Characterization of neuromuscular transmission and projections of muscle motor neurons in the rat stomach.

The stomach is the primary reservoir of the gastrointestinal tract, where ingested content is broken down into small particles. Coordinated relaxation and contraction is essential for rhythmic motility and digestion, but how the muscle motor innervation is organized to provide appropriate graded regional control is not established. In this study, we recorded neuromuscular transmission to the circular muscle using intracellular microelectrodes to investigate the spread of the influence of intrinsic motor neurons. In addition, microanatomical investigations of neuronal projections and pharmacological analysis were conducted to investigate neuromuscular relationships. We found that inhibitory neurotransmission to the circular muscle is graded with stimulus strength and circumferential distance from the stimulation site. The influence of inhibitory neurons declined between 1 and 11 mm from the stimulation site. In the antrum, corpus, and fundus, the declines at 11 mm were about 20%, 30%, and 50%, respectively. Stimulation of inhibitory neurons elicited biphasic hyperpolarizing potentials often followed by prolonged depolarizing events in the distal stomach, but only hyperpolarizing events in the proximal stomach. Excitatory neurotransmission influence varied greatly between proximal stomach, where depolarizing events occurred, and distal stomach, where no direct electrical effects in the muscle were observed. Structural studies using microlesion surgeries confirmed a dominant circumferential projection. We conclude that motor neuron influences extend around the gastric circumference, that the effectiveness can be graded by the recruitment of different numbers of motor neuron nerve terminals to finely control gastric motility, and that the ways in which the neurons influence the muscle differ between anatomical regions.NEW & NOTEWORTHY This study provides a detailed mapping of nerve transmission to the circular muscle of the different anatomical regions of rat stomach. It shows that excitatory and inhibitory influences extend around the gastric circumference and that there is a summation of neural influence that allows for finely graded control of muscle tension and length. Nerve-mediated electrical events are qualitatively and quantitatively different between regions, for example, excitatory neurons have direct effects on fundus but not antral muscle.

Surface Mapping of Gastric Motor Functions Using MRI: A Comparative Study between Humans and Rats.

BACKGROUND: The stomach's ability to store, mix, propel, and empty its content requires highly coordinated motor functions. However, current diagnostic tools cannot simultaneously assess these motor processes. This study aimed to use magnetic resonance imaging (MRI) to map multifaceted gastric motor functions, including accommodation, tonic and peristaltic contractions, and emptying, through a single non-invasive experiment for both humans and rats. METHODS: Ten humans and ten Sprague-Dawley rats consumed MRI-visible semi-solid meals and underwent MRI scans. We used a surface model to analyze MRI data, capturing the deformation of the stomach wall upon ingestion or during digestion. We inferred muscle activity, mapped motor processes, parcellated the stomach into functional regions, and revealed cross-species distinctions. RESULTS: In humans, both the fundus and antrum distended post-meal, followed by sustained tonic contractions to regulate intragastric pressure. Peristaltic contractions initiate from the distal fundus, including three concurrent wavefronts oscillating at 3.3 cycles per minute (cpm) and traveling at 1.7 to 2.9 mm/s. These motor functions facilitate linear gastric emptying with a 61-min half-time. In contrast, rats exhibited peristalsis from the mid-corpus, showing two wavefronts oscillating at 5 cpm and traveling at 0.3 to 0.9 mm/s. For both species, motility features allowed functional parcellation of the stomach along a mid-corpus division. CONCLUSIONS: This study maps region- and species-specific gastric motor functions. We demonstrate the value of MRI with surface modeling in understanding gastric physiology and its potential to become a new standard for clinical and preclinical investigations of gastric disorders at both individual and group levels.

The vagus nerve mediates the stomach-brain coherence in rats.

Interactions between the brain and the stomach shape both cognitive and digestive functions. Recent human studies report spontaneous synchronization between brain activity and gastric slow waves in the resting state. However, this finding has not been replicated in any animal models. The neural pathways underlying this apparent stomach-brain synchrony is also unclear. Here, we performed functional magnetic resonance imaging while simultaneously recording body-surface gastric slow waves from anesthetized rats in the fasted vs. postprandial conditions and performed a bilateral cervical vagotomy to assess the role of the vagus nerve. The coherence between brain fMRI signals and gastric slow waves was found in a distributed "gastric network", including subcortical and cortical regions in the sensory, motor, and limbic systems. The stomach-brain coherence was largely reduced by the bilateral vagotomy and was different between the fasted and fed states. These findings suggest that the vagus nerve mediates the spontaneous coherence between brain activity and gastric slow waves, which is likely a signature of real-time stomach-brain interactions. However, its functional significance remains to be established.

Organisation of the musculature of the rat stomach.

The strengths, directions and coupling of the movements of the stomach depend on the organisation of its musculature. Although the rat has been used as a model species to study gastric function, there is no detailed, quantitative study of the arrangement of the gastric muscles in rat. Here we provide a descriptive and quantitative account, and compare it with human gastric anatomy. The rat stomach has three components of the muscularis externa, a longitudinal coat, a circular coat and an internal oblique (sling) muscle in the region of the gastro-oesophageal junction. These layers are similar to human. Unlike human, the rat stomach is also equipped with paired muscular oesophago-pyloric ligaments that lie external to the longitudinal muscle. There is a prominent muscularis mucosae throughout the stomach and strands of smooth muscle occur in the mucosa, between the glands of the corpus and antrum. The striated muscle of the oesophageal wall reaches to the stomach, unlike the human, in which the wall of the distal oesophagus is smooth muscle. Thus, the continuity of gastric and oesophageal smooth muscle bundles, that occurs in human, does not occur in rat. Circular muscle bundles extend around the circumference of the stomach, in the fundus forming a cap of parallel muscle bundles. This arrangement favours co-ordinated circumferential contractions. Small bands of muscle make connections between the circular muscle bundles. This is consistent with a slower conduction of excitation orthogonal to the circular muscle bundles, across the corpus towards the distal antrum. The oblique muscle merged and became continuous with the circular muscle close to the gastro-oesophageal junction at the base of the fundus, and in the corpus, lateral to the lesser curvature. Quantitation of muscle thickness revealed gradients of thickness of both the longitudinal and circular muscle. This anatomical study provides essential data for interpreting gastric movements.

BIG Modulates Stem Cell Niche and Meristem Development via SCR/SHR Pathway in Arabidopsis Roots.

BIG, a regulator of polar auxin transport, is necessary to regulate the growth and development of Arabidopsis. Although mutations in the BIG gene cause severe root developmental defects, the exact mechanism remains unclear. Here, we report that disruption of the BIG gene resulted in decreased quiescent center (QC) activity and columella cell numbers, which was accompanied by the downregulation of WUSCHEL-RELATED HOMEOBOX5 (WOX5) gene expression. BIG affected auxin distribution by regulating the expression of PIN-FORMED proteins (PINs), but the root morphological defects of big mutants could not be rescued solely by increasing auxin transport. Although the loss of BIG gene function resulted in decreased expression of the PLT1 and PLT2 genes, genetic interaction assays indicate that this is not the main reason for the root morphological defects of big mutants. Furthermore, genetic interaction assays suggest that BIG affects the stem cell niche (SCN) activity through the SCRSCARECROW (SCR)/SHORT ROOT (SHR) pathway and BIG disruption reduces the expression of SCR and SHR genes. In conclusion, our findings reveal that the BIG gene maintains root meristem activity and SCN integrity mainly through the SCR/SHR pathway.

Representation learning of resting state fMRI with variational autoencoder.

Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available in each subject. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.

Visual speech differentially modulates beta, theta, and high gamma bands in auditory cortex.

Speech perception is a central component of social communication. Although principally an auditory process, accurate speech perception in everyday settings is supported by meaningful information extracted from visual cues. Visual speech modulates activity in cortical areas subserving auditory speech perception including the superior temporal gyrus (STG). However, it is unknown whether visual modulation of auditory processing is a unitary phenomenon or, rather, consists of multiple functionally distinct processes. To explore this question, we examined neural responses to audiovisual speech measured from intracranially implanted electrodes in 21 patients with epilepsy. We found that visual speech modulated auditory processes in the STG in multiple ways, eliciting temporally and spatially distinct patterns of activity that differed across frequency bands. In the theta band, visual speech suppressed the auditory response from before auditory speech onset to after auditory speech onset (-93 to 500 ms) most strongly in the posterior STG. In the beta band, suppression was seen in the anterior STG from -311 to -195 ms before auditory speech onset and in the middle STG from -195 to 235 ms after speech onset. In high gamma, visual speech enhanced the auditory response from -45 to 24 ms only in the posterior STG. We interpret the visual-induced changes prior to speech onset as reflecting crossmodal prediction of speech signals. In contrast, modulations after sound onset may reflect a decrease in sustained feedforward auditory activity. These results are consistent with models that posit multiple distinct mechanisms supporting audiovisual speech perception.

Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.

Gastric stimulation drives fast BOLD responses of neural origin.

Functional magnetic resonance imaging (fMRI) is commonly thought to be too slow to capture any neural dynamics faster than 0.1 Hz. However, recent findings demonstrate the feasibility of detecting fMRI activity at higher frequencies beyond 0.2 Hz. The origin, reliability, and generalizability of fast fMRI responses are still under debate and await confirmation through animal experiments with fMRI and invasive electrophysiology. Here, we acquired single-echo and multi-echo fMRI, as well as local field potentials, from anesthetized rat brains given gastric electrical stimulation modulated at 0.2, 0.4 and 0.8 Hz. Such gastric stimuli could drive widespread fMRI responses at corresponding frequencies from the somatosensory and cingulate cortices. Such fast fMRI responses were linearly dependent on echo times and thus indicative of blood oxygenation level dependent nature (BOLD). Local field potentials recorded during the same gastric stimuli revealed transient and phase-locked broadband neural responses, preceding the fMRI responses by as short as 0.5 s. Taken together, these results suggest that gastric stimulation can drive widespread and rapid fMRI responses of BOLD and neural origin, lending support to the feasibility of using fMRI to detect rapid changes in neural activity up to 0.8 Hz under visceral stimulation.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

BIG regulates stomatal immunity and jasmonate production in Arabidopsis.

Plants have evolved an array of responses that provide them with protection from attack by microorganisms and other predators. Many of these mechanisms depend upon interactions between the plant hormones jasmonate (JA) and ethylene (ET). However, the molecular basis of these interactions is insufficiently understood. Gene expression and physiological assays with mutants were performed to investigate the role of Arabidopsis BIG gene in stress responses. BIG transcription is downregulated by methyl JA (MeJA), necrotrophic infection or mechanical injury. BIG deficiency promotes JA-dependent gene induction, increases JA production but restricts the accumulation of both ET and salicylic acid. JA-induced anthocyanin accumulation and chlorophyll degradation are enhanced and stomatal immunity is impaired by BIG disruption. Bacteria- and lipopolysaccaride (LPS)-induced stomatal closure is reduced in BIG gene mutants, which are hyper-susceptible to microbial pathogens with different lifestyles, but these mutants are less attractive to phytophagous insects. Our results indicate that BIG negatively and positively regulate the MYC2 and ERF1 arms of the JA signalling pathway. BIG warrants recognition as a new and distinct regulator that regulates JA responses, the synergistic interactions of JA and ET, and other hormonal interactions that reconcile the growth and defense dilemma in Arabidopsis.

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.

Transferring and generalizing deep-learning-based neural encoding models across subjects.

Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a target subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a limited amount of data from the target subject. To train encoding models for a population, the models were progressively trained and updated with incremental data from different subjects. For the proof of principle, we applied these methods to functional magnetic resonance imaging (fMRI) data from three subjects watching tens of hours of naturalistic videos, while a deep residual neural network driven by image recognition was used to model visual cortical processing. Results demonstrate that the methods developed herein provide an efficient and effective strategy to establish both subject-specific and population-wide predictive models of cortical representations of high-dimensional and hierarchical visual features.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision.

Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions.

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal.

UNLABELLED: Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ∼5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels. SIGNIFICANCE STATEMENT: This study provides new insights into the neural origin of resting-state fMRI. Results demonstrate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized and directly coupled with the global component of spontaneous fMRI signals, in contrast to modularly synchronized fluctuations in oscillatory neural activity. These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for global and modular patterns of functional connectivity observed with resting-state fMRI, respectively.

Mapping white-matter functional organization at rest and during naturalistic visual perception.

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts.

Spontaneous activity in the visual cortex is organized by visual streams.

Large-scale functional networks have been extensively studied using resting state functional magnetic resonance imaging (fMRI). However, the pattern, organization, and function of fine-scale network activity remain largely unknown. Here, we characterized the spontaneously emerging visual cortical activity by applying independent component (IC) analysis to resting state fMRI signals exclusively within the visual cortex. In this subsystem scale, we observed about 50 spatially ICs that were reproducible within and across subjects, and analyzed their spatial patterns and temporal relationships to reveal the intrinsic parcellation and organization of the visual cortex. The resulting visual cortical parcels were aligned with the steepest gradient of cortical myelination, and were organized into functional modules segregated along the dorsal/ventral pathways and foveal/peripheral early visual areas. Cortical distance could partly explain intra-hemispherical functional connectivity, but not interhemispherical connectivity; after discounting the effect of anatomical affinity, the fine-scale functional connectivity still preserved a similar visual-stream-specific modular organization. Moreover, cortical retinotopy, folding, and cytoarchitecture impose limited constraints to the organization of resting state activity. Given these findings, we conclude that spontaneous activity patterns in the visual cortex are primarily organized by visual streams, likely reflecting feedback network interactions. Hum Brain Mapp 38:4613-4630, 2017. © 2017 Wiley Periodicals, Inc.

Over-expression of SlJA2 decreased heat tolerance of transgenic tobacco plants via salicylic acid pathway.

KEY MESSAGE: Over-expression of SlJA2 decreased the accumulation of SA, which resulted in significant physiological and gene expression changes in transgenic tobacco plants, leading to the decreased heat tolerance of transgenic tobacco. NAC family, the largest transcription factors in plants, responses to different environmental stimuli. Here, we isolated a typical NAC transcription factor (SlJA2) from tomato and got transgenic tobacco with SlJA2 over-expression. Expression of SlJA2 was induced by heat stress (42 °C), chilling stress (4 °C), drought stress, osmotic stress, abscisic acid, and salicylic acid. Over-expression of SlJA2 decreased the accumulation of salicylic acid by regulating expression of salicylic acid degradation gene under heat stress. Compared to WT plants, stomatal apertures and water loss increased in transgenic plants, and the damage of photosynthetic apparatus and chlorophyll breakdown were more serious in transgenic plants under heat stress. Meanwhile, more H2O2 and O2·- were accumulated transgenic plants and proline synthesis was restricted, which resulted in more serious oxidative damage compared to WT. qRT-PCR analysis showed that over-expression of SlJA2 could down-regulate genes involved in reactive oxygen species scavenging, proline biosynthesis, and response to heat stress. All the above results indicated that SlJA2 may be a negative regulator responded to plant's heat tolerance. Thus, this study provides new insight into roles of NAC family member in plant response to abiotic stress.

Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal.

Neurophysiological field-potential signals consist of both arrhythmic and rhythmic patterns indicative of the fractal and oscillatory dynamics arising from likely distinct mechanisms. Here, we present a new method, namely the irregular-resampling auto-spectral analysis (IRASA), to separate fractal and oscillatory components in the power spectrum of neurophysiological signal according to their distinct temporal and spectral characteristics. In this method, we irregularly resampled the neural signal by a set of non-integer factors, and statistically summarized the auto-power spectra of the resampled signals to separate the fractal component from the oscillatory component in the frequency domain. We tested this method on simulated data and demonstrated that IRASA could robustly separate the fractal component from the oscillatory component. In addition, applications of IRASA to macaque electrocorticography and human magnetoencephalography data revealed a greater power-law exponent of fractal dynamics during sleep compared to wakefulness. The temporal fluctuation in the broadband power of the fractal component revealed characteristic dynamics within and across the eyes-closed, eyes-open and sleep states. These results demonstrate the efficacy and potential applications of this method in analyzing electrophysiological signatures of large-scale neural circuit activity. We expect that the proposed method or its future variations would potentially allow for more specific characterization of the differential contributions of oscillatory and fractal dynamics to distributed neural processes underlying various brain functions.