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Speech and language characteristics differentiate Alzheimer's disease and dementia with Lewy bodies

Abstract Introduction Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated. Methods We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features. Results The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine‐learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB. Discussion Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

onset. 14,18 In particular, numerous studies have investigated spontaneous, connected speech during picture description tasks [14][15][16] and reported linguistic differences in AD patients such as increased repetition and reduced informative content related to word-finding difficulties. 7,8,15,16 In addition, speech and language impairments do not constitute a central feature for DLB, 19 but they are also observed in the course of DLB. 9,17,20,21 DLB patients showing discernable differences in language characteristics encompassing all linguistic aspects have been suggested, with the exception of the lexico-semantic domain, although these deficits have been suggested to be less severe compared with AD patients by study with a communication test evaluated by human experts. 22 In the speech of DLB patients, prosodic differences, such as slower speech rate and longer pauses, have been consistently observed, 9,20,17,21 and these differences are thought to be at least partially related to motor impairment. 17 Furthermore, although there is no study for DLB, acoustic impairments, such as increased variabilities in voice frequency and amplitude (i.e., jitter and shimmer), have been known to be significant features of Parkinson's disease (another form of Lewy body spectrum disorders). [23][24][25][26] By contrast, prosodic and acoustic differences in AD patients such as pauses and variability in voice frequency have yielded mixed results with several studies showing no significant difference and others showing statistically significant differences. [27][28][29] Together, these previous studies suggest different profiles of speech and language impairments between AD and DLB, 17,22,30 but there is no study directly comparing them using speech analysis.
In this study, we aimed to identify differences in features characterizing speech and language impairments between AD and DLB and to examine the feasibility of using these features to identify and differentiate AD and DLB. On the basis of previous studies, we hypothesized that AD and DLB patients would have different profiles of speech and language impairments. Specifically, we hypothesized that patients with AD would show larger differences in linguistic features compared with DLB patients, and patients with DLB would show larger differences in prosodic and acoustic features than AD patients. A second hypothesis was that these features would be used for reliably classifying patients with AD, those with DLB, and cognitively normal (CN) individuals. We collected speech responses with a tablet-based application from participants consisting of three clinical diagnostic groups: AD, DLB, and CN. We then extracted speech features characterizing acoustic, prosodic, and linguistic aspects. Finally, we tested the first hypothesis by statistically comparing the speech features between the diagnostic groups, and we tested the second hypothesis by assessing the performance of machine-learning models using these features to identify and differentiate AD and DLB.

Participants
We recruited outpatients from the Department of Psychiatry, University of Tsukuba Hospital, along with the spouses of the patients,

RESEARCH IN CONTEXT
• (e.g., frontotemporal dementia or vascular dementia) or other serious diseases or disabilities that would interfere with the collection of speech data. Thus, the participants formed three clinical diagnostic groups of cognitive impairment due to AD (AD group) or Lewy body disease (DLB group), and cognitively normal controls (CN group).
Patients in the AD and DLB groups ranged from MCI to moderate dementia. 34 The participants were administered cognitive and clinical examinations, which comprised 12 variables (see Table 1  The study was conducted with the approval of the Ethics Committee, University of Tsukuba Hospital (H29-065), and it followed the ethical code for research with humans as stated in the Declaration of Helsinki. All participants provided written informed consent to participate in the study. All examinations were conducted in Japanese.
Experiment periods are described in the Supplementary Methods.

Speech data collection and speech features
The participants sat in front of an iPad Air 2 tablet and answered questions presented by a voice-based application on the tablet in a quiet room with low reverberation in a lab setting (for more details,  Table S1. From the participants' speech responses to the five tasks, we Parkinson's disease. 8,9,16,24,28,36 They were extracted from each task response and investigated separately in subsequent analyses. A full list of speech features and tasks from which these features were extracted is given in Table S2. The acoustic and prosodic features were extracted from the speech responses to all five tasks except where otherwise indicated. Specifically, prosodic features included a proportion of the pause duration and pitch variation (i.e., inflection) in addition to the phoneme rate in the picture description task. Acoustic features included jitter and shimmer, which measure cycle-to-cycle variations of the fundamental frequency and amplitude, respectively.
Increased shimmer and jitter have been reported in patients of neurodegenerative diseases. 23

Statistical analysis
Group differences between CN, AD, and DLB were examined by using the chi-square test followed by a post hoc chi-square test for categorical data and one-way analysis of variance (ANOVA) followed by a post hoc Tukey-Kramer test for continuous data. Sex, disease stage (MCI, dementia), and the use of antipsychotic medication are categorical data, and the other data including all speech features are continuous data. For the multiple testing of the 30 speech features, the Benjamini-Hochberg correction was applied. We calculated a Bayes factor to assess the magnitude of evidence in favor of an alternative hypothesis versus the null hypothesis. 38 We also calculated the gen-

Machine-learning analysis
To evaluate the feasibility of using speech features to identify and differentiate AD and DLB, we used supervised machine-learning models to classify the clinical diagnostic groups of AD, DLB, and CN via the speech features. The input variables for the model were the 42 speech features. Binary-classification models were evaluated by using accuracy, sensitivity, specificity, F1 score, and the area under receiver operating characteristic curve (AUC) obtained from 10 iterations of a leave-two-subjects-out cross-validation procedure. To reduce overfitting through automatic feature selection, we also performed a sequential forward selection algorithm. For the classification algorithm, we used a support vector machine with a radial basis function kernel 40 implemented with the Python package scikit-learn (version 0.23.2). The hyperparameters in this study have been found in a prior study. 41 For missing values, we applied multivariate imputation by chained equations (version 3.13.3). 42 Spearman's rank correlation coefficient was used to assess the correlations between cognitive scores and output probability measures of the binary-classification models.

RESULTS
A total of 123 participants met the inclusion criteria. Among the 123 participants, two were excluded from this analysis because they did not complete any of the five speech tasks. This yielded a total of 121 partic- We investigated whether the speech features had statistically discernable differences between the clinical diagnostic groups of AD, DLB, and CN. We found that nine speech features showed statistically significant differences between the groups (Benjamini-Hochberg adjusted P < 0.05; Table 2). These nine features all showed a medium effect size (η 2 > 0.06). 39 Post hoc pairwise comparisons with the Tukey-Kramer test revealed the following patterns of statistically significant differences between the CN groups and the AD or DLB groups ( Table 2 and Figure 1).
Regarding the overall trends, the AD group demonstrated larger differences from the CN group in linguistic features, while the DLB group demonstrated larger differences in acoustic and prosodic features.   Table S4).
The ANOVA post hoc Tukey-Kramer tests also revealed that the AD and DLB groups had significant differences in two acoustic features: jitter in the semantic verbal fluency (P = 0.026) and shimmer in the picture description tasks (P = 0.045) ( Table 2). We further conducted a two-way ANOVA, 2 dementia types (AD, DLB) × 2 disease stages (MCI, dementia), for these two acoustic features. The results showed significant effects of the dementia type, in which both jitter and shimmer were higher in the DLB group compared with the AD group ( Figure 2):     [23][24][25][26] which were also suggested to further increase as the disease progresses. 26 These differences are thought to be due in part to motor impairments including deteriorated control of respiratory and laryngeal muscles. 43 Comparing these acoustic features across Lewy body disorders may provide useful insights into neuropathological mechanisms underlying the speech impairments. 23 The results of models using speech features could achieve a high performance for identifying and differentiating the AD and DLB groups. Our models differentiated the AD and DLB groups from the CN group primarily by capturing impairments in Logical Memorydelayed scores and Trail Making Test part-B scores, respectively. While AD is characterized clinically by prominent memory impairment, 19 DLB is characterized by more executive, attentional, and visuospatial impairment relative to memory impairment, 19 which may support the relevance of our results. Aligning with studies on gait and balance analysis for differentiating dementia types, [44][45][46] our results suggest the possibility of using speech and language characteristics as behavioral markers, reflecting dementia-type-specific cognitive profiles and underlying pathologies. To confirm this suggestion, we will need further study with validated neuropathological biomarkers. From a clinical perspective, a classification model using speech analysis may assist clinicians in differential diagnosis as a screening tool because speech data can be acquired in routine clinical practice. In fact, a number of studies on speech analysis for data collected during neuropsychological assessment have succeeded in detecting MCI and dementia, 14,16,[47][48][49] and several of them have shown that the addition of speech analysis to the neuropsychological assessment has been found to improve its accuracy. 48,49 Another clinical implication of this study is that our findings using the tablet-based application might help develop a self-administrated tool for the early detection of AD and DLB. According to the World Alzheimer Report published in 2021, 50 83% of clinicians maintain that the COVID-19 pandemic has delayed access to diagnostic assessments. Thus, a self-administered screening tool may be especially important in the current COVID-19 pandemic given the difficulties of an in-person evaluation in a clinical setting. In our future research, we will endeavor to investigate the operability and acceptability of real-world data collection.
There were several limitations in this study. First, our analysis did In summary, our results provide initial, empirical evidence of (1) the discriminative differences in speech features characterizing acoustic, prosodic, and linguistic aspects in AD and DLB, and (2) the feasibility of using these speech features as a screening tool for identifying and differentiating AD and DLB. Our findings require further validation with neuropathological biomarkers or postmortem follow-up.

ACKNOWLEDGMENTS
This work was supported by the Japan Society for the Promotion of Science, KAKENHI (grant number 19H01084). The funders did not play any active role in the scientific investigation and reporting of the study.

CONFLICT OF INTEREST
YY is employed by the IBM Corporation.
KS is employed by the IBM Corporation.
MN received funding from the Japan Society for the Promotion of Science, KAKENHI (grant number 19H01084).
MO has nothing to disclose.
KN has nothing to disclose.
TA received funding from Japan Society for the Promotion of Science, KAKENHI (grant number 19H01084). TA reports honoraria for lectures from Eisai, Daiichi-Sankyo, and Sumitomo Pharma.