Modular co-organization of functional connectivity and scale-free dynamics in the human brain

Scale-free neuronal dynamics and interareal correlations are emergent characteristics of spontaneous brain activity. How such dynamics and the anatomical patterns of neuronal connectivity are mutually related in brain networks has, however, remained unclear. We addressed this relationship by quantifying the network colocalization of scale-free neuronal activity—both neuronal avalanches and long-range temporal correlations (LRTCs)—and functional connectivity (FC) by means of intracranial and noninvasive human resting-state electrophysiological recordings. We found frequency-specific colocalization of scale-free dynamics and FC so that the interareal couplings of LRTCs and the propagation of neuronal avalanches were most pronounced in the predominant pathways of FC. Several control analyses and the frequency specificity of network colocalization showed that the results were not trivial by-products of either brain dynamics or our analysis approach. Crucially, scale-free neuronal dynamics and connectivity also had colocalized modular structures at multiple levels of network organization, suggesting that modules of FC would be endowed with partially independent dynamic states. These findings thus suggest that FC and scale-free dynamics—hence, putatively, neuronal criticality as well—coemerge in a hierarchically modular structure in which the modules are characterized by dense connectivity, avalanche propagation, and shared dynamic states.

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Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena

10.21203/rs.3.rs-1195514/v1 ◽

2021 ◽

Author(s):

Aditya Nanda ◽

Graham Johnson ◽

Yu Mu ◽

Misha Ahrens ◽

Catie Chang ◽

...

Keyword(s):

Brain Function ◽

Brain Activity ◽

Theoretical Frameworks ◽

Time Resolved ◽

Scale Free ◽

Systems Neuroscience ◽

Temporal Correlations ◽

Emergent Phenomena ◽

Weakly Stable ◽

Generate Model

Abstract Much of systems neuroscience posits that emergent neural phenomena underpin important aspects of brain function. Studies in the field variously emphasize the importance of distinct emergent phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract phenomena with interpretable properties of neural activity. Here, we developed a method to efficiently and unbiasedly generate model data constrained by interpretable empirical features in long neurophysiological recordings. We used this method to ground several major emergent neural phenomena to time-resolved smoothness, the correlation of distributed brain activity between adjacent timepoints. We first found that in electrocorticography recordings, time-resolved smoothness closely tracked transitions between conscious and anesthetized states. We then showed that a minimal model constrained by time-resolved smoothness, variance, and mean, captured dynamical and statistical emergent neural phenomena across modalities and species. Our results thus decouple major emergent neural phenomena from network mechanisms of brain function, and instead couple these phenomena to spatially nonspecific, time-resolved changes of brain activity. These results anchor several theoretical frameworks to a single interpretable property of the neurophysiological signal and, in this way, ultimately help bridge abstract theories of brain function with observed properties of brain activity.

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Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets

Frontiers in Physiology ◽

10.3389/fphys.2020.578537 ◽

2021 ◽

Vol 11 ◽

Author(s):

Daria La Rocca ◽

Herwig Wendt ◽

Virginie van Wassenhove ◽

Philippe Ciuciu ◽

Patrice Abry

Keyword(s):

Functional Connectivity ◽

Visual Motion ◽

Temporal Dynamics ◽

Brain Plasticity ◽

Brain Activity ◽

Task Completion ◽

Phase Lag ◽

Scale Free ◽

Connectivity Indices ◽

Complex Wavelet

The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.

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Scale-invariance in brain activity predicts practice effects in cognitive performance

10.1101/2020.05.25.114959 ◽

2020 ◽

Cited By ~ 2

Author(s):

Omid Kardan ◽

Elliot Layden ◽

Kyoung Whan Choe ◽

Muxuan Lyu ◽

Xihan Zhang ◽

...

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Brain Activity ◽

Brain Networks ◽

Video Clip ◽

Practice Effect ◽

Network Activity ◽

Practice Effects ◽

Scale Free ◽

Memory And Attention

AbstractAlthough practicing a task generally benefits later performance on that same task (‘practice effect’), there are large—and mostly unexplained—individual differences in reaping the benefits from practice. One promising avenue to model and predict such differences comes from recent research showing that brain networks can extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. As such, we hypothesized that individuals with more scale-free fMRI activity, indicated by BOLD time series with a higher Hurst exponent (H), gain more benefits from practice. In this study, participants practiced a test of working memory and attention, the dual n-back task (DNB), watched a video clip as a break, and then performed the DNB again, during MRI. To isolate the practice effect, we divided the participants into two groups based on improvement in performance from the first to second DNB task run. We identified regions and connections in which H and functional connectivity related to practice effects in the last run. More scale-free brain activity in these regions during the preceding runs (either first DNB or video) distinguished individuals who showed greater DNB performance improvements over time. In comparison, functional connectivity (r2) in the identified connections did not reliably classify the two groups in the preceding runs. Finally, we replicated both H and r2 results from study 1 in an independent fMRI dataset of participants performing multiple runs of another working memory and attention task (word completion). We conclude that the brain networks can accommodate further practice effects in individuals with higher scale-free BOLD activity.

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Long-range Temporal Correlations in the Broadband Resting state Activity of the Human Brain revealed by Neuronal Avalanches

10.1101/2020.02.03.930966 ◽

2020 ◽

Author(s):

Fabrizio Lombardi ◽

Oren Shriki ◽

Hans J. Herrmann ◽

Lucilla de Arcangelis

Keyword(s):

Long Range ◽

Power Law ◽

Resting State ◽

Brain Activity ◽

Neural Systems ◽

Scaling Exponents ◽

Long Range Correlations ◽

Temporal Correlations ◽

Neuronal Avalanches ◽

The Brain

AbstractResting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. At criticality, a crucial role is played by long-range power-law correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, this approach is not suitable for assessing long-range correlations in broadband resting-state cortical signals. To overcome such limitation, here we propose to characterize the correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms.

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Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

Journal of Neurosurgery ◽

10.3171/2018.11.jns181684 ◽

2020 ◽

Vol 132 (4) ◽

pp. 1234-1242 ◽

Cited By ~ 1

Author(s):

Paolo Belardinelli ◽

Ramin Azodi-Avval ◽

Erick Ortiz ◽

Georgios Naros ◽

Florian Grimm ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Network Effects ◽

Brain Activity ◽

Dynamic Imaging ◽

Oscillatory Activity ◽

Network Information ◽

Novel Approach ◽

Electrophysiological Recordings ◽

Intraoperative Localization

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

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Abnormal interhemispheric functional connectivity in patients with strabismic amblyopia: a resting-state fMRI study using voxel-mirrored homotopic connectivity

BMC Ophthalmology ◽

10.1186/s12886-021-02015-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shuang Zhang ◽

Gui-Ping Gao ◽

Wen-Qing Shi ◽

Biao Li ◽

Qi Lin ◽

...

Keyword(s):

Functional Connectivity ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Brain Activity ◽

Characteristic Curve ◽

Clinical Manifestations ◽

Resting State Fmri ◽

Diagnostic Value ◽

Functional Connections ◽

Receiver Operating

Abstract Background Previous studies have demonstrated that strabismus amblyopia can result in markedly brain function alterations. However, the differences in spontaneous brain activities of strabismus amblyopia (SA) patients still remain unclear. Therefore, the current study intended to employthe voxel-mirrored homotopic connectivity (VMHC) method to investigate the intrinsic brain activity changes in SA patients. Purpose To investigate the changes in cerebral hemispheric functional connections in patients with SA and their relationship with clinical manifestations using the VMHC method. Material and methods In the present study, a total of 17 patients with SA (eight males and nine females) and 17 age- and weight-matched healthy control (HC) groups were enrolled. Based on the VMHC method, all subjects were examined by functional magnetic resonance imaging. The functional interaction between cerebral hemispheres was directly evaluated. The Pearson’s correlation test was used to analyze the clinical features of patients with SA. In addition, their mean VMHC signal values and the receiver operating characteristic curve were used to distinguish patients with SA and HC groups. Results Compared with HC group, patients with SA had higher VMHC values in bilateral cingulum ant, caudate, hippocampus, and cerebellum crus 1. Moreover, the VMHC values of some regions were positively correlated with some clinical manifestations. In addition, receiver operating characteristic curves presented higher diagnostic value in these areas. Conclusion SA subjects showed abnormal brain interhemispheric functional connectivity in visual pathways, which might give some instructive information for understanding the neurological mechanisms of SA patients.

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Association Between Brain Activation and Functional Connectivity

Cerebral Cortex ◽

10.1093/cercor/bhy077 ◽

2018 ◽

Vol 29 (5) ◽

pp. 1984-1996 ◽

Cited By ~ 13

Author(s):

Dardo Tomasi ◽

Nora D Volkow

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Low Frequency ◽

Brain Activation ◽

Blood Oxygen Level Dependent ◽

Common Source ◽

Functional Connectivity Density ◽

Density Mapping ◽

Human Connectome Project ◽

Bold Responses

Abstract The origin of the “resting-state” brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

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