Slow Resting State Fluctuations Enhance Neuronal and Behavioral Responses to Looming Sounds.

We investigate both experimentally and using a computational model how the power of the electroencephalogram (EEG) recorded in human subjects tracks the presentation of sounds with acoustic intensities that increase exponentially (looming) or remain constant (flat). We focus on the link between this EEG tracking response, behavioral reaction times and the time scale of fluctuations in the resting state, which show considerable inter-subject variability. Looming sounds are shown to generally elicit a sustained power increase in the alpha and beta frequency bands. In contrast, flat sounds only elicit a transient upsurge at frequencies ranging from 7 to 45 Hz. Likewise, reaction times (RTs) in an audio-tactile task at different latencies from sound onset also present significant differences between sound types. RTs decrease with increasing looming intensities, i.e. as the sense of urgency increases, but remain constant with stationary flat intensities. We define the reaction time variation or "gain" during looming sound presentation, and show that higher RT gains are associated with stronger correlations between EEG power responses and sound intensity. Higher RT gain further entails higher relative power differences between loom and flat in the alpha and beta bands. The full-width-at-half-maximum of the autocorrelation function of the eyes-closed resting state EEG also increases with RT gain. The effects are topographically located over the central and frontal electrodes. A computational model reveals that the increase in stimulus-response correlation in subjects with slower resting state fluctuations is expected when EEG power fluctuations at each electrode and in a given band are viewed as simple coupled low-pass filtered noise processes jointly driven by the sound intensity. The model assumes that the strength of stimulus-power coupling is proportional to RT gain in different coupling scenarios, suggesting a mechanism by which slower resting state fluctuations enhance EEG response and shorten reaction times.

Framing deductive reasoning with emotional content: an fMRI study.

In the literature concerning the study of emotional effect on cognition, several researches highlight the mechanisms of reasoning ability and the influence of emotions on this ability. However, up to now, no neuroimaging study was specifically devised to directly compare the influence on reasoning performance of visual task-unrelated with semantic task-related emotional information. In the present functional fMRI study, we devised a novel paradigm in which emotionally negative vs. neutral visual stimuli (context) were used as primes, followed by syllogisms composed of propositions with emotionally negative vs. neutral contents respectively. Participants, in the MR scanner, were asked to assess the logical validity of the syllogisms. We have therefore manipulated the emotional state and arousal induced by the visual prime as well as the emotional interference exerted by the syllogism content. fMRI data indicated a medial prefrontal cortex deactivation and lateral/dorsolateral prefrontal cortex activation in conditions with negative context. Furthermore, a lateral/dorsolateral prefrontal cortex modulation caused by syllogism content was observed. Finally, behavioral data confirmed the influence of emotional task-related stimuli on reasoning ability, since the performance was worse in conditions with syllogisms involving negative emotions. Therefore, on the basis of these data, we conclude that emotional states can impair the performance in reasoning tasks by means of the delayed general reactivity, whereas the emotional content of the target may require a larger amount of top-down resources to be processed.

Persistent genital arousal disorder associated with functional hyperconnectivity of an epileptic focus.

Persistent Genital Arousal Disorder (PGAD) refers to the experience of persistent sensations of genital arousal that are felt to be unprovoked, intrusive and unrelieved by one or several orgasms. It is often mistaken for hypersexuality since PGAD often results in a high frequency of sexual behaviour. At present little is known with certainty about the etiology of this condition. We described a woman with typical PGAD symptoms and orgasmic seizures that we found to be related to a specific epileptic focus. We performed a EEG/MEG and fMRI spontaneous activity study during genital arousal symptoms and after the chronic administration of 300 mg/day of topiramate. From MEG data an epileptic focus was localized in the left posterior insular gyrus (LPIG). FMRI data evidenced that sexual excitation symptoms with PGAD could be correlated with an increased functional connectivity (FC) between different brain areas: LPIG (epileptic focus), left middle frontal gyrus, left inferior and superior temporal gyrus and left inferior parietal lobe. The reduction of the FC observed after antiepileptic therapy was more marked in the left than in the right hemisphere in agreement with the lateralization identified by MEG results. Treatment completely abolished PGAD symptoms and functional hyperconnectivity. The functional hyperconnectivity found in the neuronal network including the epileptic focus could suggest a possible central mechanism for PGAD.

Electrophysiological signatures of resting state networks in the human brain.

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.

The use of standardized infinity reference in EEG coherency studies.

The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the measured EEG signals introduced by volume conduction. In this paper, we propose an approach to study large scale EEG coherency in which these factors are eliminated. Artifactual self-interaction by volume conduction is eliminated by using the imaginary part of the complex coherency as a measure of interaction and the Reference Electrode Standardization Technique (REST) is used for the approximate standardization of the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference. The application of our approach to simulated and real EEG data shows that the detection of interaction, as opposed to artifacts due to reference and volume conduction, is a goal that can be achieved from the study of a large scale parameter.

Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis.

The simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRI scanner are contaminated by imaging, ballistocardiographic (BCG) and ocular artifacts. A number of processing techniques for the cancellation of fMRI environment disturbances exist: the most popular is averaged artifact subtraction (AAS), which performs well for the imaging artifact, but has some limitations in removing the BCG artifact, due to the variability in cardiac wave duration and shape; furthermore, no processing method to attenuate ocular artifact is currently used in EEG/fMRI, and contaminated epochs are simply rejected before signal analysis. In this work, we present a comprehensive method based on independent component analysis (ICA) for simultaneously removing BCG and ocular artifacts from the EEG recordings, as well as residual MRI contamination left by AAS. The ICA method has been tested on event-related potentials (ERPs) obtained from a visual oddball paradigm: it is very effective in attenuating artifacts in order to reconstruct clear brain signals from EEG acquired in the MRI scanner. It performs significantly better than the AAS method in removing the BCG artifact. Furthermore, since ocular artifacts can be completely suppressed, a larger number of trials is available for analysis. A comparison of ERPs inside the magnetic environment with those obtained out of the MRI scanner confirms that no systematic bias in the ERP waveform is produced by the ICA method.