Objective-Despite the high prevalence of non-motor impairments reported in patients with amyotrophic lateral sclerosis (ALS), little is known about the functional neural markers underlying such dysfunctions. In this study, a new dual-task multimodal framework relying on simultaneous electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) recordings was developed to characterize integrative non-motor neural functions in people with ALS.Approach-Simultaneous EEG-fNIRS data were recorded from six subjects with ALS and twelve healthy controls. Through a proposed visuo-mental paradigm, subjects performed a set of visuo-mental arithmetic operations. The data recorded were analyzed with respect to event-related changes both in the time and frequency domains for EEG and de/oxygen-hemoglobin level (HbR/ HbO) changes for fNIRS. The correlation of EEG spectral features with fNIRS HbO/HbR features were then evaluated to assess the mechanisms of ALS on the electrical (EEG)-vascular (fNIRS) interrelationships.Main results-We observed overall smaller increases in EEG delta and theta power, decreases in beta power, reductions in HbO responses, and distortions both in early and later EEG event-related potentials in ALS subjects compared to healthy controls. While significant correlations between EEG features and HbO responses were observed in healthy controls, these patterns were absent in ALS patients. Distortions in both electrical and hemodynamic responses are speculated to be associated with cognitive deficits in ALS that center primarily on attentional and working memory processing.Significance-Our results highlight the important role of ALS non-motor dysfunctions in electrical and hemodynamic neural dynamics as well as their interrelationships. The insights
Multimodal data fusion is one of the current primary neuroimaging
research directions to overcome the fundamental limitations of
individual modalities by exploiting complementary information from
different modalities. Electroencephalography (EEG) and functional
near-infrared spectroscopy (fNIRS) are especially compelling
modalities due to their potentially complementary features reflecting
the electro-hemodynamic characteristics of neural responses. However,
the current multimodal studies lack a comprehensive systematic
approach to properly merge the complementary features from their
multimodal data. Identifying a systematic approach to properly fuse
EEG-fNIRS data and exploit their complementary potential is crucial in
improving performance. This paper proposes a framework for classifying
fused EEG-fNIRS data at the feature level, relying on a mutual
information-based feature selection approach with respect to the
complementarity between features. The goal is to optimize the
complementarity, redundancy and relevance between multimodal features
with respect to the class labels as belonging to a pathological
condition or healthy control. Nine amyotrophic lateral sclerosis (ALS)
patients and nine controls underwent multimodal data recording during
a visuo-mental task. Multiple spectral and temporal features were
extracted and fed to a feature selection algorithm followed by a
classifier, which selected the optimized subset of features through a
cross-validation process. The results demonstrated considerably
improved hybrid classification performance compared to the individual
modalities and compared to conventional classification without feature
selection, suggesting a potential efficacy of our proposed framework
for wider neuro-clinical applications.
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