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Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use

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Abstract

Introduction

The majority of imaging studies in ALS infer group-level imaging signatures from group comparisons, as opposed to estimating disease burden in individual patients. In a condition with considerable clinical heterogeneity, the characterisation of individual patterns of pathology is hugely relevant. In this study, we evaluate a strategy to track progressive cortical involvement in single patients by using subject-specific reference cohorts.

Methods

We have interrogated a multi-timepoint longitudinal dataset of 61 ALS patients to demonstrate the utility of estimating cortical disease burden and the expansion of cerebral atrophy over time. We contrast our strategy to the gold-standard approach to gauge the advantages and drawbacks of our method. We modelled the evolution of cortical integrity in a conditional growth model, in which we accounted for age, gender, disability, symptom duration, education and handedness. We hypothesised that the variance associated with demographic variables will be successfully eliminated in our approach.

Results

In our model, the only covariate which modulated the expansion of atrophy was motor disability as measured by the ALSFRS-r (t(153) = − 2.533, p = 0.0123). Using the standard approach, age also significantly influenced progression of CT change (t(153) = − 2.151, p = 0.033) demonstrating the validity and potential clinical utility of our approach.

Conclusion

Our strategy of estimating the extent of cortical atrophy in individual patients with ALS successfully corrects for demographic factors and captures relevant cortical changes associated with clinical disability. Our approach provides a framework to interpret single T1-weighted images in ALS and offers an opportunity to track cortical propagation patterns both at individual subject level and at cohort level.

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Abbreviations

ALS:

Amyotrophic lateral sclerosis

ALSFRS-r:

Revised amyotrophic lateral sclerosis functional rating scale

ANOVA:

Analysis of variance

Cam-CAN:

Cambridge Centre for Ageing and Neuroscience

CIFTI:

Connectivity Informatics Technology Initiative

CGM:

Conditional growth model

CT:

Cortical thickness

DLPFC:

Dorsolateral prefrontal cortex

EMG:

Electromyography

EP:

Electrophysiology

FA:

Flip angle

FDR:

False discovery rate

FLAIR:

Fluid-attenuated inversion recovery

FOV:

Field of view

FTD:

Frontotemporal dementia

FWER:

Family-wise error rate

GRAPPA:

GeneRalised Autocalibrating Partial Parallel Acquisition

HC:

Healthy control

IR-SPGR:

Inversion recovery-prepared spoiled gradient-recalled echo

IR-TSE:

Inversion recovery turbo spin echo

LL:

Log-likelihoods

M:

Arithmetic mean

ML:

Machine learning

MPRAGE:

Magnetisation-prepared rapid gradient echo

MND:

Motor neuron disease

Mo:

Months

MS:

Multiple sclerosis

PLS:

Primary lateral sclerosis

QC:

Quality control

ROI:

Region of interest

SD:

Standard deviation

SENSE:

SENSitivity Encoding

SR:

Spatial resolution

T:

Tesla

T1w:

T1 weighted

TE:

Echo time

TI:

Inversion time

TMS:

Transcranial magnetic stimulation

TR:

Repetition time

UGM:

Unconditional growth model

UMM:

Unconditional means model

V:

Version

VS:

Versus

Y:

Year

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Acknowledgements

We are grateful for all patients with ALS for agreeing to participate in this research study, and we are also indebted to their caregivers and families for their support. Without their generosity, this study would have not been possible. We also thank all patients who expressed interest in this study, but were unable to participate for medical or logistical reasons. We also thank the Irish Motor Neuron Disease Association for facilitating recruitment and providing unrelenting support to all patients with ALS and PLS.

Funding

Doctor Marlene Tahedl is funded by the Deutsche Multiple Sklerose Gesellschaft (DMSG), Grant number 2018_DMSG_08. Professor Peter Bede is supported by the Health Research Board (HRB EIA-2017-019), Spastic Paraplegia Foundation, Inc. (SPF), the EU Joint Programme—Neurodegenerative Disease Research (JPND), the Andrew Lydon scholarship, the Irish Institute of Clinical Neuroscience (IICN) and the Iris O'Brien Foundation. The sponsors of the authors had no bearing on the opinions expressed herein.

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Authors and Affiliations

  1. Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland

    Marlene Tahedl, Rangariroyashe H. Chipika, Jasmin Lope, Stacey Li Hi Shing, Orla Hardiman & Peter Bede

  2. Department of Psychiatry and Psychotherapy, University of Regensburg, 93053, Regensburg, Germany

    Marlene Tahedl

  3. Institute for Psychology, University of Regensburg, 93053, Regensburg, Germany

    Marlene Tahedl

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  1. Marlene Tahedl
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  2. Rangariroyashe H. Chipika
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  4. Stacey Li Hi Shing
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  5. Orla Hardiman
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  6. Peter Bede
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Contributions

MT and PB were involved in the conceptualisation of the study, neuroimaging analyses and drafting the manuscript. RC, JL, SLHS and OH contributed to clinical profiling and data acquisition. MT, RC, JL, SLHS, OH and PB were all involved in the revision of the manuscript for intellectual content.

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Correspondence to Peter Bede.

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The authors have no conflicts of interest to declare.

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All participants provided informed consent in accordance with the approval of the Ethics (Medical Research) Committee—Beaumont Hospital, Dublin, Ireland.

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Tahedl, M., Chipika, R.H., Lope, J. et al. Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use. J Neurol 268, 1913–1926 (2021). https://doi.org/10.1007/s00415-020-10368-7

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