Marie Odile Habert

Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria

In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimers Association have contributed criteria for the diagnosis of Alzheimers disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimers pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD.

Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database

Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimers disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. However, these methods were assessed on different populations, making it difficult to compare their performance. In this paper, we evaluated the performance of ten approaches (five voxel-based methods, three methods based on cortical thickness and two methods based on the hippocampus) using 509 subjects from the ADNI database. Three classification experiments were performed: CN vs AD, CN vs MCIc (MCI who had converted to AD within 18 months, MCI converters - MCIc) and MCIc vs MCInc (MCI who had not converted to AD within 18 months, MCI non-converters - MCInc). Data from 81 CN, 67 MCInc, 39 MCIc and 69 AD were used for training and hyperparameters optimization. The remaining independent samples of 81 CN, 67 MCInc, 37 MCIc and 68 AD were used to obtain an unbiased estimate of the performance of the methods. For AD vs CN, whole-brain methods (voxel-based or cortical thickness-based) achieved high accuracies (up to 81% sensitivity and 95% specificity). For the detection of prodromal AD (CN vs MCIc), the sensitivity was substantially lower. For the prediction of conversion, no classifier obtained significantly better results than chance. We also compared the results obtained using the DARTEL registration to that using SPM5 unified segmentation. DARTEL significantly improved six out of 20 classification experiments and led to lower results in only two cases. Overall, the use of feature selection did not improve the performance but substantially increased the computation times.

Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria

During the past decade, a conceptual shift occurred in the field of Alzheimers disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this “silent” stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

Neural correlates of cognitive impairment in posterior cortical atrophy

With the prospect of disease-modifying drugs that will target the physiopathological process of Alzheimers disease, it is now crucial to increase the understanding of the atypical focal presentations of Alzheimers disease, such as posterior cortical atrophy. This study aimed to (i) characterize the brain perfusion profile in posterior cortical atrophy using regions of interest and a voxel-based approach; (ii) study the influence of the disease duration on the clinical and imaging profiles; and (iii) explore the correlations between brain perfusion and cognitive deficits. Thirty-nine patients with posterior cortical atrophy underwent a specific battery of neuropsychological tests, mainly targeting visuospatial functions, and a brain perfusion scintigraphy with 99mTc-ethyl cysteinate dimer. The imaging analysis included a comparison with a group of 24 patients with Alzheimers disease, matched for age, disease duration and Mini-Mental State Examination, and 24 healthy controls. The single-photon emission computed tomography profile in patients with posterior cortical atrophy was characterized by extensive and severe hypoperfusion in the occipital, parietal, posterior temporal cortices and in a smaller cortical area corresponding to the frontal eye fields (Brodmann areas 6/8). Compared with patients with Alzheimers disease, the group with posterior cortical atrophy showed more severe occipitoparietal hypoperfusion and higher perfusion in the frontal, anterior cingulate and mesiotemporal regions. When considering the disease duration, the functional changes began and remained centred on the posterior lobes, even in the late stage. Correlation analyses of brain perfusion and neuropsychological scores in posterior cortical atrophy highlighted the prominent role of left inferior parietal damage in acalculia, Gerstmanns syndrome, left-right indistinction and limb apraxia, whereas damage to the bilateral dorsal occipitoparietal regions appeared to be involved in Bálints syndrome. Our findings provide new insight into the natural history of functional changes according to disease duration and highlight the role of parietal and occipital cortices in the cognitive syndromes that characterize the posterior cortical atrophy.

Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses.

Imaging cerebral glucose metabolism with positron emission tomography (PET) in Alzheimers disease (AD) has allowed for improved characterisation of this pathology. Such patterns are typically analysed using either univariate or multivariate statistical techniques. In this work we combined voxel-based group analysis and independent component analysis to extract differential characteristic patterns from PET data of glucose metabolism in a large cohort of normal elderly controls and patients with AD. The patterns were used in conjunction with a support vector machine to discriminate between subjects with mild cognitive impairment (MCI) at risk or not of converting to AD. The method was applied to baseline fluoro-deoxyglucose (FDG)-PET images of subjects from the ADNI database. Our approach achieved improved early detection and differentiation of typical versus pathological metabolic patterns in the MCI population, reaching 80% accuracy (85% sensitivity and 75% specificity) when using selected regions. The method has the potential to assist in the advance diagnosis of Alzheimers disease, and to identify early in the development of the disease those individuals at high risk of rapid cognitive decline who could be candidates for new therapeutic approaches.

Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements.

Cognitive decline in normal ageing and Alzheimers disease (AD) emerges from functional disruption in the coordination of large-scale brain systems sustaining cognition. Integrity of these systems can be examined by correlation methods based on analysis of resting state functional magnetic resonance imaging (fMRI). Here we investigate functional connectivity within the default mode network (DMN) in normal ageing and AD using resting state fMRI. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN sub-systems were identified: Frontal (medial and superior), parietal (precuneus-posterior cingulate, lateral parietal), temporal (medial temporal), and hippocampal (bilateral). There was a decrease in antero-posterior interactions (lower global efficiency), but increased interactions within the frontal and parietal sub-systems (higher local clustering) in elderly compared to young controls. This decreased antero-posterior integration was more pronounced in AD patients compared to elderly controls, particularly in the precuneus-posterior cingulate region. Conjoint knowledge of integration measures and graph indices in the same data helps in the interpretation of functional connectivity results, as comprehension of one measure improves with understanding of the other. The approach allows for complete characterisation of connectivity changes and could be applied to other resting state networks and different pathologies.

Differential automatic diagnosis between Alzheimer's disease and frontotemporal dementia based on perfusion SPECT images

OBJECTIVEnAlzheimers disease (AD) and frontotemporal dementia (FTD) are among the most frequent neurodegenerative cognitive disorders, but their differential diagnosis is difficult. The aim of this study was to evaluate an automatic method returning the probability that a patient suffers from AD or FTD from the analysis of brain perfusion single photon emission computed tomography images.nnnMETHODS AND MATERIALSnA set of 116 descriptors corresponding to the average activity in regions of interest was calculated from the images of 82 AD and 91 FTD patients. A set of linear (logistic regression and linear discriminant analysis) and non-linear (support vector machines, k-nearest neighbours, multilayer perceptron and kernel logistic PLS) classification methods was subsequently used to ascertain diagnoses. Validation was carried out by means of the leave-one-out protocol. Diagnoses by the classifier and by four physicians (visual assessment) were compared. Since images were acquired in different hospitals, the impact of the medical centre on the diagnosis of both the classifier and the physicians was investigated.nnnRESULTSnBest results were obtained with support vector machine and partial least squares regression coupled with k-nearest neighbours methods (PLS+K-NN), with an overall accuracy of 88%. PLS+K-NN was however considered as the best method since performances obtained with leave-one-out cross-validation were closer to whole-database learning. The performances of the classifier were higher than those of experts (accuracy ranged from 65 to 72%). Physicians found it more difficult to diagnose the images from centres other than their own, and it affected their performances.nnnCONCLUSIONSnThe performances obtained by the classifier for the differential diagnosis of AD and FTD were found convincing. It could help physicians in daily practice, particularly when visual assessment is inconclusive, or when dealing with multicentre data.

Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept

Preclinical Alzheimers disease (AD) is a relatively recent concept describing an entity characterized by the presence of a pathophysiological biomarker signature characteristic for AD in the absence of specific clinical symptoms. There is rising interest in the scientific community to define such an early target population mainly because of failures of all recent clinical trials despite evidence of biological effects on brain amyloidosis for some compounds. A conceptual framework has recently been proposed for this preclinical phase of AD. However, few data exist on this silent stage of AD. We performed a systematic review to investigate how the concept is defined across studies. The review highlights the substantial heterogeneity concerning the three main determinants of preclinical AD: “normal cognition,” “cognitive decline,” and “AD pathophysiological signature.” We emphasize the need for a harmonized nomenclature of the preclinical AD concept and standardized population‐based and case‐control studies using unified operationalized criteria.

Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline

There is evolving evidence that individuals categorized with subjective cognitive decline (SCD) are potentially at higher risk for developing objective and progressive cognitive impairment compared to cognitively healthy individuals without apparent subjective complaints. Interestingly, SCD, during advancing preclinical Alzheimers disease (AD), may denote very early, subtle cognitive decline that cannot be identified using established standardized tests of cognitive performance. The substantial heterogeneity of existing SCD-related research data has led the Subjective Cognitive Decline Initiative (SCD-I) to accomplish an international consensus on the definition of a conceptual research framework on SCD in preclinical AD. In the area of biological markers, the cerebrospinal fluid signature of AD has been reported to be more prevalent in subjects with SCD compared to healthy controls; moreover, there is a pronounced atrophy, as demonstrated by magnetic resonance imaging, and an increased hypometabolism, as revealed by positron emission tomography, in characteristic brain regions affected by AD. In addition, SCD individuals carrying an apolipoprotein ɛ4 allele are more likely to display AD-phenotypic alterations. The urgent requirement to detect and diagnose AD as early as possible has led to the critical examination of the diagnostic power of biological markers, neurophysiology, and neuroimaging methods for AD-related risk and clinical progression in individuals defined with SCD. Observational studies on the predictive value of SCD for developing AD may potentially be of practical value, and an evidence-based, validated, qualified, and fully operationalized concept may inform clinical diagnostic practice and guide earlier designs in future therapy trials.

Lateral Temporal Lobe: An Early Imaging Marker of the Presymptomatic GRN Disease?

Abstract The preclinical stage of frontotemporal lobar degeneration (FTLD) is not well characterized. We conducted a brain metabolism (FDG-PET) and structural (cortical thickness) study to detect early changes in asymptomatic GRN mutation carriers (aGRN+) that were evaluated longitudinally over a 20-month period. At baseline, a left lateral temporal lobe hypometabolism was present in aGRN+ without any structural changes. Importantly, this is the first longitudinal study and, across time, the metabolism more rapidly decreased in aGRN+ in lateral temporal and frontal regions. The main structural change observed in the longitudinal study was a reduction of cortical thickness in the left lateral temporal lobe in carriers. A limit of this study is the relatively small sample (nu200a=u200a16); nevertheless, it provides important results. First, it evidences that the pathological processes develop a long time before clinical onset, and that early neuroimaging changes might be detected approximately 20 years before the clinical onset of disease. Second, it suggests that metabolic changes are detectable before structural modifications and cognitive deficits. Third, both the baseline and longitudinal studies provide converging results implicating lateral temporal lobe as early involved in GRN disease. Finally, our study demonstrates that structural and metabolic changes could represent possible biomarkers to monitor the progression of disease in the presymptomatic stage toward clinical onset.