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

Ally Dworetsky

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 27, 2022

ABSTRACT 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, HIGHLIGHTS Past BOLD “events” fMRI connectivity, Here, were both stationary null model to test In >50% modularity similarity time- averaged Stationary models with similar behavior Events may transient driver rather an expected outcome it.

Language: Английский

Uncovering individual differences in fine-scale dynamics of functional connectivity DOI
Sarah A. Cutts, Joshua Faskowitz, Richard F. Betzel

et al.

Cerebral Cortex, Journal Year: 2022, Volume and Issue: 33(5), P. 2375 - 2394

Published: June 12, 2022

Abstract Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain–behavior relationships. Most prior work has focused on spatial (nodes systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in while also maintaining the pattern node-based activity. To this end, we leverage recently proposed decomposition into edge series (eTS). systematically analyze functional magnetic resonance frames define enhance identifiability multiple fingerprinting metrics, similarity data sets. Results show metrics characteristically vary with eTS cofluctuation amplitude, within run, transition velocity, expression systems. further data-driven optimization maximize isolates patterns system at time. Selecting just 10% can yield stronger fingerprints than obtained from full set. Our findings support idea differentially expressed suggest distinct be identified when temporal characteristics considered simultaneously.

Language: Английский

Citations

20
Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI DOI Creative Commons
Francesca Mandino, Corey Horien, Xilin Shen

et al.

Communications Biology, Journal Year: 2025, Volume and Issue: 8(1)

Published: April 26, 2025

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment various heterogeneous neurological conditions diseases. Despite many advantages, prevailing modality this field-blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-suffers from low spatiotemporal resolution specificity as well a propensity noise spurious signal corruption. To better understand individual BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, be accessed. Here, apply simultaneous wide-field fluorescence calcium mice interrogate using connectome-based identification framework adopted human fMRI literature. This approach yields high cell-type specific signals (here, glia, excitatory, inhibitory interneurons) whole cortex. We found mouse multimodal successful explored features these data.

Language: Английский

Citations

0
Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain DOI Creative Commons
James W. Madole, Colin R. Buchanan, Mijke Rhemtulla

et al.

NeuroImage, Journal Year: 2023, Volume and Issue: 275, P. 120160 - 120160

Published: May 9, 2023

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic index distinct versus overlapping information with respect interindividual differences in brain organization. Using unthresholded, FA-weighted networks found that all other than Participation Coefficient were highly intercorrelated, both each (mean |r| 0.788) a topologically-naïve summary structure edge weight; mean 0.873). series sensitivity analyses, overlap between is influenced by sparseness network magnitude variation weights. Simulation analyses representing range population structures indicated individual graph may be intrinsically difficult separate weight. particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, Small Worldness nearly perfectly collinear one another 0.939) weight 0.952) across observed simulated conditions. measures are valuable their ability distill high-dimensional system connections into indices organization, but they more limited utility when goal separable components specific properties connectome.

Language: Английский

Citations

9
Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study DOI Creative Commons
Nicholas Theis, Jyotika Bahuguna, Jonathan Rubin

et al.

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

Published: Jan. 24, 2024

Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) altered pairwise correlation based functional connectivity (second-order properties). However, both approaches certain limitations that can be overcome by integrating them in a maximum entropy model (MEM) better represents comprehensive picture fMRI signal patterns provides system-wide summary measure called energy. This study examines the applicability individual-level MEM for psychiatry identifies image-derived coefficients related to parameters. Method MEMs are fit resting state data from each individual with schizophrenia/schizoaffective disorder, bipolar major depression (n=132) demographically matched healthy controls UK Biobank different subsets default mode network (DMN) regions. Results The satisfactorily explained observed energy occurrence probabilities across all participants, parameters were significantly correlated groups. Within clinical groups, averaged level distributions higher disorder but lower compared bilateral unilateral DMN. Major only right hemisphere Conclusions Diagnostically distinct states suggest probability temporal changes synchronously active nodes may underlie diagnostic entity. Subject-specific allow factoring variations traditional group-level inferences, offering an improved biologically meaningful correlates activity potential utility.

Language: Английский

Citations

3
Individualized event structure drives individual differences in whole-brain functional connectivity DOI Creative Commons
Richard F. Betzel, Sarah A. Cutts,

Sarah Greenwell

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown

Published: March 12, 2021

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It studied widely, both to gain insight into brain’s intrinsic organization but also develop markers sensitive changes in an individual’s cognitive, clinical, and developmental state. Despite this, origins drivers connectivity, especially at level densely sampled individuals, remain elusive. Here, we leverage novel methodology decompose its precise framewise contributions. Using two dense sampling datasets, investigate individualized focusing specifically on role brain network “events” – short-lived peaked patterns high-amplitude cofluctuations. a statistical test identify events empirical recordings. We show that cofluctuation expressed during are repeated across multiple scans same individual represent idiosyncratic variants template group level. Lastly, propose simple model based event cofluctuations, demonstrating group-averaged cofluctuations suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants primary static, extends those studies, individualized, positing dynamic basis

Language: Английский

Citations

20
Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder DOI Creative Commons
Wei Hong, Peipeng Liang, Yu Pan

et al.

Journal of Behavioral Addictions, Journal Year: 2023, Volume and Issue: 12(2), P. 458 - 470

Published: May 20, 2023

Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion core its alteration plays an important role in addiction. However, few studies explored it internet gaming disorder patients (IGD).In this study, IGD (PIGD) healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences loss aversion, brain networks node-centric connectivity (nFC) overlapping community features edge-centric (eFC) IGT.PIGD worse with lower average net score IGT. The computational model results showed that PIGD significantly reduced aversion. There was no difference nFC. there were significant eFC1. Furthermore, Con-PIGD, positively correlated edge profile similarity edge2 between left IFG right hippocampus at caudate. This relationship suppressed by response consistency3 PIGD. In addition, negatively promoted bottom-to-up neuromodulation from to PIGD.The decision making their related support same deficit as use other addictive disorders. These findings may have significance for understanding definition mechanism future.

Language: Английский

Citations

8
Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI DOI Creative Commons
Richard F. Betzel, Sarah A. Cutts, Jacob Tanner

et al.

Network Neuroscience, Journal Year: 2023, Volume and Issue: 7(3), P. 926 - 949

Published: Jan. 1, 2023

Abstract Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when global co-fluctuation amplitude takes largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in but lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: MyConnectome project Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak all magnitudes nested multiscale clusters based pairwise concordance. At coarse scale, find evidence three large that, collectively, engage virtually canonical brain systems. finer scales, however, each dissolved, giving way increasingly refined patterns involving specific sets also an increase magnitude with scale. Finally, comment amount needed estimate pattern implications for brain-behavior studies. Collectively, findings reported here fill several gaps current knowledge concerning heterogeneity richness as estimated edge while providing some practical guidance future

Language: Английский

Citations

7
Neural connectome features of procrastination: Current progress and future direction DOI
Zhiyi Chen, Tingyong Feng

Brain and Cognition, Journal Year: 2022, Volume and Issue: 161, P. 105882 - 105882

Published: June 6, 2022

Language: Английский

Citations

8
Leveraging edge-centric networks complements existing network-level inference for functional connectomes DOI
Raimundo Rodriguez, Stephanie Noble, Link Tejavibulya

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 264, P. 119742 - 119742

Published: Nov. 8, 2022

Language: Английский

Citations

7
Multimodal identification of the mouse brain using simultaneous Ca2+imaging and fMRI DOI Creative Commons
Francesca Mandino, Corey Horien, Xilin Shen

et al.

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

Published: May 26, 2024

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment various heterogeneous neurological conditions diseases. Despite many advantages, workhorse this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution specificity as well a propensity noise spurious signal corruption. To better understand individual BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, be accessed. Here, apply simultaneous wide-field fluorescence calcium mice interrogate using connectome-based identification framework adopted human fMRI literature. This approach yields high cell-type specific signals (here, glia, excitatory, inhibitory interneurons) whole cortex. We found mouse multimodal successful explored features these data.

Language: Английский

Citations

1