John C. Mazziotta

Rapid automated algorithm for aligning and reslicing PET images

A computer algorithm for the three-dimensional (3D) alignment of PET images is described. To align two images, the algorithm calculates the ratio of one image to the other on a voxel-by-voxel basis and then iteratively moves the images relative to one another to minimize the variance of this ratio across voxels. Since the method relies on anatomic information in the images rather than on external fiducial markers, it can be applied retrospectively. Validation studies using a 3D brain phantom show that the algorithm aligns images acquired at a wide variety of positions with maximum positional errors that are usually less than the width of a voxel (1.745 mm). Simulated cortical activation sites do not interfere with alignment. Global errors in quantitation from realignment are <2%. Regional errors due to partial volume effects are largest when the gantry is rotated by large angles or when the bed is translated axially by one-half the interplane distance. To minimize such partial volume effects, the algorithm can be used prospectively, during acquisition, to reposition the scanner gantry and bed to match an earlier study. Computation requires 3–6 min on a Sun SPARCstation 2.

Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

PURPOSE We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. METHOD Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. RESULTS All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. CONCLUSION The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.

MRI-PET registration with automated algorithm.

Objective We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject. Methods This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm. Results and Conclusion The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of <3 mm and mean three-dimensional errors of <2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H215O PET images typically requires 20–30 min.

Assessing the significance of focal activations using their spatial extent.

Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger, suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified volume, or larger, could have occurred by chance. These results mean that detecting significant activations no longer depends on a fixed (and high) threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region. The substantial improvement in sensitivity that ensues is illustrated using a power analysis and a simulated phantom activation study.

Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas

How do we empathize with others? A mechanism according to which action representation modulates emotional activity may provide an essential functional architecture for empathy. The superior temporal and inferior frontal cortices are critical areas for action representation and are connected to the limbic system via the insula. Thus, the insula may be a critical relay from action representation to emotion. We used functional MRI while subjects were either imitating or simply observing emotional facial expressions. Imitation and observation of emotions activated a largely similar network of brain areas. Within this network, there was greater activity during imitation, compared with observation of emotions, in premotor areas including the inferior frontal cortex, as well as in the superior temporal cortex, insula, and amygdala. We understand what others feel by a mechanism of action representation that allows empathy and modulates our emotional content. The insula plays a fundamental role in this mechanism.

PATTERNS OF BRAIN ACTIVATION IN PEOPLE AT RISK FOR ALZHEIMER’S DISEASE

BACKGROUND The epsilon4 allele of the apolipoprotein E gene (APOE) is the chief known genetic risk factor for Alzheimers disease, the most common cause of dementia late in life. To determine the relation between brain responses to tasks requiring memory and the genetic risk of Alzheimers disease, we performed APOE genotyping and functional magnetic resonance imaging (MRI) of the brain in older persons with intact cognition. METHODS We studied 30 subjects (age, 47 to 82 years) who were neurologically normal, of whom 16 were carriers of the APOE epsilon4 allele and 14 were homozygous for the APOE epsilon3 allele. The mean age and level of education were similar in the two groups. Patterns of brain activation during functional MRI scanning were determined while subjects memorized and recalled unrelated pairs of words and while subjects rested between such periods. Memory was reassessed in 14 subjects two years later. RESULTS Both the magnitude and the extent of brain activation during memory-activation tasks in regions affected by Alzheimers disease, including the left hippocampal, parietal, and prefrontal regions, were greater among the carriers of the APOE epsilon4 allele than among the carriers of the APOE epsilon3 allele. During periods of recall, the carriers of the APOE epsilon4 allele had a greater average increase in signal intensity in the hippocampal region (1.03 percent vs. 0.62 percent, P<0.001) and a greater mean (+/-SD) number of activated regions throughout the brain (15.9+/-6.2 vs. 9.4+/-5.5, P=0.005) than did carriers of the APOE epsilon3 allele. Longitudinal assessment after two years indicated that the degree of base-line brain activation correlated with degree of decline in memory. CONCLUSIONS Patterns of brain activation during tasks requiring memory differ depending on the genetic risk of Alzheimers disease and may predict a subsequent decline in memory.

Modulating emotional responses: effects of a neocortical network on the limbic system.

Humans share with animals a primitive neural system for processing emotions such as fear and anger. Unlike other animals, humans have the unique ability to control and modulate instinctive emotional reactions through intellectual processes such as reasoning, rationalizing, and labeling our experiences. This study used functional MRI to identify the neural networks underlying this ability. Subjects either matched the affect of one of two faces to that of a simultaneously presented target face (a perceptual task) or identified the affect of a target face by choosing one of two simultaneously presented linguistic labels (an intellectual task). Matching angry or frightened expressions was associated with increased regional cerebral blood flow (rCBF) in the left and right amygdala, the brains primary fear centers. Labeling these same expressions was associated with a diminished rCBF response in the amygdalae. This decrease correlated with a simultaneous increase in rCBF in the right prefrontal cortex, a neocortical region implicated in regulating emotional responses. These results provide evidence for a network in which higher regions attenuate emotional responses at the most fundamental levels in the brain and suggest a neural basis for modulating emotional experience through interpretation and labeling.

Automated image registration : II. Intersubject validation of Linear and nonlinear models

PURPOSE Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity. METHOD PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks. RESULTS Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration. CONCLUSION Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.

Bias between MNI and talairach coordinates analyzed using the ICBM-152 brain template

MNI coordinates determined using SPM2 and FSL/FLIRT with the ICBM‐152 template were compared to Talairach coordinates determined using a landmark‐based Talairach registration method (TAL). Analysis revealed a clear‐cut bias in reference frames (origin, orientation) and scaling (brain size). Accordingly, ICBM‐152 fitted brains were consistently larger, oriented more nose down, and translated slightly down relative to TAL fitted brains. Whole brain analysis of MNI/Talairach coordinate disparity revealed an ellipsoidal pattern with disparity ranging from zero at a point deep within the left hemisphere to greater than 1‐cm for some anterior brain areas. MNI/Talairach coordinate disparity was generally less for brains fitted using FSL. The mni2tal transform generally reduced MNI/Talairach coordinate disparity for inferior brain areas but increased disparity for anterior, posterior, and superior areas. Coordinate disparity patterns differed for brain templates (MNI‐305, ICBM‐152) using the same fitting method (FSL/FLIRT) and for different fitting methods (SPM2, FSL/FLIRT) using the same template (ICBM‐152). An MNI‐to‐Talairach (MTT) transform to correct for bias between MNI and Talairach coordinates was formulated using a best‐fit analysis in one hundred high‐resolution 3‐D MR brain images. MTT transforms optimized for SPM2 and FSL were shown to reduced group mean MNI/Talairach coordinate disparity from a 5‐13 mm to 1‐2 mm for both deep and superficial brain sites. MTT transforms provide a validated means to convert MNI coordinates to Talairach compatible coordinates for studies using either SPM2 or FSL/FLIRT with the ICBM‐152 template. Hum Brain Mapp 2007.

Stereotaxic White Matter Atlas Based on Diffusion Tensor Imaging in an ICBM Template

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.