scholarly journals Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

2019 ◽  
Author(s):  
Joshua Faskowitz ◽  
Farnaz Zamani Esfahlani ◽  
Youngheun Jo ◽  
Olaf Sporns ◽  
Richard F. Betzel
Keyword(s):  
Network Model ◽  
Sensory Input ◽  
System Level ◽  
The Novel ◽  
Human Cerebral Cortex ◽  
Functional Connections ◽  
Brain Circuits ◽  
Multiple Scan ◽  
Future Work ◽  
The Brain

Network neuroscience has relied on a node-centric network model in which cells, populations, and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. Here, we develop an edge-centric network model, which generates the novel constructs of “edge time series” and “edge functional connectivity” (eFC). Using network analysis, we show that at rest eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We go on to show that eFC is systematically and consistently modulated by variation in sensory input. In future work, the edge-centric approach could be used to map the connectional architecture of brain circuits and for the development of brain-based biomarkers of disease and development.

scholarly journals Functional dedifferentiation of the brain during healthy aging

2020 ◽  
Vol 123 (4) ◽  
pp. 1279-1282
Author(s):  
Divyangana Rakesh ◽  
Kavisha B. Fernando ◽  
Sina Mansour L.
Keyword(s):  
Older Adults ◽  
Cognitive Decline ◽  
Cognitive Deficits ◽  
Theoretical Approach ◽  
Healthy Aging ◽  
Functional Specialization ◽  
The Novel ◽  
Graph Theoretical Approach ◽  
Future Work ◽  
The Brain

Nonpathological aging is associated with significant cognitive deficits. Thus, the underlying neurobiology of aging-associated cognitive decline warrants investigation. In a recent study, Chong et al. (Chong JSX, Ng KK, Tandi J, Wang C, Poh J-H, Lo JC, Chee MWL, Zhou JH. J Neurosci 39: 5534–5550, 2019) provided insights into the association between cognitive decline and the loss of functional specialization in the brains of older adults. Here, we introduce the novel graph theoretical approach utilized and discuss the significance of their findings and broader implications on aging. We also provide alternate perspectives of their findings and suggest directions for future work.

How asymmetry in animals starts

2005 ◽  
Vol 13 (S2) ◽  
pp. 105-118 ◽  
Author(s):  
ONUR GÜNTÜRKÜN
Keyword(s):  
Sensory Input ◽  
Avian Brain ◽  
Light Stimulation ◽  
Present Evidence ◽  
Avian Embryos ◽  
Cerebral Asymmetries ◽  
Brain Circuits ◽  
The Right ◽  
The Brain ◽  
Stimulation Of

This review aims to present a speculation about mechanisms that shape the brains of humans and other animals into an asymmetrical organization. To this end, I will proceed in two steps: first, I want to recapitulate evidence from various experiments that show that some but not all asymmetries of the avian brain result from a prehatch light stimulation asymmetry. This should make it clear that avian embryos have a genetic predisposition to turn their head to the right. This results in a higher level of prehatch light stimulation of their right eye. The concomitant left–right difference in sensory input alters the brain circuits of the animal for the entire lifespan in a lateralized way. In the second part of the paper I will present evidence that some of the asymmetries of the human brain take a similar ontogenetic path as those observed in birds. This review provides the evidence that critical ontogenetic processes discovered in animal models could also be involved in the ontogeny of human cerebral asymmetries.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Cinematic rendering of a burst sagittal suture caused by an occipito-frontal gunshot wound

2021 ◽  
Author(s):  
Dominic Gascho ◽  
Michael J. Thali ◽  
Rosa M. Martinez ◽  
Stephan A. Bolliger
Keyword(s):  
Gunshot Wound ◽  
Three Dimensional ◽  
The Novel ◽  
Sagittal Suture ◽  
Resonance Imaging ◽  
Cinematic Rendering ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

AbstractThe computed tomography (CT) scan of a 19-year-old man who died from an occipito-frontal gunshot wound presented an impressive radiating fracture line where the entire sagittal suture burst due to the high intracranial pressure that arose from a near-contact shot from a 9 mm bullet fired from a Glock 17 pistol. Photorealistic depictions of the radiating fracture lines along the cranial bones were created using three-dimensional reconstruction methods, such as the novel cinematic rendering technique that simulates the propagation and interaction of light when it passes through volumetric data. Since the brain had collapsed, depiction of soft tissue was insufficient on CT images. An additional magnetic resonance imaging (MRI) examination was performed, which enabled the diagnostic assessment of cerebral injuries.

scholarly journals HIV Infection Is Associated with Attenuated Frontostriatal Intrinsic Connectivity: A Preliminary Study

2015 ◽  
Vol 21 (3) ◽  
pp. 203-213 ◽  
Author(s):  
Jonathan C. Ipser ◽  
Gregory G. Brown ◽  
Amanda Bischoff-Grethe ◽  
Colm G. Connolly ◽  
Ronald J. Ellis ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Regions ◽  
Group Differences ◽  
Hiv Rna ◽  
Functional Connections ◽  
Intrinsic Connectivity ◽  
Dorsolateral Prefrontal ◽  
Independent Replication ◽  
Subcortical Regions ◽  
The Brain

AbstractHIV-associated cognitive impairments are prevalent, and are consistent with injury to both frontal cortical and subcortical regions of the brain. The current study aimed to assess the association of HIV infection with functional connections within the frontostriatal network, circuitry hypothesized to be highly vulnerable to HIV infection. Fifteen HIV-positive and 15 demographically matched control participants underwent 6 min of resting-state functional magnetic resonance imaging (RS-fMRI). Multivariate group comparisons of age-adjusted estimates of connectivity within the frontostriatal network were derived from BOLD data for dorsolateral prefrontal cortex (DLPFC), dorsal caudate and mediodorsal thalamic regions of interest. Whole-brain comparisons of group differences in frontostriatal connectivity were conducted, as were pairwise tests of connectivity associations with measures of global cognitive functioning and clinical and immunological characteristics (nadir and current CD4 count, duration of HIV infection, plasma HIV RNA). HIV – associated reductions in connectivity were observed between the DLPFC and the dorsal caudate, particularly in younger participants (<50 years, N=9). Seropositive participants also demonstrated reductions in dorsal caudate connectivity to frontal and parietal brain regions previously demonstrated to be functionally connected to the DLPFC. Cognitive impairment, but none of the assessed clinical/immunological variables, was also associated with reduced frontostriatal connectivity. In conclusion, our data indicate that HIV is associated with attenuated intrinsic frontostriatal connectivity. Intrinsic connectivity of this network may therefore serve as a marker of the deleterious effects of HIV infection on the brain, possibly via HIV-associated dopaminergic abnormalities. These findings warrant independent replication in larger studies. (JINS, 2015, 21, 1–11)

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals The emulation theory of representation: Motor control, imagery, and perception

2004 ◽  
Vol 27 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Rick Grush
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Sensory Input ◽  
Neural Circuits ◽  
Sensory Information ◽  
The Body ◽  
Feedback Delay ◽  
Run Off ◽  
Effects Of Feedback ◽  
The Brain

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

Adaptation of Rehabilitation System Based on User’s Mental Engagement

2015 ◽  
Author(s):  
Ehsan T. Esfahani ◽  
Shrey Pareek ◽  
Pramod Chembrammel ◽  
Mostafa Ghobadi ◽  
Thenkurussi Kesavadas
Keyword(s):  
Brain Activity ◽  
Difficulty Level ◽  
The Novel ◽  
Theta Band ◽  
Robotic Rehabilitation ◽  
Designed Experiments ◽  
Power Spectral ◽  
Rehabilitation System ◽  
Alpha Beta ◽  
The Brain

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated. Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.

Sign in / Sign up

Export Citation Format

Share Document