The interacting brain: Dynamic functional connectivity among canonical brain networks dissociates cooperative from competitive social interactions.

We spend much our lives interacting with others in various social contexts. Although we deal with this myriad of interpersonal exchanges with apparent ease, each one relies upon a broad array of sophisticated cognitive processes. Recent research suggests that the cognitive operations supporting interactive behaviour are themselves underpinned by several canonical functional brain networks (CFNs) that integrate dynamically with one another in response to changing situational demands. Dynamic integrations among these CFNs should therefore play a pivotal role in coordinating interpersonal behaviour. Further, different types of interaction should present different demands on cognitive systems, thereby eliciting distinct patterns of dynamism among these CFNs. To investigate this, the present study performed functional magnetic resonance imaging (fMRI) on 30 individuals while they interacted with one another cooperatively or competitively. By applying a novel combination of analytical techniques to these brain imaging data, we identify six states of dynamic functional connectivity characterised by distinct patterns of integration and segregation among specific CFNs that differ systematically between these opposing types of interaction. Moreover, applying these same states to fMRI data acquired from an independent sample engaged in the same kinds of interaction, we were able to classify interpersonal exchanges as cooperative or competitive. These results provide the first direct evidence for the systematic involvement of CFNs during social interactions, which should guide neurocognitive models of interactive behaviour and investigations into biomarkers for the interpersonal dysfunction characterizing many neurological and psychiatric disorders.

You took the words right out of my mouth: Dual-fMRI reveals intra- and inter-personal neural processes supporting verbal interaction.

Verbal communication relies heavily upon mutual understanding, or common ground. Inferring the intentional states of our interaction partners is crucial in achieving this, and social neuroscience has begun elucidating the intra- and inter-personal neural processes supporting such inferences. Typically, however, neuroscientific paradigms lack the reciprocal to-and-fro characteristic of social communication, offering little insight into the way these processes operate online during real-world interaction. In the present study, we overcame this by developing a "hyperscanning" paradigm in which pairs of interactants could communicate verbally with one another in a joint-action task whilst both undergoing functional magnetic resonance imaging simultaneously. Successful performance on this task required both interlocutors to predict their partner's upcoming utterance in order to converge on the same word as each other over recursive exchanges, based only on one another's prior verbal expressions. By applying various levels of analysis to behavioural and neuroimaging data acquired from 20 dyads, three principal findings emerged: First, interlocutors converged frequently within the same semantic space, suggesting that mutual understanding had been established. Second, assessing the brain responses of each interlocutor as they planned their upcoming utterances on the basis of their co-player's previous word revealed the engagement of the temporo-parietal junctional (TPJ), precuneus and dorso-lateral pre-frontal cortex. Moreover, responses in the precuneus were modulated positively by the degree of semantic convergence achieved on each round. Second, effective connectivity among these regions indicates the crucial role of the right TPJ in this process, consistent with the Nexus model. Third, neural signals within certain nodes of this network became aligned between interacting interlocutors. We suggest this reflects an interpersonal neural process through which interactants infer and align to one another's intentional states whilst they establish a common ground.

Acoustic evaluation of short-term effects of repetitive transcranial magnetic stimulation on motor aspects of speech in Parkinson's disease.

Hypokinetic dysarthria in Parkinson's disease (PD) can be characterized by monotony of pitch and loudness, reduced stress, variable rate, imprecise consonants, and a breathy and harsh voice. Using acoustic analysis, we studied the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over the primary orofacial sensorimotor area (SM1) and the left dorsolateral prefrontal cortex (DLPFC) on motor aspects of voiced speech in PD. Twelve non-depressed and non-demented men with PD (mean age 64.58 ± 8.04 years, mean PD duration 10.75 ± 7.48 years) and 21 healthy age-matched men (a control group, mean age 64 ± 8.55 years) participated in the speech study. The PD patients underwent two sessions of 10 Hz rTMS over the dominant hemisphere with 2,250 stimuli/day in a random order: (1) over the SM1; (2) over the left DLPFC in the "on" motor state. Speech examination comprised the perceptual rating of global speech performance and an acoustic analysis based upon a standardized speech task. The Mann-Whitney U test was used to compare acoustic speech variables between controls and PD patients. The Wilcoxon test was used to compare data prior to and after each stimulation in the PD group. rTMS applied over the left SM1 was associated with a significant increase in harmonic-to-noise ratio and net speech rate in the sentence tasks. With respect to the vowel task results, increased median values and range of Teager-Kaiser energy operator, increased vowel space area, and significant jitter decrease were observed after the left SM1 stimulation. rTMS over the left DLPFC did not induce any significant effects. The positive results of acoustic analysis were not reflected in a subjective rating of speech performance quality as assessed by a speech therapist. Our pilot results indicate that one session of rTMS applied over the SM1 may lead to measurable improvement in voice quality and intensity and an increase in speech rate and tongue movements. Nevertheless, these changes were not accompanied by changes in a perceptual evaluation of speech performance by a speech therapist. Future placebo-controlled studies in larger patient cohorts should verify if rTMS would be clinically useful for treating hypokinetic dysarthria in PD.

The default mode network integrity in patients with Parkinson's disease is levodopa equivalent dose-dependent.

Disturbances in the default mode network (DMN) have been described in many neurological and psychiatric disorders including Parkinson's disease (PD). The DMN is characterized by basal activity that increases during rest or passive visual fixation and decreases ("deactivates") during cognitive tasks. The network is believed to be involved in cognitive processes. We examined the DMN in PD patients on dopaminergic medication with normal cognitive performance compared to age- and gender-matched healthy controls (HC) using fMRI and three methodological procedures: independent component analysis of resting-state data, analysis of deactivation during a complex visual scene-encoding task, and seed-based functional connectivity analysis. In the PD group, we also studied the effect of dopaminergic medication on the DMN integrity. We did not find any difference between the PD and HC groups in the DMN, but using the daily levodopa equivalent dose as a covariate, we observed an enhanced functional connectivity of the DMN in the posterior cingulate cortex and decreased activation in the left parahippocampal gyrus during the cognitive task. We conclude that dopaminergic therapy has a specific effect on both the DMN integrity and task-related brain activations in cognitively unimpaired PD patients, and these effects seem to be dose-dependent.

The role of the right dorsolateral prefrontal cortex in the Tower of London task performance: repetitive transcranial magnetic stimulation study in patients with Parkinson's disease.

We studied whether one session of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over either the right or left dorsolateral prefrontal cortex would induce any measurable changes in the Tower of London spatial planning task performance in patients with Parkinson's disease (PD). Ten patients with PD (with no dementia and/or depression) entered the randomized, sham-stimulation-controlled study with a crossover design. Active and placebo rTMS were applied over either the left or the right dorsolateral prefrontal cortex (in four separate sessions) in each patient. The order of sessions was randomized. The Tower of London task was performed prior to and immediately after each appropriate session. The "total problem-solving time" was our outcome measure. Only active rTMS of the right dorsolateral prefrontal cortex induced significant enhancement of the total problem-solving time, p = 0.038. Stimulation of the left prefrontal cortex or sham stimulations induced no significant effects. Only rTMS applied over the right dorsolateral prefrontal cortex induced positive changes in the spatial planning task performance in PD, which further supports the results of functional imaging studies indicating the causal engagement of the right-sided hemispheric structures in solving the task in this patient population.

Default mode network and extrastriate visual resting state network in patients with Parkinson's disease dementia.

AIMS: Using fMRI, we evaluated the default mode network (DMN) and the extrastriate visual resting state network (ESV-RSN) in 14 patients with Parkinson's disease dementia (PDD) as compared with 18 patients with Parkinson's disease (PD) without dementia and 18 healthy controls (HC). METHODS: We analyzed the seed-based functional connectivity of both resting state data and deactivations during a visual complex scene-encoding task. RESULTS: Using the posterior cingulate cortex/precuneus as a seed for the DMN analysis, we observed significant decreases of connectivity in the right inferior frontal gyrus in PDD as compared to PD and HC. Using the caudate nucleus as a seed for the ESV-RSN analysis, we found significant decreases of connectivity in the left and right inferior occipital gyrus in PDD as compared to HC. CONCLUSION: Differences in functional connectivity patterns between PDD and PD/HC were observed in areas known to be engaged in stimulus-driven reorienting of attention and in visual processing.

What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study.

OBJECTIVE: The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. APPROACH: We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. MAIN RESULTS: Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. SIGNIFICANCE: These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.

Sensitivity of PPI analysis to differences in noise reduction strategies.

BACKGROUND: In some fields of fMRI data analysis, using correct methods for dealing with noise is crucial for achieving meaningful results. This paper provides a quantitative assessment of the effects of different preprocessing and noise filtering strategies on psychophysiological interactions (PPI) methods for analyzing fMRI data where noise management has not yet been established. METHODS: Both real and simulated fMRI data were used to assess these effects. Four regions of interest (ROIs) were chosen for the PPI analysis on the basis of their engagement during two tasks. PPI analysis was performed for 32 different preprocessing and analysis settings, which included data filtering with RETROICOR or no such filtering; different filtering of the ROI "seed" signal with a nuisance data-driven time series; and the involvement of these data-driven time series in the subsequent PPI GLM analysis. The extent of the statistically significant results was quantified at the group level using simple descriptive statistics. Simulated data were generated to assess statistical improvement of different filtering strategies. RESULTS: We observed that different approaches for dealing with noise in PPI analysis yield differing results in real data. In simulated data, we found RETROICOR, seed signal filtering and the addition of data-driven covariates to the PPI design matrix significantly improves results. CONCLUSIONS: We recommend the use of RETROICOR, and data-driven filtering of the whole data, or alternatively, seed signal filtering with data-driven signals and the addition of data-driven covariates to the PPI design matrix.

Functional neuroanatomy of vocalization in patients with Parkinson's disease.

UNLABELLED: In Parkinson's disease (PD) both speech production and self-monitoring of voiced speech are altered. METHODS: In our previous study we used functional magnetic resonance imaging (fMRI) to examine which brain areas are involved in overt reading in nine female PD patients (mean age 66.0 ± 11.6 years) compared with eight age-matched healthy female controls (mean age 62.2 years ± 12.3). Here we performed the post-hoc seed-based functional connectivity analysis of our data to assess the functional connectivity between the periaqueductal gray matter (PAG; i.e. the core subcortical structure involved in human vocalization) and other brain regions in the same groups of PD patients and controls. RESULTS: In PD patients as compared with controls we observed increased connectivity between PAG and basal ganglia, posterior superior temporal gyrus, supramarginal and fusiform gyri and inferior parietal lobule on the right side. In the PD group, the connectivity strength in the right putamen and the right sypramarginal gyrus was correlated with variability of pitch while the connectivity strength in the right posterior superior temporal gyrus and in the right inferior parietal lobule was correlated with speech loudness. CONCLUSION: We observed functional reorganization in PD patients as compared with controls in both the motor basal ganglia-thalamo-cortical circuitry and cortical areas known to be engaged in-auditory and somatosensory feedback control of voiced speech. These changes were hemisphere-specific and might either reflect effects of dopaminergic treatment or at least partially successful compensatory mechanisms involved in early-stage PD.