Functional annotation of human cognitive states using deep graph convolution DOI Creative Commons
Yu Zhang, Loïc Tetrel, Bertrand Thirion

et al.

NeuroImage, Journal Year: 2021, Volume and Issue: 231, P. 117847 - 117847

Published: Feb. 15, 2021

A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach "brain decoding", which consists inferring a set experimental conditions performed by participant, using pattern classification activity. Few works so far have attempted train decoding model that would generalize across many different tasks drawn from multiple domains. To tackle this problem, we proposed multidomain decoder automatically learns the spatiotemporal dynamics response within short time window deep learning approach. We evaluated on large population 1200 participants, under 21 spanning six domains, acquired Human Connectome Project task-fMRI database. Using 10s fMRI response, states were identified with test accuracy 90% (chance level 4.8%). Performance remained good when 6s (82%). It was even feasible decode single volume (720ms), performance following shape hemodynamic response. Moreover, saliency map analysis demonstrated high driven biologically meaningful regions. Together, provide an automated tool annotate human activity fine temporal resolution and granularity. Our shows potential applications as reference for domain adaptation, possibly making contributions variety including neurological psychiatric disorders.

Language: Английский

The complex ecological network’s resilience of the Wuhan metropolitan area DOI Creative Commons
Tao Wang, Hongbo Li, Yue Huang

et al.

Ecological Indicators, Journal Year: 2021, Volume and Issue: 130, P. 108101 - 108101

Published: Aug. 12, 2021

With rapid urbanization and frequent disasters, regional ecosystem resilience decreased continuously. Strengthening the of ecological network is conducive to improving benefits quality products. The research on networks increasingly concerned, it necessary construct a comprehensive framework evaluate networks. Taking Wuhan metropolitan area as case, this aimed constructs an evaluates from perspective complex Firstly, we evaluation Index structure function dimensions. Secondly, regions with high importance are selected sources according landscape connectivity. Thirdly, MCR model used establish network. Finally, analyzed characteristics under different node failure scenarios. results show that: (1) Ecological nodes correspond wide variety land types, including forest, water bodies, croplands, urban build-up; (2) overall connection corridor relatively main components forest bodies; (3) trend structural functional does not always convergence shock simulation which related redundancy will help analyze provide references for optimization improvement sustainable management restoration.

Language: Английский

Citations

136
Distributed harmonic patterns of structure-function dependence orchestrate human consciousness DOI Creative Commons
Andrea I. Luppi, Jakub Vohryzek, Morten L. Kringelbach

et al.

Communications Biology, Journal Year: 2023, Volume and Issue: 6(1)

Published: Jan. 28, 2023

Abstract A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals pathological pharmacologically-induced perturbations into distributed patterns structure-function dependence across scales: harmonic modes human structural connectome. We show that coupling a generalisable indicator under bi-directional neuromodulatory control. find increased scales during loss consciousness, whether due to anaesthesia or injury, capable discriminating between behaviourally indistinguishable sub-categories brain-injured patients, tracking presence covert consciousness. The opposite signature characterises altered state induced by LSD ketamine, reflecting psychedelic-induced decoupling function correlating with physiological subjective scores. Overall, connectome decomposition reveals neuromodulation network architecture jointly shape activation scales.

Language: Английский

Citations

69
Graph Signal Processing: History, development, impact, and outlook DOI Open Access
Geert Leus, Antonio G. Marqués, José M. F. Moura

et al.

IEEE Signal Processing Magazine, Journal Year: 2023, Volume and Issue: 40(4), P. 49 - 60

Published: June 1, 2023

Graph signal processing (GSP) generalizes (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able model irregular interactions, easy interpret, and endowed with corpus of mathematical results, rendering them natural candidates serve as the basis for theory in more domains. In this article, we provide an overview evolution GSP, from its origins challenges ahead. The first half is devoted reviewing history GSP explaining how it gave rise encompassing framework that shares multiple similarities SP. A key message has been critical develop novel technically sound tools, theory, algorithms that, leveraging analogies insights digital SP, new ways analyze, process, learn graph signals. second half, shift focus review impact other disciplines. First, look at use data science problems, including learning graph-based deep learning. Second, discuss applications, neuroscience image video processing. We conclude brief discussion emerging future directions GSP.

Language: Английский

Citations

50
Structure–function coupling in macroscale human brain networks DOI
Panagiotis Fotiadis, Linden Parkes, Kathryn A. Davis

et al.

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(10), P. 688 - 704

Published: Aug. 5, 2024

Language: Английский

Citations

27
Structural-functional brain network coupling predicts human cognitive ability DOI Creative Commons
Johanna L. Popp, Jonas A. Thiele, Joshua Faskowitz

et al.

NeuroImage, Journal Year: 2024, Volume and Issue: 290, P. 120563 - 120563

Published: March 16, 2024

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of human brain. Network neuroscience investigations revealed neural correlates GCA structural as well functional brain networks. However, whether relationship between networks, structural-functional network coupling (SC-FC coupling), is related to individual remains an open question. We used data from 1030 adults Human Connectome Project, derived connectivity diffusion weighted imaging, resting-state fMRI, assessed latent g-factor 12 tasks. Two similarity measures six communication were model possible interactions arising SC-FC was estimated degree which these align with actual connectivity, providing insights into different strategies. At whole-brain level, higher associated coupling, but only when considering path transitivity strategy. Taking region-specific variations strategy account differentiating positive negative associations GCA, allows for prediction scores cross-validated framework (correlation predicted observed scores: r = .25, p < .001). The same also predicts completely independent sample (N 567, .19, Our results propose neurobiological correlate suggest strategies efficient information processing predictive ability.

Language: Английский

Citations

18
Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression DOI Creative Commons
Ming Xu, Xuemei Li, Teng Teng

et al.

JAMA Network Open, Journal Year: 2024, Volume and Issue: 7(3), P. e241933 - e241933

Published: March 12, 2024

Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the alterations adolescent MDD focused on single-modal images or analyzed different modalities separately, ignoring potential role aberrant interactions between structure function psychopathology.

Language: Английский

Citations

17
Diversity of meso-scale architecture in human and non-human connectomes DOI Creative Commons
Richard F. Betzel, John D. Medaglia, Danielle S. Bassett

et al.

Nature Communications, Journal Year: 2018, Volume and Issue: 9(1)

Published: Jan. 18, 2018

The brain's functional diversity is reflected in the meso-scale architecture of its connectome, i.e. division into clusters and communities topologically-related brain regions. dominant view, one that reinforced by current analysis techniques, are strictly assortative segregated from another, purportedly for purpose carrying out specialized information processing. Such a however, precludes possibility non-assortative could engender richer repertoire allowing more complex set inter-community interactions. Here, we use weighted stochastic blockmodels to uncover \emph{Drosophila}, mouse, rat, macaque, human connectomes. We confirm while many assortative, others form core-periphery disassortative structures, which better recapitulate observed patterns connectivity mouse gene co-expression than other community detection techniques. define network measures quantifying types regions participate. Finally, show peaked control subcortical systems humans, individual differences within those predicts cognitive performance on Stroop Navon tasks. In summary, our report paints diverse portrait connectome structure demonstrates relevance performance.

Language: Английский

Citations

151
The two-fold model of creativity: the neural underpinnings of the generation and evaluation of creative ideas DOI
Oded M. Kleinmintz, Tal Ivancovsky, Simone Shamay‐Tsoory

et al.

Current Opinion in Behavioral Sciences, Journal Year: 2018, Volume and Issue: 27, P. 131 - 138

Published: Dec. 17, 2018

Language: Английский

Citations

133
Modeling and interpreting mesoscale network dynamics DOI Creative Commons
Ankit N. Khambhati, Ann E. Sizemore, Richard F. Betzel

et al.

NeuroImage, Journal Year: 2017, Volume and Issue: 180, P. 337 - 349

Published: June 21, 2017

Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain mechanistic understanding not just circuit structure, but also dynamics, its role cognition disease. Such necessitates description the raw observations, delineation computational models mathematical theories that accurately capture fundamental principles behind observations. Here we review recent range modeling approaches embrace temporally-evolving interconnected structure summarize dynamic graph. We describe efforts model patterns connectivity, activity, activity atop connectivity. In context these models, important considerations statistical testing, including parametric non-parametric approaches. Finally, offer thoughts on careful accurate interpretation graph architecture, outline future directions for method development.

Language: Английский

Citations

130
Driving the brain towards creativity and intelligence: A network control theory analysis DOI
Yoed N. Kenett, John D. Medaglia, Roger E. Beaty

et al.

Neuropsychologia, Journal Year: 2018, Volume and Issue: 118, P. 79 - 90

Published: Jan. 4, 2018

Language: Английский

Citations

111