Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm

2019 ◽  
Vol 51 ◽  
pp. 128-137 ◽  
Author(s):  
Anmin Gong ◽  
Jianping Liu ◽  
Ling Lu ◽  
Gengrui Wu ◽  
Changhao Jiang ◽  
...  
Keyword(s):  
Phase Locking ◽  
Brain Networks ◽  
High Level ◽  
Phase Locking Value ◽  
The Brain

SOURCE RECONSTRUCTION AND SYNCHRONY MEASUREMENTS FOR REVEALING FUNCTIONAL BRAIN NETWORKS AND CLASSIFYING MENTAL STATES

2010 ◽  
Vol 20 (06) ◽  
pp. 1703-1721 ◽  
Author(s):  
FRANÇOIS LAURENT ◽  
MICHEL BESSERVE ◽  
LINE GARNERO ◽  
MATTHIEU PHILIPPE ◽  
GENEVIÈVE FLORENCE ◽  
...  
Keyword(s):  
Phase Locking ◽  
Brain Networks ◽  
Mental States ◽  
Source Reconstruction ◽  
Functional Brain ◽  
High Dimension Data ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Phase Locking Value ◽  
The Relationship

We classified performance-related mental states from EEG-derived measurements. We investigated the usefulness of massively distributed source reconstruction, comparing scalp and cortical scales. This approach provides a more detailed picture of the functional brain networks underlying the changes related to the mental state of interest. Local and distant synchrony measurements (coherence, phase locking value) were used for both scalp measurements and cortical current density sources, and were fed into a SVM-based classifier. We designed two simulations where classification scores increased when our 60-electrode scalp measurements were reconstructed on 60 sources and on a 500-source cortex. Source reconstruction appeared to be most useful in these simulations, in particular, when distant synchronies were involved and local synchronies did not prevail. Despite the simplicity of the model used, certain flaws in accuracy were observed in the localization of informative activities, due to the relationship between amplitude and phase for mixed signals. Our results with real EEG data suggested that the phenomenon of interest was characterized merely by modulations in local amplitudes, but also in strength of distant couplings. After source reconstruction, classification rates also increased for real EEG data when seeking distant phase-related couplings. When reconstructing a large number of sources, the regularization coefficient should be carefully selected on a subject-by-subject basis. We showed that training classifiers using such high-dimension data is useful for localizing discriminating patterns of activity.

scholarly journals 40 Hz acoustic stimulation decreases amyloid beta and modulates brain rhythms in a mouse model of Alzheimer’s disease

2018 ◽  
Author(s):  
Juho Lee ◽  
Seungjun Ryu ◽  
Hyun-Ju Kim ◽  
Jieun Jung ◽  
Boreom Lee ◽  
...  
Keyword(s):  
Mouse Model ◽  
Therapeutic Potential ◽  
Phase Locking ◽  
Acoustic Stimulation ◽  
Gamma Power ◽  
Frequency Coupling ◽  
Phase Locking Value ◽  
Gamma Band Oscillations ◽  
Cross Frequency Coupling ◽  
The Brain

AbstractIntroductionThe accumulation of amyloid-beta (Aβ) is one of the neuropathologic hallmarks of Alzheimer’s disease (AD) and abnormal gamma band oscillations and brain connectivity have been observed. Recently, a therapeutic potential of gamma entrainment of the brain was reported by Iaccarino et al. However, the affected areas were limited to hippocampus and visual cortex. Therefore, we sought to test the effects of acoustic stimulation in a mouse model of AD.MethodsFreely moving 6-month-old 5XFAD mice with electroencephalogram (EEG) electrodes were treated with daily two-hour acoustic stimulation at 40Hz for 2 weeks. Aβ and microglia were evaluated by immunohistochemistry and ELISA. Evoked and spontaneous gamma power were analyzed by wavelet analysis. Coherence, phase locking value (PLV), and cross-frequency coupling were analyzed.ResultsThe number of Aβ plaques decreased in the pre-and infralimbic (PIL) and hippocampus regions and soluble Aβ-40 and Aβ-42 peptides in PIL in the acoustic stimulation group. We also found that the number of microglia increased in PIL and hippocampus. In EEG analysis, evoked gamma power was decreased and spontaneous gamma power was increased. Gamma coherence and phase locking value did not show significant changes. Cross-frequency coupling was shifted from gamma-delta to gamma-theta rhythm.ConclusionIn summary, we found that acoustic stimulation at 40Hz can reduce Aβ in the brain and restore the gamma band oscillations and the frontoparietal connectivity. Our data suggest that acoustic stimulation might alter the natural deterioration processes of AD and have a therapeutic potential in AD.

Lessons From the Neural Bases of Speech and Voice

2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow
Keyword(s):  
Brain Networks ◽  
Neural Substrates ◽  
Voice Behavior ◽  
Therapeutic Approaches ◽  
Neural Bases ◽  
Sound Foundation ◽  
The Brain ◽  
Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Phase-Locked Responses to Pure Tones in the Inferior Colliculus

2006 ◽  
Vol 95 (3) ◽  
pp. 1926-1935 ◽  
Author(s):  
Liang-Fa Liu ◽  
Alan R. Palmer ◽  
Mark N. Wallace
Keyword(s):  
Guinea Pig ◽  
Inferior Colliculus ◽  
Phase Locking ◽  
Single Units ◽  
Central Nucleus ◽  
Dorsal Cortex ◽  
Upper Limits ◽  
Ascending Pathways ◽  
Pure Tones ◽  
The Brain

In the auditory system, some ascending pathways preserve the precise timing information present in a temporal code of frequency. This can be measured by studying responses that are phase-locked to the stimulus waveform. At each stage along a pathway, there is a reduction in the upper frequency limit of the phase-locking and an increase in the steady-state latency. In the guinea pig, phase-locked responses to pure tones have been described at various levels from auditory nerve to neocortex but not in the inferior colliculus (IC). Therefore we made recordings from 161 single units in guinea pig IC. Of these single units, 68% (110/161) showed phase-locked responses. Cells that phase-locked were mainly located in the central nucleus but also occurred in the dorsal cortex and external nucleus. The upper limiting frequency of phase-locking varied greatly between units (80−1,034 Hz) and between anatomical divisions. The upper limits in the three divisions were central nucleus, >1,000 Hz; dorsal cortex, 700 Hz; external nucleus, 320 Hz. The mean latencies also varied and were central nucleus, 8.2 ± 2.8 (SD) ms; dorsal cortex, 17.2 ms; external nucleus, 13.3 ms. We conclude that many cells in the central nucleus receive direct inputs from the brain stem, whereas cells in the external and dorsal divisions receive input from other structures that may include the forebrain.

scholarly journals A neurocognitive model of ideological thinking

2021 ◽  
pp. 1-15
Author(s):  
Leor Zmigrod
Keyword(s):  
Empirical Evidence ◽  
Motivational Factors ◽  
Cognitive Systems ◽  
Social Forces ◽  
Low Level ◽  
Cognitive Principles ◽  
High Level ◽  
Ideological Attitudes ◽  
The Brain

Abstract Ideological behavior has traditionally been viewed as a product of social forces. Nonetheless, an emerging science suggests that ideological worldviews can also be understood in terms of neural and cognitive principles. The article proposes a neurocognitive model of ideological thinking, arguing that ideological worldviews may be manifestations of individuals’ perceptual and cognitive systems. This model makes two claims. First, there are neurocognitive antecedents to ideological thinking: the brain’s low-level neurocognitive dispositions influence its receptivity to ideological doctrines. Second, there are neurocognitive consequences to ideological engagement: strong exposure and adherence to ideological doctrines can shape perceptual and cognitive systems. This article details the neurocognitive model of ideological thinking and synthesizes the empirical evidence in support of its claims. The model postulates that there are bidirectional processes between the brain and the ideological environment, and so it can address the roles of situational and motivational factors in ideologically motivated action. This endeavor highlights that an interdisciplinary neurocognitive approach to ideologies can facilitate biologically informed accounts of the ideological brain and thus reveal who is most susceptible to extreme and authoritarian ideologies. By investigating the relationships between low-level perceptual processes and high-level ideological attitudes, we can develop a better grasp of our collective history as well as the mechanisms that may structure our political futures.

scholarly journals Perceptual decisions are biased toward relevant prior choices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helen Feigin ◽  
Shira Baror ◽  
Moshe Bar ◽  
Adam Zaidel
Keyword(s):  
Sensory Information ◽  
Irrelevant Stimulus ◽  
Stimulus Feature ◽  
Serial Dependence ◽  
Low Level ◽  
Subsequent Test ◽  
Key Factor ◽  
High Level ◽  
Motor Actions ◽  
The Brain

AbstractPerceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

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