scholarly journals Global Signal Topography of the Human Brain: A Novel Framework of Functional Connectivity for Psychological and Pathological Investigations

2021 ◽  
Vol 15 ◽  
Author(s):  
Yujia Ao ◽  
Yujie Ouyang ◽  
Chengxiao Yang ◽  
Yifeng Wang
Keyword(s):  
Human Brain ◽  
Spatial Information ◽  
Brain Regions ◽  
Intrinsic Structure ◽  
Global Signal ◽  
Neural Information ◽  
Sensory Cortices ◽  
Brain States ◽  
Global And Local ◽  
Local Brain

The global signal (GS), which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography.

scholarly journals A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

2021 ◽  
Author(s):  
Jennifer S Goldman ◽  
Lionel Kusch ◽  
Bahar Hazal Yalcinkaya ◽  
Damien Depannemaecker ◽  
Trang-Anh Estelle Nghiem ◽  
...  
Keyword(s):  
Open Access ◽  
Human Brain ◽  
Slow Wave Sleep ◽  
Spatial Scales ◽  
Mean Field ◽  
Brain Regions ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Healthy Human ◽  
Brain States

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

scholarly journals Temporal pattern recognition in the human brain: a dual simultaneous processing

2021 ◽  
Author(s):  
Leonardo Bonetti ◽  
Elvira Brattico ◽  
Silvia EP Bruzzone ◽  
Giulia Donati ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Temporal Pattern ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Brain Areas ◽  
Simultaneous Processing ◽  
Neural Information ◽  
Complex Procedure ◽  
Magnetic Resonance Imaging Mri

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

scholarly journals Switch independent task representations in frontal and parietal cortex

2017 ◽  
Author(s):  
Lasse S. Loose ◽  
David Wisniewski ◽  
Marco Rusconi ◽  
Thomas Goschke ◽  
John-Dylan Haynes
Keyword(s):  
Human Brain ◽  
Parietal Cortex ◽  
Task Switching ◽  
Brain Regions ◽  
Task Representation ◽  
Related Information ◽  
Time To Build ◽  
Additional Control ◽  
Neural Information ◽  
Task Representations

AbstractAlternating between two tasks is effortful and impairs performance. Previous functional magnetic resonance imaging (fMRI) studies have found increased activity in fronto-parietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. MVPA was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification we found that the fronto-parietal cortex encodes tasks using a similar spatial pattern in switch and repeat trials. Thus, task representations in frontal and parietal cortex are largely switch-independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching.Significance statementAlternating between two tasks is effortful and slows down performance. One possible explanation is that the representations in the human brain need time to build up and are thus weaker on switch trials, explaining performance costs. Alternatively, task representations might even be enhanced in order to overcome the previous task. Here we used a combination of fMRI and a brain classifier to test whether the additional control demands under switching conditions lead to an increased or decreased strength of task representations in fronto-parietal brain regions. We found that task representations are not significantly modulated by switching processes. Thus, task representations in the human brain cannot account for the performance costs associated with alternating between tasks.

scholarly journals Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan Baijot ◽  
Stijn Denissen ◽  
Lars Costers ◽  
Jeroen Gielen ◽  
Melissa Cambron ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Signal To Noise Ratio ◽  
Pearson Correlation ◽  
Signal Quality ◽  
Network Parameters ◽  
Fmri Signal ◽  
Noise Ratio ◽  
Global And Local ◽  
The Brain ◽  
Local Brain

AbstractGraph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

scholarly journals Alcohol use disorder causes global changes in splicing in the human brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Derek Van Booven ◽  
Mengying Li ◽  
J. Sunil Rao ◽  
Ilya O. Blokhin ◽  
R. Dayne Mayfield ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Human Brain ◽  
Alcohol Use Disorder ◽  
Brain Regions ◽  
Gene Set Enrichment Analysis ◽  
Splicing Factor ◽  
Central Nucleus ◽  
Global Changes ◽  
Aberrant Expression ◽  
Altered Expression

AbstractAlcohol use disorder (AUD) is a widespread disease leading to the deterioration of cognitive and other functions. Mechanisms by which alcohol affects the brain are not fully elucidated. Splicing constitutes a nuclear process of RNA maturation, which results in the formation of the transcriptome. We tested the hypothesis as to whether AUD impairs splicing in the superior frontal cortex (SFC), nucleus accumbens (NA), basolateral amygdala (BLA), and central nucleus of the amygdala (CNA). To evaluate splicing, bam files from STAR alignments were indexed with samtools for use by rMATS software. Computational analysis of affected pathways was performed using Gene Ontology Consortium, Gene Set Enrichment Analysis, and LncRNA Ontology databases. Surprisingly, AUD was associated with limited changes in the transcriptome: expression of 23 genes was altered in SFC, 14 in NA, 102 in BLA, and 57 in CNA. However, strikingly, mis-splicing in AUD was profound: 1421 mis-splicing events were detected in SFC, 394 in NA, 1317 in BLA, and 469 in CNA. To determine the mechanism of mis-splicing, we analyzed the elements of the spliceosome: small nuclear RNAs (snRNAs) and splicing factors. While snRNAs were not affected by alcohol, expression of splicing factor heat shock protein family A (Hsp70) member 6 (HSPA6) was drastically increased in SFC, BLA, and CNA. Also, AUD was accompanied by aberrant expression of long noncoding RNAs (lncRNAs) related to splicing. In summary, alcohol is associated with genome-wide changes in splicing in multiple human brain regions, likely due to dysregulation of splicing factor(s) and/or altered expression of splicing-related lncRNAs.

scholarly journals RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Amy Webb ◽  
Audrey C. Papp ◽  
Amanda Curtis ◽  
Leslie C. Newman ◽  
Maciej Pietrzak ◽  
...  
Keyword(s):  
Human Brain ◽  
Rna Sequencing ◽  
Brain Regions ◽  
Protein Coding ◽  
Non Coding Rnas

scholarly journals Capturing the non-stationarity of whole-brain dynamics underlying human brain states

NeuroImage ◽  
2021 ◽  
pp. 118551
Author(s):  
J.A. Galadí ◽  
S. Silva Pereira ◽  
Y. Sanz Perl ◽  
M.L. Kringelbach ◽  
I. Gayte ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

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