Abrahams, S

Agosta, F

Atassi, N

Bede, P

Benali, H

Benatar, M

Enzinger, C

Filippi, M

Gaser, C

Goldstein, LH

Grosskreutz, J

Jelsone-Swain, L

Kalra, S

Kassubek, J

Lule, D

Mohammadi, B

Mueller, H-P

Orrell, RW

Pradat, P-F

Prudlo, J

Ropele, S

Sharma, R

Silani, V

Simmons, A

Smith, S

Teipel, S

Toosy, A

Tsermentseli, S

Turner, MR

Van Damme, P

van den Heuvel, M

Verstraete, E

Welsh, R

Wittstock, M

English

UCL Discovery

                             Elsevier Editorial System(tm) for The Lancet Neurology                                   Manuscript Draft   Manuscript Number:   Title: Towards a neuroimaging biomarker in amyotrophic lateral sclerosis   Article Type: Commentary (Reflection & Reaction)  Corresponding Author: Dr Martin R Turner, PhD  Corresponding Author's Institution: University of Oxford  First Author: Martin R Turner, PhD  Order of Authors: Martin R Turner, PhD; Julian Grosskreutz; Jan Kassubek; Sharon Abrahams; Federica Agosta; Michael Benatar; Massimo Filippi; Laura H Goldstein; Martijn van den Heuvel; Sanjay Kalra; Dorothée Lulé; Bahram Mohammadi; Other members of The 1st NISALS  Manuscript Region of Origin: UNITED KINGDOM      Turner et al 1  Towards a neuroimaging biomarker in amyotrophic lateral sclerosis  Martin R Turner1 Julian Grosskreutz2 Jan Kassubek3 Sharon Abrahams4 Federica Agosta5 Michael Benatar6 Massimo Filippi5 Laura H. Goldstein7 Martijn van den Heuvel8 Sanjay Kalra9 Dorothée Lulé3 Bahram Mohammadi10 & other members of ‘The 1st NISALS’*  *Other contributors to The 1st Neuroimaging Symposium in ALS (NISALS): Nazem Atassi11, Peter Bede12, Habib Benali13, Christian Enzinger14, Christian Gaser2, Laura Jelsone-Swain15, Hans-Peter Müller3, Richard W. Orrell16, Pierre-François Pradat17, Johannes Prudlo18, Stefan Ropele14, Rakesh Sharma1, Vincenzo Silani19, Andrew Simmons7, Stephen Smith1, Stefan Teipel18, Ahmed Toosy16, Stella Tsermentseli7, Philip Van Damme20, Esther Verstraete8, Robert Welsh15, Matthias Wittstock17  1Nuffield Department of Clinical Neurosciences, University of Oxford, UK 2Friedrich-Schiller-University of Jena, Germany 3Department of Neurology, University of Ulm, Germany 4Human Cognitive Neuroscience, Centre for Cognitive Aging and Cognitive   Epidemiology, Euan MacDonald Centre, University of Edinburgh, UK 5Neuroimaging Research Unit, Department of Neuroscience, Scientific Institute and  University San Raffaele, Milano, Italy 6Department of Neurology, Miller School of Medicine, University of Miami, USA 7King’s College London, Institute of Psychiatry, UK ManuscriptTurner et al 2  8Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht,  The Netherlands 9Division of Neurology, Department of Medicine, University of Alberta, Edmonton,  Alberta, Canada 10CNS-Lab, International Neuroscience Institute, Hannover & Department of Neurology,  University of Lübeck, Germany 11Massachusetts General Hospital-Harvard Medical School, Boston, USA 12Trinity College Dublin, Ireland 13INSERM, Paris, France 14Department of Neurology, Medical University of Graz, Austria 15University of Michigan, USA 16UCL Institute of Neurology, University College London, UK 17Hôpital de la Pitié-Salpêtrière, Paris, France 18Department of Psychiatry, University of Rostock and DZNE, Germany 19Department of Neurology, Universita' degli Studi di Milano - IRCCS Istituto Auxologico  Italiano, Milano, Italy 20University of Leuven, Belgium  Corresponding author:  Dr Martin Turner     Nuffield Department of Clinical Neurosciences     West Wing Level 3, John Radcliffe Hospital     Oxford OX3 9DU     UK     Tel: +44 (0)1865 231893     Fax: +44 (0)1865 231885 martin.turner@clneuro.ox.ac.uk   Counts:    Manuscript: 825 Refs: 5 Tables: 2   Turner et al 3  To be fully prepared for the emergence of candidate neuroprotective drugs in Amyotrophic Lateral Sclerosis (ALS), the development of robust biomarkers of disease activity, as well as those for diagnosis and prognosis in a notoriously heterogeneous disorder, is axiomatic [1]. Guidelines on the use of neuroimaging in the management of ALS recognised the enormous contribution of magnetic resonance imaging (MRI) in the exclusion of ‘mimic’ (largely spinal) disorders [2], but the expanding repertoire of MR sequences with sensitivity to the inherent cerebral motor and extra-motor pathology, now makes it a frontrunner in the search for biomarkers. The Alzheimer Disease Neuroimaging Initiative (ADNI) recognised the power of data-sharing, and a similar multi-centre collaborative approach might generate the large sample sizes needed to fully explore the feasibility of MRI as a future outcome measure in ALS therapeutic trials.  The 1st Neuroimaging Symposium in ALS (NISALS) was held at St. Edmund Hall, Oxford University, UK on 3rd-5th November 2010. The initial focus was on four MRI techniques, recognising the need to balance a multi-parametric approach (increasing the potential biomarker yield), with simplicity, reproducibility and tolerability.  1. Voxel-based morphometry (VBM) refers to the automated analysis of volumetric grey or white matter changes in high resolution 3D T1-weighted images of the brain, and is currently the primary MRI measure of disease progression in both Alzheimer’s and Huntington’s diseases. In ALS, VBM has been consistently sensitive (at a group level) to extra-motor, largely fronto-temporal cerebral changes (reviewed in [1]), reflecting the clinicopathological overlap of ALS with some types of frontotemporal dementia. However, the surprising lack of consistent motor cortical atrophy, and a paucity of large longitudinal MRI studies, makes the sensitivity of VBM to disease progression in ALS much more uncertain.  2. Diffusion tensor imaging (DTI) is an established tool for the detection of pathology within white matter neuronal tracts, and in ALS appears to accurately reflect the pathology observed historically in post-mortem histological studies Turner et al 4  [3]. The most consistent results in ALS have come from studies using a DTI measure of white matter integrity known as fractional anisotropy (FA), which is sensitive to involvement of both the cerebral and cervical corticospinal tract, as well as extra-motor regions (reviewed in [1]). However, overlapping changes are observed in other motor neuron disorders such as hereditary spastic paraparesis, and results from longitudinal studies of FA change in ALS are conflicting at present, so that the true potential of DTI as a diagnostic biomarker or in monitoring disease progression requires further study.   3. Functional MRI using blood oxygenation level-dependent (BOLD) contrast has, like PET studies a decade prior, provided evidence for widespread alterations in cortical activity as a consistent feature of ALS pathology. More recently however, the exploration of the task-free resting state image of discrete cortical networks (resting state functional MRI, rs-fMRI) heralds a new era exploring ALS as a ‘system’ failure of interconnected networks. Application of rs-fMRI to ALS patients suggests that reduced inter-hemispheric functional connectivity between motor cortices is a feature of early clinical disease [4], a finding consistent with the structural callosal involvement seen using DTI [3].   4. Magnetic resonance spectroscopy (MRS) has proved sensitive to cerebral pathology in ALS using common proton-based cerebral metabolites, mainly N-acetylaspartate, commonly expressed as a ratio with creatine or choline (reviewed in [1]). Higher field strengths (3T and above) permit greater separation of metabolite peaks, with the potential to study those with more specific relevance to ALS pathogenesis, for example glutamate and GABA, as well as myo-inositol. A lack of acquisition standardisation, including single versus multi-voxel sampling, and the technical expertise needed to perform high quality MRS are currently barriers to multi-centre collaboration.  The combination of different MRI techniques may improve sensitivity and specificity for ALS, demonstrated in a study of heterogeneous patients where combining grey matter VBM and DTI improved both indices to 90% [3]. MRI also permits the linking of Turner et al 5  structure with function in ALS, through the combination of rs-fMRI with DTI and VBM [5]. The study of pre-symptomatic individuals carrying mutations in genes linked to the ~5% of familial ALS cases is regarded as a priority, as it is the only way to study key events around the ‘clinical horizon’ at present, which may be where the optimal therapeutic window lies.  Consensus was reached on ‘essential’ and ‘desirable’ MRI protocols (Table 1) and clinical information (Table 2) for future ALS studies, with an aim for multi-centre and crucially longitudinal studies. The first stage for MRI-based collaboration will involve exploration of the feasibility of pooling longitudinal data to establish an estimate of the sensitivity of VBM, DTI and rs-fMRI to disease progression in ALS, with a view to a prospective multi-centre study comparing modalities.  A biomarker-focused era has arrived in ALS research, preceding the emergence of multiple disease-modifying drugs, the discovery of which may be facilitated through more efficient therapeutic trials. The 1st NISALS has catalysed a growing international spirit of collaboration with the hope of translation into a better future for patients.    Turner et al 6  Acknowledgements The authors are grateful to Professors Kevin Talbot and Nick Fox for their active support of the 1st NISALS, and their comments on the initial version of this manuscript. MRT is supported by the Medical Research Council/Motor Neuron Disease Association Lady Edith Wolfson Clinician Scientist Fellowship.  Competing interests There are none.  Financial disclosures The Motor Neuron Disease Association (UK) and Oxford Radcliffe Hospitals NHS Trust Charitable Funds provided funding for the Oxford NISALS meeting. Venue hire costs were met by the MRC/MNDA Lady Edith Wolfson Clinician Scientist Fellowship (MRT). Travel and accommodation costs were borne by individual delegates. Turner et al 7  Table 1  Consensus guidelines on MRI protocol for ALS studies.  MRI modality Essential Desirable Scanner field strength 1.5T 3T Voxel-based morphometry  T1 (MP-RAGE or equivalent high resolution 3D pulse sequence);  Isotropic voxels: max. 1mm3   High GM–WM contrast  Diffusion tensor imaging  Gradient directions: min. 12  Isotropic voxels: max. 2.5mm slice thickness   T2, FLAIR (to consider other WM pathology e.g. cerebrovascular disease)   Axial acquisition (to maximise brainstem coverage)  More than one cycle to allow ‘averaging’  Cervical cord as well as brain  Consideration of parallel imaging  Functional MRI  Resting state sequence (in addition to any task-based paradigm)  EPI, isotropic voxels, max. 3mm slice thickness  Consistent, either ‘eyes open-fixed target’ or ‘eyes closed-not asleep’ for resting state acquisition    Axial acquisition (to maximise brainstem coverage)  Pulse and respiratory waveform monitoring to allow physiological noise correction  Task-based protocol for both motor and cognitive functions  Spectroscopy  Standardised methodology  NAA-based measures within PMC   Myo-inositol, Glutamate and GABA measurements EPI – echo planar imaging FLAIR – fluid attenuation inversion recovery GM – grey matter MP-RAGE - magnetization prepared rapid gradient echo PMC – primary motor cortex WM – white matter Turner et al 8  Table 2  Consensus guidelines on the clinical dataset for MRI studies in ALS.  Category Essential Desirable Demographics  Date of birth  Gender   Handedness  Date of death (retrospectively)  Diagnostic aspects  Diagnosis (ALS, other MND, control)  Date of symptom onset (first weakness, month and year)  Date of diagnosis by neurologist (ALS tertiary centre)  Family history   Revised El Escorial EMG staging  Genotype for familial cases  Co-morbidities  Clinical features  Site of first weakness (bulbar, upper limb R/L, lower limb R/L, respiratory, trunk)  ALSFRS-R score (with sub-scores)  A simple cognitive battery, including verbal (letter) fluency, to classify patients as: ALS-cu, ALS-ci or ALS-FTD (Strong/Neary criteria)  Pattern and timing of regional spread of symptom  Distribution of clinical UMN (and LMN) findings within territories, considering:  a. A ‘pathological reflex’ sum score (e.g. [3]) b. Tapping speed (finger and foot bilaterally) c. Spasticity measure (e.g. Ashworth score)  Forced vital capacity (% predicted)  Detailed neuropsychological profile and behavioural assessment (e.g. FrSBE)  Any atypical findings e.g. sphincter or sensory symptoms  Concomitant medications (riluzole at any time)  ALS-ci – ALS cognitively impaired ALS-cu – ALS cognitively unimpaired ALS-FTD – ALS with frontotemporal dementia FrSBE – Frontal System Behaviour Scale  Turner et al 9   References  [1] Turner MR, Kiernan MC, Leigh PN, Talbot K. Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. 2009 Jan;8(1):94-109. [2] Filippi M, Agosta F, Abrahams S, Fazekas F, Grosskreutz J, Kalra S, et al. EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases. Eur J Neurol. 2010 Apr;17(4):526-e20. [3] Filippini N, Douaud G, Mackay CE, Knight S, Talbot K, Turner MR. Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis. Neurology. 2010;75(18):1645-52. [4] Jelsone-Swain LM, Fling BW, Seidler RD, Hovatter R, Gruis K, Welsh RC. Reduced interhemispheric functional connectivity in the motor cortex during rest in limb-onset Amyotrophic Lateral Sclerosis. Frontiers in Systems Neuroscience. 2010 epub 2010-December-31;1:12. [5] Verstraete E, van den Heuvel MP, Veldink JH, Blanken N, Mandl RC, Hulshoff Pol HE, et al. Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study. PLoS One. 2010;5(10):e13664.   

Towards a neuroimaging biomarker for amyotrophic lateral sclerosis

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Towards a neuroimaging biomarker for amyotrophic lateral sclerosis

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