Magnetic Resonance Imaging (MRI)

Research in recent decades has suggested the existence of a dedicated brain network devoted to the organization and execution of grasping, one of the most important and skilled movements of primates. Grasping an object requires the transformation of intrinsic object properties such as size, orientation, and shape into an appropriate motor scheme shaping the hand.

Subcortical nuclei are increasingly targeted for deep brain stimulation (DBS) and for gene transfer to treat neurological and psychiatric disorders. For a successful outcome in patients, it is critical to place DBS electrodes or infuse viral vectors accurately within targeted nuclei. However current MRI approaches are still limited to localize brainstem and basal ganglia nuclei accurately.

Functional imaging studies in humans and monkeys have shown category-selective regions in the temporal cortex, in particular for faces and bodies. Although the body-selective regions have been well studied in humans, little is understood about the functional properties of such regions in macaques.

The inferior temporal (IT) cortex in monkeys plays a central role in visual object recognition and learning. Previous studies have observed patches in IT cortex with strong selectivity for highly familiar object classes (e.g., faces), but the principles behind this functional organization are largely unknown due to the many properties that distinguish different object classes. To unconfound shape from meaning and memory, we scanned monkeys with functional magnetic resonance imaging while they viewed classes of initially novel objects.

Monkey electrophysiology suggests that the activity of the ventral tegmental area (VTA) helps regulate reinforcement learning and motivated behavior, in part by broadcasting prediction error signals throughout the reward system. However, electrophysiological studies do not allow causal inferences regarding the activity of VTA neurons with respect to these processes because they require artificial manipulation of neuronal firing. Rodent studies fulfilled this requirement by demonstrating that electrical and optogenetic VTA stimulation can induce learning and modulate downstream structures.

UNLABELLED: Juvenile neuronal ceroid lipofuscinosis (JNCL, CLN3) is an inherited lysosomal disease. We used longitudinal MRI, for the first time, to evaluate the rate of brain volume alterations in JNCL. Six patients (mean ages of 12.4 years and 17.3 years) and 12 healthy controls were studied twice with 1.5 T MRI. White matter (WM), gray matter (GM) and CSF volumes were measured from the sets of T1-weighted 3-dimensional MR images using a fully automated image-processing procedure. The brain volume alterations were calculated as percentage change per year.

Juvenile neuronal ceroid lipofuscinosis (CLN3) is characterized by progressive cerebral atrophy. The purpose of this study was to re-evaluate the three-dimensional magnetic resonance (3D-MR) images of patients with CLN3 using voxel-based morphometry (VBM) to achieve a detailed understanding of the affected brain regions. T1-weighted 3D-MR images of 15 patients with CLN3 (age range: 12-25 years, mean age 17.6 years) and 15 age- and sex-matched controls were analyzed using VBM.

BACKGROUND: Opioid-dependent patients have been shown to have structural brain alterations. This study focuses on magnetic resonance imaging (MRI) measurements of brain and their correlation with the onset age and the duration of opioid abuse.

Subjects attending full-time special education (SE) often have multifactorial background for their cognitive impairment, and brain MRI may show nonspecific changes. As voxel-based morphometry reveals regional volume differences, we applied this method to 119 subjects with cognitive impairments and familial need for full-time SE--graded into three levels from specific disorders of cognitive processes (level 1) to intellectual disability (IQ

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field.