scholarly journals Cortical 3-hinges could serve as hubs in cortico-cortical connective network

2020 ◽  
Vol 14 (6) ◽  
pp. 2512-2529
Author(s):  
Tuo Zhang ◽  
Xiao Li ◽  
Xi Jiang ◽  
Fangfei Ge ◽  
Shu Zhang ◽  
...  
Keyword(s):  
Brain Function ◽  
Functional Networks ◽  
Functional Brain ◽  
Network Analyses ◽  
Folding Pattern ◽  
New Findings ◽  
Cortical Parcellation ◽  
High Level ◽  
Human Brains ◽  
Anatomical Connection

Abstract Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as ‘connector’ hubs rather than ‘provincial’ hubs and they account for a dominant proportion of nodes in the high-level ‘backbone’ of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.

Language and Aging

2019 ◽  
pp. 294-316
Author(s):  
Jonathan E. Peelle
Keyword(s):  
Language Processing ◽  
Brain Function ◽  
Neural Systems ◽  
Language Abilities ◽  
Physiological Mechanisms ◽  
Cognitive Changes ◽  
Older Adulthood ◽  
Neural Organization ◽  
Age Related ◽  
High Level

Language processing in older adulthood is a model of balance between preservation and decline. Despite widespread changes to physiological mechanisms supporting perception and cognition, older adults’ language abilities are frequently well preserved. At the same time, the neural systems engaged to achieve this high level of success change, and individual differences in neural organization appear to differentiate between more and less successful performers. This chapter reviews anatomical and cognitive changes that occur in aging and popular frameworks for age-related changes in brain function, followed by an examination of how these principles play out in the context of language comprehension and production.

scholarly journals Cortical response to naturalistic stimuli is largely predictable with deep neural networks

2021 ◽  
Vol 7 (22) ◽  
pp. eabe7547
Author(s):  
Meenakshi Khosla ◽  
Gia H. Ngo ◽  
Keith Jamison ◽  
Amy Kuceyeski ◽  
Mert R. Sabuncu
Keyword(s):  
Brain Function ◽  
Deep Neural Networks ◽  
Neural Responses ◽  
Dynamic Interactions ◽  
Hierarchical Processing ◽  
Human Connectome Project ◽  
Neural Information ◽  
Neural Information Processing ◽  
Visual Interactions ◽  
High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

scholarly journals The role of cultural practices in the emergence of modern human intelligence

2008 ◽  
Vol 363 (1499) ◽  
pp. 2011-2019 ◽  
Author(s):  
Edwin Hutchins
Keyword(s):  
Embodied Cognition ◽  
Cognitive Processes ◽  
Human Activities ◽  
Cultural Practices ◽  
Modern Human ◽  
Human Intelligence ◽  
Symbolic Representations ◽  
High Level ◽  
Human Brains

Innate cognitive capacities are orchestrated by cultural practices to produce high-level cognitive processes. In human activities, examples of this phenomenon range from everyday inferences about space and time to the most sophisticated reasoning in scientific laboratories. A case is examined in which chimpanzees enter into cultural practices with humans (in experiments) in ways that appear to enable them to engage in symbol-mediated thought. Combining the cultural practices perspective with the theories of embodied cognition and enactment suggests that the chimpanzees' behaviour is actually mediated by non-symbolic representations. The possibility that non-human primates can engage in cultural practices that give them the appearance of symbol-mediated thought opens new avenues for thinking about the coevolution of human culture and human brains.

Deep Learning for the Automated Classification of Functional Brain Networks in fMRI (Preprint)

2021 ◽  
Author(s):  
Lukman Ismael ◽  
Pejman Rasti ◽  
Florian Bernard ◽  
Philippe Menei ◽  
Aram Ter Minassian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Brain Networks ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Deep Learning Model

BACKGROUND The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks. OBJECTIVE We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms METHODS We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers. RESULTS Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task. CONCLUSIONS The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status. CLINICALTRIAL Comité de protection des personnes Ouest II, decision reference CPP 2012-25)

Neuroplasticity in Music Learning

2018 ◽  
pp. 545-563
Author(s):  
Vesa Putkinen ◽  
Mari Tervaniemi
Keyword(s):  
Speech Processing ◽  
Cognitive Functions ◽  
Brain Function ◽  
Musical Training ◽  
Event Related Potentials ◽  
Training Transfer ◽  
Neural Correlates ◽  
Music Learning ◽  
Functional Brain ◽  
Related Potentials

Studies conducted during the last three decades have identified numerous differences between musicians and non-musicians in neural correlates of sensory, motor, and higher-order cognitive functions. Research employing event-related potentials/fields has been particularly important in this framework. This chapter reviews the evidence that has emerged from these studies with emphasis on longitudinal studies comparing functional brain development in children taking music lessons and those engaged in non-musical activities. The literature provides empirical and theoretical grounds for concluding that musical training enhances sound encoding skills that are relevant for both music and speech processing. The question whether the benefits of musical training transfer to more distantly related cognitive functions remains controversial, however. Finally, it appears likely that training-induced plasticity alone does not account for the differences in brain function between musicians and non-musicians and, conversely, that predisposing factors also play a role.

scholarly journals Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks

2021 ◽  
Author(s):  
Ning Mei ◽  
Roberto Santana ◽  
David Soto
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Model Simulation ◽  
Conscious Experience ◽  
Neural Representation ◽  
Visual Stream ◽  
Order Processing ◽  
High Level ◽  
Unseen Objects ◽  
Human Brains

AbstractDespite advances in the neuroscience of visual consciousness over the last decades, we still lack a framework for understanding the scope of unconscious processing and how it relates to conscious experience. Previous research observed brain signatures of unconscious contents in visual cortex, but these have not been identified in a reliable manner, with low trial numbers and signal detection theoretic constraints not allowing to decisively discard conscious perception. Critically, the extent to which unconscious content is represented in high-level processing stages along the ventral visual stream and linked prefrontal areas remains unknown. Using a within-subject, high-precision, highly-sampled fMRI approach, we show that unconscious contents, even those associated with null sensitivity, can be reliably decoded from multivoxel patterns that are highly distributed along the ventral visual pathway and also involving prefrontal substrates. Notably, the neural representation in these areas generalised across conscious and unconscious visual processing states, placing constraints on prior findings that fronto-parietal substrates support the representation of conscious contents and suggesting revisions to models of consciousness such as the neuronal global workspace. We then provide a computational model simulation of visual information processing/representation in the absence of perceptual sensitivity by using feedforward convolutional neural networks trained to perform a similar visual task to the human observers. The work provides a novel framework for pinpointing the neural representation of unconscious knowledge across different task domains.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

scholarly journals Some Useful Pedagogical Practices: Educational Neuroscience Perspective

2015 ◽  
Vol 5 (4) ◽  
pp. 191
Author(s):  
Chandana Watagodakumbura
Keyword(s):  
Neural Plasticity ◽  
Frontal Lobes ◽  
Brain Regions ◽  
Pedagogical Practices ◽  
Educational Neuroscience ◽  
Effective Learning ◽  
Problem Solvers ◽  
High Level ◽  
Human Brains ◽  
Multiple Domain

<p>We have reviewed the goals of education by approaching them from the direction of educational neuroscience; through education, we have to achieve transfer of learning in order to produce individuals who are better problem solvers and decision makers. To achieve this goal, learners will have to transform what they have learned explicitly into implicit memories and vice versa by attaching sense and meaning, ideally across multiple domain areas. Further, through education, we enhance learner consciousness and/or wisdom that give abilities to spontaneously recall retained memories readily, whenever necessary. A number of pedagogical practices that are useful in achieving the above goals are identified. When new contents are presented to learners, high-level, generalised concepts need to be emphasised; concepts are likely to penetrate through multiple domain areas and last longer in memory, thus helping learners to attach sense and meaning better. In order to reach out to multiple brain regions, inducing creativity, we need to get frontal lobes involved essentially, with an appropriate pace and form of presentation. The important task of motivating learners can be done by presenting learners with educational neuroscience facts that can be enlightening; even difficult content can be mastered by simply paying attention fully and through elaborate rehearsal; human brains have the feature of neural plasticity and neural networks can grow throughout the lifespan through effective learning. When setting assessment, we should focus on open-ended, novel and conceptual/generalised questions so that learners use their frontal lobes, engaging in a higher-order, divergent and/or inductive thinking process to provide answers.</p>

Synaptic Transmission with the Glia

Physiology ◽  
2001 ◽  
Vol 16 (4) ◽  
pp. 178-184 ◽  
Author(s):  
Sabino Vesce ◽  
Paola Bezzi ◽  
Andrea Volterra
Keyword(s):  
Synaptic Transmission ◽  
Glial Cells ◽  
Brain Function ◽  
Synaptic Integration ◽  
New Findings ◽  
The Brain

For decades, scientists thought that all of the missing secrets of brain function resided in neurons. However, a wave of new findings indicates that glial cells, formerly considered mere supporters and subordinate to neurons, participate actively in synaptic integration and processing of information in the brain.

scholarly journals Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

2019 ◽  
Vol 30 (3) ◽  
pp. 1087-1102
Author(s):  
Shi Gu ◽  
Cedric Huchuan Xia ◽  
Rastko Ciric ◽  
Tyler M Moore ◽  
Ruben C Gur ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Function ◽  
Brain Networks ◽  
Brain Regions ◽  
Imaging Data ◽  
Modular Architecture ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Networks ◽  
Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

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