2,176 research outputs found
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity
Functional connectivity network (FCN) data from functional magnetic resonance
imaging (fMRI) is increasingly used for the diagnoses of brain disorders.
However, state-of-the-art studies used to build the FCN using a single brain
parcellation atlas at a certain spatial scale, which largely neglected
functional interactions across different spatial scales in hierarchical
manners. In this study, we propose a novel framework to perform multiscale FCN
analysis for brain disorder diagnosis. We first use a set of well-defined
multiscale atlases to compute multiscale FCNs. Then, we utilize biologically
meaningful brain hierarchical relationships among the regions in multiscale
atlases to perform nodal pooling across multiple spatial scales, namely
"Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based
Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers
of graph convolution and the atlas-guided pooling, for a comprehensive
extraction of diagnostic information from multiscale FCNs. Experiments on
neuroimaging data from 1792 subjects demonstrate the effectiveness of our
proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal
stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum
disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All
results show significant advantages of our proposed method over other competing
methods. This study not only demonstrates the feasibility of brain disorder
diagnosis using resting-state fMRI empowered by deep learning, but also
highlights that the functional interactions in the multiscale brain hierarchy
are worth being explored and integrated into deep learning network
architectures for better understanding the neuropathology of brain disorders
Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications
Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic
resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of
Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity
underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the
use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to
cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers
have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic,
and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity
across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power
and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral
reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains
in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163
Cyber-physical systems in manufacturing: Future trends and research priorities
In the last decades, the manufacturing ecosystem witnessed an unprecedented evolution of disruptive technologies forging new opportunities for manufacturing companies to cope the ever-growing market pressure. Moreover, the race to create value for the customers has been hindered by several issues that both small and large companies have been facing, such as shorter product life cycles, rapid time-to-market, product complexity, cost pressure, increased international competition, etc. In this scenario, ICT represent a crucial enabler for preserving competitiveness and fostering industry innovation. In particular, among these technologies, Cyber-Physical Systems (CPS) is growing an ever-high interest of industry stakeholders, researchers, practitioners and policy makers as they are considered the key technology that will transform manufacturing industry to the next generation. Indeed, CPS is a breakthrough research area for ICT in manufacturing and represents the cornerstone for achieving the EU2020 "smart everywhere" vision. At this early development phase, there is the urgent need to set the ground for future research streams, create a common understanding and consensus, define viable migration paths and support standards definition. This paper describes the identified research challenges and the future trends that will drive to the adoption of CPS in manufacturing. The main evidences on researches challenges expected for CPS in manufacturing are outlined by the authors that have been involved in the sCorPiuS project 'European Roadmap for Cyber- Physical Systems in Manufacturing', promoted by the European Commission to define a roadmap for future CPS in manufacturing adoption research agenda
Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis
The human brain’s resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person’s rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis)
Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis
The online version contains supplementary material
available at https://doi.org/10.1038/s41380-023-02039-6The human brain’s resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual,
cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person’s
rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for
vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the
same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder
(n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of
the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of
diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory
hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences
were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the
subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal
and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups
strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of
personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and
mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic
psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive
systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction
(which differs by diagnosis).EU FEDER grants through the Spanish Ministry of Science and Technology
PID2021-125017OB-I00,
RTI2018-098983-B-I00,
D43 TW011793-06A1,
PID2021-125017OB-I00,
RTI2018-098983-B-I00,
D43 TW011793-06A1United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
R01-MH124060Psychosis-Risk Outcomes Network
U01 MH12463
Neuro-WiFi: A Novel Neuronal Connection Underlies the Potential Interventional Target
Neuro-WiFi, as a non-physical connection-related neural network that efficiently links various regions of the brain, facilitates swift transfer of information and fostering communication among neurons. It is a significant advancement in neuroscience, providing valuable understanding of the intricate connections between neurons and opening up possibilities for precise interventions. This unique neural connection entails the transfer of information between remote parts of the brain via a network resembling WiFi signal. Neuro-WiFi has the potential to greatly enhance our understanding of how information is processed and sent in the brain by facilitating fast and accurate communication over long distances. Envision the ability to modify the neuro-WiFi network to enhance cognitive performance or restore impaired neural circuits. Furthermore, this neuronal connection could have substantial ramifications for the development of therapeutic approaches to address neurological conditions like Alzheimer’s disease or epilepsy. Despite the remaining knowledge gaps around this remarkable phenomenon, through additional investigations, we believe that the mysteries of neuro-WiFi would be extensively uncovered and precise therapies that could profoundly transform our comprehension of brain function and enhance patient outcomes would be provided in the future.
 
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