Preferential signal pathways during the perception and imagery of familiar scenes: An effective connectivity study.

The perception and imagery of landmarks activate similar content-dependent brain areas, including occipital and temporo-medial brain regions. However, how these areas interact during visual perception and imagery of scenes, especially when recollecting their spatial location, remains unknown. Here, we combined functional magnetic resonance imaging (fMRI), resting-state functional connectivity (rs-fc), and effective connectivity to assess spontaneous fluctuations and task-induced modulation of signals among regions entailing scene-processing, the primary visual area and the hippocampus (HC), responsible for the retrieval of stored information. First, we functionally defined the scene-selective regions, that is, the occipital place area (OPA), the retrosplenial complex (RSC) and the parahippocampal place area (PPA), by using the face/scene localizer, observing that two portions of the PPA-anterior and posterior PPA-were consistently activated in all subjects. Second, the rs-fc analysis (n = 77) revealed a connectivity pathway similar to the one described in macaques, showing separate connectivity routes linking the anterior PPA with RSC and HC, and the posterior PPA with OPA. Third, we used dynamic causal modelling to evaluate whether the dynamic couplings among these regions differ between perception and imagery of familiar landmarks during a fMRI task (n = 16). We found a positive effect of HC on RSC during the retrieval of imagined places and an effect of occipital regions on both RSC and pPPA during the perception of scenes. Overall, we propose that under similar functional architecture at rest, different neural interactions take place between regions in the occipito-temporal higher-level visual cortex and the HC, subserving scene perception and imagery.

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex.

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.

The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI.

The influence of global BOLD fluctuations on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting state networks (RSNs) - as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in resting state fMRI.

Connectivity between the cerebrum and cerebellum during social and non-social sequencing using dynamic causal modelling.

This analysis explores the effective connectivity of the cerebellum with the cerebral cortex during the generation of correct sequences of social and non-social events, using dynamic causal modelling (DCM). Our hypothesis is that during human evolution, the cerebellum's function evolved from a mere coordinator of fluent sequences of motions and actions, to an interpreter of action sequences without overt movements that are important for social understanding. This requires efficient neural communication between the cerebellum and cerebral cortex. In a functional magnetic resonance imaging (fMRI) study, participants generated the correct chronological order of (non-)social events, including stories involving mechanical and social scripts, and true or false beliefs. Across all stories, a DCM analysis of these data revealed, as predicted, bidirectional (closed-loop) connections linking the bilateral posterior cerebellum with the bilateral temporo-parietal junction (TPJ) associated with behavior understanding, and this connectivity pattern was almost entirely significant. There was also a unidirectional connection from the right posterior cerebellum to the precuneus, but no direct connections with the dorsomedial prefrontal cortex (dmPFC). Moreover, all connections emanating from the bilateral posterior cerebellum were negative, indicative of some kind of error signal. Within the cerebral cortex, there were unidirectional connections from the bilateral TPJ to the dmPFC, as well as bidirectional connections between the precuneus and dmPFC, and between the bilateral TPJ. These results confirm that the effective connectivity between the posterior cerebellum and mentalizing areas in the cerebral cortex play a critical role in the understanding and construction of the correct order of social and non-social action sequences.

Effective connectivity modulations related to win and loss outcomes.

Previous studies have characterized the brain regions involved in encoding monetary reward and punishment outcomes. The question of how this information is integrated across brain regions has received less attention. Here, we investigated changes in effective connectivity related to the processing of positive and negative monetary outcomes using functional magnetic resonance imaging data from the Human Connectome Project. Specifically, subjects engaged in a card guessing game which could yield win, loss, or neutral outcomes. A general linear model was used to define a network of regions involved in win and loss outcome processing, including anterior insula, anterior cingulate cortex, and ventral striatum. Dynamic causal modelling (DCM) was implemented to study between-region couplings and outcome-related modulations thereof within this network. In addition, we explored the relation between effective connectivity patterns and choice behavior in the gambling task. Parametric empirical Bayesian modelling was conducted for group-level inferences of both DCM and the choice behavior. Behaviorally, both win and loss outcomes increased the probability of choice switches in subsequent gambles. In terms of connectivity, win outcomes were associated with increased extrinsic connectivity across the network, while loss outcomes featured a balance between increased and decreased extrinsic connectivity. Moreover, self-inhibitory connections tended to decrease for both win and loss outcomes. Interestingly, a substantial discrepancy was observed for occipital cortex connectivity, which was characterized by intrinsic disinhibition in loss but not in win trials. The observed differences in effective connectivity during the processing of positive and negative outcomes, despite similarities in average regional activity and choice behavior, highlight the value of exploring network dynamics in the context of incentive manipulations.

Disambiguating the role of blood flow and global signal with partial information decomposition.

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.

Dynamic causal modelling of fluctuating connectivity in resting-state EEG.

Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1- min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.

Dynamic causal modeling of the effective connectivity between the cerebrum and cerebellum in social mentalizing across five studies.

In this analysis we explored the effective connectivity of the cerebellum with the cerebrum in social mentalizing, across five studies (n = 91) involving abstract and complex forms of mentalizing, such as (a) person and group impression formation, based on behavioral descriptions, and (b) constructing personal counterfactual events. Connectivity was analyzed by applying dynamic causal model analysis, which revealed effective connectivity between the mentalizing areas of the cerebellum and cerebrum. The results revealed a significant pattern of bidirectional (closed-loop) connectivity linking the right posterior cerebellum with bilateral temporo-parietal junction (TPJ), associated with behavior understanding. These connections are consistent with known anatomical data on closed loops between the cerebellum and cerebrum, although contralateral closed loops typically dominate. This analysis improves on an earlier psychophysiological interaction analysis of this dataset, which had failed to reveal such evidence of closed loops. Within the cerebrum, there were connections between the bilateral areas of TPJ, as well as connections between bilateral TPJ and the (ventral and dorsal) medial prefrontal cortex. The discussion centers on the function of cerebro-cerebellar connections in generating internal cerebellar "forward" models, potentially serving the automatic understanding, prediction, and error correction of behavioral sequences.

Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources.

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG "inverse problem". Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, linearly constrained minimum variance beamforming and 'Exact' low-resolution tomography (eLORETA). In source space, directed connectivity was estimated using multi-variate Granger causality and time-reversed Granger causality, and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis.

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations cannot be seen as an approximation of a source's anatomical location and (2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study.

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

Individual differences in mental imagery modulate effective connectivity of scene-selective regions during resting state.

Successful navigation relies on the ability to identify, perceive, and correctly process the spatial structure of a scene. It is well known that visual mental imagery plays a crucial role in navigation. Indeed, cortical regions encoding navigationally relevant information are also active during mental imagery of navigational scenes. However, it remains unknown whether their intrinsic activity and connectivity reflect the individuals' ability to imagine a scene. Here, we primarily investigated the intrinsic causal interactions among scene-selective brain regions such as Parahipoccampal Place Area (PPA), Retrosplenial Complex, and Occipital Place Area (OPA) using Dynamic Causal Modelling for resting-state functional magnetic resonance data. Second, we tested whether resting-state effective connectivity parameters among scene-selective regions could reflect individual differences in mental imagery in our sample, as assessed by the self-reported Vividness of Visual Imagery Questionnaire. We found an inhibitory influence of occipito-medial on temporal regions, and an excitatory influence of more anterior on more medial and posterior brain regions. Moreover, we found that a key role in imagery is played by the connection strength from OPA to PPA, especially in the left hemisphere, since the influence of the signal between these scene-selective regions positively correlated with good mental imagery ability. Our investigation contributes to the understanding of the complexity of the causal interaction among brain regions involved in navigation and provides new insight in understanding how an essential ability, such as mental imagery, can be explained by the intrinsic fluctuation of brain signal.

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine.

Chronic migraine is characterised by persistent headaches for >15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 min). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients. The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

Cognitive-motor telerehabilitation in multiple sclerosis (CoMoTeMS): study protocol for a randomised controlled trial.

BACKGROUND: The management of cognitive impairment is an important goal in the treatment of multiple sclerosis (MS). While cognitive rehabilitation has been proven to be effective in improving cognitive performance in MS, research in the elderly indicates a higher effectiveness of combined cognitive-motor rehabilitation. Here, we present the protocol of a randomised controlled clinical trial to assess whether a combined cognitive-motor telerehabilitation programme is more effective in improving working memory than only cognitive or motor training. METHODS/DESIGN: The CoMoTeMS-trial is a two-centre, randomised, controlled and blinded clinical trial. A total of 90 patients with MS will receive 12 weeks of either a combined cognitive-motor telerehabilitation programme or only cognitive or motor training. The primary outcome is a change in the digit span backwards. Secondary outcomes are other cognitive changes (Brief International Cognitive Assessment for Multiple Sclerosis and Backward Corsi), Expanded Disability Status Scale (EDSS), 6-Min Walk Test, 25-Foot Walk Test, 9-Hole Peg Test, anxiety and depression, fatigue, quality of life, cognitive and physical activity level, electroencephalography and magnetic resonance imaging of the brain. DISCUSSION: We hypothesise that the improvement in digit span backwards after 12 weeks of treatment will be significantly higher in the group treated with the combined cognitive-motor telerehabilitation programme, compared to the groups receiving only cognitive and only motor training. TRIAL REGISTRATION: ClinicalTrials.gov NCT05355389. Registered on 2 May 2022.

Dynamic Causal Modelling of the Reduced Habituation to Painful Stimuli in Migraine: An EEG Study.

A consistent finding in migraine is reduced cortical habituation to repetitive sensory stimuli. This study investigated brain dynamics underlying the atypical habituation to painful stimuli in interictal migraine. We investigated modulations in effective connectivity between the sources of laser evoked potentials (LEPs) from a first to final block of trigeminal LEPs using dynamic causal modelling (DCM) in a group of 23 migraine patients and 20 controls. Additionally, we looked whether the strength of dynamical connections in the migrainous brain is initially different. The examined network consisted of the secondary somatosensory areas (lS2, rS2), insulae (lIns, rIns), anterior cingulate cortex (ACC), contralateral primary somatosensory cortex (lS1), and a hidden source assumed to represent the thalamus. Results suggest that migraine patients show initially heightened communication between lS1 and the thalamus, in both directions. After repetitive stimulations, connection strengths from the thalamus to all somatosensory areas habituated in controls whereas this was not apparent in migraine. Together with further abnormalities in initial connectivity strengths and modulations between the thalamus and the insulae, these results are in line with altered thalamo-cortical network dynamics in migraine. Group differences in connectivity from and to the insulae including interhemispheric connections, suggests an important role of the insulae.

The influence of nociceptive and neuropathic pain states on the processing of acute electrical nociceptive stimulation: A dynamic causal modeling study.

INTRODUCTION: Despite the worldwide increase in prevalence of chronic pain and the subsequent scientific interest, researchers studying the brain and brain mechanisms in pain patients have not yet clearly identified the exact underlying mechanisms. Quantifying the neuronal interactions in electrophysiological data could help us gain insight into the complexity of chronic pain. Therefore, the aim of this study is to examine how different underlying pain states affect the processing of nociceptive information. METHODS: Twenty healthy participants, 20 patients with non-neuropathic low back-related leg pain and 20 patients with neuropathic failed back surgery syndrome received nociceptive electrical stimulation at the right sural nerve with simultaneous electroencephalographic recordings. Dynamic Causal Modeling (DCM) was used to infer hidden neuronal states within a Bayesian framework. RESULTS: Pain intensity ratings and stimulus intensity of the nociceptive stimuli did not differ between groups. Compared to healthy participants, both patient groups had the same winning DCM model, with an additional forward and backward connection between the somatosensory cortex and right dorsolateral prefrontal cortex. DISCUSSION: The additional neuronal connection with the prefrontal cortex as seen in both pain patient groups could be a reflection of the higher attention towards pain in pain patients and might be explained by the higher levels of pain catastrophizing in these patients. CONCLUSION: In contrast to the similar pain intensity ratings of an acute nociceptive electrical stimulus between pain patients and healthy participants, the brain is processing these stimuli in a different way.

Complete hemispherotomy leads to lateralized functional organization and lower level of consciousness in the isolated hemisphere.

OBJECTIVE: To quantify whole-brain functional organization after complete hemispherotomy, characterizing unexplored plasticity pathways and the conscious level of the dissected hemispheres. METHODS: Evaluation with multimodal magnetic resonance imaging in two pediatric patients undergoing right hemispherotomy including complete callosotomy with a perithalamic section. Regional cerebral blood flow and fMRI network connectivity assessed the functional integrity of both hemispheres after surgery. The level of consciousness was tested by means of a support vector machine classifier which compared the intrinsic organization of the dissected hemispheres with those of patients suffering from disorders of consciousness. RESULTS: After hemispherotomy, both patients showed typical daily functionality. We found no interhemispheric transfer of functional connectivity in either patient as predicted by the operation. The healthy left hemispheres displayed focal blood hyperperfusion in motor and limbic areas, with preserved network-level organization. Unexpectedly, the disconnected right hemispheres showed sustained network organization despite low regional cerebral blood flow. Subcortically, functional connectivity was increased in the left thalamo-cortical loop and between the cerebelli. One patient further showed unusual ipsilateral right cerebello-cortical connectivity, which was explained by the mediation of the vascular system. The healthy left hemisphere had higher probability to be classified as in a minimally conscious state compared to the isolated right hemisphere. SIGNIFICANCE: Complete hemispherotomy leads to a lateralized whole-brain organization, with the remaining hemisphere claiming most of the brain's energetic reserves supported by subcortical structures. Our results further underline the contribution of nonneuronal vascular signals on contralateral connectivity, shedding light on the nature of network organization in the isolated tissue. The disconnected hemisphere is characterized by a level of consciousness which is necessary but insufficient for conscious processing, paving the way for more specific inquiries about its role in awareness in the absence of behavioral output.

Electroencephalogram Resting State Frequency Power Characteristics of Suicidal Behavior in Female Patients With Major Depressive Disorder.

BACKGROUND: Difficulties in predicting suicidal behavior hamper effective suicide prevention. Therefore, there is a great need for reliable biomarkers, and neuroimaging may help to identify such markers. METHODS: Electroencephalography (EEG) was used to investigate resting state spatial-frequency power characteristics of female patients with major depressive disorder (MDD); 19 were recent suicide attempters (within the previous 30 days), 36 were suicide ideators, and 23 were nonsuicidal. Patients were enrolled at neuroCare Clinic Nijmegen (Nijmegen, the Netherlands) between May 2007 and November 2016, and the primary diagnosis of nonpsychotic MDD was confirmed using the Mini-International Neuropsychiatric Interview, DSM-IV criteria, and a score of ≥ 14 on the 21-item Beck Depression Inventory. Nonparametric, cluster-based permutation tests were applied to detect robust power differences between the study groups on the EEG broadband signal (2-100 Hz). Furthermore, a nonadaptive distributed source imaging method (eLORETA) was utilized to examine if these suicide-based frequency characteristics are localized in brain areas previously reported in the neuroimaging literature. RESULTS: When compared to nonsuicidal depressed patients, attempters and ideators displayed both decreased beta and low gamma activity in the frontal regions. Moreover, ideators had increased alpha activity over the posterior regions and increased high beta, low gamma activity over the left occipital region when compared to psychiatric controls. Attempters had reduced beta and low gamma activity over the right temporal region when compared to ideators. In addition, eLORETA localized attempter and ideator reduced frontal activity within the orbito-, medial-, middle-, superior-, and inferior-frontal areas and the anterior cingulate cortex. In attempters, reduced right temporal activity was localized within the right inferior-, middle-, and superior-temporal cortices and the fusiform gyrus. CONCLUSIONS: Frequency power characteristics of attempters and ideators are consistent with findings from the neuroimaging literature concerning suicide, implying EEG resting state assessment could become a potential biomarker to predict suicide risk.

Functional Connectivity of EEG Signals Under Laser Stimulation in Migraine.

In previous studies, migraine patients showed abnormalities in pain-related evoked responses, as reduced habituation to repetitive stimulation. In this study, we aimed to apply a novel analysis of EEG bands synchronization and directed dynamical influences under painful stimuli in migraine patients compared to non-migraine healthy volunteers. Thirty-one migraine without aura outpatients (MIGR) were evaluated and compared to 19 controls (CONT). The right hand was stimulated by means of 30 consecutive CO2 laser stimuli. EEG signal was examined by means of Morlet wavelet, synchronization entropy (SE), and Granger causality (GC), and the statistically validated results were mapped on the corresponding scalp locations. The vertex complex of averaged laser-evoked responses (LEPs) showed reduced habituation compared to CONT. In the prestimulus phase, enhanced SE in the 0, 5-30 Hz range was present in MIGR and CONT between the bilateral temporal-parietal and the frontal regions around the midline. Migraine patients showed an anticipation of EEG changes preceding the painful stimulation compared to CONT. In the poststimulus phase, the same cortical areas were more connected in MIGR vs CONT. In both groups of patients and CONT, the habituation index was negatively correlated with the GC scores. A different pattern of cortical activation after painful stimulation was present in migraine. The increase in cortical connections during repetitive painful stimulation may subtend the phenomenon of LEPs reduced habituation. Brain network analysis may give an aid in understanding subtle changes of pain processing under laser stimuli in migraine patients.