Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

Calzà, Laura; Gagliardi, Gloria; Favretti, Rema Rossini; Tamburini, Fabio

doi:10.1016/j.csl.2020.101113

Cited by 53 publications

(40 citation statements)

References 84 publications

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“…Machine learning approaches are found to be quite useful for discriminating MCI patients from normal controls (NCs) ( Tanveer et al, 2020 ). Some researchers made use of linguistic and/or acoustic features ( Gosztolya et al, 2019 ; Orimaye et al, 2020 ; Calzà et al, 2021 ), while the overwhelming majority of previous studies focused on utilizing the neuroimaging biomarkers for the identification of MCI ( Tanveer et al, 2020 ). Growing functional magnetic resonance imaging (fMRI) studies have been devoted to the classification task between MCI patients and NCs ( Chen et al, 2011 ; Jie et al, 2013 , 2014 ; Suk et al, 2013 ; Wang et al, 2013 ; Wee et al, 2013a , b ; Cui et al, 2018 ; Xu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment

Zhang

et al. 2020

Front. Neurosci.

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Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer's disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.

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Section: Introductionmentioning

confidence: 99%

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment

Zhang

et al. 2020

Front. Neurosci.

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“…Decision tree is a kind of machine learning, which is a prediction model [ 16 , 22 , 23 ]. The process of decision analysis of decision tree [ 24 ] is like the trunk of a tree, which reflects the process of grouping learning data, so it is called decision tree.…”

Section: Methodsmentioning

confidence: 99%

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Chen

Pan

2021

IJERPH

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Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.

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“…Alzheimer's disease leads to a decline in cognitive and functional abilities, such as memory loss and language impairments. There have been numerous review studies on linguistic biomarkers that have been used for detecting the progression of AD (Mueller et al, 2018;Slegers et al, 2018;Voleti et al, 2019;de la Fuente Garcia et al, 2020;Calzà et al, 2020). These include both acoustic features (prosodic, spectral, vocal and fluency), and textual features (lexical, syntactic, semantic, and pragmatic).…”

Section: Speech and Languagementioning

confidence: 99%

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

Jonell

Moëll

Håkansson

et al. 2021

Front. Comput. Sci.

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Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.

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Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

Cited by 53 publications

References 84 publications

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

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