From rest to task

A primary goal of neuroscience research on psychiatric disorders such as schizophrenia is to enhance the current understanding of underlying biological mechanisms in order to develop novel interventions. Human brain functions are maintained through activity of large-scale brain networks. Accordingly, deficient perceptual and cognitive processing can be caused by failures of functional integration within networks, as reflected by the disconnection hypothesis of schizophrenia. Various neuroimaging techniques can be applied to study functional brain networks, each having different strengths. Frequently used complementary methods are the electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which were shown to have a common basis. Given the feasibility of combined EEG and fMRI measurement, EEG signatures of functional networks have been described, providing complimentary information about the functional state of networks. Both at rest and during task completion, many independent EEG and fMRI studies confirmed deficient network connectivity in schizophrenia. However, a rather diffuse picture with hyper- and hypo- activations within and between specific networks was reported. Furthermore, the theory of state dependent information processing argues that spontaneous and prestimulus brain activity interacts with upcoming task-related processes. Consequently, observed network deficits that vary according to task conditions could be caused by differences in resting or prestimulus state in schizophrenia. Based on that background, the present thesis aimed to increase the understanding of aberrant functional networks in schizophrenia by using simultaneous EEG-fMRI under different conditions. One study investigated integrative mechanisms of networks during eyes-open (EO) resting state using a common-phase synchronization measure in an EEG-informed fMRI analysis (study 3). The other two studies (studies 1&2) used an fMRI-informed EEG analysis: The second study was an extension of the first, which was performed in healthy subjects only. Hence, the same methodologies and analyses were applied in both studies, but in the second study schizophrenia patients were compared to healthy controls. The associations between four temporally coherent networks (TCNs) – the default mode network (DMN), the dorsal attention network (dAN), left and right working memory networks (WMNs) – and power of three EEG frequency bands (theta, alpha, and beta band) during a verbal working memory (WM) task were investigated. Both resting state and task-related studies performed in schizophrenia patients (studies 2&3) revealed altered activation strength, functional states and interaction of TCNs, especially of the DMN. During rest (study 3), the DMN was differently integrated through common-phase synchronization in the delta (0.5 – 3.5Hz) and beta (13 – 30Hz) band. At prestimulus states of a verbal WM task, however, study 2 did not reveal differences in the activation level of the DMN between groups. Furthermore, from pre-to-post stimulus, the association of the DMN with frontal-midline (FM) theta (3 – 7Hz) band was altered, and a reduced suppression of the DMN during WM retention was detected. Schizophrenia patients also demonstrated abnormal interactions between networks: the DMN and dAN showed a reduced anti-correlation and the WMNs demonstrated an absent lateralization effect (study 2). The view that schizophrenia patients display TCN deficiencies is supported by the results of the present thesis. Especially the DMN and its interaction to the task-positive dAN showed specific alterations at different mental states and their interaction (during rest and from pre-to-post stimulus). Those alterations might at least partly explain observed symptomatology as attentional orientation deficits in patients. To conclude, functional networks as the DMN might represent promising targets for novel treatment options such as neurofeedback or transcranial direct current stimulation (tDCS)

15 years of microstate research in schizophrenia - Where are we? A meta-analysis

Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options

FROM REST TO TASK: Functional brain networks in achizophrenia

A primary goal of neuroscience research on psychiatric disorders such as schizophrenia is to enhance the current understanding of underlying biological mechanisms in order to develop novel interventions. Human brain functions are maintained through activity of large-scale brain networks. Accordingly, deficient perceptual and cognitive processing can be caused by failures of functional integration within networks, as reflected by the disconnection hypothesis of schizophrenia. Various neuroimaging techniques can be applied to study functional brain networks, each having different strengths. Frequently used complementary methods are the electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which were shown to have a common basis. Given the feasibility of combined EEG and fMRI measurement, EEG signatures of functional networks have been described, providing complimentary information about the functional state of networks. Both at rest and during task completion, many independent EEG and fMRI studies confirmed deficient network connectivity in schizophrenia. However, a rather diffuse picture with hyper- and hypo- activations within and between specific networks was reported. Furthermore, the theory of state dependent information processing argues that spontaneous and prestimulus brain activity interacts with upcoming task-related processes. Consequently, observed network deficits that vary according to task conditions could be caused by differences in resting or prestimulus state in schizophrenia. Based on that background, the present thesis aimed to increase the understanding of aberrant functional networks in schizophrenia by using simultaneous EEG-fMRI under different conditions. One study investigated integrative mechanisms of networks during eyes-open (EO) resting state using a common-phase synchronization measure in an EEG-informed fMRI analysis (study 3). The other two studies (studies 1&2) used an fMRI-informed EEG analysis: The second study was an extension of the first, which was performed in healthy subjects only. Hence, the same methodologies and analyses were applied in both studies, but in the second study schizophrenia patients were compared to healthy controls. The associations between four temporally coherent networks (TCNs) – the default mode network (DMN), the dorsal attention network (dAN), left and right working memory networks (WMNs) – and power of three EEG frequency bands (theta, alpha, and beta band) during a verbal working memory (WM) task were investigated. Both resting state and task-related studies performed in schizophrenia patients (studies 2&3) revealed altered activation strength, functional states and interaction of TCNs, especially of the DMN. During rest (study 3), the DMN was differently integrated through common-phase synchronization in the delta (0.5 – 3.5Hz) and beta (13 – 30Hz) band. At prestimulus states of a verbal WM task, however, study 2 did not reveal differences in the activation level of the DMN between groups. Furthermore, from pre-to-post stimulus, the association of the DMN with frontal-midline (FM) theta (3 – 7Hz) band was altered, and a reduced suppression of the DMN during WM retention was detected. Schizophrenia patients also demonstrated abnormal interactions between networks: the DMN and dAN showed a reduced anti-correlation and the WMNs demonstrated an absent lateralization effect (study 2). The view that schizophrenia patients display TCN deficiencies is supported by the results of the present thesis. Especially the DMN and its interaction to the task-positive dAN showed specific alterations at different mental states and their interaction (during rest and from pre-to-post stimulus). Those alterations might at least partly explain observed symptomatology as attentional orientation deficits in patients. To conclude, functional networks as the DMN might represent promising targets for novel treatment options such as neurofeedback or transcranial direct current stimulation (tDCS)

The brain’s default state interacts with working memory processes in a complex and load-dependent manner

Recently, many studies about a network active during rest and deactivated during tasks emerged in the literature: the default mode network (DMN). Spatial and temporal DMN features are important markers for psychiatric diseases. Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects. From these findings, the conclusion can be drawn that an increase in resting state DMN activity may go along with an increase in theta power in high-load WM conditions. We followed this hypothesis in a study on 17 healthy subjects performing a visual Sternberg WM task. The DMN was obtained by a BOLD-ICA approach and its dynamics represented by the percent-strength during pre-stimulus periods. DMN dynamics were temporally correlated with EEG theta spectral power from retention intervals. This so-called covariance mapping yielded the spatial distribution of the theta EEG fluctuations associated with the dynamics of the DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention. However, load-dependent correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance during later retention. Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over the course of the retention period. Since both, WM performance and DMN activity, are markers of mental health, our results could be important for further investigations of psychiatric populations

The brain’s pre-stimulus default state interacts with working memory in a load-dependent manner

Recently, multiple studies showed that spatial and temporal features of a task-negative default mode network (DMN) (Greicius et al., 2003) are important markers for psychiatric diseases (Balsters et al., 2013). Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG and MEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects (Brookes et al., 2011). Negative correlations between DMN activity and theta amplitude have been found during resting state (Jann et al., 2010) as well as during WM (Michels et al., 2010). Likewise, WM training resulted in higher resting state theta power as well as increased small-worldness of the resting brain (Langer et al., 2013). Further, increased fMRI connectivity between nodes of the DMN correlated with better WM performance (Hampson et al., 2006). Hence, the brain’s default state might influence it’s functioning during task. We therefore hypothesized correlations between pre-stimulus DMN activity and EEG-theta power during WM maintenance, depending on the WM load. 17 healthy subjects performed a Sternberg WM task while being measured simultaneously with EEG and fMRI. Data was recorded within a multicenter-study: 12 subjects were measured in Zurich with a 64-channels MR-compatible system (Brain Products) in a 3T Philips scanner, 5 subjects with a 96-channel MR-compatible system (Brain Products) in a 3T Siemens Scanner in Bern. The DMN components was obtained by a group BOLD-ICA approach over the full task duration (figure 1). The subject-wise dynamics were obtained by back-reconstructed onto each subject’s fMRI data and normalized to percent signal change values. The single trial pre-stimulus-DMN activation was then temporally correlated with the single trial EEG-theta (3-8 Hz) spectral power during retention intervals. This so-called covariance mapping (Jann et al., 2010) yielded the spatial distribution of the theta EEG fluctuations during retention associated with the dynamics of the pre-stimulus DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention (figure 2). However, correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance in the middle of the retention period (TANOVA, p<0.05) (figure 3). Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over time. While at a more global, load-independent view pre-stimulus DMN activity correlated positively with theta power during retention, the correlation was inversed during certain time windows in high-load trials, meaning that in trials with enhanced pre-stimulus DMN activity theta power decreases during retention. Since both WM performance and DMN activity are markers of mental health our results could be important for further investigations of psychiatric populations

Towards Using Microstate-Neurofeedback for the Treatment of Psychotic Symptoms in Schizophrenia. A Feasibility Study in Healthy Participants

Spontaneous EEG signal can be parsed into sub-second periods of stable functional states (microstates) that assumingly correspond to brief large scale synchronization events. In schizophrenia, a specific class of microstate (class "D") has been found to be shorter than in healthy controls and to be correlated with positive symptoms. To explore potential new treatment options in schizophrenia, we tested in healthy controls if neurofeedback training to self-regulate microstate D presence is feasible and what learning patterns are observed. Twenty subjects underwent EEG-neurofeedback training to up-regulate microstate D presence. The protocol included 20 training sessions, consisting of baseline trials (resting state), regulation trials with auditory feedback contingent on microstate D presence, and a transfer trial. Response to neurofeedback was assessed with mixed effects modelling. All participants increased the percentage of time spent producing microstate D in at least one of the three conditions (p < 0.05). Significant between-subjects across-sessions results showed an increase of 0.42 % of time spent producing microstate D in baseline (reflecting a sustained change in the resting state), 1.93 % of increase during regulation and 1.83 % during transfer. Within-session analysis (performed in baseline and regulation trials only) showed a significant 1.65 % increase in baseline and 0.53 % increase in regulation. These values are in a range that is expected to have an impact upon psychotic experiences. Additionally, we found a negative correlation between alpha power and microstate D contribution during neurofeedback training. Given that microstate D has been related to attentional processes, this result provides further evidence that the training was to some degree specific for the attentional network. We conclude that microstate-neurofeedback training proved feasible in healthy subjects. The implementation of the same protocol in schizophrenia patients may promote skills useful to reduce positive symptoms by means of EEG-neurofeedback

Pre-stimulus BOLD-network activation modulates EEG spectral activity during working memory retention

Working memory (WM) processes depend on our momentary mental state and therefore exhibit considerable fluctuations. Here, we investigate the interplay of task-preparatory and task-related brain activity as represented by pre-stimulus BOLD-fluctuations and spectral EEG from the retention periods of a visual WM task. Visual WM is used to maintain sensory information in the brain enabling the performance of cognitive operations and is associated with mental health. We tested 22 subjects simultaneously with EEG and fMRI while performing a visuo-verbal Sternberg task with two different loads, allowing for the temporal separation of preparation, encoding, retention and retrieval periods. Four temporally coherent networks (TCNs)-the default mode network (DMN), the dorsal attention, the right and the left WM network-were extracted from the continuous BOLD data by means of a group ICA. Subsequently, the modulatory effect of these networks' pre-stimulus activation upon retention-related EEG activity in the theta, alpha, and beta frequencies was analyzed. The obtained results are informative in the context of state-dependent information processing. We were able to replicate two well-known load-dependent effects: the frontal-midline theta increase during the task and the decrease of pre-stimulus DMN activity. As our main finding, these two measures seem to depend on each other as the significant negative correlations at frontal-midline channels suggested. Thus, suppressed pre-stimulus DMN levels facilitated later task related frontal midline theta increases. In general, based on previous findings that neuronal coupling in different frequency bands may underlie distinct functions in WM retention, our results suggest that processes reflected by spectral oscillations during retention seem not only to be "online" synchronized with activity in different attention-related networks but are also modulated by activity in these networks during preparation intervals

Differential recruitment of brain networks during visuospatial and color processing: Evidence from ERP microstates

Recent functional magnetic resonance imaging (fMRI) studies consistently revealed contributions of fronto-parietal and related networks to the execution of a visuospatial judgment task, the so-called "Clock Task". However, due to the low temporal resolution of fMRI, the exact cortical dynamics and timing of processing during task performance could not be resolved until now. In order to clarify the detailed cortical activity and temporal dynamics, 14 healthy subjects performed an established version of the "Clock Task", which comprises a visuospatial task (angle discrimination) and a control task (color discrimination) with the same stimulus material, in an electroencephalography (EEG) experiment. Based on the time-resolved analysis of network activations (microstate analysis), differences in timing between the angle compared to the color discrimination task were found after sensory processing in a time window starting around 200ms. Significant differences between the two tasks were observed in an analysis window from 192ms to 776ms. We divided this window in two parts: an early phase - from 192ms to ∼440ms, and a late phase - from ∼440ms to 776ms. For both tasks, the order of network activations and the types of networks were the same, but, in each phase, activations for the two conditions were dominated by differing network states with divergent temporal dynamics. Our results provide an important basis for the assessment of deviations in processing dynamics during visuospatial tasks in clinical populations

Abnormal coupling between DMN and delta and beta band EEG in psychotic patients

Common-phase synchronization of neuronal oscillations is a mechanism by which distributed brain regions can be integrated into transiently stable networks. Based on the hypothesis that schizophrenia is characterized by deficits in functional integration within neuronal networks, this study aimed to explore whether psychotic patients exhibit differences in brain regions involved in integrative mechanisms. We report an EEG-informed fMRI analysis of eyes-open resting state data collected from patients and healthy controls at two study sites. Global field synchronization (GFS) was chosen as an EEG measure indicating common-phase synchronization across electrodes. Several brain clusters appeared to be coupled to GFS differently in patients and controls: Activation in brain areas belonging to the default mode network (DMN) were negatively associated to GFS delta (1 - 3.5Hz) and positively to GFS beta (13 - 30Hz) bands in patients, whereas controls showed an opposite pattern for both GFS frequency bands in those regions; activation in extrastriate visual cortex was inversely related to GFS alpha1 (8.5 - 10.5Hz) band in healthy controls, while patients had a tendency towards a positive relationship. Taken together, the GFS measure might be useful for detecting additional aspects of deficient functional network integration in psychosis

Neurofeedback-Based Enhancement of Single Trial Auditory Evoked Potentials: Feasibility in Healthy Subjects.

Previous studies showed a global reduction of the event-related potential component N100 in patients with schizophrenia, a phenomenon that is even more pronounced during auditory verbal hallucinations. This reduction assumingly results from dysfunctional activation of the primary auditory cortex by inner speech, which reduces its responsiveness to external stimuli. With this study, we tested the feasibility of enhancing the responsiveness of the primary auditory cortex to external stimuli with an upregulation of the event-related potential component N100 in healthy control subjects. A total of 15 healthy subjects performed 8 double-sessions of EEG-neurofeedback training over 2 weeks. The results of the used linear mixed effect model showed a significant active learning effect within sessions ( t = 5.99, P < .001) against an unspecific habituation effect that lowered the N100 amplitude over time. Across sessions, a significant increase in the passive condition ( t = 2.42, P = .03), named as carry-over effect, was observed. Given that the carry-over effect is one of the ultimate aims of neurofeedback, it seems reasonable to apply this neurofeedback training protocol to influence the N100 amplitude in patients with schizophrenia. This intervention could provide an alternative treatment option for auditory verbal hallucinations in these patients