scholarly journals What The Brain Does As We Speak

2021 ◽  
Author(s):  
KJ Forseth ◽  
X Pitkow ◽  
S Fischer-Baum ◽  
N Tandon
Keyword(s):  
Cognitive Processes ◽  
Transient Dynamics ◽  
Network Motifs ◽  
Single Trial ◽  
Single Word ◽  
Network States ◽  
Intracranial Recordings ◽  
The Brain ◽  
Trial State ◽  
Better Than

AbstractLanguage is a defining human behavior and is dependent on networks interactions amongst broadly distributed neuronal substrates. Transient dynamics between language regions that underpin speech production have long been postulated, yet have proven challenging to evaluate empirically. We used direct intracranial recordings during single word production to create a finely resolved spatiotemporal atlas (134 patients, 25810 electrodes, 40278 words) of the entire language-dominant cortex and used this to derive single-trial state-space sequences of network motifs. We derived 5 discrete neural states during the production of each word, distinguished by unique patterns of distributed cortical interaction. This interactive model was significantly better than a model of the same design but lacking interactions between regions in explaining observed activity. Our results eschew strict functional attribution to localized cortical populations, supporting instead the idea that cognitive processes are better explained by distributed metastable network states.

scholarly journals Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

2021 ◽  
Author(s):  
Javier Rasero ◽  
Richard Betzel ◽  
Amy Isabella Sentis ◽  
Thomas E. Kraynak ◽  
Peter J. Gianaros ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Nonlinear Process ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Subject ◽  
Network States ◽  
Task Representations ◽  
The Brain ◽  
Degree Of Similarity ◽  
Differential Connectivity

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of a distributed nonlinear process (dynamical systems theory). The former predicts that tasks that recruit similar brain areas should have an equivalent degree of similarity in their connectivity. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views by comparing activation and connectivity patterns from a large sample of healthy subjects (N=242) that performed two executive control tasks, color-word Stroop task and Multi-Source Interference Task (MSIT), known to recruit similar brain areas. Using a measure of instantaneous connectivity based on edge time series as outcome variables, we estimated task-related network profiles as connectivity changes between incongruent and congruent information conditions. The degree of similarity of such profiles at the group level between both tasks was substantially smaller than their overlapping activation responses. A similar finding was observed at the subject level and when employing a different method for defining task-related connectivity. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be understood at both node and edge (connectivity) levels.

Brain stem – from general view to computational model based on switchboard rules of operation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Włodzisław Duch ◽  
Dariusz Mikołajewski
Keyword(s):  
Brain Stem ◽  
Large Scale ◽  
Ionic Channels ◽  
General View ◽  
The Central Nervous System ◽  
Network States ◽  
Large Scale Simulations ◽  
Influence Of Noise ◽  
The Brain ◽  
Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

Priors and Prejudice: How prior knowledge shapes intergroup perception

2021 ◽  
Author(s):  
Hugh McGovern ◽  
Marte Otten
Keyword(s):  
Social Psychology ◽  
Prior Knowledge ◽  
Cognitive Processes ◽  
Social Neuroscience ◽  
Intergroup Bias ◽  
Predictive Processing ◽  
Future Research ◽  
Intergroup Perception ◽  
The Brain ◽  
Processing Framework

Bayesian processing has become a popular framework by which to understand cognitive processes. However, relatively little has been done to understand how Bayesian processing in the brain can be applied to understanding intergroup cognition. We assess how categorization and evaluation processes unfold based on priors about the ethnic outgroup being perceived. We then consider how the precision of prior knowledge about groups differentially influence perception depending on how the information about that group was learned affects the way in which it is recalled. Finally, we evaluate the mechanisms of how humans learn information about other ethnic groups and assess how the method of learning influences future intergroup perception. We suggest that a predictive processing framework for assessing prejudice could help accounting for seemingly disparate findings on intergroup bias from social neuroscience, social psychology, and evolutionary psychology. Such an integration has important implications for future research on prejudice at the interpersonal, intergroup, and societal levels.

scholarly journals Comparative Analysis of Methods for Estimation of Undirected Coupling from Time Series of Intracranial Eegs of Cortex of Rats-Genetic Models of Absence Epilepsy

2017 ◽  
Vol 12 (2) ◽  
pp. 317-326
Author(s):  
А.А. Грищенко ◽  
A.A. Grishchenko
Keyword(s):  
Time Window ◽  
Surrogate Data ◽  
Epileptic Activity ◽  
Absence Epilepsy ◽  
Information Function ◽  
Brain Areas ◽  
To Receive ◽  
Intracranial Recordings ◽  
The Brain ◽  
Mutual Information Function

Studying coupling between brain areas from its electromagnetic activity is one of the key approaches in epilepsy research now, since epileptic activity has been considered to be a result of pathological synchronization in the brain. Often, research is conducted on animal models, because this allows to perform intracranial measurement, and to get rid of interference caused by the skull and to receive signals from deeper regions of the brain such as thalamus or hippocampus. In this study, the intracranial recordings from the frontal and parietal areas of cortex are investigated with a nonlinear correlation coefficient and a mutual information function in a sliding time window. The coupling estimates obtained were subjected for statistical analysis for significance using surrogate data. The dynamics of connectivity between the frontal cortex and the parietal cortex was shown to vary from seizure to seizure and from animal to animal. Therefore, estimates of the significant change in connectivity associated with initiation of the absense seizure, found previously based on averaging over a large number of animals and a large number of seizures for an each animal, can be a result of contribution of a relatively small number of seizures (less than a half of considered), for which the changes are significant.

scholarly journals Downregulation of a satiety signal from peripheral fat bodies improves visual attention while reducing sleep need in Drosophila

2019 ◽  
Author(s):  
Deniz Ertekin ◽  
Leonie Kirszenblat ◽  
Richard Faville ◽  
Bruno van Swinderen
Keyword(s):  
Visual Attention ◽  
Cognitive Processes ◽  
Fat Body ◽  
Sleep Loss ◽  
Genetically Engineered ◽  
Night Time ◽  
Selective Visual Attention ◽  
Negative Side Effects ◽  
Fat Bodies ◽  
The Brain

AbstractSleep is vital for survival. Yet, under environmentally challenging conditions such as starvation, animals suppress their need for sleep. Interestingly, starvation-induced sleep loss does not evoke a subsequent sleep rebound. Little is known about how starvation-induced sleep deprivation differs from other types of sleep loss, or why some sleep functions become dispensable during starvation. Here we demonstrate that downregulation of unpaired-2 (upd2, the Drosophila ortholog of leptin), is sufficient to mimic a starved-like state in flies. We use this ‘genetically starved’ state to investigate the consequences of a starvation signal on visual attention and sleep in otherwise well-fed flies, thereby sidestepping the negative side-effects of undernourishment. We find that knockdown of upd2 in the fat body is sufficient to suppress sleep while also increasing selective visual attention and promoting night-time feeding. Further, we show that this peripheral signal is integrated in the fly brain via insulin-expressing cells. Together, these findings identify a role for peripheral tissue-to-brain interactions in the simultaneous regulation of sleep and attention, to potentially promote adaptive behaviors necessary for survival in hungry animals.Author SummarySleep is important for maintaining both physiological (e.g., metabolic, immunological, and developmental) and cognitive processes, such as selective attention. Under nutritionally impoverished conditions, animals suppress sleep and increase foraging to locate food. Yet it is currently unknown how an animal is able to maintain well-tuned cognitive processes, despite being sleep deprived. Here we investigate this question by studying flies that have been genetically engineered to lack a satiety signal, and find that signaling from fat bodies in the periphery to insulin-expressing cells in the brain simultaneously regulates sleep need and attention-like processes.

scholarly journals Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Shui-Hua Wang ◽  
Xianwei Jiang ◽  
Yu-Dong Zhang
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Adaptive Genetic Algorithm ◽  
Biorthogonal Wavelet ◽  
Wide Range ◽  
Wavelet Features ◽  
The Brain ◽  
Better Than

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

scholarly journals Algebra dissociates from arithmetic in the brain semantic network

2022 ◽  
Vol 18 (1) ◽  
Author(s):  
Dazhi Cheng ◽  
Mengyi Li ◽  
Jiaxin Cui ◽  
Li Wang ◽  
Naiyi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Supplementary Motor Area ◽  
Semantic Network ◽  
Brain Activation ◽  
Motor Area ◽  
Angular Gyrus ◽  
Single Trial ◽  
Positive Functional ◽  
Brain Behavior ◽  
The Brain

Abstract Background Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebra (such as a − (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing. Methods Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis. Results Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network. Conclusion These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

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]

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