Classification of resting-state fMRI for olfactory dysfunction in parkinson's disease using evolutionary algorithms

Dehsarvi, Amir; Smith, Stephen L.

doi:10.1145/3205651.3205681

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Computational methods, such as EAs, have been recently used for the measurement and analysis of clinical data (e.g., patient movements data and neuroimaging, among others; Dehsarvi and Smith, 2018 ). A core advantage when applying EAs with an expressive dynamical representation is that multiple classifiers can be examined.…”

Section: Methodsmentioning

confidence: 99%

“…Upon the completion of the classification process, EAs allows for looking into the classification graphs generated by the algorithm and, for instance, exploring how specific features have been chosen to evolve the models. Finally, EAs have proved to perform well with relatively small datasets (Picardi et al, 2017 ; Dehsarvi and Smith, 2018 ; Muhamed et al, 2018 ). To our knowledge, our study is the first to investigate whether the use of EAs and conversational speech may enhance the classification of MCI and/or AD.…”

Section: Methodsmentioning

confidence: 99%

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Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Looze¹,

Dehsarvi²,

Crosby³

et al. 2021

Front. Aging Neurosci.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Looze¹,

Dehsarvi²,

Crosby³

et al. 2021

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Computational methods, such as EAs, have been recently used for the measurement and analysis of clinical data (e.g., patient movements data and neuroimaging, among others; Dehsarvi and Smith, 2018). A core advantage when applying EAs with an expressive dynamical representation is that multiple classifiers can be examined.…”

Section: Classification Of MCI and Ad Based On Speech Featuresmentioning

confidence: 99%

“…Upon the completion of the classification process, EAs allows for looking into the classification graphs generated by the algorithm and, for instance, exploring how specific features have been chosen to evolve the models. Finally, EAs have proved to perform well with relatively small datasets (Picardi et al, 2017;Dehsarvi and Smith, 2018;Muhamed et al, 2018). To our knowledge, our study is the first to investigate whether the use of EAs and conversational speech may enhance the classification of MCI and/or AD.…”

Section: Classification Of MCI and Ad Based On Speech Featuresmentioning

confidence: 99%

Alzheimer's Dementia Recognition Through Spontaneous Speech

2021

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease

Looze

Dehsarvi

Suleyman

et al. 2022

CAR

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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.

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Classification of resting-state fMRI for olfactory dysfunction in parkinson's disease using evolutionary algorithms

Cited by 3 publications

References 3 publications

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease: Relevance for Early Detection Approaches

Alzheimer's Dementia Recognition Through Spontaneous Speech

Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease

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