Defining the exact mechanisms by which the brain processes visual objects and scenes remains an unresolved challenge. Valuable clues to this process have emerged from the demonstration that clusters of neurons ("modules") in inferior temporal cortex apparently respond selectively to specific categories of visual stimuli, such as places/scenes. However, the higher-order "category-selective" response could also reflect specific lower-level spatial factors.
Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls. We derived eight cognitive dimensions and subsequently applied a cluster analysis to identify possible schizophrenia subtypes.
The corpus callosum (CC) is the major conduit for information transfer between the cerebral hemispheres and plays an integral role in relaying sensory, motor and cognitive information between homologous cortical regions. The majority of fibers that make up the CC arise from large pyramidal neurons in layers III and V, which project contra-laterally. These neurons degenerate in Huntington's disease (HD) in a topographically and temporally selective way.
Human neuroimaging studies suggest that localization and identification of relevant auditory objects are accomplished via parallel parietal-to-lateral-prefrontal "where" and anterior-temporal-to-inferior-frontal "what" pathways, respectively.
BACKGROUND: Acupuncture stimulation elicits deqi, a composite of unique sensations that is essential for clinical efficacy according to traditional Chinese medicine (TCM). There is lack of adequate experimental data to indicate what sensations comprise deqi, their prevalence and intensity, their relationship to acupoints, how they compare with conventional somatosensory or noxious response.
The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units.
Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains.
Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the information contained in the time course of individual voxels. Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from fMRI data sets.
Early imaging or blood biomarkers of tumor response are desperately needed to customize antiangiogenic therapy for cancer patients. Anti-vascular endothelial growth factor (VEGF) therapy can "normalize" brain tumor vasculature by decreasing vessel diameter and permeability, and thinning the abnormally thick basement membrane. We hypothesized that the extent of vascular normalization will be predictive of outcome of anti-VEGF therapy in glioblastoma.
