Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The identification and staging of Alzheimer's Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC). Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD. METHODS: One hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses. RESULTS: Two episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI. CONCLUSIONS: Our findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

Distribution of the C9orf72 hexanucleotide repeat expansion in healthy subjects: a multicenter study promoted by the Italian IRCCS network of neuroscience and neurorehabilitation.

Introduction: High repeat expansion (HRE) alleles in C9orf72 have been linked to both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD); ranges for intermediate allelic expansions have not been defined yet, and clinical interpretation of molecular data lacks a defined genotype-phenotype association. In this study, we provide results from a large multicenter epidemiological study reporting the distribution of C9orf72 repeats in healthy elderly from the Italian population. Methods: A total of 967 samples were collected from neurologically evaluated healthy individuals over 70 years of age in the 13 institutes participating in the RIN (IRCCS Network of Neuroscience and Neurorehabilitation) based in Italy. All samples were genotyped using the AmplideXPCR/CE C9orf72 Kit (Asuragen, Inc.), using standardized protocols that have been validated through blind proficiency testing. Results: All samples carried hexanucleotide G4C2 expansion alleles in the normal range. All samples were characterized by alleles with less than 25 repeats. In particular, 93.7% of samples showed a number of repeats ≤10, 99.9% ≤20 repeats, and 100% ≤25 repeats. Conclusion: This study describes the distribution of hexanucleotide G4C2 expansion alleles in an Italian healthy population, providing a definition of alleles associated with the neurological healthy phenotype. Moreover, this study provides an effective model of federation between institutes, highlighting the importance of sharing genomic data and standardizing analysis techniques, promoting translational research. Data derived from the study may improve genetic counseling and future studies on ALS/FTD.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network.

PURPOSE: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS: The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS: The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

Aß1-6A2V(D) peptide, effective on Aß aggregation, inhibits tau misfolding and protects the brain after traumatic brain injury.

Alzheimer's disease (AD), the leading cause of dementia in older adults, is a double proteinopathy characterized by amyloid-ß (Aß) and tau pathology. Despite enormous efforts that have been spent in the last decades to find effective therapies, late pharmacological interventions along the course of the disease, inaccurate clinical methodologies in the enrollment of patients, and inadequate biomarkers for evaluating drug efficacy have not allowed the development of an effective therapeutic strategy. The approaches followed so far for developing drugs or antibodies focused solely on targeting Aß or tau protein. This paper explores the potential therapeutic capacity of an all-D-isomer synthetic peptide limited to the first six amino acids of the N-terminal sequence of the A2V-mutated Aß, Aß1-6A2V(D), that was developed following the observation of a clinical case that provided the background for its development. We first performed an in-depth biochemical characterization documenting the capacity of Aß1-6A2V(D) to interfere with the aggregation and stability of tau protein. To tackle Aß1-6A2V(D) in vivo effects against a neurological decline in genetically predisposed or acquired high AD risk mice, we tested its effects in triple transgenic animals harboring human PS1(M146 V), APP(SW), and MAPT(P301L) transgenes and aged wild-type mice exposed to experimental traumatic brain injury (TBI), a recognized risk factor for AD. We found that Aß1-6A2V(D) treatment in TBI mice improved neurological outcomes and reduced blood markers of axonal damage. Exploiting the C. elegans model as a biosensor of amyloidogenic proteins' toxicity, we observed a rescue of locomotor defects in nematodes exposed to the brain homogenates from TBI mice treated with Aß1-6A2V(D) compared to TBI controls. By this integrated approach, we demonstrate that Aß1-6A2V(D) not only impedes tau aggregation but also favors its degradation by tissue proteases, confirming that this peptide interferes with both Aß and tau aggregation propensity and proteotoxicity.

Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN-Neuroimaging Network case study.

Initiatives for the collection of harmonized MRI datasets are growing continuously, opening questions on the reliability of results obtained in multi-site contexts. Here we present the assessment of the brain anatomical variability of MRI-derived measurements obtained from T1-weighted images, acquired according to the Standard Operating Procedures, promoted by the RIN-Neuroimaging Network. A multicentric dataset composed of 77 brain T1w acquisitions of young healthy volunteers (mean age = 29.7 ± 5.0 years), collected in 15 sites with MRI scanners of three different vendors, was considered. Parallelly, a dataset of 7 "traveling" subjects, each undergoing three acquisitions with scanners from different vendors, was also used. Intra-site, intra-vendor, and inter-site variabilities were evaluated in terms of the percentage standard deviation of volumetric and cortical thickness measures. Image quality metrics such as contrast-to-noise and signal-to-noise ratio in gray and white matter were also assessed for all sites and vendors. The results showed a measured global variability that ranges from 11% to 19% for subcortical volumes and from 3% to 10% for cortical thicknesses. Univariate distributions of the normalized volumes of subcortical regions, as well as the distributions of the thickness of cortical parcels appeared to be significantly different among sites in 8 subcortical (out of 17) and 21 cortical (out of 68) regions of i nterest in the multicentric study. The Bland-Altman analysis on "traveling" brain measurements did not detect systematic scanner biases even though a multivariate classification approach was able to classify the scanner vendor from brain measures with an accuracy of 0.60 ± 0.14 (chance level 0.33).

Corrigendum: PMCA-based detection of prions in the olfactory mucosa of patients with sporadic Creutzfeldt-Jakob disease.

[This corrects the article DOI: 10.3389/fnagi.2022.848991.].

Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative cohort.

Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as 'normal' or 'abnormal' based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the 'normal' and 'abnormal' groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials.

Italian, European, and international neuroinformatics efforts: An overview.

Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.

Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia.

INTRODUCTION: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS: We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia.

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Humoral and T-Cell Immune Response After 3 Doses of Messenger RNA Severe Acute Respiratory Syndrome Coronavirus 2 Vaccines in Fragile Patients: The Italian VAX4FRAIL Study.

BACKGROUND: Patients with solid or hematological tumors or neurological and immune-inflammatory disorders are potentially fragile subjects at increased risk of experiencing severe coronavirus disease 2019 and an inadequate response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination. METHODS: We designed a prospective Italian multicenter study to assess humoral and T-cell responses to SARS-CoV-2 vaccination in patients (n = 378) with solid tumors (ST), hematological malignancies (HM), neurological disorders (ND), and immunorheumatological diseases (ID). A group of healthy controls was also included. We analyzed the immunogenicity of the primary vaccination schedule and booster dose. RESULTS: The overall seroconversion rate in patients after 2 doses was 62.1%. Significantly lower rates were observed in HM (52.4%) and ID (51.9%) than in ST (95.6%) and ND (70.7%); a lower median antibody level was detected in HM and ID versus ST and ND (P < .0001). Similar rates of patients with a positive SARS-CoV-2 T-cell response were found in all disease groups, with a higher level observed in ND. The booster dose improved the humoral response in all disease groups, although to a lesser extent in HM patients, whereas the T-cell response increased similarly in all groups. In the multivariable logistic model, independent predictors of seroconversion were disease subgroup, treatment type, and age. Ongoing treatment known to affect the immune system was associated with the worst humoral response to vaccination (P < .0001) but had no effect on T-cell responses. CONCLUSIONS: Immunosuppressive treatment more than disease type per se is a risk factor for a low humoral response after vaccination. The booster dose can improve both humoral and T-cell responses.

Educational interventions to improve detection and management of cognitive decline in primary care-An Italian multicenter pragmatic study.

Introduction: Timely detection of cognitive decline in primary care is essential to promote an appropriate care pathway and enhance the benefits of interventions. We present the results of a study aimed to evaluate the effectiveness of an educational intervention addressed to Italian family physicians (FPs) to improve timely detection and management of cognitive decline. Materials and methods: We conducted a pre-post study in six Italian health authorities (HAs) involving 254 FPs and 3,736 patients. We measured process and outcome indicators before the intervention (1 January 2014 to 31 December 2016) and after the intervention (1 January 2018 to 31 December 2019). One interactive face-to-face session workshop was delivered by local cognitive disorders and dementia specialists and FP advisors at each HA, in the period September 2017-December 2017. The session focused on key messages of the local Diagnostic and Therapeutic Care Pathway (DTCP) or regional guidelines: (a) the role of the FP for a timely suspicion of cognitive decline is fundamental; (b) when cognitive decline is suspected, the role of the FP is active in the diagnostic work-up; (c) FP's knowledge on pharmacological and non-pharmacological interventions is essential to improve the management of patients with cognitive decline. Results: An overall improvement in diagnostic procedures and management of patients with cognitive decline by FPs after the intervention was observed. The number of visits per year performed by FPs increased, and the time interval between the first FP consultation and the diagnosis was optimized. Neuroleptic use significantly decreased, whereas the use of benzodiazepines remained steadily high. Non-pharmacological interventions, or use of support services, were underrepresented even in the post-intervention. Differences among the participating HAs were identified and discussed. Discussion: Results from this study suggest the success of the educational intervention addressed to FPs in improving early detection and management of cognitive decline, highlighting the importance to continue medical education in this field. At the same time, further initiatives of care pathway dissemination and implementation should promote strategies to enhance interactions between primary and secondary care optimizing the collaboration between FPs and specialists.

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms.

PURPOSE: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). CONCLUSIONS: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.