Functional integration between brain regions at rest occurs in multiple-frequency bands

Circadian challenge of astronauts' unconscious mind adapting to microgravity in space, estimated by heart rate variability

It is critical that the regulatory system functions well in space's microgravity. However, the "intrinsic" cardiovascular regulatory system (β), estimated by the fractal scaling of heart rate variability (HRV) (0.0001-0.01 Hz), does not adapt to the space environment during long-duration (6-month) space flights. Neuroimaging studies suggest that the default mode network (DMN) serves a broad adaptive purpose, its topology changing over time in association with different brain states of adaptive behavior. Hypothesizing that HRV varies in concert with changes in brain's functional connectivity, we analyzed 24-hour HRV records from 8 healthy astronauts (51.8 ± 3.7 years; 6 men) on long (174.5 ± 13.8 days) space missions, obtained before launch, after about 21 (ISS01), 73 (ISS02), and 156 (ISS03) days in space, and after return to Earth. Spectral power in 8 frequency regions reflecting activity in different brain regions was computed by maximal entropy. Improved β (p < 0.05) found in 4 astronauts with a positive activation in the "HRV slow-frequency oscillation" (0.10-0.20 Hz) occurred even in the absence of consciousness. The adaptive response was stronger in the evening and early sleep compared to morning (p = 0.039). Brain functional networks, the DMN in particular, can help adapt to microgravity in space with help from the circadian clock.

Neural mechanisms for selectively tuning in to the target speaker in a naturalistic noisy situation

The neural mechanism for selectively tuning in to a target speaker while tuning out the others in a multi-speaker situation (i.e., the cocktail-party effect) remains elusive. Here we addressed this issue by measuring brain activity simultaneously from a listener and from multiple speakers while they were involved in naturalistic conversations. Results consistently show selectively enhanced interpersonal neural synchronization (INS) between the listener and the attended speaker at left temporal–parietal junction, compared with that between the listener and the unattended speaker across different multi-speaker situations. Moreover, INS increases significantly prior to the occurrence of verbal responses, and even when the listener’s brain activity precedes that of the speaker. The INS increase is independent of brain-to-speech synchronization in both the anatomical location and frequency range. These findings suggest that INS underlies the selective process in a multi-speaker situation through neural predictions at the content level but not the sensory level of speech.

When many people are speaking, e.g. at a party, we can selectively attend to just one speaker. Here, using ‘hyperscanning’, the authors show that interpersonal neural synchronization is selectively increased between a listener and the attended speaker, compared to between the listener and an unattended speaker.

Advances in functional magnetic resonance imaging data analysis methods using Empirical Mode Decomposition to investigate temporal changes in early Parkinson's disease

Introduction

Previous neuroimaging studies of Parkinson's disease (PD) patients have shown changes in whole-brain functional connectivity networks. Whether connectivity changes can be detected in the early stages (first 3 years) of PD by resting-state functional magnetic resonance imaging (fMRI) remains elusive. Research infrastructure including MRI and analytic capabilities is required to investigate this issue. The National Institutes of Health/National Institute of General Medical Sciences Center for Biomedical Research Excellence awards support infrastructure to advance research goals.

Methods

Static and dynamic functional connectivity analyses were conducted on early stage never-medicated PD subjects (N = 18) and matched healthy controls (N = 18) from the Parkinson's Progression Markers Initiative.

Results

Altered static and altered dynamic functional connectivity patterns were found in early PD resting-state fMRI data. Most static networks (with the exception of the default mode network) had a reduction in frequency and energy in specific low-frequency bands. Changes in dynamic networks in PD were associated with a decreased switching rate of brain states.

Discussion

This study demonstrates that in early PD, resting-state fMRI networks show spatial and temporal differences of fMRI signal characteristics. However, the default mode network was not associated with any measurable changes. Furthermore, by incorporating an optimum window size in a dynamic functional connectivity analysis, we found altered whole-brain temporal features in early PD, showing that PD subjects spend significantly more time than healthy controls in a specific brain state. These findings may help in improving diagnosis of early never-medicated PD patients. These key observations emerged in a Center for Biomedical Research Excellence–supported research environment.

Changes in Dynamics Within and Between Resting-State Subnetworks in Juvenile Myoclonic Epilepsy Occur at Multiple Frequency Bands

Time-varying connectivity analyses have indicated idiopathic generalized epilepsy (IGE) could cause significant abnormalities in dynamic connective pattern within and between resting-state sub-networks (RSNs). However, previous studies mainly focused on the IGE-induced dynamic changes of functional connectivity (FC) in specific frequency band (0.01–0.08 Hz or 0.01–0.15 Hz), ignoring the changes across different frequency bands. Here, 24 patients with IGE characterized by juvenile myoclonic epilepsy (JME) and 24 matched healthy controls were studied using a data-driven frequency decomposition approach and a sliding window approach. The RSN dynamics, including intra-RSN dynamics and inter-RSN dynamics, was further calculated to investigate dynamic FC changes within and between RSNs in JME patients in each decomposed frequency band. Compared to healthy controls, JME patients not only showed frequency-dependent decrease in intra-RSN dynamics within multiple RSNs but also exhibited fluctuant alterations in inter-RSN dynamics among several RSNs over different frequency bands especially in the ventral/dorsal attention network and the subcortical network. Additionally, the disease severity had significantly negative correlations with both intra-RSN dynamics within the subcortical network and inter-RSN dynamics between the subcortical network and the default network at the lower frequency band (0.0095–0.0195 Hz). These results suggested that abnormal dynamic FC within and between RSNs in JME occurs at multiple frequency bands and the lower frequency band (0.0095–0.0195 Hz) was probably more sensitive to JME-caused dynamic FC abnormalities. The frequency subdivision and selection are potentially helpful for detecting particular changes of dynamic FC in JME.

Limitations of Resting-State Functional MR Imaging in the Setting of Focal Brain Lesions

Methods of image acquisition and analysis for resting-state functional MR imaging (rsfMR imaging) are still evolving. Neurovascular uncoupling and susceptibility artifact are important confounds of rsfMR imaging in the setting of focal brain lesions such as brain tumors. This article reviews the detection of these confounds using rsfMR imaging metrics in the setting of focal brain lesions. In the near future, with the wide range of ongoing research in rsfMR imaging, these issues likely will be overcome and will open new windows into brain function and connectivity.

Evolution of spatial and temporal features of functional brain networks across the lifespan

Development and aging are associated with functional changes in the brain across the lifespan. These changes manifest in a variety of spatial and temporal features of resting state functional MRI (rs-fMRI) but have seldom been explored exhaustively. We present a comprehensive study assessing age-related changes in spatial and temporal features of blind-source separated components identified by independent vector analysis (IVA) in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale network configurations remain consistent throughout the lifespan, changes persist in both local and global organization of these networks. We show that the spatial extent of the majority of functional networks exhibits linear decreases and both positive and negative quadratic trajectories across the lifespan. Network connectivity revealed nuanced patterns of linear and quadratic relationships with age, primarily in higher order cognitive networks. We also show divergent age-related patterns across the frequency spectrum in lower and higher frequencies. Taken together, these results point to the presence of sophisticated patterns of age-related changes that have previously not been considered collectively. We suggest that established patterns of lifespan changes in rs-fMRI features may be driven by changes in the spectral composition of BOLD signals.

Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective

Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.

Value of Frequency Domain Resting-State Functional Magnetic Resonance Imaging Metrics Amplitude of Low-Frequency Fluctuation and Fractional Amplitude of Low-Frequency Fluctuation in the Assessment of Brain Tumor-Induced Neurovascular Uncoupling

The aim of this study was to explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) (rsfMRI) may also affect the resting-state fMRI (rsfMRI) frequency domain metrics the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). Twelve de novo brain tumor patients, who underwent clinical fMRI examinations, including task-based fMRI (tbfMRI) and rsfMRI, were included in this Institutional Review Board-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional (IL) sensorimotor cortex in the absence of a corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from general linear model analysis (reflecting motor activation vs. rest). Seed-based correlation analysis (SCA) maps of sensorimotor network, ALFF, and fALFF were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and IL hemispheres were parcellated using an automated anatomical labeling template for each patient. Region of interest (ROI) analysis was performed on four maps: tbfMRI, SCA, ALFF, and fALFF. Voxel values in the CL and IL ROIs of each map were divided by the corresponding global mean of ALFF and fALFF in the cortical brain tissue. Group analysis revealed significantly decreased IL ALFF (p = 0.02) and fALFF (p = 0.03) metrics compared with CL ROIs, consistent with similar findings of significantly decreased IL BOLD signal for tbfMRI (p = 0.0005) and SCA maps (p = 0.0004). The frequency domain metrics ALFF and fALFF may be markers of lesion-induced NVU in rsfMRI similar to previously reported alterations in tbfMRI activation and SCA-derived resting-state functional connectivity maps.

Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State

Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms.

Enhancement of teaching outcome through neural prediction of the students' knowledge state

The neural mechanism for the dyadic process of teaching is poorly understood. Although theories about teaching have proposed that before any teaching takes place, the teacher will predict the knowledge state of the student(s) to enhance the teaching outcome, this theoretical Prediction-Transmission hypothesis has not been tested with any neuroimaging studies. Using functional near-infrared spectroscopy-based hyperscanning, this study measured brain activities of the teacher-student pairs simultaneously. Results showed that better teaching outcome was associated with higher time-lagged interpersonal neural synchronization (INS) between right temporal-parietal junction (TPJ) of the teacher and anterior superior temporal cortex (aSTC) of the student, when the teacher's brain activity preceded that of the student. Moreover, time course analyses suggested that such INS could mark the quality of the teaching outcome at an early stage of the teaching process. These results provided key neural evidence for the Prediction-Transmission hypothesis about teaching, and suggested that the INS plays an important role in the successful teaching.

Dual regression physiological modeling of resting-state EPI power spectra: Effects of healthy aging

Aging and disease-related changes in the arteriovasculature have been linked to elevated levels of cardiac cycle-induced pulsatility in the cerebral microcirculation. Functional magnetic resonance imaging (fMRI), acquired fast enough to unalias the cardiac frequency contributions, can be used to study these physiological signals in the brain. Here, we propose an iterative dual regression analysis in the frequency domain to model single voxel power spectra of echo planar imaging (EPI) data using external recordings of the cardiac and respiratory cycles as input. We further show that a data-driven variant, without external physiological traces, produces comparable results. We use this framework to map and quantify cardiac and respiratory contributions in healthy aging. We found a significant increase in the spatial extent of cardiac modulated white matter voxels with age, whereas the overall strength of cardiac-related EPI power did not show an age effect.

Detection of fast oscillating magnetic fields using dynamic multiple TR imaging and Fourier analysis

Neuronal oscillations produce oscillating magnetic fields. There have been trials to detect neuronal oscillations using MRI, but the detectability in in vivo is still in debate. Major obstacles to detecting neuronal oscillations are (i) weak amplitudes, (ii) fast oscillations, which are faster than MRI temporal resolution, and (iii) random frequencies and on/off intervals. In this study, we proposed a new approach for direct detection of weak and fast oscillating magnetic fields. The approach consists of (i) dynamic acquisitions using multiple times to repeats (TRs) and (ii) an expanded frequency spectral analysis. Gradient echo echo-planar imaging was used to test the feasibility of the proposed approach with a phantom generating oscillating magnetic fields with various frequencies and amplitudes and random on/off intervals. The results showed that the proposed approach could precisely detect the weak and fast oscillating magnetic fields with random frequencies and on/off intervals. Complex and phase spectra showed reliable signals, while no meaningful signals were observed in magnitude spectra. A two-TR approach provided an absolute frequency spectrum above Nyquist sampling frequency pixel by pixel with no a priori target frequency information. The proposed dynamic multiple-TR imaging and Fourier analysis are promising for direct detection of neuronal oscillations and potentially applicable to any pulse sequences.

Developmental implications of children's brain networks and learning

The human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children's brains. We first focused on the general rules of brain network development and on the typical and atypical development of children's brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

Frequency-specific alteration of functional connectivity density in antipsychotic-naive adolescents with early-onset schizophrenia

Early-onset schizophrenia (EOS) is a severe mental illness associated with dysconnectivity that widespread in the brain. However, the functional dysconnectivity in EOS are still mixed. Recently, studies have identified that functional connectivity (FC) arises from a band-limited slow rhythmic mechanism and suggested that the dysconnectivity at specific frequency bands may provide more robust biomarkers for schizophrenia. The frequency-specific changes of FC pattern in EOS remain unclear. To address this issue, resting-state functional magnetic resonance imaging data scans from 39 EOS patients (drug-naive) and 31 healthy controls (HCs) were used to assess the FC density (FCD) across slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz). Results revealed that a remarkable difference between the FCD of the two bands existed mainly in the default mode network (DMN) and subcortical areas. Compared with the HCs, EOS patients showed significantly altered FCD involved in audiovisual information processing, sensorimotor system, and social cognition. Importantly, a significant frequency-by-group interaction was observed in the left precuneus with significantly lower FCD in the slow-4 frequency band, but no significant effect in the slow-5 frequency band. In addition, decreased FC was found between the precuneus and other DMN regions in the slow-4 band. Furthermore, the change in FCD in precuneus was inversely proportional to the clinical symptom in slow-4 band, indicating the key role of precuneus in schizophrenia progress. Our findings demonstrated that the dysconnectivity pattern in EOS could be frequency-dependent.

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

Functional MR imaging (fMR imaging) studies have recently begun to examine spontaneous changes in interregional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiologic states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, analysis of these time-varying phenomena requires careful differentiation between neural and nonneural contributions to the fMR imaging signal and thorough validation and statistical testing. In this article, the authors present an overview of methodological and interpretational considerations in this emerging field.

Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution

Psychophysiological interaction (PPI) is a regression based method to study task modulated brain connectivity. Despite its popularity in functional MRI (fMRI) studies, its reliability and reproducibility have not been evaluated. We investigated reproducibility and reliability of PPI effects during a simple visual task, and examined the effect of deconvolution on the PPI results. A large open-access dataset was analyzed (n = 138), where a visual task was scanned twice with repetition times (TRs) of 645 and 1,400 ms, respectively. We first replicated our previous results by using the left and right middle occipital gyrus as seeds. Then regions of interest (ROI)-wise analysis was performed among 20 visual-related thalamic and cortical regions, and negative PPI effects were found between many ROIs with the posterior fusiform gyrus as a hub region. Both the seed-based and ROI-wise results were similar between the two runs and between the two PPI methods with and without deconvolution. The non-deconvolution method and the short TR run in general had larger effect sizes and greater extents. However, the deconvolution method performed worse in the 645 ms TR run than the 1,400 ms TR run in the voxel-wise analysis. Given the general similar results between the two methods and the uncertainty of deconvolution, we suggest that deconvolution may be not necessary for PPI analysis on block-designed data. Lastly, intraclass correlations (ICC) between the two runs were much lower for the PPI effects than the activation main effects, which raise cautions on performing inter-subject correlations and group comparisons on PPI effects.

Frequency dependent hub role of the dorsal and ventral right anterior insula

The right anterior insula (rAI) plays a crucial role in generating adaptive behavior by orchestrating multiple brain networks. Based on functional separation findings of the insula and spectral fingerprints theory of cognitive functions, we hypothesize that the hub role of the rAI is region and frequency dependent. Using the Human Connectome Project dataset and backtracking approach, we segregate the rAI into dorsal and ventral parts at frequency bands from slow 6 to slow 3, indicating the frequency dependent functional separation of the rAI. Functional connectivity analysis shows that, within lower than 0.198 Hz frequency range, the dorsal and ventral parts of rAI form a complementary system to synchronize with externally and internally-oriented networks. Moreover, the relationship between the dorsal and ventral rAIs predicts the relationship between anti-correlated networks associated with the dorsal rAI at slow 6 and slow 5, suggesting a frequency dependent regulation of the rAI to brain networks. These findings could improve our understanding of the rAI by supporting the region and frequency dependent function of rAI and its essential role in coordinating brain systems relevant to internal and external environments.

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task.

A Comprehensive Analysis of the Correlations between Resting-State Oscillations in Multiple-Frequency Bands and Big Five Traits

Recently, the association between human personality traits and resting-state brain activity has gained interest in neuroimaging studies. However, it remains unclear if Big Five personality traits are represented in frequency bands (~0.25 Hz) of resting-state functional magnetic resonance imaging (fMRI) activity. Based on earlier neurophysiological studies, we investigated the correlation between the five personality traits assessed by the NEO Five-Factor Inventory (NEO-FFI), and the fractional amplitude of low-frequency fluctuation (fALFF) at four distinct frequency bands (slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz)). We enrolled 835 young subjects and calculated the correlations of resting-state fMRI signals using a multiple regression analysis. We found a significant and consistent correlation between fALFF and the personality trait of extraversion at all frequency bands. Furthermore, significant correlations were detected in distinct brain regions for each frequency band. This finding supports the frequency-specific spatial representations of personality traits as previously suggested. In conclusion, our data highlight an association between human personality traits and fALFF at four distinct frequency bands.

On the detection of high frequency correlations in resting state fMRI

Current studies of resting-state connectivity rely on coherent signal fluctuations at frequencies below 0.1 Hz, however, recent studies using high-speed fMRI have shown that fluctuations above 0.5 Hz may exist. This study replicates the feasibility of measuring high frequency (HF) correlations in six healthy controls and a patient with a brain tumor while analyzing non-physiological signal sources via simulation. Resting-state data were acquired using a high-speed multi-slab echo-volumar imaging pulse sequence with 136 ms temporal resolution. Bandpass frequency filtering in combination with sliding window seed-based connectivity analysis using running mean of the correlation maps was employed to map HF correlations up to 3.7 Hz. Computer simulations of Rician noise and the underlying point spread function were analyzed to estimate baseline spatial autocorrelation levels in four major networks (auditory, sensorimotor, visual, and default-mode). Using seed regions based on Brodmann areas, the auditory and default-mode networks were observed to have significant frequency band dependent HF correlations above baseline spatial autocorrelation levels. Correlations in the sensorimotor network were at trend level. The auditory network was still observed using a unilateral single voxel seed. In the patient, HF auditory correlations showed a spatial displacement near the tumor consistent with the displacement seen at low frequencies. In conclusion, our data suggest that HF connectivity in the human brain may be observable with high-speed fMRI, however, the detection sensitivity may depend on the network observed, data acquisition technique, and analysis method.

Abnormal Coupling Between Default Mode Network and Delta and Beta Band Brain Electric Activity in Psychotic Patients

Common-phase synchronization of neuronal oscillations is a mechanism by which distributed brain regions can be integrated into transiently stable networks. Based on the hypothesis that schizophrenia is characterized by deficits in functional integration within neuronal networks, this study aimed to explore whether psychotic patients exhibit differences in brain regions involved in integrative mechanisms. We report an electroencephalography (EEG)-informed functional magnetic resonance imaging analysis of eyes-open resting-state data collected from patients and healthy controls at two study sites. Global field synchronization (GFS) was chosen as an EEG measure indicating common-phase synchronization across electrodes. Several brain clusters appeared to be coupled to GFS differently in patients and controls. Activation in brain areas belonging to the default mode network was negatively associated to GFS delta (1-3.5 Hz) and positively to GFS beta (13-30 Hz) bands in patients, whereas controls showed an opposite pattern for both GFS frequency bands in those regions; activation in the extrastriate visual cortex was inversely related to GFS alpha1 (8.5-10.5 Hz) band in healthy controls, while patients had a tendency toward a positive relationship. Taken together, the GFS measure might be useful for detecting additional aspects of deficient functional network integration in psychosis.

Towards prognostic biomarkers from BOLD fluctuations to differentiate a first epileptic seizure from new-onset epilepsy

OBJECTIVE

The diagnosis of epilepsy cannot be reliably made prior to a patient's second seizure in most cases. Therefore, adequate diagnostic tools are needed to differentiate subjects with a first seizure from those with a seizure preceding the onset of epilepsy. The objective was to explore spontaneous blood oxygen level-dependent (BOLD) fluctuations in subjects with a first-ever seizure and patients with new-onset epilepsy (NOE), and to find characteristic biomarkers for seizure recurrence after the first seizure.

METHODS

We examined 17 first-seizure subjects, 19 patients with new-onset epilepsy (NOE), and 18 healthy controls. All subjects underwent clinical investigation and received electroencephalography and resting-state functional magnetic resonance imaging (MRI). The BOLD time series were analyzed in terms of regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFFs).

RESULTS

We found significantly stronger amplitudes (higher fALFFs) in patients with NOE relative to first-seizure subjects and healthy controls. The frequency range of 73-198 mHz (slow-3 subband) appeared most useful for discriminating patients with NOE from first-seizure subjects. The ReHo measure did not show any significant differences.

SIGNIFICANCE

The fALFF appears to be a noninvasive measure that characterizes spontaneous BOLD fluctuations and shows stronger amplitudes in the slow-3 subband of patients with NOE relative first-seizure subjects and healthy controls. A larger study population with follow-up is required to determine whether fALFF holds promise as a potential biomarker for identifying subjects at increased risk to develop epilepsy.

Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy

Recently, emerging studies have demonstrated the existence of brain resting-state spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood oxygen level-dependent contrasts (the upper limit of the canonical model is ∼0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue) or rather is artificially introduced by improper preprocessing operations. In this study, we examined the influence of a common preprocessing step-whole-band linear nuisance regression (WB-LNR)-on resting-state functional connectivity (RSFC) and demonstrated through both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of functional magnetic resonance imaging (fMRI) signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.

Investigating resting-state functional connectivity in the cervical spinal cord at 3T

The study of spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord. Two ultra-high field functional magnetic resonance imaging (fMRI) studies in humans have provided evidence for reproducible resting-state connectivity between the dorsal horns as well as between the ventral horns, and a study in non-human primates has shown that these resting-state signals are impacted by spinal cord injury. As these studies were carried out at ultra-high field strengths using region-of-interest (ROI) based analyses, we investigated whether such resting-state signals could also be observed at the clinically more prevalent field strength of 3 T. In a reanalysis of a sample of 20 healthy human participants who underwent a resting-state fMRI acquisition of the cervical spinal cord, we were able to observe significant dorsal horn connectivity as well as ventral horn connectivity, but no consistent effects for connectivity between dorsal and ventral horns, thus replicating the human 7 T results. These effects were not only observable when averaging along the acquired length of the spinal cord, but also when we examined each of the acquired spinal segments separately, which showed similar patterns of connectivity. Finally, we investigated the robustness of these resting-state signals against variations in the analysis pipeline by varying the type of ROI creation, temporal filtering, nuisance regression and connectivity metric. We observed that – apart from the effects of band-pass filtering – ventral horn connectivity showed excellent robustness, whereas dorsal horn connectivity showed moderate robustness. Together, our results provide evidence that spinal cord resting-state connectivity is a robust and spatially consistent phenomenon that could be a valuable tool for investigating the effects of pathology, disease progression, and treatment response in neurological conditions with a spinal component, such as spinal cord injury.

Mental rotation task specifically modulates functional connectivity strength of intrinsic brain activity in low frequency domains: A maximum uncertainty linear discriminant analysis

Neuroimaging studies have highlighted that intrinsic brain activity is modified to implement task demands. However, the relation between mental rotation and intrinsic brain activity remains unclear. To answer this question, we collected functional MRI (fMRI) data from 30 healthy participants in two mental rotation task periods (1st-task state, 2nd-task state) and two rest periods before (pre-task resting state) and after the task (post-task resting state) respectively. By combining the spatial independent component analysis (ICA) and voxel-wise functional connectivity strength (FCS), we identified FCS maps of 10 brain resting state networks (RSNs) within six different bands (i.e., 0-0.05, 0.05-0.1, 0.1-0.15, 0.15-0.2, 0.2-0.25, and 0.01-0.08Hz) corresponding to the four states for each subject. The maximum uncertainty linear discriminant analysis (MLDA) method showed that the FCS within the low frequency bandwidth of 0.05-0.1Hz could effectively classify the mental rotation task state from pre-/post-task resting states but failed to discriminate the pre- and post-task resting states. Discriminative FCSs were observed in the cognitive executive-control network (central executive and attention) and the imagery-based internal mental manipulation network (default mode, primary sensorimotor, and primary visual). Imagery manipulation is a stable mental element of mental rotation, and the involvement of executive control is dependent on the degree of task familiarity. Together, the present study provides evidence that mental rotation task specifically modifies intrinsic brain activity to complement cognitive demands, which provides further insight into the neural basis of mental rotation manipulation.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.

Effects of anesthesia on resting state BOLD signals in white matter of non-human primates

Resting state functional magnetic resonance imaging (rsfMRI) has been widely used to measure functional connectivity between cortical regions of the brain. However, there have been minimal reports of bold oxygenation level dependent (BOLD) signals in white matter, and even fewer attempts to detect resting state connectivity. Recently, there has been growing evidence that suggests that reliable detection of white matter BOLD signals may be possible. We have previously shown that nearest neighbor inter-voxel correlations of resting state BOLD signal fluctuations in white matter are anisotropic and can be represented by a functional correlation tensor, but the biophysical origins of these signal variations are not clear. We aimed to assess whether MRI signal fluctuations in white matter vary for different baseline levels of neural activity. We performed imaging studies on live squirrel monkeys under different levels of isoflurane anesthesia at 9.4T. We found 1) the fractional power (0.01-0.08Hz) in white matter was between 60 to 75% of the level in gray matter; 2) the power in both gray and white matter low frequencies decreased monotonically in similar manner with increasing levels of anesthesia; 3) the distribution of fractional anisotropy values of the functional tensors in white matter were significantly higher than those in gray matter; and 4) the functional tensor eigenvalues decreased with increasing level of anesthesia. Our results suggest that as anesthesia level changes baseline neural activity, white matter signal fluctuations behave similarly to those in gray matter, and functional tensors in white matter are affected in parallel.

Mapping Multiplex Hubs in Human Functional Brain Networks

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.

Influences of Head Motion Regression on High-Frequency Oscillation Amplitudes of Resting-State fMRI Signals

High-frequency oscillations (HFOs, >0.1 Hz) of resting-state fMRI (rs-fMRI) signals have received much attention in recent years. Denoising is critical for HFO studies. Previous work indicated that head motion (HM) has remarkable influences on a variety of rs-fMRI metrics, but its influences on rs-fMRI HFOs are still unknown. In this study, we investigated the impacts of HM regression (HMR) on HFO results using a fast sampling rs-fMRI dataset. We demonstrated that apparent high-frequency (∼0.2–0.4 Hz) components existed in the HM trajectories in almost all subjects. In addition, we found that individual-level HMR could robustly reveal more between-condition (eye-open vs. eye-closed) amplitude differences in high-frequency bands. Although regression of mean framewise displacement (FD) at the group level had little impact on the results, mean FD could significantly account for inter-subject variance of HFOs even after individual-level HMR. Our findings suggest that HM artifacts should not be ignored in HFO studies, and HMR is necessary for detecting HFO between-condition differences.

Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder

BACKGROUND

Major depressive disorder is a disabling neuropsychiatric condition that is associated with disrupted functional connectivity across brain networks. The precise nature of altered connectivity, however, remains incompletely understood. The current study was designed to examine the coherence of large-scale connectivity in depression using a recently developed technique termed global brain connectivity.

METHODS

A total of 82 subjects, including medication-free patients with major depression (n = 57) and healthy volunteers (n = 25) underwent functional magnetic resonance imaging with resting data acquisition for functional connectivity analysis. Global brain connectivity was computed as the mean of each voxel's time series correlation with every other voxel and compared between study groups. Relationships between global connectivity and depressive symptom severity measured using the Montgomery-Åsberg Depression Rating Scale were examined by means of linear correlation.

RESULTS

Relative to the healthy group, patients with depression evidenced reduced global connectivity bilaterally within multiple regions of medial and lateral prefrontal cortex. The largest between-group difference was observed within the right subgenual anterior cingulate cortex, extending into ventromedial prefrontal cortex bilaterally (Hedges' g = -1.48, P < 0.000001). Within the depressed group, patients with the lowest connectivity evidenced the highest symptom severity within ventromedial prefrontal cortex (r = -0.47, P = 0.0005).

CONCLUSIONS

Patients with major depressive evidenced abnormal large-scale functional coherence in the brain that was centered within the subgenual cingulate cortex, and medial prefrontal cortex more broadly. These data extend prior studies of connectivity in depression and demonstrate that functional disconnection of the medial prefrontal cortex is a key pathological feature of the disorder. Hum Brain Mapp 37:3214-3223, 2016. © 2016 Wiley Periodicals, Inc.

Regional Patterns of Cortical Phase Synchrony in the Resting State

Synchronized phase estimates between oscillating neuronal signals at the macroscale level reflect coordinated activities between neuronal assemblies. Recent electrophysiological evidence suggests the presence of significant spontaneous phase synchrony within the resting state. The purpose of this study was to investigate phase synchrony, including directional interactions, in resting state subdural electrocorticographic recordings to better characterize patterns of regional phase interactions across the lateral cortical surface during the resting state. We estimated spontaneous phase locking value (PLV) as a measure of functional connectivity, and phase slope index (PSI) as a measure of pseudo-causal phase interactions, across a broad range of canonical frequency bands and the modulation of the amplitude envelope of high gamma (amHG), a band that is believed to best reflect the physiological processes giving rise to the functional magnetic resonance imaging BOLD signal. Long-distance interactions had higher PLVs in slower frequencies (≤theta) than in higher ones (≥beta) with amHG behaving more like slow frequencies, and a general trend of increasing frequency band of significant PLVs when moving across the lateral surface along an anterior-posterior axis. Moreover, there was a strong trend of frontal-to-parietal directional phase synchronization, measured by PSI across multiple frequencies. These findings, which are likely indicative of coordinated and structured spontaneous cortical interactions, are important in the study of time scales and directional nature of resting state functional connectivity, and may ultimately contribute to a better understanding of how spontaneous synchrony is linked to variation in regional architecture across the lateral cortical surface.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.

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We introduce distance correlation as a new measure of ROI-based connectivity.

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It can be used for functional connectivity and structural covariance analyses.

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Simulations show that distance correlation copes with inhomogeneous ROIs.

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In real data, distance correlation is more reliable and robust than Pearson correlation.

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Distance correlation improves correspondence between different ROI definitions.

Differing Patterns of Altered Slow-5 Oscillations in Healthy Aging and Ischemic Stroke

The 'default-mode' network (DMN) has been investigated in the presence of various disorders, such as Alzheimer's disease and Autism spectrum disorders. More recently, this investigation has expanded to include patients with ischemic injury. Here, we characterized the effects of ischemic injury in terms of its spectral distribution of resting-state low-frequency oscillations and further investigated whether those specific disruptions were unique to the DMN, or rather more general, affecting the global cortical system. With 43 young healthy adults, 42 older healthy adults, 14 stroke patients in their early stage (<7 days after stroke onset), and 16 stroke patients in their later stage (between 1 to 6 months after stroke onset), this study showed that patterns of cortical system disruption may differ between healthy aging and following the event of an ischemic stroke. The stroke group in the later stage demonstrated a global reduction in the amplitude of the slow-5 oscillations (0.01-0.027 Hz) in the DMN as well as in the primary visual and sensorimotor networks, two 'task-positive' networks. In comparison to the young healthy group, the older healthy subjects presented a decrease in the amplitude of the slow-5 oscillations specific to the components of the DMN, while exhibiting an increase in oscillation power in the task-positive networks. These two processes of a decrease DMN and an increase in 'task-positive' slow-5 oscillations may potentially be related, with a deficit in DMN inhibition, leading to an elevation of oscillations in non-DMN systems. These findings also suggest that disruptions of the slow-5 oscillations in healthy aging may be more specific to the DMN while the disruptions of those oscillations following a stroke through remote (diaschisis) effects may be more widespread, highlighting a non-specificity of disruption on the DMN in stroke population. The mechanisms underlying those differing modes of network disruption need to be further explored to better inform our understanding of brain function in healthy individuals and following injury.

Interpersonal frontopolar neural synchronization in group communication: An exploration toward fNIRS hyperscanning of natural interactions

Research of interpersonal neural synchronization (INS) using functional near-infrared spectroscopy (fNIRS) hyperscanning is an expanding nascent field. This field still requires the accumulation of findings and establishment of analytic standards. In this study, we therefore intend to extend fNIRS-based INS research in three directions: (1) verifying the enhancement of frontopolar INS by natural and unstructured verbal communication involving more than two individuals; (2) examining timescale dependence of the INS modulation; and (3) evaluating the effects of artifact reduction methods in capturing INS. We conducted an fNIRS hyperscanning study while 12 groups of four subjects were engaged in cooperative verbal communication. Corresponding to the three objectives, our analyses of the data (1) confirmed communication-enhanced frontopolar INS, as expected from the region's roles in social communication; (2) revealed the timescale dependency in the INS modulation, suggesting the merit of evaluating INS in fine timescale bins; and (3) determined that removal of the skin blood flow component engenders substantial improvement in sensitivity to communication-enhanced INS and segregation from artifactual synchronization, and that caution for artifact reduction preprocessing is needed to avoid excessive removal of the neural fluctuation component. Accordingly, this study provides a prospective technical basis for future hyperscanning studies during daily communicative activities.

Recovery of slow-5 oscillations in a longitudinal study of ischemic stroke patients

Functional networks in resting-state fMRI are identified by characteristics of their intrinsic low-frequency oscillations, more specifically in terms of their synchronicity. With advanced aging and in clinical populations, this synchronicity among functionally linked regions is known to decrease and become disrupted, which may be associated with observed cognitive and behavioral changes. Previous work from our group has revealed that oscillations within the slow-5 frequency range (0.01–0.027 Hz) are particularly susceptible to disruptions in aging and following a stroke. In this study, we characterized longitudinally the changes in the slow-5 oscillations in stroke patients across two different time-points. We followed a group of ischemic stroke patients (n = 20) and another group of healthy older adults (n = 14) over two visits separated by a minimum of three months (average of 9 months). For the stroke patients, one visit occurred in their subacute window (10 days to 6 months after stroke onset), the other took place in their chronic window (> 6 months after stroke). Using a mid-order group ICA method on 10-minutes eyes-closed resting-state fMRI data, we assessed the frequency distributions of a component's representative time-courses for differences in regards to slow-5 spectral power. First, our stroke patients, in their subacute stage, exhibited lower amplitude slow-5 oscillations in comparison to their healthy counterparts. Second, over time in their chronic stage, those same patients showed a recovery of those oscillations, reaching near equivalence to the healthy older adult group. Our results indicate the possibility of an eventual recovery of those initially disrupted network oscillations to a near-normal level, providing potentially a biomarker for stroke recovery of the cortical system. This finding opens new avenues in infra-slow oscillation research and could serve as a useful biomarker in future treatments aimed at recovery.

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Slow-5 oscillation amplitudes are reduced in stroke patients at the subacute stage.

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Slow-5 oscillation amplitudes correlate with cognitive performance.

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Slow-5 oscillations recover in the same patients at the chronic stage.

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Findings support the high implication of slow-5 oscillations in network disruption.

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Slow-5 oscillations may serve as a bio-marker of functional network health.

Frequency-Specific Local Synchronization Changes in Paroxysmal Kinesigenic Dyskinesia

The neurobiological basis of paroxysmal kinesigenic dyskinesia (PKD) is poorly defined due to the lack of reliable neuroimaging differences that can distinguish PKD with dystonia (PKD-D) from PKD with chorea (PKD-C). Consequently, diagnosis of PKD remains largely based on the clinical phenotype. Understanding the pathophysiology of PKD may facilitate discrimination between PKD-D and PKD-C, potentially contributing to more accurate diagnosis. We conducted resting-state functional magnetic resonance imaging on patients with PKD-D (n = 22), PKD-C (n = 10), and healthy controls (n = 32). Local synchronization was measured in all 3 groups via regional homogeneity (ReHo) and evaluated using receiver operator characteristic analysis to distinguish between PKD-C and PKD-D. Cortical-basal ganglia circuitry differed significantly between the 2 groups at a specific frequency. Furthermore, the PKD-D and PKD-C patients were observed to show different spontaneous brain activity in the right precuneus, right putamen, and right angular gyrus at the slow-5 frequency band (0.01-0.027 Hz). The frequency-specific abnormal local synchronization between the 2 types of PKD offers new insights into the pathophysiology of this disorder to some extent.

Reproducibility of resting state spinal cord networks in healthy volunteers at 7 Tesla

We recently reported our findings of resting state functional connectivity in the human spinal cord: in a cohort of healthy volunteers we observed robust functional connectivity between left and right ventral (motor) horns and between left and right dorsal (sensory) horns (Barry et al., 2014). Building upon these results, we now quantify the within-subject reproducibility of bilateral motor and sensory networks (intraclass correlation coefficient=0.54-0.56) and explore the impact of including frequencies up to 0.13Hz. Our results suggest that frequencies above 0.08Hz may enhance the detectability of these resting state networks, which would be beneficial for practical studies of spinal cord functional connectivity.

Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations

Steady-state responses (SSRs) reflect the synchronous neural oscillations evoked by noninvasive and consistently repeated stimuli at the fundamental or harmonic frequencies. The steady-state evoked potentials (SSEPs; the representative form of the SSRs) have been widely used in the cognitive and clinical neurosciences and brain–computer interface research. However, the steady-state evoked potentials have limitations in examining high-frequency neural oscillations and basic cognition. In addition, synchronous neural oscillations in the low frequency range (&lt;1 Hz) and in higher-order cognition have received a little attention. Therefore, we examined the SSRs in the low frequency range using a new index, the steady-state BOLD responses (SSBRs) evoked by semantic stimuli. Our results revealed that the significant SSBRs were induced at the fundamental frequency of stimuli and the first harmonic in task-related regions, suggesting the enhanced variability of neural oscillations entrained by exogenous stimuli. The SSBRs were independent of neurovascular coupling and characterized by sensorimotor bias, an indication of regional-dependent neuroplasticity. Furthermore, the amplitude of SSBRs may predict behavioral performance and show the psychophysiological relevance. Our findings provide valuable insights into the understanding of the SSRs evoked by higher-order cognition and how the SSRs modulate low-frequency neural oscillations.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

Segmentation of the thalamus based on BOLD frequencies affected in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy is associated with functional changes throughout the brain, particularly including a putative seizure propagation network involving the hippocampus, insula, and thalamus. We identified a specified frequency range where functional connectivity in this network was related to duration of disease. Then, to identify specific thalamic nuclei involved in seizure propagation, we determined the subregions of the thalamus that have increased resting functional oscillations in this frequency range.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) was acquired from 20 patients with unilateral temporal lobe epilepsy (TLE; 14 right and 6 left) and 20 healthy controls who were each age and gender matched to a specific patient. Wavelet-based fMRI connectivity mapping across the network was computed at each frequency to determine those frequencies where connectivity significantly decreases with duration of disease consistent with impairment due to repeated seizures. The voxel-wise power of the spontaneous blood oxygenation fluctuations of this frequency band was computed in the thalamus of each subject.

RESULTS

Functional connectivity was impaired in the proposed seizure propagation network over a specific range (0.0067-0.013 Hz and 0.024-0.032 Hz) of blood oxygenation oscillations. Increased power in this frequency band (<0.032 Hz) was detected bilaterally in the pulvinar and anterior nucleus of the thalamus of healthy controls, and was increased over the ipsilateral thalamus compared to the contralateral thalamus in TLE.

SIGNIFICANCE

This study identified frequencies of impaired connectivity in a TLE seizure propagation network and used them to localize the anterior nucleus and pulvinar of the thalamus as subregions most susceptible to TLE seizures. Further examinations of these frequencies in healthy and TLE subjects may provide unique information relating to the mechanism of seizure propagation and potential treatment using electrical stimulation.

Functional Magnetic Resonance Imaging Methods

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

Frequency-Specific Alterations of Local Synchronization in Idiopathic Generalized Epilepsy

Recurrently and abnormally hypersynchronous discharge is a striking feature of idiopathic generalized epilepsy (IGE). Resting-state functional magnetic resonance imaging has revealed aberrant spontaneous brain synchronization, predominately in low-frequency range (<0.1 Hz), in individuals with IGE. Little is known, however, about these changes in local synchronization across different frequency bands. We examined alterations to frequency-specific local synchronization in terms of spontaneous blood oxygen level-dependent (BOLD) fluctuations across 5 bands, spanning 0 to 0.25 Hz. Specifically, we compared brain activity in a large cohort of IGE patients (n = 86) to age- and sex-matched normal controls (n = 86). IGE patients showed decreased local synchronization in low frequency (<0.073 Hz), primarily in the default mode network (DMN). IGE patients also exhibited increased local synchronization in high-frequency (>0.073 Hz) in a "conscious perception network," which is anchored by the pregenual and dorsal anterior cingulate cortex, as well as the bilateral insular cortices, possibly contributing to impaired consciousness. Furthermore, we found frequency-specific alternating local synchronization in the posterior portion of the DMN relative to the anterior part, suggesting an interaction between the disease and frequency bands. Importantly, the aberrant high-frequency local synchronization in the middle cingulate cortex was associated with disease duration, thus linking BOLD frequency changes to disease severity. These findings provide an overview of frequency-specific local synchronization of BOLD fluctuations, and may be helpful in uncovering abnormal synchronous neuronal activity in patients with IGE at specific frequency bands.

Investigation of Multiple Frequency Ranges Using Discrete Wavelet Decomposition of Resting-State Functional Connectivity in Mild Traumatic Brain Injury Patients

The aim of this study was to investigate if discrete wavelet decomposition provides additional insight into resting-state processes through the analysis of functional connectivity within specific frequency ranges within the default mode network (DMN) that may be affected by mild traumatic brain injury (mTBI). Participants included 32 mTBI patients (15 with postconcussive syndrome [PCS+] and 17 without [PCS-]). mTBI patients received resting-state functional magnetic resonance imaging (rs-fMRI) at acute (within 10 days of injury) and chronic (6 months postinjury) time points and were compared with 31 controls (healthy control [HC]). The wavelet decomposition divides the time series into multiple frequency ranges based on four scaling factors (SF1: 0.125-0.250 Hz, SF2: 0.060-0.125 Hz, SF3: 0.030-0.060 Hz, SF4: 0.015-0.030 Hz). Within each SF, wavelet connectivity matrices for nodes of the DMN were created for each group (HC, PCS+, PCS-), and bivariate measures of strength and diversity were calculated. The results demonstrate reduced strength of connectivity in PCS+ patients compared with PCS- patients within SF1 during both the acute and chronic stages of injury, as well as recovery of connectivity within SF1 across the two time points. Furthermore, the PCS- group demonstrated greater network strength compared with controls at both time points, suggesting a potential compensatory or protective mechanism in these patients. These findings stress the importance of investigating resting-state connectivity within multiple frequency ranges; however, many of our findings are within SF1, which may overlap with frequencies associated with cardiac and respiratory activities.

High frequency functional brain networks in neonates revealed by rapid acquisition resting state fMRI

Understanding how spatially remote brain regions interact to form functional brain networks, and how these develop during the neonatal period, provides fundamental insights into normal brain development, and how mechanisms of brain disorder and recovery may function in the immature brain. A key imaging tool in characterising functional brain networks is examination of T2*-weighted fMRI signal during rest (resting state fMRI, rs-fMRI). The majority of rs-fMRI studies have concentrated on slow signal fluctuations occurring at <0.1 Hz, even though neuronal rhythms, and haemodynamic responses to these fluctuate more rapidly, and there is emerging evidence for crucial information about functional brain connectivity occurring more rapidly than these limits. The characterisation of higher frequency components has been limited by the sampling frequency achievable with standard T2* echoplanar imaging (EPI) sequences. We describe patterns of neonatal functional brain network connectivity derived using accelerated T2*-weighted EPI MRI. We acquired whole brain rs-fMRI data, at subsecond sampling frequency, from preterm infants at term equivalent age and compared this to rs-fMRI data acquired with standard EPI acquisition protocol. We provide the first evidence that rapid rs-fMRI acquisition in neonates, and adoption of an extended frequency range for analysis, allows identification of a substantial proportion of signal power residing above 0.2 Hz. We thereby describe changes in brain connectivity associated with increasing maturity which are not evident using standard rs-fMRI protocols. Development of optimised neonatal fMRI protocols, including use of high speed acquisition sequences, is crucial for understanding the physiology and pathophysiology of the developing brain.

Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.

BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz

Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤ 0.1 Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8 Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5 Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1 Hz (non-BOLD/BOLD contrast at 0.4-0.5 Hz is ~4 times that at <0.1 Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1 Hz are discussed.