Discriminant analysis of intermediate brain atrophy rates in longitudinal diagnosis of alzheimer's disease

Diagnosing Alzheimer's disease through MRI neuroimaging biomarkers has been used as a complementary marker for traditional clinical markers to improve diagnostic accuracy and also help in developing new pharmacotherapeutic trials. It has been revealed that longitudinal analysis of the whole brain atrophy has the power of discriminating Alzheimer's disease and elderly normal controls. In this work, effect of involving intermediate atrophy rates and impact of using uncorrelated principal components of these features instead of original ones on discriminating normal controls and Alzheimer's disease subjects, is inspected. In fact, linear discriminative analysis of atrophy rates is used to classify subjects into Alzheimer's disease and controls. Leave-one-out cross-validation has been adopted to evaluate the generalization rate of the classifier along with its memorization. Results show that incorporating uncorrelated version of intermediate features leads to the same memorization performance as the original ones but higher generalization rate. As a conclusion, it is revealed that in a longitudinal study, using intermediate MRI scans and transferring them to an uncorrelated feature space can improve diagnostic accuracy

Anatomical and Functional Deficits in Patients with Amnestic Mild Cognitive Impairment

Background: Anatomical and functional deficits have been studied in patients with amnestic mild cognitive impairment (MCI). However, it is unclear whether and how the anatomical deficits are related to the functional alterations. Present study aims to characterize the association between anatomical and functional deficits in MCI patients. Methods: Seventeen amnestic MCI patients and 18 healthy aging controls were scanned using a T1 Weighted MPRAGE sequence and a gradient-echo echo-planar imaging sequence. Clinical severity of MCI patients was evaluated by usin

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study

Background Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour

The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis

Mindfulness-based programs (MBPs) are increasingly utilized to improve mental health. Interest in the putative effects of MBPs on cognitive function is also growing. This is the first meta-analysis of objective cognitive outcomes across multiple domains from randomized MBP studies of adults. Seven databases were systematically searched to January 2020. Fifty-six unique studies (n = 2,931) were included, of which 45 (n = 2,238) were synthesized using robust variance estimation meta-analysis. Meta-regression and subgroup analyses evaluated moderators. Pooling data across cognitive domains, the summary effect size for all studies favored MBPs over comparators and was small in magnitude (g = 0.15; [0.05, 0.24]). Across subgroup analyses of individual cognitive domains/subdomains, MBPs outperformed comparators for executive function (g = 0.15; [0.02, 0.27]) and working memory outcomes (g = 0.23; [0.11, 0.36]) only. Subgroup analyses identified significant effects for studies of non-clinical samples, as well as for adults aged over 60. Across all studies, MBPs outperformed inactive, but not active comparators. Limitations include the primarily unclear within-study risk of bias (only a minority of studies were considered low risk), and that statistical constraints rendered some p-values unreliable. Together, results partially corroborate the hypothesized link between mindfulness practices and cognitive performance. This review was registered with PROSPERO [CRD42018100904]

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury

Discrete Ladders for Parallel Transport in Transformation Groups with an Affine Connection Structure

International audienceModeling the temporal evolution of the tissues of the body is an important goal of medical image analysis, for instance for understanding the structural changes of organs affected by a pathology, or for studying the physiological growth during the life span. For such purposes we need to analyze and compare the observed anatomical differences between follow-up sequences of anatomical images of different subjects. Non-rigid registration is one of the main instruments for modeling anatomical differences from images. The aim of non-rigid registration is to encode the observed structural changes as deformation fields of the image space, which represent the warping required to match observed differences. This way, anatomical changes can be modeled and quantified by analyzing the associated deformations. The comparison of temporal evolutions thus requires the transport (or "normalization") of longitudinal deformations in a common reference frame. Normalization of longitudinal deformations can be done in different ways, depending on the feature of interest. For instance, local volume changes encoded by the scalar Jacobian determinant of longitudinal deformations can be compared by scalar resampling in a common reference frame via inter-subject registration. However, if we consider vector-valued deformation trajectories instead of scalar quantities, the transport is not uniquely defined anymore. Among the different normalization methods for deformation trajectories, the parallel transport is a powerful and promising tool which can be used within the ''diffeomorphic registration'' setting. Mathematically, parallel transporting a vector along a curve consists in translating it across the tangent spaces to the curve by preserving its parallelism according to a given derivative operation called (affine) connection. This chapter focuses on explicitly discrete algorithms for parallel transporting diffeomorphic deformations. Schild's ladder is an efficient and simple method proposed in theoretical Physics for the parallel transport of vectors along geodesics paths by iterative construction of infinitesimal geodesics parallelograms on the manifold. The base vertices of the parallelogram are given by the initial tangent vector to be transported. By iteratively building geodesic diagonals along the path, Schild's Ladder computes the missing vertex which corresponds to the transported vector. In this chapter we first show that the Schild ladder can lead to an effective computational scheme for the parallel transport of diffeomorphic deformations parameterized by tangent velocity fields. Schild's ladder may be however inefficient for transporting longitudinal deformations from image time series of multiple time points, in which the computation of the geodesic diagonals is required several times. We propose therefore a new parallel transport method based on the Schild's ladder, the "pole ladder", in which the computation of geodesics diagonals is minimized. Differently from the Schild's ladder, the pole ladder is symmetric with respect to the baseline-to-reference frame geodesic. From the theoretical point of view, we show that the pole ladder is rigorously equivalent to the Schild's ladder when transporting along geodesics. From the practical point of view, we establish the computational advantages and demonstrate the effectiveness of this very simple method by comparing with standard methods of transport on simulated images with progressing brain atrophy. Finally, we illustrate its application to a clinical problem: the measurement of the longitudinal progression in Alzheimer's disease. Results suggest that an important gain in sensitivity could be expected in group-wise comparisons

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest

Subcortical amyloid load is associated with shape and volume in cognitively normal individuals

Amyloid-beta (Aβ) deposition is one of the main hallmarks of Alzheimer’s disease. The study assessed the associations between cortical and subcortical 11C-Pittsburgh Compound B retention, namely in the hippocampus, amygdala, putamen, caudate, pallidum, and thalamus, and subcortical morphology in cognitively normal individuals. We recruited 104 cognitive normal individuals who underwent extensive neuropsychological assessment, PiB-positron emission tomography (PET) scan and 3-tesla magnetic resonance imaging (MRI) acquisition of T1-weighted images. Global, cortical, and subcortical regional PiB retention values were derived from each scan and subcortical morphology analyses were performed to investigate vertex-wise local surface and global volumes, including the hippocampal subfields volumes. We found that subcortical regional Aβ was associated with the surface of the hippocampus, thalamus, and pallidum, with changes being due to volume and shape. Hippocampal Aβ was marginally associated with volume of the whole hippocampus as well as with the CA1 subfield, subiculum, and molecular layer. Participants showing higher subcortical Aβ also showed worse cognitive performance and smaller hippocampal volumes. In contrast, global and cortical PiB uptake did not associate with any subcortical metrics. This study shows that subcortical Aβ is associated with subcortical surface morphology in cognitively normal individuals. This study highlights the importance of quantifying subcortical regional PiB retention values in these individuals

Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden

Disruption of functional connectivity between brain regions may represent an early functional consequence of β-amyloid pathology prior to clinical Alzheimer's disease. We aimed to investigate if non-demented older individuals with increased amyloid burden demonstrate disruptions of functional whole-brain connectivity in cortical hubs (brain regions typically highly connected to multiple other brain areas) and if these disruptions are associated with neuronal dysfunction as measured with fluorodeoxyglucose-positron emission tomography. In healthy subjects without cognitive symptoms and patients with mild cognitive impairment, we used positron emission tomography to assess amyloid burden and cerebral glucose metabolism, structural magnetic resonance imaging to quantify atrophy and novel resting state functional magnetic resonance imaging processing methods to calculate whole-brain connectivity. Significant disruptions of whole-brain connectivity were found in amyloid-positive patients with mild cognitive impairment in typical cortical hubs (posterior cingulate cortex/precuneus), strongly overlapping with regional hypometabolism. Subtle connectivity disruptions and hypometabolism were already present in amyloid-positive asymptomatic subjects. Voxel-based morphometry measures indicate that these findings were not solely a consequence of regional atrophy. Whole-brain connectivity values and metabolism showed a positive correlation with each other and a negative correlation with amyloid burden. These results indicate that disruption of functional connectivity and hypometabolism may represent early functional consequences of emerging molecular Alzheimer's disease pathology, evolving prior to clinical onset of dementia. The spatial overlap between hypometabolism and disruption of connectivity in cortical hubs points to a particular susceptibility of these regions to early Alzheimer's-type neurodegeneration and may reflect a link between synaptic dysfunction and functional disconnection