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1 Supplementary Online Content Van Laere K, Vanhee A, Verschueren J, et al. Value of 18 fluorodeoxyglucose positron-emission tomography in amyotrophic lateral sclerosis. JAMA Neurol. Published online March 10, doi: /jamaneurol eappendix 1. Methods eappendix 2. Results efigure 1. Glucose Metabolism in Controls, ALS (C9- and C9+), and PLS efigure 2. Canonical Discriminant Function and Scatterplot of ALS, PLS, and CON efigure 3. Correlation Between Prefrontal Glucose Metabolism and ALS-FRS Functional Score etable 1. Global Cluster Peak Coordinates (Talairach) and Statistics of Resting Metabolism Comparisons for ALS Patients and the C9+ Subgroup Versus Controls etable 2. Global Cluster Peak Coordinates (Talairach) and Statistics of Resting Metabolism Comparisons for PLS Patients Versus Controls and ALS etable 3. Clinical Characteristic of 7 ALS Patients With Extensive Hypometabolism in Frontal/Temporal Regions This supplementary material has been provided by the authors to give readers additional information about their work American Medical Association. All rights reserved.

2 eappendix 1. Methods Healthy volunteers The twenty control subjects (demographic data in Table 1), were part of a recent reference database set of 30 carefully screened healthy volunteers, aged between years who were prospectively recruited during the same time period as the patient data acquisition ( ). Exclusion criteria were amnestic neurological or psychiatric disorders, first-degree relatives of persons with dementia and psychiatric disorders needing treatment, major internal disorders (including diabetes), abnormalities on blood and urine screening, drug/alcohol abuse, abnormalities in clinical neurological examination or on a neuropsychologic battery, structural brain abnormalties on 3 Tesla MPRAGE T1 or FLAIR MRI sequences..the subjects were selected from this sample for obtaining an age- and gender matched group and randomized in alphabetical order until matching was reached. This substudy was separately approved by the local Ethics Committee of the University Hospital. PET acquisition All subjects fasted at least 6 hours before FDG PET. Prior to radiopharmaceutical injection, blood glucose was measured and below 130 mg/dl in all subjects. PET-scans were acquired using an ECAT HR+ camera (Siemens, Erlangen, Germany) operated in 3D mode. 185 MBq of 18 F-FDG was injected intravenously under standard conditions, i.e. subjects lying supine in a dimly lit, quiet room, with ears and eyes open. Thirty minutes after 18 F- FDG injections, a dynamic scan of 30 minutes (6 frames of 5 minutes each) was started. During the acquisition the subject s head was immobilized by means of a vacuum pillow. Prescan attenuation correction was performed using a 68 Ge/ 68 Ga transmission scan, scatter correction was done and the images were reconstructed using 3D filtered back projection with a Hanning postfilter resulting in a full-width-at-half-maximum (FWHM) of 6 mm. PET image analysis For qualitative patient scan analysis, FDG PET scans were firstly evaluated visually using clinical routine software MIM Vista (MIM Software Inc, Cleveland, OH, USA), with surface projections and z-map rendering. PET data were normalized to the mean grey matter value and thus relative hyper- and hypometabolism was evaluated. To explore the prognostic value of FDG PET in ALS, a survival analysis was performed for ALS patients with extensive areas of hypometabolism in the frontal and or anterior temporal regions (suggestive for a FDG PET pattern of FTLD), versus ALS patients with a more restricted pattern of hypometabolism. Visual evaluation of the z- images at 2 SD were scored for frontotemporal involvement. Patients with reduced uptake in 1/5 of a frontal lobe, or 1/2 of an anterior temporal lobe were considered to have extensive frontotemporal involvement. The data were analysed quantitatively in two ways. First, a volume-of-interest (VOI) analysis was performed in PMOD v3.0 (PMOD Inc, Zurich, Switzerland), using an in-house created predefined template onto the spatially normalized datasets, as used in previous work 1,2. This VOI template consists of 88 volumes-of-interest, covering all grey cortical, subcortical and infratentorial matter. Left- and right-hemispheric VOIs were considered separately. Cortical VOIs were defined by Brodmann areas from the Talairach atlas, while for subcortical areas thalamus, caudate nucleus, putamen, insula and pons were delineated in both hemisperes. Relative metabolic activity was calculated by dividing the VOI activity by the volume-weighted average of all VOIs. The VOI sizes ranged from 55 2

3 (BA27, parahippocampus) to 9408 (left cerebellum) voxels of 2x2x2 mm 3 (average ). Secondly, a voxel-based analysis was conducted using Statistical Parametric Mapping (SPM8; Wellcome Trust Centre for Neuroimaging, London, UK), implemented in MATLAB 7 (The MathWorks, Massachusetts, USA). All scans were spatially normalized to MNI (Montreal Neurological Institute) space using the SPM PET-template and non-rigid registration with 16 iterations, and isotropic Gaussian smoothing with FWHM 8 mm). The dimensions of the resulting voxels were 2x2x2mm. Data were normalized to the average grey matter activity of each image using proportional scaling. There were no significant age and gender differences between groups, so SPM analyses were conducted without age and gender as nuisance variables. Unless specified otherwise, for group comparisons a t-test was performed with a threshold p height (uncorrected) < and minimum extent threshold set at k E = 20 (p cluster < 0.05, false-discovery rate (FDR) corrected). Statistics Standard statistics were conducted using Statistica v11.0 (Statsoft, Tulsa, OK, USA). Significance was considered when p < All data are shown as mean ± standard deviation. To compare clinical parameters between patients with ALS and PLS, a Mann-Whitney U test was used for numerical variables (because the clinical parameters deviated from a normal distribution) and a Chi-square for categorical variables. The correlation between the metabolic changes and ALS-FRS were calculated using the VOI approach and linear regression, as some data were missing for the whole group. For the survival analysis, a Log-rank test was used. Conventional discriminant analysis between patients and controls was done using the above defined 88 VOI regions, and was specifically computed in SPSS v22.0 for Mac (IBM, New York, USA), as SPSS allows inclusion of crossvalidation using the leave-one-out technique. A forward stepwise analysis was done, using Wilks lambda method, and F-values for entry of 3.84 and removal As conservative estimate, prior probabilities were considered as equal instead of computed by group size. Furthermore, a voxel-based support vector machine (SVM) approach was conducted within the whole brain. 3 A SVM with a linear kernel using the default soft margin option (The Spider version 1.71 running under Matlab version 7.9; was used to classify the subjects. An image of a subject represents a single point in a high dimensional space in which each dimension corresponds to one voxel within a brain mask, and the coordinate is the intensity value in that voxel. The weight of a feature (voxel) is the component of the normal vector of the hyperplane along that voxel's dimension. The brain mask was defined by those voxels exceeding 50% of the mean of the FDG PET images and the same mask was used in all analyses. Also, a leave-one-out approach was used to obtain realistic estimates of sensitivity, specificity and accuracy for novel cases, the SVM was trained to classify an image into one of two classes using all images less one. The test set consisted of the remaining image. By means of permutations we cycled through the entire set so that each scan was used once as the test set. In addition to the simple binary classification, we will also report the distance of each scan to the separating hyperplane. Larger absolute distances indicate a more robust classification. Surival analysis was done using a Log-rank test. For the analysis of prognostic factors a Cox regression was performed with clinical parameters such as site of onset, age of onset, forced vital capacity and extensive FDG PET changes in frontal and/or temporal lobes as predictors. 3

4 eappendix 2. Results Patients characteristics In 70/81 patients a diagnosis of ALS was made, in 7/81 the clinical diagnosis was PLS. In 4/81 patients with pure lower motor neuron disease a diagnosis of PMA was made. All ALS patients had clinical evidence of combined upper and lower motor neuron involvement in at least one region. The patient characteristics are given in Table 1 and can be regarded typical for ALS patients around the time of diagnosis. The mean duration of disease was similar in all ALS patients and slightly over one year, which is compatible with an expected diagnostic delay. A C9orf72 repeat expansion was present in 11/70 ALS patients (C9+), and absent in 59/70 (C9-). In 3/70, a SOD1 mutation was found, 1 ALS patient had a FUS mutation. The mean time interval between tests conducted at clinical diagnosis and date of PET scan was 2.4 ± 5.4 months. The diagnostic delay (and hence the time from symptom onset to FDG PET) was significantly longer in PLS patients. This was due to 2 PLS patients with a slow disease progression and a diagnostic delay of 4 and 10 years. Correlation between frontal hypometabolism and ALS FRS To evaluate if cerebral hypometabolism correlated with clinical parameters such as age at onset, FVC or ALS-FRS, univariate regression analyses were performed in the areas that were significantly different with respect to controls. Only ALS-FRS was moderately correlated to metabolic activity in the left prefrontal cortex (BA9) (Pearson r=0.41, p=0.004, Supplemental efigure 2). Excluding the outlier with low metabolic activity in this region did not alter the significance of findings. ereferences 1. Van Laere K, Clerinx K, D'Hondt E, de Groot T, Vandenberghe W. Combined striatal binding and cerebral influx analysis of dynamic 11C-raclopride PET improves early differentiation between multiple-system atrophy and Parkinson disease. J Nucl Med. 2010;51: Van Laere K, Goffin K, Casteels C, et al. Gender-dependent increases with healthy aging of the human cerebral cannabinoid-type 1 receptor binding using [(18)F]MK-9470 PET. Neuroimage. 2008;39: Vandenberghe R, Nelissen N, Salmon E, et al. Binary classification of (1)(8)F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI. Neuroimage. 2013;64:

5 efigure 1. Glucose Metabolism in Controls, ALS (C9- and C9+), and PLS Box-and-whisker plots of relative glucose metabolism (normalized to mean grey matter value) in the posterior cingulate (Brodmann areas BA 23 and 31), in healthy controls, ALS (wild type without and patients with the C9orf72 mutation) and PLS. 5

6 efigure 2. Canonical Discriminant Function and Scatterplot of ALS, PLS, and CON Two-dimensional plot of the canonical discriminant functions for discriminant analysis of ALS versus PLS versus controls. The centroid of each group is given by the black square. 6

7 efigure 3. Correlation Between Prefrontal Glucose Metabolism and ALS-FRS Functional Score Correlation graph showing a significant positive relationship between relative glucose metabolism (normalized to mean grey matter value) in the left prefrontal cortex (Brodmann areas BA9) in ALS patients and the total Amyotrophic Lateral Sclerosis Functional Rating Scale (ALS-FRS) score. 7

8 etable 1. Global Cluster Peak Coordinates (Talairach) and Statistics of Resting Metabolism Comparisons for ALS Patients and the C9+ Subgroup Versus Controls (Figure 1) SPM condition (contrast) x,y,z (mm) brain area p height cluster p cluster uncorr extent corr(fdr) t-value All ALS versus controls Relatively decreased metabolism prefrontal cortex,, thalamus L+R, posterior cingulate L+R < < All ALS versus controls Relatively increased metabolism Cerebellum L+R, brain stem, occipital cortex L+R, medial temporal cortex L+R < < C9+ ALS versus controls Relatively decreased metabolism prefrontal cortex L+R, < < post cingulate and precuneus L+R < C9+ ALS versus controls Relatively increased metabolism cerebellum L+R brain stem < <

9 occipital cortex L+R < < C9+ ALS versus C9- ALS Relatively decreased metabolism posterior cingulate < posterior thalamus L < L = left, R = right 9

10 etable 2. Global Cluster Peak Coordinates (Talairach) and Statistics of Resting Metabolism Comparisons for PLS Patients Versus Controls and ALS (Figure 2) SPM condition (contrast) x,y,z (mm) brain area p height cluster p cluster uncorr extent corr(fdr) t-value PLS vs. controls Relatively decreased metabolism prefrontal cortex, L+R < < motor cortex L+R < < PLS vs. controls Relatively increased metabolism cerebellum R + L occipital cortex R + L < < temporal cortex R < < PLS vs. ALS Relatively decreased metabolism prefrontal cortex and anterior cingulate L+R <

11 posterior cingulate < L = left, R = right 11

12 etable 3. Clinical Characteristic of 7 ALS Patients With Extensive Hypometabolism in Frontal/Temporal Regions Patient Sex Age at onset Diagnostic delay (months) Site of onset FVC (% Expected) Survival (months) Mutation status Region of severe hypometabolism 1 Male Upper limbs Negative Bilateral frontal cortex 2 Male 66 6 Bulbar Negative Bilateral frontal cortex and left temporal pole 3 Male 70 5 Upper limbs Negative Left > right anterior temporal lobe 4 Male 75 8 Lower limbs Negative Left > right frontal cortex 5 Male 72 6 Bulbar Negative Left > right frontal cortex 6 Female 76 6 Lower limbs C9orf72 positive 7 Male 60 8 Upper limbs C9orf72 positive Bilateral frontal cortex Left > right anterior temporal lobe 12