Evidence map›Paper›PMID 38874653›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

Alfonso Estudillo Romero, Raffaella Migliaccio, Bénédicte Batrancourt, Pierre Jannin, John S H Baxter

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Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Alfonso Estudillo RomeroLaboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.
Raffaella MigliaccioFrontal Functions and Pathology Laboratory (FrontLab), Institut du Cerveau, Paris, France.
Bénédicte BatrancourtFrontal Functions and Pathology Laboratory (FrontLab), Institut du Cerveau, Paris, France.
Pierre JanninLaboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.
John S H BaxterLaboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France. john.baxter@univ-rennes.fr.ORCID http://orcid.org/0000-0003-3548-4343

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeFrontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As these variants are detected based on their symptoms, it can be unclear if they represent different types of FTD or different symptomatological axes. The goal of this paper is to investigate this question with a constrained cohort of FTD patients in order to see if the heterogeneity within this cohort can be inferred from medical images rather than symptom severity measurements.

methodsAn ensemble of convolutional neural networks (CNNs) is used to classify diffusion tensor images collected from two databases consisting of 72 patients with behavioural variant FTD and 120 healthy controls. FTD biomarkers were found using voxel-based analysis on the sensitivities of these CNNs. Sparse principal components analysis (sPCA) is then applied on the sensitivities arising from the patient cohort in order to identify the axes along which the patients express these biomarkers. Finally, this is correlated with their symptom severity measurements in order to interpret the clinical presentation of each axis.

resultsThe CNNs result in sensitivities and specificities between 83 and 92%. As expected, our analysis determines that all the robust biomarkers arise from the frontal and temporal lobes. sPCA identified four axes in terms of biomarker expression which are correlated with symptom severity measurements.

conclusionOur analysis confirms that behavioural variant FTD is not a singular type or spectrum of FTD, but rather that it has multiple symptomatological axes that relate to distinct regions of the frontal and temporal lobes. This analysis suggests that medical images can be used to understand the heterogeneity of FTD patients and the underlying anatomical changes that lead to their different clinical presentations.

Indexed as

BiomarkersDiffusion Tensor ImagingFrontotemporal DementiaNeural Networks, ComputerAgedFemaleHumansMaleMiddle AgedPrincipal Component AnalysisSensitivity and SpecificityBiomarkersConvolutional neural networksDisease sub-typingFrontotemporal lobe dementiaVoxel-based diktiometry

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.