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

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Abstract

Purpose:

Frontotemporal 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.

Methods:

An 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.

Results:

The 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.

Conclusion:

Our 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.

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Availability of data and materials

Data from the ECOCAPTURE dataset [12, 13] was collected by the Insitut du Cerveau (Paris, France). NIFD data are available at https://ida.loni.usc.edu, https://memory.ucsf.edu/research-trials/research/allftd.

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Funding

Alfonso Estudillo Romero is supported through the SAD Région Bretagne programme and the Institut des Neurosciences Cliniques de Rennes (INCR).

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Authors and Affiliations

  1. Laboratoire Traitement du Signal et de l’Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France

    Alfonso Estudillo Romero, Pierre Jannin & John S. H. Baxter

  2. Frontal Functions and Pathology Laboratory (FrontLab), Institut du Cerveau, Paris, France

    Raffaella Migliaccio & Bénédicte Batrancourt

Authors
  1. Alfonso Estudillo Romero
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  2. Raffaella Migliaccio
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  3. Bénédicte Batrancourt
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  4. Pierre Jannin
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  5. John S. H. Baxter
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Corresponding author

Correspondence to John S. H. Baxter.

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The authors have no Conflict of interest to declare.

Ethics approval

All ECOCAPTURE data were collected with institutional ethics board approval (Clinicaltrials.gov:NTC02496312, NCT03272230).

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Estudillo Romero, A., Migliaccio, R., Batrancourt, B. et al. Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03197-w

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