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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.

[EN] 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., The data collection and labeling of the cerebellum was supported in part by the NIH/NINDS grant R01 NS056307 (PI: J.L. Prince) and NIH/NIMH grants R01 MH078160 & R01 MH085328 (PI: S.H. Mostofsky). PMT is supported in part by the NIH/NIBIB grant U54 EB020403. CERES2 development was supported by grant UPV2016-0099 from the Universitat Politecnica de Valencia (PI: J.V. Manjon); the French National Research Agency through the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project; PI: P. Coupe) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57; PI: P. Coupe). Support for the development of LiviaNET was provided by the National Science and Engineering Research Council of Canada (NSERC), discovery grant program, and by the ETS Research Chair on Artificial Intelligence in Medical Imaging. The authors wish to acknowledge the invaluable contributions offered by Dr. George Fein (Dept. of Medicine and Psychology, University of Hawaii) in preparing this manuscript.

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)

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.

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.

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.

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.

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.

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.