Vonk Jet M J, Morin Brittany T, Pillai Janhavi, Rolon David Rosado, Bogley Rian, Baquirin David Paul, Ezzes Zoe, Tee Boon Lead, DeLeon Jessica, Wauters Lisa, Lukic Sladjana, Montembeault Maxime, Younes Kyan, Miller Zachary, García Adolfo M, Mandelli Maria Luisa, Sturm Virginia E, Miller Bruce L, Gorno-Tempini Maria Luisa
Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA.
Department of Speech, Language and Hearing Sciences, University of Texas Austin, Austin, TX.
medRxiv. 2024 Aug 30:2024.08.29.24312807. doi: 10.1101/2024.08.29.24312807.
Within frontotemporal dementia (FTD), the behavioral variant (bvFTD) characterized by frontal atrophy, and semantic behavioral variant (sbvFTD) characterized by right anterior temporal lobe (rATL) atrophy, present diagnostic challenges due to overlapping symptoms and neuroanatomy. Accurate differentiation is crucial for clinical trial inclusion targeting TDP-43 proteinopathies. This study investigated whether automated speech analysis can distinguish between FTD-related rATL and frontal atrophy, potentially offering a non-invasive diagnostic tool.
In a cross-sectional design, we included 40 participants with FTD-related predominant frontal atrophy (n=16) or predominant rATL atrophy (n=24) and 22 healthy controls from the UCSF Memory and Aging Center. Using stepwise logistic regression and receiver operating characteristic (ROC) curve analysis, we analyzed 16 linguistic and acoustic features that were extracted automatically from audio-recorded picture description tasks. Neuroimaging data were analyzed using voxel-based morphometry to examine brain-behavior relationships of regional atrophy with the features selected in the regression models.
Logistic regression identified three features (content units, lexical frequency, familiarity) differentiating the overall FTD group from controls (AUC=.973), adjusted for age. Within the FTD group, five features (adpositions/total words ratio, arousal, syllable pause duration, restarts, words containing 'thing') differentiated frontal from rATL atrophy (AUC=.943). Neuroimaging analyses showed that semantic features (lexical frequency, content units, 'thing' words) were linked to bilateral inferior temporal lobe structures, speech and lexical features (syllable pause duration, adpositions/total words ratio) to bilateral inferior frontal gyri, and socio-emotional features (arousal) to areas known to mediate social cognition including the right insula and bilateral anterior temporal structures. As a composite score, this set of five features was uniquely associated with rATL atrophy.
Automated speech analysis effectively distinguished the overall FTD group from controls and differentiated between frontal and rATL atrophy. The neuroimaging findings for individual features highlight the neural basis of language impairments in these FTD variants, and when considered together, underscore the importance of utilizing features' combined power to identify impaired language patterns. Automated speech analysis could enhance early diagnosis and monitoring of FTD, offering a scalable, non-invasive alternative to traditional methods, particularly in resource-limited settings. Further research should aim to integrate automated speech analysis into multi-modal diagnostic frameworks.
在额颞叶痴呆（FTD）中，以额叶萎缩为特征的行为变异型（bvFTD）和以右侧颞前叶（rATL）萎缩为特征的语义行为变异型（sbvFTD），由于症状和神经解剖结构重叠，存在诊断挑战。准确区分对于针对TDP-43蛋白病的临床试验纳入至关重要。本研究调查了自动语音分析是否能够区分与FTD相关的rATL萎缩和额叶萎缩，从而有可能提供一种非侵入性诊断工具。
采用横断面设计，我们纳入了40名来自加州大学旧金山分校记忆与衰老中心的FTD患者，其中以额叶萎缩为主（n = 16）或rATL萎缩为主（n = 24），以及22名健康对照者。使用逐步逻辑回归和受试者工作特征（ROC）曲线分析，我们分析了从音频记录的图片描述任务中自动提取的16种语言和声学特征。使用基于体素的形态计量学分析神经影像数据，以检查区域萎缩与回归模型中选定特征之间的脑-行为关系。
逻辑回归确定了三个特征（内容单元、词汇频率、熟悉度）可将整个FTD组与对照组区分开来（AUC = 0.973），并对年龄进行了校正。在FTD组中，五个特征（介词/总词数比、唤醒度、音节停顿持续时间、重新开始、包含“thing”的词）可区分额叶萎缩和rATL萎缩（AUC = 0.943）。神经影像分析表明，语义特征（词汇频率、内容单元、含“thing”的词）与双侧颞下回结构相关，语音和词汇特征（音节停顿持续时间、介词/总词数比）与双侧额下回相关，社会情感特征（唤醒度）与已知介导社会认知的区域相关，包括右侧岛叶和双侧颞前叶结构。作为一个综合评分，这组五个特征与rATL萎缩具有独特的相关性。
自动语音分析有效地将整个FTD组与对照组区分开来，并区分了额叶萎缩和rATL萎缩。个体特征的神经影像研究结果突出了这些FTD变异型语言障碍的神经基础，综合考虑时，强调了利用特征的综合力量来识别受损语言模式的重要性。自动语音分析可以加强FTD的早期诊断和监测，为传统方法提供一种可扩展的、非侵入性的替代方法，特别是在资源有限的环境中。进一步的研究应旨在将自动语音分析纳入多模态诊断框架。
