The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8569182 | PMC |
| http://dx.doi.org/10.3389/fnins.2021.715861 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Technologies for studying phase-separated biomolecular condensates.
Adv Biotechnol (Singap)
March 2024
Guangdong Provincial Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, GuangZhou, GuangDong, China.
Biomolecular condensates, also referred to as membrane-less organelles, function as fundamental organizational units within cells. These structures primarily form through liquid-liquid phase separation, a process in which proteins and nucleic acids segregate from the surrounding milieu to assemble into micron-scale structures. By concentrating functionally related proteins and nucleic acids, these biomolecular condensates regulate a myriad of essential cellular processes.
View Article and Find Full Text PDFSpatiotemporal variation in size-dependent growth rates in small isolated populations of Arctic charr ().
R Soc Open Sci
January 2025
School of Biology, University of St Andrews, St Andrews, UK.
As a key life-history trait, growth rates are often used to measure individual performance and to inform parameters in demographic models. Furthermore, intraspecific trait variation generates diversity in nature. Therefore, partitioning out and understanding drivers of spatiotemporal variation in growth rate is of fundamental interest in ecology and evolution.
View Article and Find Full Text PDFTransforming CCTV cameras into NO sensors at city scale for adaptive policymaking.
Sci Rep
January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
View Article and Find Full Text PDFHigh-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.
J Environ Manage
January 2025
Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China.
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.
View Article and Find Full Text PDFOrientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields.
J Comput Neurosci
January 2025
Computational Brain Science Lab, Division of Computational Science and Technology, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
This paper presents an in-depth theoretical analysis of the orientation selectivity properties of simple cells and complex cells, that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation. A detailed mathematical analysis is presented for the three different cases of either: (i) purely spatial receptive fields, (ii) space-time separable spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal receptive fields. Closed-form theoretical expressions for the orientation selectivity curves for idealized models of simple and complex cells are derived for all these main cases, and it is shown that the orientation selectivity of the receptive fields becomes more narrow, as a scale parameter ratio , defined as the ratio between the scale parameters in the directions perpendicular to vs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!