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Background: The novel coronavirus disease (COVID‐19) was first detected in Mainland China in December 2019, and soon it spread throughout the world, with multiple physical and psychological consequences across the affected populations. Aims: The aim of the current study was to analyze the impact of COVID‐19 pandemic on older adults with mild cognitive impairment (MCI)/dementia and their caregivers as well. Materials and Methods: Two hundred and four caregivers took part in the study, completing a self‐reported questionnaire about the person with MCI/dementia and their own, since the lockdown period which started in February and ended in May of 2020 in Greece. Results: Results indicated a significant overall decline of the people with MCI/dementia. Further, the domains in which people with MCI/dementia were mostly affected were: communication, mood, movement and compliance with the new measures. Caregivers also reported a great increase in their psychological and physical burden during this period, where the available support sources were limited. Discussion: The pandemic threatens to disrupt the basic routines that promote mental and physical health of both people with MCI/dementia and t heir caregivers. Conclusion: Further measures to protect and provide support to people who suffer and their families are needed. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to assess the ability of Neuropsychiatric Inventory (NPI) scale profiles to differentiate between distinct frontotemporal dementia (FTD) subtypes. Methods: The NPI was used to assess 311 older patients who had been clinically diagnosed with FTD. FTD subtypes included behavioural variant FTD (bvFTD, n = 121), primary progressive aphasia (semantic variant (n = 69), non‐fluent agrammatic variant (n = 31), and logopenic variant (n = 0)), FTD‐motor neuron disease (n = 4), progressive supranuclear palsy (n = 43), and corticobasal syndrome (n = 43). Total NPI score and scores for each NPI item were correlated across the distinct FTD subtypes. Results: Patients with bvFTD showed significantly greater impairment on their total NPI score than patients with corticobasal syndrome (P < 0.001), non‐fluent agrammatic variant primary progressive aphasia (P < 0.001), progressive supranuclear palsy (P = 0.002), and semantic variant primary progressive aphasia (P = 0.010). Aggressiveness, euphoria, apathy, disinhibition, irritability, aberrant motor behaviours, and appetite disturbance were significantly higher in bvFTD than in the other subgroups. The lowest NPI scores were generally shown among those with CBS. However, NPI total and specific item values overlapped among the subtypes. Conclusions: Patients with bvFTD showed significantly greater neuropsychiatric dysfunction than those with the other FTD subtypes, as measured by the NPI scale. In contrast, patients with corticobasal syndrome had a comparatively healthier profile. Therefore, differential diagnosis among the FTD subtypes may be guided by the NPI, although the subtype is unlikely to be confirmed on the basis of NPI alone. [ABSTRACT FROM AUTHOR]

Background: Multiple neurodegenerative and psychiatric diseases can affect speech, manifesting as subtle changes in spoken language. We perform a rigorous evaluation of a Machine Learning (ML) pipeline on a large‐scale multiaxial experimental setting, to investigate its capacity for detecting early signs of cognitive decline as manifested in speech. Using a diverse dataset of speech samples spanning different languages, patient cohorts and cognitive status diagnoses, we propose that the developed pipeline showcases robust performance, facilitating efficient decision support for early cognitive decline detection. Method: We build upon previous research efforts on data‐driven cognitive decline prediction. Random forest models are constructed from a dataset of more than 3000 audio recordings and ASR‐generated transcripts, constituting cognitive assessment test tasks (e.g. picture description, everyday activity recounting, etc.). A multimodal NLP and audio analysis procedure derives sophisticated predictive biomarkers, using three methods for feature selection, feature decorrelation preprocessing and rigorous, grid search‐based hyperparameter optimization. We introduce novel experimental axes of investigation, including the spoken language (English and Greek), single and multi‐task verbal task settings, and different neurodegenerative diseases for the pathological cohorts (Alzheimer's Disease or Dementia, along with age and education‐matched healthy cohorts). Four different NLP analysis tools for tokenization, syntax and dependency extraction are considered, using both ML and Deep Learning (DL) techniques. Model stability and generalization is supported via 5‐fold cross‐validation and the use of a separate test set. Result: Results are validated against medical expert diagnosis. Αverage performance across all investigative axes reaches high scores for accuracy (0.73), sensitivity (0.72), specificity (0.74), precision (0.77), F1 (0.74), and AUC (0.78). Additional findings suggest that single‐task models outperform multitask ones (e.g. reaching AUC scores of 0.85). Moreover, cognitive decline detection is more efficient for pathological cohorts of Alzheimer's, versus patients with Dementias. Conclusion: The broad empirical evaluation verifies the effectiveness of the deployed ML system, adding evidence in favor of multimodal AI solutions for predicting early cognitive decline and showcasing their robustness in diverse evaluation settings. Future work includes leveraging DL methods for classification and evaluation on differential diagnosis discrimination tasks. [ABSTRACT FROM AUTHOR]

Background: Various neurodegenerative and psychiatric diseases are related with changes in spoken language [1], although they have seldom been investigated. We evaluate the effectiveness of our language‐agnostic Machine Learning (ML) system to detect subtle changes in spoken language that manifest early signs of cognitive decline, thus assisting with its diagnosis. We evaluate our methodology using recordings of speech samples from multiple languages obtained from patient cohorts in early stages of cognitive decline and matched healthy controls. Method: Our methodology involves capturing patient audio recordings while they are performing one or more predefined cognitive assessment tasks, involving, e.g., the description of a picture or recounting of an everyday activity. Afterwards, multi‐language audio recordings and generated transcripts are analyzed with audio and NLP feature extraction methods [3], ranging from semantic, morpho‐syntactic, phonological representations of the input, as well as, more sophisticated linguistic measures. The feature pool is filtered by a Pearson's rho threshold of 0.85. We build a Random Forest classifier out of 100 Decision trees, using the Gini impurity criterion, 5‐fold cross‐validation for training, elimination and composition‐based feature selection, as well as post‐selection retraining / fine‐tuning. The model's diagnostic performance is evaluated on a test set unseen during training. Result: Our results are validated against the diagnosis that is provided by medical experts. Our performance in terms of accuracy (∼82%), f1 (84%) and ROC‐AUC score (∼82%) are clear indicators of the effectiveness of speech analysis towards detecting cognitive decline. Moreover, our tree‐based classifier produces probability scores that closely follow the proportion of pathological cases in the input data, with a correlation of 94%. Conclusion: In the current evaluation we verified our conjectures regarding the strong capacity of speech to predict cognitive decline. Audio analysis and machine learning are proven to be invaluable tools in the prediction of early signs of cognitive decline, which are coupled with a wide spectrum of neurodegenerative and psychiatric diseases. [1] Boschi, Veronica, et al., Frontiers in psychology 8 (2017): 269. [2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020. [3] Alberdi, Ane et al., Artificial intelligence in medicine 71 (2016): 1‐29. [ABSTRACT FROM AUTHOR]

Background: There are multiple neurodegenerative diseases that directly affect speech [1]. However, its utilization as a robust indicator for cognitive impairment is under‐investigated. In many cases, mild cognitive decline progresses to a neurodegenerative disease and its detection is of utmost importance, since it is at this stage that treatment is most effective. One of our core goals is developing techniques for differentiating between patients with cognitive decline and healthy cohorts, by utilizing only speech samples [2]. Such samples are obtained from verbal elicitation tasks designed for cognitive assessment, e.g. picture descriptions and narration of everyday activities. Method: Audio recordings from cognitive assessment tasks are fed through our platform to a Natural Language Processing and Machine Learning pipeline, employing an automatic discovery procedure of predictive salient biomarkers, to train an advanced classification system. The biomarker collection includes features that characterize voice, speech, language structure, composition and usage, and are engineered to highlight symptoms of neurodegenerative disorders. Biomarkers undergo multiple stages of filtering, processing and transformation to train and fine‐tune the final model. Diagnostic performance of the output classifier is obtained on an unseen test set, to ensure a robust generalization of the platform. Result: Our platform utilizes the set of automatically selected, cross‐linguistic digital biomarkers to obtain sensitivity and specificity scores of 81% and 84% respectively, compared against medical expert diagnosis. The most salient biomarkers with respect to identifying our pathological cohort, relate to feature categories of Syntactic Complexity, Content Word usage, Lexical Repetition, Syntactic Errors and Function Words, with weight contributions of ∼ 16%, 14%, 13%, 12% and 11% respectively. Our platform provides additional, detailed population and patient‐based descriptive analytics to enhance transparency and explainability of the results. Conclusion: Early detection of cognitive decline facilitates early intervention, treatment and proactive care, delaying disease progression and reducing symptom severity. We believe that our platform provides an effective solution for risk factor estimation and our findings incentivize further research into speech analysis techniques for the prediction of cognitive decline. [1] Boschi, Veronica, et al.,Frontiers in psychology 8 (2017): 269. [2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020. [ABSTRACT FROM AUTHOR]