A symbolic information approach to determine anticipated and delayed synchronization in neuronal circuit models

The phenomenon of synchronization between two or more areas of the brain coupled asymmetrically is a relevant issue for understanding mechanisms and functions within the cerebral cortex. Anticipated synchronization (AS) refers to the situation in which the receiver system synchronizes to the future dynamics of the sender system while the intuitively expected delayed synchronization (DS) represents exactly the opposite case. AS and DS are investigated in the context of causal information formalism. More specifically, we use a multi-scale symbolic information-theory approach for discriminating the time delay displayed between two areas of the brain when they exchange information.

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Engineered glycomaterial implants orchestrate large-scale functional repair of brain tissue chronically after severe traumatic brain injury

Science Advances ◽

10.1126/sciadv.abe0207 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabe0207

Author(s):

Charles-Francois V. Latchoumane ◽

Martha I. Betancur ◽

Gregory A. Simchick ◽

Min Kyoung Sun ◽

Rameen Forghani ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Severe Traumatic Brain Injury ◽

Large Scale ◽

Growth Factor Signaling ◽

Neuronal Circuit ◽

Reach To Grasp ◽

Function Recovery ◽

Cellular Repair ◽

The Brain

Severe traumatic brain injury (sTBI) survivors experience permanent functional disabilities due to significant volume loss and the brain’s poor capacity to regenerate. Chondroitin sulfate glycosaminoglycans (CS-GAGs) are key regulators of growth factor signaling and neural stem cell homeostasis in the brain. However, the efficacy of engineered CS (eCS) matrices in mediating structural and functional recovery chronically after sTBI has not been investigated. We report that neurotrophic factor functionalized acellular eCS matrices implanted into the rat M1 region acutely after sTBI significantly enhanced cellular repair and gross motor function recovery when compared to controls 20 weeks after sTBI. Animals subjected to M2 region injuries followed by eCS matrix implantations demonstrated the significant recovery of “reach-to-grasp” function. This was attributed to enhanced volumetric vascularization, activity-regulated cytoskeleton (Arc) protein expression, and perilesional sensorimotor connectivity. These findings indicate that eCS matrices implanted acutely after sTBI can support complex cellular, vascular, and neuronal circuit repair chronically after sTBI.

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A symbolic information approach to characterize response-related differences in cortical activity during a Go/No-Go task

Nonlinear Dynamics ◽

10.1007/s11071-021-06477-1 ◽

2021 ◽

Author(s):

Helena Bordini de Lucas ◽

Steven L. Bressler ◽

Fernanda Selingardi Matias ◽

Osvaldo Anibal Rosso

Keyword(s):

Cortical Activity ◽

Information Approach ◽

Symbolic Information

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Aggregation-and-Attention Network for brain tumor segmentation

BMC Medical Imaging ◽

10.1186/s12880-021-00639-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chih-Wei Lin ◽

Yu Hong ◽

Jinfu Liu

Keyword(s):

Brain Tumor ◽

Semantic Information ◽

Spatial Relationship ◽

Tumor Segmentation ◽

Computer Assisted ◽

Brain Tumor Segmentation ◽

Attention Network ◽

Multi Scale ◽

Assisted Diagnosis ◽

The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

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A Computational Network Control Theory Analysis of Depression Symptoms

Personality Neuroscience ◽

10.1017/pen.2018.15 ◽

2018 ◽

Vol 1 ◽

Cited By ~ 2

Author(s):

Yoed N. Kenett ◽

Roger E. Beaty ◽

John D. Medaglia

Keyword(s):

Control Theory ◽

Diffusion Tensor ◽

Brain Regions ◽

Brain Network ◽

Network Control ◽

Depression Symptoms ◽

Theory Approach ◽

Imaging Data ◽

The Brain ◽

Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

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Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

Journal of Neural Engineering ◽

10.1088/1741-2552/aa9dae ◽

2018 ◽

Vol 15 (3) ◽

pp. 033001 ◽

Cited By ~ 40

Author(s):

Nicholas J Michelson ◽

Alberto L Vazquez ◽

James R Eles ◽

Joseph W Salatino ◽

Erin K Purcell ◽

...

Keyword(s):

Modal Analysis ◽

Brain Tissue ◽

Neural Interface ◽

Complex Relationship ◽

Multi Scale ◽

Biological Interface ◽

The Brain

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Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion

eLife ◽

10.7554/elife.50316 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 7

Author(s):

David W Sims ◽

Nicolas E Humphries ◽

Nan Hu ◽

Violeta Medan ◽

Jimena Berni

Keyword(s):

Synaptic Activity ◽

Suboesophageal Ganglion ◽

Searching Behaviour ◽

Multi Scale ◽

Lévy Walks ◽

Environmental Interactions ◽

Drosophila Larvae ◽

Spatial Exploration ◽

Lévy Random Walk ◽

The Brain

Efficient searching for resources such as food by animals is key to their survival. It has been proposed that diverse animals from insects to sharks and humans adopt searching patterns that resemble a simple Lévy random walk, which is theoretically optimal for ‘blind foragers’ to locate sparse, patchy resources. To test if such patterns are generated intrinsically, or arise via environmental interactions, we tracked free-moving Drosophila larvae with (and without) blocked synaptic activity in the brain, suboesophageal ganglion (SOG) and sensory neurons. In brain-blocked larvae, we found that extended substrate exploration emerges as multi-scale movement paths similar to truncated Lévy walks. Strikingly, power-law exponents of brain/SOG/sensory-blocked larvae averaged 1.96, close to a theoretical optimum (µ ≅ 2.0) for locating sparse resources. Thus, efficient spatial exploration can emerge from autonomous patterns in neural activity. Our results provide the strongest evidence so far for the intrinsic generation of Lévy-like movement patterns.

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TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

10.21203/rs.3.rs-264855/v2 ◽

2021 ◽

Author(s):

Rastko Ciric ◽

William Thompson ◽

Romy Lorenz ◽

Mathias Goncalves ◽

Eilidh MacNicol ◽

...

Keyword(s):

Access Management ◽

Multi Scale ◽

Important Challenge ◽

Brain Models ◽

The Brain ◽

Fair Sharing ◽

Insight Into

Abstract Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

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The future of human cerebral cartography: a novel approach

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2014.0171 ◽

2015 ◽

Vol 370 (1668) ◽

pp. 20140171 ◽

Cited By ~ 22

Author(s):

Richard Frackowiak ◽

Henry Markram

Keyword(s):

Scale Model ◽

Quarter Century ◽

Multi Scale ◽

Novel Approach ◽

Brain Structure And Function ◽

Recent Developments ◽

Spatio Temporal ◽

And Function ◽

The Brain ◽

Different Levels

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context.

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