Frequency-phase analysis of resting-state functional MRI

Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls

BACKGROUND AND PURPOSE

Resting-state functional magnetic resonance imaging (rsfMRI) has been proposed as an alternative to task-based fMRI including clinical situations such as preoperative brain tumor planning, due to advantages including ease of performance and time savings. However, one of its drawbacks is the limited ability to accurately lateralize language function.

METHODS

Using the rsfMRI data of healthy controls, we carried out a power spectra analysis on three regions of interest (ROIs): Broca's area (BA) in the frontal cortex for language, hand motor (HM) area in the primary motor cortex, and the primary visual cortex (V1). Spike removal, motion correction, linear trend removal, and spatial smoothing were applied. Spontaneous low-frequency fluctuations (0.01-0.1 Hz) were filtered to enable functional integration.

RESULTS

BA showed greater power on the left hemisphere relative to the right (p = .0055), while HM (p = .1563) and V1 (p = .4681) were not statistically significant. A novel index, termed the power laterality index (PLI), computed to estimate the degree of power lateralization for each brain region, revealed a statistically significant difference between BA and V1 (p < .00001), where V1 was used as a control since the primary visual cortex does not lateralize. Validation studies used to compare PLI to a laterality index computed using phonemic fluency, a task-based, language fMRI paradigm, demonstrated good correlation.

CONCLUSIONS

The power spectra for BA revealed left language lateralization, which was not replicated in HM or V1. This work demonstrates the feasibility and validity of an ROI-based power spectra analysis on rsfMRI data for language lateralization.

Modes of cognition: Evidence from metastable brain dynamics

Managing cognitive load depends on adequate resource allocation by the human brain through the engagement of metastable substates, which are large-scale functional networks that change over time. We employed a novel analysis method, deep autoencoder dynamical analysis (DADA), with 100 healthy adults selected from the Human Connectome Project (HCP) data set in rest and six cognitive tasks. The deep autoencoder of DADA described seven recurrent stochastic metastable substates from the functional connectome of BOLD phase coherence matrices. These substates were significantly differentiated in terms of their probability of appearance, time duration, and spatial attributes. We found that during different cognitive tasks, there was a higher probability of having more connected substates dominated by a high degree of connectivity in the thalamus. In addition, compared with those during tasks, resting brain dynamics have a lower level of predictability, indicating a more uniform distribution of metastability between substates, quantified by higher entropy. These novel findings provide empirical evidence for the philosophically motivated cognitive theory, suggesting on-line and off-line as two fundamentally distinct modes of cognition. On-line cognition refers to task-dependent engagement with the sensory input, while off-line cognition is a slower, environmentally detached mode engaged with decision and planning. Overall, the DADA framework provides a bridge between neuroscience and cognitive theory that can be further explored in the future.

Inter-Brain Synchronization During Sandplay Therapy: Individual Analyses

Interactions between the client (Cl) and therapist (Th) evolve therapeutic relationships in psychotherapy. An interpersonal link or therapeutic space is implicitly developed, wherein certain important elements are expressed and shared. However, neural basis of psychotherapy, especially of non-verbal modalities, have scarcely been explored. Therefore, we examined the neural backgrounds of such therapeutic alliances during sandplay, a powerful art/play therapy technique. Real-time and simultaneous measurement of hemodynamics was conducted in the prefrontal cortex (PFC) of Cl-Th pairs participating in sandplay and subsequent interview sessions through multichannel near-infrared spectroscopy. As sandplay is highly individualized, and no two sessions and products (sandtrays) are the same, we expected variation in interactive patterns in the Cl–Th pairs. Nevertheless, we observed a statistically significant correlation between the spatio-temporal patterns in signals produced by the homologous regions of the brains. During the sandplay condition, significant correlations were obtained in the lateral PFC and frontopolar (FP) regions in the real Cl-Th pairs. Furthermore, a significant correlation was observed in the FP region for the interview condition. The correlations found in our study were explained as a “remote” synchronization (i.e., unconnected peripheral oscillators synchronizing through a hub maintaining free desynchronized dynamics) between two subjects in a pair, possibly representing the neural foundation of empathy, which arises commonly in sandplay therapy (ST).

Specific Oscillatory Power Changes and Their Efficacy for Determining Laterality in Mesial Temporal Lobe Epilepsy: A Magnetoencephalographic Study

Appropriate determination of the epileptic focus and its laterality are important for the diagnosis of mesial temporal lobe epilepsy (MTLE). The aims of this study are to establish a specific oscillatory distribution and laterality of the oscillatory power in unilateral MTLE with frequency analysis of magnetoencephalography (MEG), and to confirm their potential to carry significant information for determining lateralization of the epileptic focus. Thirty-five patients with MTLE [left (LtMTLE), 16; right (RtMTLE), 19] and 102 healthy control volunteers (CTR) were enrolled. Cortical oscillatory powers were compared among the groups by contrasting the source images using a one-way ANOVA model for each frequency band. Further, to compare the lateralization of regional oscillatory powers between LtMTLEs and RtMTLEs, the laterality index (LI) was calculated for four brain regions (frontal, temporal, parietal, and occipital) in each frequency band, which were compared between patient groups (LtMTLE, RtMTLE, and CTR), and used for machine learning prediction of the groups with support vector machine (SVM) with linear kernel function. Significant oscillatory power differences between MTLE and CTR were found in certain areas. In the theta to high-frequency oscillation bands, there were marked increases in the parietal lobe, especially on the left side, in LtMTLE. In the theta, alpha, and high-gamma bands, there were marked increases in the parietal lobe, especially on the right side in RtMTLE. Compared with CTR, LIs were significantly higher in 24/28 regions in LtMTLE, but lower in 4/28 regions and higher in 10/28 regions in RtMTLE. LI at the temporal lobe in the theta band was significantly higher in LtMTLE and significantly lower in RtMTLE. Comparing LtMTLE and RtMTLE, there were significant LI differences in most regions and frequencies (21/28 regions). In all frequency bands, there were significant differences between LtMTLE and RtMTLE in the temporal and parietal lobes. The leave-one-out cross-validation of the linear-SVM showed the classification accuracy to be over 91%, where the model had high specificity over 96% and mild sensitivity ~68–75%. Using MEG frequency analysis, the characteristics of the oscillatory power distribution in the MTLE were demonstrated. Compared with CTR, LIs shifted to the side of the epileptic focus in the temporal lobe in the theta band. The machine learning approach also confirmed that LIs have significant predictive ability in the lateralization of the epileptic focus. These results provide useful additional information for determining the laterality of the focus.

Spatiotemporal functional interactivity among large-scale brain networks

The macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N = 200) we applied prediction correlation techniques to four resting-state fMRI scans (each scan has TRs = 1200). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual, and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in the hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

Bidirectional signal exchanges and their mechanisms during joint attention interaction - A hyperscanning fMRI study

Social interactions are essential to our daily life. We tested the hypothesis that social interactions during joint attention (JA) require bidirectional communication, each with a different mechanism. We used a novel multivariate functional connectivity analysis, which enables obtaining directed pathways between four regions at each time-frequency point, with hyper-scanning MRI data of real-time JA interaction. Constructing multiple "4-regional directed pathways" and counting the number of times, regions engaged in feedforward or feedback processes in the 'sender' or the 'receiver brains, we obtained the following. (1) There were more regions in feedforward than in feedback processes (125 versus 99). (2) The right hemisphere was more involved in feedforward (74 versus 33), while the left hemisphere in feedback (66 versus 51). (3) The dmPFC was more engaged in feedforward (73 versus 44) while the TPJ in both (49 versus 45). (4) The dmPFC was more involved in the sending processes (i.e. initiation of feedforward and feedback) while the TPJ in the receiving processes. (5) JA interaction was involved with high MRI frequencies (0.04-0.1 Hz), while continues interactions by low MRI frequencies (0.01-0.04 Hz). (6) Initiation and responding to JA (i.e. IJA and RJA) evolved with composite neural systems: similar systems for pathways that included the dmPFC, vmPFC and the STS, and different systems for pathways that included the TPJ, vmPFC, PCC and the STS. These findings have important consequences in the basic understanding of social interaction and could help in diagnose and follow-up of social impairments.

Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data

A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data.

Spectral Dynamics of Resting State fMRI Within the Ventral Tegmental Area and Dorsal Raphe Nuclei in Medication-Free Major Depressive Disorder in Young Adults

Background: Dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) are major brainstem monamine nuclei consisting of serotonin and dopamine neurons respectively. Animal studies show that firing patterns in both nuclei are altered when animals exhibit depression like behaviors. Functional MRI studies in humans have shown reduced VTA activation and DRN connectivity in depression. This study for the first time aims at investigating the functional integrity of local neuronal firing concurrently in both the VTA and DRN in vivo in humans using spectral analysis of resting state low frequency fluctuation fMRI. Method: A total of 97 medication-free subjects-67 medication-free young patients (ages 18-30) with major depressive disorder and 30 closely matched healthy controls were included in the study to detect aberrant dynamics in DRN and VTA. For the investigation of altered localized dynamics we conducted power spectral analysis and above this spectral cross correlation between the two groups. Complementary to this, spectral dependence of permutation entropy, an information theoretical measure, was compared between groups. Results: Patients displayed significant spectral slowing in VTA vs. controls (p = 0.035, corrected). In DRN, spectral slowing was less pronounced, but the amount of slowing significantly correlated with 17-item Hamilton Depression Rating scores of depression severity (p = 0.038). Signal complexity as assessed via permutation entropy showed spectral alterations inline with the results on spectral slowing. Conclusion: Our results indicate that altered functional dynamics of VTA and DRN in depression can be detected from regional fMRI signal. On this basis, impact of antidepressant treatment and treatment response can be assessed using these markers in future studies.

Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra

We present a shape matching approach for functional magnetic resonance imaging (fMRI) time course and spectral alignment. We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.

Dynamic local connectivity uncovers altered brain synchrony during propofol sedation

Human consciousness is considered a result of the synchronous "humming" of multiple dynamic networks. We performed a dynamic functional connectivity analysis using resting state functional magnetic resonance imaging (rsfMRI) in 14 patients before and during a propofol infusion to characterize the sedation-induced alterations in consciousness. A sliding 36-second window was used to derive 59 time points of whole brain integrated local connectivity measurements. Significant changes in the connectivity strength (Z Corr) at various time points were used to measure the connectivity fluctuations during awake and sedated states. Compared with the awake state, sedation was associated with reduced cortical connectivity fluctuations in several areas connected to the default mode network and around the perirolandic cortex with a significantly decreased correlation of connectivity between their anatomical homologues. In addition, sedation was associated with increased connectivity fluctuations in the frequency range of 0.027 to 0.063 Hz in several deep nuclear regions, including the cerebellum, thalamus, basal ganglia and insula. These findings advance our understanding of sedation-induced altered consciousness by visualizing the altered dynamics in several cortical and subcortical regions and support the concept of defining consciousness as a dynamic and integrated network.

Dissonance encoding in human inferior colliculus covaries with individual differences in dislike of dissonant music

Harmony is one of the most fundamental elements of music that evokes emotional response. The inferior colliculus (IC) has been known to detect poor agreement of harmonics of sound, that is, dissonance. Electrophysiological evidence has implicated a relationship between a sustained auditory response mainly from the brainstem and unpleasant emotion induced by dissonant harmony. Interestingly, an individual’s dislike of dissonant harmony of an individual correlated with a reduced sustained auditory response. In the current paper, we report novel evidence based on functional magnetic resonance imaging (fMRI) for such a relationship between individual variability in dislike of dissonance and the IC activation. Furthermore, for the first time, we show how dissonant harmony modulates functional connectivity of the IC and its association with behaviourally reported unpleasantness. The current findings support important contributions of low level auditory processing and corticofugal interaction in musical harmony preference.