Profiling cognition and brain metabolism in amyotrophic lateral sclerosis and frontotemporal dementia

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are described as a disease continuum, given their shared clinical, genetic and pathological characteristics. Comparisons of clinical and biomarker features within the ALS and behavioural variant FTD (bvFTD) spectrum are of utmost importance for diagnostic and prognostic purposes. This study investigated biomarker differences between cognitively normal patients with ALS (ALS-cn), ALS-FTD patients and bvFTD patients. Participants, genetically screened for known ALS- and FTD-associated mutations, underwent neuropsychological assessments, CSF analysis and 18F-fluorodeoxyglucose PET (18F-FDG-PET). Neuropsychological data were analysed by calculating a composite score for each cognitive domain, averaging the rank-transformed z-scores of all tests measuring the same domain. 18F-FDG-PET was performed using a validated voxel-based SPM method at the single-subject and group levels. To evaluate the ability to differentiate ALS-cn, ALS-FTD and bvFTD using the identified markers, machine-learning models-support vector machine (SVM) and random forest (RF)-were applied, offering a streamlined, data-driven approach to improve diagnostic precision across this spectrum of disorders. Twenty ALS-cn, 19 ALS-FTD and 21 bvFTD patients were included. Neuropsychological composite z-scores revealed significant differences across groups, underlining worse performance in bvFTD regarding memory, visuospatial, language and executive functions. Brain 18F-FDG-PET showed a pattern of hypometabolism that increased from ALS-cn to ALS-FTD and reached its greatest extent in bvFTD. Specifically, brain hypometabolism was confined mainly to the sensorimotor cortices and frontobasal regions in the ALS-cn group, whereas in the ALS-FTD group, it extended to the supplementary motor area and dorsolateral frontal cortex, and in the bvFTD group, it was widespread, further affecting the frontomesial and orbitofrontal cortices. No significant differences in CSF biomarkers were observed between groups. SVM correctly classified 83% of patients, indicating good classification performance, while RF achieved perfect accuracy (100%). The two models shared eight to ten most relevant features for classification, namely age, disease duration from symptom onset to diagnosis, total composite z-score, superior frontal gyrus (left), middle frontal gyrus (left), middle frontal gyrus-pars orbitalis (left and right) and anterior cingulate cortex (left). Our study identified significant differences in biomarkers across neurodegenerative clinical groups within the same disease spectrum. These differences were evident in the neuropsychological profiles and brain hypometabolism patterns among the patient groups, providing valuable insights into the heterogeneity of the ALS-FTD continuum and highlighting distinct disease-related patterns to enhance diagnostic accuracy.