Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

AbstractIt is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinson’s disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous α-synuclein via axonal projections. However, the mechanisms that translate local “synucleinopathy” to large-scale network dysfunction and atrophy remain unknown. Here we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity and gene expression, and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability. Functional co-activation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

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Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

ACM Transactions on Design Automation of Electronic Systems ◽

10.1145/3480961 ◽

2022 ◽

Vol 27 (1) ◽

pp. 1-30

Author(s):

Mengke Ge ◽

Xiaobing Ni ◽

Xu Qi ◽

Song Chen ◽

Jinglei Huang ◽

...

Keyword(s):

Large Scale ◽

Small World ◽

Brain Network ◽

Graph Processing ◽

Hop Count ◽

Scale Free ◽

Large Scale Network ◽

Average Latency ◽

Scale Network ◽

The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

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Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

10.1101/2021.04.12.439440 ◽

2021 ◽

Author(s):

Florian Krause ◽

Nikolaos Kogias ◽

Martin Krentz ◽

Michael Luehrs ◽

Rainer Goebel ◽

...

Keyword(s):

Real Time ◽

Executive Control ◽

Large Scale ◽

Acute Stress ◽

Self Regulation ◽

Real Life ◽

Brain Network ◽

Large Scale Network ◽

Novel Approach ◽

Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

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Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

Brain Topography ◽

10.1007/s10548-018-0688-x ◽

2018 ◽

Vol 32 (2) ◽

pp. 304-314 ◽

Cited By ~ 5

Author(s):

Fali Li ◽

Chanlin Yi ◽

Limeng Song ◽

Yuanling Jiang ◽

Wenjing Peng ◽

...

Keyword(s):

Network Analysis ◽

Motor Imagery ◽

Large Scale ◽

Brain Network ◽

Network Reconfiguration ◽

Scalp Eeg ◽

Large Scale Network ◽

Scale Network

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How brain reacts to targeted attack at a hub region

10.1101/767863 ◽

2019 ◽

Author(s):

Wenyu Tu ◽

Zilu Ma ◽

Yuncong Ma ◽

Nanyin Zhang

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Network ◽

Anterior Cingulate ◽

Dorsal Anterior Cingulate Cortex ◽

Node Activity ◽

Large Scale Network ◽

Scale Network ◽

Network Properties ◽

Multiple Species

AbstractThe architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in awake rodents, we investigated the causal impact of acutely inactivating a hub region (i.e. dorsal anterior cingulate cortex) on brain network properties. We found that disrupting hub activity profoundly changed the function the default-mode network (DMN), and this change was associated with altered DMN-related behavior. Suppressing hub activity also impacted the topological architecture of the whole-brain network in network resilience, segregation and small worldness, but not network integration. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. This study opens an avenue of manipulating brain networks by controlling hub-node activity.

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Uncovering the transcriptional signatures of hub connectivity in neural networks

10.31234/osf.io/7j4s2 ◽

2018 ◽

Author(s):

Aurina Arnatkeviciute ◽

Ben Fulcher ◽

Alex Fornito

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Large Scale ◽

Expression Patterns ◽

Genetic Influences ◽

Gene Expression Patterns ◽

Small Subset ◽

Present Evidence ◽

Large Scale Network ◽

Scale Network

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different of species and scales ranging from C.elegans to mouse, rat, cat, macaque and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

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Visual Attention

Oxford Research Encyclopedia of Neuroscience ◽

10.1093/acrefore/9780190264086.013.79 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sabine Kastner ◽

Timothy J. Buschman

Keyword(s):

Visual Processing ◽

Large Scale ◽

Relevant Information ◽

Neural Mechanisms ◽

Natural Scenes ◽

Subcortical Structures ◽

Network Nodes ◽

Large Scale Network ◽

Scale Network ◽

Selection Of

Natural scenes are cluttered and contain many objects that cannot all be processed simultaneously. Due to this limited processing capacity, neural mechanisms are needed to selectively enhance the information that is most relevant to one’s current behavior and to filter unwanted information. We refer to these mechanisms as “selective attention.” Attention has been studied extensively at the behavioral level in a variety of paradigms, most notably, Treisman’s visual search and Posner’s paradigm. These paradigms have also provided the basis for studies directed at understanding the neural mechanisms underlying attentional selection, both in the form of neuroimaging studies in humans and intracranial electrophysiology in non-human primates. The selection of behaviorally relevant information is mediated by a large-scale network that includes regions in all major lobes as well as subcortical structures. Attending to a visual stimulus modulates processing across the visual processing hierarchy with stronger effects in higher-order areas. Current research is aimed at characterizing the functions of the different network nodes as well as the dynamics of their functional connectivity.

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Neuropsychological evidence of multi-domain network hubs in the human thalamus

10.1101/2021.05.10.443403 ◽

2021 ◽

Author(s):

Kai Hwang ◽

James M. Shine ◽

Joel Bruss ◽

Daniel Tranel ◽

Aaron Boes

Keyword(s):

Large Scale ◽

Brain Network ◽

Human Cognition ◽

Cognitive Domains ◽

Network Analyses ◽

Large Scale Network ◽

Human Thalamus ◽

Scale Network ◽

Neuropsychological Evidence ◽

Thalamic Function

Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.

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