Aurora Cermelli

Alzheimer’s disease (AD) is the most common cause of neurocognitive disorder, and the integration of cognitive assessment with biological markers remains essential for clinical characterization. The Clock Drawing Test (CDT) is a brief and widely used screening tool assessing visuospatial and executive functions, which may reflect underlying neurodegenerative processes. This study investigated the diagnostic performance of the CDT and its association with cerebrospinal fluid (CSF) biomarkers within the A/T/(N) research framework. Ninety-seven patients with mild or major neurocognitive disorder were classified as AD or non-AD according to CSF amyloid-β, phosphorylated tau, and total tau profiles, and compared with 36 healthy participants. All subjects underwent a comprehensive neuropsychological evaluation, including the CDT scored using the quantitative–qualitative method proposed by Rouleau et al. Group comparisons, ROC analyses, and regression models adjusted for age, sex, and education were performed. CDT scores effectively distinguished patients from healthy participants, showing large effect sizes, and modestly differentiated AD from non-AD profiles, particularly on the Hands subscale. Diagnostic accuracy was fair, with adjusted AUC values ranging from 0.65 to 0.75. Lower CDT performance was significantly associated with higher CSF total tau levels, while associations with amyloid-β and phosphorylated tau were not robust after correction. These findings suggest that the CDT is sensitive to cognitive impairment severity and shows limited but meaningful relationships with neurodegenerative biomarkers, supporting its role as a practical complementary tool alongside biological assessment.

Background: Frailty is a multidimensional syndrome reflecting reduced physiological reserve, increasingly recognized as a relevant factor in the clinical assessment of older adults with cognitive disorders. Objective: To explore the association between frailty, as measured by the Multidimensional Prognostic Index (MPI), cognitive performance, and plasma biomarkers of Alzheimer’s disease (AD), and to examine the correlation between plasma and cerebrospinal fluid (CSF) biomarkers. Methods: This cross-sectional observational study included 40 patients (mean age 68.0 ± 9.0 years; 42.5% female) undergoing a diagnostic workup for cognitive decline. Patients were classified into AD (n = 20) and non-AD (n = 20) groups based on CSF AT[N] profiles. Frailty was assessed using the MPI. Linear and logistic regression models adjusted for age, sex, and education examined associations between MPI, cognitive scores, and plasma biomarkers (Aβ42, Aβ42/40, p-tau181, NfL). Correlations between plasma and CSF biomarkers and ROC analyses were also performed. Results: The AD group showed significantly higher plasma p-tau181 levels and MPI scores. MPI was positively associated with plasma p-tau181 levels (β = 4.26, p = 0.009). Plasma p-tau181 correlated strongly with CSF p-tau181 (R = 0.523, p < 0.001) and with CSF Aβ42/40 ratio (R = −0.541, p < 0.001) and showed high diagnostic accuracy (AUC = 0.910). Combining MPI with plasma biomarkers improved classification between AD and non-AD cases (AUC = 0.941). Conclusions: These findings support the value of incorporating frailty assessment in the diagnostic process of AD. The integration of geriatric tools and blood-based biomarkers may improve early detection and promote a more comprehensive approach in dementia evaluation.

Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of 64 participants exposed to standardized audio-visual stimuli. Facial emotion features in terms of valence and arousal were extracted and used to train machine learning models on multiple classification tasks, including distinguishing individuals with mild cognitive impairment (MCI) and overt dementia from healthy controls (HCs) and differentiating Alzheimer’s disease (AD) from other types of cognitive impairment. Nested cross-validation was adopted to evaluate the performance of different tested models (K-Nearest Neighbors, Logistic Regression, and Support Vector Machine models) and optimize their hyperparameters. The system achieved a cross-validation accuracy of 76.0% for MCI vs. HCs, 73.6% for dementia vs. HCs, and 64.1% in the three-class classification (MCI vs. dementia vs. HCs). Among cognitively impaired individuals, a 75.4% accuracy was reached in distinguishing AD from other etiologies. These results demonstrated the potential of AI-driven facial emotion analysis as a non-invasive tool for early detection of cognitive impairment and for supporting differential diagnosis of AD in clinical settings.

Background: Neurofilament light chain (NfL) is a promising biomarker of neuroaxonal injury, increasingly used to monitor neurodegeneration in Alzheimer’s disease (AD). Multiple analytical platforms are available for NfL quantification in cerebrospinal fluid (CSF), but data on cross-platform consistency remain limited. Objective: This pilot study aimed to provide CSF NfL concentrations measured using Simoa and Lumipulse immunoassays in patients with biologically confirmed AD. Methods: Twenty-eight patients with cognitive impairment fulfilling the biological criteria for AD were enrolled. CSF NfL levels were measured using both Simoa and Lumipulse immunoassays. Statistical analyses assessed intra-individual agreement, correlation between platforms, and associations with cognitive status. Results: NfL concentrations measured with Simoa and Lumipulse showed a strong positive correlation between platforms (Spearman’s ρ = 0.965, p < 0.001), demonstrating excellent analytical concordance. Conclusions: In this pilot study, Simoa and Lumipulse yielded strongly correlated CSF NfL measurements, providing initial evidence of cross-platform consistency. However, these findings require confirmation in larger and diverse cohorts before definitive validation.

Altered facial expressivity is frequently recognized in cognitively impaired individuals. This makes facial emotion identification a promising tool with which to support the diagnostic process. We propose a novel, non-invasive approach for detecting cognitive impairment based on facial emotion analysis. We design a protocol for emotion elicitation using visual and auditory standardized stimuli. We collect facial emotion video recordings from 32 cognitively impaired and 28 healthy control subjects. To track the evolution of emotions during the experiment, we train a deep convolutional neural network on the AffectNet dataset for emotion recognition from facial images. Emotions are described using a dimensional affect model, namely the continuous dimensions of valence and arousal, rather than discrete categories, enabling a more nuanced analysis. The collected facial emotion data are used to train a classifier to distinguish cognitively impaired and healthy subjects. Our k-nearest neighbors model achieves a cross-validation accuracy of 76.7%, demonstrating the feasibility of automatic cognitive impairment detection from facial expressions. These results highlight the potential of facial expressions as early markers of cognitive impairment, which could enhance non-invasive screening methods for early diagnosis.

Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of 64 participants exposed to standardized audio-visual stimuli. Facial emotion features in terms of valence and arousal were extracted and used to train machine learning models on multiple classification tasks, including distinguishing individuals with mild cognitive impairment (MCI) and overt dementia from healthy controls (HC), and differentiating Alzheimer’s disease (AD) from other types of cognitive impairment. The system achieved a cross-validation accuracy of 76.0% for MCI vs HC, 73.6% for dementia vs HC, and 64.1% in the three-class classification (MCI vs dementia vs HC). Among cognitively impaired individuals, 75.4% accuracy was reached in distinguishing AD from other etiologies. These results demonstrated the potential of AI-driven facial emotion analysis as a non-invasive tool for early detection of cognitive impairment, and for supporting differential diagnosis of AD in clinical settings.

This study evaluated the diagnostic performance of plasma biomarkers for Alzheimer's disease (AD) using an automated platform. In a cohort of 74 consecutive patients, plasma p-Tau181 levels were significantly higher in AD compared to non-AD groups and showed correlation with cerebrospinal fluid biomarkers. Plasma p-Tau181 demonstrated high diagnostic accuracy for AD, with an area under the curve of 0.854. The findings suggest that plasma biomarkers, particularly p-Tau181, could improve the accessibility and efficiency of AD diagnostics in clinical settings, offering a less invasive and cost-effective alternative to traditional methods.

Objective: In this systematic review and meta-analysis, we critically evaluate available evidence regarding the association between primary headaches and subsequent decline of cognitive function and dementia. Background: Recent studies suggested that headache disorders may increase the risk for dementia. However, available studies are conflicting. Methods: To identify qualifying studies, we searched scientific databases, including Pubmed, Scopus, Web of Science, Science Direct and BMC, screening for relevant papers. In order to reduce the heterogeneity between different studies, the analyses were further subdivided according to the clinical diagnoses and the study methodologies. Results: We identified 23 studies investigating the association between primary headaches and the risk of dementia. Of these, 18 met our inclusion criteria for meta-analysis (covering 924.140 individuals). Overall effect-size shows that primary headaches were associated with a small increase in dementia risk (OR = 1,15; CI 95%: 1,03–1,28; p = 0,02). Analyzing subgroups, we found that migraine was associated with both a moderate increased risk of all-cause dementia (OR = 1,26; p = 0,00; 95% CI: 1,13–1,40) as well as a moderate increased risk of Alzheimer’s disease (OR = 2,00; p = 0,00; 95% CI: 1,46–2,75). This association was significant in both case–control and retrospective cohort studies but not in prospective studies. Conclusions: Our study supports the presence of a link between primary headaches and dementia. However, in the subgroup analysis, only patients with migraine showed a moderate increase risk for all-cause dementia and for Alzheimer’s disease. Additional rigorous studies are needed to elucidate the possible role of primary headaches on the risk of developing cognitive impairment and dementia.

Migraine is a common neurovascular disorder characterized by recurrent episodes of headache and associated neurological symptoms. At present, a significant portion of patients do not obtain a satisfactory response to acute pain-relieving therapies, including NSAIDs and triptans. In this context, pharmacogenetics plays a key role in the understanding of such a diverse response. In order to investigate whether functional polymorphisms in proinflammatory cytokine genes (IL-1α, IL-1β, IL-1RN; IL-6 and TNF-α) may influence the response to acute treatment, 313 consecutive patients with episodic migraine without aura were enrolled. Pain relief by administration of NSAIDs or triptans for three consecutive migraine attacks was evaluated. We found a significant association between A allele of the TNF-α promoter (−308 A/G) and a lack of efficacy after NSAID administration (p < 0.01, OR 2.51, 95% CI: 1.33 < OR < 4.75 compared to the G allele). Remaining polymorphisms had no significant effect on pain relief. Our study showed that a functional polymorphism in the TNF-α gene significantly modulates the clinical response to NSAID administration in acute attacks. Patients with higher production of the active cytokine during stress showed a significantly lower anti-migraine effect. Our results further support a role for TNF-α in the pathophysiological mechanisms of migraine attack.