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  • A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers

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Neurodegeneration
Research paper
A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers
  1. Rogier A Feis1,2,3,
  2. Mark J R J Bouts1,2,3,
  3. Frank de Vos1,2,3,
  4. Tijn M Schouten1,2,3,
  5. Jessica L Panman1,4,
  6. Lize C Jiskoot1,4,
  7. Elise G P Dopper4,
  8. Jeroen van der Grond1,
  9. John C van Swieten4,5,
  10. Serge A R B Rombouts1,2,3
  1. 1 Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
  2. 2 Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
  3. 3 Institute of Psychology, Leiden University, Leiden, The Netherlands
  4. 4 Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
  5. 5 Department of Clinical Genetics, VU University Medical Centre, Amsterdam, The Netherlands
  1. Correspondence to Mr Rogier A Feis, Department of Radiology, Leiden University Medical Centre, Leiden 2333 ZA, The Netherlands; r.a.feis{at}lumc.nl

Abstract

Background Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up (‘converters’) and non-converting carriers (‘non-converters’).

Methods We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time.

Results Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001).

Conclusions Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.

  • frontotemporal dementia
  • mapt protein, human
  • grn protein, human
  • c9orf72, human
  • diffusion tensor imaging
  • resting-state functional mri
  • multimodal mri
  • classification
  • machine learning

https://doi.org/10.1136/jnnp-2019-320774

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    Footnotes

    • Correction notice This article has been corrected since it was published Online First. Serge Rombouts has been linked to affiliation 3.

    • Contributors RAF was involved in the study design, analyses, interpretation of the data, and the drafting and revision of the manuscript. MJRJB was involved in the analysis and interpretation of the data, and the revision of the manuscript. FdV was involved in the interpretation of the data and the revision of the manuscript. TMS was involved in the interpretation of the data and the revision of the manuscript. JLP was involved in the data acquisition and the revision of the manuscript. LCJ was involved in the data acquisition and the revision of the manuscript. EGPD was involved in the data acquisition and the revision of the manuscript. JvdG was involved in the interpretation of the data and the revision of the manuscript. JCvS was involved in the study design, interpretation of the data and the revision of the manuscript. SARBR was involved in the study design, interpretation of the data and the revision of the manuscript.

    • Funding The authors of this work were supported by the Leiden University Medical Centre MD/PhD Scholarship (to RAF), ZonMw programme Memorabel project 733050103, JPND PreFrontAls consortium project 733051042 (to JCvS) and NWO VICI grant 016-130-667 (to SARBR).

    • Disclaimer The views expressed are those of the authors and not necessarily those of the funding sources. The funding sources were not involved in the design of the study; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

    • Competing interests None declared.

    • Patient consent for publication Obtained.

    • Ethics approval Erasmus Medical Centre and Leiden University Medical Centre local medical ethics committees.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available on reasonable request.

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