scholarly journals Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions

2022 ◽  
Vol 12 ◽  
Author(s):  
Olivia Campbell ◽  
Tamara Vanderwal ◽  
Alexander Mark Weber
Keyword(s):  
Task Difficulty ◽  
Brain State ◽  
Bold Signal ◽  
Self Similarity ◽  
Attention Networks ◽  
Human Connectome Project ◽  
Fractal Signal ◽  
Brain States ◽  
Test Retest Reliability ◽  
7T Fmri

Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions.Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest.Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10−7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal.Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.

scholarly journals Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI

2021 ◽  
Author(s):  
Anant Mittal ◽  
Priya Aggarwal ◽  
Luiz Pessoa ◽  
Anubha Gupta
Keyword(s):  
Short Term Memory ◽  
Fmri Data ◽  
Brain State ◽  
Short Term ◽  
Term Memory ◽  
Feature Representations ◽  
Human Connectome Project ◽  
Brain States ◽  
Long Short Term Memory ◽  
The Brain

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information avail- able in the fMRI data. Long short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bidirectional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18 percent better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.

scholarly journals Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

2019 ◽  
Author(s):  
Filip Sobczak ◽  
Yi He ◽  
Terrence J. Sejnowski ◽  
Xin Yu
Keyword(s):  
Low Frequency ◽  
Calcium Oscillations ◽  
Brain State ◽  
Fmri Signal ◽  
Human Connectome Project ◽  
Vascular Origin ◽  
Signal Fluctuation ◽  
Brain States ◽  
Human Brains ◽  
Global Fluctuations

AbstractResting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to oscillations in neural activity through several mechanisms. Although the vascular origin of the fMRI signal is well established, the neural correlates of global rs-fMRI signal fluctuations are difficult to separate from other confounding sources. Recently, we reported that single-vessel fMRI slow oscillations are directly coupled to brain state changes. Here, we used an echo-state network (ESN) to predict the future temporal evolution of the rs-fMRI slow oscillatory feature from both rodent and human brains. rs-fMRI signals from individual blood vessels that were strongly correlated with neural calcium oscillations were used to train an ESN to predict brain state-specific rs-fMRI signal fluctuations. The ESN-based prediction model was also applied to recordings from the Human Connectome Project (HCP), which classified variance-independent brain states based on global fluctuations of rs-fMRI features. The ESN revealed brain states with global synchrony and decoupled internal correlations within the default-mode network.

scholarly journals Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons

2021 ◽  
Vol 118 (51) ◽  
pp. e2114549118
Author(s):  
Ricardo Martins Merino ◽  
Carolina Leon-Pinzon ◽  
Walter Stühmer ◽  
Martin Möck ◽  
Jochen F. Staiger ◽  
...  
Keyword(s):  
Low Frequency ◽  
Gamma Oscillations ◽  
Brain State ◽  
Gabaergic Interneurons ◽  
Time Resolved ◽  
Cortical Rhythms ◽  
Brain States ◽  
Fast Spiking ◽  
Fast Oscillations ◽  
Cross Frequency Coupling

Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling.

scholarly journals Segregated brain state during hypnosis

2019 ◽  
Author(s):  
Jarno Tuominen ◽  
Sakari Kallio ◽  
Valtteri Kaasinen ◽  
Henry Railo
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Activation ◽  
Brain State ◽  
Neural Connectivity ◽  
Single Word ◽  
Electrophysiological Response ◽  
Hypnotic Induction ◽  
Brain States ◽  
The Brain

Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotisable subjects would suggest? Demonstrating an altered brain state is difficult. Brain activation varies greatly during wakefulness and can be voluntarily influenced. We measured the complexity of electrophysiological response to transcranial magnetic stimulation (TMS) in one “hypnotic virtuoso”. Such a measure produces a response outside the subject’s voluntary control and has been proven adequate for discriminating conscious from unconscious brain states. We show that a single-word hypnotic induction robustly shifted global neural connectivity into a state where activity remained sustained but failed to ignite strong, coherent activity in frontoparietal cortices. Changes in perturbational complexity indicate a similar move toward a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding. [preprint updated 20/02/2020]

scholarly journals Clinical Electroencephalography for Anesthesiologists

2015 ◽  
Vol 123 (4) ◽  
pp. 937-960 ◽  
Author(s):  
Patrick L. Purdon ◽  
Aaron Sampson ◽  
Kara J. Pavone ◽  
Emery N. Brown
Keyword(s):  
Neural Circuits ◽  
Brain State ◽  
Anesthesia Care ◽  
Depth Of Anesthesia ◽  
State Monitoring ◽  
Inhaled Anesthetics ◽  
Anesthesia Monitoring ◽  
Brain States ◽  
Index Value ◽  
The Brain

Abstract The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.

scholarly journals Temporal circuit of macroscale dynamic brain activity supports human consciousness

2020 ◽  
Vol 6 (11) ◽  
pp. eaaz0087 ◽  
Author(s):  
Zirui Huang ◽  
Jun Zhang ◽  
Jinsong Wu ◽  
George A. Mashour ◽  
Anthony G. Hudetz
Keyword(s):  
Default Mode Network ◽  
Functional Role ◽  
Brain Activity ◽  
Human Consciousness ◽  
Attention Network ◽  
Attention Networks ◽  
Default Mode ◽  
Dorsal Attention Network ◽  
Brain States

The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

scholarly journals Hyperoxia enhances slow-wave forebrain states in urethane-anesthetized and naturally sleeping rats

2018 ◽  
Vol 120 (4) ◽  
pp. 1505-1515 ◽  
Author(s):  
Brandon E. Hauer ◽  
Biruk Negash ◽  
Kingsley Chan ◽  
Wesley Vuong ◽  
Frederick Colbourne ◽  
...  
Keyword(s):  
Slow Wave ◽  
Brain Function ◽  
Field Potential ◽  
Blood Levels ◽  
Brain State ◽  
Wave State ◽  
Wave States ◽  
Brain States ◽  
Eeg Recordings ◽  
Spontaneously Breathing

Oxygen (O2) is a crucial element for physiological functioning in mammals. In particular, brain function is critically dependent on a minimum amount of circulating blood levels of O2 and both immediate and lasting neural dysfunction can result following anoxic or hypoxic episodes. Although the effects of deficiencies in O2 levels on the brain have been reasonably well studied, less is known about the influence of elevated levels of O2 (hyperoxia) in inspired gas under atmospheric pressure. This is of importance due to its typical use in surgical anesthesia, in the treatment of stroke and traumatic brain injury, and even in its recreational or alternative therapeutic use. Using local field potential (EEG) recordings in spontaneously breathing urethane-anesthetized and naturally sleeping rats, we characterized the influence of different levels of O2 in inspired gases on brain states. While rats were under urethane anesthesia, administration of 100% O2 elicited a significant and reversible increase in time spent in the deactivated (i.e., slow-wave) state, with concomitant decreases in both heartbeat and respiration rates. Increasing the concentration of carbon dioxide (to 5%) in inspired gas produced the opposite result on EEG states, mainly a decrease in the time spent in the deactivated state. Consistent with this, decreasing concentrations of O2 (to 15%) in inspired gases decreased time spent in the deactivated state. Further confirmation of the hyperoxic effect was found in naturally sleeping animals where it similarly increased time spent in slow-wave (nonrapid eye movement) states. Thus alterations of O2 in inspired air appear to directly affect forebrain EEG states, which has implications for brain function, as well as for the regulation of brain states and levels of forebrain arousal during sleep in both normal and pathological conditions. NEW & NOTEWORTHY We show that alterations of oxygen concentration in inspired air biases forebrain EEG state. Hyperoxia increases the prevalence of slow-wave states. Hypoxia and hypercapnia appear to do the opposite. This suggests that oxidative metabolism is an important stimulant for brain state.

Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI

2013 ◽  
Vol 109 (5) ◽  
pp. 1250-1258 ◽  
Author(s):  
Oliver Hinds ◽  
Todd W. Thompson ◽  
Satrajit Ghosh ◽  
Julie J. Yoo ◽  
Susan Whitfield-Gabrieli ◽  
...  
Keyword(s):  
Real Time ◽  
Default Mode Network ◽  
Human Performance ◽  
Supplementary Motor Area ◽  
Brain Regions ◽  
Motor Area ◽  
Neural Systems ◽  
Brain State ◽  
Default Mode ◽  
Brain States

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

scholarly journals The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficienc

2018 ◽  
Author(s):  
Ian M. McDonough ◽  
Jonathan T. Siegel
Keyword(s):  
White Matter ◽  
Time Scales ◽  
Brain Structure ◽  
Brain Networks ◽  
Simulation Models ◽  
Multiscale Entropy ◽  
Fast Time ◽  
Bold Signal ◽  
White Matter Microstructure ◽  
Human Connectome Project

AbstractBrain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal—a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks. Using multivariate analysis techniques (Partial Least Squares), we found that greater WM integrity was associated with greater network complexity at fast time scales, but less network complexity at slower time scales. These findings implicate two separate pathways through which WM integrity affects brain function in the prefrontal cortex—an executive-prefrontal pathway and a perceptuo-occipital pathway. In two additional samples, the main patterns of WM and network complexity were replicated. These findings support simulation models of WM integrity and network complexity and provide new insights into brain structure-function relationships.

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