Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are related neurodegenerative disorders. Amyotrophic lateral sclerosis primarily affects the motor system with upper and lower motor neuron involvement, but extramotor manifestations may occur.^1-3 Frontotemporal dementia is the second most common form of presenile dementia, caused by degeneration of frontal and anterior temporal cortices. It affects brain regions implicated in executive control, language, behavior, and personality.⁴ The disease course of both ALS and FTD is progressive and invariably fatal. The molecular link between ALS and FTD has been confirmed by the discovery of the hexanucleotide repeat expansions in the 3′ untranslated region of the chromosome 9 open reading frame 72 gene (C9orf72, OMIM 614260), the most common known monogenetic cause of both ALS and FTD.^5-7

Amyotrophic lateral sclerosis (ALS) is characterized by progressive loss of upper and lower motor neurons. In 10% of patients, the disorder runs in the family. Our aim was to study the impact of ALS-causing gene mutations on cerebral glucose metabolism. Between October 2010 and October 2022, 538 patients underwent genetic testing for mutations with strong evidence of causality for ALS and ¹⁸F-2-fluoro-2-deoxy-D-glucose-PET (FDG PET), at University Hospitals Leuven. We identified 48 C9orf72-ALS and 22 SOD1-ALS patients. After propensity score matching, two cohorts of 48 and 21 matched sporadic ALS patients, as well as 20 healthy controls were included. FDG PET images were assessed using a voxel-based and volume-of-interest approach. We observed widespread frontotemporal involvement in all ALS groups, in comparison to healthy controls. The degree of relative glucose metabolism in SOD1-ALS in motor and extra-motor regions did not differ significantly from matched sporadic ALS patients. In C9orf72-ALS, we found more pronounced hypometabolism in the peri-rolandic region and thalamus, and hypermetabolism in the medulla extending to the pons, in comparison to matched sporadic ALS patients. Our study revealed C9orf72-dependent differences in glucose metabolism in the peri-rolandic region, thalamus, and brainstem (i.e., medulla, extending to the pons) in relation to matched sporadic ALS patients.

18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores. ALS patients (n = 70) and PP patients (n = 33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n = 29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient. Classification between ALS and PP resulted in a sensitivity and specificity ≥ 0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ≥ 0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ≥ 0.91 was found between pairs of ALS- and PP-specific pattern expression scores. We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers.

Background Translation, validation, and use of a computerized battery for cognitive evaluation of patients with systemic lupus erythematosus (SLE) as part of an investigation of neuropsychiatric manifestations is useful and allows multicenter studies. Methods This is a quantitative transversal study with control group conducted in pediatric and adult rheumatology clinics. The computer test batteries ANAM (Automated Neuropsychological Assessment Metrics) or PedAnam (Pediatric version) was submitted to the Guidelines for the Process of Cross-Cultural Adaptation. This process has five stages: Initial translation, synthesis of the translations, back translation, harmonization, test of the prefinal version and final version. After the process, we apply the ANAM in 98 SLE patients and healthy age-matched individuals. All individuals underwent an evaluation through the battery tests taking 30 min on average to solve the problems. Results 98 SLE patients, 69 female and 29 male, ages between 6 and 68 years with mean of 28.6. The control group had 84 people, 73 female and 11 male, ages between 6 and 65 years with mean of 25. For the evaluation was used the Performance Validity Index score, that provides a performance indicator, between 0 and 14, for someone with good effort, or above for someone outside the range of that expected for someone providing good effort. Patients presented an average performance of 8.05, with a minimum of 0 and a maximum of 33, while the control group had a mean of 4.4, minimum 0 and maximum of 27. As the smaller score results in a better effort, it is possible to notice meaningful differences between the groups (p<0.05). Conclusions Cognitive difficulties are often observed in SLE and practical tools like ANAM and PedAnan should be used to measure the cognitive loss that patients may have; these losses should be monitored more closely if there are other neuropsychiatric symptoms. Funding Source(s): CNPq CAPES FAPESP