Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study.

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.

White Matter Hyperintensities Are No Major Confounder for Alzheimer's Disease Cerebrospinal Fluid Biomarkers.

BACKGROUND: The cerebrospinal fluid (CSF) biomarkers amyloid-ß 1-42 (Aß42), total and phosphorylated tau (t-tau, p-tau) are increasingly used to assist in the clinical diagnosis of Alzheimer's disease (AD). However, CSF biomarker levels can be affected by confounding factors. OBJECTIVE: To investigate the association of white matter hyperintensities (WMHs) present in the brain with AD CSF biomarker levels. METHODS: We included CSF biomarker and magnetic resonance imaging (MRI) data of 172 subjects (52 controls, 72 mild cognitive impairment (MCI), and 48 AD patients) from 9 European Memory Clinics. A computer aided detection system for standardized automated segmentation of WMHs was used on MRI scans to determine WMH volumes. Association of WMH volume with AD CSF biomarkers was determined using linear regression analysis. RESULTS: A small, negative association of CSF Aß42, but not p-tau and t-tau, levels with WMH volume was observed in the AD (r2 = 0.084, p = 0.046), but not the MCI and control groups, which was slightly increased when including the distance of WMHs to the ventricles in the analysis (r2 = 0.105, p = 0.025). Three global patterns of WMH distribution, either with 1) a low, 2) a peak close to the ventricles, or 3) a high, broadly-distributed WMH volume could be observed in brains of subjects in each diagnostic group. CONCLUSION: Despite an association of WMH volume with CSF Aß42 levels in AD patients, the occurrence of WMHs is not accompanied by excess release of cellular proteins in the CSF, suggesting that WMHs are no major confounder for AD CSF biomarker assessment.

Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors.

BACKGROUND: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. METHODS: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. RESULTS: Significant MRI site and vendor effects (p â€‹< â€‹.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman's r correlations >0.43, p â€‹< â€‹1.39E-36). In particular, volumes larger than 200 â€‹mm3 (for amygdalar nuclei) and 300 â€‹mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε â€‹< â€‹5% and DICE â€‹> â€‹0.80). CONCLUSION: Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.

Incremental value of amyloid-PET versus CSF in the diagnosis of Alzheimer's disease.

PURPOSE: To compare the incremental diagnostic value of amyloid-PET and CSF (Aß42, tau, and phospho-tau) in AD diagnosis in patients with mild cognitive impairment (MCI) or mild dementia, in order to improve the definition of diagnostic algorithm. METHODS: Two independent dementia experts provided etiological diagnosis and relative diagnostic confidence in 71 patients on 3 rounds, based on (1) clinical, neuropsychological, and structural MRI information alone; (2) adding one biomarker (CSF amyloid and tau levels or amyloid-PET with a balanced randomized design); and (3) adding the other biomarker. RESULTS: Among patients with a pre-biomarker diagnosis of AD, negative PET induced significantly more diagnostic changes than amyloid-negative CSF at both rounds 2 (CSF 67%, PET 100%, P = 0.028) and 3 (CSF 0%; PET 78%, P < 0.001); PET induced a diagnostic confidence increase significantly higher than CSF on both rounds 2 and 3. CONCLUSIONS: Amyloid-PET should be prioritized over CSF biomarkers in the diagnostic workup of patients investigated for suspected AD, as it provides greater changes in diagnosis and diagnostic confidence. TRIAL REGISTRATION: EudraCT no.: 2014-005389-31.

Prediction of cognitive worsening in de novo Parkinson's disease: Clinical use of biomarkers.

BACKGROUND: Cognitive impairment is a frequent and disabling feature of Parkinson's disease. Identifying the factors able to predict cognitive worsening since the early stage may improve disease management. The objective of this study was to define the best predictors of future cognitive worsening in a group of patients with newly diagnosed PD and to propose cutoff values potentially useful at the individual level. METHODS: Fifty-four consecutive drug-naive patients with de novo PD were prospectively evaluated by clinical and neuropsychological assessment, resting EEG, and 123 I-FP-CIT-SPECT and clinically classified into mainly motor, diffuse/malignant, and intermediate PD subtypes; they were then followed up for an average of 5 years. Cognitive outcome was defined by identifying cognitively stable or worsened patients. RESULTS: Step-wise logistic regression selected the posterior qEEG mean frequency and 123 I-FP-CIT-SPECT uptake at caudate level (P < 0.0001). The posterior qEEG mean frequency (cut point, 8.3 Hz) and the caudate 123 I-FP-CIT-SPECT uptake (cut point, 2.3, specific to nondisplaceable binding ratio) achieved 82% and 80% of accuracy, respectively, in predicting cognitive outcome. Survival analysis showed decreasing expected time to cognitive worsening associated with scores below the established thresholds for qEEG and 123 I-FP-CIT-SPECT and with the presence of a malignant clinical phenotype. CONCLUSIONS: Resting EEG and 123 I-FP-CIT-SPECT are good predictors of future cognitive worsening, in de novo drug-naive PD patients. Wherever available, these biomarkers could add valuable prognostic information to classification into different clinical phenotypes. © 2017 International Parkinson and Movement Disorder Society.

18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer's disease (AD) patients at the mild cognitive impairment (MCI) stage.

PURPOSE: We aimed to identify the cortical regions where hypometabolism can predict the speed of conversion to dementia in mild cognitive impairment due to Alzheimer's disease (MCI-AD). METHODS: We selected from the clinical database of our tertiary center memory clinic, eighty-two consecutive MCI-AD that underwent 18F-fluorodeoxyglucose (FDG) PET at baseline during the first diagnostic work-up and were followed up at least until their clinical conversion to AD dementia. The whole group of MCI-AD was compared in SPM8 with a group of age-matched healthy controls (CTR) to verify the presence of AD diagnostic-pattern; then the correlation between conversion time and brain metabolism was assessed to identify the prognostic-pattern. Significance threshold was set at p < 0.05 False-Discovery-Rate (FDR) corrected at peak and at cluster level. Each MCI-AD was then compared with CTR by means of a SPM single-subject analysis and grouped according to presence of AD diagnostic-pattern and prognostic-pattern. Kaplan-Meier-analysis was used to evaluate if diagnostic- and/or prognostic-patterns can predict speed of conversion to dementia. RESULTS: Diagnostic-pattern corresponded to typical posterior hypometabolism (BA 7, 18, 19, 30, 31 and 40) and did not correlate with time to conversion, which was instead correlated with metabolic levels in right middle and inferior temporal gyri as well as in the fusiform gyrus (prognostic-pattern, BA 20, 21 and 38). At Kaplan-Meier analysis, patients with hypometabolism in the prognostic pattern converted to AD-dementia significantly earlier than patients not showing significant hypometabolism in the right middle and inferior temporal cortex (9 versus 19 months; Log rank p < 0.02, Breslow test: p < 0.003, Tarone-Ware test: p < 0.007). CONCLUSION: The present findings support the role of FDG PET as a robust progression biomarker even in a naturalist population of MCI-AD. However, not the AD-typical diagnostic-pattern in posterior regions but the middle and inferior temporal metabolism captures speed of conversion to dementia in MCI-AD since baseline. The highlighted prognostic pattern is a further, independent source of heterogeneity in MCI-AD and affects a primary-endpoint on interventional clinical trials (time of conversion to dementia).

Early identification of MCI converting to AD: a FDG PET study.

PURPOSE: Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer's disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not. METHODS: FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy. RESULTS: The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients. CONCLUSION: In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.

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

The diagnosis of Alzheimer's 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 Alzheimer's 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 Alzheimer's 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.

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study.

INTRODUCTION: Hippocampal volume is a core biomarker of Alzheimer's disease (AD). However, its contribution over the standard diagnostic workup is unclear. METHODS: Three hundred fifty-six patients, under clinical evaluation for cognitive impairment, with suspected AD and Mini-Mental State Examination ≥20, were recruited across 17 European memory clinics. After the traditional diagnostic workup, diagnostic confidence of AD pathology (DCAD) was estimated by the physicians in charge. The latter were provided with the results of automated hippocampal volumetry in standardized format and DCAD was reassessed. RESULTS: An increment of one interquartile range in hippocampal volume was associated with a mean change of DCAD of -8.0% (95% credible interval: [-11.5, -5.0]). Automated hippocampal volumetry showed a statistically significant impact on DCAD beyond the contributions of neuropsychology, 18F-fluorodeoxyglucose positron emission tomography/single-photon emission computed tomography, and cerebrospinal fluid markers (-8.5, CrI: [-11.5, -5.6]; -14.1, CrI: [-19.3, -8.8]; -10.6, CrI: [-14.6, -6.1], respectively). DISCUSSION: There is a measurable effect of hippocampal volume on DCAD even when used on top of the traditional diagnostic workup.

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 Alzheimer's 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.

Clinical validity of increased cortical uptake of amyloid ligands on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework.

The use of biomarkers has been proposed for diagnosing Alzheimer's disease in recent criteria, but some biomarkers have not been sufficiently investigated to justify their routine clinical use. Here, we evaluate in a literature review the clinical validity of amyloid positron emission tomography (PET) imaging using a structured framework developed for the assessment of oncological biomarkers. Homogenous criteria have been addressed in reviews of other Alzheimer's disease biomarkers. There is adequate evidence that the main aims of phases 1 (rationale for use) and 2 (discriminative ability) have been achieved. The aims of phase 3 (early detection ability) have been partly achieved, while phase 4 studies (performance in representative mild cognitive impairment patients) are currently ongoing. Phase 5 studies (quantification of impact and costs) are still to come. This review highlights the priorities to be pursued to enable the proper use of amyloid PET imaging in a clinical setting. Future investigations will primarily be large, phase 4 studies that will assess the utility of amyloid PET imaging in routine clinical practice.

Clinical validity of presynaptic dopaminergic imaging with 123I-ioflupane and noradrenergic imaging with 123I-MIBG in the differential diagnosis between Alzheimer's disease and dementia with Lewy bodies in the context of a structured 5-phase development framework.

The use of biomarkers (BMs) for accurate diagnosis of Alzheimer's disease (AD) has been proposed by recent diagnostic criteria; however, their maturity is not sufficient to grant implementation in the clinical routine. A proper diagnostic process requires not only confirmation of the disease but also the exclusion of similar disorders entering differential diagnosis, like dementia with Lewy bodies (DLB). This review is aimed at evaluating the clinical validity of 123I-ioflupane brain single photon emission tomography and 123I-MIBG cardiac scintigraphy as imaging BMs for DLB. For this purpose, we used an adapted version of the 5-phase oncology framework for BMs development. A review of the literature was conducted using homogenous search criteria with other BMs addressed in parallel reviews. Results of our literature search showed that the rationale for the use of both BMs in the differential diagnosis of DLB and AD is strong (phase 1) and that they allow a good discrimination ability (phase 2), but studies investigating BMs distribution antemortem and postmortem on pathology are lacking. Moreover, thresholds for test positivity have not been defined for 123I-MIBG. The 2 BMs have not been yet assessed in early phases of DLB and AD (phase 3). No phase 4 and phase 5 studies have so far been carried out. This review highlights the priorities to address in future investigations to enable the proper use of 123I-ioflupane and 123I-MIBG for the differential diagnosis of dementia.

Improved Cerebrospinal Fluid-Based Discrimination between Alzheimer's Disease Patients and Controls after Correction for Ventricular Volumes.

Cerebrospinal fluid (CSF) biomarkers may support the diagnosis of Alzheimer's disease (AD). We studied if the diagnostic power of AD CSF biomarker concentrations, i.e., Aß42, total tau (t-tau), and phosphorylated tau (p-tau), is affected by differences in lateral ventricular volume (VV), using CSF biomarker data and magnetic resonance imaging (MRI) scans of 730 subjects, from 13 European Memory Clinics. We developed a Matlab-algorithm for standardized automated segmentation analysis of T1 weighted MRI scans in SPM8 for determining VV, and computed its ratio with total intracranial volume (TIV) as proxy for total CSF volume. The diagnostic power of CSF biomarkers (and their combination), either corrected for VV/TIV ratio or not, was determined by ROC analysis. CSF Aß42 levels inversely correlated to VV/TIV in the whole study population (Aß42: r = -0.28; p < 0.0001). For CSF t-tau and p-tau, this association only reached statistical significance in the combined MCI and AD group (t-tau: r = -0.15; p-tau: r = -0.13; both p < 0.01). Correction for differences in VV/TIV improved the differentiation of AD versus controls based on CSF Aß42 alone (AUC: 0.75 versus 0.81) or in combination with t-tau (AUC: 0.81 versus 0.91). In conclusion, differences in VV may be an important confounder in interpreting CSF Aß42 levels.

Free water elimination improves test-retest reproducibility of diffusion tensor imaging indices in the brain: A longitudinal multisite study of healthy elderly subjects.

Free water elimination (FWE) in brain diffusion MRI has been shown to improve tissue specificity in human white matter characterization both in health and in disease. Relative to the classical diffusion tensor imaging (DTI) model, FWE is also expected to increase sensitivity to microstructural changes in longitudinal studies. However, it is not clear if these two models differ in their test-retest reproducibility. This study compares a bi-tensor model for FWE with DTI by extending a previous longitudinal-reproducibility 3T multisite study (10 sites, 7 different scanner models) of 50 healthy elderly participants (55-80 years old) scanned in two sessions at least 1 week apart. We computed the reproducibility of commonly used DTI metrics (FA: fractional anisotropy, MD: mean diffusivity, RD: radial diffusivity, and AXD: axial diffusivity), derived either using a DTI model or a FWE model. The DTI metrics were evaluated over 48 white-matter regions of the JHU-ICBM-DTI-81 white-matter labels atlas, and reproducibility errors were assessed. We found that relative to the DTI model, FWE significantly reduced reproducibility errors in most areas tested. In particular, for the FA and MD metrics, there was an average reduction of approximately 1% in the reproducibility error. The reproducibility scores did not significantly differ across sites. This study shows that FWE improves sensitivity and is thus promising for clinical applications, with the potential to identify more subtle changes. The increased reproducibility allows for smaller sample size or shorter trials in studies evaluating biomarkers of disease progression or treatment effects. Hum Brain Mapp 38:12-26, 2017. © 2016 Wiley Periodicals, Inc.

Predicting the transition from normal aging to Alzheimer's disease: A statistical mechanistic evaluation of FDG-PET data.

The assessment of the degree of order of brain metabolism by means of a statistical mechanistic approach applied to FDG-PET, allowed us to characterize healthy subjects as well as patients with mild cognitive impairment and Alzheimer's Disease (AD). The intensity signals from 24 volumes of interest were submitted to principal component analysis (PCA) giving rise to a major first principal component whose eigenvalue was a reliable cumulative index of order. This index linearly decreased from 77 to 44% going from normal aging to AD patients with intermediate conditions between these values (r=0.96, p<0.001). Bootstrap analysis confirmed the statistical significance of the results. The progressive detachment of different brain regions from the first component was assessed, allowing for a purely data driven reconstruction of already known maximally affected areas. We demonstrated for the first time the reliability of a single global index of order in discriminating groups of cognitively impaired patients with different clinical outcome. The second relevant finding was the identification of clusters of regions relevant to AD pathology progressively separating from the first principal component through different stages of cognitive impairment, including patients cognitively impaired but not converted to AD. This paved the way to the quantitative assessment of the functional networking status in individual patients.

Functional neuroimaging and clinical features of drug naive patients with de novo Parkinson's disease and probable RBD.

INTRODUCTION: The association between Parkinson Disease (PD) and REM sleep behavior disorder (RBD) has been related to a specific, malignant clinical phenotype. Definite RBD diagnosis requires video-polysomnography that is often unfeasible. A malignant clinical PD-RBD phenotype could be expected also in PD patients with probable RBD. Aim of this cross-sectional study was to evaluate whether a more severe neuropsychological and functional neuroimaging phenotype can be identified in PD patients with probable RBD. METHODS: Thirty-eight de novo, drug naïve PD patients underwent a first-line clinical assessment and a second-line multimodal assessment, including neuropsychological evaluation, (123)I-FP-CIT-SPECT and (18)F-FDG-PET, which were compared between PD patients with (PD + RBD+) and without (PD + RBD-) probable RBD. RESULTS: On first-line assessment, PD + RBD + patients had significantly more constipation (p = 0.02) and showed worse olfaction (p = 0.01) compared with PD + RBD-while the two groups were similar as for age, presence of orthostatic hypotension, UPDRS-III and MMSE scores. On second-line assessment, PD + RBD + patients showed a worse neuropsychological test profile, more severe nigro-striatal dopaminergic impairment, mainly at caudate level in the less affected hemisphere (p = 0.004) and impaired brain glucose metabolism, with relative hypometabolism in posterior cortical regions and relative hypermetabolism mainly in anterior regions of the more affected hemisphere (p = 0.015). CONCLUSIONS: PD patients with probable RBD are likely to have a more severe neuropsychological and functional brain-imaging phenotype already at the time of diagnosis.

Mapping brain morphological and functional conversion patterns in predementia late-onset bvFTD.

PURPOSE: The diagnosis of behavioural variant frontotemporal dementia (bvFTD) is challenging during the predementia stage when symptoms are subtle and confounding. Morphological and functional neuroimaging can be particularly helpful during this stage but few data are available. METHODS: We retrospectively selected 25 patients with late-onset probable bvFTD. Brain structural MRI and FDG PET were performed during the predementia stage (mean MMSE score 27.1 ± 2.5) on average 2 years before. The findings with the two imaging modalities were compared (SPM8) with those in a group of 20 healthy subjects. The bvFTD patients were divided into two subgroups: those with predominant disinhibition (bvFTD+) and those with apathy (bvFTD-). RESULTS: Hypometabolism exceeded grey matter (GM) density reduction in terms of both extension and statistical significance in all comparisons. In the whole bvFTD group, hypometabolism involved the bilateral medial, inferior and superior lateral frontal cortex, anterior cingulate, left temporal and right parietal cortices and the caudate nuclei. GM density reduction was limited to the right frontal cortex and the left medial temporal lobe. In bvFTD+ patients hypometabolism was found in the bilateral medial and basal frontal cortex, while GM reduction involved the left anterior cingulate and left inferior frontal cortices, and the right insula. In bvFTD- patients, atrophy and mainly hypometabolism involved the lateral frontal cortex and the inferior parietal lobule. CONCLUSION: These findings suggest that hypometabolism is more extensive than, and thus probably precedes, atrophy in predementia late-onset bvFTD, underscoring different topographic involvement in disinhibited and apathetic presentations. If confirmed in a larger series, these results should prompt biomarker operationalization in bvFTD, especially for patient selection in therapeutic clinical trials.

Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques.

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.

Parkinson's Disease Sleep Scale 2: application in an Italian population.

Sleep disturbances and nocturnal disabilities are common in Parkinson's Disease (PD). The PD sleep scale, second version (PDSS-2), has been proposed as a helpful tool for measuring sleep disorders in PD. We aimed to validate the Italian version of the PDSS-2. One hundred and twenty-three consecutive PD outpatients (76 males) were evaluated by means of PDSS-2, Epworth Sleepiness Scale, Hamilton Depression Rating Scale, Parkinson's Disease Quality of Life Questionnaire (self-administered scales), Unified Parkinson's Disease Rating (motor section) and Hoehn and Yahr Scales, and Mini Mental State Examination. PDSS-2 internal consistency was satisfactory (Cronbach's α: 0.77) with significant item to total score correlation and high intra-class correlation coefficient for test-retest reliability (0.943). Total PDSS-2 score was correlated with the scores on all other clinical scales. The factor analysis identified five factors, related to five areas of nocturnal disturbances, similarly as the original PDSS-2. The five factors mainly reflected: (1) nocturnal movement-related problems, (2) quality of sleep, (3) dreaming distress, (4) fragmentation of sleep and (5) insomnia symptoms. The PDSS-2 scale has confirmed its usefulness in evaluating sleep problems in Italian PD patients.

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.