The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

The object of this study was to extensively characterize a region of periventricular nodular heterotopia (PVNH) in an epilepsy patient to reveal its possible neurocognitive functional role(s). The authors used 3-T MRI approaches to exhaustively characterize a single, right hemisphere heterotopion in a high-functioning adult male with medically responsive epilepsy, which had manifested during late adolescence. The heterotopion proved to be spectroscopically consistent with a cortical-like composition and was interconnected with nearby ipsilateral cortical fundi, as revealed by fiber tractography (diffusion-weighted imaging) and resting-state functional connectivity MRI (rsfMRI). Moreover, the region of PVNH demonstrated two novel characterizations for a heterotopion. First, functional MRI (fMRI), as distinct from rsfMRI, showed that the heterotopion was significantly modulated while the patient watched animated video scenes of biological motion (i.e., cartoons). Second, rsfMRI, which demonstrated correlated brain activity during a task-negative state, uniquely showed directionality within an interconnected network, receiving positive path effects from patent cortical and cerebellar foci while outputting only negative path effects to specific brain foci.These findings are addressed in the context of the impact on noninvasive presurgical brain mapping strategies for adult and pediatric patient workups, as well as the impact of this study on an understanding of the functional cortical architecture underlying cognition from a neurodiversity and evolutionary perspective.

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Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

10.1101/2020.05.25.114603 ◽

2020 ◽

Author(s):

Niv Tik ◽

Abigail Livny ◽

Shachar Gal ◽

Karny Gigi ◽

Galia Tsarfaty ◽

...

Keyword(s):

Functional Connectivity ◽

Prediction Model ◽

Resting State ◽

Brain Activity ◽

Strong Association ◽

Brain Activation ◽

Individual Variability ◽

Resting State Functional Connectivity ◽

Successful Prediction ◽

And Task

AbstractBACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia.METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia.RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001).CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.

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Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes

Frontiers in Neurology ◽

10.3389/fneur.2021.665338 ◽

2021 ◽

Vol 12 ◽

Author(s):

James R. Williamson ◽

Doug Sturim ◽

Trina Vian ◽

Joseph Lacirignola ◽

Trey E. Shenk ◽

...

Keyword(s):

Cognitive Functioning ◽

Resting State ◽

Brain Activity ◽

American Football ◽

Future Application ◽

Diagnostic Tools ◽

Fine Motor ◽

Latent Factor ◽

Motor Movements ◽

The Impact

Repeated subconcussive blows to the head during sports or other contact activities may have a cumulative and long lasting effect on cognitive functioning. Unobtrusive measurement and tracking of cognitive functioning is needed to enable preventative interventions for people at elevated risk of concussive injury. The focus of the present study is to investigate the potential for using passive measurements of fine motor movements (smooth pursuit eye tracking and read speech) and resting state brain activity (measured using fMRI) to complement existing diagnostic tools, such as the Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), that are used for this purpose. Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Hypotheses were that (1) measures of complexity of fine motor coordination and of resting state brain activity are predictive of cognitive functioning measured by the ImPACT test, and (2) within-subject changes in these measures over the course of a sports season are predictive of changes in ImPACT scores. The first principal component of the six ImPACT composite scores was used as a latent factor that represents cognitive functioning. This latent factor was positively correlated with four of the ImPACT composites: verbal memory, visual memory, visual motor speed and reaction speed. Strong correlations, ranging between r = 0.26 and r = 0.49, were found between this latent factor and complexity features derived from each sensor modality. Based on a regression model, the complexity features were combined across sensor modalities and used to predict the latent factor on out-of-sample subjects. The predictions correlated with the true latent factor with r = 0.71. Within-subject changes over time were predicted with r = 0.34. These results indicate the potential to predict cognitive performance from passive monitoring of fine motor movements and brain activity, offering initial support for future application in detection of performance deficits associated with subconcussive events.

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Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

10.1101/250894 ◽

2018 ◽

Author(s):

Antonio Ulloa ◽

Barry Horwitz

Keyword(s):

Computational Model ◽

Resting State ◽

Short Term Memory ◽

Brain Activity ◽

Brain Regions ◽

Intrinsic Activity ◽

Task Execution ◽

Short Term ◽

Visual Short Term Memory ◽

And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

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Interactions between Resting-State and Task-Evoked Brain Activity Suggest a Different Approach to fMRI Analysis

Journal of Neuroscience ◽

10.1523/jneurosci.2580-13.2013 ◽

2013 ◽

Vol 33 (32) ◽

pp. 12912-12914 ◽

Cited By ~ 13

Author(s):

D. Mastrovito

Keyword(s):

Resting State ◽

Brain Activity ◽

Fmri Analysis ◽

And Task

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The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

10.21203/rs.2.15754/v1 ◽

2019 ◽

Author(s):

Gang Li ◽

Youdong Luo ◽

Weidong Jiao ◽

Yonghua Jiang ◽

Zhao Gao ◽

...

Keyword(s):

Resting State ◽

Mental Arithmetic ◽

Brain Activity ◽

Mental Fatigue ◽

Relative Power ◽

Arithmetic Task ◽

Eeg Data ◽

Mental Arithmetic Task ◽

The Brain ◽

And Task

Abstract Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom researches explore the differences of mental fatigue on electrophysiological activity between resting state and task state. In the present study, 20 healthy male individuals were recruited to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) data were collected before and after the task. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences between resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

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STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG

Frontiers in Physiology ◽

10.3389/fphys.2021.775172 ◽

2021 ◽

Vol 12 ◽

Author(s):

Simon J. Houtman ◽

Hanna C. A. Lammertse ◽

Annemiek A. van Berkel ◽

Ganna Balagura ◽

Elena Gardella ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Brain Activity ◽

Neurodevelopmental Disorder ◽

Epileptic Encephalopathy ◽

Frequency Resolution ◽

Similar Frequency ◽

Psychomotor Delay ◽

Local Circuitry ◽

The Impact

STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.

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On the complexity of resting state spiking activity in monkey motor cortex

10.1101/2020.05.28.121095 ◽

2020 ◽

Author(s):

Paulina Anna Dąbrowska ◽

Nicole Voges ◽

Michael von Papen ◽

Junji Ito ◽

David Dahmen ◽

...

Keyword(s):

Motor Cortex ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Population Level ◽

Eyes Open ◽

Simulated Network ◽

Monkey Motor Cortex ◽

And Task ◽

Spiking Activity

AbstractResting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons’ spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance of distinguishing between rest with eyes open and closed.

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Frequency-Dependent Amplitude Alterations of Resting-State Spontaneous Fluctuations in Late-Onset Depression

BioMed Research International ◽

10.1155/2015/505479 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 14

Author(s):

Yingying Yue ◽

Xize Jia ◽

Zhenghua Hou ◽

Yufeng Zang ◽

Yonggui Yuan

Keyword(s):

Resting State ◽

Late Onset ◽

Brain Activity ◽

Low Frequency ◽

Anterior Lobe ◽

Posterior Lobe ◽

Frequency Bands ◽

Relationship Analysis ◽

Intrinsic Brain Activity ◽

The Impact

There is limited amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (fMRI) studies in late-onset depression (LOD) but reported different results. This may be due to the impact of different frequency bands. In this study, we examined the ALFF in five different frequency bands (slow-6: 0–0.01 Hz; slow-5: 0.01–0.027 Hz; slow-4: 0.027–0.073 Hz; slow-3: 0.073–0.167 Hz, and slow-2: 0.167–0.25 Hz) within the whole brain during resting-state fMRI in 16 LOD patients and 16 normal control (NC) subjects. The ALFF of primary effect of disease was widely distributed over left cerebellum anterior lobe, left cerebellum posterior lobe, left middle orbitofrontal gyrus, left superior occipital, and right superior parietal, while the interaction effect of disease and frequency was distributed over right superior frontal gyrus. Further relationship analysis findings suggest these abnormal ALFF may relate to cognitive dysfunction of LOD. Therefore, our data show that LOD patients have widespread abnormalities in intrinsic brain activity, which is dependent on the frequency band, and suggest that future studies should take the frequency bands into account when measuring intrinsic brain activity.

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