scholarly journals Expert programmers have fine-tuned cortical representations of source code

2020 ◽  
Author(s):  
Yoshiharu Ikutani ◽  
Takatomi Kubo ◽  
Satoshi Nishida ◽  
Hideaki Hata ◽  
Kenichi Matsumoto ◽  
...  
Keyword(s):  
Brain Activity ◽  
Source Code ◽  
Knowledge Structure ◽  
Brain Regions ◽  
Data Driven ◽  
Behavioral Performance ◽  
Functional Categories ◽  
Domain Specific ◽  
Cortical Representations ◽  
The Brain

ABSTRACTExpertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

scholarly journals EEGG: An Analytic Brain-computer Interface Algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Classification Performance ◽  
Data Driven ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Data Driven Approach ◽  
Left And Right ◽  
The Brain

<div><div> <p><a></a></p><div> <p><a></a><a><i>Objective. </i></a>Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. </p> <p><i>Approach. </i>Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG’s classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. </p> <p><i>Main results. </i>(1) EEGG was more robust than typical “CSP+” algorithms for the poorquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. </p> <p><i>Significance. </i>EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (<i>in analogy with the data-driven but human-readable Fourier transform and frequency spectrum</i>), which offers a novel frame for analysis of the brain.</p> </div> </div></div><div><p></p></div>

scholarly journals Category representations in the brain are both discretely localized and widely distributed

2018 ◽  
Vol 119 (6) ◽  
pp. 2256-2264 ◽  
Author(s):  
Zarrar Shehzad ◽  
Gregory McCarthy
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Strong Support ◽  
Brain Regions ◽  
Distributed Representations ◽  
Domain Specific ◽  
Category Information ◽  
Shared Information ◽  
Unique Domain ◽  
The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals The Cross-Modal Effects of Sensory Deprivation on Spatial and Temporal Processes in Vision and Audition: A Systematic Review on Behavioral and Neuroimaging Research since 2000

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Laura Bell ◽  
Lisa Wagels ◽  
Christiane Neuschaefer-Rube ◽  
Janina Fels ◽  
Raquel E. Gur ◽  
...  
Keyword(s):  
Systematic Review ◽  
Temporal Processing ◽  
Neural Plasticity ◽  
Sensory Deprivation ◽  
Brain Regions ◽  
Behavioral Performance ◽  
Brain Organization ◽  
Processing Resources ◽  
The Cross ◽  
The Brain

One of the most significant effects of neural plasticity manifests in the case of sensory deprivation when cortical areas that were originally specialized for the functions of the deprived sense take over the processing of another modality. Vision and audition represent two important senses needed to navigate through space and time. Therefore, the current systematic review discusses the cross-modal behavioral and neural consequences of deafness and blindness by focusing on spatial and temporal processing abilities, respectively. In addition, movement processing is evaluated as compiling both spatial and temporal information. We examine whether the sense that is not primarily affected changes in its own properties or in the properties of the deprived modality (i.e., temporal processing as the main specialization of audition and spatial processing as the main specialization of vision). References to the metamodal organization, supramodal functioning, and the revised neural recycling theory are made to address global brain organization and plasticity principles. Generally, according to the reviewed studies, behavioral performance is enhanced in those aspects for which both the deprived and the overtaking senses provide adequate processing resources. Furthermore, the behavioral enhancements observed in the overtaking sense (i.e., vision in the case of deafness and audition in the case of blindness) are clearly limited by the processing resources of the overtaking modality. Thus, the brain regions that were previously recruited during the behavioral performance of the deprived sense now support a similar behavioral performance for the overtaking sense. This finding suggests a more input-unspecific and processing principle-based organization of the brain. Finally, we highlight the importance of controlling for and stating factors that might impact neural plasticity and the need for further research into visual temporal processing in deaf subjects.

scholarly journals Brain Activity during Visual and Auditory Word Rhyming Tasks in Cantonese–Mandarin–English Trilinguals

2020 ◽  
Vol 10 (12) ◽  
pp. 936
Author(s):  
Yujia Wu ◽  
Jingwen Ma ◽  
Lei Cai ◽  
Zengjian Wang ◽  
Miao Fan ◽  
...  
Keyword(s):  
Phonological Processing ◽  
Brain Activity ◽  
Brain Regions ◽  
Writing Systems ◽  
Significant Interaction Effect ◽  
Chinese College Students ◽  
Second Languages ◽  
Auditory Word ◽  
Visual Tasks ◽  
The Brain

It is unclear whether the brain activity during phonological processing of second languages (L2) is similar to that of the first language (L1) in trilingual individuals, especially when the L1 is logographic, and the L2s are logographic and alphabetic, respectively. To explore this issue, this study examined brain activity during visual and auditory word rhyming tasks in Cantonese–Mandarin–English trilinguals. Thirty Chinese college students whose L1 was Cantonese and L2s were Mandarin and English were recruited. Functional magnetic resonance imaging (fMRI) was conducted while subjects performed visual and auditory word rhyming tasks in three languages (Cantonese, Mandarin, and English). The results revealed that in Cantonese–Mandarin–English trilinguals, whose L1 is logographic and the orthography of their L2 is the same as L1—i.e., Mandarin and Cantonese, which share the same set of Chinese characters—the brain regions for the phonological processing of L2 are different from those of L1; when the orthography of L2 is quite different from L1, i.e., English and Cantonese who belong to different writing systems, the brain regions for the phonological processing of L2 are similar to those of L1. A significant interaction effect was observed between language and modality in bilateral lingual gyri. Regions of interest (ROI) analysis at lingual gyri revealed greater activation of this region when using English than Cantonese and Mandarin in visual tasks.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

scholarly journals Making the brain-activity-to-information leap using a novel framework: Stimulus Information Representation (SIR)

2019 ◽  
Author(s):  
Philippe G. Schyns ◽  
Robin A.A. Ince
Keyword(s):  
Brain Imaging ◽  
Brain Activity ◽  
Behavioral Responses ◽  
Data Driven ◽  
Information Representation ◽  
Stimulus Information ◽  
Fundamental Challenge ◽  
Per Se ◽  
The Brain ◽  
Different Levels

AbstractA fundamental challenge in neuroscience is to understand how the brain processes information. Neuroscientists have approached this question partly by measuring brain activity in space, time and at different levels of granularity. However, our aim is not to discover brain activity per se, but to understand the processing of information that this activity reflects. To make this brain-activity-to-information leap, we believe that we should reconsider brain imaging from the methodological foundations of psychology. With this goal in mind, we have developed a new data-driven framework, called Stimulus Information Representation (SIR), that enables us to better understand how the brain processes information from measures of brain activity and behavioral responses. In this article, we explain this approach, its strengths and limitations, and how it can be applied to understand how the brain processes information to perform behavior in a task.“It is no good poking around in the brain without some idea of what one is looking for. That would be like trying to find a needle in a haystack without having any idea what needles look like. The theorist is the [person] who might reasonably be asked for [their] opinion about the appearance of needles.” HC Longuet-Higgins, 1969.

scholarly journals Experience-dependent plasticity modulates ongoing activity in the antennal lobe and enhance odor representations

2021 ◽  
Author(s):  
Luis M. Franco ◽  
Emre Yaksi
Keyword(s):  
Antennal Lobe ◽  
Internal State ◽  
Brain Activity ◽  
Fruit Fly ◽  
Brain Regions ◽  
Projection Neurons ◽  
Ongoing Activity ◽  
Dependent Plasticity ◽  
Olfactory Glomeruli ◽  
The Brain

ABSTRACTOngoing neural activity has been observed across several brain regions and thought to reflect the internal state of the brain. Yet, it is not fully understood how ongoing brain activity interacts with sensory experience and shape sensory representations. Here, we show that projection neurons of the fruit fly antennal lobe exhibit spatiotemporally organized ongoing activity in the absence of odor stimulation. Upon repeated exposure to odors, we observe a gradual and long-lasting decrease in the amplitude and frequency of spontaneous calcium events, as well as a reorganization of correlations between olfactory glomeruli during ongoing activity. Accompanying these plastic changes, we find that repeated odor experience reduces trial-to-trial variability and enhances the specificity of odor representations. Our results reveal a previously undescribed experience-dependent plasticity of ongoing and sensory driven activity at peripheral levels of the fruit fly olfactory system.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

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