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Original Investigation |

Value of 18Fluorodeoxyglucose–Positron-Emission Tomography in Amyotrophic Lateral Sclerosis A Prospective Study FREE

Koen Van Laere, MD, PhD, DSc1,3,4; Annelies Vanhee, MD2; Jolien Verschueren, MD1; Liesbeth De Coster, MD1; An Driesen, MD2; Patrick Dupont, PhD2; Wim Robberecht, MD, PhD2,3,4; Philip Van Damme, MD, PhD2,3,4
[+] Author Affiliations
1KU Leuven, Nuclear Medicine and Molecular Imaging, University Hospital Leuven, Leuven, Belgium
2KU Leuven, Department of Neurology, University Hospital Leuven, Leuven, Belgium
3KU Leuven, Department of Neurosciences, Experimental Neurology, and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium
4VIB, Vesalius Research Center, Laboratory of Neurobiology, Leuven, Belgium
JAMA Neurol. 2014;71(5):553-561. doi:10.1001/jamaneurol.2014.62.
Text Size: A A A
Published online

Importance  Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder primarily affecting the motor system, with extramotor involvement to a variable extent. Biomarkers for early differential diagnosis and prognosis are needed. An autosomal dominant hexanucleotide (GGGGCC) expansion in the noncoding region of the chromosome 9 open reading frame 72 (C9orf72) gene is the most frequent genetic cause of ALS, but its metabolic pattern has not been studied systematically.

Objectives  To evaluate the use of 18fluorodeoxyglucose–positron-emission tomography as a marker of ALS pathology and investigate whether a specific metabolic signature is present in patients with C9orf72 mutations.

Design, Setting, and Participants  In total, 81 patients with a suspected diagnosis of ALS at University Hospital Leuven were prospectively investigated. All underwent detailed neurological examination and electrodiagnostic and genetic testing for the major known genetic causes of ALS (C9orf72, SOD1, TARDBP, and FUS). A diagnosis of ALS was made in 70 of 81 patients. Of these, 11 were C9orf72 positive and 59 were C9orf72 negative. In 7 patients, the diagnosis of primary lateral sclerosis was made; 4 patients had progressive muscular atrophy. A screened healthy control population was used for comparison.

Main Outcomes and Measures  Positron-emission tomographic data were spatially normalized and analyzed using a predefined volume of interest and a voxel-based analysis (SPM8). Discriminant analysis was done both volume of interest based and voxel based using a support vector machine approach.

Results  Compared with control participants, 18fluorodeoxyglucose–positron-emission tomography showed perirolandic and variable prefrontal hypometabolism in most patients. Patients with primary lateral sclerosis showed a similar pattern. Patients with C9orf72-positive ALS had discrete relative hypometabolism in the thalamus and posterior cingulate compared with those with C9orf72-negative ALS. A posteriori-corrected discriminant analysis was able to correctly classify 95% of ALS cases and 71% of primary lateral sclerosis cases. Prefrontal hypometabolism was associated with reduced clinical functioning (ALS Functional Rating Scale). Extensive hypometabolism in the prefrontal or anterior temporal areas was present in 10% of patients and associated with significantly shorter survival as an independent factor (n = 63, P < .001). Patients who were C9orf72 positive did not differ in survival compared with those who were C9orf72 negative.

Conclusions and Relevance  18Fluorodeoxyglucose–positron-emission tomography is a useful early diagnostic and prognostic marker for ALS. Amyotrophic lateral sclerosis that is positive for C9orf72 is characterized by only mild cerebral metabolic differences that show no prognostic difference.

Figures in this Article

Motor neuron disorders (MNDs) comprise a group of disorders involving preferential damage to upper motor neurons (UMNs) and/or lower motor neurons (LMNs).1,2 In adulthood, the clinical spectrum of MNDs is wide, ranging from simultaneous involvement of UMNs and LMNs (classic amyotrophic lateral sclerosis [ALS], about 90% of all MND cases), a pure UMN syndrome (primary lateral sclerosis [PLS]), and an isolated LMN involvement (progressive muscular atrophy [PMA]).1 It has not been settled whether the latter 2 are entities fully separated from ALS because a considerable number of patients with PLS and PMA progress to ALS.

A diagnosis of ALS predominantly relies on the interpretation of clinical symptoms and signs, with the use of ancillary tests to exclude other causes.13 Currently, the revised El Escorial and Awaji-Shima diagnostic criteria are used, the latter being more sensitive for early disease assessment.4 Upper motor neuron symptoms are spasticity, hyperreflexia, and the Babinski sign, whereas LMN loss leads to muscle weakness and wasting with fasciculations. Depending on symptom onset, clinical presentations of ALS may be separated into bulbar or spinal-onset disease.

It is now recognized that ALS and PLS are characterized by significant extramotor cerebral pathology that, to a variable extent, overlaps with the clinicopathological features of frontotemporal lobar degeneration (FTLD),5 and cognitive decline may range from mild abnormalities to manifest FTLD in about 5% to 15% of cases.6

Although most MND patients are sporadic, approximately 10% have a positive family history.6 Previously, the most prevalent and best-known mutation was in the Cu/Zn superoxide dismutase 1 gene (SOD1), whereas other genes that cause autosomal-dominant ALS (such as TARDBP, FUS, VAPB, ANG, OPTN, UBQLN2, and ATXN2) only accounted for a small percentage of cases. In 2011, a new gene defect was identified, namely, a massively expanded GGGGCC hexanucleotide repeat in a noncoding region of the chromosome 9 open reading frame 72 (C9orf72) gene.7,8 Phenotypically, patients with the expansion have a younger age at onset, shortened survival, increased rates of cognitive and behavioral impairment, and a strong family history of neurodegenerative disease.916 This mutation is currently recognized as a major genetic cause of ALS and FTLD, responsible for up to 40% of familial ALS, 5% to 10% of sporadic ALS, about 20% of familial FTLD, and 5% of sporadic FTLD.15,1720

At the neuropathological level, most forms of ALS are characterized by the presence of inclusions in degenerating motor neurons staining positively for ubiquitin and TAR DNA-binding protein 43. Whereas the presence of LMN degeneration can be confirmed by electrodiagnostic testing, there is no widely accepted biomarker for UMN involvement.21 However, reliable objective biomarkers are critical for the early diagnosis, monitoring of disease progression, prognosis, and patient stratification for clinical trials.

Whereas magnetic resonance imaging (MRI) of the brain and spinal cord remains a most useful neuroimaging technique in ALS, this is mainly to exclude syndromes that mimic ALS.6 Voxel-based morphometry reveals regional atrophy, and magnetic resonance spectroscopy and diffusion-tensor MRI can detect corticospinal lesions. However, because of their relative lack of sensitivity and specificity, these techniques are currently inadequate for use as diagnostic tools in individual patients.1,22,23

Early 18fluorodeoxyglucose (FDG)–positron-emission tomography (PET) imaging studies conducted in limited numbers of patients (n < 20) and dating back 25 years have shown that patients with ALS have decreased glucose uptake that is more extended than motor areas.2427 Frontal hypometabolism has been associated with neuropsychological deficits,27,28 especially to disturbances of word fluency.26,29 Detecting extramotor involvement is important because comorbid cognitive dysfunction has been associated with functional decline, shortened survival, and poor compliance with life-prolonging interventions.30,31

Voxel-based approaches have led to a more detailed, group-based characterization of the disorder spectrum. In a study in 32 patients, the pattern of UMN (bulbar) and LMN (spinal) onset could be differentiated by FDG-PET.32 To our knowledge, the individual discrimination capacity of FDG-PET as an aid in early and differential diagnosis in MND has not been studied.

The aim of this study was 3-fold: first, to evaluate glucose metabolism in a large group of patients with ALS and those with PLS, in comparison to age-matched healthy control participants, to assess the diagnostic value of voxel-based classification. Second, our goal was to study the metabolic impact of the C9orf72 mutation in patients with ALS. And third, we aimed to relate survival of patients with ALS associated with the presence of frontotemporal hypometabolism.

Patients (n = 81) were consecutively recruited from referrals made to the neuromuscular clinic at the University Hospital Leuven (Leuven, Belgium) between January 2011 and January 2013. None of the patients had a history of other neurological disorders. All underwent full neurological evaluation and electrodiagnostic testing as part of their clinical workup by an experienced specialist in neuromuscular disorders (P.V.D. or W.R.). Both the revised El Escorial and Awaji-Shima criteria were applied.4 All patients underwent FDG-PET planned at the initial visit. All patients had undergone a routine MRI scan of the brain. All patients underwent genetic testing for C9orf72, SOD1, TARDBP, and FUS at the time of diagnosis. A C9orf72 repeat expansion was checked for by repeat-primed polymerase chain reaction, and mutations in SOD1, TARDBP, and FUS were as sought for by Sanger sequencing, as previously described.15,3335

Onset of the disease was determined by the patient’s recollection of the month in which the first symptoms occurred. None of the patients showed evidence of respiratory compromise or nutritional abnormalities, such as dehydration or ketosis, at the time of the FDG-PET scan. The functional status of the patients near the time of PET imaging was scored by the revised version of the ALS Functional Rating Scale (FRS) in most patients. Also, forced vital capacity as a marker of lung function was scored (Table 1). Control participants were part of a prospective study in healthy volunteers aged 50 to 80 years acquired over the same period on the same equipment. Inclusion and exclusion details for this substudy are given in eAppendix 1 in Supplement.

Table Graphic Jump LocationTable 1.  Demographic and Clinical Characteristics of the Study Participants

The study was approved by the local university ethics committee and written informed consent was given by patients and control participants, according to the Declaration of Helsinki. Positron-emission tomography acquisition, processing, and statistics are provided in eAppendix 1 in Supplement. Patient characteristics are given in eAppendix 2 in Supplement and Table 1.

Categorical Differences of ALS vs Control Cases

On an individual visual level, as diagnosed in clinical routine using surface projections and z-maps, FDG-PET showed areas of hypometabolism in virtually all individual patients with ALS and those with PLS, with variable intensity ranging from very mild to severe. The most common regions with hypometabolism clinically reported were the perirolandic and frontal brain regions.

Voxel-based group analysis showed that, compared with healthy control participants, patients with ALS had significant, symmetrical hypometabolism in the prefrontal, lateral frontal, and premotor cortex. Patients with ALS showed also clusters of relative hypermetabolism in the cerebellum, occipital cortex, upper brain stem, and medial temporal cortex, encompassing the hippocampus and amygdala (Figure 1A and eTable 1 in Supplement for cluster statistics and locations). In 3 of 4 patients diagnosed as having PMA, a similar pattern of hypometabolism was seen. One patient with PMA, with a mutation in SOD1, had a visually normal FDG-PET.

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Figure 1.
Relative Glucose Metabolism in C9orf72-Positive and C9orf72-Negative Patients With Amyotrophic Lateral Sclerosis (ALS) and Control Cases

A, Surface and interhemispheric projections of areas with relative hypometabolism (red) and hypermetabolism (blue) for patients with ALS vs healthy control cases (Pheight < .001). B, Patients with C9orf72-positive ALS vs healthy control cases (Pheight < .001). C, Patients with C9orf72-positive vs those with C9orf72-negative ALS (Pheight < .005).

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Categorical Differences of C9orf72 Negative vs C9orf72 Positive

There were no significant differences in patients with C9orf72-positive vs C9orf72-negative ALS in time of disease onset to PET, bulbar vs spinal onset, forced vital capacity measurements, or ALS-FRS values. Patients with C9orf72-positive ALS showed a similar pattern of hypometabolism compared with control participants, slightly more pronounced bilaterally in the thalamus and posterior cingulate and precuneus (Figure 1B and eTable 1 in Supplement). These results were confirmed by volume of interest (VOI)–based analysis of variance, with a difference for the posterior cingulate of 4.0% (P = .003, post hoc–corrected univariate analysis of variance) (eFigure 1 in Supplement).

Direct comparison of C9orf72-positive ALS vs C9orf72-negative ALS showed no differences at the preset thresholds. Exploratively, at a less stringent threshold of significance, patients with C9orf72-positive ALS showed relatively lower metabolism in the anterior and posterior cingulate, posterior thalami, right lateral frontal cortex, and right temporoparietal junction (Figure 1C and eTable 1 in Supplement).

Categorical Differences of ALS vs PLS

Patients with PLS also showed symmetrically decreased metabolism bilaterally in the prefrontal cortex, anterior cingulate, pericentral cortex, and thalamus (Figure 2A and eTable 2 in Supplement). Relative hypermetabolism was also present in the cerebellum, occipital cortex, and left lateral temporal cortex.

Place holder to copy figure label and caption
Figure 2.
Relative Glucose Metabolism in Primary Lateral Sclerosis (PLS) vs Controls and vs Amyotrophic Lateral Sclerosis (ALS)

A, Surface and interhemispheric projections of areas with relative hypometabolism (red) and hypermetabolism (blue) for patients with PLS vs healthy control cases (Pheight < .001). B, Patients with PLS vs those with ALS (Pheight < .005).

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Direct comparison between PLS and ALS on a group level showed more severe metabolic involvement of the prefrontal cortex and posterior cingulate in ALS (Pheight < .005; kE = 200; Figure 2B and eTable 1 in Supplement for cluster locations and statistics). The inverse contrast showed lower metabolism in the primary sensorimotor cortex in PLS in bilateral clusters, which was only significant after small-volume correction (PFDR < .05; T statistic, 3.99 left and 3.76 right) based on the a priori spatial restriction of known primary motor cortex involvement in MND (data not shown in Figure 2B).

Discriminant Analysis
VOI Based

To evaluate the use of FDG-PET to discriminate between different forms of motor neuron degeneration, a discriminant analysis was performed. In total, 88 VOI regions were used for discriminant analysis between control participants, patients with ALS, and patients with PLS. Using equal a priori probabilities as the most conservative estimation, the classification matrix after leave-one-out cross-validation (Table 2) shows an overall classification accuracy of 89.7% (91.8% without cross-validation). The most discriminating regions were the prefrontal cortex, thalamus, posterior cingulate, and anterior cingulate (Figure 3). In total, 10 of 97 individuals were misclassified: 4 ALS cases as control participants, 4 ALS cases as PLS cases, and 2 PLS cases as ALS cases. eFigure 2 in Supplement shows the canonical discriminant function values with classification plot.

Table Graphic Jump LocationTable 2.  Stepwise Forward Discriminant Analysisa
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Figure 3.
Most Discriminative Areas of Reduced Glucose Metabolism in Amyotrophic Lateral Sclerosis (ALS) and Primary Lateral Sclerosis (PLS)

Box-and-whisker plots of relative glucose metabolism (normalized to mean gray matter value) in volume of interest areas that are most discriminative between healthy control cases, patients with ALS, and patients with PLS: prefrontal (Brodmann Area [BA]9), motor cortex (BA4), anterior cingulate (BA 24,32), and thalamus. L indicates left; PMA, progressive muscular atrophy; R, right.

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When including patients with PMA in the analysis, cross-validated classification accuracy dropped to 62.4%, as the centroid of the PMA group was very close to ALS, and discriminative features of motor cortex and thalamic dysfunction were not noted for PMA (Figure 3). However, because of the very small PMA group, these findings have to be interpreted with caution.

In the clinically relevant setting of only patients with ALS vs control participants (ie, when a priori clinical information was included), overall classification accuracy was 94.4% with 5 ALS cases misclassified as control cases. Discriminant analysis could not separate C9orf72-negative and C9orf72-positive ALS with an accuracy greater than 60%, confirming the high overlap between both groups of patients.

Support Vector Machine Analysis

To further refine the discriminating regions in patients with ALS and those with PLS, a support vector machine (SVM) analysis was performed. The classifier image between ALS and control participants is shown in Figure 4A, and the individual SVM distances are given in Figure 4B. The most important clusters of discrimination by SVM were found bilateral in the thalamus, primary motor cortex, striatum, prefrontal and lateral prefrontal cortex, and posterior cingulate. For ALS vs control cases, the leave-one-out approach had a sensitivity of 94.8, a specificity of 80.0%, and an accuracy of 91.8%. Similarly, for the subgroups of patients with C9orf72-negative ALS, sensitivity, specificity, and accuracy were 89.8%, 85.0%, and 88.6%, respectively. For patients with C9orf72-positive ALS, these were 90.9%, 100%, and 96.8%, respectively.

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Figure 4.
Support Vector Machine (SVM) Voxel-Based Discriminant Analysis of Amyotrophic Lateral Sclerosis (ALS) vs Control Cases

A, Feature weights of the classifier for ALS vs healthy control cases projected onto a normalized structural magnetic resonance image in Montreal Neurological Institute space. The scale of the feature weights represents how much a voxel contributes. The scale was normalized so that the sum of all weights is 1. Only voxels with a weight of more than 0.002 in absolute value are shown. Clusters indicate areas with high discriminative impact based on relative hypometabolism (yellow-red) and relative hypermetabolism (blue). B, Plots of distance to the classifier for healthy control cases (green) vs patients with ALS (C9orf72 positive and C9orf72 negative grouped together) using a leave-one-out approach.

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For PLS vs control cases, these values decreased to 57.1% for sensitivity (specificity 100%). An SVM was not able to discriminate between C9orf72-positive and C9orf72-negative ALS (sensitivity 0%), in concordance with the small differences found by voxel-based group comparison.

Survival vs Frontotemporal Metabolism

At the time of the writing of this article, 20 of 70 (28.6%) of this patient cohort had died (mean [SD] survival after symptom onset in these patients, 19.9 [7.1] months). For all surviving patients (50 of 70), more than 14 months of follow-up after disease onset was available (average [SD], 39.9 [16.9] months) and 48 of 50 (96%) of the surviving patients were beyond the point of the 20-month disease duration.

In 7 of 70 patients (10%), large areas with a more than 2 SD decreased uptake in frontal and/or anterior temporal regions were observed. Of these, 6 of 7 patients had died after surviving a mean (SD) of only 20.0 (7.0) months (Figure 5A; for clinical details of these patients, see eTable 3 in Supplement). The lower survival in the patients with ALS with severe frontotemporal metabolic involvement was highly significant (P < .001). This shortened survival was not owing to the contribution of C9orf72-positive patients (Figure 5B, P = .45). In this cohort, no correlation between survival and site of onset (P = .46) was found. After correction for other prognostic factors, such as age at onset (P = .001; hazard ratio, 1.08; 95% CI, 1.03-1.13) and forced vital capacity (P = .01; hazard ratio, 0.98; 95% CI, 0.96-0.99), the extensive hypometabolism on FDG-PET remained significant (P = .005; HR, 4.1; 95% CI, 1.6-11.0).

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Figure 5.
Survival Plots of Patients With Amyotrophic Lateral Sclerosis (ALS) With and Without Extensive Frontotemporal Hypometabolism

Extensive hypometabolism in frontotemporal areas is a negative prognostic factor. A, Kaplan-Meier survival plots of patients with ALS with (n = 7) vs without (n = 63) extensive frontotemporal hypometabolism (P < .001). B, Kaplan-Meier survival plots of patients with ALS with (C9orf72+, n = 11) or without (C9orf72−, n = 59) the C9orf72 repeat expansion (P = .45).

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The correlation between frontal hypometabolism and ALS FRS is described in eAppendix 2 and eFigure 3 in Supplement.

Reliable diagnostic and disease-state biomarkers of motor neuron degeneration would facilitate the management and clinical trials of patients with ALS.23 Imaging techniques, especially MRI sequences, have shown their potential to track cerebral motor and extramotor pathology in ALS.36,37 Conventional MRI findings—such as corticospinal tract hyperintensities on fluid-attenuated inversion recovery, T2-weighted, and proton density images or a hypointense rim at the margin of the precentral gyrus in T2-weighted images—are frequently found in patients with ALS, but the overlap with control cases is high and therefore lacking specificity.38 Voxel-based morphometry and cortical thickness measurements have revealed atrophy in the motor cortex and frontal involvement in patients with ALS. Diffusion-tensor imaging of the corticospinal tract shows a significantly decreased fractional anisotropy in patients with ALS and UMN degeneration but does not provide sufficient discriminatory power at the individual level. Magnetic resonance spectroscopy of the motor cortex has shown abnormally low N-acetylaspartate levels in patients with advanced ALS that is correlated with verbal fluency deterioration,39 but studies at the early stage of the disease and with an adequate follow-up are scarce.40 Also, previous FDG-PET studies in smaller sample sizes have shown significant group differences but have not focused yet on classification accuracy on the level of an individual patient.2427

Advanced classification techniques using whole-brain analytic approaches have the potential to ameliorate the accuracy of discrimination on an individual patient level. Therefore, in this study, we evaluated the value of FDG-PET in a large prospective cohort of patients with ALS around the time of diagnosis using whole-brain region-based discriminant and SVM tools.

To our knowledge, this study is the first to report on glucose metabolic patterns on a group basis in a cohort of patients with C9orf72-positive ALS. So far, only cases of C9orf72 ALS with FDG-PET and 99mTc-ECD perfusion single-photon emission computed tomography have been described, each in 5 patients with combined ALS–frontotemporal dementia.19 Concordant with this study, large heterogeneity in individual metabolic patterns is found. The frequency of the C9orf72 mutation in our cohort was very similar to that previously reported series of patients with ALS (20%-50% of familial ALS and 4%-10% of sporadic ALS).20,41

We found that perirolandic and prefrontal hypometabolism is a sensitive marker of ALS. C9orf72-positive patients had a similar pattern, but the involvement of the posterior cingulate, thalamus, and prefrontal cortex was on average slightly more severe. Similar affected areas were observed in a study with diffusion-tensor MRI.42

Our findings are consistent with previous PET studies in smaller samples, where metabolic abnormalities in the dorsolateral prefrontal cortex and anteromedial cingulate cortex were the most consistent findings. In C9orf72-negative ALS, extramotor involvement has been studied before and consistently reported,24,25 although not all studies found resting-state hypometabolism or hypoperfusion.43

As was previously reported by Cistaro et al,32 relative hypermetabolism in the posterior areas, including the cerebellum, occipital cortex, brain stem, and mesial temporal, is also seen. Although this can purely be due to the relative normalization procedure used, it has been suggested that this pattern could reflect true hypermetabolism and a measure of astrocytosis or microglial activation in these areas. This question of true hypermetabolism should be addressed by absolute regional cerebral metabolic rates of glucose measurements to enable definite conclusions. In the same study, hypermetabolism in the upper brain stem was particularly described in spinal-onset forms only, but this was not confirmed in our study, where both patients with spinal and bulbar onset ALS showed completely similar patterns of hypometabolism (data not shown).

The correlation between the ALS-FRS scores and prefrontal hypometabolism is congruent with previous reports showing that executive impairment is related to overall functioning of patients. Lower ALS-FRS scores are also associated with poorer prognosis.6 Extensive hypometabolism in the frontal or temporal lobes was associated with shorter survival after correction for other known prognostic factors. This is in line with other studies showing a negative correlation between concomitant frontotemporal dementia or cognitive or behavioral impairment and survival.30,31

From a clinical point of view, this study has shown that, although deviations from normality are relatively modest and some variability exists, the pattern of affection in patients with ALS and those with PLS does allow for an accurate discrimination with healthy control cases, on a quantitative basis, and even between patients with PLS and ALS, albeit with lower accuracy and only tested in a limited group of patients with PLS. The discrimination of patients with ALS using FDG-PET could be an important additional tool in early diagnoses of new cases and in the assessment of disease prognosis. And both the VOI- and SVM-based analyses performed in this group remain to be validated in a novel prospective cohort of patients.

Our findings also support the notion that PLS is part of the wider MND spectrum. The corollary is that the modifying factors (genetic and environmental) that account for the delayed progression of disease in patients with PLS, and which are not clearly reflected by major metabolic differences as resulting from this study, remain to be discovered.

A number of limitations of the study need to be mentioned. First, although routine MRI was performed in all patients, we did not apply a volume correction on the FDG-PET of the participants. Atrophy has been described in patients with ALS,4447 but as for most patients, no volumetric MRI data were available; no partial volume correction was possible in this dataset. This does not diminish the validity of the results and their indication of discriminatory power in the functional metabolic information, which is a measure of implicit cellular metabolism and macroscopic atrophy effects.

Second, we did not assess the issue of laterality, which may have caused some levelling out of information in possible larger deviations. In most patients, however, relatively symmetric cerebral cortical involvement seems to be the rule, and in previous studies, left-right (metabolic or perfusion) asymmetry has not been regarded as a feature of ALS. Third, we did not perform extensive cognitive testing in our patients. Therefore, we cannot correlate the FDG-PET findings to cognitive disability. Previous studies have shown that patients with cognitive or behavioral impairment have a worse prognosis.2,31 How FDG-PET findings relate to cognitive testing requires additional study to further elucidate the mechanism by which frontotemporal hypometabolism is associated with worse prognosis as was found here.

Fourth, the accuracy of the classification analysis was based on a relatively small, but well-defined, control group of 20 sex- and age-matched participants. Enlargement of the control group may increase overall performance of the technique. Fifth, we have not defined a subset of patients and control cases as a training set on which subsequent cases could be evaluated, but we have used the leave-one-out technique to estimate future applicability in novel clinical samples. Whereas this is a necessary correction to establish robustness of classification accuracy, future studies in novel patient groups with clinical follow-up are needed to fully validate the current findings. Finally, the clinical diagnosis of ALS was used as the gold standard for diagnosis and compared with a single FDG-PET scan. We did not collect longitudinal FDG-PET data, perform FDG-PET around the time of symptom onset before a clinical diagnosis of ALS was made, nor assess the specificity of the FDG-PET changes to discriminate ALS from ALS mimics.

This study shows the promise on the individual patient level of an effective metabolic PET marker that has value in early and differential diagnosis in ALS and characterization of C9orf72-positive ALS. It also holds prognostic information, with frontotemporal metabolic decrease related to poorer survival.

Corresponding Author: Koen Van Laere, MD, PhD, DSc, University Hospital Leuven, Herestraat 49, E901, 3000 Leuven, Belgium (koen.vanlaere@uzleuven.be).

Accepted for Publication: January 16, 2014.

Published Online: March 10, 2014. doi:10.1001/jamaneurol.2014.62.

Author Contributions: Dr Van Laere had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Van Laere, Robberecht, Van Damme.

Acquisition of data: Van Laere, Vanhee, Verschueren, De Coster, Driesen, Robberecht, Van Damme.

Analysis and interpretation of data: Van Laere, Vanhee, De Coster, Dupont, Robberecht, Van Damme.

Drafting of the manuscript: Van Laere, Vanhee, Verschueren, De Coster, Driesen, Robberecht, Van Damme.

Critical revision of the manuscript for important intellectual content: Van Laere, Dupont, Robberecht, Van Damme.

Statistical analysis: Van Laere, Driesen, Van Damme.

Obtained funding: Robberecht.

Administrative, technical, and material support: Van Laere, Vanhee, Verschueren, Driesen.

Study supervision: Van Laere, Robberecht, Van Damme.

Conflict of Interest Disclosures: Dr Robberecht is supported through the E. von Behring Chair for Neuromuscular and Neurodegenerative Disorders. Dr Van Damme is supported by the Belgian ALS League. Drs Van Laere and Van Damme are senior clinical investigators of the Flemish Fund for Scientific Research (FWO, Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Belgium). No other disclosures were reported.

Funding/Support: This work was supported by a grant from the KU Leuven (GOA/11/014) and by the Interuniversity Attraction Poles (IUAP) program P7/16 of the Belgian Federal Science Policy Office.

Role of the Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We acknowledge the skilled help of the radiopharmacy team at UZ Leuven, which included Marva Bex, Tjibbe de Groot, PhD, and Kim Serdons, Pharm, PhD. They did not receive compensation for their contributions.

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Majounie  E, Renton  AE, Mok  K,  et al; Chromosome 9-ALS/FTD Consortium; French research network on FTLD/FTLD/ALS; ITALSGEN Consortium.  Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol. 2012;11(4):323-330.
PubMed   |  Link to Article
Gijselinck  I, Van Langenhove  T, van der Zee  J,  et al.  A C9orf72 promoter repeat expansion in a Flanders-Belgian cohort with disorders of the frontotemporal lobar degeneration-amyotrophic lateral sclerosis spectrum: a gene identification study. Lancet Neurol. 2012;11(1):54-65.
PubMed   |  Link to Article
Chiò  A, Borghero  G, Restagno  G,  et al; ITALSGEN consortium.  Clinical characteristics of patients with familial amyotrophic lateral sclerosis carrying the pathogenic GGGGCC hexanucleotide repeat expansion of C9ORF72Brain. 2012;135(pt 3):784-793.
PubMed   |  Link to Article
Byrne  S, Elamin  M, Bede  P,  et al.  Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study. Lancet Neurol. 2012;11(3):232-240.
PubMed   |  Link to Article
Millecamps  S, Boillée  S, Le Ber  I,  et al.  Phenotype difference between ALS patients with expanded repeats in C9ORF72 and patients with mutations in other ALS-related genes. J Med Genet. 2012;49(4):258-263.
PubMed   |  Link to Article
Smith  BN, Newhouse  S, Shatunov  A,  et al.  The C9ORF72 expansion mutation is a common cause of ALS+/-FTD in Europe and has a single founder. Eur J Hum Genet. 2013;21(1):102-108.
PubMed   |  Link to Article
Debray  S, Race  V, Crabbé  V,  et al.  Frequency of C9orf72 repeat expansions in amyotrophic lateral sclerosis: a Belgian cohort study. Neurobiol Aging. 2013;34(12):e7-e12.
PubMed   |  Link to Article
Snowden  JS, Rollinson  S, Thompson  JC,  et al.  Distinct clinical and pathological characteristics of frontotemporal dementia associated with C9ORF72 mutations. Brain. 2012;135(pt 3):693-708.
PubMed   |  Link to Article
van Rheenen  W, van Blitterswijk  M, Huisman  MH,  et al.  Hexanucleotide repeat expansions in C9ORF72 in the spectrum of motor neuron diseases. Neurology. 2012;79(9):878-882.
PubMed   |  Link to Article
Stewart  H, Rutherford  NJ, Briemberg  H,  et al.  Clinical and pathological features of amyotrophic lateral sclerosis caused by mutation in the C9ORF72 gene on chromosome 9p. Acta Neuropathol. 2012;123(3):409-417.
PubMed   |  Link to Article
Boeve  BF, Graff-Radford  NR.  Cognitive and behavioral features of c9FTD/ALS. Alzheimers Res Ther. 2012;4(4):29.
PubMed   |  Link to Article
van Blitterswijk  M, DeJesus-Hernandez  M, Rademakers  R.  How do C9ORF72 repeat expansions cause amyotrophic lateral sclerosis and frontotemporal dementia: can we learn from other noncoding repeat expansion disorders? Curr Opin Neurol. 2012;25(6):689-700.
PubMed   |  Link to Article
Turner  MR, Hardiman  O, Benatar  M,  et al.  Controversies and priorities in amyotrophic lateral sclerosis. Lancet Neurol. 2013;12(3):310-322.
PubMed   |  Link to Article
van der Graaff  MM, de Jong  JM, Baas  F, de Visser  M.  Upper motor neuron and extra-motor neuron involvement in amyotrophic lateral sclerosis: a clinical and brain imaging review. Neuromuscul Disord. 2009;19(1):53-58.
PubMed   |  Link to Article
Turner  MR, Kiernan  MC, Leigh  PN, Talbot  K.  Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. 2009;8(1):94-109.
PubMed   |  Link to Article
Dalakas  MC, Hatazawa  J, Brooks  RA, Di Chiro  G.  Lowered cerebral glucose utilization in amyotrophic lateral sclerosis. Ann Neurol. 1987;22(5):580-586.
PubMed   |  Link to Article
Hatazawa  J, Brooks  RA, Dalakas  MC, Mansi  L, Di Chiro  G.  Cortical motor-sensory hypometabolism in amyotrophic lateral sclerosis: a PET study. J Comput Assist Tomogr. 1988;12(4):630-636.
PubMed   |  Link to Article
Abrahams  S, Leigh  PN, Kew  JJ, Goldstein  LH, Lloyd  CM, Brooks  DJ.  A positron emission tomography study of frontal lobe function (verbal fluency) in amyotrophic lateral sclerosis. J Neurol Sci. 1995;129(suppl):44-46.
PubMed   |  Link to Article
Kew  JJ, Goldstein  LH, Leigh  PN,  et al.  The relationship between abnormalities of cognitive function and cerebral activation in amyotrophic lateral sclerosis: a neuropsychological and positron emission tomography study. Brain. 1993;116(pt 6):1399-1423.
PubMed   |  Link to Article
Ludolph  AC, Langen  KJ, Regard  M,  et al.  Frontal lobe function in amyotrophic lateral sclerosis: a neuropsychologic and positron emission tomography study. Acta Neurol Scand. 1992;85(2):81-89.
PubMed   |  Link to Article
Abrahams  S, Goldstein  LH, Kew  JJ,  et al.  Frontal lobe dysfunction in amyotrophic lateral sclerosis: a PET study. Brain. 1996;119(pt 6):2105-2120.
PubMed   |  Link to Article
Elamin  M, Bede  P, Byrne  S,  et al.  Cognitive changes predict functional decline in ALS: a population-based longitudinal study. Neurology. 2013;80(17):1590-1597.
PubMed   |  Link to Article
Chiò  A, Ilardi  A, Cammarosano  S, Moglia  C, Montuschi  A, Calvo  A.  Neurobehavioral dysfunction in ALS has a negative effect on outcome and use of PEG and NIV. Neurology. 2012;78(14):1085-1089.
PubMed   |  Link to Article
Cistaro  A, Valentini  MC, Chiò  A,  et al.  Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging. 2012;39(2):251-259.
PubMed   |  Link to Article
Herdewyn  S, Zhao  H, Moisse  M,  et al.  Whole-genome sequencing reveals a coding non-pathogenic variant tagging a non-coding pathogenic hexanucleotide repeat expansion in C9orf72 as cause of amyotrophic lateral sclerosis. Hum Mol Genet. 2012;21(11):2412-2419.
PubMed   |  Link to Article
Lemmens  R, Race  V, Hersmus  N,  et al.  TDP-43 M311V mutation in familial amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2009;80(3):354-355.
PubMed   |  Link to Article
Damme  PV, Goris  A, Race  V,  et al.  The occurrence of mutations in FUS in a Belgian cohort of patients with familial ALS. Eur J Neurol. 2010;17(5):754-756.
PubMed   |  Link to Article
Turner  MR, Grosskreutz  J, Kassubek  J,  et al; first Neuroimaging Symposium in ALS (NISALS).  Towards a neuroimaging biomarker for amyotrophic lateral sclerosis. Lancet Neurol. 2011;10(5):400-403.
PubMed   |  Link to Article
Foerster  BR, Welsh  RC, Feldman  EL.  25 years of neuroimaging in amyotrophic lateral sclerosis. Nat Rev Neurol. 2013;9(9):513-524.
PubMed   |  Link to Article
Agosta  F, Chiò  A, Cosottini  M,  et al.  The present and the future of neuroimaging in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol. 2010;31(10):1769-1777.
PubMed   |  Link to Article
Quinn  C, Elman  L, McCluskey  L,  et al.  Frontal lobe abnormalities on MRS correlate with poor letter fluency in ALS. Neurology. 2012;79(6):583-588.
PubMed   |  Link to Article
Sivák  S, Bittšanský  M, Kurča  E,  et al.  Proton magnetic resonance spectroscopy in patients with early stages of amyotrophic lateral sclerosis. Neuroradiology. 2010;52(12):1079-1085.
PubMed   |  Link to Article
Van Damme  P, Robberecht  W.  Clinical implications of recent breakthroughs in amyotrophic lateral sclerosis. Curr Opin Neurol. 2013;26(5):466-472.
PubMed   |  Link to Article
Bede  P, Bokde  AL, Byrne  S,  et al.  Multiparametric MRI study of ALS stratified for the C9orf72 genotype. Neurology. 2013;81(4):361-369.
PubMed   |  Link to Article
Hoffman  JM, Mazziotta  JC, Hawk  TC, Sumida  R.  Cerebral glucose utilization in motor neuron disease. Arch Neurol. 1992;49(8):849-854.
PubMed   |  Link to Article
Grosskreutz  J, Kaufmann  J, Frädrich  J, Dengler  R, Heinze  HJ, Peschel  T.  Widespread sensorimotor and frontal cortical atrophy in amyotrophic lateral sclerosis. BMC Neurol. 2006;6:17.
PubMed   |  Link to Article
Tsujimoto  M, Senda  J, Ishihara  T,  et al.  Behavioral changes in early ALS correlate with voxel-based morphometry and diffusion tensor imaging. J Neurol Sci. 2011;307(1-2):34-40.
PubMed   |  Link to Article
Agosta  F, Gorno-Tempini  ML, Pagani  E,  et al.  Longitudinal assessment of grey matter contraction in amyotrophic lateral sclerosis: a tensor based morphometry study. Amyotroph Lateral Scler. 2009;10(3):168-174.
PubMed   |  Link to Article
Chang  JL, Lomen-Hoerth  C, Murphy  J,  et al.  A voxel-based morphometry study of patterns of brain atrophy in ALS and ALS/FTLD. Neurology. 2005;65(1):75-80.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Relative Glucose Metabolism in C9orf72-Positive and C9orf72-Negative Patients With Amyotrophic Lateral Sclerosis (ALS) and Control Cases

A, Surface and interhemispheric projections of areas with relative hypometabolism (red) and hypermetabolism (blue) for patients with ALS vs healthy control cases (Pheight < .001). B, Patients with C9orf72-positive ALS vs healthy control cases (Pheight < .001). C, Patients with C9orf72-positive vs those with C9orf72-negative ALS (Pheight < .005).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Relative Glucose Metabolism in Primary Lateral Sclerosis (PLS) vs Controls and vs Amyotrophic Lateral Sclerosis (ALS)

A, Surface and interhemispheric projections of areas with relative hypometabolism (red) and hypermetabolism (blue) for patients with PLS vs healthy control cases (Pheight < .001). B, Patients with PLS vs those with ALS (Pheight < .005).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Most Discriminative Areas of Reduced Glucose Metabolism in Amyotrophic Lateral Sclerosis (ALS) and Primary Lateral Sclerosis (PLS)

Box-and-whisker plots of relative glucose metabolism (normalized to mean gray matter value) in volume of interest areas that are most discriminative between healthy control cases, patients with ALS, and patients with PLS: prefrontal (Brodmann Area [BA]9), motor cortex (BA4), anterior cingulate (BA 24,32), and thalamus. L indicates left; PMA, progressive muscular atrophy; R, right.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.
Support Vector Machine (SVM) Voxel-Based Discriminant Analysis of Amyotrophic Lateral Sclerosis (ALS) vs Control Cases

A, Feature weights of the classifier for ALS vs healthy control cases projected onto a normalized structural magnetic resonance image in Montreal Neurological Institute space. The scale of the feature weights represents how much a voxel contributes. The scale was normalized so that the sum of all weights is 1. Only voxels with a weight of more than 0.002 in absolute value are shown. Clusters indicate areas with high discriminative impact based on relative hypometabolism (yellow-red) and relative hypermetabolism (blue). B, Plots of distance to the classifier for healthy control cases (green) vs patients with ALS (C9orf72 positive and C9orf72 negative grouped together) using a leave-one-out approach.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 5.
Survival Plots of Patients With Amyotrophic Lateral Sclerosis (ALS) With and Without Extensive Frontotemporal Hypometabolism

Extensive hypometabolism in frontotemporal areas is a negative prognostic factor. A, Kaplan-Meier survival plots of patients with ALS with (n = 7) vs without (n = 63) extensive frontotemporal hypometabolism (P < .001). B, Kaplan-Meier survival plots of patients with ALS with (C9orf72+, n = 11) or without (C9orf72−, n = 59) the C9orf72 repeat expansion (P = .45).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Demographic and Clinical Characteristics of the Study Participants
Table Graphic Jump LocationTable 2.  Stepwise Forward Discriminant Analysisa

References

Filippi  M, Agosta  F, Abrahams  S,  et al; European Federation of Neurological Societies.  EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases. Eur J Neurol. 2010;17(4):526-e20.
PubMed   |  Link to Article
Elamin  M, Phukan  J, Bede  P,  et al.  Executive dysfunction is a negative prognostic indicator in patients with ALS without dementia. Neurology. 2011;76(14):1263-1269.
PubMed   |  Link to Article
Andersen  PM, Abrahams  S, Borasio  GD,  et al; EFNS Task Force on Diagnosis and Management of Amyotrophic Lateral Sclerosis.  EFNS guidelines on the clinical management of amyotrophic lateral sclerosis (MALS): revised report of an EFNS task force. Eur J Neurol. 2012;19(3):360-375.
PubMed   |  Link to Article
Schrooten  M, Smetcoren  C, Robberecht  W, Van Damme  P.  Benefit of the Awaji diagnostic algorithm for amyotrophic lateral sclerosis: a prospective study. Ann Neurol. 2011;70(1):79-83.
PubMed   |  Link to Article
Phukan  J, Pender  NP, Hardiman  O.  Cognitive impairment in amyotrophic lateral sclerosis. Lancet Neurol. 2007;6(11):994-1003.
PubMed   |  Link to Article
Hardiman  O, van den Berg  LH, Kiernan  MC.  Clinical diagnosis and management of amyotrophic lateral sclerosis. Nat Rev Neurol. 2011;7(11):639-649.
PubMed   |  Link to Article
DeJesus-Hernandez  M, Mackenzie  IR, Boeve  BF,  et al.  Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011;72(2):245-256.
PubMed   |  Link to Article
Renton  AE, Majounie  E, Waite  A,  et al; ITALSGEN Consortium.  A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron. 2011;72(2):257-268.
PubMed   |  Link to Article
Majounie  E, Renton  AE, Mok  K,  et al; Chromosome 9-ALS/FTD Consortium; French research network on FTLD/FTLD/ALS; ITALSGEN Consortium.  Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol. 2012;11(4):323-330.
PubMed   |  Link to Article
Gijselinck  I, Van Langenhove  T, van der Zee  J,  et al.  A C9orf72 promoter repeat expansion in a Flanders-Belgian cohort with disorders of the frontotemporal lobar degeneration-amyotrophic lateral sclerosis spectrum: a gene identification study. Lancet Neurol. 2012;11(1):54-65.
PubMed   |  Link to Article
Chiò  A, Borghero  G, Restagno  G,  et al; ITALSGEN consortium.  Clinical characteristics of patients with familial amyotrophic lateral sclerosis carrying the pathogenic GGGGCC hexanucleotide repeat expansion of C9ORF72Brain. 2012;135(pt 3):784-793.
PubMed   |  Link to Article
Byrne  S, Elamin  M, Bede  P,  et al.  Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study. Lancet Neurol. 2012;11(3):232-240.
PubMed   |  Link to Article
Millecamps  S, Boillée  S, Le Ber  I,  et al.  Phenotype difference between ALS patients with expanded repeats in C9ORF72 and patients with mutations in other ALS-related genes. J Med Genet. 2012;49(4):258-263.
PubMed   |  Link to Article
Smith  BN, Newhouse  S, Shatunov  A,  et al.  The C9ORF72 expansion mutation is a common cause of ALS+/-FTD in Europe and has a single founder. Eur J Hum Genet. 2013;21(1):102-108.
PubMed   |  Link to Article
Debray  S, Race  V, Crabbé  V,  et al.  Frequency of C9orf72 repeat expansions in amyotrophic lateral sclerosis: a Belgian cohort study. Neurobiol Aging. 2013;34(12):e7-e12.
PubMed   |  Link to Article
Snowden  JS, Rollinson  S, Thompson  JC,  et al.  Distinct clinical and pathological characteristics of frontotemporal dementia associated with C9ORF72 mutations. Brain. 2012;135(pt 3):693-708.
PubMed   |  Link to Article
van Rheenen  W, van Blitterswijk  M, Huisman  MH,  et al.  Hexanucleotide repeat expansions in C9ORF72 in the spectrum of motor neuron diseases. Neurology. 2012;79(9):878-882.
PubMed   |  Link to Article
Stewart  H, Rutherford  NJ, Briemberg  H,  et al.  Clinical and pathological features of amyotrophic lateral sclerosis caused by mutation in the C9ORF72 gene on chromosome 9p. Acta Neuropathol. 2012;123(3):409-417.
PubMed   |  Link to Article
Boeve  BF, Graff-Radford  NR.  Cognitive and behavioral features of c9FTD/ALS. Alzheimers Res Ther. 2012;4(4):29.
PubMed   |  Link to Article
van Blitterswijk  M, DeJesus-Hernandez  M, Rademakers  R.  How do C9ORF72 repeat expansions cause amyotrophic lateral sclerosis and frontotemporal dementia: can we learn from other noncoding repeat expansion disorders? Curr Opin Neurol. 2012;25(6):689-700.
PubMed   |  Link to Article
Turner  MR, Hardiman  O, Benatar  M,  et al.  Controversies and priorities in amyotrophic lateral sclerosis. Lancet Neurol. 2013;12(3):310-322.
PubMed   |  Link to Article
van der Graaff  MM, de Jong  JM, Baas  F, de Visser  M.  Upper motor neuron and extra-motor neuron involvement in amyotrophic lateral sclerosis: a clinical and brain imaging review. Neuromuscul Disord. 2009;19(1):53-58.
PubMed   |  Link to Article
Turner  MR, Kiernan  MC, Leigh  PN, Talbot  K.  Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. 2009;8(1):94-109.
PubMed   |  Link to Article
Dalakas  MC, Hatazawa  J, Brooks  RA, Di Chiro  G.  Lowered cerebral glucose utilization in amyotrophic lateral sclerosis. Ann Neurol. 1987;22(5):580-586.
PubMed   |  Link to Article
Hatazawa  J, Brooks  RA, Dalakas  MC, Mansi  L, Di Chiro  G.  Cortical motor-sensory hypometabolism in amyotrophic lateral sclerosis: a PET study. J Comput Assist Tomogr. 1988;12(4):630-636.
PubMed   |  Link to Article
Abrahams  S, Leigh  PN, Kew  JJ, Goldstein  LH, Lloyd  CM, Brooks  DJ.  A positron emission tomography study of frontal lobe function (verbal fluency) in amyotrophic lateral sclerosis. J Neurol Sci. 1995;129(suppl):44-46.
PubMed   |  Link to Article
Kew  JJ, Goldstein  LH, Leigh  PN,  et al.  The relationship between abnormalities of cognitive function and cerebral activation in amyotrophic lateral sclerosis: a neuropsychological and positron emission tomography study. Brain. 1993;116(pt 6):1399-1423.
PubMed   |  Link to Article
Ludolph  AC, Langen  KJ, Regard  M,  et al.  Frontal lobe function in amyotrophic lateral sclerosis: a neuropsychologic and positron emission tomography study. Acta Neurol Scand. 1992;85(2):81-89.
PubMed   |  Link to Article
Abrahams  S, Goldstein  LH, Kew  JJ,  et al.  Frontal lobe dysfunction in amyotrophic lateral sclerosis: a PET study. Brain. 1996;119(pt 6):2105-2120.
PubMed   |  Link to Article
Elamin  M, Bede  P, Byrne  S,  et al.  Cognitive changes predict functional decline in ALS: a population-based longitudinal study. Neurology. 2013;80(17):1590-1597.
PubMed   |  Link to Article
Chiò  A, Ilardi  A, Cammarosano  S, Moglia  C, Montuschi  A, Calvo  A.  Neurobehavioral dysfunction in ALS has a negative effect on outcome and use of PEG and NIV. Neurology. 2012;78(14):1085-1089.
PubMed   |  Link to Article
Cistaro  A, Valentini  MC, Chiò  A,  et al.  Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging. 2012;39(2):251-259.
PubMed   |  Link to Article
Herdewyn  S, Zhao  H, Moisse  M,  et al.  Whole-genome sequencing reveals a coding non-pathogenic variant tagging a non-coding pathogenic hexanucleotide repeat expansion in C9orf72 as cause of amyotrophic lateral sclerosis. Hum Mol Genet. 2012;21(11):2412-2419.
PubMed   |  Link to Article
Lemmens  R, Race  V, Hersmus  N,  et al.  TDP-43 M311V mutation in familial amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2009;80(3):354-355.
PubMed   |  Link to Article
Damme  PV, Goris  A, Race  V,  et al.  The occurrence of mutations in FUS in a Belgian cohort of patients with familial ALS. Eur J Neurol. 2010;17(5):754-756.
PubMed   |  Link to Article
Turner  MR, Grosskreutz  J, Kassubek  J,  et al; first Neuroimaging Symposium in ALS (NISALS).  Towards a neuroimaging biomarker for amyotrophic lateral sclerosis. Lancet Neurol. 2011;10(5):400-403.
PubMed   |  Link to Article
Foerster  BR, Welsh  RC, Feldman  EL.  25 years of neuroimaging in amyotrophic lateral sclerosis. Nat Rev Neurol. 2013;9(9):513-524.
PubMed   |  Link to Article
Agosta  F, Chiò  A, Cosottini  M,  et al.  The present and the future of neuroimaging in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol. 2010;31(10):1769-1777.
PubMed   |  Link to Article
Quinn  C, Elman  L, McCluskey  L,  et al.  Frontal lobe abnormalities on MRS correlate with poor letter fluency in ALS. Neurology. 2012;79(6):583-588.
PubMed   |  Link to Article
Sivák  S, Bittšanský  M, Kurča  E,  et al.  Proton magnetic resonance spectroscopy in patients with early stages of amyotrophic lateral sclerosis. Neuroradiology. 2010;52(12):1079-1085.
PubMed   |  Link to Article
Van Damme  P, Robberecht  W.  Clinical implications of recent breakthroughs in amyotrophic lateral sclerosis. Curr Opin Neurol. 2013;26(5):466-472.
PubMed   |  Link to Article
Bede  P, Bokde  AL, Byrne  S,  et al.  Multiparametric MRI study of ALS stratified for the C9orf72 genotype. Neurology. 2013;81(4):361-369.
PubMed   |  Link to Article
Hoffman  JM, Mazziotta  JC, Hawk  TC, Sumida  R.  Cerebral glucose utilization in motor neuron disease. Arch Neurol. 1992;49(8):849-854.
PubMed   |  Link to Article
Grosskreutz  J, Kaufmann  J, Frädrich  J, Dengler  R, Heinze  HJ, Peschel  T.  Widespread sensorimotor and frontal cortical atrophy in amyotrophic lateral sclerosis. BMC Neurol. 2006;6:17.
PubMed   |  Link to Article
Tsujimoto  M, Senda  J, Ishihara  T,  et al.  Behavioral changes in early ALS correlate with voxel-based morphometry and diffusion tensor imaging. J Neurol Sci. 2011;307(1-2):34-40.
PubMed   |  Link to Article
Agosta  F, Gorno-Tempini  ML, Pagani  E,  et al.  Longitudinal assessment of grey matter contraction in amyotrophic lateral sclerosis: a tensor based morphometry study. Amyotroph Lateral Scler. 2009;10(3):168-174.
PubMed   |  Link to Article
Chang  JL, Lomen-Hoerth  C, Murphy  J,  et al.  A voxel-based morphometry study of patterns of brain atrophy in ALS and ALS/FTLD. Neurology. 2005;65(1):75-80.
PubMed   |  Link to Article

Correspondence

CME


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Multimedia

Supplement.

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

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