Showing papers by "Jerome N. Sanes published in 2002"

Frontal and Parietal Lobe Activation during Transitive Inference in Humans

Abstract: Cortical areas engaged in knowledge manipulation during reasoning were identified with functional magnetic resonance imaging (MRI) while participants performed transitive inference (TI) on an ordered list of 11 items (e.g. if A < B and B < C, then A < C). Initially, participants learned a list of arbitrarily ordered visual shapes. Learning occurred by exposure to pairs of list items that were adjacent in the sequence. Subsequently, functional MR images were acquired as participants performed TI on non-adjacent sequence items. Control tasks consisted of height comparisons (HT) and passive viewing (VIS). Comparison of the TI task with the HT task identified activation resulting from TI, termed 'reasoning', while controlling for rule application, decision processes, perception, and movement, collectively termed 'support processes'. The HT-VIS comparison revealed activation related to support processes. The TI reasoning network included bilateral prefrontal cortex (PFC), pre-supplementary motor area (preSMA), premotor area (PMA), insula, precuneus, and lateral posterior parietal cortex. By contrast, cortical regions activated by support processes included the bilateral supplementary motor area (SMA), primary motor cortex (M1), somatic sensory cortices, and right PMA. These results emphasize the role of a prefrontal-parietal network in manipulating information to form new knowledge based on familiar facts. The findings also demonstrate PFC activation beyond short-term memory to include mental operations associated with reasoning.

Cognitive mechanisms of transitive inference.

Abstract: We examined how the brain organizes interrelated facts during learning and how the facts are subsequently manipulated in a transitive inference (TI) paradigm (e.g., if A

Real-time quantification of T(2)(*) changes using multiecho planar imaging and numerical methods.

Abstract: 2mapping) that directly calculates T* by a linear combination of images obtained at three or more different echo times was developed. NumART*, linear least-squares, and nonlinear regression techniques were applied to multiecho planar images of the human brain and to simulated data. Although NumART* 2 may overestimate T*, it yields comparable values to regression techniques in cortical and subcortical areas, with only moderate deviations for echo spacings between 18 and 40 ms. NumART* 2, like linear regression, requires 2% of the computational time needed for nonlinear regression and compares favorably with linear regression due to its higher precision. The use of NumART* 2 for continuous on-line T* mapping in real time fMRI studies is shown. Magn Reson Med 48:877– 882, 2002. © 2002 Wiley-Liss, Inc. 2; real-time In blood oxygenation level-dependent (BOLD) functional MRI (fMRI), the major source of contrast is the effective transverse relaxation time, T* 2, which is sensitive to the presence of paramagnetic deoxyhemoglobin molecules. In most fMRI studies, relative T* 2 changes are detected by EPI (echo planar imaging) at a fixed echo time, TE, and, typically, experimenters chose a TE close to the resting tissue T* 2 to produce maximal BOLD signal change (1,2). However, the use of a fixed, single TE does not necessarily guarantee the best contrast, since baseline T* 2 values may have spatial heterogeneity within and between individuals. The resulting contrast variation may cause inconsistent activation patterns and detectability, as shown for somatosensory-related activation in rats at 7 T (3) and motor activation in human subjects at 1.5 T (4). Consequently, it may be advantageous to measure several echoes instead of a single echo in fMRI studies and quantify T* 2, with a possible added advantage of reducing the variability of activation patterns. Recent developments of the EPI technique have made multiple-echo measurements possible, encompassing the acquisition of several images at different echo times in a single shot (5‐11). With these new techniques, whole brain coverage can be obtained in a time-frame comparable to conventional single echo methods. Consequently, limits in imaging time no longer prevent T* 2 mapping in fMRI studies, although excessive postprocessing times could restrict their use. Available methods for T* 2 quantification currently use voxel-wise nonlinear regression of the signal relaxation observed from single-echo images. These techniques are computationally intensive and may not be readily implemented on many currently available MRI scanners deployed at clinical sites. A proposed method to overcome this limit is weighted averaging of the echo images, with weights based on expected T* 2 values in the resting condition (5). This method is fast and can be implemented in real-time but, similar to single-echo acquisition techniques, may also be affected by differences in baseline T* 2. Here, we propose an alternative numerical method, NumART* 2 (numerical algorithm for real-time T* 2 mapping) that, to our knowledge, has not been previously used in the context of MRI. The method is based on the acquisition of MR images with three or more echo times and then performing a linear image combination to obtain quantitative T* 2 maps. THEORY