Cerebrospinal fluid neurofilament light chain and total-tau as biomarkers of neurodegeneration in Alzheimer's disease and frontotemporal dementia.

INTRODUCTION: CSF Neurofilament light chain(NfL) is a promising biomarker of neurodegeneration, but its utility in discriminating between Alzheimer's disease(AD) and frontotemporal dementia(FTD) is limited. METHODS: 105 patients with clinical-biological diagnosis of mild cognitive impairment(MCI) due to AD (N = 72) or clinical diagnosis of FTD (N = 33) underwent neuropsychological assessment and CSF Aß42/40, p-tau181, total-tau and NfL quantification. Group comparisons, correlations between continuous variables and ROC curve analysis were carried out to assess NfL role in discriminating between MCI due to AD and FTD, exploring the associations between NfL, ATN biomarkers and neuropsychological measures. RESULTS: NfL levels were significantly lower in the AD group, while levels of total-tau were higher. In the FTD group, significant correlations were found between NfL, p-tau181 and total-tau, and between NfL and cognitive performances. In the AD group, NfL levels were directly correlated with total-tau and p-tau181; Aß42/40 ratio was inversely correlated with total-tau and p-tau181, but not with NfL. Moreover, p-tau181 and t-tau levels were found to be associated with episodic memory and lexical-semantic impairment. Total-tau/NfL ratio differentiated prodromal-AD from FTD with an AUC of 0.951, higher than the individual measures. DISCUSSION & CONCLUSIONS: The results support that NfL and total-tau levels reflect distinct pathophysiological neurodegeneration mechanisms, independent and dependent of Aß pathology, respectively, Combining them may enhance both markers reliability, their ratio showing high accuracy in distinguishing MCI due to AD from FTD. Moreover, our results revealed associations between NfL and disease severity in FTD and between tauopathy and episodic memory and lexical-semantic impairment in prodromal-AD.

A true isolated cognitive relapse in multiple sclerosis.

Isolated cognitive relapses (ICRs) have been a matter of debate for the past few years. Currently, there is no clear consensus on such an entity, as cognitive decline usually accompanies typical multiple sclerosis (MS) relapses. Herein, we present the neuropsychological and neurophysiological manifestations of a patient who suddenly complained of confusion and memory loss, showing insight into her deficit, in absence of sensorimotor disturbances. Neuroimaging revealed a large tumefactive gadolinium-enhancing lesion localized in the left medial temporal lobe. The patient's symptoms persisted for months afterwards, despite corticosteroid treatment. We believe our patient experienced a true ICR. ICRs are rare entities in MS, but we should be alert to their existence in order to treat them promptly. Deepening their pathophysiology is equally important and neuropsychology combined with neurophysiology may be useful in this regard.

A multiple hits hypothesis for memory dysfunction in Parkinson disease.

Cognitive disorders are increasingly recognized in Parkinson disease (PD), even in early disease stages, and memory is one of the most affected cognitive domains. Classically, hippocampal cholinergic system dysfunction was associated with memory disorders, whereas nigrostriatal dopaminergic system impairment was considered responsible for executive deficits. Evidence from PD studies now supports involvement of the amygdala, which modulates emotional attribution to experiences. Here, we propose a tripartite model including the hippocampus, striatum and amygdala as key structures for cognitive disorders in PD. First, the anatomo-functional relationships of these structures are explored and experimental evidence supporting their role in cognitive dysfunction in PD is summarized. We then discuss the potential role of α-synuclein, a pathological hallmark of PD, in the tripartite memory system as a key mechanism in the pathogenesis of memory disorders in the disease.

Associations Between Free and Cued Selective Reminding Test and Cerebrospinal Fluid Biomarkers in Amnestic Mild Cognitive Impairment.

Background: The Free and Cued Selective Reminding Test (FCSRT), assessing verbal episodic memory with controlled learning and semantic cueing, has been recommended for detecting the genuine encoding and storage deficits characterizing AD-related memory disorders. Objective: The present study aims at investigating the ability of FCSRT in predicting cerebrospinal fluid (CSF) evidence of amyloid-ß positivity in subjects with amnestic mild cognitive impairment (aMCI) and exploring its associations with amyloidopathy, tauopathy and neurodegeneration biomarkers. Methods: 120 aMCI subjects underwent comprehensive neurological and neuropsychological examinations, including the FCSRT assessment, and CSF collection; CSF Aß42/40 ratio, p-tau181, and total-tau quantification were conducted by an automated CLEIA method on Lumipulse G1200. Based on the Aß42/40 ratio value, subjects were classified as either A+ or A-. Results: All FCSRT subitem scores were significantly lower in A+ group and significantly predicted the amyloid-ß status, with Immediate Total Recall (ITR) being the best predictor. No significant correlations were found between FCSRT and CSF biomarkers in the A- aMCI group, while in the A+ aMCI group, all FCSRT subitem scores were negatively correlated with CSF p-tau181 and total-tau, but not with the Aß42/40 ratio. Conclusions: FCSRT confirms its validity as a tool for the diagnosis of AD, being able to predict the presence of amyloid-ß deposition with high specificity. The associations between FCSRT subitem scores and CSF p-tau-181 and total-tau levels in aMCI due to AD could further encourage the clinical use of this simple and cost-effective test in the evaluation of individuals with aMCI.

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.

Medical Information Extraction With NLP-Powered QABots: A Real-World Scenario.

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.

Neuropsychological predictors of conversion from mild cognitive impairment to dementia at different timepoints.

INTRODUCTION: Impairment of episodic memory is largely considered the main cognitive marker of prodromic Alzheimer's disease (AD). Nevertheless, the neuropathological process in AD starts several years before and, apart from biomarkers well defined in the Amyloid (A), Tauopathy (T), Neurodegeneration (N) framework, early clinical and neuropsychological markers able to detect mild cognitive impairment (MCI) due to AD before the appearance of memory disorders are lacking in clinical practice. Investigations on semantic memory have shown promising results in providing an earlier marker of dementia in MCI patients. METHODS: A total of 253 MCI subjects were followed up every 6 months for 6 years-186 converted to dementia and 67 remained stable at the sixth year of follow-up. Twenty-seven patients progressed in the first 2 years (fast converters), 107 in the third to fourth year (intermediate converters), and 51 after the fourth year of follow-up (slow converters). RESULTS: Stable MCI subjects performed better than fast decliners in Mini-Mental State Examination (MMSE), several long-term memory scores, and category verbal fluency test (CFT); stable and intermediate converters differ only in MMSE and CFT tests; and stable and slow converters differ only in MMSE and phonological/semantic discrepancy score. CONCLUSION: Early impairment of semantic memory could predict the evolution to AD before the onset of episodic memory disorders, and the discrepancy between phonological and semantic verbal fluency could be able to detect this impairment in advance in respect of simple CFT tests. The assessment of different aspects of semantic memory and its degradation could represent an early cognitive marker to intercept MCI due to AD in clinical practice.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Defining the disease course of TNFα blockers-associated Multiple Sclerosis.

Tumour Necrosis Factor alpha (TNFα) blockers are common and effective treatments for several autoimmune diseases but can be associated with neuroinflammatory events. We describe the disease course of ten patients who developed CNS demyelinating events while exposed to TNFα blockers. We divided them into two groups: eight patients with Relapsing Multiple Sclerosis and two isolated optic neuritis. In our cohort, TNFα blockers-associated Multiple Sclerosis does not seem to be associated with a more aggressive course and can be managed with MS-specific DMTs, chosen considering the clinical course and the concomitant autoimmune disease. Our findings need confirmation in larger cohorts to further characterize the disease course of TNFα blockers-associated Multiple Sclerosis.