Clinical relevance of single-subject brain metabolism patterns in amyotrophic lateral sclerosis mutation carriers

Abstract

Background and objectives: The ALS diagnosis requires an integrative approach, combining the clinical examination and supporting tests. Nevertheless, in several cases, the diagnosis proves to be suboptimal, and for this reason, new diagnostic methods and novel biomarkers are catching on. The ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-PET could be a helpful method, but it still requires additional research for sensitivity and specificity. We performed an ¹⁸F-FDG-PET single-subject analysis in a sample of familial ALS patients carrying different gene mutations, investigating the genotype-phenotype correlations and exploring metabolism correlations with clinical and neuropsychological data.

Methods: We included ten ALS patients with pathogenic gene mutation who underwent a complete clinical and neuropsychological evaluation and an ¹⁸F-FDG-PET scan at baseline. Patients were recruited between 2018 and 2022 at the ALS Tertiary Centre in Novara, Italy. Patients were selected based on the presence of ALS gene mutation (C9orf72, SOD1, TBK1, and KIF5A). Following a validated voxel-based Statistical Parametric Mapping (SPM) procedure, we obtained hypometabolism maps at single-subject level. We extracted regional hypometabolism from the SPM maps, grouping significant hypometabolism regions into three meta-ROIs (motor, prefrontal association and limbic). Then, the corresponding ¹⁸F-FDG-PET regional hypometabolism was correlated with clinical and neuropsychological features.

Results: Classifying the patients with C9orf72-ALS based on the rate of disease progression from symptoms onset to the time of scan, we observed two different patterns of brain hypometabolism: an extensive motor and prefrontal hypometabolism in patients classified as fast progressors, and a more limited brain hypometabolism in patients grouped as slow progressors. Patients with SOD1-ALS showed a hypometabolic pattern involving the motor cortex and prefrontal association regions, with a minor involvement of the limbic regions. The patient with TBK1-ALS showed an extended hypometabolism, in limbic systems, along with typical motor involvement, while the hypometabolism in the patient with KIF5A-ALS involved almost exclusively the motor regions, supporting the predominantly motor impairment linked to this gene mutation. Additionally, we observed strong correlations between the hypometabolism in the motor, prefrontal association and limbic meta-ROI and the specific neuropsychological performances.

Conclusions: To our knowledge, this is the first study investigating brain hypometabolism at the single-subject level in genetic ALS patients carrying different mutations. Our results show high heterogeneity in the hypometabolism maps and some commonalities in groups sharing the same mutation. Specifically, in patients with C9orf72-ALS the brain hypometabolism was larger in patients classified as fast progressors than slow progressors. In addition, in the whole group, the brain metabolism showed specific correlations with clinical and neuropsychological impairment, confirming the ability of ¹⁸F-FDG-PET in revealing pattern of neuronal dysfunction, aiding the diagnostic workup in genetic ALS patients.

Keywords: (18)F-FDG-PET; Amyotrophic lateral sclerosis; Brain metabolism; Genetic; Neurodegenerative diseases; Positron emission tomography.

Fig. 1

18F-FDG-PET imaging finding in ALS genetic patients. Brain hypometabolism maps are presented in single cases. Patients ALS1, ALS2 and ALS3 are considered as C9orf72 fast progressors. Patients ALS4, ALS5 and ALS6 are considered as C9orf72 slow progressors. Patient ALS7 and ALS8 were carriers of the SOD1 mutation. The bottom row shows the results of the single-subject analysis in TBK1 (ALS9) and KIF5A (ALS10) patient. The hypometabolism regions are overlaid on a 3D brain template using Brainnet toolbox. The statistical threshold was set at a p-value < 0.05 uncorrected, minimal cluster extent = 100.

Fig. 2

Hypometabolism cluster extraction according to the limbic, prefrontal association and motor meta-ROIs. Mean regional hypometabolism in the three meta-ROI is reported in each single patient. The figure underlines the large variability among the whole sample in the hypometabolism patterns and some commonalities. ALS1, ALS2 and ALS3 are patients with C9orf72-ALS showing widespread hypometabolism involving all the three meta-ROIs and worse clinical functional status. ALS4 ALS5 and ALS6 are patients with C9orf72-ALS showing slight clinical impairment and minor hypometabolism (sole motor involvement has present in ALS5). ALS7 and ALS8 share the same metabolism meta-ROI pattern, even with a different rate of involvement.

Fig. 3

Significant correlation between hypometabolism peak expression and neuropsychological scores. From Panel A and Panel B: correlations between the hypometabolism in the motor meta-ROI and the neuropsychological tests; from Panel C to Panel F: correlations between the hypometabolism in the prefrontal association meta-ROI and the neuropsychological tests; from Panel G to Panel L: correlations between the hypometabolism in the limbic meta-ROI and the neuropsychological tests. X-axis: hypometabolism values; Y-axis: neuropsychological scores.