scholarly journals Lateral prefrontal cortex as a hub for music production with gradation from structural rules to movement sequences

2020 ◽  
Author(s):  
R. Bianco ◽  
G. Novembre ◽  
H. Ringer ◽  
N. Kohler ◽  
P.E. Keller ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Motor Planning ◽  
Rule Based ◽  
Structural Rules ◽  
Movement Sequences ◽  
Music Production ◽  
Multi Level ◽  
High Level ◽  
The Brain ◽  
And Diffusion

Complex sequential behaviours, such as speaking or playing music, often entail the flexible, rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into concrete series of movements. Here we demonstrate a multi-level contribution of anatomically distinct cognitive and motor networks to the execution of novel musical sequences. We combined functional and diffusion-weighted neuroimaging to dissociate high-level structural and low-level motor planning of musical chord sequences executed on a piano. Fronto-temporal and fronto-parietal neural networks were involved when sequences violated pianists’ structural or motor plans, respectively. Prefrontal cortex is identified as a hub where both networks converge within an anterior-to-posterior gradient of action control linking abstract structural rules to concrete movement sequences.

Concepts and Compositionality: In Search of the Brain's Language of Thought

2020 ◽  
Vol 71 (1) ◽  
pp. 273-303 ◽  
Author(s):  
Steven M. Frankland ◽  
Joshua D. Greene
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Neuroscience ◽  
Default Mode Network ◽  
Computational Models ◽  
Language Of Thought ◽  
Default Mode ◽  
Episodic Simulation ◽  
High Level ◽  
The Brain ◽  
Language Of Thought Hypothesis

Imagine Genghis Khan, Aretha Franklin, and the Cleveland Cavaliers performing an opera on Maui. This silly sentence makes a serious point: As humans, we can flexibly generate and comprehend an unbounded number of complex ideas. Little is known, however, about how our brains accomplish this. Here we assemble clues from disparate areas of cognitive neuroscience, integrating recent research on language, memory, episodic simulation, and computational models of high-level cognition. Our review is framed by Fodor's classic language of thought hypothesis, according to which our minds employ an amodal, language-like system for combining and recombining simple concepts to form more complex thoughts. Here, we highlight emerging work on combinatorial processes in the brain and consider this work's relation to the language of thought. We review evidence for distinct, but complementary, contributions of map-like representations in subregions of the default mode network and sentence-like representations of conceptual relations in regions of the temporal and prefrontal cortex.

SPECIFIC PROPERTIES OF TUBULIN IN THE PREFRONTAL CORTEX IN CONTROL, SCHIZOPHRENIA AND VASCULAR DEMENTIA

2020 ◽  
pp. 65-71
Author(s):  
Burbaeva G.Sh. ◽  
Androsova L.V. ◽  
Vorobyeva E.A. ◽  
Savushkina O.K.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Vascular Dementia ◽  
Binding Activity ◽  
Nephelometric Method ◽  
Rate Of Polymerization ◽  
Mental Diseases ◽  
And Control ◽  
The Brain

The aim of the study was to evaluate the rate of polymerization of tubulin into microtubules and determine the level of colchicine binding (colchicine-binding activity of tubulin) in the prefrontal cortex in schizophrenia, vascular dementia (VD) and control. Colchicine-binding activity of tubulin was determined by Sherlinе in tubulin-enriched extracts of proteins from the samples. Measurement of light scattering during the polymerization of the tubulin was carried out using the nephelometric method at a wavelength of 450-550 nm. There was a significant decrease in colchicine-binding activity and the rate of tubulin polymerization in the prefrontal cortex in both diseases, and in VD to a greater extent than in schizophrenia. The obtained results suggest that not only in Alzheimer's disease, but also in other mental diseases such as schizophrenia and VD, there is a decrease in the level of tubulin in the prefrontal cortex of the brain, although to a lesser extent than in Alzheimer's disease, and consequently the amount of microtubules.

Activation of Melanocortin-4 Receptor by a Synthetic Agonist Inhibits Ethanolinduced Neuroinflammation in Rats

2020 ◽  
Vol 25 (45) ◽  
pp. 4799-4805 ◽  
Author(s):  
Osvaldo Flores-Bastías ◽  
Gonzalo I. Gómez ◽  
Juan A. Orellana ◽  
Eduardo Karahanian
Keyword(s):  
Prefrontal Cortex ◽  
Alcohol Consumption ◽  
Ethanol Intake ◽  
Neuroinflammatory Response ◽  
Agonist Peptide ◽  
Melanocortin 4 Receptor ◽  
Tnf Α ◽  
Cord Injury ◽  
High Ethanol ◽  
The Brain

Background: High ethanol intake induces a neuroinflammatory response resulting in the subsequent maintenance of chronic alcohol consumption. The melanocortin system plays a pivotal role in the modulation of alcohol consumption. Interestingly, it has been shown that the activation of melanocortin-4 receptor (MC4R) in the brain decreases the neuroinflammatory response in models of brain damage other than alcohol consumption, such as LPS-induced neuroinflammation, cerebral ischemia, glutamate excitotoxicity, and spinal cord injury. Objectives: In this work, we aimed to study whether MC4R activation by a synthetic MC4R-agonist peptide prevents ethanol-induced neuroinflammation, and if alcohol consumption produces changes in MC4R expression in the hippocampus and hypothalamus. Methods: Ethanol-preferring Sprague Dawley rats were selected offering access to 20% ethanol on alternate days for 4 weeks (intermittent access protocol). After this time, animals were i.p. administered an MC4R agonist peptide in the last 2 days of the protocol. Then, the expression of the proinflammatory cytokines interleukin 6 (IL-6), interleukin 1-beta (IL-1β), and tumor necrosis factor-alpha (TNF-α) were measured in the hippocampus, hypothalamus and prefrontal cortex. It was also evaluated if ethanol intake produces alterations in the expression of MC4R in the hippocampus and the hypothalamus. Results: Alcohol consumption increased the expression of MC4R in the hippocampus and the hypothalamus. The administration of the MC4R agonist reduced IL-6, IL-1β and TNF-α levels in hippocampus, hypothalamus and prefrontal cortex, to those observed in control rats that did not drink alcohol. Conclusion: High ethanol consumption produces an increase in the expression of MC4R in the hippocampus and hypothalamus. The administration of a synthetic MC4R-agonist peptide prevents neuroinflammation induced by alcohol consumption in the hippocampus, hypothalamus, and prefrontal cortex. These results could explain the effect of α-MSH and other synthetic MC4R agonists in decreasing alcohol intake through the reduction of the ethanol-induced inflammatory response in the brain.

025 Sleep Disruption on an Orbital Shaker alters Glutamate in Prairie Vole Prefrontal Cortex

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A11-A12
Author(s):  
Carolyn Jones ◽  
Randall Olson ◽  
Alex Chau ◽  
Peyton Wickham ◽  
Ryan Leriche ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Prefrontal Cortex ◽  
Early Life ◽  
Autism Spectrum ◽  
Prairie Vole ◽  
Sleep Disruption ◽  
Glutamatergic Synapses ◽  
The Brain ◽  
Orbital Shaker

Abstract Introduction Glutamate concentrations in the cortex fluctuate with the sleep wake cycle in both rodents and humans. Altered glutamatergic signaling, as well as the early life onset of sleep disturbances have been implicated in neurodevelopmental disorders such as autism spectrum disorder. In order to study how sleep modulates glutamate activity in brain regions relevant to social behavior and development, we disrupted sleep in the socially monogamous prairie vole (Microtus ochrogaster) rodent species and quantified markers of glutamate neurotransmission within the prefrontal cortex, an area of the brain responsible for advanced cognition and complex social behaviors. Methods Male and female prairie voles were sleep disrupted using an orbital shaker to deliver automated gentle cage agitation at continuous intervals. Sleep was measured using EEG/EMG signals and paired with real time glutamate concentrations in the prefrontal cortex using an amperometric glutamate biosensor. This same method of sleep disruption was applied early in development (postnatal days 14–21) and the long term effects on brain development were quantified by examining glutamatergic synapses in adulthood. Results Consistent with previous research in rats, glutamate concentration in the prefrontal cortex increased during periods of wake in the prairie vole. Sleep disruption using the orbital shaker method resulted in brief cortical arousals and reduced time in REM sleep. When applied during development, early life sleep disruption resulted in long-term changes in both pre- and post-synaptic components of glutamatergic synapses in the prairie vole prefrontal cortex including increased density of immature spines. Conclusion In the prairie vole rodent model, sleep disruption on an orbital shaker produces a sleep, behavioral, and neurological phenotype that mirrors aspects of autism spectrum disorder including altered features of excitatory neurotransmission within the prefrontal cortex. Studies using this method of sleep disruption combined with real time biosensors for excitatory neurotransmitters will enhance our understanding of modifiable risk factors, such as sleep, that contribute to the altered development of glutamatergic synapses in the brain and their relationship to social behavior. Support (if any) NSF #1926818, VA CDA #IK2 BX002712, Portland VA Research Foundation, NIH NHLBI 5T32HL083808-10, VA Merit Review #I01BX001643

scholarly journals Veto and Vacillation: A Neural Precursor of the Decision to Withhold Action

2014 ◽  
Vol 26 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Erman Misirlisoy ◽  
Patrick Haggard
Keyword(s):  
Human Behavior ◽  
Temporal Pattern ◽  
Neural Precursor ◽  
Rule Based ◽  
Action Sequences ◽  
Readiness Potentials ◽  
Preparatory Activity ◽  
The Brain ◽  
Motor Areas

The capacity to inhibit a planned action gives human behavior its characteristic flexibility. How this mechanism operates and what factors influence a decision to act or not act remain relatively unexplored. We used EEG readiness potentials (RPs) to examine preparatory activity before each action of an ongoing sequence, in which one action was occasionally omitted. We compared RPs between sequences in which omissions were instructed by a rule (e.g., “omit every fourth action”) and sequences in which the participant themselves freely decided which action to omit. RP amplitude was reduced for actions that immediately preceded a voluntary omission but not a rule-based omission. We also used the regular temporal pattern of the action sequences to explore brain processes linked to omitting an action by time-locking EEG averages to the inferred time when an action would have occurred had it not been omitted. When omissions were instructed by a rule, there was a negative-going trend in the EEG, recalling the rising ramp of an RP. No such component was found for voluntary omissions. The results are consistent with a model in which spontaneously fluctuating activity in motor areas of the brain could bias “free” decisions to act or not.

Transfer Reinforcement Learning for Autonomous Driving

2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Problem ◽  
Autonomous Driving ◽  
High Fidelity ◽  
Rule Based ◽  
High Level ◽  
Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

scholarly journals Glia Not Neurons: Uncovering Brain Dysmaturation in a Rat Model of Alzheimer’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 823
Author(s):  
Ekaterina A. Rudnitskaya ◽  
Tatiana A. Kozlova ◽  
Alena O. Burnyasheva ◽  
Natalia A. Stefanova ◽  
Nataliya G. Kolosova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Brain Structures ◽  
Time Frame ◽  
Oxys Rats ◽  
Unknown Etiology ◽  
Suitable Model ◽  
Model Of Alzheimer’S Disease ◽  
The Brain

Sporadic Alzheimer’s disease (AD) is a severe disorder of unknown etiology with no definite time frame of onset. Recent studies suggest that middle age is a critical period for the relevant pathological processes of AD. Nonetheless, sufficient data have accumulated supporting the hypothesis of “neurodevelopmental origin of neurodegenerative disorders”: prerequisites for neurodegeneration may occur during early brain development. Therefore, we investigated the development of the most AD-affected brain structures (hippocampus and prefrontal cortex) using an immunohistochemical approach in senescence-accelerated OXYS rats, which are considered a suitable model of the most common—sporadic—type of AD. We noticed an additional peak of neurogenesis, which coincides in time with the peak of apoptosis in the hippocampus of OXYS rats on postnatal day three. Besides, we showed signs of delayed migration of neurons to the prefrontal cortex as well as disturbances in astrocytic and microglial support of the hippocampus and prefrontal cortex during the first postnatal week. Altogether, our results point to dysmaturation during early development of the brain—especially insufficient glial support—as a possible “first hit” leading to neurodegenerative processes and AD pathology manifestation later in life.

scholarly journals Comparison of Activation in the Prefrontal Cortex of Native Speakers of Mandarin by Ability of Japanese as a Second Language Using a Novel Speaking Task

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 412
Author(s):  
Li Cong ◽  
Hideki Miyaguchi ◽  
Chinami Ishizuki
Keyword(s):  
Second Language ◽  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Near Infrared ◽  
Native Speakers ◽  
Brain Activation ◽  
Brain Regions ◽  
Strong Inhibition ◽  
Functional Near Infrared Spectroscopy ◽  
High Level

Evidence shows that second language (L2) learning affects cognitive function. Here in this work, we compared brain activation in native speakers of Mandarin (L1) who speak Japanese (L2) between and within two groups (high and low L2 ability) to determine the effect of L2 ability in L1 and L2 speaking tasks, and to map brain regions involved in both tasks. The brain activation during task performance was determined using prefrontal cortex blood flow as a proxy, measured by functional near-infrared spectroscopy (fNIRS). People with low L2 ability showed much more brain activation when speaking L2 than when speaking L1. People with high L2 ability showed high-level brain activation when speaking either L2 or L1. Almost the same high-level brain activation was observed in both ability groups when speaking L2. The high level of activation in people with high L2 ability when speaking either L2 or L1 suggested strong inhibition of the non-spoken language. A wider area of brain activation in people with low compared with high L2 ability when speaking L2 is considered to be attributed to the cognitive load involved in code-switching L1 to L2 with strong inhibition of L1 and the cognitive load involved in using L2.

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

scholarly journals Rule-based multi-level modeling of cell biological systems

2011 ◽  
Vol 5 (1) ◽  
pp. 166 ◽  
Author(s):  
Carsten Maus ◽  
Stefan Rybacki ◽  
Adelinde M Uhrmacher
Keyword(s):  
Biological Systems ◽  
Rule Based ◽  
Multi Level
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