Agnese Picco

Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers

The diagnosis of Alzheimers disease can be improved by the use of biological measures. Biomarkers of functional impairment, neuronal loss, and protein deposition that can be assessed by neuroimaging (ie, MRI and PET) or CSF analysis are increasingly being used to diagnose Alzheimers disease in research studies and specialist clinical settings. However, the validation of the clinical usefulness of these biomarkers is incomplete, and that is hampering reimbursement for these tests by health insurance providers, their widespread clinical implementation, and improvements in quality of health care. We have developed a strategic five-phase roadmap to foster the clinical validation of biomarkers in Alzheimers disease, adapted from the approach for cancer biomarkers. Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete. To complete these phases, research priorities include the standardisation of the readout of these assays and thresholds for normality, the evaluation of their performance in detecting early disease, the development of diagnostic algorithms comprising combinations of biomarkers, and the development of clinical guidelines for the use of biomarkers in qualified memory clinics.

Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects

Large-scale longitudinal neuroimaging studies with diffusion imaging techniques are necessary to test and validate models of white matter neurophysiological processes that change in time, both in healthy and diseased brains. The predictive power of such longitudinal models will always be limited by the reproducibility of repeated measures acquired during different sessions. At present, there is limited quantitative knowledge about the across-session reproducibility of standard diffusion metrics in 3T multi-centric studies on subjects in stable conditions, in particular when using tract based spatial statistics and with elderly people. In this study we implemented a multi-site brain diffusion protocol in 10 clinical 3T MRI sites distributed across 4 countries in Europe (Italy, Germany, France and Greece) using vendor provided sequences from Siemens (Allegra, Trio Tim, Verio, Skyra, Biograph mMR), Philips (Achieva) and GE (HDxt) scanners. We acquired DTI data (2 × 2 × 2 mm(3), b = 700 s/mm(2), 5 b0 and 30 diffusion weighted volumes) of a group of healthy stable elderly subjects (5 subjects per site) in two separate sessions at least a week apart. For each subject and session four scalar diffusion metrics were considered: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial (AD) diffusivity. The diffusion metrics from multiple subjects and sessions at each site were aligned to their common white matter skeleton using tract-based spatial statistics. The reproducibility at each MRI site was examined by looking at group averages of absolute changes relative to the mean (%) on various parameters: i) reproducibility of the signal-to-noise ratio (SNR) of the b0 images in centrum semiovale, ii) full brain test-retest differences of the diffusion metric maps on the white matter skeleton, iii) reproducibility of the diffusion metrics on atlas-based white matter ROIs on the white matter skeleton. Despite the differences of MRI scanner configurations across sites (vendors, models, RF coils and acquisition sequences) we found good and consistent test-retest reproducibility. White matter b0 SNR reproducibility was on average 7 ± 1% with no significant MRI site effects. Whole brain analysis resulted in no significant test-retest differences at any of the sites with any of the DTI metrics. The atlas-based ROI analysis showed that the mean reproducibility errors largely remained in the 2-4% range for FA and AD and 2-6% for MD and RD, averaged across ROIs. Our results show reproducibility values comparable to those reported in studies using a smaller number of MRI scanners, slightly different DTI protocols and mostly younger populations. We therefore show that the acquisition and analysis protocols used are appropriate for multi-site experimental scenarios.

Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study

An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimers disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine (SVM) and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.

Longitudinal reproducibility of default-mode network connectivity in healthy elderly participants: A multicentric resting-state fMRI study.

To date, limited data are available regarding the inter-site consistency of test-retest reproducibility of functional connectivity measurements, in particular with regard to integrity of the Default Mode Network (DMN) in elderly participants. We implemented a harmonized resting-state fMRI protocol on 13 clinical scanners at 3.0T using vendor-provided sequences. Each site scanned a group of 5 healthy elderly participants twice, at least a week apart. We evaluated inter-site differences and test-retest reproducibility of both temporal signal-to-noise ratio (tSNR) and functional connectivity measurements derived from: i) seed-based analysis (SBA) with seed in the posterior cingulate cortex (PCC), ii) group independent component analysis (ICA) separately for each site (site ICA), and iii) consortium ICA, with group ICA across the whole consortium. Despite protocol harmonization, significant and quantitatively important inter-site differences remained in the tSNR of resting-state fMRI data; these were plausibly driven by hardware and pulse sequence differences across scanners which could not be harmonized. Nevertheless, the tSNR test-retest reproducibility in the consortium was high (ICC=0.81). The DMN was consistently extracted across all sites and analysis methods. While significant inter-site differences in connectivity scores were found, there were no differences in the associated test-retest error. Overall, ICA measurements were more reliable than PCC-SBA, with site ICA showing higher reproducibility than consortium ICA. Across the DMN nodes, the PCC yielded the most reliable measurements (≈4% test-retest error, ICC=0.85), the medial frontal cortex the least reliable (≈12%, ICC=0.82) and the lateral parietal cortices were in between (site ICA). Altogether these findings support usage of harmonized multisite studies of resting-state functional connectivity to characterize longitudinal effects in studies that assess disease progression and treatment response.

What predicts cognitive decline in de novo Parkinson's disease?

Subtle cognitive impairment can be detected in early Parkinsons disease (PD). In a consecutive series of de novo, drug-naive PD patients, we applied stepwise regression analysis to assess which clinical, neuropsychological, and functional neuroimaging (dopamine transporter [DAT] and perfusion single photon emission computed tomography [SPECT]) characteristics at baseline was predictive of cognitive decline during an average follow-up time of about 4 years. Decline both in executive (R(2) = 0.54; p = 0.0001) and visuospatial (R(2) = 0.56; p = 0.0001) functions was predicted by the couple of Unified Parkinsons Disease Rating Scale (UPDRS)-III score and caudate dopamine transporter (DAT) uptake in the less affected hemisphere (LAH). Verbal memory and language decline was predicted instead by caudate DAT uptake and brain perfusion in a posterior parieto-temporal area of the less affected hemisphere (R(2) = 0.42; p = 0.0005). No significant effect was shown for age, baseline neuropsychological scores, and levodopa equivalent dose at follow-up. The combined use of clinical structured examination and brain functional assessment by means of dual single photon emission computed tomography imaging appears as a powerful approach to predict cognitive decline in de novo PD patients.

Visual versus semi-quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer's Disease Consortium (EADC) project.

We aimed to investigate the accuracy of FDG-PET to detect the Alzheimers disease (AD) brain glucose hypometabolic pattern in 142 patients with amnestic mild cognitive impairment (aMCI) and 109 healthy controls. aMCI patients were followed for at least two years or until conversion to dementia. Images were evaluated by means of visual read by either moderately-skilled or expert readers, and by means of a summary metric of AD-like hypometabolism (PALZ score). Seventy-seven patients converted to AD-dementia after 28.6 ± 19.3 months of follow-up. Expert reading was the most accurate tool to detect these MCI converters from healthy controls (sensitivity 89.6%, specificity 89.0%, accuracy 89.2%) while two moderately-skilled readers were less (p < 0.05) specific (sensitivity 85.7%, specificity 79.8%, accuracy 82.3%) and PALZ score was less (p < 0.001) sensitive (sensitivity 62.3%, specificity 91.7%, accuracy 79.6%). Among the remaining 67 aMCI patients, 50 were confirmed as aMCI after an average of 42.3 months, 12 developed other dementia, and 3 reverted to normalcy. In 30/50 persistent MCI patients, the expert recognized the AD hypometabolic pattern. In 13/50 aMCI, both the expert and PALZ score were negative while in 7/50, only the PALZ score was positive due to sparse hypometabolic clusters mainly in frontal lobes. Visual FDG-PET reads by an expert is the most accurate method but an automated, validated system may be particularly helpful to moderately-skilled readers because of high specificity, and should be mandatory when even a moderately-skilled reader is unavailable.

Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

The use of Alzheimers disease (AD) biomarkers is supported in diagnostic criteria, but their maturity for clinical routine is still debated. Here, we evaluate brain fluorodeoxyglucose positron emission tomography (FDG PET), a measure of cerebral glucose metabolism, as a biomarker to identify clinical and prodromal AD according to the framework suggested for biomarkers in oncology, using homogenous criteria with other biomarkers addressed in parallel reviews. FDG PET has fully achieved phase 1 (rational for use) and most of phase 2 (ability to discriminate AD subjects from healthy controls or other forms of dementia) aims. Phase 3 aims (early detection ability) are partly achieved. Phase 4 studies (routine use in prodromal patients) are ongoing, and only preliminary results can be extrapolated from retrospective observations. Phase 5 studies (quantify impact and costs) have not been performed. The results of this study show that specific efforts are needed to complete phase 3 evidence, in particular comparing and combining FDG PET with other biomarkers, and to properly design phase 4 prospective studies as a basis for phase 5 evaluations.

Nigro-caudate dopaminergic deafferentation: A marker of REM sleep behavior disorder?

Forty-nine consecutive, drug naïve outpatients with de novo Parkinsons disease (PD) and 12 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) underwent clinical examination and dopamine transporter single photon emission computed tomography with [(123)I]-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)nortropane as a biomarker of nigro-striatal function. PD patients were grouped into rapid eye movement sleep behavior disorder (RBD) negative (PD-RBD-) and RBD positive (PD-RBD+). Repeated measures and univariate analysis of variance were used to compare dopaminergic and clinical impairment among groups. The variations of dopamine transporter-single photon emission computed tomography specific binding ratios (SBR) as a function of group belonging were significantly different (p = 0.0013) at caudate with respect to putamen level. Indeed, putamen SBR progressively decreased from iRBD to PD-RBD- and PD-RBD+ groups while caudate SBR were higher in PD-RBD- group than in PD-RBD+ and even than in iRBD group. Motor impairment was more severe in PD patients with RBD than in those without RBD. Our data suggest that a more severe nigro-caudate dopaminergic deafferentation is related to RBD, both in its idiopathic form and in PD patients.

Plasma antioxidants and brain glucose metabolism in elderly subjects with cognitive complaints.

PurposeThe role of oxidative stress is increasingly recognized in cognitive disorders of the elderly, notably Alzheimer’s disease (AD). In these subjects brain18F-FDG PET is regarded as a reliable biomarker of neurodegeneration. We hypothesized that oxidative stress could play a role in impairing brain glucose utilization in elderly subjects with increasing severity of cognitive disturbance.MethodsThe study group comprised 85 subjects with cognitive disturbance of increasing degrees of severity including 23 subjects with subjective cognitive impairment (SCI), 28 patients with mild cognitive impairment and 34 patients with mild AD. In all subjects brain FDG PET was performed and plasma activities of extracellular superoxide dismutase (eSOD), catalase and glutathione peroxidase were measured. Voxel-based analysis (SPM8) was used to compare FDG PET between groups and to evaluate correlations between plasma antioxidants and glucose metabolism in the whole group of subjects, correcting for age and Mini-Mental State Examination score.ResultsBrain glucose metabolism progressively decreased in the bilateral posterior temporoparietal and cingulate cortices across the three groups, from SCI to mild AD. eSOD activity was positively correlated with glucose metabolism in a large area of the left temporal lobe including the superior, middle and inferior temporal gyri and the fusiform gyrus.ConclusionThese results suggest a role of oxidative stress in the impairment of glucose utilization in the left temporal lobe structures in elderly patients with cognitive abnormalities, including AD and conditions predisposing to AD. Further studies exploring the oxidative stress–energy metabolism axis are considered worthwhile in larger groups of these patients in order to identify pivotal pathophysiological mechanisms and innovative therapeutic opportunities.

Brain Functional Network in Alzheimer's Disease: Diagnostic Markers for Diagnosis and Monitoring

Alzheimers disease (AD) is the most common type of dementia that is clinically characterized by the presence of memory impairment and later by impairment in other cognitive domains. The clinical diagnosis is based on interviews with the patient and his/her relatives and on neuropsychological assessment, which are also used to monitor cognitive decline over time. Several biomarkers have been proposed for detecting AD in its earliest stages, that is, in the predementia stage. In an attempt to find noninvasive biomarkers, researchers have investigated the feasibility of neuroimaging tools, such as MR, SPECT, and FDG-PET imaging, as well as neurophysiological measurements using EEG. In this paper, we investigate the brain functional networks in AD, focusing on main neurophysiological techniques, integrating with most relevant functional brain imaging findings.