brains of humans presented with a general-content database defined using a set of features and includes a mapping between the set of features and a set of extracted brain activities. The model is applied to the extracted features, to calculate, using the labeling, a set of brain-responses for the specific discipline. Given a new content item associated with the discipline, a category of the discipline best matching the new content item is estimated, based on the model and the discipline-specific brain responses.

Abstract: A method of identifying brain structures is disclosed. The method comprises imaging the cortex of the brain via inversion recovery magnetic resonance imaging, such that at least two areas are zeroed in terms of their longitudinal relaxation times, and associating the two areas with different brain structures, thereby identifying brain structures.

Abstract: A computer implemented method includes supplying stimuli to an organism, measuring brain activity of the organism responsive to the stimuli and producing a brain feature activity map characterizing the brain activity. The operations of supplying, measuring and producing are repeated for different stimuli to form a brain feature activity map database. New stimuli are received. New stimuli features are mapped to a projected brain activity map. The projected brain activity map is compared to the brain feature activity map database to identify similarities and dissimilarities between the projected brain activity map and entries in the brain feature activity map database to designate a match. The new stimuli are characterized based upon the match.

Abstract: A method of identifying brain structures is disclosed. The method comprises imaging the cortex of the brain via inversion recovery magnetic resonance imaging, such that at least two areas are zeroed in terms of their longitudinal relaxation times, and associating the two areas with different brain structures, thereby identifying brain structures.

Abstract: A method of identifying brain structures is disclosed. The method comprises imaging the cortex of the brain via inversion recovery magnetic resonance imaging, such that at least two areas are zeroed in terms of their longitudinal relaxation times, and associating the two areas with different brain structures, thereby identifying brain structures.

Abstract: Magnetic resonance methods include modeling magnetic resonance signals obtained from specimens at low and high q-values to obtain parameters and distributions of parameters associated with specimen structure and orientation. In evaluation of brain white matter specimens, diffusion within axons can be modeled based on hindered diffusion parallel to an axis of the axon and restricted diffusion perpendicular to the axis. Diffusion exterior to axons can be modeled as hindered diffusion with differing diffusivities parallel to and perpendicular to the axis. Based on extracted parameters and associated model functions, distributions of specimen properties such as intra and extra-axonal principal diffusivities and the corresponding principal directions can be estimated. Features of the axon diameter distribution can also be estimated using this approach.

Abstract: A method of identifying brain structures is disclosed. The method comprises imaging the cortex of the brain via inversion recovery magnetic resonance imaging, such that at least two areas are zeroed in terms of their longitudinal relaxation times, and associating the two areas with different brain structures, thereby identifying brain structures.

Abstract: Magnetic resonance methods include modeling magnetic resonance signals obtained from specimens at low and high q-values to obtain parameters associated with specimen structure and orientation. In evaluation of brain white matter specimens, diffusion within axons can be modeled based on hindered diffusion parallel to an axis of the axon and restricted diffusion perpendicular to the axis. Diffusion exterior to axons can be modeled as hindered diffusion with differing diffusivities parallel to and perpendicular to the axis. Based on extracted parameters and associated model functions, specimen properties such as intra and extra-axonal principal diffusivities and the corresponding principal directions can be estimated.

Abstract: Magnetic resonance methods include modeling magnetic resonance signals obtained from specimens at low and high q-values to obtain parameters and distributions of parameters associated with specimen structure and orientation. In evaluation of brain white matter specimens, diffusion within axons can be modeled based on hindered diffusion parallel to an axis of the axon and restricted diffusion perpendicular to the axis. Diffusion exterior to axons can be modeled as hindered diffusion with differing diffusivities parallel to and perpendicular to the axis. Based on extracted parameters and associated model functions, distributions of specimen properties such as intra and extra-axonal principal diffusivities and the corresponding principal directions can be estimated. Features of the axon diameter distribution can also be estimated using this approach.

Abstract: Magnetic resonance methods include modeling magnetic resonance signals obtained from specimens at low and high q-values to obtain parameters associated with specimen structure and orientation. In evaluation of brain white matter specimens, diffusion within axons can be modeled based on hindered diffusion parallel to an axis of the axon and restricted diffusion perpendicular to the axis. Diffusion exterior to axons can be modeled as hindered diffusion with differing diffusivities parallel to and perpendicular to the axis. Based on extracted parameters and associated model functions, specimen properties such as intra and extra-axonal principal diffusivities and the corresponding principal directions can be estimated.

Abstract: A method for spatial imaging of neuronal biological material using diffusion based magnetic resonance imaging (MRI) techniques. The method is comprised of (i) exposing a neuronal biological material region to a series of diffusion weighted MRI sequences producing plurality of non mono-exponential decay signals; (ii) analyzing the non mono-exponential decay signals and obtaining a parameter reflecting the diffusion characteristic of the slow diffusing component; and (iii) forming an array of these parameters thereby obtaining the spatial image of the neuronal biological material region.