Cortical Surface Mapping Research Articles

G-RMOS: GPU-accelerated Riemannian Metric Optimization on Surfaces

Surface mapping is used in various brain imaging studies, such as for mapping gray matter atrophy patterns in Alzheimer’s disease. Riemannian metrics on surface (RMOS) is a state-of-the-art surface mapping algorithm that optimizes Riemannian metrics to establish one-to-one correspondences between surfaces in the Laplace–Beltrami embedding space. However, owing to the complex calculation with accurate one-to-one correspondences, RMOS registration takes a long time. In this study, we propose G-RMOS, a graphics processing unit (GPU)-accelerated RMOS registration pipeline that uses three GPU kernel design strategies: 1. using GPU computing capability with a batch scheme; 2. using the cache in the GPU block to minimize memory latency in register and shared memory; and 3. maximizing the effective number of instructions per GPU cycle using instruction level parallelism. Using the experimental results, we compare the acceleration speed of the G-RMOS framework with that of RMOS using hippocampus and cortical surfaces, and show that G-RMOS achieves a significant speedup in surface mapping. We also compare the memory requirements for cortical surface mapping and show that G-RMOS uses less memory than RMOS.

Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species- and region-specific differences

Precise spatiotemporal control of gene expression in the developing brain is critical for neural circuit formation, and comprehensive expression mapping in the developing primate brain is crucial to understand brain function in health and disease. Here, we developed an unbiased, automated, large-scale, cellular-resolution in situ hybridization (ISH)-based gene expression profiling system (GePS) and companion analysis to reveal gene expression patterns in the neonatal New World marmoset cortex, thalamus, and striatum that are distinct from those in mice. Gene-ontology analysis of marmoset-specific genes revealed associations with catalytic activity in the visual cortex and neuropsychiatric disorders in the thalamus. Cortically expressed genes with clear area boundaries were used in a three-dimensional cortical surface mapping algorithm to delineate higher-order cortical areas not evident in two-dimensional ISH data. GePS provides a powerful platform to elucidate the molecular mechanisms underlying primate neurobiology and developmental psychiatric and neurological disorders.

Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture.

In diffusion MRI (dMRI), it remains unclear to know how much increase of b-value is conveying additional biological meaning. We tested the correlations between cortical microarchitecture and diffusion metrics computed from standard (1000s/mm2), high (3000s/mm2), to very high (5000s/mm2) b-value dMRI. Healthy volunteers were scanned with a dMRI pulse sequence that was first optimized together with a T1-WI and T2-WI. Averaged cortical surface map of estimated myelin (T1-WI/T2-WI) was compared with surface maps of mean diffusivity (MD) computed from each b-value (MD1000, MD3000, and MD5000) and to surface map of mean kurtosis (MK computed from the 0-, 1000-, to 3000-s/mm2 shells) in 360 cortical parcels using Spearman correlations, multiple linear regressions, and Akaike information criteria (AIC). Surface map from MD1000 showed variations not related to myelin but the MD3000 and MD5000 maps inversely mirrored estimated myelin map; lower MD values being observed in more myelinated cortical areas. MK mirrored myelinated cortical areas. Quantitatively, Spearman correlations between myelin and MD became more and more negative as long as b-values increased while the correlation was positive between myelin and MK. Multiple regression models confirmed negative associations between myelin and MD that were significantly better from MD1000 to MD3000 and MD5000 (R2 = 0.33, p < 0.001; R2 = 0.43, p < 0.001; and R2 = 0.50, p < 0.001) and positive association between myelin and MK (R2 = 0.53, p < 0.001). Comparisons of the 3 statistical models showed the best performances with MK and MD5000 (AICMK < AICMD5000 < AICMD3000 < AICMD1000). Higher b-values are more closely related to subtle cellular variations of the cortical microarchitecture.

A novel Tungsten-based fiducial marker for multi-modal brain imaging

BackgroundMulti-modal brain image registration is a prerequisite for accurate mapping of brain structure and function in neuroscience. Image registration is commonly performed using automated software; however, its accuracy decreases when images differ in modality, contrast, uniformity, and resolution. This limitation could be overcome by using an external reference point; however, high-contrast agents in multi-modal imaging have not been previously reported. New methodsHere, we propose a novel multi-modal fiducial marker that contains Tungsten solution and provides high contrast in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). The basic characteristics of this multi-modal marker were investigated by assessing major sources of image contrast in the following modalities: density and T1-, T2-relaxivity in comparison with conventional contrast agents. ResultsTungsten solution had lower T1- and T2-relaxivity and high solubility, and showed high contrast in T1- and T2-weighted MR and CT images at a high-density concentration (˜3.0 g/mL), whereas other conventional solutions did not show sufficient contrast in either CT or MRI. Comparison with existing methodsThe use of this Tungsten-based multi-modal marker allowed more accurate registration than a software-only method in phantom and animal experiments. Application of this method demonstrated accurate cortical surface mapping of neurotransmitter function (dopamine transporter, DAT) using PET and MRI, and provided a neurobiologically relevant cortical distribution consistent with previous literature on histology-based DAT immunoreactivity. ConclusionsThe Tungsten-based multi-modal fiducial marker is non-radioactive, easy to handle, and aids precise registration across different modalities of brain imaging.

TMSmap - Software for Quantitative Analysis of TMS Mapping Results.

The use of the MRI-navigation system ensures accurate targeting of TMS. This, in turn, results in TMS motor mapping becoming a routinely used procedure in neuroscience and neurosurgery. However, currently, there is no standardized methodology for assessment of TMS motor-mapping results. Therefore, we developed TMSmap—free standalone graphical interface software for the quantitative analysis of the TMS motor mapping results (http://tmsmap.ru/). In addition to the estimation of standard parameters (such as the size of cortical muscle representation and the center of gravity location), it allows estimation of the volume of cortical representations, excitability profile of the cortical surface map, and the overlap between cortical representations. The input data for the software includes the coordinates of the coil position (or electric field maximum) and the corresponding response in each stimulation point. TMSmap has been developed for versatile assessment and comparison of TMS maps relating to different experimental interventions including, but not limited to longitudinal, pharmacological and clinical studies (e.g., stroke recovery). To illustrate the use of TMSmap we provide examples of the actual TMS motor-mapping analysis of two healthy subjects and one chronic stroke patient.

Spin-glass model predicts metastable brain states that diminish in anesthesia.

Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm2 area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity—a presumed condition of consciousness.

An empirical comparison of surface-based and volume-based group studies in neuroimaging

Being able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random effects (RFX) and mixed-effects analyses (MFX). We consider different schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We find that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this effect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps.

Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET

A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed. Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models. Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6. FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.

Automated VOI Analysis in FDDNP PET Using Structural Warping: Validation through Classification of Alzheimer's Disease Patients

We evaluate an automated approach to the cortical surface mapping (CSM) method of VOI analysis in PET. Although CSM has been previously shown to be successful, the process can be long and tedious. Here, we present an approach that removes these difficulties through the use of 3D image warping to a common space. We test this automated method using studies of FDDNP PET in Alzheimer's disease and mild cognitive impairment. For each subject, VOIs were created, through CSM, to extract regional PET data. After warping to the common space, a single set of CSM-generated VOIs was used to extract PET data from all subjects. The data extracted using a single set of VOIs outperformed the manual approach in classifying AD patients from MCIs and controls. This suggests that this automated method can remove variance in measurements of PET data and can facilitate accurate, high-throughput image analysis.

Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease

Magnetic resonance (MR) provides a non-invasive way to investigate changes in the brain resulting from aging or neurodegenerative disorders such as Alzheimer's disease (AD). Performing accurate analysis for population studies is challenging because of the interindividual anatomical variability. A large set of tools is found to perform studies of brain anatomy and population analysis (FreeSurfer, SPM, FSL). In this paper we present a newly developed surface-based processing pipeline (MILXCTE) that allows accurate vertex-wise statistical comparisons of brain modifications, such as cortical thickness (CTE). The brain is first segmented into the three main tissues: white matter, gray matter and cerebrospinal fluid, after CTE is computed, a topology corrected mesh is generated. Partial inflation and non-rigid registration of cortical surfaces to a common space using shape context are then performed. Each of the steps was firstly validated using MR images from the OASIS database. We then applied the pipeline to a sample of individuals randomly selected from the AIBL study on AD and compared with FreeSurfer. For a population of 50 individuals we found correlation of cortical thickness in all the regions of the brain (average r=0.62 left and r=0.64 right hemispheres). We finally computed changes in atrophy in 32 AD patients and 81 healthy elderly individuals. Significant differences were found in regions known to be affected in AD. We demonstrated the validity of the method for use in clinical studies which provides an alternative to well established techniques to compare different imaging biomarkers for the study of neurodegenerative diseases.

Quantitative mapping of diffusion characteristics under the cortical surface

Recent studies have demonstrated regional segregations on several peripheral white matter (WM) regions, which may imply different anatomical or functional characteristics [ Cereb Cortex 17(4) 2007 816–25; Neuroimage 37(2) 2007 599–610; J Cogn Neurosci 16(7) 2004 1227–33]. Nonetheless, little is known about overall patterns of peripheral WM across the regions. In this study, diffusion tensor imaging with 2-mm isovoxel resolution and cortical surface mapping were combined to determine peripheral WM structure. Fractional anisotropy (FA) mapping showed consistent regional patterns across the young normal subjects while significant high or low FA values were shown in the motor-somatosensory cortex, prefrontal cortex, temporal, and medial occipital cortex. By adopting both region of interest and connectivity analysis, results were then discussed with structural network properties as well as WM maturation process.

Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping

Cortical surface mapping has been widely used to compensate for individual variability of cortical shape and topology in anatomical and functional studies. While many surface mapping methods were proposed based on landmarks, curves, spherical or native cortical coordinates, few studies have extensively and quantitatively evaluated surface mapping methods across different methodologies. In this study we compared five cortical surface mapping algorithms, including large deformation diffeomorphic metric mapping (LDDMM) for curves (LDDMM-curve), for surfaces (LDDMM-surface), multi-manifold LDDMM (MM-LDDMM), FreeSurfer, and CARET, using 40 MRI scans and 10 simulated datasets. We computed curve variation errors and surface alignment consistency for assessing the mapping accuracy of local cortical features (e.g., gyral/sulcal curves and sulcal regions) and the curvature correlation for measuring the mapping accuracy in terms of overall cortical shape. In addition, the simulated datasets facilitated the investigation of mapping error distribution over the cortical surface when the MM-LDDMM, FreeSurfer, and CARET mapping algorithms were applied. Our results revealed that the LDDMM-curve, MM-LDDMM, and CARET approaches best aligned the local curve features with their own curves. The MM-LDDMM approach was also found to be the best in aligning the local regions and cortical folding patterns (e.g., curvature) as compared to the other mapping approaches. The simulation experiment showed that the MM-LDDMM mapping yielded less local and global deformation errors than the CARET and FreeSurfer mappings.

Steal physiology is spatially associated with cortical thinning

BackgroundThe physiological impact of severely impaired cerebral autoregulatory vascular reactivity on cortical integrity is unknown. The purpose of this study is to determine the relationship between severe impairment of autoregulatory...

FDDNP binding using MR derived cortical surface maps

ObjectivesTo assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size. MethodsFDDNP DVR PET images from 23 subjects (7 with Alzheimer's disease (AD), 6 with mild cognitive impairment and 10 controls) were obtained from Logan analysis using cerebellum as reference. A hemispheric cortical surface model for each subject was extracted from the MRI. The same transformations were applied to the FDDNP DVR PET images to map them into the same space. The cortical map with PV correction was calculated as the ratio of the DVR cortical surface and that of the simulated map, created from the mask derived from MRI and smoothed to the PET resolution. Discriminant analysis was used to order the FDDNP DVR cortical surfaces based on subjects' disease state. Linear regression was used to assess the rate of change of DVR vs. MMSE for each hemispheric cortical surface point. ResultsThe FDDNP DVR cortical surface corrected for movement and PV had less hemispheric asymmetry. Optimal kernel size was determined to be 9 mm. The corrected cortical surface map of FDDNP DVR showed clear spatial pattern that was consistent with the known pathological progression of AD. ConclusionCorrecting for movement, PV as well as optimizing kernel size provide sensitive statistical analysis of FDDNP distribution which confirms in the living brain known pathology patterns earlier observed with cognitive decline with brain specimens.

Structural MRI biomarkers for preclinical and mild Alzheimer's disease

Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. We applied high-throughput image analyses procedures to these data to demonstrate the feasibility of detecting subtle structural changes in prodromal AD. Raw DICOM scans (139 NC, 175 MCI, and 84 AD) were downloaded for analysis. Volumetric segmentation and cortical surface reconstruction produced continuous cortical surface maps and region-of-interest (ROI) measures. The MCI cohort was subdivided into single- (SMCI) and multiple-domain MCI (MMCI) based on neuropsychological performance. Repeated measures analyses of covariance were used to examine group and hemispheric effects while controlling for age, sex, and, for volumetric measures, intracranial vault. ROI analyses showed group differences for ventricular, temporal, posterior and rostral anterior cingulate, posterior parietal, and frontal regions. SMCI and NC differed within temporal, rostral posterior cingulate, inferior parietal, precuneus, and caudal midfrontal regions. With MMCI and AD, greater differences were evident in these regions and additional frontal and retrosplenial cortices; evidence for non-AD pathology in MMCI also was suggested. Mesial temporal right-dominant asymmetries were evident and did not interact with diagnosis. Our findings demonstrate that high-throughput methods provide numerous measures to detect subtle effects of prodromal AD, suggesting early and later stages of the preclinical state in this cross-sectional sample. These methods will enable a more complete longitudinal characterization and allow us to identify changes that are predictive of conversion to AD.

Auditory Attention Activates Peripheral Visual Cortex

BackgroundRecent neuroimaging studies have revealed that putatively unimodal regions of visual cortex can be activated during auditory tasks in sighted as well as in blind subjects. However, the task determinants and functional significance of auditory occipital activations (AOAs) remains unclear.Methodology/Principal FindingsWe examined AOAs in an intermodal selective attention task to distinguish whether they were stimulus-bound or recruited by higher-level cognitive operations associated with auditory attention. Cortical surface mapping showed that auditory occipital activations were localized to retinotopic visual cortex subserving the far peripheral visual field. AOAs depended strictly on the sustained engagement of auditory attention and were enhanced in more difficult listening conditions. In contrast, unattended sounds produced no AOAs regardless of their intensity, spatial location, or frequency.Conclusions/SignificanceAuditory attention, but not passive exposure to sounds, routinely activated peripheral regions of visual cortex when subjects attended to sound sources outside the visual field. Functional connections between auditory cortex and visual cortex subserving the peripheral visual field appear to underlie the generation of AOAs, which may reflect the priming of visual regions to process soon-to-appear objects associated with unseen sound sources.

Beautiful Minds—For How Long?

Beautiful Minds—For How Long?

Amyloid and tau imaging, neuronal losses and function in mild cognitive impairment.

Establish new approaches for early diagnosis of dementia, based on imaging amyloid and tau pathology, cell losses and neuronal function, in subjects with mild cognitive impairment (MCI),. The overall aim is to develop effective tools for monitoring disease progression in the living patient to facilitate discovery of early therapeutic interventions to modify the course of the disease. Use 2-(1-{6-[(2-[F- 18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile ([F-18]FDDNP) in combination with positron emission tomography (PET) to produce dynamic images for quantification of regional cortical brain deposition in MCI patients and compare them with controls subjects and patients with Alzheimer's disease (AD). Comparison with other molecular imaging probes for neuronal losses and function were also made. Patients are positioned supine in the tomograph bed with his/her head in the detector ring field. Upon injection of the molecular imaging probe (e.g., [F-18]FDDNP) images are obtained at very short time intervals for up to two hours. This results in dynamic sequences of brain distribution of the probe. Patients with clinical diagnosis of AD, MCI and control subjects. Subjects in the categories established above were scanned with [F-18]FDDNP-PET and quantification performed using Logan parametric graphical analysis to measure relative quantitative amyloid loads throughout the brain within patient groups. These results were compared in the same patients with cell losses in hippocampus using 4-[F-18]fluoro-N-{2-[4-(2-methoxyphenyl)- 1-piperazinyl]ethyl}-N-(2-pyridinyl)benzamide,([F-18]MPPF) and regional cerebral glucose metabolic rates using 2-deoxy-2-[F-18]fluoro-2-deoxy-D-glucose (2-[F-18]FDG). [F-18]FDDNP reliably follows neuropathological progression (amyloid plaques [SP]; neurofibrillary tangles [NFT]) in the living brain of AD patients and those with MCI. The distribution of [F-18]FDDNP brain cortical accumulation correlates well with behavioral measures (e.g., MMSE scores) and follows known patterns of pathological distribution observed at autopsy. We have also established conversion of controls to MCI and MCI to AD with precision and sensitivity in patients and control subjects in follow-up studies. Moreover, we have established that hemispheric cortical surface mapping of [F-18]FDDNP binding is a powerful tool for assessment and visualization of the rate of brain pathology deposition. A strong correlation of [F-18]FDDNP binding, cell losses in hippocampus and decreased glucose utilization ([F-18]FDG PET) in several neocortical regions was found in the same AD and MCI subjects. The combined evaluation of [F-18]FDDNP PET (targeting NFT and_SP) with neuronal losses in the hippocampus and with [F-18]FDG PET (targeting neuronal function) offers the opportunity for reliable, noninvasive detection of MCI patients at risk for AD. The approach offers a glimpse to the molecular and cellular mechanisms associated with dementia and provides a means for their assessment in the living patient. Monitoring disease progression in MCI patients demonstrates the usefulness of this imaging approach for early diagnosis and provides a means for evaluation of neuroprotective agents and drugs aimed at prevention and modification of disease progression.

O2-02-05: FDDNP-PET binding values from cortical hemispheric surface maps correlate with MMSE scores

PET scans after intravenous injection of 2-(1-{6-{(2-[F-18]fluoroethyl)(methyl) amino}-2-naphthyl} ethylidene)malononitrile (FDDNP), a molecule that binds to amyloid senile plaques and neurofibrillary tangles, differentiate patients with AD, those with MCI, and healthy controls. Cortical surface maps of PET images provide a standardized space that allows easy inter-subject comparisons and three-dimensional visualization while retaining the quantitative aspect of PET and may be useful tools for monitoring AD prevention treatments. We used brain cortical surface maps to determine FDDNP binding values and their relationship to the mini-mental state examination (MMSE), a common measure of cognitive function. PET scans of 23 subjects (7 ADs, 6 MCIs, 10 controls) were used to generate FDDNP distribution volume (DVR) images, using Logan graphical plots with the cerebellum as reference region. Affine transformation was applied to the MRI to place the data into International Consortium of Brain Mapping (ICBM) space. A hemispheric cortical surface model for each subject was extracted from the MRI in ICBM space. The same transformations were applied to the DVR images to put them into the same cortical space. Regional FDDNP values were obtained directly on the cortical surface. For partial volume correction (PVC), a simulated PET was created from a mask based on the MR derived cortical surface. A cortical map with PVC also was calculated as the ratio of the DVR cortical surface and that of the simulated PET. Linear regression and Spearman correlations were used to determine relationships between MMSE scores and regional and global DVR measures. MMSE scores ranged from 19 to 30 (mean±SD=26.4±3.06) and correlated with FDDNP DVRs in cortical regions controlling cognitive function, including temporal, frontal, parietal, and posterior cingulate (F statistic, 4.32 with alpha of 0.05; figure shows significant regions of correlation in green and yellow), and these relationships increased after PVC. Global DVR correlated significantly with MMSE scores before and after PVC (rs=-0.5, p=0.01). Hemispheric cortical surface mapping of FDDNP binding values demonstrate correlations with cognitive scores in people at risk for dementia and those with AD, suggesting their utility as diagnostic and treatment monitoring tools in future AD prevention trials.

Distributed cortical networks for focused auditory attention and distraction

We used behavioral measures and functional magnetic resonance imaging (fMRI) to study the effects of parametrically varied task-irrelevant pitch changes in attended sounds on loudness-discrimination performance and brain activity in cortical surface maps. Ten subjects discriminated tone loudness in sequences that also included infrequent task-irrelevant pitch changes. Consistent with results of previous studies, the task-irrelevant pitch changes impaired performance in the loudness discrimination task. Auditory stimulation, attention-enhanced processing of sounds and motor responding during the loudness discrimination task activated supratemporal (auditory cortex) and inferior parietal areas bilaterally and left-hemisphere (contralateral to the hand used for responding) motor areas. Large pitch changes were associated with right hemisphere supratemporal activations as well as widespread bilateral activations in the frontal lobe and along the intraparietal sulcus. Loudness discrimination and distracting pitch changes activated common areas in the right supratemporal gyrus, left medial frontal cortex, left precentral gyrus, and left inferior parietal cortex.