Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome - a comprehensive morphometric analysis with active staining contrast-enhanced MRI.

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study.

Frontotemporal dementia is a heterogeneous neurodegenerative disorder with around a third of cases having autosomal dominant inheritance. There is wide variability in phenotype even within affected families, raising questions about the determinants of the progression of disease and age at onset. It has been recently demonstrated that cognitive reserve, as measured by years of formal schooling, can counteract the ongoing pathological process. The TMEM106B genotype has also been found to be a modifier of the age at disease onset in frontotemporal dementia patients with TDP-43 pathology. This study therefore aimed to elucidate the modulating effect of environment (i.e. cognitive reserve as measured by educational attainment) and genetic background (i.e. TMEM106B polymorphism, rs1990622 T/C) on grey matter volume in a large cohort of presymptomatic subjects bearing frontotemporal dementia-related pathogenic mutations. Two hundred and thirty-one participants from the GENFI study were included: 108 presymptomatic MAPT, GRN, and C9orf72 mutation carriers and 123 non-carriers. For each subject, cortical and subcortical grey matter volumes were generated using a parcellation of the volumetric T1-weighted magnetic resonance imaging brain scan. TMEM106B genotyping was carried out, and years of education recorded. First, we obtained a composite measure of grey matter volume by graph-Laplacian principal component analysis, and then fitted a linear mixed-effect interaction model, considering the role of (i) genetic status; (ii) educational attainment; and (iii) TMEM106B genotype on grey matter volume. The presence of a mutation was associated with a lower grey matter volume (P = 0.002), even in presymptomatic subjects. Education directly affected grey matter volume in all the samples (P = 0.02) with lower education attainment being associated with lower volumes. TMEM106B genotype did not influence grey matter volume directly on its own but in mutation carriers it modulated the slope of the correlation between education and grey matter volume (P = 0.007). Together, these results indicate that brain atrophy in presymptomatic carriers of common frontotemporal dementia mutations is affected by both genetic and environmental factors such that TMEM106B enhances the benefit of cognitive reserve on brain structure. These findings should be considered in evaluating outcomes in future disease-modifying trials, and support the search for protective mechanisms in people at risk of dementia that might facilitate new therapeutic strategies.

Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups.

BACKGROUND: Pathological abnormalities including demyelination and neuronal loss are reported in the outer cortex in multiple sclerosis (MS). OBJECTIVE: We investigated for in vivo evidence of outer cortical abnormalities by measuring the magnetisation transfer ratio (MTR) in MS patients of different subgroups. METHODS: Forty-four relapsing-remitting (RR) (mean age 41.9 years, median Expanded Disability Status Scale (EDSS) 2.0), 25 secondary progressive (SP) (54.1 years, EDSS 6.5) and 19 primary progressive (PP) (53.1 years, EDSS 6.0) MS patients and 35 healthy control subjects (mean age 39.2 years) were studied. Three-dimensional (3D) 1×1×1mm(3) T1-weighted images and MTR data were acquired. The cortex was segmented, then subdivided into outer and inner bands, and MTR values were calculated for each band. RESULTS: In a pairwise analysis, mean outer cortical MTR was lower than mean inner cortical MTR in all MS groups and controls (p<0.001). Compared with controls, outer cortical MTR was decreased in SPMS (p<0.001) and RRMS (p<0.01), but not PPMS. Outer cortical MTR was lower in SPMS than PPMS (p<0.01) and RRMS (p<0.01). CONCLUSIONS: Lower outer than inner cortical MTR in healthy controls may reflect differences in myelin content. The lowest outer cortical MTR was seen in SPMS and is consistent with more extensive outer cortical (including subpial) pathology, such as demyelination and neuronal loss, as observed in post-mortem studies of SPMS patients.

Faciobrachial dystonic seizures: the influence of immunotherapy on seizure control and prevention of cognitive impairment in a broadening phenotype.

Voltage-gated potassium channel complex antibodies, particularly those directed against leucine-rich glioma inactivated 1, are associated with a common form of limbic encephalitis that presents with cognitive impairment and seizures. Faciobrachial dystonic seizures have recently been reported as immunotherapy-responsive, brief, frequent events that often predate the cognitive impairment associated with this limbic encephalitis. However, these observations were made from a retrospective study without serial cognitive assessments. Here, we undertook the first prospective study of faciobrachial dystonic seizures with serial assessments of seizure frequencies, cognition and antibodies in 10 cases identified over 20 months. We hypothesized that (i) faciobrachial dystonic seizures would show a differential response to anti-epileptic drugs and immunotherapy; and that (ii) effective treatment of faciobrachial dystonic seizures would accelerate recovery and prevent the development of cognitive impairment. The 10 cases expand both the known age at onset (28 to 92 years, median 68) and clinical features, with events of longer duration, simultaneously bilateral events, prominent automatisms, sensory aura, and post-ictal fear and speech arrest. Ictal epileptiform electroencephalographic changes were present in three cases. All 10 cases were positive for voltage-gated potassium channel-complex antibodies (346-4515 pM): nine showed specificity for leucine-rich glioma inactivated 1. Seven cases had normal clinical magnetic resonance imaging, and the cerebrospinal fluid examination was unremarkable in all seven tested. Faciobrachial dystonic seizures were controlled more effectively with immunotherapy than anti-epileptic drugs (P = 0.006). Strikingly, in the nine cases who remained anti-epileptic drug refractory for a median of 30 days (range 11-200), the addition of corticosteroids was associated with cessation of faciobrachial dystonic seizures within 1 week in three and within 2 months in six cases. Voltage-gated potassium channel-complex antibodies persisted in the four cases with relapses of faciobrachial dystonic seizures during corticosteroid withdrawal. Time to recovery of baseline function was positively correlated with time to immunotherapy (r = 0.74; P = 0.03) but not time to anti-epileptic drug administration (r = 0.55; P = 0.10). Of 10 cases, the eight cases who received anti-epileptic drugs (n = 3) or no treatment (n = 5) all developed cognitive impairment. By contrast, the two who did not develop cognitive impairment received immunotherapy to treat their faciobrachial dystonic seizures (P = 0.02). In eight cases without clinical magnetic resonance imaging evidence of hippocampal signal change, cross-sectional volumetric magnetic resonance imaging post-recovery, after accounting for age and head size, revealed cases (n = 8) had smaller brain volumes than healthy controls (n = 13) (P < 0.001). In conclusion, faciobrachial dystonic seizures can be prospectively identified as a form of epilepsy with an expanding phenotype. Immunotherapy is associated with excellent control of the frequently anti-epileptic drug refractory seizures, hastens time to recovery, and may prevent the subsequent development of cognitive impairment observed in this study.

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation.

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

Using florbetapir positron emission tomography to explore cerebrospinal fluid cut points and gray zones in small sample sizes.

INTRODUCTION: We aimed to assess the feasibility of determining Alzheimer's disease cerebrospinal fluid (CSF) cut points in small samples through comparison with amyloid positron emission tomography (PET). METHODS: Twenty-three individuals (19 patients, four controls) had CSF measures of amyloid beta (Aß)1-42 and total tau/Aß1-42 ratio, and florbetapir PET. We compared CSF measures with visual and quantitative (standardized uptake value ratio [SUVR]) PET measures of amyloid. RESULTS: Seventeen of 23 were amyloid-positive on visual reads, and 14 of 23 at an SUVR of ≥1.1. There was concordance (positive/negative on both measures) in 20 of 23, of whom 19 of 20 were correctly classified at an Aß1-42 of 630 ng/L, and 20 of 20 on tau/Aß1-42 ratio (positive ≥0.88; negative ≤0.34). Three discordant cases had Aß1-42 levels between 403 and 729 ng/L and tau/Aß1-42 ratios of 0.54-0.58. DISCUSSION: Comparing amyloid PET and CSF biomarkers provides a means of assessing CSF cut points in vivo, and can be applied to small sample sizes. CSF tau/Aß1-42 ratio appears robust at predicting amyloid status, although there are gray zones where there remains diagnostic uncertainty.

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

Cardiac arrhythmia detection by parameters sharing and MMIE training of Hidden Markov Models.

This paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models and Maximum Mutual Information Estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fibrillation and normal beats are morphologically similar except that the former does not exhibit the P wave. In fact there are more differences as the irregularity of the RR interval, but ventricular conduction in AF is normal in morphology. Regarding to the Hidden Markov Models (HMM) modelling this can mean that these two classes can be modelled by HMM's of similar topology and sharing some parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMIE training, and saving computational resources at run-time decoding. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where Maximum Likelihood Estimation training is used alone.