Longitudinal Magnetic Resonance Imaging in Asymptomatic C9orf72 Mutation Carriers Distinguishes Phenoconverters to Amyotrophic Lateral Sclerosis or Amyotrophic Lateral Sclerosis With Frontotemporal Dementia

Overview

Journal Ann Neurol

Specialty Neurology

Date 2024 Nov 2

PMID 39487710

Authors

Kevin van Veenhuijzen

Harold H G Tan

Abram D Nitert

Michael A van Es

Jan H Veldink

Leonard H van den Berg

Henk-Jan Westeneng

Affiliations

Soon will be listed here.

Abstract

Objective: We prospectively studied asymptomatic C9orf72 mutation carriers, identifying those developing amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD).

Methods: We enrolled 56 asymptomatic family members (AFM) with a C9orf72 mutation (AFM C9+), 132 non-carriers (AFM C9-), and 359 population-based controls. Using 3 T magnetic resonance imaging, we measured cortical thickness, gyrification, and subcortical volumes longitudinally. Linear mixed-effects models on non-converting AFM C9+ scans (n = 107) created a reference for these measurements, establishing individual atrophy patterns. Atrophy patterns from presymptomatic phenoconverters (n = 10 scans) served as a template for group comparisons and similarity assessments. Similarity with phenoconverters was quantified using Dice similarity coefficient (DSC) for cortical and Kullback-Leibler similarity (KLS) for subcortical measures. Using longitudinal similarity assessments, we predicted when participants would reach the average similarity level of phenoconverters at their first post-onset scan.

Results: Five AFM C9+ converted to ALS or ALS-FTD. Up to 6 years before symptoms, these phenoconverters exhibited significant atrophy in frontal, temporal, parietal, and cingulate cortex, along with smaller thalamus, hippocampus, and amygdala compared to other AFM C9+. Some non-converted AFM C9+ had high DSC and KLS, approaching values of phenoconverters, whereas others, along with AFM C9- and controls, had lower values. At age 80, we predicted 27.9% (95% confidence interval, 13.2-40.1%) of AFM C9+ and no AFM C9- would reach the same DSC as phenoconverters.

Interpretation: Distinctive atrophy patterns are visible years before symptom onset on presymptomatic scans of phenoconverters. Combining baseline and follow-up similarity measures may serve as a promising imaging biomarker for identifying those at risk of ALS or ALS-FTD. ANN NEUROL 2025;97:281-295.

References

van Rheenen W, van Blitterswijk M, Huisman M, Vlam L, van Doormaal P, Seelen M . Hexanucleotide repeat expansions in C9ORF72 in the spectrum of motor neuron diseases. Neurology. 2012; 79(9):878-82. DOI: 10.1212/WNL.0b013e3182661d14. View

Van Wijk I, van Eijk R, Van Boxmeer L, Westeneng H, van Es M, van Rheenen W . Assessment of risk of ALS conferred by the GGGGCC hexanucleotide repeat expansion in among first-degree relatives of patients with ALS carrying the repeat expansion. Amyotroph Lateral Scler Frontotemporal Degener. 2023; 25(1-2):188-196. DOI: 10.1080/21678421.2023.2272187. View

Wagstyl K, Larocque S, Cucurull G, Lepage C, Cohen J, Bludau S . BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. PLoS Biol. 2020; 18(4):e3000678. PMC: 7159250. DOI: 10.1371/journal.pbio.3000678. View

Iglesias J, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve D . A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage. 2018; 183:314-326. PMC: 6215335. DOI: 10.1016/j.neuroimage.2018.08.012. View

Benatar M, Wuu J, Lombardi V, Jeromin A, Bowser R, Andersen P . Neurofilaments in pre-symptomatic ALS and the impact of genotype. Amyotroph Lateral Scler Frontotemporal Degener. 2019; 20(7-8):538-548. PMC: 6768722. DOI: 10.1080/21678421.2019.1646769. View

Grossman M, Seeley W, Boxer A, Hillis A, Knopman D, Ljubenov P . Frontotemporal lobar degeneration. Nat Rev Dis Primers. 2023; 9(1):40. DOI: 10.1038/s41572-023-00447-0. View

Murphy N, Arthur K, Tienari P, Houlden H, Chio A, Traynor B . Age-related penetrance of the C9orf72 repeat expansion. Sci Rep. 2017; 7(1):2116. PMC: 5437033. DOI: 10.1038/s41598-017-02364-1. View

van Veenhuijzen K, Westeneng H, Tan H, Nitert A, van der Burgh H, Gosselt I . Longitudinal Effects of Asymptomatic C9orf72 Carriership on Brain Morphology. Ann Neurol. 2022; 93(4):668-680. DOI: 10.1002/ana.26572. View

Jiskoot L, Panman J, Meeter L, Dopper E, Donker Kaat L, Franzen S . Longitudinal multimodal MRI as prognostic and diagnostic biomarker in presymptomatic familial frontotemporal dementia. Brain. 2018; 142(1):193-208. PMC: 6308313. DOI: 10.1093/brain/awy288. View

10.

Le Blanc G, Pomerleau V, McCarthy J, Borroni B, Van Swieten J, Galimberti D . Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic C9orf72 Repeat Expansion Adult Carriers. Ann Neurol. 2020; 88(1):113-122. DOI: 10.1002/ana.25748. View

11.

Bachmann T, Schroeter M, Chen K, Reiman E, Weise C . Longitudinal changes in surface based brain morphometry measures in amnestic mild cognitive impairment and Alzheimer's Disease. Neuroimage Clin. 2023; 38:103371. PMC: 10025277. DOI: 10.1016/j.nicl.2023.103371. View

12.

Feis R, van der Grond J, Bouts M, Panman J, Poos J, Schouten T . Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Brain Commun. 2021; 2(2):fcaa079. PMC: 7846185. DOI: 10.1093/braincomms/fcaa079. View

13.

Yao A, Cheng D, Pan I, Kitamura F . Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology. Radiol Artif Intell. 2021; 2(2):e190026. PMC: 8017426. DOI: 10.1148/ryai.2020190026. View

14.

Cao B, Mwangi B, Passos I, Wu M, Keser Z, Zunta-Soares G . Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders. Sci Rep. 2017; 7(1):511. PMC: 5428697. DOI: 10.1038/s41598-017-00582-1. View

15.

Pandya S, Maia P, Freeze B, Menke R, Talbot K, Turner M . Modeling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis. Neuroimage. 2022; 251:118968. PMC: 10729776. DOI: 10.1016/j.neuroimage.2022.118968. View

16.

Smith S, Nichols T . Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2008; 44(1):83-98. DOI: 10.1016/j.neuroimage.2008.03.061. View

17.

Rascovsky K, Hodges J, Knopman D, Mendez M, Kramer J, Neuhaus J . Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011; 134(Pt 9):2456-77. PMC: 3170532. DOI: 10.1093/brain/awr179. View

18.

Benatar M, Granit V, Andersen P, Grignon A, McHutchison C, Cosentino S . Mild motor impairment as prodromal state in amyotrophic lateral sclerosis: a new diagnostic entity. Brain. 2022; 145(10):3500-3508. PMC: 9586537. DOI: 10.1093/brain/awac185. View

19.

Staffaroni A, Quintana M, Wendelberger B, Heuer H, Russell L, Cobigo Y . Temporal order of clinical and biomarker changes in familial frontotemporal dementia. Nat Med. 2022; 28(10):2194-2206. PMC: 9951811. DOI: 10.1038/s41591-022-01942-9. View

20.

Huisman M, de Jong S, van Doormaal P, Weinreich S, Jurgen Schelhaas H, van der Kooi A . Population based epidemiology of amyotrophic lateral sclerosis using capture-recapture methodology. J Neurol Neurosurg Psychiatry. 2011; 82(10):1165-70. DOI: 10.1136/jnnp.2011.244939. View