How networks communicate: propagation patterns in spontaneous brain activity

Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety

Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.

Static and temporal dynamic alterations of local functional connectivity in chronic insomnia

Several studies have revealed altered intrinsic neural activity in chronic insomnia (CI). However, the temporal variability of intrinsic neural activity in CI is rarely mentioned. This study aimed to explore static and temporal dynamic alterations of regional homogeneity (ReHo) in CI and excavate the potential associations between these changes and clinical characteristics. Eighty-seven patients with CI and seventy-eight healthy controls (HCs) were included. Resting-state functional magnetic resonance imaging was performed on all subjects and both static and dynamic ReHo were used to detect local functional connectivity. We then tested the relationship between altered brain regions, disease duration, and clinical scales. The receiver operating characteristic curve analysis was used to reveal the potential capability of these indicators to screen CI patients from HCs. CI showed increased dynamic ReHo in the right precuneus and decreased static ReHo in the right cerebellum_6. The dynamic ReHo values of the right precuneus were negatively correlated with the self-rating depression score and the static ReHo values of the right cerebellum_6 were positively correlated with the Montreal Cognitive Assessment-Naming score. In addition, the combination of the two metrics showed a potential capacity to distinguish CI patients from HCs, which was better than a single metric alone. The present study has revealed the altered local functional connectivity under static and temporal dynamic conditions in patients with CI, and found the relationships between these changes, mood-related scales, and cognitive-related scales. These may be useful in elucidating the neurological mechanisms of CI and accompanying symptoms.

Claustrum modulation drives altered prefrontal cortex dynamics and connectivity

This study delves into the claustrum's role in modulating spontaneous and sensory-evoked network activity across cortical regions. Using mesoscale calcium imaging and Gi and Gq DREADDs in anesthetized mice, we show that decreasing claustral activity enhances prefrontal cortical activity, while activation reduces prefrontal cortical activity. This claustrum modulation also caused changes to the brain's large-scale functional networks, emphasizing the claustrum's ability to influence long-range functional connectivity in the cortex. Claustrum inhibition increased the local coupling between frontal cortex areas, but reduced the correlation between anterior medial regions and lateral/posterior regions, while also enhancing sensory-evoked responses in the visual cortex. These findings indicate the claustrum can participate in orchestrating neural communication across cortical regions through modulation of prefrontal cortical activity. These insights deepen our understanding of the claustrum's impact on prefrontal connectivity, large-scale network dynamics, and sensory processing, positioning the claustrum as a key node modulating large-scale cortical dynamics.

Mechanisms of Action of TMS in the Treatment of Depression

Transcranial magnetic stimulation (TMS) is entering increasingly widespread use in treating depression. The most common stimulation target, in the dorsolateral prefrontal cortex (DLPFC), emerged from early neuroimaging studies in depression. Recently, more rigorous casual methods have revealed whole-brain target networks and anti-networks based on the effects of focal brain lesions and focal brain stimulation on depression symptoms. Symptom improvement during therapeutic DLPFC-TMS appears to involve directional changes in signaling between the DLPFC, subgenual and dorsal anterior cingulate cortex, and salience-network regions. However, different networks may be involved in the therapeutic mechanisms for other TMS targets in depression, such as dorsomedial prefrontal cortex or orbitofrontal cortex. The durability of therapeutic effects for TMS involves synaptic neuroplasticity, and specifically may depend upon dopamine acting at the D1 receptor family, as well as NMDA-receptor-dependent synaptic plasticity mechanisms. Although TMS protocols are classically considered 'excitatory' or 'inhibitory', the actual effects in individuals appear quite variable, and might be better understood at the level of populations of synapses rather than individual synapses. Synaptic meta-plasticity may provide a built-in protective mechanism to avoid runaway facilitation or inhibition during treatment, and may account for the relatively small number of patients who worsen rather than improve with TMS. From an ethological perspective, the antidepressant effects of TMS may involve promoting a whole-brain attractor state associated with foraging/hunting behaviors, centered on the rostrolateral periaqueductal gray and salience network, and suppressing an attractor state associated with passive threat defense, centered on the ventrolateral periaqueductal gray and default-mode network.

As without, so within: how the brain's temporo-spatial alignment to the environment shapes consciousness

Consciousness is constituted by a structure that includes contents as foreground and the environment as background. This structural relation between the experiential foreground and background presupposes a relationship between the brain and the environment, often neglected in theories of consciousness. The temporo-spatial theory of consciousness addresses the brain-environment relation by a concept labelled 'temporo-spatial alignment'. Briefly, temporo-spatial alignment refers to the brain's neuronal activity's interaction with and adaption to interoceptive bodily and exteroceptive environmental stimuli, including their symmetry as key for consciousness. Combining theory and empirical data, this article attempts to demonstrate the yet unclear neuro-phenomenal mechanisms of temporo-spatial alignment. First, we suggest three neuronal layers of the brain's temporo-spatial alignment to the environment. These neuronal layers span across a continuum from longer to shorter timescales. (i) The background layer comprises longer and more powerful timescales mediating topographic-dynamic similarities between different subjects' brains. (ii) The intermediate layer includes a mixture of medium-scaled timescales allowing for stochastic matching between environmental inputs and neuronal activity through the brain's intrinsic neuronal timescales and temporal receptive windows. (iii) The foreground layer comprises shorter and less powerful timescales for neuronal entrainment of stimuli temporal onset through neuronal phase shifting and resetting. Second, we elaborate on how the three neuronal layers of temporo-spatial alignment correspond to their respective phenomenal layers of consciousness. (i) The inter-subjectively shared contextual background of consciousness. (ii) An intermediate layer that mediates the relationship between different contents of consciousness. (iii) A foreground layer that includes specific fast-changing contents of consciousness. Overall, temporo-spatial alignment may provide a mechanism whose different neuronal layers modulate corresponding phenomenal layers of consciousness. Temporo-spatial alignment can provide a bridging principle for linking physical-energetic (free energy), dynamic (symmetry), neuronal (three layers of distinct time-space scales) and phenomenal (form featured by background-intermediate-foreground) mechanisms of consciousness.

Targeted neurostimulation reverses a spatiotemporal biomarker of treatment-resistant depression

Major depressive disorder (MDD) is widely hypothesized to result from disordered communication across brain-wide networks. Yet, prior resting-state-functional MRI (rs-fMRI) studies of MDD have studied zero-lag temporal synchrony (functional connectivity) in brain activity absent directional information. We utilize the recent discovery of stereotyped brain-wide directed signaling patterns in humans to investigate the relationship between directed rs-fMRI activity, MDD, and treatment response to FDA-approved neurostimulation paradigm termed Stanford neuromodulation therapy (SNT). We find that SNT over the left dorsolateral prefrontal cortex (DLPFC) induces directed signaling shifts in the left DLPFC and bilateral anterior cingulate cortex (ACC). Directional signaling shifts in the ACC, but not the DLPFC, predict improvement in depression symptoms, and moreover, pretreatment ACC signaling predicts both depression severity and the likelihood of SNT treatment response. Taken together, our findings suggest that ACC-based directed signaling patterns in rs-fMRI are a potential biomarker of MDD.

From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.

Development of top-down cortical propagations in youth

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.

Information flow across the cortical timescale hierarchy during narrative construction

When listening to spoken narratives, we must integrate information over multiple, concurrent timescales, building up from words to sentences to paragraphs to a coherent narrative. Recent evidence suggests that the brain relies on a chain of hierarchically organized areas with increasing temporal receptive windows to process naturalistic narratives. We hypothesized that the structure of this cortical processing hierarchy should result in an observable sequence of response lags between networks comprising the hierarchy during narrative comprehension. This study uses functional MRI to estimate the response lags between functional networks during narrative comprehension. We use intersubject cross-correlation analysis to capture network connectivity driven by the shared stimulus. We found a fixed temporal sequence of response lags-on the scale of several seconds-starting in early auditory areas, followed by language areas, the attention network, and lastly the default mode network. This gradient is consistent across eight distinct stories but absent in data acquired during rest or using a scrambled story stimulus, supporting our hypothesis that narrative construction gives rise to internetwork lags. Finally, we build a simple computational model for the neural dynamics underlying the construction of nested narrative features. Our simulations illustrate how the gradual accumulation of information within the boundaries of nested linguistic events, accompanied by increased activity at each level of the processing hierarchy, can give rise to the observed lag gradient.

Reaction-diffusion models in weighted and directed connectomes

Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.

Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.

In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

The global neuronal workspace as a broadcasting network

A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

High-definition transcranial infraslow pink noise stimulation for chronic low back pain: protocol for a pilot, safety and feasibility randomised placebo-controlled trial

INTRODUCTION

Chronic low back pain (CLBP) is a common disabling health condition. Current treatments demonstrate modest effects, warranting newer therapies. Brain imaging demonstrates altered electrical activities in cortical areas responsible for pain modulation, emotional and sensory components of pain experience. Treatments targeting to change electrical activities of these key brain regions may produce clinical benefits. This pilot study aims to (1) evaluate feasibility, safety and acceptability of a novel neuromodulation technique, high-definition transcranial infraslow pink noise stimulation (HD-tIPNS), in people with CLBP, (2) explore the trend of effect of HD-tIPNS on pain and function, and (3) derive treatment estimates to support sample size calculation for a fully powered trial should trends of effectiveness be present.

METHODS AND ANALYSIS

A pilot, triple-blinded randomised two-arm placebo-controlled parallel trial. Participants (n=40) with CLBP will be randomised to either sham stimulation or HD-tIPNS (targeting somatosensory cortex and dorsal and pregenual anterior cingulate cortex). Primary outcomes include feasibility and safety measures, and clinical outcomes of pain (Brief Pain Inventory) and disability (Roland-Morris disability questionnaire). Secondary measures include clinical, psychological, quantitative sensory testing and electroencephalography collected at baseline, immediately postintervention, and at 1-week, 1-month and 3 months postintervention. All data will be analysed descriptively. A nested qualitative study will assess participants perceptions about acceptability of intervention and analysed thematically.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from Health and Disability Ethics Committee (Ref:20/NTB/67). Findings will be reported to regulatory and funding bodies, presented at conferences, and published in a scientific journal.

TRIAL REGISTRATION NUMBER

ACTRN12620000505909p.

Latency structure of BOLD signals within white matter in resting-state fMRI

PURPOSE

Previous studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter.

METHODS

We used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-covariances between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states.

RESULTS

Projections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states.

CONCLUSIONS

These findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.

Neural network involvement for religious experiences in worship measured by EEG microstate analysis

To date, not much is known about large-scale brain activation patterns in religious states of mind and previous studies have not set an emphasis on experience. The present study investigated the phenomenon of religious experiences through microstate analysis, and it was the first neurocognitive research to tackle the dimension of experience directly. Hence, a total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. With a bar slider, people were able to continuously rate how strongly they sensed God's presence at any given moment. A selection of songs was used to help in the induction of the desired experience. With 64 electroencephalography (EEG) electrodes, the brain activity was assessed and analyzed with five clusters of microstate classes. First, we hypothesized that the neural network for multisensory integration was involved in the religious experience. Second, we hypothesized that the same was true for the Default Mode Network (DMN). Our results suggested an association between the auditory network and the religious experience, and an association with the salience network as well as with the DMN. No associations with the network thought to be involved with multisensory integration was detected.

Dissociable multi-scale patterns of development in personalized brain networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Oscillator decomposition of infant fNIRS data

The functional near-infrared spectroscopy (fNIRS) can detect hemodynamic responses in the brain and the data consist of bivariate time series of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) on each channel. In this study, we investigate oscillatory changes in infant fNIRS signals by using the oscillator decompisition method (OSC-DECOMP), which is a statistical method for extracting oscillators from time series data based on Gaussian linear state space models. OSC-DECOMP provides a natural decomposition of fNIRS data into oscillation components in a data-driven manner and does not require the arbitrary selection of band-pass filters. We analyzed 18-ch fNIRS data (3 minutes) acquired from 21 sleeping 3-month-old infants. Five to seven oscillators were extracted on most channels, and their frequency distribution had three peaks in the vicinity of 0.01-0.1 Hz, 1.6-2.4 Hz and 3.6-4.4 Hz. The first peak was considered to reflect hemodynamic changes in response to the brain activity, and the phase difference between oxy-Hb and deoxy-Hb for the associated oscillators was at approximately 230 degrees. The second peak was attributed to cardiac pulse waves and mirroring noise. Although these oscillators have close frequencies, OSC-DECOMP can separate them through estimating their different projection patterns on oxy-Hb and deoxy-Hb. The third peak was regarded as the harmonic of the second peak. By comparing the Akaike Information Criterion (AIC) of two state space models, we determined that the time series of oxy-Hb and deoxy-Hb on each channel originate from common oscillatory activity. We also utilized the result of OSC-DECOMP to investigate the frequency-specific functional connectivity. Whereas the brain oscillator exhibited functional connectivity, the pulse waves and mirroring noise oscillators showed spatially homogeneous and independent changes. OSC-DECOMP is a promising tool for data-driven extraction of oscillation components from biological time series data.

Sex differences in the intrinsic reading neural networks of Chinese children

Sex differences in reading performance have been considered a relatively stable phenomenon. However, there is no general agreement about their neural basis, which might be due to that sex differences are largely influenced by age. This paper focuses on the sex differences in the reading-related neural network of Chinese children and its interaction with age. We also attempt to predict reading abilities based on neural network. Fifty-three boys and 56 girls (8.2–14.6 years of age) were recruited. We collected their resting-state fMRI and behavioural data. Restricted sex differences were found in the resting-state reading neural network compared to extensive age by sex interaction effect. Specifically, the interactions between sex and age indicated that with increasing age, girls showed greater connectivity strength between visual orthographic areas and other brain areas within the reading network, while boys showed an opposite trend. After controlling age, the prediction models of reading performance for the girls mainly included interhemispheric connections, while the intrahemispheric connections (particularly the phonological route) mainly contributed to predicting the reading ability for boys. Taken together, these findings suggest that sex differences in reading neural networks are modulated by age. Partialling out age, boys and girls also show the stable sex differences in relationship between reading neural circuit and reading behaviour.

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Sex differences in reading neural networks are modulated by age.

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Girls’ RSFCs within reading neural networks increase with age, contrary to boys.

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Intra- and interhemispheric RSFCs predict the reading ability of boys and girls, respectively.

Whole-brain propagating patterns in human resting-state brain activities

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy

The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain's functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. Dynamic analysis techniques have shown that functional connectivity is a mere summary of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain's intrinsic activity that cannot be inferred by functional connectivity or the spatial maps derived from it, such as functional networks and gradients. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale functional gradient, simultaneously across the cerebral cortex and in most other brain regions. In some regions, like the thalamus, the propagation suggests previously-undescribed gradients. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between functional networks is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder

Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.

High-amplitude cofluctuations in cortical activity drive functional connectivity

Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network's modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.

Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy

Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.

Brain states govern the spatio-temporal dynamics of resting-state functional connectivity

Previously, using simultaneous resting-state functional magnetic resonance imaging (fMRI) and photometry-based neuronal calcium recordings in the anesthetized rat, we identified blood oxygenation level-dependent (BOLD) responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized correlate of locally recorded neuronal activity (Schwalm et al., 2017). Here, using the same techniques, we investigate two distinct cortical activity states: persistent activity, in which compartmentalized network dynamics were observed; and slow wave activity, dominated by a cortex-wide BOLD component, suggesting a strong functional coupling of inter-cortical activity. During slow wave activity, we find a correlation between the occurring slow wave events and the strength of functional connectivity between different cortical areas. These findings suggest that down-up transitions of neuronal excitability can drive cortex-wide functional connectivity. This study provides further evidence that changes in functional connectivity are dependent on the brain's current state, directly linked to the generation of slow waves.

Space: A Missing Piece of the Dynamic Puzzle

There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial patterns of functional structures are not well characterized. Here, we present the concepts of spatially, temporally, and spatiotemporally dynamics and use this terminology to categorize existing approaches. We review current spatially dynamic connectivity work, emphasizing that explicit incorporation of space into dynamic analyses can expand our understanding of brain function and disorder.

Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI

The resting state fMRI time series appears to have cyclic patterns, which indicates presence of cyclic interactions between different brain regions. Such interactions are not easily captured by pre-established resting state functional connectivity methods including zero-lag correlation, lagged correlation, and dynamic time warping distance. These methods formulate the functional interaction between different brain regions as similar temporal patterns within the time series. To use information related to temporal ordering, cyclicity analysis has been introduced to capture pairwise interactions between multiple time series. In this study, we compared the efficacy of cyclicity analysis with aforementioned similarity-based techniques in representing individual-level and group-level information. Additionally, we investigated how filtering and global signal regression interacted with these techniques. We obtained and analyzed fMRI data from patients with tinnitus and neurotypical controls at two different days, a week apart. For both patient and control groups, we found that the features generated by cyclicity and correlation (zero-lag and lagged) analyses were more reliable than the features generated by dynamic time warping distance in identifying individuals across visits. The reliability of all features, except those generated by dynamic time warping, improved as the global signal was regressed. Nevertheless, removing fluctuations >0.1 Hz deteriorated the reliability of all features. These observations underscore the importance of choosing appropriate preprocessing steps while evaluating different analytical methods in describing resting state functional interactivity. Further, using different machine learning techniques including support vector machines, discriminant analyses, and convolutional neural networks, our results revealed that the manifestation of the group-level information within all features was not sufficient enough to dissociate tinnitus patients from controls with high sensitivity and specificity. This necessitates further investigation regarding the representation of group-level information within different features to better identify tinnitus-related alternation in the functional organization of the brain. Our study adds to the growing body of research on developing diagnostic tools to identify neurological disorders, such as tinnitus, using resting state fMRI data.

Aberrant executive control networks and default mode network in patients with right-sided temporal lobe epilepsy: a functional and effective connectivity study

Objective: We aimed to explore functional connectivity (FC) and effective connectivity (EC) of the executive control networks (ECNs) and the default mode network (DMN) in patients with right-sided TLE (rTLE) by applying independent component analysis (ICA) and Granger causal analysis (GCA).Methods: Twenty-seven patients with rTLE and 20 healthy controls (HCs) matched for age, gender underwent resting-state functional magnetic resonance imaging and Attention Network Test (ANT).Results: The FC analysis showed compared to HCs, patients with rTLE demonstrated reduced FC strength in the right inferior parietal gyrus (IPG) and the right middle temporal gyrus (MTG). The left superior temporal gyrus (STG) displayed reduced FC values whereas the left thalamus revealed increased FC values in rTLE. ROI-wise GCA revealed that patients with rTLE displayed increased EC from the left thalamus to the left STG, and as well as enhanced EC from the right IPG to the right MTG compared to HCs. Voxel-wise GCA showed positive EC from the left thalamus to the left insula while the right middle occipital gyrus (MOG) exhibited increased EC to the right MTG in patients. The ANT results demonstrated executive dysfunction in patients compared to HCs. The increased FC in the left thalamus showed a negative association with ECF in patients.Conclusion: We speculated that recurrent seizures take effect on disruption among the brain networks, and self-modulation occurs simultaneously to compensate for cognitive decline. Our findings revealed new insights on the neuropathophysiological mechanisms of rTLE.

Expanding our horizons: central pattern generation in the context of complex activity sequences

Central pattern generators (CPGs) are central nervous system (CNS) networks that can generate coordinated output in the absence of patterned sensory input. For decades, this concept was applied almost exclusively to simple, innate, rhythmic movements with essentially identical cycles that repeat continually (e.g. respiration) or episodically (e.g. locomotion). But many natural movement sequences are not simple rhythms, as they include different elements in a complex order, and some involve learning. The concepts and experimental approaches of CPG research have also been applied to the neural control of complex movement sequences, such as birdsong, though this is not widely appreciated. Experimental approaches to the investigation of CPG networks, both for simple rhythms and for complex activity sequences, have shown that: (1) brief activation of the CPG elicits a long-lasting naturalistic activity sequence; (2) electrical stimulation of CPG elements alters the timing of subsequent cycles or sequence elements; and (3) warming or cooling CPG elements respectively speeds up or slows down the rhythm or sequence rate. The CPG concept has also been applied to the activity rhythms of populations of mammalian cortical neurons. CPG concepts and methods might further be applied to a variety of fixed action patterns typically used in courtship, rivalry, nest building and prey capture. These complex movements could be generated by CPGs within CPGs (‘nested’ CPGs). Stereotypical, non-motor, non-rhythmic neuronal activity sequences may also be generated by CPGs. My goal here is to highlight previous applications of the CPG concept to complex but stereotypical activity sequences and to suggest additional possible applications, which might provoke new hypotheses and experiments.

Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose, we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

Propagations of spontaneous brain activity in awake rats

Slow propagations of spontaneous brain activity have been reported in multiple species. However, systematical investigation of the organization of such brain activity is still lacking. In this study, we analyzed propagations of spontaneous brain activity using a reference library of characteristic resting-state functional connectivity (RSFC) patterns in awake rodents. We found that transitions through multiple distinct RSFC patterns were reproducible not only in transition sequences but also in transition time delays. In addition, the organization of these transitions and their spatiotemporal dynamic patterns were revealed using a graphical model. We further identified prominent brain regions involved in these transitions. These results provide a comprehensive framework of brainwide propagations of spontaneous activity in awake rats. This study also offers a new tool to study the spatiotemporal dynamics of activity in the resting brain.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

On time delay estimation and sampling error in resting-state fMRI

Accumulating evidence indicates that resting-state functional magnetic resonance imaging (rsfMRI) signals correspond to propagating electrophysiological infra-slow activity (<0.1 Hz). Thus, pairwise correlations (zero-lag functional connectivity (FC)) and temporal delays among regional rsfMRI signals provide useful, complementary descriptions of spatiotemporal structure in infra-slow activity. However, the slow nature of fMRI signals implies that practical scan durations cannot provide sufficient independent temporal samples to stabilize either of these measures. Here, we examine factors affecting sampling variability in both time delay estimation (TDE) and FC. Although both TDE and FC accuracy are highly sensitive to data quantity, we use surrogate fMRI time series to study how the former is additionally related to the magnitude of a given pairwise correlation and, to a lesser extent, the temporal sampling rate. These contingencies are further explored in real data comprising 30-min rsfMRI scans, where sampling error (i.e., limited accuracy owing to insufficient data quantity) emerges as a significant but underappreciated challenge to FC and, even more so, to TDE. Exclusion of high-motion epochs exacerbates sampling error; thus, both sides of the bias-variance (or data quality-quantity) tradeoff associated with data exclusion should be considered when analyzing rsfMRI data. Finally, we present strategies for TDE in motion-corrupted data, for characterizing sampling error in TDE and FC, and for mitigating the influence of sampling error on lag-based analyses.

Possible links between the lag structure in visual cortex and visual streams using fMRI

Conventional functional connectivity analysis using functional magnetic resonance imaging (fMRI) measures the correlation of temporally synchronized brain activities between brain regions. Lag structure analysis relaxes the synchronicity constraint of fMRI signals, and thus, this approach might be better at explaining functional connectivity. However, the sources of the lag structure in fMRI are primarily unknown. Here, we applied lag structure analysis to the human visual cortex to identify the possible sources of lag structure. A total of 1,250 fMRI data from two independent databases were considered. We explored the temporal lag patterns between the central and peripheral visual fields in early visual cortex and those in two visual pathways of dorsal and ventral streams. We also compared the lag patterns with effective connectivity obtained with dynamic causal modeling. We found that the lag structure in early visual cortex flows from the central to peripheral visual fields and the order of the lag structure flow was consistent with the order of signal flows in visual pathways. The effective connectivity computed by dynamic causal modeling exhibited similar patterns with the lag structure results. This study suggests that signal flows in visual streams are possible sources of the lag structure in human visual cortex.

Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment

Pain is a subjective sensory experience that can, mostly, be reported but cannot be directly measured or quantified. Nevertheless, a suite of biomarkers related to mechanisms, neural activity, and susceptibility offer the possibility-especially when used in combination-to produce objective pain-related indicators with the specificity and sensitivity required for diagnosis and for evaluation of risk of developing pain and of analgesic efficacy. Such composite biomarkers will also provide improved understanding of pain pathophysiology.

A review of hippocampal activation in post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is often characterized by deficits in memory encoding and retrieval and aberrant fear and extinction learning. The hippocampus plays a critical role in memory and contextual processing and has been implicated in intrinsic functional connectivity networks involved in self-referential thought and memory-related processes. This review focuses on hippocampal activation findings during memory and fear and extinction learning tasks, as well as resting state hippocampal connectivity in individuals with PTSD. A preponderance of functional neuroimaging studies to date, using memory, fear learning, and extinction tasks, report decreased or "controls comparable" hippocampal activation in individuals with PTSD, which is usually associated with poorer performance on the task imaged. Existing evidence thus raises the possibility that greater hippocampal recruitment in PTSD participants may be required for similar performance levels. Studies of resting state functional connectivity in PTSD predominantly report reduced within-network connectivity in the default mode network (DMN), as well as greater coupling between the DMN and salience network (SN) via the hippocampus. Together, these findings suggest that deficient hippocampal activation in PTSD may be associated with poorer performance during memory, extinction recall, and fear renewal tasks. Furthermore, studies of resting state connectivity implicate the hippocampus in decreased within-network DMN connectivity and greater coupling with SN regions characteristic of PTSD.

High-resolution mapping of infraslow cortical brain activity enabled by graphene microtransistors

Recording infraslow brain signals (<0.1 Hz) with microelectrodes is severely hampered by current microelectrode materials, primarily due to limitations resulting from voltage drift and high electrode impedance. Hence, most recording systems include high-pass filters that solve saturation issues but come hand in hand with loss of physiological and pathological information. In this work, we use flexible epicortical and intracortical arrays of graphene solution-gated field-effect transistors (gSGFETs) to map cortical spreading depression in rats and demonstrate that gSGFETs are able to record, with high fidelity, infraslow signals together with signals in the typical local field potential bandwidth. The wide recording bandwidth results from the direct field-effect coupling of the active transistor, in contrast to standard passive electrodes, as well as from the electrochemical inertness of graphene. Taking advantage of such functionality, we envision broad applications of gSGFET technology for monitoring infraslow brain activity both in research and in the clinic.

Unilateral thalamic glioma disrupts large-scale functional architecture of human brain during resting state

BACKGROUND

The thalamus is an important deep brain structure for the synchronization of brain rhythm and the integration of cortical activity. Human brain imaging and computational modeling have non-invasively revealed its role in maintaining the cortical network architecture and functional hierarchy.

PURPOSE

The objective of this study was to identify the effect of unilateral thalamic damage on the human brain intrinsic functional architecture.

PATIENTS AND METHODS

We collected an 8-minute resting-state functional magnetic resonance imaging (R-fMRI) data on a 3.0 T magnetic resonance scanner for all the participants: a preoperative patient with left thalamus destroyed by anaplastic astrocytoma (WHO grade III type of astrocytoma) and 20 matched healthy controls. The R-fMRI data was analyzed for functional connectivity and amplitude of spontaneous fluctuations.

RESULTS

The patient showed prominent decrease in functional connectivity within primary sensory networks and advanced cognitive networks, and extensive alterations in between-network coupling. Further analysis of the amplitude of spontaneous activity suggested significant decrease especially in the topographies of default mode network and the Papez circuit.

CONCLUSION

This result provided evidence about the consequences of thalamic destruction on the correlation and landscape of spontaneous brain activity, promoting our understanding of the effects of thalamic damage on large-scale brain networks.

Principles of cross-network communication in human resting state fMRI

Directed signaling among and within the large-scale networks of the human brain is functionally critical. Recent advances in our understanding of spontaneous fluctuations of the fMRI BOLD signal have provided strategies to study the spatial-temporal properties of directed signaling at infra-slow frequencies. Herein we explore the relationship between two canonical systems of the human brain, the default mode network (DMN) and the dorsal attention network (DAN) whose anti-correlated relationship is well known but poorly understood. We find that within the DMN, activity moves from retrosplenial to prefrontal cortex whereas in the DAN activity moves from the frontal eye fields to the parietal cortex. Bi-directional communication between the two networks occurs via their earliest elements (i.e., from the retrosplenial cortex of the DMN to the frontal eye fields of the DAN). This framework for network communication appears to generalize across all networks providing an expanded basis for understanding human brain function.

Normative pathways in the functional connectome

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.

Advances in cellular and integrative control of oxygen homeostasis within the central nervous system

Mammals must continuously regulate the levels of O2 and CO2 , which is particularly important for the brain. Failure to maintain adequate O2 /CO2 homeostasis has been associated with numerous disorders including sleep apnoea, Rett syndrome and sudden infant death syndrome. But, O2 /CO2 homeostasis poses major regulatory challenges, even in the healthy brain. Neuronal activities change in a differentiated, spatially and temporally complex manner, which is reflected in equally complex changes in O2 demand. This raises important questions: is oxygen sensing an emergent property, locally generated within all active neuronal networks, and/or the property of specialized O2 -sensitive CNS regions? Increasing evidence suggests that the regulation of the brain's redox state involves properties that are intrinsic to many networks, but that specialized regions in the brainstem orchestrate the integrated control of respiratory and cardiovascular functions. Although the levels of O2 in arterial blood and the CNS are very different, neuro-glial interactions and purinergic signalling are critical for both peripheral and CNS chemosensation. Indeed, the specificity of neuroglial interactions seems to determine the differential responses to O2 , CO2 and the changes in pH.

Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans

Resting-state networks offer a unique window into the brain’s functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with partial overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics.

The brain is always active. Even when it is not receiving sensory input, it generates its own spontaneous activity. This activity shapes how we interpret future sensory signals and creates our inner mental world. Moreover, this spontaneous activity is not random. When a healthy volunteer lies inside a brain scanner without performing any task, his or her brain shows predictable patterns of activity. Specific groups of brain regions – often with related roles – become active at the same time as one another. Each set of regions is referred to as a resting state network.

Of course, the brain does not operate in isolation from the rest of the body. Our internal organs continuously send signals to the brain via the spinal cord and cranial nerves. Specialized cells in the stomach wall in particular produce a slow rhythmic pattern of electrical activity. Known as the gastric rhythm, this activity helps ensure that the stomach muscles contract at the correct speed for digestion. But the stomach also produces this rhythm even when empty, suggesting that it has other roles too.

To find out what these might be, Rebollo et al. placed electrodes on the abdomen of healthy volunteers lying inside brain scanners. By examining the volunteers’ spontaneous brain activity, Rebollo et al. identified a new resting state network that is active in synchrony with the gastric rhythm. The regions within this so-called gastric network are not active at the same time as each other, but instead become active in a specific sequence that is repeated at each gastric cycle. Many of the regions within the gastric network belong to other resting state networks too. Some of the regions help regulate automatic bodily functions such as heart rate, while others process information about the body’s position in space.

The existence of the gastric network suggests a link between the automatic regulation of processes such as digestion, and spontaneous brain activity. Future studies could examine whether this link impacts perception and cognition, and whether this link plays a role in disorders where the connection between the digestive system and the brain appears to be altered.