Hormonal dynamics shape brain structural plasticity across the menstrual cycle: Insights from dense-sampling structural brain imaging of females with and without endometriosis DOI Creative Commons
Carina Heller, Christian Gaser, Lejla Čolić

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 19, 2023

Abstract Gonadal hormone fluctuations in females have been associated with symptoms of mental health, yet the underlying brain mechanisms remain understudied. Recent advances neuroscience shifted paradigm towards longitudinal tracking, enabling detection subtle changes overlooked conventional cross-sectional analyses. This dense-sampling approach acknowledges rhythmic nature gonadal production. Our study employed three densely sampled who underwent imaging and venipuncture (5 to 7 days per week) over full menstrual cycle investigate impact variation on structure. In two healthy typical cycles, progesterone progesterone/estradiol ratios were inversely spatiotemporal structural patterns across cycle. To probe neural effects hormonal dysregulation, we a participant endometriosis, an endocrine disorder affecting 10% their reproductive years. Here, pattern was only estradiol fluctuations. findings suggest that hormones are short-term changes, distinctions observed between endometriosis cycles. emphasizes consideration individual dynamics understanding plasticity.

Language: Английский

Whole-brain dynamics across the menstrual cycle: the role of hormonal fluctuations and age in healthy women DOI Creative Commons
Daniela S. Avila‐Varela, Esmeralda Hidalgo-Lopez, Paulina Clara Dagnino

et al.

npj Women s Health, Journal Year: 2024, Volume and Issue: 2(1)

Published: April 1, 2024

Abstract Recent neuroimaging research suggests that female sex hormone fluctuations modulate brain activity. Nevertheless, how network dynamics change across the menstrual cycle remains largely unknown. Here, we investigated dynamical complexity underlying three phases (i.e., early follicular, pre-ovulatory, and mid-luteal) in 60 healthy naturally-cycling women scanned using resting-state fMRI. Our results revealed pre-ovulatory phase exhibited highest (variability over time) whole-brain functional compared to follicular mid-luteal phases, while showed lowest. Furthermore, found large-scale networks reconfigure along phases. Multilevel mixed-effects models age-related changes whole-brain, control, dorsal attention networks, estradiol progesterone influenced DMN, limbic, attention, somatomotor, subcortical networks. Overall, these findings evidence age ovarian hormones cycle.

Language: Английский

Citations

14
Living on the edge: network neuroscience beyond nodes DOI Creative Commons
Richard F. Betzel, Joshua Faskowitz, Olaf Sporns

et al.

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(11), P. 1068 - 1084

Published: Sept. 15, 2023

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This come at expense anatomical functional connections that link these to one another. A new perspective namely emphasizes 'edges' may prove fruitful in addressing outstanding questions network neuroscience. We highlight recently proposed 'edge-centric' method review its current applications, merits, limitations. also seek establish conceptual mathematical links between this previously approaches science neuroimaging literature. conclude by presenting several avenues for future work extend refine existing edge-centric analysis.

Language: Английский

Citations

22
BOLD cofluctuation ‘events’ are predicted from static functional connectivity DOI Creative Commons
Zach Ladwig, Benjamin A. Seitzman,

Ally Dworetsky

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 260, P. 119476 - 119476

Published: July 14, 2022

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low points. This suggested that events might be a discrete, temporally sparse signal drives connectivity (FC) over the timeseries. However, different, not yet explored possibility is differences between are driven by sampling variability on constant, static, noisy signal. Using combination real and simulated data, we examined relationship structure asked if this was unique, or it could arise from alone. First, show discrete - there gradually increasing cofluctuation; ∼50% samples very strong structure. Second, using simulations predicted static FC. Finally, randomly selected can capture about as well events, largely because their temporal spacing. Together, these results suggest that, while exhibit particularly representations FC, little evidence unique timepoints drive FC Instead, parsimonious explanation for data but noisy,

Language: Английский

Citations

27
Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery DOI Creative Commons
Sebastián Idesis, Joshua Faskowitz, Richard F. Betzel

et al.

NeuroImage Clinical, Journal Year: 2022, Volume and Issue: 35, P. 103055 - 103055

Published: Jan. 1, 2022

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given importance nonlocal and diffuse damage, we use an edge-centric approach order provide alternative description this disorder. These techniques allow for rendering metrics such as normalized entropy, which describes diversity edge communities at each node. Moreover, enables identification high amplitude co-fluctuations fMRI time series. We found that entropy is associated with lesion severity continually increases across patients' recovery. Furthermore, not only relate but are also level The current study first application a clinical population longitudinal dataset demonstrates how different perspective data analysis can further characterize topographic modulations brain dynamics.

Language: Английский

Citations

26
The promise of precision functional mapping for neuroimaging in psychiatry DOI
Damion V. Demeter, Deanna J. Greene

Neuropsychopharmacology, Journal Year: 2024, Volume and Issue: 50(1), P. 16 - 28

Published: July 31, 2024

Language: Английский

Citations

6
Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains DOI Creative Commons

Elisabeth Ragone,

Jacob Tanner, Youngheun Jo

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 24, 2024

Abstract Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no applied this data collected from model organisms. Here, we analyze structural and functional imaging lightly anesthetized mice through lens. We find evidence of “bursty” events - brief periods high-amplitude connectivity. Further, show that on a per-frame basis best explain static FC can be divided into series hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas largely adhere the boundaries algorithmically detected brain systems. then investigate connectivity undergirding patterns. induce modular bipartitions inter-areal axonal projections. Finally, replicate these same findings dataset. In summary, report recapitulates organism many phenomena observed previously analyses data. However, unlike subjects, murine nervous system is amenable invasive experimental perturbations. Thus, sets stage for future investigation causal origins co-fluctuations. Moreover, cross-species consistency reported enhances likelihood translation.

Language: Английский

Citations

5
A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke DOI Creative Commons
Sebastián Idesis, Michele Allegra, Jakub Vohryzek

et al.

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

Published: Sept. 21, 2023

Abstract Large-scale brain networks reveal structural connections as well functional synchronization between distinct regions of the brain. The latter, referred to connectivity (FC), can be derived from neuroimaging techniques such magnetic resonance imaging (fMRI). FC studies have shown that are severely disrupted by stroke. However, since data usually large and high-dimensional, extracting clinically useful information this vast amount is still a great challenge, our understanding consequences stroke remains limited. Here, we propose dimensionality reduction approach simplify analysis complex neural data. By using autoencoders, find low-dimensional representation encoding fMRI which preserves typical anomalies known present in patients. employing latent representations emerging enhanced patients’ diagnostics severity classification. Furthermore, showed how increased accuracy recovery prediction.

Language: Английский

Citations

11
Reliability of variability and complexity measures for task and task‐free BOLD fMRI DOI Creative Commons
Maren H. Wehrheim, Joshua Faskowitz, Anna‐Lena Schubert

et al.

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(10)

Published: July 9, 2024

Abstract Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest understanding complexity of these temporal variations, for example with respect to developmental changes function or between‐person differences healthy and clinical populations. However, psychometric reliability signal variability measures—which important precondition robust individual as well longitudinal research—is not yet sufficiently studied. We examined (split‐half correlations) test–retest correlations task‐free (resting‐state) BOLD fMRI split‐half seven functional task data sets from Human Connectome Project evaluate their reliability. observed good excellent measures derived rest activation time series (standard deviation, mean absolute successive difference, squared difference), moderate same under conditions. estimates (several entropy dimensionality measures) showed reliabilities both, calculated also time‐resolved (dynamic) connectivity measures, but poor series. Global (i.e., across cortical regions) tended show higher than region‐specific estimates. Larger subcortical regions similar regions, small lower reliability, especially measures. Lastly, we that scores are only minorly dependent on scan length replicate our results different parcellation denoising strategies. These suggest well‐suited research. Temporal global provides novel approach robustly quantifying dynamics function. Practitioner Points Variability Measures can quantify neural dynamics. Length has a minor effect

Language: Английский

Citations

3
Menstrual cycle‐driven hormone concentrations co‐fluctuate with white and gray matter architecture changes across the whole brain DOI Creative Commons
Elizabeth Rizor, Viktoriya Babenko, Neil M. Dundon

et al.

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(11)

Published: July 19, 2024

Abstract Cyclic fluctuations in hypothalamic–pituitary–gonadal axis (HPG‐axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these alter structural nodes information highways of human brain. In a study 30 naturally cycling women, we employed multidimensional diffusion T 1 ‐weighted imaging during three estimated menstrual cycle phases (menses, ovulation, mid‐luteal) to investigate whether HPG‐axis hormone concentrations co‐fluctuate with alterations white matter (WM) microstructure, cortical thickness (CT), brain volume. Across whole brain, 17β‐estradiol luteinizing (LH) were directly proportional anisotropy (μFA; 17β‐estradiol: β = 0.145, highest density interval (HDI) [0.211, 0.4]; LH: 0.111, HDI [0.157, 0.364]), while follicle‐stimulating (FSH) was CT ( 0 .162, [0.115, 0.678]). Within several individual regions, FSH progesterone demonstrated opposing relationships mean diffusivity D iso ) CT. These regions mainly reside within temporal occipital lobes, implications for limbic visual systems. Finally, associated increased tissue 0.66, [0.607, 15.845]) decreased cerebrospinal fluid (CSF; −0.749, [−11.604, −0.903]) volumes, total volume remaining unchanged. results are first report simultaneous brain‐wide changes WM microstructure coinciding cycle‐driven rhythms. Effects observed both classically receptor‐dense (medial lobe, prefrontal cortex) other located across frontal, occipital, temporal, parietal lobes. Our suggest that may have significant impacts entire

Language: Английский

Citations

3
Controllability analysis of macaque structural connectome from an edge centric perspective DOI Open Access
Subham Dey,

Eesha Bharti,

Zhi‐De Deng

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this paper, we investigate the edge controllability properties of macaque structural connectome, which is reconstructed using optimal tractography parameters. We derive expression modal and average controllability, providing a mathematical framework to analyze their roles from network systems perspective. Further, establish relationship between two measures, insights into functional implications. also identify top edges with highest values, may be critical in facilitating state transitions within brain network. These findings have implications for neurostimulation interventions.

Language: Английский

Citations

0