Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis

Abstract

Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success.

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

“…For example, Iyer et al, ( Iyer et al, 2015 ) showed increased assortativity in the alpha-band in ALS patients as compared to controls, as well as higher clustering coefficient values based on Partial Directed connectivity (PCD). Similarly, an EEG study conducted by Perez Ortiz et al ( Perez-Ortiz et al, 2021 ) displayed the presence of connectivity alterations (in the sense of reduced synchronisation) in the alpha band in ALS patients during the execution of cognitive tasks. According to Fraschini et al ( Fraschini et al, 2018 ) ALS patients showed reduced brain functional connectivity compared to healthy subjects, which was linked to different synchrony patterns in the alpha band.…”

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

“…This enables the measure to reveal temporal changes of connectivity and opens a new direction for further research. This can be especially useful for analyzing human brain networks in auditory and visual tasks [39,40] and is also promising for assessing motor skills [41]. This also allows us to observe changes in the organization of brain network connectivity over time using well-known measures from complex network graph theory [42].…”

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases—A Review

“…Moreover, support vector machines were used by Chang et al to classify ALS patients and healthy subjects using plasma biomarkers metrics, achieving a mean accuracy of 88% [44]. Additionally, the research presented by Perez-Ortiz et al showed that EEG activity monitoring, coupled with a support vector machine classifier, can also be used for ALS diagnosis [45], leading to a success rate of 89%. Research by Bandini et al was conducted to distinguish between presymptomatic and symptomatic ALS stages using SVM and KNN algorithms [46].…”