The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprintingrepresents a reliable approach to assess subject-specific connectivity features within a given population (healthyor diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructedmagnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients andthirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which eachpatient was recognisable based on his/her connectome, as compared to healthy controls. The analysis wasperformed in the five canonical frequency bands. Then, we built a multilinear regression model to test the abilityof the “clinical fingerprint” to predict the clinical evolution of the disease, as assessed by the Amyotrophic LateralSclerosis Functional Rating Scale-Revised (ALSFRS-r), the King’s disease staging system, and the Milano-TorinoStaging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha bandcompared to the healthy controls. Furthermore, the “clinical fingerprint” was predictive of the ALSFRS-r (p =0.0397; β = 32.8), the King’s (p = 0.0001; β = − 7.40), and the MiToS (p = 0.0025; β = − 4.9) scores.Accordingly, it negatively correlated with the King’s (Spearman’s rho = -0.6041, p = 0.0003) and MiToS scales(Spearman’s rho = − 0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predictthe individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, wehope to further exploit it to improve disease management.