Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission and potentially cognitive impairment. Current methods for measuring the excitation/inhibition (E/I) ratio are mostly invasive, but recent research combining neurocomputational modeling with measurements of local field potentials has indicated that the slope with which the power spectrum of neuronal activity captured by electro- and/or magnetoencephalography rolls off, is a non-invasive biomarker of the E/I ratio. A steeper roll-off is associated with a stronger inhibition. This novel method can be applied to assess the E/I ratio in people with multiple sclerosis (pwMS), detect the effect of medication such as benzodiazepines, and explore its utility as a biomarker for cognition. We recruited 44 healthy control subjects and 95 pwMS who underwent resting-state magnetoencephalographic recordings. The 1/f spectral slope of the neural power spectra was calculated for each subject and for each brain region. As expected, the spectral slope was significantly steeper in pwMS treated with benzodiazepines (BZDs) compared to pwMS not receiving BZDs (p = .01). In the sub-cohort of pwMS not treated with BZDs, we observed a steeper slope in cognitively impaired pwMS compared to cognitively preserved pwMS (p = .01) and healthy subjects (p = .02). Furthermore, we observed a significant correlation between 1/f spectral slope and verbal and spatial working memory functioning in the brain regions located in the prefrontal and parietal cortex. In this study, we highlighted the value of the spectral slope in MS by quantifying the effect of benzodiazepines and by putting it forward as a potential biomarker of cognitive deficits in pwMS.

Decreased aperiodic neural activity in Parkinson's disease and dementia with Lewy bodies

Neural oscillations and signal complexity have been widely studied in neurodegenerative diseases, whereas aperiodic activity has not been explored yet in those disorders. Here, we assessed whether the study of aperiodic activity brings new insights relating to disease as compared to the conventional spectral and complexity analyses. Eyes-closed resting-state electroencephalography (EEG) was recorded in 21 patients with dementia with Lewy bodies (DLB), 28 patients with Parkinson's disease (PD), 27 patients with mild cognitive impairment (MCI) and 22 age-matched healthy controls. Spectral power was differentiated into its oscillatory and aperiodic components using the Irregularly Resampled Auto-Spectral Analysis. Signal complexity was explored using the Lempel-Ziv algorithm (LZC). We found that DLB patients showed steeper slopes of the aperiodic power component with large effect sizes compared to the controls and MCI and with a moderate effect size compared to PD. PD patients showed steeper slopes with a moderate effect size compared to controls and MCI. Oscillatory power and LZC differentiated only between DLB and other study groups and were not sensitive enough to detect differences between PD, MCI, and controls. In conclusion, both DLB and PD are characterized by alterations in aperiodic dynamics, which are more sensitive in detecting disease-related neural changes than the traditional spectral and complexity analyses. Our findings suggest that steeper aperiodic slopes may serve as a marker of network dysfunction in DLB and PD features.

Methodological considerations for studying neural oscillations

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.