Accumulating evidence over the past decade suggests that semantic deficits represent a consistent feature of Mild Cognitive Impairment (MCI). A meta-analysis was performed to examine if semantic ...deficits are consistently found in patients with MCI. Studies meeting all inclusion criteria were selected for the current meta-analysis. An effect size and a weight were calculated for each study. A random effect model was performed to assess the overall difference in semantic performances between MCI patients and healthy subjects. 22 studies (476 healthy participants, 476 MCI patients, mean Mini Mental Status Examination of the MCI patients: 27.05 ± 0.58) were included in the meta-analysis. Results indicate that MCI patients systematically performed significantly worse than healthy matched controls in terms of overall semantic performance (mean effect size of 1.02; 95% CI 0.80; 1.24). Semantic deficits are a key feature of MCI. Semantic tests should be incorporated in routine clinical assessments.
Large-scale longitudinal multi-site MRI brain morphometry studies are becoming increasingly crucial to characterize both normal and clinical population groups using fully automated segmentation ...tools. The test–retest reproducibility of morphometry data acquired across multiple scanning sessions, and for different MR vendors, is an important reliability indicator since it defines the sensitivity of a protocol to detect longitudinal effects in a consortium. There is very limited knowledge about how across-session reliability of morphometry estimates might be affected by different 3T MRI systems. Moreover, there is a need for optimal acquisition and analysis protocols in order to reduce sample sizes. A recent study has shown that the longitudinal FreeSurfer segmentation offers improved within session test–retest reproducibility relative to the cross-sectional segmentation at one 3T site using a nonstandard multi-echo MPRAGE sequence. In this study we implement a multi-site 3T MRI morphometry protocol based on vendor provided T1 structural sequences from different vendors (3D MPRAGE on Siemens and Philips, 3D IR-SPGR on GE) implemented in 8 sites located in 4 European countries. The protocols used mild acceleration factors (1.5–2) when possible. We acquired across-session test–retest structural data of a group of healthy elderly subjects (5 subjects per site) and compared the across-session reproducibility of two full-brain automated segmentation methods based on either longitudinal or cross-sectional FreeSurfer processing. The segmentations include cortical thickness, intracranial, ventricle and subcortical volumes. Reproducibility is evaluated as absolute changes relative to the mean (%), Dice coefficient for volume overlap and intraclass correlation coefficients across two sessions. We found that this acquisition and analysis protocol gives comparable reproducibility results to previous studies that used longer acquisitions without acceleration. We also show that the longitudinal processing is systematically more reliable across sites regardless of MRI system differences. The reproducibility errors of the longitudinal segmentations are on average approximately half of those obtained with the cross sectional analysis for all volume segmentations and for entorhinal cortical thickness. No significant differences in reliability are found between the segmentation methods for the other cortical thickness estimates. The average of two MPRAGE volumes acquired within each test–retest session did not systematically improve the across-session reproducibility of morphometry estimates. Our results extend those from previous studies that showed improved reliability of the longitudinal analysis at single sites and/or with non-standard acquisition methods. The multi-site acquisition and analysis protocol presented here is promising for clinical applications since it allows for smaller sample sizes per MRI site or shorter trials in studies evaluating the role of potential biomarkers to predict disease progression or treatment effects.
•We implemented a multi-site 3T MRI protocol for brain morphometry on 8EU sites.•We acquired across-session test-retest data on 40 healthy elderly subjects.•We calculated the reproducibility of cortical and volumetric FreeSurfer estimates.•Longitudinal segmentation was more reliable than cross-sectional on all sites.
Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion ...of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks.
In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.
•Sleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.•Whole-brain level slowing of dFC speed does not selectively correlate with fine and task-specific changes in performance.•We quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”.•Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.
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.
•We implement a multi-site 3T MRI protocol for resting state fMRI in 13 sites.•We acquire across-session test–retest (TRT) data on 64 healthy elderly participants.•Despite harmonization strong phantom and brain tSNR differences remain across sites.•TRT error of regional DMN functional connectivity is consistent across sites.•ICA yields more reliable DMN connectivity measurements relative to SBA.
Abstract Neuroimaging biomarkers differ between patients with early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). Whether these changes reflect cognitive heterogeneity ...or differences in disease severity is still unknown. This study aimed at investigating changes in neuroimaging biomarkers, according to the age of onset of the disease, in mild amnestic Alzheimer's disease patients with positive amyloid biomarkers in cerebrospinal fluid. Both patient groups were impaired on tasks assessing verbal and visual recognition memory. EOAD patients showed greater executive and linguistic deficits, while LOAD patients showed greater semantic memory impairment. In EOAD and LOAD, hypometabolism involved the bilateral temporoparietal junction and the posterior cingulate cortex. In EOAD, atrophy was widespread, including frontotemporoparietal areas, whereas it was limited to temporal regions in LOAD. Atrophic volumes were greater in EOAD than in LOAD. Hypometabolic volumes were similar in the 2 groups. Greater extent of atrophy in EOAD, despite similar extent of hypometabolism, could reflect different underlying pathophysiological processes, different glucose-based compensatory mechanisms or distinct level of premorbid atrophic lesions.
Valosin-containing protein (VCP) mutations are rare causes of autosomal dominant frontotemporal dementias associated with Paget's disease of bone, inclusion body myopathy, and amyotrophic lateral ...sclerosis. We analyzed the VCP gene in a cohort of 199 patients with frontotemporal dementia and identified 7 heterozygous mutations in unrelated families, including 3 novel mutations segregating with dementia. This expands the VCP mutation spectrum and suggests that although VCP mutations are rare (3.5% in this study), the gene should be analyzed even in absence of the full syndromic complex. Reporting genetic variants with convincing arguments for pathogenicity is important considering the large amount of data generated by next-generation sequencing and the growing difficulties to interpret rare genetic variants identified in isolated cases.
Abstract We investigated whether dementia risk factors were associated with prodromal Alzheimer’s disease (AD) according to the International Working Group-2 and National Institute of ...Aging-Alzheimer’s Association criteria, and with cognitive decline. 1394 subjects from with Mild Cognitive Impairment (MCI) from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of ten risk factors between the subgroups and used Cox-regression to examine the effect of risk factors on cognitive decline. Depression, obesity and hypercholesterolemia occurred more often in individuals with low-AD-likelihood, compared to those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with MCI. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e. preclinical) of AD.
Functional Connectivity, describing the interaction between brain regions beyond their anatomical interconnection, is highly dynamic even when no task is performed (“resting state”) and it remains a ...methodological challenge to properly describe its changes in time without strong assumptions. We have developed a framework to describe the dynamics of Functional Connectivity (dFC) estimated from brain activity time-series as a as a smooth reconfiguration process, combining “liquid” and “coordinated” aspects. Our framework considers dFC as a complex random walk in the space of possible functional networks. Unlike other previous approaches, our method does not require the explicit extraction of discrete connectivity states but tracks changes in a continuous time fashion.
•We introduced several dFC random walk metrics. First, dFC speed analyses extract the distribution of the time-resolved rate of reconfiguration of FC along time. These distributions have a clear peak (typical dFC speed, that can already serve as a biomarker) and fat tails (denoting deviations from Gaussianity that can be detected by suitable scaling analyses of FC network streams).•Second, meta-connectivity (MC) analyses identify groups of functional links whose fluctuations co-vary in time and that define veritable dFC modules organized along specific dFC meta-hub controllers (differing from conventional FC modules and hubs). The decomposition of whole-brain dFC by MC allows performing dFC speed analyses separately for each of the detected dFC modules.•We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLABⓇ toolbox (dFCwalk), openly downloadable. Although we applied such analyses mostly to fMRI resting state data, in principle our methods can be extended to any type of neural activity (from Local Field Potentials to EEG, MEG, fNIRS, etc.) or even non-neural time-series.
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