Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/404418

Organization of state transitions in the resting-state human cerebral cortex

Kang, J.·Pae, C.·Park, H.-J.
View original source ↗Export metadata ↓

Abstract

The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretic analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by nonlinear systematic approach.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kang, J., Pae, C., Park, H.-J.. 2018-08-31. Organization of state transitions in the resting-state human cerebral cortex. https://doi.org/10.1101/404418

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Why probe registration is crucial for infant fNIRS and HD-DOT? An evaluation across 49 newborns

Source-detector probe registration, i.e., the mapping of optode positions on the scalp to their true anatomical locations in head/brain space, is crucial for brain imaging with fNIRS or HD-DOT. While individual, MRI-based registration is desirable, it is not always feasible, motivating the development of alternative registration approaches. This study uses the STORM-Net registration protocol and quantifies the variation of cap alignment in a cohort of 49 newborns to evaluate how this affects reconstructed images using HD-DOT. Relative to a centrally aligned template, the cohort showed a median probe-position error of 10.12 mm, while the least and most misaligned subjects exhibited mean errors of 4.41 mm and 20.60 mm, respectively. The results demonstrate that the level of cap misalignment typically observed in newborn imaging can affect the spatial accuracy of reconstructed functional images, particularly in cases with larger probe displacements. These findings provide a quantitative basis for adopting more conservative interpretations of group-level functional brain activity and functional connectivity results, and strongly support the routine use of registration methods in infant HD-DOT studies.

neuroscience↗
bioRxiv2026

Donor Aware Reanalysis of Neuronal Nuclei Supports STMN2 Depletion Associated with Nuclear TDP 43 loss in FTD and ALS

Background. Nuclear TDP-43 depletion is a central pathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but donor-to-donor variation can obscure transcriptomic associations in small postmortem cohorts. We reanalyzed public RNA-seq data from FACS-isolated NeuN-positive frontal-cortex neuronal nuclei to quantify gene-expression changes associated with nuclear TDP-43 loss while explicitly accounting for donor structure. Methods. The supplied GSE126542-derived matrix contained 62,754 genes across 12 libraries from seven donors, including five complete matched pairs. Source labels TDP-43 positive and TDP-43 negative were harmonized as nuclear TDP-43 retained and lost, respectively. After validation against featureCounts-assigned read counts, 44,225 genes with at least 10 total counts were analyzed using PyDESeq2. The primary model was ~ donor + nuclear_status; status-only and diagnosis-adjusted models were sensitivity analyses. The contrast was TDP-43 loss minus retained. Results. The donor-aware model identified 3,442 genes at false discovery rate (FDR) < 0.05: 1,865 lower and 1,577 higher in TDP-43-loss nuclei. STMN2 was consistently depleted (log2 fold change = -1.813, 95% Wald interval -2.229 to -1.396; FDR = 6.22e-15), corresponding to approximately 71.5% lower expression. Normalized STMN2 counts were lower in TDP-43-loss nuclei in all five complete donor pairs. TARDBP expression was also lower (log2 fold change = -0.606; FDR = 4.98e-6). In contrast, UNC13A whole-gene expression was modestly higher in the donor-aware model (log2 fold change = 0.372; FDR = 0.00622) and was not significant in either sensitivity model. Conclusions. Donor-aware analysis supports a robust association between nuclear TDP-43 loss and STMN2 depletion in this neuronal-nuclei cohort, alongside extensive but model-dependent transcriptomic variation. Whole-gene UNC13A expression cannot be used to infer UNC13A cryptic-exon inclusion or functional UNC13A depletion. No splice-junction, percent-spliced-in, isoform, or protein measurements were available; mechanistic claims therefore require junction-level and experimental validation.

neuroscience↗
bioRxiv2026

Photoentrainment as Bayesian Inference: How the central circadian clock may weight and integrate the intensity and wavelength of natural light to estimate astronomical time

Synchronising with the Earth's 24-hour rotation using natural light as a timing cue presents a problem for the mammalian brain's central circadian clock. Natural light is highly variable and can take a range of values at any given time of day. For example, it fluctuates due to differences in weather conditions and across seasons. Therefore, an absolute value of light provides an uncertain indication of the time of day. Despite this uncertainty, the central clock maintains close alignment with astronomical time, and here we describe how this may be achieved computationally. We present a Bayesian model in which the clock's endogenous oscillation is used to predict the probability of observing features of light, including intensity and spectral composition, and their co-occurrence given the time of day. These predictions are drawn from a distribution of values typically experienced. When observed light falls outside the predicted range, a prediction error provides evidence that the clock may be misaligned with astronomical time. How much the clock adjusts its phase depends on the relative uncertainty associated with its internal prediction and observed light. These are combined as a weighted average according to their relative reliability, drawing the clock's updated phase towards whichever provides the more reliable estimate of astronomical time. Using natural light measurements collected over one week from 59 human observers and 100 days of environmental spectral measurements, we characterize how the variability of natural light systematically changes across the day and show how the clock may exploit this structure for circadian timekeeping. The model explains how the canonical shape of the phase response curve emerges from the relationship between light distributions and time of day, and how properties of light, including intensity and spectral composition, can be integrated according to their reliability. The central clock's synchrony to light may represent a specific instance of a general computational strategy employed by the mammalian nervous system, whereby sensory information is interpreted relative to an internally generated prediction.

neuroscience↗