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Objective. The characterization of brain functional connectivity is a helpful tool in the study of the neuronal substrates and mechanisms that are altered in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Recently, there has been a shift towards the characterization of dynamic functional connectivity (dFC), discarding the assumption of connectivity stationarity during the resting-state. The majority of these studies have been performed with functional magnetic resonance imaging recordings, with only a small subset being based on magnetoencephalography/electroencephalography (MEG/EEG). However, only these modalities enable the characterization of potentially fast brain dynamics, which is mandatory for an accurate understanding of the transmission and processing of neuronal information. The aim of this study was to characterize the dFC of resting-state EEG activity in AD and MCI. Approach. Three measures: the phase lag index (PLI), leakage-corrected magnitude squared coherence (MSCOH) and leakage-corrected amplitude envelope correlation (AEC) were computed for 45 patients with dementia due to AD, 51 subjects with MCI due to AD and 36 cognitively healthy controls. All measures were estimated in epochs of 60 s using a sliding window approach. An epoch length of 15 s was used to provide reliable results. We tested whether the observed PLI, MSCOH and AEC fluctuations reflected actual variations in functional connectivity, as well as whether between-group differences could be found. Main results. We found dFC using PLI, MSCOH and AEC, with AEC having the highest number of statistically significant connections, followed by MSCOH and PLI. Furthermore, a significant reduction in AEC dFC for patients with AD compared to controls was found in the alpha (8–13 Hz) and beta-1 (13–30 Hz) bands. Significance. Our results suggest that patients with AD (and MCI subjects to a lesser degree) show less variation in neuronal connectivity during resting-state, supporting the notion that dFC can be found at the EEG time scale and is abnormal in the MCI-AD continuum. Measures of dFC have the potential of being used as biomarkers of AD. Moreover, they could also suggest that AD resting-state networks may operate at a state of low firing activity induced by the observed reduction in coupling strength. Furthermore, the statistically significant correlation between dFC and relative power in the beta-1 band could be related to pathologically high levels of neural activity inducing a loss of dFC. These findings show that the stability of neuronal coupling is affected in AD and MCI.

Objective. Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information. Approach. A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography data from patients with schizophrenia and cognitively healthy controls. Main results. The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations. Significance. Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Objective. Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. Approach. Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). Main results. Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. Significance. To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.

Dementia is a syndrome characterised by cognitive impairments, with a loss of learning/memory abilities at the earlier stages and executive dysfunction at the later stages. However, recent studies have suggested that impairments in both learning/memory abilities and executive functioning might co-exist. Cognitive impairments have been primarily evaluated using neuropsychological assessments, such as the Mini-Mental State Examination (MMSE). Recently, neuroimaging techniques such as magnetoencephalography (MEG), which assess changes in resting-state brain activity, have also been used as biomarkers for cognitive impairment. However, it is unclear whether these changes reflect dysfunction in executive function as well as learning and memory. In this study, parameters from the MEG for brain activity, MMSE for learning/memory, and Frontal Assessment Battery (FAB) for executive function were compared within 207 individuals. Three MEG parameters were used as representatives of resting-state brain activity: median frequency, individual alpha frequency, and Shannon’s spectral entropy. Regression analysis showed that median frequency was predicted by both the MMSE and FAB scores, while individual alpha frequency and Shannon’s spectral entropy were predicted by MMSE and FAB scores, respectively. Our results indicate that MEG spectral parameters reflect both learning/memory and executive functions, supporting the utility of MEG as a biomarker of cognitive impairment.