Speech silence character as a diagnostic biomarker of early cognitive decline and its functional mechanism: a multicenter cross-sectional cohort study

Wang, Hua-Long; Tang, Ran; Ren, Rong; Dammer, Eric B.; Guo, Qihao; Peng, Guoping; Cui, Hai-lun; Yu, Xiang; Wang, Jintao; Xie, Xinyi; Huang, Qiang; Li, Jianping; Yan, Fuhua; Chen, Shengdi; He, Naying; Wang, Gang

doi:10.1186/s12916-022-02584-x

Cited by 9 publications

(12 citation statements)

References 45 publications

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“…In Mazzeo et al ( 2022 ), researchers observed associations between disease progression, language lesions, and brain hypometabolism. In addition, evidence shows that MCI patients show longer speech and phonation time (Tóth et al, 2018 ; Gosztolya et al, 2019 ), an incremented length of silent pauses (voiceless) (Wang et al, 2022 ), lower speech rate (Tóth et al, 2018 ), presence of stammers and articulatory disfluencies that interrupt speech with longer hesitations (López-de Ipiña et al, 2013 ; Tóth et al, 2018 ), and impairments in formant features in phonological planning formants (Themistocleous et al, 2018 ). For a more detailed description of these alterations, (see Martínez-Nicolás et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…In our study, several voice parameters were identified as the most discriminatory using ML approaches for differentiating MCI with positive and negative amyloid status. Among the most important voice parameters identified ( Figure 4 ), there were spectral features (relative energy in different frequency bands), associated with vocal emotional expre ssions Sauter et al ( 2010 ), voiced segments (the portion of speech with relatively constant phonetic features) useful to differentiate AD from healthy individuals (López-de Ipiña et al, 2013 ; Wang et al, 2022 ), and measures of the Hammarberg index, a spectral measure of voice quality, which has been identified as a discriminant feature for MCI (Themistocleous et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Mazzeo et al ( 2022 ) showed that a single-word comprehension impairment could be an indicator for identifying patients who may need assistance with self-care in the upcoming years. Moreover, employing neuroimaging techniques, numerous studies have revealed alterations in brain connectivity (Montembeault et al, 2019 ; Rafiq et al, 2022 ; Wang et al, 2022 ) and atrophy (Smits et al, 2014 ; Wei et al, 2018 ) directly associated with language functions.…”

Section: Introductionmentioning

confidence: 99%

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Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

García-Gutiérrez,

Marquié,

Muñoz

et al. 2023

Front. Neurosci.

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Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

García-Gutiérrez,

Marquié,

Muñoz

et al. 2023

Front. Neurosci.

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“…In the Pitt centre cohort, PSD was verified as a reliable marker to differentiate mild AD from normal controls. 49…”

Section: General Psychiatrymentioning

confidence: 99%

Consensus on rapid screening for prodromal Alzheimer’s disease in China

Huang,

Li,

et al. 2024

Gen Psych

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Alzheimer’s disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD’s pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD’s pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD’s pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.

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“…Language functioning can provide valuable insights into cognition and behavior, serving as a window into an individual's cognitive functioning [7][8][9][10][11][12]. Speech and language offer an opportunity to use ecologically valid methods to assess key symptoms related to cognitive function, a unique advantage in the assessment and monitoring of cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

Cay,

Pfeifer,

Lee

et al. 2024

Gerontology

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Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable-microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's D effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho=0.529, p<0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho=-0.441, p<0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech to pause ratio, similarity via DTW, and ratio of extra words. Precision and balanced accuracy scores were found to be 84.3 ± 1.5% and 75.0 ± 1.4%, respectively. Discussion: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.

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Speech silence character as a diagnostic biomarker of early cognitive decline and its functional mechanism: a multicenter cross-sectional cohort study

Cited by 9 publications

References 45 publications

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

Consensus on rapid screening for prodromal Alzheimer’s disease in China

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

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