Search

The nexus model of social processing proposes that the right temporal parietal junction (rTPJ) serves as a neural hub for cognitive social functions. We test the hypothesis that the rTPJ is a domain general region including somatosensory social functions. Neuroimaging findings and cross-brain coherence for right- and left-hand handclasps with real vs. simulated hands were consistent with the domain general model.

It is not known whether lateralized olfactory sensitivity deficits are present in MS. Since projections from the olfactory bulb to the olfactory cortex are largely ipsilateral, and since both functional imaging and psychophysical studies suggest that the right side of the brain may be more involved in olfactory processing than the left, we addressed this issue by administering well-validated tests of odor detection, along with tests of odor identification, to each side of the nose of 73 MS patients and 73 age-, gender-, and race-matched normal controls. We also determined, in 63 of the MS patients, whether correlations were present between the olfactory test measures and MRI-determined lesions in brain regions ipsilateral and contralateral to the nose side that was tested. No significant left:right differences in either olfactory sensitivity or identification were present, although in both cases mean performance was lower in the MS than in the control subjects (ps < 0.0001). Scores on the two sides of the nose were positively correlated with one another (identification r = 0.71, p < 0.0001; threshold r = 0.56, p < 0.0001). The percent of MS patients whose bilateral test scores fell below the 10th percentile of controls did not differ between the odor identification and detection threshold tests. Both left and right odor identification and detection test scores were weakly correlated with lesion volumes in temporal and frontal lobe brain regions (r’s < 0.40). Our findings demonstrate that MS does not differentially influence odor perception on left and right sides of the nose, regardless of whether sensitivity or identification is being measured. They also indicate that tests of odor identification and detection are similarly influenced by MS and that such influences are associated with central brain lesions.

Empirical studies of taste function in multiple sclerosis (MS) are rare. Moreover, a detailed assessment of whether quantitative measures of taste function correlate with the punctate and patchy myelin-related lesions found throughout the CNS of MS patients has not been made. We administered a 96-trial test of sweet (sucrose), sour (citric acid), bitter (caffeine) and salty (NaCl) taste perception to the left and right anterior (CN VII) and posterior (CN IX) tongue regions of 73 MS patients and 73 matched controls. The number and volume of lesions were assessed using quantitative MRI in 52 brain regions of 63 of the MS patients. Taste identification scores were significantly lower in the MS patients for sucrose (p = 0.0002), citric acid (p = 0.0001), caffeine (p = 0.0372) and NaCl (p = 0.0004) and were present in both anterior and posterior tongue regions. The percent of MS patients with identification scores falling below the 5th percentile of controls was 15.07 % for caffeine, 21.9 % for citric acid, 24.66 % for sucrose, and 31.50 % for NaCl. Such scores were inversely correlated with lesion volumes in the temporal, medial frontal, and superior frontal lobes, and with the number of lesions in the left and right superior frontal lobes, right anterior cingulate gyrus, and left parietal operculum. Regardless of the subject group, women outperformed men on the taste measures. These findings indicate that a sizable number of MS patients exhibit taste deficits that are associated with MS-related lesions throughout the brain., U.S. National Institutes of Health ( RO1 DC 02974 -- R01 NS 37172 -- R01 NS070906)

We hypothesized that integrated motor-visual functions measured by manipulative manual dexterity are affected by white matter lesion (WML) burden as measured on cranial MRI across relevant brain regions in subjects at risk of preclinical occult vascular disease.A real-time cross-sectional study of healthy subjects aged 29 to 74 years with a family history of early-onset coronary artery disease (n = 714; mean age, 51 ± 11 years; mean education, 14 ± 3 years; 42% male; 38% black) were identified from probands with coronary artery disease diagnosed before age 60 years. WMLs on 3-tesla brain MRI and Grooved Pegboard scores were measured.WMLs were observed at all ages. Mean pegboard scores were 108 ± 18, similar to normal populations. In unadjusted analysis, WMLs and pegboard scores were significantly correlated by region (total WMLs, r = 0.34, p = 0.0001; frontal [r = 0.34, p0.0001], insula [r = 0.31, p0.0001], parietal [r = 0.31, p0.0001], and temporal [r = 0.17, p0.0001]). In multivariate analysis predicting (log) pegboard score adjusted for age, sex, race, education, regional or total volumes, and familial non-independence, total WML volume (p = 5.79E - 05) and regional WML volumes (p0.01) retained statistical significance in all but the youngest age quartile (29-43 years).Greater WML volumes in different brain regions are associated with higher pegboard scores (worse performance) independent of age, sex, race, education, and total or regional volumes. This suggests that small vessel cerebrovascular disease may be present in healthy individuals in a preclinical state with measurable impact on complex integrative functions in individuals with excess risk of clinical vascular disease.

Dizziness, postural instability, and ataxia are among the most debilitating symptoms of multiple sclerosis (MS), reflecting, in large part, dysfunctional integration of visual, somatosensory, and vestibular sensory cues. However, the role of MS-related supratentorial lesions in producing such symptoms is poorly understood. In this study, motor control test (MCT) and dynamic sensory organization test (SOT) scores of 58 MS patients were compared to those of 72 healthy controls; correlations were determined between the MS scores of 49 patients and lesion volumes within 26 brain regions. Depending upon platform excursion direction and magnitude, MCT latencies, which were longer in MS patients than controls (p

Background: African Americans (AAs) have a higher prevalence of extreme ischemic white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) than do European Americans (EAs) based on the Cardiovascular Health Study (CHS) score. Ischemic white matter disease, limited to the deep white matter, may be biologically distinct from disease in other regions and may reflect a previously observed trend toward an increased risk of subcortical lacunar infarcts in AAs. We hypothesized that extreme deep WMH volume (DWMV) or periventricular volume (PV) may also have a higher prevalence in AAs. Thus, we studied extreme CHS scores and extreme DWMV and PV in a healthy population enriched for cardiovascular disease risk factors. Methods: We imaged the brains of 593 subjects who were first-degree relatives of probands with early onset coronary disease prior to 60 years of age. WMHs were manually delineated on 3-tesla cranial MRI by a trained radiology reader; the location and volume of lesions were characterized using automated software. DWMV and PV were measured directly with automated software, and the CHS score was determined by a neuroradiologist. Volumes were characterized as being in the upper 25% versus lower 75% of total lesion volume. Volumes in the upper versus the remaining quartiles were examined for AA versus EA race using multiple logistic regression (generalized estimating equations adjusted for family relatedness) and adjusted for major vascular disease risk factors including age ≥55 years versus 160 mg/dl. Results: Participants were 58% women and 37% AAs, with a mean age of 51.5 ± 11.0 years (range, 29-74 years). AAs had significantly higher odds of having extreme DWMVs (odds ratio, OR, 1.8; 95% confidence interval, CI, 1.2-2.9; p = 0.0076) independently of age, sex, hypertension and all other risk factors. AAs also had significantly higher odds of having extreme CHS scores ≥3 (OR, 1.3; 95% CI, 1.1-3.6; p = 0.025). Extreme PV was not significantly associated with AA race (OR, 1.3; 95% CI, 0.81-2.1; p = 0.26). Conclusions: AAs from families with early-onset cardiovascular disease are more likely to have extreme DWMVs (a subclinical form of cerebrovascular disease) and an extreme CHS score, but not extreme PV, independently of age and other cardiovascular disease risk factors. These findings suggest that this AA population is at an increased risk for DWMV and may be at an increased risk for future subcortical stroke. Longitudinal studies are required to see if DWMV is predictive of symptomatic subcortical strokes in this population.

Cortical atrophy has been reported in a number of diseases, such as multiple sclerosis and Alzheimer's disease, that are also associated with white matter (WM) lesions. However, most cortical reconstruction techniques do not account for these pathologies, thereby requiring additional processing to correct for the effect of WM lesions. In this work, we introduce CRUISE+, an automated process for cortical reconstruction from magnetic resonance brain images with WM lesions. The process extends previously well validated methods to allow for multichannel input images and to accommodate for the presence of WM lesions. We provide new validation data and tools for measuring the accuracy of cortical reconstruction methods on healthy brains as well as brains with multiple sclerosis lesions. Using this data, we validate the accuracy of CRUISE+ and compare it to another state-of-the-art cortical reconstruction tool. Our results demonstrate that CRUISE+ has superior performance in the cortical regions near WM lesions, and similar performance in other regions. Hum Brain Mapp 35:3385–3401, 2014. © 2013 Wiley Periodicals, Inc.

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images., Highlights • We developed an automated segmentation method for MS lesions in brain MRIs. • We use logistic regression models with multiple MRI modalities. • We trained and validated our method on MRI images from two imaging sites.

Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access) In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

In this study, we used manual delineation of high-resolution magnetic resonance imaging (MRI) to determine the spatial and temporal characteristics of the cerebellar atrophy in spinocerebellar ataxia type 2 (SCA2). Ten subjects with SCA2 were compared to ten controls. The volume of the pons, the total cerebellum, and the individual cerebellar lobules were calculated via manual delineation of structural MRI. SCA2 showed substantial global atrophy of the cerebellum. Furthermore, the degeneration was lobule specific, selectively affecting the anterior lobe, VI, Crus I, Crus II, VIII, uvula, corpus medullare, and pons, while sparing VIIB, tonsil/paraflocculus, flocculus, declive, tuber/folium, pyramis, and nodulus. The temporal characteristics differed in each cerebellar subregion: (1) duration of disease: Crus I, VIIB, VIII, uvula, corpus medullare, pons, and the total cerebellar volume correlated with the duration of disease; (2) age: VI, Crus II, and flocculus correlated with age in control subjects; and (3) clinical scores: VI, Crus I, VIIB, VIII, corpus medullare, pons, and the total cerebellar volume correlated with clinical scores in SCA2. No correlations were found with the age of onset. Our extrapolated volumes at the onset of symptoms suggest that neurodegeneration may be present even during the presymptomatic stages of disease. The spatial and temporal characteristics of the cerebellar degeneration in SCA2 are region specific. Furthermore, our findings suggest the presence of presymptomatic atrophy and a possible developmental component to the mechanisms of pathogenesis underlying SCA2. Our findings further suggest that volumetric analysis may aid in the development of a non-invasive, quantitative biomarker.

Several studies have shown marked differences in the neural localization of language functions in the brains of left-handed individuals when compared with right-handers. Previous experiments involving functional lateralization have demonstrated cerebral blood flow patterns that differ concordantly with subject handedness while performing language-related tasks. The effect of handedness on function in specific stages of memory processing, however, is a largely unexplored area. We used a paired-associates verbal memory task to elicit activation of neural areas related to declarative memory, examining the hypothesis that there are differences in activation in the medial temporal lobe (MTL) between handedness groups. 15 left-handed and 25 right-handed healthy adults were matched for all major demographic and neuropsychological variables. Functional and structural imaging data were acquired and analyzed for group differences within MTL subregions. Our results show that activation of the MTL during declarative memory processing varies with handedness. While both groups showed activation in left and right MTL subregions, the left-handed group showed a statistically significant increase in the left hippocampus and amygdala during both encoding and recall. No increases in activation were found in the right-handed group. This effect was found in the absence of any differences in performance on the verbal memory task, structural volumetric disparities, or functional asymmetries. This provides evidence of functional differences between left-handers and right-handers, which extends to declarative memory processes.

Deep white matter hyperintensity (DWMH) and periventricular (PV) white matter lesion volumes are associated with age and subsequent stroke. We studied age differences in these volumes accounting for collinearity and risk factors. Subjects were 563 healthy family members of early-onset coronary artery disease patients. Using 3T magnetic resonance imaging, lesions were classified as DWMH or PV. Age association with lesion classification was analyzed using random effects Tobit regression, adjusting for intracranial volume (ICV) and risk factors. Subjects were 60% women, 36% African-American, mean age 51 ± 11 years. In multivariable analysis adjusted for PV and ICV, DWMH was associated with age (p < 0.001) and female sex (p = 0.003). PV, adjusted for DWMH and ICV, was age associated (p < 0.001). For each age decade, DWMH showed 0.07 log units/decade greater volume (95% CI = 0.04-0.11); PV was 0.18 log units/decade greater (95% CI = 0.14-0.23); slope differences (p < 0.001). In people with a family history of coronary artery disease, PV and DWMH are independently and differentially associated with age controlling for traditional risk factors.

Dear Sirs, Although cerebellar atrophy is a key sign in cerebellar ataxia, interpretation of magnetic resonance imaging (MRI) may be hindered by region-specific age-related degeneration and poor visualization of posterior fossa structures. Direct quantification of true cerebellar cortical volume on MRI is a time-consuming process that may be confounded by complex foliation, which introduces partial volume effects. It may be simpler to calculate an index for disease progression for the deep cerebellar nuclei (DCN), which mediate virtually all cerebellar outflow along with the vestibular nuclei. Quantitative assessment of the DCN could be invaluable to diagnosis, staging, and prognosis in neurodegenerative diseases with cerebellar involvement. Progress in structural assessment has enabled DCN identification [1–3]; however, there may be variable signal and contrast on T1- and T2-weighted scans such that DCN are sometimes not detectable at all [2, 4]. Recently, our team developed techniques for diffusion tensor imaging (DTI)-based delineation of the dentate and interposed nuclei—the largest, most reliably distinguishable DCN components [4, 5]. Although this indirect method would be expected to systematically overestimate true DCN volumes, it could still produce a clinically relevant DCN index measurement that is indirectly based on volume. As proof of concept, we investigated the sensitivity of these observations to differences in DCN index between ataxia patients and controls. DTI evaluation of the DCN could serve as a simple imaging biomarker of cerebellar disease. A multi-slice, single-shot EPI sequence achieved whole brain coverage (2.2 mm isotropic resolution) in 19 control participants (6 M/13 F) and 28 patients—18 (11 M/7 F) with idiopathic isolated cerebellar disease (ICD), six (2 M/4 F) with spinocerebellar ataxia type 6 (SCA6), and four (2 M/2F) with olivopontocerebellar atrophy (OPCA). Each sequence utilized 32 diffusion encoding directions and five averaged, minimally-weighted (b0) volumes with a 3T MR scanner (Intera, Philips Medical Systems, Netherlands). Neurological function was assessed using the International Cooperative Ataxia Rating Scale [6] and subsections of the Unified Ataxic Disorders Rating Scale. Bilateral DCN delineations were performed on DTI colormaps with Medical Image Processing and Visualization software [7] by one rater (A.X.D.), who was naive to participant status. Measurement accuracy was assessed by an expert neurologist with reference to histological and structural MRI sections [8]. Volumes and ataxia scores were evaluated with Student’s t test. Eight scans were chosen for repeat rating by the original rater and a second rater (J.L.C.). Controls had an average DCN index of 1,722 ± 341 mm3, while patients had an average DCN index of 1,142 ± 494 mm3 (P < 0.001). Analysis of patient groups showed correlations between lower DCN index and higher total ataxia scores, indicating greater disability (Fig. 1) (P < 0.05). Fig. 1 Deep cerebellar nuclei (DCN) index in ataxia patients is smaller than expected when compared with control participant data. a Mean DCN index decreases with age, and is smaller in patients compared with controls (P < 0.001). b Age-adjusted DCN ... Intra- and inter-rater reliability testing showed high correlation between repeated measurements of DCN structures (R2 = 0.95 and 0.97, Fig. 2). Fig. 2 Intra- and inter-rater reliability. The graph on the left shows the intra-rater repeated volumes and reliability correlation score for rater 1 (A.X.D.), whose delineations were used to evaluate our data. The graph on the right shows the inter-rater repeated ... DTI colormaps enabled successful delineation of the DCN for all participants, despite varying degrees of functional deficit, volumetric atrophy, and T2 signal (Fig. 3). Ataxia patients had smaller DCN indices, which were associated with higher ataxia scores. It is important to note that our data show unexpectedly low mean DCN index in all patient groups, including our largest subpopulation (SCA6, Fig. 2b). This supports the possibility that the DCN are affected in SCA6, which is a point of controversy in the literature [9–11]. Of note, our index would be expected to correlate only with macroscopic volumetric loss, whereas “degeneration” classically refers to histopathologic evidence. Presumably, several distinct patterns of DCN involvement exist within the heterogeneous population affected with cerebellar degeneration. It would be of interest to test our method in SCA3 and Friedreich’s ataxia, which show significant dentate involvement. Fig. 3 DCN is clearly visualized on DTI images. a Patients with SCA6 (hereditary disease primarily affecting the cerebellum) exhibited reduced DCN index when compared to the control group. b In the T2w images on the left, the DCN appears as a well-defined black ... In summary, reliable identification of the DCN is possible with DTI, which exposes clinically relevant differences in DCN characteristics in ataxia.

Introduction: Persons with a family history of early-onset coronary artery disease (CAD) have an excess risk of stroke and CAD. White matter lesions (WML) on MRI represent small vessel ischemic cerebrovascular disease and are associated with incident stroke and neurocognitive decline with age. We hypothesized manipulative manual dexterity, an integration of fine motor, visual spatial, and cognition function, may be affected by increased WML burden in task-relevant brain regions across age ranges in persons at risk for pre-clinical occult vascular disease. We tested this in a large population with a family history of early CAD. Methods: Healthy 29-74 year old subjects (N=714; mean age 51± 11 years; mean education 14 ± 3 years; 42% male, 38% Black) were identified from probands with CAD Results: WML were observed in all age groups; mean overall pegboard scores were 108±18, and were within reference norms. In unadjusted analysis, pegboard scores were highly correlated in the expected direction with total WML volumes, r=0.34, p= Conclusions: Our findings in a large population-based sample with a family history of early CAD confirm that greater WML volumes in multiple brain locations are associated with higher pegboard scores (worse performance) independent of age, sex, race, and education. This suggests that small vessel cerebrovascular disease is present in an early preclinical state and that WML volumes impact manipulative manual dexterity in healthy middle-aged and younger individuals with excess risk for clinical vascular disease.

Introduction: The loss of total brain volume (TBV) due to aging is associated with increasing ischemic white matter lesion volume (WMH) and dementia. Lower TBV may be secondary to chronic ischemia and hypoperfusion throughout the periventricular white matter rather than the burden of focal ischemia in the deep white matter. We hypothesized lower TBV would be more closely correlated with increasing periventricular white matter lesion volume (PV) rather than deep white matter lesion volume (DWMH). Methods: We enrolled 593 asymptomatic family members of probands with premature coronary artery disease ( Results: Participants were 58% women, 37% African-American, 29-74 years old. TBV/ICV was more correlated to PV than DWMH (correlation coefficients -0.26 and -0.11, p= Conclusions: PV is more strongly correlated to TBV than DWMH and the age associated changes in PV and TBV were more similar to one another than DWMH. The association of increasing PV with lower TBV may help to explain the association of cognitive and motor decline with increasing PV. Future longitudinal studies are needed to verify this association.

Introduction: White matter hyperintensities (WMH) on MRI represent small vessel ischemic cerebrovascular disease. Greater WMH burden is associated with higher levels of circulating inflammatory cytokines in persons > 65 years with dementia, suggesting a pro-inflammatory vascular process. We hypothesized that middle-aged, asymptomatic, apparently healthy high risk people with high WMH burden would demonstrate increased inflammatory gene expression in monocytes analyzed with microarray. Methods: Subjects (N=70) were identified from a larger MRI study in 593 healthy family members of persons with early-onset CAD (< 60 years). We obtained monocytes and examined gene expression in all subjects (mean age 58.1 ± 10 years, range 30-73; 55% female; 36% African American). These included 35 subjects with the greatest WMH burden using volumetric methods in the larger study, and 35 unrelated age-sex-race matched controls with the lowest WMH burden. Monocyte mRNA was analyzed on Illumina Human HT12 v4 microarrays. We performed unsupervised principal component analysis (PCA) followed by ANOVA between high and low WMH groups. Genes with 2 SD differences in expression between groups were included for Gene Ontology permutation analysis using 1000 permutations within “GoMiner”. Only genes with the lowest false discovery rate (FDR) were summarized. Results: PCA identified no significant clustering. A total of 1,315 genes were included in gene ontology analysis and resulted in 10 ontological categories with an FDR Conclusions: Gene microarray ontological analysis of monocytes in healthy middle aged high risk people with greater WMH burden shows increased activation of genes of known innate inflammatory pathways. These findings likely reflect greater pro-inflammatory processes in persons with greater WMH, consistent with the presence of inflammation-mediated occult small vessel cerebrovascular disease in a healthy middle-aged population at increased risk for vascular diseases.

Despite the fact that acute cases of multiple sclerosis (MS)-related pure-tone hearing loss have been reported in the literature, consensus is lacking as to the chronic influences of MS on pure-tone thresholds. Most studies examining such influences have been limited by small sample sizes, lack of statistical comparisons between patients and controls, and confounding of the hearing measure with influences from sex and age. To date, associations between pure-tone thresholds and central MS-related brain lesions have not been assessed. In this study, pure-tone thresholds ranging from 0.5 to 8 kHz were measured in 73 MS patients and 73 individually age- and gender-matched normal controls. In 63 MS patients, correlations were computed between the threshold values and MRI-determined lesion activity in 26 central brain regions. Although thresholds were strongly influenced by sex, age, and tonal frequency, no meaningful influences of MS were discerned. Moreover, no significant association between the threshold values and central MS-related lesion activity was evident in any brain region evaluated. This study, the largest on this topic to use carefully matched control subjects and the sole study to assess relationships between auditory thresholds and central MS-related lesions, strongly suggests that (a) MS is not chronically associated with pure-tone hearing loss and (b) pure-tone thresholds are unrelated to MS lesion activity in higher brain regions. These findings, along with general reports from the literature, support the concept that when MS-related hearing threshold deficits are found, they are episodic and primarily dependent on lesions within the eighth nerve or brainstem.

Extraction of the cerebrum, cerebellum, and brain stem from structural magnetic resonances images (MRIs) is an important initial step in neuroimaging. We present an automated algorithm that solves this difficult problem, often referred to as skull stripping, which is novel for its use of registration, segmentation, and morphological operations. Our algorithm is also concerned with an accurate representation of the grey matter boundary, which is a unique feature. We also present results demonstrating the accuracy of this approach

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.