Background: Increasing efforts have focused on the establishment of novel biomarkers for the early detection of Alzheimer’s disease (AD) and prediction of Mild Cognitive Impairment (MCI)-to-AD conversion. Behavioral changes over the course of healthy ageing, at disease onset and during disease progression, have been recently put forward as promising markers for the detection of MCI and AD. The present study examines whether the temporal characteristics of speech in a collaborative referencing task are associated with cognitive function and the volumes of brain regions involved in speech production and known to be reduced in MCI and AD pathology. We then explore the discriminative ability of the temporal speech measures for the classification of MCI and AD.Method: Individuals with MCI, mild-to-moderate AD and healthy controls (HCs) underwent a structural MRI scan and a battery of neuropsychological tests. They also engaged in a collaborative referencing task with a caregiver. The associations between the conversational speech timing features, cognitive function (domain-specific) and regional brain volumes were examined by means of linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of the conversational speech features.Results: MCI and mild-to-moderate AD are characterized by a general slowness of speech, attributed to slower speech rate and slower turn-taking in conversational settings. The speech characteristics appear to be reflective of episodic, lexico-semantic, executive functioning and visuospatial deficits and underlying volume reductions in frontal, temporal and cerebellar areas.Conclusion: The implementation of conversational speech timing-based technologies in clinical and community settings may provide additional markers for the early detection of cognitive deficits and structural changes associated with MCI and AD.

Accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for olfactory dysfunction in early stage Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. Classification will be applied to PD patients with severe hyposmia, patients with no/mild hyposmia, and healthy controls. An additional novel element is the use of evolutionary algorithms to map and predict the functional connectivity using rs-fMRI. Cartesian Genetic Programming (CGP) will be used to classify dynamic causal modelling (DCM) data as well as timeseries data. The findings will be validated using two other commonly used classification methods (ANN and SVM) and by employing k-fold cross-validation. Developing methods for identifying early stage PD patients with hyposmia is relevant since current methods of diagnosing early stage PD have low reliability and accuracy. Furthermore, exploring the performance of CGP relative to other methods is crucial given the additional benefits it provides regarding easy classifier decoding. Hence, this research underscores the potential relevance of DCM analyses for classification and CGP as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages.

Background: Overt sentence reading in mild cognitive impairment (MCI) and mild-to-moderate Alzheimer’s disease (AD) has been associated with slowness of speech, characterized by a higher number of pauses, shorter speech units and slower speech rate and attributed to reduced working memory/ attention and language capacity. Objective: This preliminary case-control study investigates whether the temporal organization of speech is associated with the volume of brain regions involved in overt sentence reading and explores the discriminative ability of temporal speech parameters and standard volumetric MRI measures for the classification of MCI and AD. Method: Individuals with MCI, mild-to-moderate AD, and healthy controls (HC) had a structural MRI scan and read aloud sentences varying in cognitive-linguistic demand (length). The association between speech features and regional brain volumes was examined by linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of temporal and MRI features. Results: Longer sentences, slower speech rate, higher number of pauses and shorter interpausal units were associated with reduced volumes of the reading network. Speech-based classifiers performed similarly to the MRI-based classifiers for MCI-HC (67% vs 68%) and slightly better for AD-HC (80% vs 64%) and AD-MCI (82% vs 59%). Adding the speech features to the MRI features slightly improved performance of MRI-based classification for AD-HC and MCI-HC but not HC-MCI. Conclusion: The temporal organization of speech in overt sentence reading reflects underlying volume reductions. It may represent a sensitive marker for early assessment of structural changes and cognitive-linguistic deficits associated with healthy aging, MCI, and AD.

No abstract