Hemoglobin signal network mapping reveals novel indicators for precision medicine DOI Creative Commons

Randall L. Barbour,

Harry L. Graber

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Oct. 25, 2023

Precision medicine currently relies on a mix of deep phenotyping strategies to guide more individualized healthcare. Despite being widely available and information-rich, physiological time-series measures are often overlooked as resource extend insights gained from such measures. Here we have explored resting-state hemoglobin applied intact whole breasts for two subject groups - women with confirmed breast cancer control subjects the goal achieving detailed assessment phenotype non-invasive measure. Invoked is novel ordinal partition network method multivariate that generates Markov chain, thereby providing access quantitative descriptions short-term dynamics in form several classes adjacency matrices. Exploration these their associated co-dependent behaviors unexpectedly reveals features structured dynamics, some which shown exhibit enzyme-like sensitivity recognized molecular markers disease. Thus, findings obtained strongly indicate despite use macroscale sensing method, typical molecular-cellular processes can be identified. Discussed factors unique our approach favor deeper depiction tissue phenotypes, its extension other forms measures, expected utility advance goals precision medicine.

Language: Английский

Partial entropy decomposition reveals higher-order information structures in human brain activity DOI Creative Commons
Thomas F. Varley, Maria Pope,

Maria Grazia

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(30)

Published: July 19, 2023

The standard approach to modeling the human brain as a complex system is with network, where basic unit of interaction pairwise link between two regions. While powerful, this limited by inability assess higher-order interactions involving three or more elements directly. In work, we explore method for capturing dependencies in multivariate data: partial entropy decomposition (PED). Our decomposes joint whole into set nonnegative atoms that describe redundant, unique, and synergistic compose system's structure. PED gives insight mathematics functional connectivity its limitation. When applied resting-state fMRI data, find robust evidence synergies are largely invisible analyses. can also be localized time, allowing frame-by-frame analysis how distributions redundancies change over course recording. We different ensembles regions transiently from being redundancy-dominated synergy-dominated temporal pattern structured time. These results provide strong there exists large space unexplored structures data have been missed focus on bivariate network models. This structure dynamic time likely will illuminate interesting links behavior. Beyond brain-specific application, provides very general understanding variety systems.

Language: Английский

Citations

58
Resting-state BOLD temporal variability of the default mode network predicts spontaneous mind wandering, which is negatively associated with mindfulness skills DOI Creative Commons
Sara Sorella, Cristiano Crescentini, Alessio Matiz

et al.

Frontiers in Human Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Jan. 22, 2025

Mind wandering (MW) encompasses both a deliberate and spontaneous disengagement of attention from the immediate external environment to unrelated internal thoughts. Importantly, MW has been suggested have an inverse relationship with mindfulness, state nonjudgmental awareness present-moment experience. Although they are, respectively, associated increased decreased activity in default mode network (DMN), specific contributions MW, their relationships mindfulness abilities resting-state macro networks remain be elucidated. Therefore, MRI scans 76 participants were analyzed group independent component analysis decompose brain into macro-networks see which them predicted aspects or traits. Our results show that temporal variability DMN predicts turn is negatively acting facet mindfulness. This finding shows not directly overall but rather demonstrates there exists close between furthermore, involvement this dynamic may secondary. In sum, our study contributes better understanding neural bases its These open up possibility intervening on cognitive abilities: for example, data suggest training would allow lessening tendency at inopportune times.

Language: Английский

Citations

1
Structure and function in artificial, zebrafish and human neural networks DOI
Peng Ji, Yufan Wang, Thomas Peron

et al.

Physics of Life Reviews, Journal Year: 2023, Volume and Issue: 45, P. 74 - 111

Published: April 25, 2023

Language: Английский

Citations

20
LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics DOI Creative Commons
Kenneth Shinozuka, Prejaas Tewarie, Andrea I. Luppi

et al.

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

Published: April 28, 2024

Abstract Psychedelics are serotonergic drugs that profoundly alter consciousness, yet their neural mechanisms not fully understood. A popular theory, RElaxed Beliefs Under pSychedelics (REBUS), posits psychedelics flatten the hierarchy of information flow in brain. Here, we investigate based on imbalance between sending and receiving brain signals, as determined by directed functional connectivity. We measure a magnetoencephalography (MEG) dataset 16 healthy human participants who were administered psychedelic dose (75 micrograms, intravenous) lysergic acid diethylamide (LSD) under four different conditions. LSD diminishes asymmetry connectivity when averaged across time. Additionally, demonstrate machine learning classifiers distinguish placebo more accurately trained one our metrics than traditional measures Taken together, these results indicate weakens increasing balance senders receivers signals.

Language: Английский

Citations

3
Unraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology’s Diagnostic Arsenal DOI Creative Commons
Yashbir Singh, Emilio Quaia

Tomography, Journal Year: 2025, Volume and Issue: 11(1), P. 6 - 6

Published: Jan. 9, 2025

This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential medical image interpretation. TDA, which is grounded mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across imaging domains, including tumor characterization, cardiovascular COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods. The synergy between TDA artificial intelligence presents promising opportunities for enhanced diagnostic accuracy. While implementation challenges exist, ability to uncover hidden patterns positions as a transformative tool modern radiology.

Language: Английский

Citations

0
Dynamical measures of developing neuroelectric fields in emerging consciousness DOI Creative Commons
William J. Bosl, Jenny Capua-Shenkar

Current Opinion in Behavioral Sciences, Journal Year: 2025, Volume and Issue: 61, P. 101480 - 101480

Published: Jan. 10, 2025

Language: Английский

Citations

0
Emerging Materials and Computing Paradigms for Temporal Signal Analysis DOI Open Access
Teng Zhang, Stanisław Woźniak, Syed Ghazi Sarwat

et al.

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Abstract In the era of relentless data generation and dynamic information streams, demand for efficient robust temporal signal analysis has intensified across diverse domains such as healthcare, finance, telecommunications. This perspective study explores unfolding landscape emerging materials computing paradigms that are reshaping way signals analyzed interpreted. Traditional processing techniques often fall short when confronted with intricacies time‐varying data, prompting exploration innovative approaches. The rise devices empowers real‐time by in situ, mitigating latency concerns. Through this perspective, untapped potential is highlighted, offering valuable insights into both challenges opportunities. Standing on cusp a new computing, understanding harnessing these pivotal unraveling complexities embedded within dimensions propelling realms previously deemed inaccessible.

Language: Английский

Citations

0
Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics DOI Creative Commons
Ramón Nartallo-Kaluarachchi, Leonardo Bonetti, Gemma Fernández-Rubio

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(10)

Published: March 7, 2025

Information processing in the human brain can be modeled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study global level nonequilibrium brain, quantifying irreversibility interactions among at levels remains an unresolved challenge. Here, we present Directed Multiplex Visibility Graph Irreversibility framework, method for analyzing neural recordings using network analysis time-series. Our approach constructs directed multilayer graphs from multivariate time-series where information about decoded marginal degree distributions across layers, which each represents variable. This framework is able to quantify every interaction system. Applying magnetoencephalography during long-term memory recognition task, between and identify combinations showed higher their interactions. For individual regions, find cognitive versus sensorial while pairs, strong relationships are uncovered pairs same hemisphere. triplets quadruplets, most cognitive-sensorial alongside medial regions. Combining these results, show that multilevel offers unique insights into higher-order, hierarchical organization dynamics perspective dynamics.

Language: Английский

Citations

0
Composite multi-span amplitude-aware ordinal transition network: Fine-grained representation and quantification of complex system time series DOI
Jun Huang, Xin Liu, Yizhou Li

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 197, P. 116487 - 116487

Published: May 6, 2025

Language: Английский

Citations

0
Machine learning approach for early onset dementia neurobiomarker using EEG network topology features DOI Creative Commons
Tomasz M. Rutkowski, Masato Abe, Tomasz Komendziński

et al.

Frontiers in Human Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: June 16, 2023

Modern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called "AI for social good" domain contributes to improving well-being of individuals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches neuro-biomarker feedback may be helpful older adults remain independent and improve their wellbeing. We report results on early-onset dementia neuro-biomarkers scrutinize cognitive-behavioral intervention management non-pharmacological therapies.We present an empirical task in EEG-based passive brain-computer interface application framework assess working memory forecasting mild impairment. The EEG responses are analyzed network neuroscience technique applied time series evaluation confirm initial hypothesis possible ML modeling impairment prediction.We findings from pilot study group Poland prediction. utilize two emotional tasks by analyzing facial emotions reproduced short videos. A reminiscent interior image oddball is also employed validate proposed methodology further.The three experimental current showcase critical utilization artificial intelligence prognosis adults.

Language: Английский

Citations

7