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Background and purpose: Biomarkers support the aetiological diagnosis of neurocognitive disorders in vivo. Incomplete evidence is available to drive clinical decisions; available diagnostic algorithms are generic and not very helpful in clinical practice. The aim was to develop a biomarker‐based diagnostic algorithm for mild cognitive impairment patients, leveraging on knowledge from recognized national experts. Methods: With a Delphi procedure, experienced clinicians making variable use of biomarkers in clinical practice and representing five Italian scientific societies (neurology – Società Italiana di Neurologia per le Demenze; neuroradiology – Associazione Italiana di Neuroradiologia; biochemistry – Società Italiana di Biochimica Clinica; psychogeriatrics – Associazione Italiana di Psicogeriatria; nuclear medicine – Associazione Italiana di Medicina Nucleare) defined the theoretical framework, relevant literature, the diagnostic issues to be addressed and the diagnostic algorithm. An N–1 majority defined consensus achievement. Results: The panellists chose the 2011 National Institute on Aging and Alzheimer's Association diagnostic criteria as the reference theoretical framework and defined the algorithm in seven Delphi rounds. The algorithm includes baseline clinical and cognitive assessment, blood examination, and magnetic resonance imaging with exclusionary and inclusionary roles; dopamine transporter single‐photon emission computed tomography (if no/unclear parkinsonism) or metaiodobenzylguanidine cardiac scintigraphy for suspected dementia with Lewy bodies with clear parkinsonism (round VII, votes (yes‐no‐abstained): 3‐1‐1); 18F‐fluorodeoxyglucose positron emission tomography for suspected frontotemporal lobar degeneration and low diagnostic confidence of Alzheimer's disease (round VII, 4‐0‐1); cerebrospinal fluid for suspected Alzheimer's disease (round IV, 4‐1‐0); and amyloid positron emission tomography if cerebrospinal fluid was not possible/accepted (round V, 4‐1‐0) or inconclusive (round VI, 5‐0‐0). Conclusions: These consensus recommendations can guide clinicians in the biomarker‐based aetiological diagnosis of mild cognitive impairment, whilst guidelines cannot be defined with evidence‐to‐decision procedures due to incomplete evidence. [ABSTRACT FROM AUTHOR]

The advent of computerized medical recording systems in healthcare facilities has made data retrieval tasks easier, compared to manual recording. Nevertheless, the potential of the information contained within medical records remains largely untapped, mostly due to the time and effort required to extract data from unstructured documents. Natural Language Processing (NLP) represents a promising solution to this challenge, as it enables the use of automated text-mining tools for clinical practitioners. In this work, we present the architecture of the Virtual Dementia Institute (IVD), a consortium of sixteen Italian hospitals, using the NLP Extraction and Management Tool (NEMT), a (semi-) automated end-to-end pipeline that extracts relevant information from clinical documents and stores it in a centralized REDCap database. After defining a common Case Report Form (CRF) across the IVD hospitals, we implemented NEMT, the core of which is a Question Answering Bot (QABot) based on a modern NLP model. This QABot is fine-tuned on thousands of examples from IVD centers. Detailed descriptions of the process to define a common minimum dataset, Inter-Annotator Agreement calculated on clinical documents, and NEMT results are provided. The best QABot performance show an Exact Match score (EM) of 78.1%, a F1-score of 84.7%, a Lenient Accuracy (LAcc) of 0.834, and a Mean Reciprocal Rank (MRR) of 0.810. EM and F1 scores outperform the same metrics obtained with ChatGPTv3.5 (68.9% and 52.5%, respectively). With NEMT the IVD has been able to populate a database that will contain data from thousands of Italian patients, all screened with the same procedure. NEMT represents an efficient tool that paves the way for medical information extraction and exploitation for new research studies.

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., (© 2023. Springer Nature Limited.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lombardi, Chipi, Arenella, Fiorani, Frisoni, Linarello, Montanucci, Muscio, Pacifico, Pelizzari, Perani, Piras, Rozzini, Sorbi, Spalletta, Tagliavini, Tiraboschi, Parnetti and Filippini.)

Background: Neuropsychological testing plays a cardinal role in the diagnosis and monitoring of Alzheimer's disease. A major concern is represented by the heterogeneity of the neuropsychological batteries currently adopted in memory clinics and healthcare centers. The current study aimed to solve this issue., Methods: Following the initiative of the University of Washington's National Alzheimer's Coordinating Center (NACC), we presented the Italian adaptation of the Neuropsychological Test Battery of the Uniform Data Set (I-UDSNB). We collected data from 433 healthy Italian individuals and employed regression models to evaluate the impact of demographic variables on the performance, deriving the reference norms., Results: Higher education and lower age were associated with a better performance in the majority of tests, while sex affected only fluency tests and Digit Span Forward., Conclusions: The I-UDSNB offers a valuable and harmonized tool for neuropsychological testing in Italy, to be used in clinical and research settings., (© 2022. The Author(s).)

Introduction: Dementia with Lewy bodies (DLB) may represent a diagnostic challenge, since its clinical picture overlaps with other dementia. Two toolkits have been developed to aid the clinician to diagnose DLB: the Lewy Body Composite Risk Score (LBCRS) and the Assessment Toolkit for DLB (AT-DLB). We aim to evaluate the reliability of these two questionnaires, and their ability to enhance the interpretation of the international consensus diagnostic criteria., Methods: LBCRS and AT-DLB were distributed to 135 Italian Neurological Centers for Cognitive Decline and Dementia (CDCDs), with the indication to administer them to all patients with dementia referred within the subsequent 3 months. We asked to subsequently apply consensus criteria for DLB diagnosis, to validate the diagnostic accuracy of the two toolkits., Results: A total of 23 Centers joined the study; 1854 patients were enrolled. We found a prevalence of possible or probable DLB of 13% each (26% total), according to the consensus criteria. LBCRS toolkit showed good reliability, with a Cronbach alpha of 0.77, stable even after removing variables from the construct. AT-DLB toolkit Cronbach alpha was 0.52 and, after the subtraction of the "cognitive fluctuation" criterion, was only 0.31. Accuracy, sensitivity, and specificity were higher for LBCRS vs. AT-DLB. However, when simultaneously considered in the logistic models, AT-DLB showed a better performance (p < 0.001). Overall, the concordance between LBCRS positive and AT-DLB possible/probable was of 78.02% CONCLUSIONS: In a clinical setting, the LBCRS and AT-DLB questionnaires have good accuracy for DLB diagnosis., (© 2022. The Author(s).)

Aim: Our aim was to evaluate the psychological impact of predictive genetic testing in individuals at-risk for inherited dementia who underwent a structured counseling and testing protocol., Methods: Participants were healthy at-risk relatives from families with at least one affected patient, in whom a disease-associated genetic variant had been ascertained. A comprehensive psychological assessment (personality, anxiety and depression, quality of life, coping strategies, resilience and health-related beliefs) was administered at baseline, at 6 months and 12 months follow-up., Results: Twenty-four participants from 13 families were included. Sixteen participants underwent blood sampling and genetic analysis; 6 resulted to be carriers of pathogenic variants (1 in PSEN1, 1 in PSEN2, 4 in GRN). Carriers showed higher score on the Resilience Scale for Adults (RSA) - social competence, and on Multidimensional Health Locus of Control - internal, than noncarriers (P=0.03 for both). Ten at-risk relatives who completed the follow-up showed improvement in RSA - planned future (P=0.01) with respect to baseline., Discussion: Our case series showed that at-risk individuals undergoing predictive testing showed benefit on personal life and no detrimental impact on a broad range of psychological outcomes. Higher social skills and lower internal health locus of control in carriers may be an early psychological correlate of preclinical dementia., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

A 40-y-old woman with severe acute respiratory syndrome coronavirus 2 infection developed neurologic manifestations (confusion, agitation, seizures, dyskinesias, and parkinsonism) a few weeks after the onset of severe acute respiratory syndrome. MRI and cerebrospinal fluid analyses were unremarkable, but 18 F-FDG PET/CT showed limbic and extralimbic hypermetabolism. A full recovery, alongside 18 F-FDG normalization in previously hypermetabolic areas, was observed after intravenous immunoglobulin administration., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Accurate and reproducible automated segmentation of human hippocampal subfields is of interest to study their roles in cognitive functions and disease processes. Multispectral structural MRI methods have been proposed to improve automated hippocampal subfield segmentation accuracy, but the reproducibility in a multicentric setting is, to date, not well characterized. Here, we assessed test-retest reproducibility of FreeSurfer 6.0 hippocampal subfield segmentations using multispectral MRI analysis pipelines (22 healthy subjects scanned twice, a week apart, at four 3T MRI sites). The harmonized MRI protocol included two 3D-T1, a 3D-FLAIR, and a high-resolution 2D-T2. After within-session T1 averaging, subfield volumes were segmented using three pipelines with different multispectral data: two longitudinal ("long&#95;T1s" and "long&#95;T1s&#95;FLAIR") and one cross-sectional ("long&#95;T1s&#95;FLAIR&#95;crossT2"). Volume reproducibility was quantified in magnitude (reproducibility error-RE) and space (DICE coefficient). RE was lower in all hippocampal subfields, except for hippocampal fissure, using the longitudinal pipelines compared to long&#95;T1s&#95;FLAIR&#95;crossT2 (average RE reduction of 0.4-3.6%). Similarly, the longitudinal pipelines showed a higher spatial reproducibility (1.1-7.8% of DICE improvement) in all hippocampal structures compared to long&#95;T1s&#95;FLAIR&#95;crossT2. Moreover, long&#95;T1s&#95;FLAIR provided a small but significant RE improvement in comparison to long&#95;T1s (p = 0.015), whereas no significant DICE differences were found. In addition, structures with volumes larger than 200 mm 3 had better RE (1-2%) and DICE (0.7-0.95) than smaller structures. In summary, our study suggests that the most reproducible hippocampal subfield FreeSurfer segmentations are derived from a longitudinal pipeline using 3D-T1s and 3D-FLAIR. Adapting a longitudinal pipeline to include high-resolution 2D-T2 may lead to further improvements.

Background: Three different amyloid tracers labeled with 18-flourine have been introduced into clinical use. The leaflets of tracers indicate different visual criteria for PET reporting. In clinical practice, it is not yet ascertained whether these criteria are equivalent in terms of diagnostic accuracy or if anyone is better than another. We aimed to evaluate the inter and intra-rater variability of visual assessment of 18 F-Florbetapir PET/CT images among six independent readers with different clinical experience., Methods: We analyzed 252 PET/CT scans, visually assessed by each reader three times, applying independently the three different reading criteria proposed. Each reader evaluated the regional uptake specifying for each cortical region a numeric value of grading of positivity in order to assign a final score. At the end of each reading a level of confidence was determined by assigning a score from 0 (negative) to 4 (positive). After first reading, those cases in which the evaluations by two experienced readers did not match (discordant cases) were independently reevaluated merging all the three different visual interpretation criteria., Results: Good agreement was observed for visual interpretation among the six readers' confidence-level using independently the three visual reading criteria: ICC=0.83 (0.80-0.86) for 18 F-florbetapir, ICC=0.84 (0.81-0.87) for 18 F-florbetaben, and ICC=0.86 (0.83-0.88) for 18 F-flutemetamol reading. A good inter-rater agreement was observed for final-score too: ICC=0.74 (0.70-0.78) for 18 F-florbetapir; ICC=0.82 (0.79-0.85) for 18 F-florbetaben; ICC=0.84 (0.81-0.87) for 18 F-flutemetamol. Intra-rater agreement was good for final-score (from 0.76 to 0.90; P<0.001) and confidence-level (Spearman's rho from 0.89 to 1.00; P<0.001). Disagreement between the two experienced readers was observed in 22 of 252 cases (9%). The agreement converged over a second round of independent reading in 12 of 22 cases (54%), by merging all the criteria., Conclusions: All the criteria proposed are useful to determine the grading of positivity or negativity of amyloid deposition and their merging improves the diagnostic confidence and provides a better agreement.

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD ( n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology., (Copyright © 2020 Redolfi, De Francesco, Palesi, Galluzzi, Muscio, Castellazzi, Tiraboschi, Savini, Nigri, Bottini, Bruzzone, Ramusino, Ferraro, Gandini Wheeler-Kingshott, Tagliavini, Frisoni, Ryvlin, Demonet, Kherif, Cappa and D'Angelo.)

The prodromal phase of dementia with Lewy bodies (DLB) includes (1) mild cognitive impairment (MCI), (2) delirium-onset, and (3) psychiatric-onset presentations. The purpose of our review is to determine whether there is sufficient information yet available to justify development of diagnostic criteria for each of these. Our goal is to achieve evidence-based recommendations for the recognition of DLB at a predementia, symptomatic stage. We propose operationalized diagnostic criteria for probable and possible mild cognitive impairment with Lewy bodies, which are intended for use in research settings pending validation for use in clinical practice. They are compatible with current criteria for other prodromal neurodegenerative disorders including Alzheimer and Parkinson disease. Although there is still insufficient evidence to propose formal criteria for delirium-onset and psychiatric-onset presentations of DLB, we feel that it is important to characterize them, raising the index of diagnostic suspicion and prioritizing them for further investigation., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background: Approximately 10-20% of people with early onset dementias (EOD) or mild cognitive impairment (MCI) are aged under 65 and, due to extended working life and increasing prevalence of dementias, they more and more frequently will be present in the active workforce. This review aimed to synthesize the available information about the ability of people with EOD or MCI to retain their participation in the labor workforce. Methods : We searched SCOPUS and EMBASE for peer-reviewed papers that reported studies assessing work ability in employees with EOD or MCI that were published in the period of January 2010 to August 2019. Results : We selected four publications, in which 1012 participants with EOD or MCI were enrolled (41.2% males). Cognitive difficulties rather than motor dysfunction were found to reduce patients' ability to work. Two main themes emerged: management of dementia in the workplace and the impact of symptoms on working status. Conclusions : EOD and MCI impact on workforce participation by determining problems in executive functions. Although this review was based on a small sample of studies, it can be shown that support in the workplace may act as a facilitator to enhance workforce participation, and occupational health professionals can help patients with EOD or MCI continue working as much as possible.

Introduction: We test the hypothesis that amyloid-positron emission tomography prescriptions, considered appropriate based on the Amyloid Imaging Taskforce (AIT) criteria, lead to greater clinical utility than AIT-inappropriate prescriptions., Methods: We compared the clinical utility between patients who underwent amyloid-positron emission tomography appropriately or inappropriately and among the subgroups of patients defined by the AIT criteria. Finally, we performed logistic regressions to identify variables associated with clinical utility., Results: We identified 171 AIT-appropriate and 67 AIT-inappropriate patients. AIT-appropriate and AIT-inappropriate cases did not differ in any outcomes of clinical utility (P > .05). Subgroup analysis denoted both expected and unexpected results. The logistic regressions outlined the primary role of clinical picture and clinical or neuropsychological profile in identifying patients benefitting from amyloid-positron emission tomography., Discussion: Contrary to our hypothesis, also AIT-inappropriate prescriptions were associated with clinical utility. Clinical or neuropsychological variables, not taken into account by the AIT criteria, may help further refine criteria for appropriateness., (Copyright © 2018 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N  = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT ( 18 F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global 18 F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVr CT, SUVr MRI ) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVr CT and SUVr MRI global uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis.

The pathway leading from amyloid-β deposition to cognitive impairment is believed to be a cornerstone of the pathogenesis of Alzheimer's disease (AD). However, what drives amyloid buildup in sporadic nongenetic cases of AD is still unknown. AD brains feature an inflammatory reaction around amyloid plaques, and a specific subset of the gut microbiota (GMB) may promote brain inflammation. We investigated the possible role of the GMB in AD pathogenesis by studying the association of brain amyloidosis with (1) GMB taxa with pro- and anti-inflammatory activity; and (2) peripheral inflammation in cognitively impaired patients. We measured the stool abundance of selected bacterial GMB taxa (Escherichia/Shigella, Pseudomonas aeruginosa, Eubacterium rectale, Eubacterium hallii, Faecalibacterium prausnitzii, and Bacteroides fragilis) and the blood expression levels of cytokines (pro-inflammatory cytokines: CXCL2, CXCL10, interleukin [IL]-1β, IL-6, IL-18, IL-8, inflammasome complex (NLRP3), tumor necrosis factor-alpha [TNF-α]; anti-inflammatory cytokines: IL-4, IL-10, IL-13) in cognitively impaired patients with (n = 40, Amy+) and with no brain amyloidosis (n = 33, Amy-) and also in a group of controls (n = 10, no brain amyloidosis and no cognitive impairment). Amy+ patients showed higher levels of pro-inflammatory cytokines (IL-6, CXCL2, NLRP3, and IL-1β) compared with both controls and with Amy- patients. A reduction of the anti-inflammatory cytokine IL-10 was observed in Amy+ versus Amy-. Amy+ showed lower abundance of E. rectale and higher abundance of Escherichia/Shigella compared with both healthy controls (fold change, FC = -9.6, p < 0.001 and FC = +12.8, p < 0.001, respectively) and to Amy- (FC = -7.7, p < 0.001 and FC = +7.4, p = 0.003). A positive correlation was observed between pro-inflammatory cytokines IL-1β, NLRP3, and CXCL2 with abundance of the inflammatory bacteria taxon Escherichia/Shigella (rho = 0.60, p < 0.001; rho = 0.57, p < 0.001; and rho = 0.30, p = 0.007, respectively) and a negative correlation with the anti-inflammatory E. rectale (rho = -0.48, p < 0.001; rho = -0.25, p = 0.024; rho = -0.49, p < 0.001). Our data indicate that an increase in the abundance of a pro-inflammatory GMB taxon, Escherichia/Shigella, and a reduction in the abundance of an anti-inflammatory taxon, E. rectale, are possibly associated with a peripheral inflammatory state in patients with cognitive impairment and brain amyloidosis. A possible causal relation between GMB-related inflammation and amyloidosis deserves further investigation., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Importance: Cerebral amyloidosis is a key abnormality in Alzheimer disease (AD) and can be detected in vivo with positron emission tomography (PET) ligands. Although amyloid PET has clearly demonstrated analytical validity, its clinical utility is debated., Objective: To evaluate the incremental diagnostic value of amyloid PET with florbetapir F 18 in addition to the routine clinical diagnostic assessment of patients evaluated for cognitive impairment., Design, Setting, and Participants: The Incremental Diagnostic Value of Amyloid PET With [18F]-Florbetapir (INDIA-FBP) Study is a multicenter study involving 18 AD evaluation units from eastern Lombardy, Northern Italy, 228 consecutive adults with cognitive impairment were evaluated for AD and other causes of cognitive decline, with a prescan diagnostic confidence of AD between 15% and 85%. Participants underwent routine clinical and instrumental diagnostic assessment. A prescan diagnosis was made, diagnostic confidence was estimated, and drug treatment was provided. At the time of this workup, an amyloid PET/computed tomographic scan was performed, and the result was communicated to physicians after workup completion. Physicians were asked to review the diagnosis, diagnostic confidence, and treatment after the scan. The study was conducted from August 5, 2013, to December 31, 2014., Main Outcomes and Measures: Primary outcomes were prescan to postscan changes of diagnosis, diagnostic confidence, and treatment., Results: Of the 228 participants, 107 (46%) were male; mean (SD) age was 70.5 (7) years. Diagnostic change occurred in 46 patients (79%) having both a previous diagnosis of AD and an amyloid-negative scan (P < .001) and in 16 (53%) of those with non-AD diagnoses and an amyloid-positive scan (P < .001). Diagnostic confidence in AD diagnosis increased by 15.2% in amyloid-positive (P < .001; effect size Cohen d = 1.04) and decreased by 29.9% in amyloid-negative (P < .001; d = -1.19) scans. Acetylcholinesterase inhibitors and memantine hydrochloride were introduced in 61 (65.6%) patients with positive scan results who had not previously received those drugs, and the use of the drugs was discontinued in 6 (33.3%) patients with negative scan results who were receiving those drugs (P < .001)., Conclusions and Relevance: Amyloid PET in addition to routine assessment in patients with cognitive impairment has a significant effect on diagnosis, diagnostic confidence, and drug treatment. The effect on health outcomes, such as morbidity and mortality, remains to be assessed.

Objective: To compare the diagnostic value of striatal (123) I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane ((123) I-FP-CIT) single photon emission computed tomography (SPECT) and (123) I-metaiodobenzylguanidine ((123) I-MIBG) myocardial scintigraphy in differentiating dementia with Lewy bodies (DLB) from other dementia types., Methods: This prospective longitudinal study included 30 patients with a clinical diagnosis of DLB and 29 patients with non-DLB dementia (Alzheimer disease, n = 16; behavioral variant frontotemporal dementia, n = 13). All patients underwent (123) I-FP-CIT SPECT and (123) I-MIBG myocardial scintigraphy within a few weeks of clinical diagnosis. All diagnoses at each center were agreed upon by the local clinician and an independent expert, both unaware of imaging data, and re-evaluated after 12 months. Each image was visually classified as either normal or abnormal by 3 independent nuclear physicians blinded to patients' clinical data., Results: Overall, sensitivity and specificity to DLB were respectively 93% and 100% for (123) I-MIBG myocardial scintigraphy, and 90% and 76% for (123) I-FP-CIT SPECT. Lower specificity of striatal compared to myocardial imaging was due to decreased (123) I-FP-CIT uptake in 7 non-DLB subjects (3 with concomitant parkinsonism) who had normal (123) I-MIBG myocardial uptake. Notably, in our non-DLB group, myocardial imaging gave no false-positive readings even in those subjects (n = 7) with concurrent medical illnesses (diabetes and/or heart disease) supposed to potentially interfere with (123) I-MIBG uptake., Interpretation: (123) I-FP-CIT SPECT and (123) I-MIBG myocardial scintigraphy have similar sensitivity for detecting DLB, but the latter appears to be more specific for excluding non-DLB dementias, especially when parkinsonism is the only "core feature" exhibited by the patient. Our data also indicate that the potential confounding effects of diabetes and heart disease on (123) I-MIBG myocardial scintigraphy results might have been overestimated. Ann Neurol 2016;80:368-378., (© 2016 American Neurological Association.)