Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

Greater leftward lateralization of the inferior frontal gyrus in second language learners with higher syntactic abilities

There is a great individual variability for acquiring syntactic knowledge in a second language (L2). Little is, however, known if there is any anatomical basis in the brain for individual differences in syntactic acquisition. Here we examined brain structures in 95 nonnative speakers of English, including 78 high-school students and 17 adult international students. We found a significant correlation between the performance of a syntactic task and leftward lateralization of a single region in the triangular part (F3t) of the inferior frontal gyrus, which has been proposed as the grammar center. Moreover, this correlation was independent of the performance of a spelling task, age, gender, and handedness. This striking result suggests that the neural basis for syntactic abilities in L2 is independent of that for lexical knowledge in L2, further indicating that the individual differences in syntactic acquisition are related to the lateralization of the grammar center.

Sexual Dimorphism in the Parietal Substrate Associated with Visuospatial Cognition Independent of General Intelligence

Sex differences in visuospatial cognition (VSC) with male advantage are frequently reported in the literature. There is evidence for sexual dimorphisms in the human brain, one of which postulates more gray matter (GM) in females and more white matter (WM) in males relative to total intracranial volume. We investigated the neuroanatomy of VSC independent of general intelligence (g) in sex-separated populations, homogenous in age, education, memory performance, a memory- and brain morphology-related gene, and g. VSC and g were assessed with the Wechsler adult intelligence scale. The influence of g on VSC was removed using a hierarchical factor analysis and the Schmid–Leiman solution. Structural high-resolution magnetic resonance images were acquired and analyzed with voxel-based morphometry. As hypothesized, the clusters of positive correlations between local volumes and VSC performance independent of g were found mainly in parietal areas, but also in pre- and postcentral regions, predominantly in the WM in males, whereas in females these correlations were located in parietal and superior temporal areas, predominantly in the GM. Our results suggest that VSC depends more strongly on parietal WM structures in males and on parietal GM structures in females. This sex difference might have to do with the increased axonal and decreased somatodendritic tissue in males relative to females. Whether such sex-specific implementations of the VSC network can be explained genetically as suggested in investigations into the Turner syndrome or as a result of structural neural plasticity upon different experience and usage remains to be shown.

Reduced occipital and prefrontal brain volumes in dysbindin-associated schizophrenia

A three-marker C-A-T dysbindin haplotype identified by Williams et al (PMID: 15066891) is associated with increased risk for schizophrenia, decreased mRNA expression, poorer cognitive performance, and early sensory processing deficits. We investigated whether this same dysbindin risk haplotype was also associated with structural variation in the gray matter volume (GMV). Using voxel-based morphometry, whole-volume analysis revealed significantly reduced GMVs in both the right dorsolateral prefrontal and left occipital cortex, corresponding to the behavioral findings of impaired spatial working memory and EEG findings of impaired visual processing already reported. These data provide important evidence of the influence of dysbindin risk variants on brain structure, and suggest a possible mechanism by which disease risk is being increased.

Structural neuroplasticity in the sensorimotor network of professional female ballet dancers

Evidence suggests that motor, sensory, and cognitive training modulates brain structures involved in a specific practice. Functional neuroimaging revealed key brain structures involved in dancing such as the putamen and the premotor cortex. Intensive ballet dance training was expected to modulate the structures of the sensorimotor network, for example, the putamen, premotor cortex, supplementary motor area (SMA), and the corticospinal tracts. We investigated gray (GM) and white matter (WM) volumes, fractional anisotropy (FA), and mean diffusivity (MD) using magnetic resonance-based morphometry and diffusion tensor imaging in 10 professional female ballet dancers compared with 10 nondancers. In dancers compared with nondancers, decreased GM volumes were observed in the left premotor cortex, SMA, putamen, and superior frontal gyrus, and decreased WM volumes in both corticospinal tracts, both internal capsules, corpus callosum, and left anterior cingulum. FA was lower in the WM underlying the dancers' left and right premotor cortex. There were no significant differences in MD between the groups. Age of dance commencement was negatively correlated with GM and WM volume in the right premotor cortex and internal capsule, respectively, and positively correlated with WM volume in the left precentral gyrus and corpus callosum. Results were not influenced by the significantly lower body mass index of the dancers. The present findings complement the results of functional imaging studies in experts that revealed reduced neural activity in skilled compared with nonskilled subjects. Reductions in brain activity are accompanied by local decreases in GM and WM volumes and decreased FA.

Why sex matters: brain size independent differences in gray matter distributions between men and women

The different brain anatomy of men and women is both a classic and continuing topic of major interest. Among the most replicated and robust sex differences are larger overall brain dimensions in men, and relative increases of global and regional gray matter (GM) in women. However, the question remains whether sex-typical differences in brain size (i.e., larger male and smaller female brains) or biological sex itself account for the observed sex effects on tissue amount and distribution. Exploring cerebral structures in men and women with similar brain size may clarify the true contribution of biological sex. We thus examined a sample of 24 male and 24 female subjects with brains identical in size, in addition to 24 male and 24 female subjects with considerable brain size differences. Using this large set of brains (n = 96), we applied a well validated and automated voxel-based approach to examine regional volumes of GM. While we revealed significant main effects of sex, there were no significant effects of brain size (and no significant interactions between sex and brain size). When conducting post hoc tests, we revealed a number of regions where women had larger GM volumes than men. Importantly, these sex effects remained evident when comparing men and women with the same brain size. Altogether, our findings suggest that the observed increased regional GM volumes in female brains constitute sex-dependent redistributions of tissue volume, rather than individual adjustments attributable to brain size.

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Topographic analysis of individual activation patterns in medial frontal cortex in schizophrenia

Individual variability in the location of neural activations poses a unique problem for neuroimaging studies employing group averaging techniques to investigate the neural bases of cognitive and emotional functions. This may be especially challenging for studies examining patient groups, which often have limited sample sizes and increased intersubject variability. In particular, medial frontal cortex (MFC) dysfunction is thought to underlie performance monitoring dysfunction among patients with schizophrenia, yet previous studies using group averaging to compare schizophrenic patients to controls have yielded conflicting results. To examine individual activations in MFC associated with two aspects of performance monitoring, interference and error processing, functional magnetic resonance imaging data were acquired while 17 patients with schizophrenia and 21 healthy controls (HCs) performed an event-related version of the multisource interference task. Comparisons of averaged data revealed few differences between the groups. By contrast, topographic analysis of individual activations for errors showed that control subjects exhibited activations spanning across both posterior and anterior regions of MFC while patients primarily activated posterior MFC, possibly reflecting an impaired emotional response to errors in schizophrenia. This discrepancy between topographic and group-averaged results may be due to the significant dispersion among individual activations, particularly in HCs, highlighting the importance of considering intersubject variability when interpreting the medial frontal response to error commission.

Segmentation of MRI brain scans using non-uniform partial volume densities

We present an algorithm that provides a partial volume segmentation of a T1-weighted image of the brain into gray matter, white matter and cerebrospinal fluid. The algorithm incorporates a non-uniform partial volume density that takes the curved nature of the cortex into account. The pure gray and white matter intensities are estimated from the image, using scanner noise and cortical partial volume effects. Expected tissue fractions are subsequently computed in each voxel. The algorithm has been tested for reliability, correct estimation of the pure tissue intensities on both real (repeated) MRI data and on simulated (brain) images. Intra-class correlation coefficients (ICCs) were above 0.93 for all volumes of the three tissue types for repeated scans from the same scanner, as well as for scans with different voxel sizes from different scanners with different field strengths. The implementation of our non-uniform partial volume density provided more reliable volumes and tissue fractions, compared to a uniform partial volume density. Applying the algorithm to simulated images showed that the pure tissue intensities were estimated accurately. Variations in cortical thickness did not influence the accuracy of the volume estimates, which is a valuable property when studying (possible) group differences. In conclusion, we have presented a new partial volume segmentation algorithm that allows for comparisons over scanners and voxel sizes.

Voxel-based morphometry and automated lobar volumetry: the trade-off between spatial scale and statistical correction

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller sub-region of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups.

Gaussian mixture model-based segmentation of MR images taken from premature infant brains

Segmentation of Magnetic Resonance multi-layer images of premature infant brain has additional challenges in comparison to normal adult brain segmentation. Images of premature infants contain lower signal to noise ratio due to shorter scanning times. Further, anatomic structure include still greater variations which can impair the accuracy of standard brain models. A fully automatic brain segmentation method for T1-weighted images is proposed in present paper. The method uses watershed segmentation with Gaussian mixture model clustering for segmenting cerebrospinal fluid from brain matter and other head tissues. The effect of the myelination process is considered by utilizing information from T2-weighted images. The performance of the new method is compared voxel-by-voxel to the corresponding expert segmentation. The proposed method is found to produce more uniform results in comparison to three accustomary segmentation methods originally developed for adults. This is the case in particular when anatomic forms are still under development and differ in their form from those of adults.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

Evaluation of uterine cervix segmentations using ground truth from multiple experts

This work is focused on the generation and utilization of a reliable ground truth (GT) segmentation for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). NCI invited twenty experts to manually segment a set of 939 cervigrams into regions of medical and anatomical interest. Based on this unique data, the objectives of the current work are to: (1) Automatically generate a multi-expert GT segmentation map; (2) Use the GT map to automatically assess the complexity of a given segmentation task; (3) Use the GT map to evaluate the performance of an automated segmentation algorithm. The multi-expert GT map is generated via the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm, which is a well-known method to generate a GT segmentation from multiple observations. A new measure of segmentation complexity, which relies on the inter-observer variability within the GT map, is defined. This measure is used to identify images that were found difficult to segment by the experts and to compare the complexity of different segmentation tasks. An accuracy measure, which evaluates the performance of automated segmentation algorithms is presented. Two algorithms for cervix boundary detection are compared using the proposed accuracy measure. The measure is shown to reflect the actual segmentation quality achieved by the algorithms. The methods and conclusions presented in this work are general and can be applied to different images and segmentation tasks. Here they are applied to the cervigram database including a thorough analysis of the available data.

Patients with pain disorder show gray-matter loss in pain-processing structures: a voxel-based morphometric study

OBJECTIVE

To investigate whether the functional changes in pain disorder might be reflected by structural brain changes. Pain disorder assessed with the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria is characterized by persistent and distressing chronic pain at one or more body sites which cannot be fully explained by a physiological process or somatic disorder. Psychological factors are thought to play a major role. Recent neuroimaging studies evidenced altered pain processing in patients suffering from this disorder.

METHODS

Fourteen right-handed women fulfilling the DSM-IV criteria for pain disorder and 25 healthy age-matched women were investigated with magnetic resonance imaging. In the voxel-based morphometry analysis, we compared both groups for changes of gray-matter density. We included age and Beck Depression Inventory scores as nuisance variables to minimize possible confounding effects of age or depressive comorbidity.

RESULTS

In the patient group, we found significant gray-matter decreases in the prefrontal, cingulate, and insular cortex. These regions are known to be critically involved in the modulation of subjective pain experiences.

CONCLUSIONS

In the context of similar results in patients with other functional pain syndromes, such as fibromyalgia and chronic back pain, we suggest that structural changes in fronto-limbic brain circuits represent not only an objective marker of these pain syndromes but also constitute a critical pathophysiological element. These findings represent a further proof of the important role of central changes in pain disorder.

Voxel-based morphometry in individual patients: a pilot study in early Huntington disease

BACKGROUND AND PURPOSE

Voxel-based morphometry (VBM) has proved a powerful method to detect subtle changes of gray matter (GM) at the group level but the role of VBM for the detection of GM changes in single subjects, especially in those with suspected neurodegenerative disorder, remains uncertain. Here, we performed single subject analyses in 22 patients in early stages of Huntington disease (HD), a neurodegenerative disorder with a well-known and characteristic pattern of GM loss.

MATERIALS AND METHODS

We applied an ANCOVA with age and gender as covariates and corrected for multiple statistical tests by false discovery rate (P < 0.05). Each patient was compared to 133 healthy controls. The same procedure was applied to 22 of the controls matched for age and gender in a pair-wise manner.

RESULTS

Our analyses yielded biologically plausible results in HD patients in which GM decrease within the caudate nucleus could be identified in 15 of the 16 most affected patients while GM decrease was found in only 1 control subject. Lowering the size of the control group yielded comparable results with 99 and 66 control subjects whereas sensitivity decreased with 33 control subjects.

CONCLUSIONS

Our pilot study demonstrates a potential role of VBM for the detection of cerebral GM changes in single subjects with suspected neurodegenerative disorder.

Reduced gray matter brain volumes are associated with variants of the serotonin transporter gene in major depression

The serotonergic system is involved in the pathophysiology of major depression as well as in the early central nervous system development and adult neuroplasticity. The aim of the study was to examine in 77 patients with major depression and 77 healthy controls the association between the triallelic polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) and gray matter (GM) brain volumes measured with 1.5 T magnetic resonance imaging. Voxel-based morphometry were estimated on magnetic resonance images and genotyping was performed. We found that healthy controls have a strong association between the 5-HTTLPR and GM volumes of the dorsolateral prefrontal cortex, left anterior gyrus cinguli, left amygdala as well as right hippocampus, whereas there is no such association in patients with major depression. Healthy subjects carrying the S- or L(G)-allele have smaller GM volumes than those with the L(A)-allele, indicating that 5-HTTLPR contributes to the development of brain structures. Patients with depression show reduced GM volumes, particularly when they are homozygous for the L(A)-allele, suggesting that these patients are more vulnerable for morphological changes during depressive episodes.

Brain MR images segmentation using statistical ratio: mapping between watershed and competitive Hopfield clustering network algorithms

Conventional watershed segmentation methods invariably produce over-segmented images due to the presence of noise or local irregularities in the source images. In this paper, a robust medical image segmentation technique is proposed, which combines watershed segmentation and the competitive Hopfield clustering network (CHCN) algorithm to minimize undesirable over-segmentation. In the proposed method, a region merging method is presented, which is based on employing the region adjacency graph (RAG) to improve the quality of watershed segmentation. The relation of inter-region similarities is then investigated using image mapping in the watershed and CHCN images to determine more appropriate region merging. The performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images. Significant and promising segmentation results were achieved on brain phantom simulated data.

Structural brain alterations at different stages of schizophrenia: a voxel-based morphometric study

Structural alterations in schizophrenia have mainly been regarded as the result of neurodevelopmental processes. However, it remains unresolved whether the pattern of morphological brain changes differs between different stages of disease. We examined structural brain changes in 93 first-episode (FES) and 72 recurrently ill (REZ) patients with schizophrenia (SZ) and 175 matched healthy control subjects (HC) using cross-sectional and conjunctional voxel-based morphometry (VBM) of whole-brain MRI data in a three-step approach. We found significant grey matter density (GMD) reductions in FES compared to HC bilaterally in the temporal and prefrontal areas, including the anterior cingulate gyrus, as well as in both thalami. Hippocampus and amygdala were affected on the left side (P<0.05, corrected). In REZ patients this pattern was spatially extended. The basal ganglia were exclusively reduced in the recurrently ill group compared to controls. Common to both disease groups were reductions in the bilateral perisylvian regions, the opercular region, the insula, prefrontal cortex, left inferior temporal gyrus, limbic system including hippocampus and amygdala, and the thalami. In FES patients there were no regions affected that were not also affected in REZ patients. In contrast, REZ patients showed extended alterations within the frontal and temporal regions, the hippocampus, amygdala and exclusively in the basal ganglia relative to the FES patients. Our findings suggest a system-specific involvement of neuronal networks in schizophrenia. Furthermore, our data suggest that in the advanced stages of schizophrenia additional cortical and subcortical brain areas become involved in the disease process. Longitudinal data will be required to further test this hypothesis.

The val158met COMT polymorphism's effect on atrophy in healthy aging and Parkinson's disease

We investigated whether the val(158)met functional polymorphism of catechol-o-methyltransferase influenced age-related changes in grey matter density and volume, both in healthy individuals (n=80, ages 18-79) and those with Parkinson's disease (n=50). Global grey matter volumes and voxelwise estimates of grey matter volume and density were determined from structural magnetic resonance images at 3T. Male and female ValVal homozygotes (low prefrontal cortical dopamine) had more grey matter in early adulthood, but this difference disappeared with increasing age. The insula and ventral prefrontal cortex had higher grey matter volume in younger, but not older, ValVal homozygotes. Conversely, the dominant premotor cortex revealed genotypic differences in grey matter density in later life. There were no global or local interactions between Parkinson's disease and COMT val(158)met genotype on morphometry. Since the val(158)met polymorphism is associated with differences in cortical dopamine metabolism, our data suggest a role for dopamine in cortical development followed by differential vulnerability to cortical atrophy across the adult life span.

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.

A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme

A fully automatic, multiscale fuzzy C-means (MsFCM) classification method for MR images is presented in this paper. We use a diffusion filter to process MR images and to construct a multiscale image series. A multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels. The objective function of the conventional fuzzy C-means (FCM) method is modified to allow multiscale classification processing where the result from a coarse scale supervises the classification in the next fine scale. The method is robust for noise and low-contrast MR images because of its multiscale diffusion filtering scheme. The new method was compared with the conventional FCM method and a modified FCM (MFCM) method. Validation studies were performed on synthesized images with various contrasts and on the McGill brain MR image database. Our MsFCM method consistently performed better than the conventional FCM and MFCM methods. The MsFCM method achieved an overlap ratio of greater than 90% as validated by the ground truth. Experiments results on real MR images were given to demonstrate the effectiveness of the proposed method. Our multiscale fuzzy C-means classification method is accurate and robust for various MR images. It can provide a quantitative tool for neuroimaging and other applications.

Gray matter increase induced by practice correlates with task-specific activation: a combined functional and morphometric magnetic resonance imaging study

The neurophysiological basis of practice-induced gray matter increase is unclear. To study the relationship of practice-induced gray matter changes and neural activation, we conducted a combined longitudinal functional and morphometric (voxel-based morphometry) magnetic resonance imaging (MRI) study on mirror reading. Compared with normal reading, mirror reading resulted in an activation of the dorsolateral occipital cortex, medial occipital cortex, superior parietal cortex, medial and dorsolateral prefrontal cortex, as well as anterior insula and cerebellum. Daily practice of 15 min for 2 weeks resulted in an increased performance of mirror reading. After correction for pure performance effects, we found a practice-related decrease of activation at the right superior parietal cortex and increase of activation at the right dorsal occipital cortex. The longitudinal voxel-based morphometry analysis yielded an increase of gray matter in the right dorsolateral occipital cortex that corresponded to the peak of mirror-reading-specific activation. This confirms that short-term gray matter signal increase corresponds to task-specific processing. We speculate that practice-related gray matter signal changes in MRI are primarily related to synaptic remodeling within specific processing areas.

Intelligence and variability in a simple timing task share neural substrates in the prefrontal white matter

General intelligence is correlated with the mean and variability of reaction time in elementary cognitive tasks, as well as with performance on temporal judgment and discrimination tasks. This suggests a link between the temporal accuracy of neural activity and intelligence. However, it has remained unclear whether this link reflects top-down mechanisms such as attentional control and cognitive strategies or basic neural properties that influence both abilities. Here, we investigated whether millisecond variability in a simple, automatic timing task, isochronous tapping, correlates with intellectual performance and, using voxel-based morphometry, whether these two tasks share neuroanatomical substrates. Stability of tapping and intelligence were correlated and related to regional volume in overlapping right prefrontal white matter regions. These results suggest a bottom-up explanation of the link between temporal stability and intellectual performance, in which more extensive prefrontal connectivity underlies individual differences in both variables.

Template-O-Matic: a toolbox for creating customized pediatric templates

Processing pediatric neuroimaging data is a challenge due to pervasive morphological changes that occur in the human brain during normal development. This is of special relevance when reference data is used as part of the processing approach, as in spatial normalization and tissue segmentation. Current approaches construct reference data (templates) by averaging brain images from a control group of subjects, or by creating custom templates from the group under study. In this technical note, we describe a new, and generalized method of constructing such appropriate reference data by statistically analyzing a large sample (n=404) of healthy children, as acquired during the NIH MRI study of normal brain development. After eliminating non-contributing demographic variables, we modeled the effects of age (first, second, and third-order terms) and gender, for each voxel in gray matter and white matter. By appropriate weighting with the parameter estimates from these analyses, complete tissue maps can be generated automatically from this database to match a pediatric population selected for study. The algorithm is implemented in the form of a toolbox for the SPM5 image data processing suite, which we term Template-O-Matic. We compare the performance of this approach with the current method of template generation and discuss the implications of our approach.

Gray Matter Atrophy in Patients With Ischemic Stroke With Cognitive Impairment

Background and Purpose— Patients with ischemic stroke are at risk for developing vascular cognitive impairment ranging from mild impairments to dementia. MRI findings of infarction, white matter hyperintensities, and global cerebral atrophy have been implicated in the development of vascular cognitive impairment. The present study investigated regional gray matter volume differences between patients with ischemic stroke with no cognitive impairment and those with impairment in at least one domain of cognitive function.

Methods— Ninety-one patients with ischemic stroke participated. Detailed neuropsychological testing was used to characterize cognitive functioning in 7 domains: orientation, attention, working memory, language, visuospatial ability, psychomotor speed, and memory. High-resolution T1-weighted 3-dimensional fast-spoiled gradient recalled structural MRIs were processed using optimized voxel-based morphometry techniques while controlling for lesions. Whole brain voxelwise regional differences in gray matter volume were assessed between patients with stroke with no impaired cognitive domains and patients with stroke with at least one impaired cognitive domain. Logistic regression models were used to assess the contribution of demographic variables, stroke-related variables, and voxel-based morphometry results to classification of cognitive impairment group membership.

Results— Fifty-one patients had no impairments in any cognitive domain and 40 patients were impaired in at least one cognitive domain. Logistic regression identified significant contributions to cognitive impairment groups for demographic variables, stroke-related variables, and cognitive domain performance. Voxel-based morphology results demonstrated significant gray matter volume reductions in patients with stroke with one or more cognitive domain impairment compared with patients with stroke without cognitive impairment that was seen mostly in the thalamus with smaller reductions found in the cingulate gyrus and frontal, temporal, parietal, and occipital lobes. These reductions were present after controlling for group differences in age, education, stroke volume, and laterality of stroke. The addition of voxel-based morphometry-derived thalamic volume significantly improved a logistic regression model predicting cognitive impairment group membership when added to demographic variables, stroke-related variables, and cognitive domain performance.

Conclusions— These results suggest a central role for the thalamus and lesser roles for other cortical regions in the development of cognitive impairment after ischemic stroke. Indeed, consideration of thalamic volumes adds significant information to the classification of cognitive impaired versus nonimpaired groups beyond information provided by demographic, stroke-related, and cognitive performance measures.

Individual differences in stressor-evoked blood pressure reactivity vary with activation, volume, and functional connectivity of the amygdala

Individuals who exhibit exaggerated blood pressure reactions to psychological stressors are at risk for hypertension, ventricular hypertrophy, and premature atherosclerosis; however, the neural systems mediating exaggerated blood pressure reactivity and associated cardiovascular risk in humans remain poorly defined. Animal models indicate that the amygdala orchestrates stressor-evoked blood pressure reactions via reciprocal signaling with corticolimbic and brainstem cardiovascular-regulatory circuits. Based on these models, we used a multimodal neuroimaging approach to determine whether human individual differences in stressor-evoked blood pressure reactivity vary with amygdala activation, gray matter volume, and functional connectivity with corticolimbic and brainstem areas implicated in stressor processing and cardiovascular regulation. We monitored mean arterial pressure (MAP) and concurrent functional magnetic resonance imaging BOLD signal changes in healthy young individuals while they completed a Stroop color-word stressor task, validated previously in epidemiological studies of cardiovascular risk. Individuals exhibiting greater stressor-evoked MAP reactivity showed (1) greater amygdala activation, (2) lower amygdala gray matter volume, and (3) stronger positive functional connectivity between the amygdala and perigenual anterior cingulate cortex and brainstem pons. Individual differences in amygdala activation, gray matter volume, and functional connectivity with corticolimbic and brainstem circuits may partly underpin cardiovascular disease risk by impacting stressor-evoked blood pressure reactivity.

Gray matter volume differences and the effects of smoking on gray matter in schizophrenia

OBJECTIVE

Many studies have evaluated differences in gray matter volume in schizophrenia, but have not considered the possible effects of smoking, which is extraordinarily common in people with the illness. The present study used voxel-based morphometry (VBM) to examine differences in gray matter in subjects with schizophrenia and evaluate the effects of smoking on this measure.

METHODS

Thirty-two subjects with schizophrenia (14 smokers, 18 non-smokers) and 32 healthy comparison subjects participated in the study. Whole brain, voxel-wise analyses of regional gray matter volume were conducted using voxel-based morphometry (VBM).

RESULTS

Reduced gray matter was observed in the schizophrenia group in the orbitofrontal cortex, bilateral insula and superior temporal gyri (STG), bilateral dorsolateral prefrontal cortices (DLPFC), medial frontal gyrus, and cingulate gyrus. Within this group, smoking subjects had greater lateral prefrontal and STG gray matter volumes relative to non-smoking subjects.

CONCLUSIONS

The finding of reduced gray matter volume in prefrontal and temporal regions in schizophrenia is consistent with prior anatomical tracing and whole-brain voxel-based studies. Greater gray matter volumes in smoking relative to non-smoking subjects with schizophrenia highlight a potential experimental confound in volumetric studies and suggests that smoking may be associated with a relative preservation of lateral prefrontal and temporal gray matter in schizophrenia.

A Dirichlet process mixture model for brain MRI tissue classification

Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.

Multiscale Fuzzy C-Means Image Classification for Multiple Weighted MR Images for the Assessment of Photodynamic Therapy in Mice

We are investigating in vivo small animal imaging and analysis methods for the assessment of photodynamic therapy (PDT), an emerging therapeutic modality for cancer treatment. Multiple weighted MR images were acquired from tumor-bearing mice pre- and post-PDT and 24-hour after PDT. We developed an automatic image classification method to differentiate live, necrotic and intermediate tissues within the treated tumor on the MR images. We used a multiscale diffusion filter to process the MR images before classification. A multiscale fuzzy C-means (FCM) classification method was applied along the scales. The object function of the standard FCM was modified to allow multiscale classification processing where the result from a coarse scale is used to supervise the classification in the next scale. The multiscale fuzzy C-means (MFCM) method takes noise levels and partial volume effects into the classification processing. The method was validated by simulated MR images with various noise levels. For simulated data, the classification method achieved 96.0 ± 1.1% overlap ratio. For real mouse MR images, the classification results of the treated tumors were validated by histologic images. The overlap ratios were 85.6 ± 5.1%, 82.4 ± 7.8% and 80.5 ± 10.2% for the live, necrotic, and intermediate tissues, respectively. The MR imaging and the MFCM classification methods may provide a useful tool for the assessment of the tumor response to photodynamic therapy in vivo.

Altered effective connectivity within the language network in primary progressive aphasia

Primary progressive aphasia (PPA) is a neurodegenerative dementia syndrome principally characterized by the gradual dissolution of language functions, especially in the early stages of disorder. In a previous functional neuroimaging study, PPA patients were found to activate core language areas similarly to control subjects when performing semantic and phonological processing tasks (Sonty et al., 2003). In the present study, functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) were used to study multiregional effective connectivity in early-stage PPA (n = 8) and control (n = 8) subjects performing semantic word matching and visual letter matching tasks. fMRI analysis showed semantic task-specific activations in the left inferior frontal (Broca's area) and posterior superior temporal (Wernicke's area) regions, in addition to other language regions, in both groups. Using a model language network consisting of six left hemisphere regions, the DCM analysis demonstrated reduced language-specific effective connectivity between Wernicke's and Broca's areas in the PPA patient group. Furthermore, this decrement in connectivity was predictive of semantic task accuracy. These results demonstrate for the first time that dysfunctional network interactions (effective connectivity), rather than hypoactivity within individual brain regions, may contribute to the emergence of language deficits seen in PPA.

Generalized overlap measures for evaluation and validation in medical image analysis

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms.

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.