scholarly journals Emergence of task information from dynamic network interactions in the human brain

2021 ◽  
Author(s):  
Ravi D. Mill ◽  
Julia L. Hamilton ◽  
Emily C. Winfield ◽  
Nicole Lalta ◽  
Richard H. Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Cognitive Task ◽  
Activity Patterns ◽  
Dynamic Network ◽  
Information Dynamics ◽  
Dynamic Activity ◽  
Flow Models ◽  
Functional Connections ◽  
Response Information

AbstractHow cognitive task information emerges from brain activity is a central question in neuroscience. We identified the spatiotemporal emergence of task information in the human brain using individualized source electroencephalography and dynamic multivariate pattern analysis. We then substantially extended recently developed brain activity flow models to predict the future emergence of task information dynamics. The model simulated the flow of task-evoked activity over causally interpretable resting-state functional connections (dynamic, lagged, direct and directional) to accurately predict response information dynamics underlying cognitive task behavior. Predicting event-related spatiotemporal activity patterns and fine-grained representational geometry confirmed the model’s faithfulness to how the brain veridically represents response information. Simulated network “lesioning” revealed cognitive control networks (CCNs) as the dominant causal drivers of response information flow. These results demonstrate the efficacy of dynamic activity flow models in predicting the emergence of task information, thereby revealing a mechanistic role for CCNs in producing behavior.

scholarly journals Sleeping While Awake: The Intrusion of Neural Activity Associated with Sleep Onset in the Awake Human Brain

2020 ◽  
Author(s):  
Stephanie Hawes ◽  
Carrie R. H. Innes ◽  
Nicholas Parsons ◽  
Sean P.A. Drummond ◽  
Karen Caeyensberghs ◽  
...  
Keyword(s):  
Human Brain ◽  
Basal Forebrain ◽  
Brain Activity ◽  
Cognitive Task ◽  
Sleep Onset ◽  
Theoretical Modelling ◽  
Related Activity ◽  
Independent Dataset ◽  
Time Graph ◽  
The Brain

AbstractSleep can intrude into the awake human brain when sleep deprived or fatigued, even while performing cognitive tasks. However, how the brain activity associated with sleep onset can co-exist with the activity associated with cognition in the awake humans remains unexplored. Here, we used simultaneous fMRI and EEG to generate fMRI activity maps associated with EEG theta (4-7 Hz) activity associated with sleep onset. We implemented a method to track these fMRI activity maps in individuals performing a cognitive task after well-rested and sleep-deprived nights. We found frequent intrusions of the fMRI maps associated with sleep-onset in the task-related fMRI data. These sleep events elicited a pattern of transient fMRI activity, which was spatially distinct from the task-related activity in the frontal and parietal areas of the brain. They were concomitant with reduced arousal as indicated by decreased pupil size and increased response time. Graph theoretical modelling showed that the activity associated with sleep onset emerges from the basal forebrain and spreads anterior-posteriorly via the brain’s structural connectome. We replicated the key findings in an independent dataset, which suggests that the approach can be reliably used in understanding the neuro-behavioural consequences of sleep and circadian disturbances in humans.

scholarly journals High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucy L. W. Owen ◽  
Thomas H. Chang ◽  
Jeremy R. Manning
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Dynamic Network ◽  
Brain Structures ◽  
High Order ◽  
Dynamic Correlations ◽  
Neuroimaging Data ◽  
Human Participants ◽  
High Level

AbstractOur thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

fMRI and Human Pain Perception

2020 ◽  
pp. 497-516
Author(s):  
Giandomenico Iannetti ◽  
A. Vania Apkarian
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Pain Perception ◽  
Single Photon ◽  
Brain Activity ◽  
Activity Patterns ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Subjective Perception ◽  
Brain Physiology

Almost 30 years ago, technology based on magnetic resonance imaging (MRI) made it possible to visualize the functional states of the human brain. This technology immediately spurred pain researchers to examine brain circuitry of human pain and relate brain activity patterns with verbal reports of subjective perception. There was a brief period prior to functional MRI (fMRI) when positron emission tomography (PET) and single-photon emission computed tomography (SPECT) technologies were used to identify brain states in humans reporting pain, but the noninvasiveness of fMRI and its higher spatial and temporal resolution quickly made the latter the preferred choice to study human brain physiology. Prior to the advent of such human brain imaging technologies, whether the neocortex was involved in pain perception was still an open question: In human brain injury studies, large cortical lesions seemed to have little effect on pain perception, and in animal electrophysiological studies (mostly done in anesthetized preparations) several years of single-unit electrophysiological explorations from large expanses of the cortex yielded a measly number of neurons responding to nociceptive stimuli and not a single neocortical column dedicated to nociception. What has been learned between the introduction of the technology and today? This chapter briefly reviews the subject, highlighting advances and novel insights and pointing to lingering gaps. It also outlines future directions from the viewpoint of understanding mechanisms for nociception, acute pain, and chronic pain. From a brain imaging viewpoint, the chapter tackles the last concepts regarding local neuronal representation and across neuronal integration of information.

Fundamental Study for Human Brain Activity Based on the Spatial Cognitive Task

2011 ◽  
pp. 218-225 ◽  
Author(s):  
Shunji Shimizu ◽  
Noboru Takahashi ◽  
Hiroyuki Nara ◽  
Hiroaki Inoue ◽  
Yukihiro Hirata
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Cognitive Task ◽  
Fundamental Study

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Genetically defined cellular correlates of the baseline brain MRI signal

2018 ◽  
Vol 115 (41) ◽  
pp. E9727-E9736 ◽  
Author(s):  
Jie Wen ◽  
Manu S. Goyal ◽  
Serguei V. Astafiev ◽  
Marcus E. Raichle ◽  
Dmitriy A. Yablonskiy
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Activity ◽  
Brain Mri ◽  
Blood Oxygenation ◽  
Brain Atlas ◽  
Functional Connections ◽  
Fmri Signal ◽  
Useful Signal

fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

scholarly journals Deciphering Cortical Number Coding from Human Brain Activity Patterns

2009 ◽  
Vol 19 (19) ◽  
pp. 1608-1615 ◽  
Author(s):  
Evelyn Eger ◽  
Vincent Michel ◽  
Bertrand Thirion ◽  
Alexis Amadon ◽  
Stanislas Dehaene ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Activity Patterns

scholarly journals Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e75257 ◽  
Author(s):  
Daniel Kroeger ◽  
Bogdan Florea ◽  
Florin Amzica
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Activity Patterns ◽  
Deep Coma

scholarly journals Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

2019 ◽  
Author(s):  
Katharina Glomb ◽  
Morten L. Kringelbach ◽  
Gustavo Deco ◽  
Patric Hagmann ◽  
Joel Pearson ◽  
...  
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Brain Activity ◽  
Activity Patterns ◽  
Small Subset ◽  
Modular Architecture ◽  
Brain Organization ◽  
Cortical Organization ◽  
Human Connectome Project ◽  
Governing Principle

ABSTRACTThe human brain consists of functionally specialized areas, which flexibly interact and integrate forming a multitude of complex functional networks. However, the nature and governing principles of these specialized areas remain controversial: a distinct modular architecture versus a smooth continuum across the whole cortex. Here, we demonstrate a candidate governing principle ubiquitous in nature, that resolves this controversy for the brain at rest, during perception, cognition and action: functional harmonic modes. We calculated the harmonic modes of the brain’s functional connectivity, called “functional harmonics”, from functional magnetic resonance imaging (fMRI) data in resting state of 812 participants. Each functional harmonic provides an elementary pattern of brain activity with a different spatial frequency. The set of all functional harmonics - ordered according to their spatial frequencies - can reconstruct any pattern of brain activity. The activity patterns elicited by 7 different tasks from the Human Connectome Project can be reconstructed from a very small subset of functional harmonics, suggesting a novel relationship between task and resting state brain activity. Further, the isolines of the continuous functional harmonic patterns delineate the borders of specialized cortical areas as well as somatotopic and retinotopic organization. Our results demonstrate a candidate scalable governing principle for functional brain organization, resolving the controversy between modular versus gradiental views, and demonstrate that a universal principle in nature also underlies human brain cortical organization.

Abstract TP42: Brain Plasticity Changes in Default Mode Network after Stroke

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
JIE SONG ◽  
VEENA A NAIR ◽  
CHRISTIAN LA ◽  
MATHEW JENSEN ◽  
MARCUS CHACON ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Brain Plasticity ◽  
Brain Activity ◽  
Cognitive Task ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Default Mode ◽  
Functional Connections

Background: Resting-state fMRI (rs-fMRI) has been used for assessing task-free brain activity changes after stroke. One prominent resting-state neural network is the default mode network (DMN) that has been suggested to be suppressed during cognitive tasks. Older adults often show difficulties in suppressing DMN compared to younger adults during cognitive task performance. Moreover, dysfunction of DMN appears to be linked with the severity of post-stroke depression. Here we explored brain plasticity changes in DMN in stroke subjects. Methods: 9 stroke subjects (mean age=60.3, 5 Male) and 5 normal healthy subjects (mean age=48, 4 Male) underwent two rs-fMRI scans. Patients participated in the 1st scan within 7 days after onset and within 6 months (mean ~3 months) postonset in the 2nd scan. Brain plasticity changes were examined by functional connectivity measures that were computed using region-of-interest analysis. Rs-fMRI data were pre-processed in AFNI. The resulting time-series from 6 common seeds in DMN were averaged over each seed and correlated with that from every other seed to generate the Pearson correlation coefficients. These correlations were then z-transformed representing the 15 unique functional connections (fconn) in DMN. Fconn changes were determined with intraclass correlation (ICC), which measures reproducibility of fconn between scans. A reliable connection, as suggested to be an ICC ≥ 0.5, requires a small within-subject plasticity change compared to the between-subject variance. Results: Shown in Table 1. Conclusion: As seen from Table 1, fconn between RtLatPar and PC may undergo plasticity changes after stroke (ICC < 0.5) as would be reliable in the normal group. Fconn between LtLatPar and PC, mPFC and PC, right and left LatPar were found to be significant and reliable, which could be due to less suppression in DMN and higher between-subject variability after stroke.

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