Low-dimensional controllability of brain networks DOI Creative Commons

Remy Ben Messaoud,

Vincent Le Du,

Brigitte C. Kaufmann

et al.

arXiv (Cornell University), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Network controllability is a powerful tool to study causal relationships in complex systems and identify the driver nodes for steering network dynamics into desired states. However, due ill-posed conditions, results become unreliable when number of drivers becomes too small compared size. This very common situation, particularly real-world applications, where possibility access multiple at same time limited by technological constraints, such as human brain. Although targeting smaller parts might improve accuracy, challenges may remain extremely unbalanced situations, example there one single driver. To address this problem, we developed mathematical framework that combines concepts from spectral graph theory modern science. Instead controlling original dynamics, aimed control its low-dimensional embedding topological space derived Laplacian. By performing extensive simulations on synthetic networks, showed relatively low projected components enough overall notably dealing with few drivers. Based these findings, introduced alternative metrics used them main areas connectome obtained N=6134 healthy individuals UK-biobank cohort. Results revealed previously unappreciated influential regions standard approaches, enabled draw maps between distinct specialized large-scale brain systems, yielded an anatomically-based understanding hemispheric functional lateralization. Taken together, our offered theoretically-grounded solution deal real-life applications provided insights interactions

Language: Английский

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
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

28
Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics DOI Creative Commons
Andrea I. Luppi, Helena M. Gellersen, Zhen-Qi Liu

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 4, 2024

Abstract Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate function through network science. Here, we systematically evaluate 768 data-processing pipelines for reconstruction from resting-state functional MRI, evaluating the effect of parcellation, connectivity definition, and global signal regression. Our criteria seek that minimise motion confounds spurious test-retest discrepancies topology, while being sensitive both inter-subject differences experimental effects interest. We reveal vast systematic variability across pipelines’ suitability connectomics. Inappropriate choice pipeline produce results are not only misleading, but so, with majority failing at least one criterion. However, set optimal consistently satisfy all different datasets, spanning minutes, weeks, months. provide full breakdown each pipeline’s performance inform future best practices in

Language: Английский

Citations

22
Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain DOI Creative Commons
Andrea I. Luppi, Lynn Uhrig, Jordy Tasserie

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 11, 2024

Abstract A central challenge of neuroscience is to elucidate how brain function supports consciousness. Here, we combine the specificity focal deep stimulation with fMRI coverage entire cortex, in awake and anaesthetised non-human primates. During propofol, sevoflurane, or ketamine anaesthesia, subsequent restoration responsiveness by electrical thalamus, investigate loss consciousness impacts distributed patterns structure-function organisation across scales. We report that activity under anaesthesia increasingly constrained structure scales, coinciding anaesthetic-induced collapse multiple dimensions hierarchical cortical organisation. These signatures are observed different anaesthetics, they reversed recovery behavioural markers arousal. No such effects were upon stimulating ventral lateral demonstrating specificity. Overall, identify consistent orchestrated specific thalamic nuclei.

Language: Английский

Citations

13
Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis DOI Creative Commons
Francesco Alberti, Arianna Menardi, Daniel S. Margulies

et al.

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(3)

Published: Feb. 15, 2025

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that interindividual variability connectivity (FC) profiles reflects differences fluid intelligence. To provide an additional perspective into this relationship, present study uses recent framework investigating cortical organization: gradients. approach places local within common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, data-driven model association between FC For one these loci, right ventral-lateral prefrontal (vlPFC), we describe intelligence relative distance area from sensory high-cognition systems. Furthermore, topological properties region indicate that, with decreasing affinity systems, vlPFC connections more evenly distributed across all networks. Participating multiple networks may reflect better ability coordinate high-order

Language: Английский

Citations

1
Unravelling consciousness and brain function through the lens of time, space, and information DOI Creative Commons
Andrea I. Luppi, Fernando Rosas, Pedro A. M. Mediano

et al.

Trends in Neurosciences, Journal Year: 2024, Volume and Issue: 47(7), P. 551 - 568

Published: May 31, 2024

Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among major challenges that neuroscientists face. Pharmacological pathological perturbations consciousness provide a lens to investigate these complex challenges. Here, we review recent advances about brain's functional organisation have been driven by common denominator: decomposing function into fundamental constituents time, space, information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple structure. Convergent effects also emerge: anaesthetics, psychedelics, disorders can exhibit similar reconfigurations unimodal-transmodal axis. Decomposition approaches reveal potential translate discoveries species, with computational modelling providing path towards mechanistic integration.

Language: Английский

Citations

5
Neural dynamics of semantic control underlying generative storytelling DOI Creative Commons
Clara Rastelli, Antonino Greco, Chiara Finocchiaro

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 18, 2024

Storytelling has been pivotal for the transmission of knowledge and cultural norms across human history. A crucial process underlying generation narratives is exertion cognitive control on semantic representations stored in memory, a phenomenon referred as control. Despite extensive literature investigating neural mechanisms generative language tasks, little effort done towards storytelling under naturalistic conditions. Here, we probed participants to generate stories response set instructions which triggered narrative that was either appropriate (ordinary), novel (random), or balanced (creative), while recording functional magnetic resonance imaging (fMRI) signal. By leveraging deep models, demonstrated how ideally level during story generation. At level, creative were differentiated by multivariate pattern activity frontal cortices compared ordinary ones fronto- temporo-parietal with respect randomly generated stories. Crucially, similar brain regions also encoding features distinguished behaviourally. Moreover, decomposed dynamics into connectome harmonic modes found specific spatial frequency patterns modulation Finally, different coupling within between default mode, salience networks when contrasting their controls. Together, our findings highlight regulation exploration ideation contribute deeper understanding underpinning role storytelling.

Language: Английский

Citations

4
Low-dimensional controllability of brain networks DOI Creative Commons

Remy Ben Messaoud,

Vincent Le Du,

Camile Bousfiha

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(1), P. e1012691 - e1012691

Published: Jan. 7, 2025

Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances control theory, results remain inaccurate as number drivers becomes too small compared size, thus limiting concrete usability many real-life applications. To overcome this issue, we introduced framework that integrates principles spectral graph theory and output controllability project state into smaller topological space formed by Laplacian structure. Through extensive simulations on synthetic real networks, showed relatively low projected components can significantly improve accuracy. By introducing new low-dimensional metric experimentally validated our method N = 6134 human connectomes obtained UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled draw directed maps between differently specialized cerebral systems, yielded insights hemispheric lateralization. Taken together, offered theoretically grounded solution deal with provided brain.

Language: Английский

Citations

0
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity DOI Creative Commons
Joan Rué‐Queralt, Katharina Glomb, David Pascucci

et al.

NeuroImage, Journal Year: 2021, Volume and Issue: 244, P. 118611 - 118611

Published: Sept. 21, 2021

The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., connectome. Here, we explore how connectivity can improve representation brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, MRI, diffusion MRI), represent electrical at cortical surface as time-varying composition harmonic modes connectivity. These are known connectome harmonics. Here describe signal combination We term this description spectrum signal. found that: first, represented more compactly than traditional area-based representation; second, characterizes fast dynamics in terms broadcasting profile, revealing different temporal regimes integration segregation that consistent across participants. And last, with fewer degrees freedom representations. Specifically, show smaller number dimensions capture differences between low-level high-level visual processing spectrum. Also, demonstrate harmonics sensitively topological properties activity. In summary, work provides statistical, functional, evidence indicating fosters comprehensive understanding large-scale dynamic functioning.

Language: Английский

Citations

24
Methods for decoding cortical gradients of functional connectivity DOI Creative Commons
Julio A. Peraza, Taylor Salo,

Michael C. Riedel

et al.

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 32

Published: Jan. 1, 2024

Abstract Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies demonstrated that principal gradient of connectivity in the human exists, with unimodal primary sensorimotor regions situated at one end and transmodal associated default mode network representative abstract functioning other. The significance interpretation macroscale remains topic discussion neuroimaging community, some demonstrating may be described using meta-analytic decoding techniques. However, additional methodological development is necessary to fully leverage available methods resources quantitatively evaluate their relative performance. Here, we conducted comprehensive series analyses investigate improve framework data-driven, methods, thereby establishing principled approach segmentation decoding. We found two-segment solution determined by k-means an LDA-based meta-analysis combined NeuroQuery database was optimal combination gradients. Finally, proposed method components decomposition. current work aims provide recommendations on best practices flexible gradient-based fMRI data.

Language: Английский

Citations

3