Apraxia screening predicts Alzheimer pathology in frontotemporal dementia

Pawlowski, Matthias; Joksch, Viktoria; Wiendl, Heinz; Meuth, Sven G.; Duning, Thomas; Johnen, Andreas

doi:10.1136/jnnp-2018-318470

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…The systematic literature search yielded 1257 records, of which 116 studies were assessed at full-text level for eligibility and 83 studies met inclusion criteria (eFigure 5 in the Supplement for flowchart). Confirmation of AD pathology was present in 91.1% of cases with bvAD based on autopsy data (36 studies; n = 334), genetic data (9 studies; n = 21), or biomarker data (31 studies; n = 262), while no information was available in 9.9% of cases (11 studies including 68 cases of bvAD). Thirteen studies were eligible for meta-analysis.…”

Section: Resultsmentioning

confidence: 99%

Research Criteria for the Behavioral Variant of Alzheimer Disease

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Research Criteria for the Behavioral Variant of Alzheimer Disease

et al. 2022

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“…These features were seen in both patients with AD pathology and the majority of those with FTLD. Apraxia has been reported more frequently in typical AD with positive biomarkers [31,32] but was not present at initial assessment in either of our cases of bvFTD with AD pathology. Previous studies suggest that apathy is non-specific in neurodegenerative diseases [4,9,32] and it was present in both AD and FTLD patients in the present study.…”

Section: Discussionmentioning

confidence: 45%

Frontotemporal dementia or frontal variant Alzheimer's disease? A case series

Powell¹,

Foxe²,

Halliday³

et al. 2023

Preprint

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Introduction: Accurate prediction of the underlying neuropathology in behavioural variant frontotemporal dementia (bvFTD) is challenging but essential for future targeted therapy trials and prognostication. Alzheimer’s disease (AD) pathology has been reported in a significant proportion of patients with clinical bvFTD. We sought to determine whether detailed clinical and neuroradiological assessment was sufficient to distinguish bvFTD with AD pathology from bvFTD with frontotemporal lobar degeneration (FTLD).Methods: Two patients with clinically diagnosed probable bvFTD but AD pathology at autopsy were identified. The clinical, neuropsychological and imaging features of these patients were compared with those of ten patients with clinically probable bvFTD and proven FTLD pathology (i.e. tau, TDP-43, FUS).Results: Both patients with AD pathology presented with behavioural symptoms typical of bvFTD as well as memory impairment. Executive function, memory and visuospatial skills were impaired in both pathologic groups. Language skills were relatively spared in those with AD pathology. Neuropsychiatric symptoms were frequent in both groups but significant depression and anxiety were seen only in those with FTLD pathology. Dementia severity and caregiver burden were similar across the groups. The degree or topographical distribution of atrophy did not differ on review of MRI data.Discussion: Alzheimer’s pathology may cause bvFTD symptoms which are otherwise indistinguishable to those caused by FTLD pathology. While there may be subtle differences in patterns of cognitive deficits, standard neuropsychological testing is insufficient to discern the underlying pathology. Similarly, structural imaging cannot be used to reliably identify AD pathology. Better access to amyloid biomarkers may be needed to more accurately define bvFTD caused by AD pathology.

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“…Recent research has demonstrated that apraxia screening can also predict dementia disease progression (Pawlowski et al, 2019), especially as a way to predict AD in early stage FTD subjects, a population that we are particularly interested in targeting with our biomarkers. For the Behavioral Variant of Fronto Temporal Dementia (bvFTD), in patients under 65 the second most common cognitive disorder caused by neurodegeneration, little tonal modulation and buccofacial apraxia, are targeted by our biomarkers and are established diagnostic domains (Johnen and Bertoux, 2019).…”

Section: Respiratory Tract Biomarkers Cough and Wake Wordmentioning

confidence: 99%

Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

Laguarta¹,

Subirana

2021

Front. Comput. Sci.

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We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. Transfer Learning is subsequently used to incorporate features from this model into various other biomarker-based OVBM architectures. This biomarker yields consistent improvements in AD detection in all the starting OBVM biomarker architecture combinations we tried. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first time cough cultural bias.

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Apraxia screening predicts Alzheimer pathology in frontotemporal dementia

Cited by 10 publications

References 39 publications

Research Criteria for the Behavioral Variant of Alzheimer Disease

Research Criteria for the Behavioral Variant of Alzheimer Disease

Frontotemporal dementia or frontal variant Alzheimer's disease? A case series

Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

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