Multiscale energy reallocation during low-frequency steady-state brain response

Intrinsic neural timescales relate to the dynamics of infraslow neural waves

The human brain is a highly dynamic organ that operates across a variety of timescales, the intrinsic neural timescales (INT). In addition to the INT, the neural waves featured by its phase-related processes including their cycles with peak/trough and rise/fall play a key role in shaping the brain's neural activity. However, the relationship between the brain's ongoing wave dynamics and INT remains yet unclear. In this study, we utilized functional magnetic resonance imaging (fMRI) rest and task data from the Human Connectome Project (HCP) to investigate the relationship of infraslow wave dynamics [as measured in terms of speed by changes in its peak frequency (PF)] with INT. Our findings reveal that: (i) the speed of phase dynamics (PF) is associated with distinct parts of the ongoing phase cycles, namely higher PF in peak/trough and lower PF in rise/fall; (ii) there exists a negative correlation between phase dynamics (PF) and INT such that slower PF relates to longer INT; (iii) exposure to a movie alters both PF and INT across the different phase cycles, yet their negative correlation remains intact. Collectively, our results demonstrate that INT relates to infraslow phase dynamics during both rest and task states.

Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan

Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.

Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity

Within the context of the computer metaphor, evoked brain activity acts as a primary carrier for the brain mechanisms of mental processing. However, many studies have found that evoked brain activity is not the major part of brain activity. Instead, spontaneous brain activity exhibits greater intensity and coevolves with evoked brain activity through continuous interaction. Spontaneous and evoked brain activities are similar but not identical. They are not separate parts, but always dynamically interact with each other. Therefore, the enactive cognition theory further states that the brain is characterized by unified and active patterns of activity. The brain adjusts its activity pattern by minimizing the error between expectation and stimulation, adapting to the ever-changing environment. Therefore, the dynamic regulation of brain activity in response to task situations is the core brain mechanism of mental processing. Beyond the evoked brain activity and spontaneous brain activity, the enactive brain activity provides a novel framework to completely describe brain activities during mental processing. It is necessary for upcoming researchers to introduce innovative indicators and paradigms for investigating enactive brain activity during mental processing.

The Infraslow Frequency Oscillatory Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Enhances Sustained Attention

Background

The vigilance fluctuation and decrement of sustained attention have large detrimental consequences to most tasks in daily life, especially among the elderly. Non-invasive brain stimulations (e.g., transcranial direct current stimulation, tDCS) have been widely applied to improve sustained attention, however, with mixed results.

Objective

An infraslow frequency oscillatory tDCS approach was designed to improve sustained attention.

Methods

The infraslow frequency oscillatory tDCS (O-tDCS) over the left dorsolateral prefrontal cortex at 0.05 Hz was designed and compared with conventional tDCS (C-tDCS) to test whether this new protocol improves sustained attention more effectively. The sustained attention was evaluated by reaction time and accuracy.

Results

Compared with the C-tDCS and sham, the O-tDCS significantly enhanced sustained attention by increasing response accuracy, reducing response time, and its variability. These effects were predicted by the evoked oscillation of response time at the stimulation frequency.

Conclusion

Similar to previous studies, the modulation effect of C-tDCS on sustained attention is weak and unstable. In contrast, the O-tDCS effectively and systematically enhances sustained attention by optimizing vigilance fluctuation. The modulation effect of O-tDCS is probably driven by neural oscillations at the infraslow frequency range.

Functional near-infrared spectroscopy can detect low-frequency hemodynamic oscillations in the prefrontal cortex during steady-state visual evoked potential-inducing periodic facial expression stimuli presentation

Brain oscillations are vital to cognitive functions, while disrupted oscillatory activity is linked to various brain disorders. Although high-frequency neural oscillations (> 1 Hz) have been extensively studied in cognition, the neural mechanisms underlying low-frequency hemodynamic oscillations (LFHO) < 1 Hz have not yet been fully explored. One way to examine oscillatory neural dynamics is to use a facial expression (FE) paradigm to induce steady-state visual evoked potentials (SSVEPs), which has been used in electroencephalography studies of high-frequency brain oscillation activity. In this study, LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy (fNIRS), in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz. The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels, indicating that the periodic events successfully elicited LFHO in the prefrontal cortex. More importantly, measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions, with happy FE presentation showing greater LFHO power than neutral FE presentation. These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex, which provides new insights into the cognitive mechanisms involved in slow oscillation.

Spatial variability of low frequency brain signal differentiates brain states

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula

The hub role of the right anterior insula (AI) has been emphasized in cognitive neurosciences and been demonstrated to be frequency-dependently organized. However, the functional organization of left AI (LAI) has not been systematically investigated. Here we used 100 unrelated datasets from the Human Connectome Project to study the frequency-dependent organization of LAI along slow 6 to slow 1 bands. The broadband functional connectivity of LAI was similar to previous findings. In slow 6-slow 3 bands, both dorsal and ventral seeds in LAI were correlated to the salience network (SN) and language network (LN) and anti-correlated to the default mode network (DMN). However, these seeds were only correlated to the LAI in slow 2-slow 1 bands. These findings indicate that broadband and narrow band functional connections reflect different functional organizations of the LAI. Furthermore, the dorsal seed had a stronger connection with the LN and anti-correlation with DMN while the ventral seed had a stronger connection within the SN in slow 6-slow 3 bands. In slow 2-slow 1 bands, both seeds had stronger connections with themselves. These observations indicate distinctive functional organizations for the two parts of LAI. Significant frequency effect and frequency by seed interaction were also found, suggesting different frequency characteristics of these two seeds. The functional integration and functional segregation of LDAI and LVAI were further supported by their cognitive associations. The frequency- and seed-dependent functional organizations of LAI may enlighten future clinical and cognitive investigations.

Low Frequency Phase-locking of Brain Signals Contribute to Efficient Face Recognition

Low frequency phase synchronization is an essential mechanism of information communication among brain regions. In the infra-slow frequency range (<0.1 Hz), inter-regional phase lag is of importance for brain function (e.g., anti-phase between the default mode network and task positive network). However, the role of phase lag in cognitive processing remains unclear. Based on the frequency tagging experimental paradigm and functional magnetic resonance imaging (fMRI) technique, we investigated inter-regional phase lag and phase coherence using a face recognition task (n = 30, 15 males/15 females). Phase coherence within the face processing system was significantly increased during task state, highlighting the importance of regular inter-regional phase relationship for face recognition. Moreover, results showed decreased phase lag within the core and extended face areas (face processing system) and increased phase lag between the face processing system and frontoparietal network, indicating a reorganization of inter-regional relationships of the two systems. Inter-regional phase lag was modulated by the task at ascending and descending phases of the fMRI signal, suggesting a phase-dependent inter-regional relationship. Furthermore, phase lags between visual cortex and amygdala and between visual cortex and motor area were positively related to reaction time, indicating better task performance depends on both rapid emotional detection pathway and visual-motor pathway. Overall, inter-regional phase synchronization in the infra-slow frequency range is of important for effective information communication and cognitive performance.

Spatial complexity of brain signal is altered in patients with generalized anxiety disorder

BACKGROUND

Is it healthy to be chaotic? Recent studies have argued that mental disorders are associated with more orderly neural activities, corresponding to a less flexible functional system. These conclusions were derived from altered temporal complexity. However, the relationship between spatial complexity and health is unknown, although spatial configuration is of importance for normal brain function.

METHODS

Based on resting-state functional magnetic resonance imaging data, we used Sample entropy (SampEn) to evaluate the altered spatial complexity in patients with generalized anxiety disorder (GAD; n = 47) compared to healthy controls (HCs; n = 38) and the relationship between spatial complexity and anxiety level.

RESULTS

Converging results showed increased spatial complexity in patients with GAD, indicating more chaotic spatial configuration. Interestingly, inverted-U relationship was revealed between spatial complexity and anxiety level, suggesting complex relationship between health and the chaos of human brain.

LIMITATIONS

Anxiety-related alteration of spatial complexity should be verified at voxel level in a larger sample and compared with results of other indices to clarify the mechanism of spatial chaotic of anxiety.

CONCLUSIONS

Altered spatial complexity in the brain of GAD patients mirrors inverted-U relationship between anxiety and behavioral performance, which may reflect an important characteristic of anxiety. These results indicate that SampEn is a good reflection of human health or trait mental characteristic.

Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is characterized by a chronic, continuous symptom of worry and exaggerated startle response. Although functional abnormality in GAD has been widely studied using functional magnetic resonance imaging (fMRI), the dynamic signatures of GAD are not fully understood. As a vital index of brain function, brain signal variability (BSV) reflects the capacity of state transition of neural activities. In this study, we recruited 47 patients with GAD and 38 healthy controls (HCs) to investigate whether or not BSV is altered in patients with GAD by measuring the standard deviation of fMRI signal of each voxel. We found that patients with GAD exhibited decreased BSV in widespread regions including the visual network, sensorimotor network, frontoparietal network, limbic system, and thalamus, indicating an inflexible brain state transfer pattern in these systems. Furthermore, the correlation between BSV and trait anxiety score was prone to be positive in patients with GAD but negative in HCs. The opposite relationships between BSV and anxiety level in the two groups indicate that the brain with moderate anxiety level may stay in the most stable rather than in the flexible state. As the first study of BSV in GAD, we revealed extensively decreased BSV in patients with GAD similar to that in other mental disorders but with a non-linear relationship between BSV and anxiety level indicating a novel neurodynamic mechanism of the anxious brain.

Multiple Frequency Bands Analysis of Large Scale Intrinsic Brain Networks and Its Application in Schizotypal Personality Disorder

The human brain is a complex system composed by several large scale intrinsic networks with distinct functions. The low frequency oscillation (LFO) signal of blood oxygen level dependent (BOLD), measured through resting-state fMRI, reflects the spontaneous neural activity of these networks. We propose to characterize these networks by applying the multiple frequency bands analysis (MFBA) to the LFO time courses (TCs) resulted from the group independent component analysis (ICA). Specifically, seven networks, including the default model network (DMN), dorsal attention network (DAN), control executive network (CEN), salience network, sensorimotor network, visual network and limbic network, are identified. After the power spectral density (PSD) analysis, the amplitude of low frequency fluctuation (ALFF) and the fractional amplitude of low frequency fluctuation (fALFF) is determined in three bands: <0.1 Hz; slow-5; and slow-4. Moreover, the MFBA method is applied to reveal the frequency-dependent alternations of fALFF for seven networks in schizotypal personality disorder (SPD). It is found that seven networks can be divided into three categories: the advanced cognitive networks, primary sensorimotor networks and limbic networks, and their fALFF successively decreases in both slow-4 and slow-5 bands. Comparing to normal control group, the fALFF of DMN, DAN and CEN in SPD tends to be higher in slow-5 band, but lower in slow-4. Higher fALFF of sensorimotor and visual networks in slow-5, higher fALFF of limbic network in both bands have been observed for SPD group. The results of ALFF are consistent with those of fALFF. The proposed MFBA method may help distinguish networks or oscillators in the human brain, reveal subtle alternations of networks through locating their dominant frequency band, and present potential to interpret the neuropathology disruptions.

Baseline Brain Activity Changes in Patients With Single and Relapsing Optic Neuritis

Purpose: To investigate spontaneous brain activity amplitude alterations in single and relapsing optic neuritis (sON and rON, respectively) and their relationships with clinical variables.

Methods: In total, 42 patients with sON, 35 patients with rON and 50 healthy volunteers were recruited. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans were acquired for all participants and compared to investigate the changes in the amplitude of low-frequency fluctuations (ALFFs) among the three groups. The relationships between the ALFFs in regions with significant differences in the groups and clinical variables, including the logarithm of minimal angle of resolution (LogMAR), Expanded Disability Status Scale (EDSS) score and disease duration, were further explored.

Results: Compared with healthy volunteers, the sON and rON patients showed significantly decreased ALFFs in several regions of the occipital and temporal lobes (i.e., inferior occipital gyrus and superior temporal gyrus; corrected p < 0.01 using AlphaSim). The sON patients showed significantly increased ALFFs in the left caudate and certain regions in the frontal lobes (i.e., medial frontal gyrus), whereas the rON patients showed increased ALFFs in the bilateral inferior temporal gyrus and left medial frontal gyrus (corrected p < 0.01 using AlphaSim). Significantly decreased ALFFs were observed in the right inferior parietal lobule (IPL), left posterior cingulate and precuneus in the rON patients compared with those in the sON patients (corrected p < 0.01 using AlphaSim). Significant correlations were observed between the disease duration and ALFF in the left middle temporal gyrus, left inferior occipital gyrus, right lingual gyrus and right IPL (p < 0.05).

Conclusion: Functional impairment and adaptation occurred in both the sON and rON patients. Impairment mainly involved the occipital cortex, and functional adaptions predominantly occurred in the frontal lobe. Functional damage was more severe in the rON patients than in the sON patients and correlated with the disease duration.

Multiscale energy reallocation during low-frequency steady-state brain response

Traditional task-evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low-frequency steady-state brain response (lfSSBR) reflects frequency-tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra-slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain-behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency-specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks.