Education moderates the association between motor involvement and executive status in ALS.

Background: This study aimed to determine whether educational attainment-a common proxy of cognitive reserve (CR)-influences the association between motor and cognitive/behavioural outcomes in a large cohort of ALS patients without dementia.

Methods: N = 726 ALS patients without FTD were assessed for motor (ALSFRS-R), cognitive (Edinburgh Cognitive and Behavioural ALS Screen, ECAS) and behavioural outcomes (ECAS-Carer Interview, ECAS-CI). CR was operationalized via educational attainment (in years).

Sex-related differences in amyotrophic lateral sclerosis: A 2-[F]FDG-PET study.

Purpose: We investigated sex-related brain metabolic differences in Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC).

Methods: We collected two equal-sized groups of male (m-ALS) and female ALS (f-ALS) patients (n = 130 each), who underwent 2-[F]FDG-PET at diagnosis, matched for site of onset, cognitive status and King's stage. We included 168 age-matched healthy controls, half female (f-HC) and half male (m-HC).

Croplands proximity is associated with amyotrophic lateral sclerosis incidence and age at onset.

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease resulting from an intricate interplay between genetics and environmental factors. Many studies have explored living in rural areas as a possible risk factor for ALS, without focusing simultaneously on incidence, age at onset and phenotypic features.

Objective: To evaluate the effect of croplands residential proximity on ALS incidence and phenotype, focusing on age of onset, site of onset and progression rate.

Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study.

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data.