Mind over chatter: plastic up-regulation of the fMRI alertness network by EEG neurofeedback

EEG neurofeedback (NFB) is a brain-computer interface (BCI) approach used to shape brain oscillations by means of real-time feedback from the electroencephalogram (EEG), which is known to reflect neural activity across cortical networks. Although NFB is being evaluated as a novel tool for treating brain disorders, evidence is scarce on the mechanism of its impact on brain function. In this study with 34 healthy participants, we examined whether, during the performance of an attentional auditory oddball task, the functional connectivity strength of distinct fMRI networks would be plastically altered after a 30-min NFB session of alpha-band reduction (n=17) versus a sham-feedback condition (n=17). Our results reveal that compared to sham, NFB induced a specific increase of functional connectivity within the alertness/salience network (dorsal anterior and mid cingulate), which was detectable 30 minutes after termination of training. Crucially, these effects were significantly correlated with reduced mind-wandering 'on-task' and were coupled to NFB-mediated resting state reductions in the alpha-band (8-12 Hz). No such relationships were evident for the sham condition. Although group default-mode network (DMN) connectivity was not significantly altered following NFB, we observed a positive association between modulations of resting alpha amplitude and precuneal connectivity, both correlating positively with frequency of mind-wandering. Our findings demonstrate a temporally direct, plastic impact of NFB on large-scale brain functional networks, and provide promising neurobehavioral evidence supporting its use as a noninvasive tool to modulate brain function in health and disease

Baseline gamma power during auditory steady-state stimulation in schizophrenia

Several studies have reported deficits in γ oscillatory activity elicited by sensory stimulation or cognitive processes in schizophrenia patients (SZ) compared to healthy control subjects (HC). However, the evidence for cortical hyperexcitability and reduced function of N-methyl-D-aspartate receptors (NMDARs) on parvalbumin-expressing inhibitory interneurons in schizophrenia leads to the prediction that γ activity should rather be increased in SZ, but data supporting this hypothesis have been lacking. One possibility is that baseline induced γ power is increased, an effect that might have gone unnoticed in studies of stimulus-locked oscillations. Here we addressed this question by re-analyzing the data from a previously published study on the 40 Hz auditory steady-state response (ASSR) in schizophrenia in which dipole source localization was used to examine γ responses in the left and right auditory cortices. Subjects were 16 HC and 18 chronic SZ, who listened to trains of clicks presented at 40 Hz during electroencephalogram recording. Independent component analysis was used to remove ocular artifacts. Power spectra were computed for the pre-stimulus baseline period. We found that baseline power was higher in SZ than HC at 40 Hz in the left auditory cortex. Baseline 40 Hz power in the left auditory cortex was also correlated with ASSR evoked power in SZ. Thus, γ oscillation abnormalities in schizophrenia may include abnormal increases in baseline power as well as deficits in evoked oscillations. These baseline increases could be the sign of NMDAR hypofunction on parvalbumin-expressing inhibitory interneurons, which would be consistent with acute NMDAR antagonism and genetic ablation models of schizophrenia

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71‐channel EEG recorded from 35 healthy adults at two sessions (1‐week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal‐to‐noise ratio, participant‐level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait‐multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low‐variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component‐based identification of spectral activity (CSD/eLORETA‐fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.Magnitude of frontal theta (rostral ACC eLORETA source amplitude) and posterior alpha (spectral components of scalp current source density) at rest have been considered candidate EEG biomarkers of depression outcomes. Given inconsistent findings, we examined the discriminant and convergent validity of these measures in healthy adults. Unlike theta, two distinct alpha components constituted reliable, convergent, and discriminant biometrics. While results have marked implications for clinical utility, we make several recommendations for improving the psychometric properties of resting frontal theta.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/1/psyp13483.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/2/psyp13483_am.pd

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tGraph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.Funding was from Epilepsy Research UK (http://www.epilepsyresearch.org.uk) via grant number A1007 and the Medical Research Council (http://www.mrc.ac.uk) via grants (MR/K013998/1 and G0701310)

Multilingual experience modulates resting-state functional connectivity and executive functioning in cognitive aging

Bi-/multilingualism has been found to act favourably on the cognitive aging (CA) trajectory due to the increased executive functioning demands that dual-language use exerts on the brain leading to contributions to neurocognitive reserve and resilience. There is a gap in the literature on how individual differences in the degree of multilingualism influence this trajectory. Furthermore, other lifestyle factors such as diet and exercise, have also been shown to influence CA, yet language experiences and lifestyle factors have rarely been examined together. This thesis aims to fill this gap by examining the unique influence of multilingual language engagement on intrinsic brain activity at-rest and working memory performance. A comprehensive language and lifestyle profile was calculated from native Norwegian multilingual speakers with English as one of their additional languages (n=90, mage=49,3, (SD=18.06), range 19-82. Resting-state Electroencephalography (rs-EEG) and working memory were assessed and regressed against a continuous measure of multilingualism (MLD) while controlling for other lifestyle-experiences. Results indicate a near-significant trend hinting that degree of multilingualism offsets the downwards aging trajectory of EEG coherence in alpha and gamma coherence across several electrode regions. A significant positive interaction between age and MLD was found for WM performance. An exploratory post-hoc analysis revealed a null relationship between functional connectivity and working memory. Results suggest that a higher degree of multilingualism leads to increased resilience against CA

A Novel Method for Reducing the Effect of Tonic Muscle Activity on the Gamma Band of the Scalp EEG

Neural oscillations in the gamma band are of increasing interest, but separating them from myogenic electrical activity has proved difficult. A novel algorithm has been developed to reduce the effect of tonic scalp and neck muscle activity on the gamma band of the EEG. This uses mathematical modelling to fit individual muscle spikes and then subtracts them from the data. The method was applied to the detection of motor associated gamma in two separate groups of eight subjects using different sampling rates. A reproducible increase in high gamma (65–85 Hz) magnitude occurred immediately after the motor action in the left central area (p = 0.02 and p = 0.0002 for the two cohorts with individually optimized algorithm parameters, compared to p = 0.03 and p = 0.16 before correction). Whilst the magnitude of this event-related gamma synchronisation was not reduced by the application of the EMG reduction algorithm, the baseline left central gamma magnitude was significantly reduced by an average of 23 % with a faster sampling rate (p < 0.05). In comparison, at left and right temporo-parietal locations the gamma amplitude was reduced by 60 and 54 % respectively (p < 0.05). The reduction of EMG contamination by fitting and subtraction of individual spikes shows promise as a method of improving the signal to noise ratio of high frequency neural oscillations in scalp EEG

Frequency and power of human alpha oscillations drift systematically with time-on-task

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8–13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8–10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9–13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation

An interplay of feedforward and feedback signals supporting visual cognition

Vast majority of visual cognitive functions from low to high level rely not only on feedforward signals carrying sensory input to downstream brain areas but also on internally-generated feedback signals traversing the brain in the opposite direction. The feedback signals underlie our ability to conjure up internal representations regardless of sensory input – when imagining an object or directly perceiving it. Despite ubiquitous implications of feedback signals in visual cognition, little is known about their functional organization in the brain. Multiple studies have shown that within the visual system the same brain region can concurrently represent feedforward and feedback contents. Given this spatial overlap, (1) how does the visual brain separate feedforward and feedback signals thus avoiding a mixture of the perceived and the imagined? Confusing the two information streams could lead to potentially detrimental consequences. Another body of research demonstrated that feedback connections between two different sensory systems participate in a rapid and effortless signal transmission across them. (2) How do nonvisual signals elicit visual representations? In this work, we aimed to scrutinize the functional organization of directed signal transmission in the visual brain by interrogating these two critical questions. In Studies I and II, we explored the functional segregation of feedforward and feedback signals in grey matter depth of early visual area V1 using 7T fMRI. In Study III we investigated the mechanism of cross-modal generalization using EEG. In Study I, we hypothesized that functional segregation of external and internally-generated visual contents follows the organization of feedforward and feedback anatomical projections revealed in primate tracing anatomy studies: feedforward projections were found to terminate in the middle cortical layer of primate area V1, whereas feedback connections project to the superficial and deep layers. We used high-resolution layer-specific fMRI and multivariate pattern analysis to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth compartments (i.e. superficial and deep). At the same time perceived contents were more strongly represented at the middle cortical compartment. These results correspond to the previous neuroanatomical findings and identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. For the more precise estimation of signal-by-depth separation revealed in Study I, next we benchmarked three MR-sequences at 7T - gradient-echo, spin-echo, and vascular space occupancy - in their ability to differentiate feedforward and feedback signals in V1. The experiment in Study II consisted of two complementary tasks: a perception task that predominantly evokes feedforward signals and a working memory task that relies on feedback signals. We used multivariate pattern analysis to read out the perceived (feedforward) and memorized (feedback) grating orientation from neural signals across cortical depth. Analyses across all the MR-sequences revealed perception signals predominantly in the middle cortical compartment of area V1 and working memory signals in the deep compartment. Despite an overall consistency across sequences, spin-echo was the only sequence where both feedforward and feedback information were differently pronounced across cortical depth in a statistically robust way. We therefore suggest that in the context of a typical cognitive neuroscience experiment manipulating feedforward and feedback signals at 7T fMRI, spin-echo method may provide a favorable trade-off between spatial specificity and signal sensitivity. In Study III we focused on the second critical question - how are visual representations activated by signals belonging to another sensory modality? Here we built our hypothesis following the studies in the field of object recognition, which demonstrate that abstract category-level representations emerge in the brain after a brief stimuli presentation in the absence of any explicit categorization task. Based on these findings we assumed that two sensory systems can reach a modality-independent representational state providing a universal feature space which can be read out by both sensory systems. We used EEG and a paradigm in which participants were presented with images and spoken words while they were conducting an unrelated task. We aimed to explore whether categorical object representations in both modalities reflect a convergence towards modality-independent representations. We obtained robust representations of objects and object categories in visual and auditory modalities; however, we did not find a conceptual representation shared across modalities at the level of patterns extracted from EEG scalp electrodes in our study. Overall, our results show that feedforward and feedback signals are spatially segregated in the grey matter depth, possibly reflecting a general strategy for implementation of multiple cognitive functions within the same brain region. This differentiation can be revealed with diverse MR-sequences at 7T fMRI, where spin-echo sequence could be particularly suitable for establishing cortical depth-specific effects in humans. We did not find modality-independent representations which, according to our hypothesis, may subserve the activation of visual representations by the signals from another sensory system. This pattern of results indicates that identifying the mechanisms bridging different sensory systems is more challenging than exploring within-modality signal circuitry and this challenge requires further studies. With this, our results contribute to a large body of research interrogating how feedforward and feedback signals give rise to complex visual cognition

Small-World Network Analysis of Cortical Connectivity in Chronic Fatigue Syndrome using EEG

The primary aim of this thesis was to explore the relationship between electroencephalography (qEEG) and brain system dysregulation in people with Chronic Fatigue Syndrome (CFS). EEG recordings were taken from an archival dataset of 30 subjects, 15 people with CFS and 15 healthy controls (HCs), evaluated during an eye-closed resting state condition. Exact low resolution electromagnetic tomography (eLORETA) was applied to the qEEG data to estimate cortical sources and perform functional connectivity analysis assessing the strength of time-varying signals between all pairwise cortical regions of interest. To obtain a comprehensive view of local and global processing, eLORETA lagged coherence was computed on 84 regions of interest representing 42 Brodmann areas for the left and right hemispheres of the cortex, for the delta (1-3 Hz) and alpha-1 (8-10 Hz) and alpha-2 (10-12 Hz) frequency bands. Graph theory analysis of eLORETA coherence matrices for each participant was conducted to derive the “small-worldness” index, a measure of the optimal balance between the functional integration (global) and segregation (local) properties known to be present in brain networks. The data were also associated with the cognitive impairment composite score on the DePaul Symptom Questionnaire (DSQ), a patient-reported symptom outcome measure of frequency and severity of cognitive symptoms. Results showed that small-worldness for the delta band was significantly lower for patients with CFS compared to HCs. Small-worldness for delta, alpha-1, and alpha-2 were associated with higher cognitive composite scores on the DSQ. Finally, small-worldness in all 3 frequency bands correctly distinguished those with CFS from HCS with a classification rate of nearly 87 percent. These preliminary findings suggest disease processes in CFS may be functionally disruptive to small-world characteristics, especially in the delta frequency band, resulting in cognitive impairments. In turn, these findings may help to confirm a biological basis for cognitive symptoms, providing clinically relevant diagnostic indicators, and characterizing the neurophysiological status of people with CFS