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Introduction: Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome., Areas Covered: The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years., Expert Opinion: Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.

Objective: Detecting and monitoring cognitive and behavioral deficits in motor neuron diseases (MND) is critical due to their considerable clinical impact. In this scenario, computer-based batteries may play an important role. In this study, we investigated the progression of cognitive and behavioral deficits in MND patients using both standard and computer-based neuropsychological batteries. Methods : This is a retrospective study on 74 MND patients (52 amyotrophic lateral sclerosis [ALS], 12 primary lateral sclerosis [PLS], and 10 progressive muscular atrophy [PMA]) who were followed up for 12 months and underwent up to three cognitive/behavioral assessments, 6 months apart, including standard and/or computerized based (the Test of Attentional Performance [TAP]) batteries. Behavioral/cognitive changes were investigated over time using generalized linear model for longitudinal data accounting for time and revised-ALS Functional Rating Scale. Results : Over 12 months, ALS patients showed a global cognitive decline (Mini Mental State Examination) at the standard battery and reduced performance in the alertness, sustained and divided attention, go/nogo, cross-modal and incompatibility TAP tasks. Most of these findings remained significant when ALSFRS-R changes over time were included as covariate in the analyses. ALS patients did not show significant behavioral abnormalities over time. No cognitive and behavioral changes were found in PLS and PMA cases. Conclusions : Computer-based neuropsychological evaluations are able to identify subtle cognitive changes in ALS, unique to this condition. This study highlights the need of specific, accurate and well-tolerated tools for the monitoring of cognitive deficits in MND.