Machine learning in the clinical and language characterisation of primary progressive aphasia variants

Matías‐Guiu, Jordi A.; Álvarez, Josefa Díaz; Cuetos, Fernando; Cabrera‐Martín, María Nieves; Segovia-Ríos, Ignacio; Pytel, Vanesa; Moreno‐Ramos, Teresa; Carreras, José; Ayala, José L.

doi:10.1016/j.cortex.2019.05.007

Cited by 34 publications

(35 citation statements)

References 38 publications

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“…In cases of nfvPPA, structural and metabolic imaging reveals a left-predominant decline in the frontal lobe involving the pars opercularis, insula, middle frontal gyrus, and supplementary motor area [ 86 ]. In a 2019 study by Matias-Guiu et al [ 248 ], it was found that in those with nfvPPA, two subtypes could be differentiated based on clustering of FDG-PET hypometabolism. In the first cluster, involvement of the left frontal lobe was predominant, impacting the left superior and inferior frontal gyri, insula, anterior cingulate, left caudate, and the left middle and medial frontal gyri.…”

Section: Neuroimaging Heterogeneity Within Ftd Clinical Phenotypes: Data-driven Approachesmentioning

confidence: 99%

Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology

et al. 2021

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Frontotemporal dementia encompasses a group of clinical syndromes defined pathologically by degeneration of the frontal and temporal lobes. Historically, these syndromes have been challenging to diagnose, with an average of about three years between the time of symptom onset and the initial evaluation and diagnosis. Research in the field of neuroimaging has revealed numerous biomarkers of the various frontotemporal dementia syndromes, which has provided clinicians with a method of narrowing the differential diagnosis and improving diagnostic accuracy. As such, neuroimaging is considered a core investigative tool in the evaluation of neurodegenerative disorders. Furthermore, patterns of neurodegeneration correlate with the underlying neuropathological substrates of the frontotemporal dementia syndromes, which can aid clinicians in determining the underlying etiology and improve prognostication. This review explores the advancements in neuroimaging and discusses the phenotypic and pathologic features of behavioral variant frontotemporal dementia, semantic variant primary progressive aphasia, and nonfluent variant primary progressive aphasia, as seen on structural magnetic resonance imaging and positron emission tomography.

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Section: Neuroimaging Heterogeneity Within Ftd Clinical Phenotypes: Data-driven Approachesmentioning

confidence: 99%

Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology

et al. 2021

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“…As in many other neurodegenerative disorders, the available treatments have limited e cacy. Three main variants are currently recognized (the non uent, semantic, and logopenic variants), although some additional subtypes have been suggested (3)(4)(5).…”

Section: Trial Registration Nct03580954 (Clinicaltrialsgov) Registered 10 July 2018 (Retrospectively Registered)mentioning

confidence: 99%

Personalized Repetitive Transcranial Magnetic Stimulation for Primary Progressive Aphasia

Pytel

Cabrera‐Martín

Delgado‐Álvarez

et al. 2021

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Background: Primary progressive aphasia (PPA) is a neurodegenerative syndrome for which no effective treatment is available. Objective: We aimed to assess the effect of repetitive transcranial magnetic stimulation (rTMS), using personalized targeting. Methods: We conducted a randomized, double-blind, pilot study of patients with PPA receiving rTMS, with a subgroup of patients receiving active- versus control-site rTMS in a cross-over design. Target for active TMS varied among the cases and was determined during a pre-treatment phase from a list of potential regions. The primary outcome was changes in spontaneous speech (word count). Secondary outcomes included changes in other language tasks, global cognition, global impression of change, neuropsychiatric symptoms, and brain metabolism using FDG-PET. Results: Twenty patients with PPA were enrolled (14 with nonfluent and 6 with semantic variant PPA). For statistical analyses, data for the two variants were combined. Compared to the control group (n = 7), the group receiving active-site rTMS (n = 20) showed improvements in spontaneous speech, other language tasks, patient and caregiver global impression of change, apathy, and depression. This group also showed improvement or stabilization of results obtained in the baseline examination. Increased metabolism was observed in several brain regions after the therapy, particularly in the left frontal and parieto-temporal lobes and in the precuneus and posterior cingulate bilaterally. Conclusion: We found an improvement in language, patient and caregiver perception of change, apathy, and depression using high frequency rTMS. The increase of regional brain metabolism suggests enhancement of synaptic activity with the treatment. Trial registration: NCT03580954 (https://clinicaltrials.gov/ct2/show/NCT03580954)

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“…One of the main strengths of ML is the combination of multiple sources of information. Thus, the application of ML to studies including multimodality assessments (cognitive testing, MRI, PET) may be of interest to disentangle the best tools (isolated or in combination) for diagnosis of PPA [54].…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study

et al. 2021

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Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.

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Machine learning in the clinical and language characterisation of primary progressive aphasia variants

Cited by 34 publications

References 38 publications

Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology

Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology

Personalized Repetitive Transcranial Magnetic Stimulation for Primary Progressive Aphasia

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study

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