Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Balance in multiple sclerosis: relationship to central brain regions.

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 < 0.0001), were correlated with lesion volumes in the cortex, medial frontal lobes, temporal lobes, and parietal opercula (r's ranging from 0.20 to 0.39). SOT test scores were also impacted by MS and correlated with lesions in these same brain regions as well as within the superior frontal lobe (r's ranging from - 0.28 to - 0.40). The strongest and most consistent correlations occurred for the most challenging tasks in which incongruent visual and proprioceptive feedback were given. This study demonstrates that supratentorial lesion volumes are associated with quantitative balance measures in MS, in accord with the concept that balance relies upon highly convergent and multimodal neural pathways involving the skin, muscles, joints, eyes, and vestibular system.

Longitudinal multiple sclerosis lesion segmentation data resource.

The data presented in this article is related to the research article entitled "Longitudinal multiple sclerosis lesion segmentation: Resource and challenge" (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

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.

Unilateral olfactory sensitivity in multiple sclerosis.

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 (threshold r=0.56, p<0.0001; Identification r=0.71, 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.

Taste dysfunction in multiple sclerosis.

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.

Effect of white matter lesions on manual dexterity in healthy middle-aged persons.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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, p < 0.0001], insula [r = 0.31, p < 0.0001], parietal [r = 0.31, p < 0.0001], and temporal [r = 0.17, p < 0.0001]). 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 (p < 0.01) retained statistical significance in all but the youngest age quartile (29-43 years). CONCLUSIONS: 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.

Age differences in periventricular and deep white matter lesions.

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.

Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

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.

Example Based Lesion Segmentation.

Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation.

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.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

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.

Pure-tone auditory thresholds are not chronically elevated in multiple sclerosis.

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.

Principal component analysis of cerebellar shape on MRI separates SCA types 2 and 6 into two archetypal modes of degeneration.

Although "cerebellar ataxia" is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes.

MRI shows a region-specific pattern of atrophy in spinocerebellar ataxia type 2.

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.

Segmentation of brain images using adaptive atlases with application to ventriculomegaly.

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.

Effect of handedness on fMRI activation in the medial temporal lobe during an auditory verbal memory task.

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.

Volumetric neuroimage analysis extensions for the MIPAV software package.

We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets.