Abstract

ObjectiveTo localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms.RationaleThe MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes.MethodsMMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography.ResultsCompared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10−6, right: p = 1.07 × 10−5) and left superior temporal gyri (p = 9.30 × 10−6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95).InterpretationSource localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI).Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials.

Highlights

  • Amyotrophic lateral sclerosis is a progressive neurodegenerative condition characterized by upper and lower motor neuron degeneration (Kiernan et al, 2011)

  • Power was significantly lower in the IFG bilaterally as well as the left STG

  • The discrepancy from complete fit indicated the presence of additional sources, which were subsequently aggregated by Exact low resolution brain electromagnetic tomography (eLORETA) and Linearly constrained minimum variance (LCMV)

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Summary

Introduction

Amyotrophic lateral sclerosis is a progressive neurodegenerative condition characterized by upper and lower motor neuron degeneration (Kiernan et al, 2011). Structural imaging can reliably record changes in grey and white matter integrity (Schuster et al, 2016) and functional imaging detects resting and activated states of metabolic activity (Erdoğan et al, 2016), there remains an unmet need for real-time measurement of different patterns of network disruption. Electrophysiological measurement of network activity during cognitive performance allows for direct objective quantification of dysfunction (Katada et al, 2004) with excellent temporal resolution (Teplan, 2002). These measures, captured by EEG or MEG, are distinct from secondary blood flow or oxygen content measures upon which fMRI is based (Erdoğan et al, 2016). The use of improved recording instrumentation with up to 256 sensors, combined with digitized data processing (Dukic et al, 2017; Muthuraman et al, 2018; Nasseroleslami et al, 2017), has substantially improved the signal to noise ratio

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