Neuroimaging Endpoints in Amyotrophic Lateral Sclerosis

Neurotherapeutics (2017) 14:11–23 DOI 10.1007/s13311-016-0484-9 REVIEW Neuroimaging Endpoints in Amyotrophic Lateral Sclerosis Ricarda A. L. Menke 1 & Federica Agosta 2 & Julian Grosskreutz 3 & Massimo Filippi 2,4 & Martin R. Turner 1 Published online: 17 October 2016 # The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative, clinically heterogeneous syndrome pathologically overlapping with frontotemporal dementia. To date, therapeutic trials in animal models have not been able to predict treatment response in humans, and the revised ALS Functional Rating Scale, which is based on coarse disability measures, remains the gold-standard measure of disease progression. Advances in neuroimaging have enabled mapping of functional, structural, and molecular aspects of ALS pathology, and these objective measures may be uniquely sensitive to the detection of propagation of pathology in vivo. Abnormalities are detectable before clinical symptoms develop, offering the potential for neuroprotective intervention in familial cases. Although promising neuroimaging biomarker candidates for diagnosis, prognosis, and disease progression have emerged, these have been from the study of necessarily select patient cohorts identified in specialized referral centers. Further multicenter research is now needed to establish their validity as therapeutic outcome measures. Key Words Amyotrophic lateral sclerosis . motor neuron disease . magnetic resonance imaging . trial . biomarker. * Martin R. Turner 1 Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK 2 Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy 3 Hans-Berger Department of Neurology, Jena University Hospital, Jena, Germany 4 Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy Introduction Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the motor system and its associated neuronal networks. Pathologically it is characterized by cytoplasmic inclusions of ubiquitinated TAR DNA-binding protein 43 in degenerating upper motor neurons (UMNs) of the primary motor and frontotemporal cortices, and lower motor neurons (LMNs) of the brainstem nuclei and spinal cord anterior horns. The syndrome is heterogeneous and overlaps clinically, pathologically, and genetically with frontotemporal dementia (FTD). Progressive muscle weakness leads eventually to death, typically caused by respiratory insufficiency, with a median survival from symptom onset of only 2 to 3 years [1]. ALS is emerging as a final common pathway from multiple upstream pathological mechanisms [2]. Approximately 10% of all cases of ALS are associated with mutations in a single gene (C9orf72, SOD1, TARDBP, FUS), and asymptomatic carriers of such mutations offer a window into the earliest pathological changes [3]. To date, animal models have not been able to predict treatment response in humans, and there are no validated biomarkers for human ALS beyond the clinically supported diagnostic application of electromyography, which is only 60% sensitive. Riluzole, thought to work by suppressing glutamatergic activity, is the only diseasemodifying treatment for ALS, despite decades of drug trials. ALS symptoms typically begin in the distal limb or bulbar musculature, and typically spread to contiguous body regions clinically [4], outwards from an apparent focus of pathology in postmortem studies [5]. The diagnosis remains clinical, and based upon the coincidence of UMN and LMN signs in the same body regions [6]. The dominance of UMN versus LMN signs is variable, with extremes of UMN involvement termed primary lateral sclerosis and those of LMN involvement, termed progressive muscular atrophy. These extremes are both 12 associated with slower rates of progression [7–9]. The clinical, pathological, and genetic overlap of ALS with FTD is an adverse prognostic factor [10]. The revised ALS Functional Rating Scale (ALSFRS-R), which is based on coarse disability measures driven by LMN dysfunction and remote from histopathological changes, remains the gold-standard measure of disease progression [11, 12]. The incorporation of objective UMN biomarkers into drug trials in ALS, such as transcranial magnetic stimulation [13] or cerebrospinal fluid (CSF) neurofilaments [14], and LMN electrophysiological measures, such as motor unit number estimation [15] and electrical impedance myography [16], have gained increased attention. As well as improved participant stratification, they may help to reduce trial length and costs by providing more objective and sensitive surrogate markers of slowed disease progression or proof of target engagement. Histopathological stages of TAR DNA-binding protein 43positive pathology based on postmortem ALS brains support concepts of prion-like connectomic spread of pathology in ALS [17–19]. Advanced brain imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) over the last 20 years have bridged the gap between basic histopathological and molecular science and in vivo structural and functional abnormalities observed in the brain and spinal cord [20]. This review will focus on their potential as surrogate markers for diagnosis, stratification and monitoring disease progression of ALS in the context of therapeutic trials. Overview: Neuroimaging Techniques MRI: The Basics Structural MRI T1-weighted structural MRI results in images with good tissue contrast (gray matter, white matter, CSF), and is the method of choice for the investigation of gray matter, with the added advantage that the respective sequences are readily available on clinical MRI scanners. The most basic analysis approach is to utilize the acquired images in order to outline a region-ofinterest (ROI) known to be affected in a disease process and to determine the volume of this structure. Conveniently, a number of currently available analysis tools now allow automated segmentation of various cortical and subcortical brain structures [21]. These techniques result not only in quantitative volumetric measures, but can also reveal local differences in thickness and surface shapes of structures and provide cortical thickness and surface area measures [22]. Automated segmentation tools help to avoid labor-intensive manual delineation, reduce interrater variability, and usually delineate structures with good accuracy, although problems can arise in morphometrically Menke et al. highly unusual brains. In addition to ROI approaches, other automated postprocessing pipelines such as voxel-based morphometry (VBM) enable statistics on gray matter density maps on a whole-brain, voxel-by-voxel basis, and can provide information on regio (...truncated)