Identification of attention and cognitive control networks in a parametric auditory fMRI study.

In the competition for limited processing resources, top-down attention and cognitive control processes are needed to separate relevant from irrelevant sensory information and to interact with the environment in a meaningful way. The demands for the recruitment of top-down control processes depend on the relative salience of the competing stimuli. In the present event-related functional magnetic resonance imaging (fMRI) study we investigated the dynamics of neuronal networks during varying degrees of top-down control demands. We tested 20 participants with a dichotic auditory discrimination task in which the relative perceptual salience of two simultaneously presented syllables was parametrically varied by manipulating the inter-aural intensity differences (IIDs) and instructing the subjects to selectively attend to either the louder or weaker of the two stimuli. A significant interaction of IID manipulation and attentional instruction was detected bilaterally in the inferior parietal lobe and pre-supplementary motor area, and in the precentral gyrus, anterior cingulate cortex, and inferior frontal gyrus of the right hemisphere. The post hoc analysis of the interaction pattern allowed for an assignment of these regions to either of two sets of regions which can be interpreted to constitute two different brain networks: a fronto-parietal attention control network, involved in the integration of saliency-based and instruction-based processing preferences, and a medial-lateral frontal cognitive control network, involved in the processing of the conflicts arising in the attempt to follow the attentional instruction in face of the varying inter-aural stimulus salience.

The effects of background noise on dichotic listening to consonant-vowel syllables: An fMRI study.

The present fMRI study attempts to identify brain areas that may underlie the effect of different background noises on functional brain asymmetry in a dichotic listening task. Previous studies have shown that the prominent right ear advantage in dichotic listening to consonant-vowel syllables is affected by background noise. To explore the underlying neuronal processes, haemodynamic brain responses using fMRI were recorded while participants performed the dichotic listening task in two different noisy backgrounds (conversational "babble" and traffic noise). The behavioural results showed a reduction of the right ear advantage in the background noise conditions, especially in the traffic noise condition. The behavioural results are discussed in terms of alertness-attentional mechanisms. The effects of background noise on brain activation involved significant activations in a speech-processing network. Specifically the changes in activations in the peri-Sylvian region of the superior temporal gyrus and in the temporo-parietal junction part in the left hemisphere, as well as in the superior temporal gyrus/sulcus area in the right hemisphere may mirror the effects of noise on behavioural performance. The effects of noise on brain activation are discussed with regard to pre-activation mechanisms.

Realignment parameter-informed artefact correction for simultaneous EEG-fMRI recordings.

In this work we introduce a new algorithm to correct the imaging artefacts in the EEG signal measured during fMRI acquisition. The correction techniques proposed so far cannot optimally represent transitions, i.e. when abrupt changes of the artefact properties due to head movements occur. The algorithm developed here takes the head movement parameters from the fMRI signal into account to calculate adequate EEG artefact templates and subsequently correct the distorted EEG data. The data reported in this work demonstrate that the realignment parameter-informed algorithm outperforms the commonly used moving average algorithm if head movements occur. The superiority is reflected by comparing the residual variance after artefact correction with either method. The residual variance is lower around head-movements that exceed head deflections of about 1 mm when applying the realignment parameter-informed algorithm. Additionally, the signal to noise ratio of a surrogate event-related potential (ERP) increased by 10-40% for head displacements larger than 1 mm. The algorithm developed here is particularly suited for studies where head movements of the subject cannot be prevented as in studies with patients, children, or during sleep. Furthermore, the enhanced signal to noise ratio of a single trial ERP indicates the power of the presented algorithm for single trial ERP-fMRI studies in which EEG signal quality is a critical factor.

Top-down and bottom-up interaction: manipulating the dichotic listening ear advantage.

The aim of the study was to investigate interactions between top-down and bottom-up information processing in the auditory domain. For this purpose, thirty-five right-handed participants with normal hearing acuity were tested with consonant-vowel dichotic stimulus pairs. Bottom-up stimulus characteristics were manipulated by gradually varying interaural intensity difference from -21 dB in favor of the left ear to +21 dB in favor of the right ear (including a no difference baseline condition). Top-down manipulation consisted of three conditions with different attention instructions: one free report condition, and each one condition requiring the participants to focus their attention on the right ear and on the left ear, respectively. The results showed a significant interaction of bottom-up and top-down manipulations with respect to the modulation of the ear advantage. Post-hoc analysis showed that the effect of directing attention was reduced when the intensity difference favored the to-be-attended ear. Thus, bottom-up intensity and top-down attention manipulations should not be regarded as independent but rather interacting factors when it comes to the manipulation of the ear advantage in a dichotic listening situation.

Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex.

Similar to the posterior alpha rhythm, pericentral (Rolandic) EEG rhythms in the alpha and beta frequency range are referred to as "idle rhythms" indicating a "resting state" of the respective system. The precise function of these rhythms is not clear. We used simultaneous EEG-fMRI during a bimanual motor task to localize brain areas involved in Rolandic alpha and beta EEG rhythms. The identification of these rhythms in the MR environment was achieved by a blind source separation algorithm. Rhythm "strength", i.e. spectral power determined by wavelet analysis, inversely correlated most strongly with the fMRI-BOLD signal in the postcentral cortex for the Rolandic alpha (mu) rhythm and in the precentral cortex for the Rolandic beta rhythm. FMRI correlates of Rolandic alpha and beta rhythms were distinct from those associated with the posterior "classical" alpha rhythm, which correlated inversely with the BOLD signal in the occipital cortex. An inverse correlation with the BOLD signal in the respective sensory area seems to be a general feature of "idle rhythms".

Single-trial coupling of EEG and fMRI reveals the involvement of early anterior cingulate cortex activation in effortful decision making.

While the precise role of the anterior cingulate cortex (ACC) is still being discussed, it has been suggested that ACC activity might reflect the amount of mental effort associated with cognitive processing. So far, not much is known about the temporal dynamics of ACC activity in effort-related decision making or auditory attention, because fMRI is limited concerning its temporal resolution and electroencephalography (EEG) is limited concerning its spatial resolution. Single-trial coupling of EEG and fMRI can be used to predict the BOLD signal specifically related to amplitude variations of electrophysiological components. The striking feature of single-trial coupling is its ability to separate different aspects of the BOLD signal according to their specific relationship to a distinct neural process. In the present study we investigated 10 healthy subjects with a forced choice reaction task under both low and high effort conditions and a control condition (passive listening) using simultaneous EEG and fMRI. We detected a significant effect of mental effort only for the N1 potential, but not for the P300 potential. In the fMRI analysis, ACC activation was present only in the high effort condition. We used single-trial coupling of EEG and fMRI in order to separate information specific to N1-amplitude variations from the unrelated BOLD response. Under high effort conditions we were able to detect circumscribed BOLD activations specific to the N1 potential in the ACC (t=4.7) and the auditory cortex (t=6.1). Comparing the N1-specific BOLD activity of the high effort condition versus the control condition we found only activation of the ACC (random effects analysis, corrected for multiple comparisons, t=4.4). These findings suggest a role of early ACC activation in effort-related decision making and provide a direct link between the N1 component and its corresponding BOLD signal.

Unmixing concurrent EEG-fMRI with parallel independent component analysis.

Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-by-voxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Sub-area-specific Suppressive Interaction in the BOLD responses to simultaneous finger stimulation in human primary somatosensory cortex: evidence for increasing rostral-to-caudal convergence.

In the primary somatosensory cortex (SI) of non-human primates, receptive field properties have been shown to differ between its sub-areas with increasing convergence in areas 1 and 2 as compared with area 3b. In this study, we searched for a similar functional organization of human SI. We performed fMRI in healthy subjects during separate or simultaneous electrical stimulation of the second and third finger of the right hand. Activation patterns in response to stimulation of single fingers reflected the somatotopical arrangement within the hand area of SI. Somatotopy was more clear-cut in area 3b as compared with areas 1 and 2. The response to simultaneous stimulation was considerably smaller than the summed responses to separate stimulation of each finger alone, pointing to a suppressive interaction effect. A region-of-interest analysis in the representational areas of the second and third finger revealed sub-area-specific differential suppressive interaction with an increase along the rostral-caudal axis (areas 3b, 1 and 2: 26, 32.7 and 42.2%, respectively). These findings on differences in the topographic as well as functional organization between sub-areas of SI support the notion of increasing convergence and integration from area 3b to areas 1 and 2 in human subjects.

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.

Visual evoked potentials recovered from fMRI scan periods.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) may allow functional imaging of the brain at high temporal and spatial resolution. Artifacts generated in the EEG signal during MR acquisition, however, continue to pose a major challenge. Due to these artifacts, an interleaved modus has often been used for "evoked potential" experiments, i.e., only EEG signals recorded between MRI scan periods were assessed. An obvious disadvantage of this approach is the loss of a portion of the EEG information, which might be relevant for the specific scientific issue. In this study, continuous, simultaneous EEG-fMRI measurements were carried out. Visual evoked potentials (VEPs) could be reconstructed reliably from periods during MR scanning and in between successive scans. No significant differences between both VEPs were detected. This indicates sufficient artifact removal as well as physiological correspondence of VEPs in both periods. Simultaneous continuous VEP-fMRI recordings are thus shown to be feasible.

Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy.

We used simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) and EEG-near infrared spectroscopy (NIRS) to investigate whether changes of the posterior EEG alpha rhythm are correlated with changes in local cerebral blood oxygenation. Cross-correlation analysis of slowly fluctuating, spontaneous rhythms in the EEG and the fMRI signal revealed an inverse relationship between alpha activity and the fMRI-blood oxygen level dependent signal in the occipital cortex. The NIRS-EEG measurements demonstrated a positive cross-correlation in occipital cortex between alpha activity and concentration changes of deoxygenated hemoglobin, which peaked at a relative shift of about 8 s. Our data suggest that alpha activity in the occipital cortex is associated with metabolic deactivation. Mapping of spontaneously synchronizing distributed neuronal networks is thus shown to be feasible.