BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Chen, Gang; Taylor, Paul A.; Reynolds, Richard C.; Leibenluft, Ellen; Pine, Daniel S.; Brotman, Melissa A.; Pagliaccio, David; Haller, Simone P.

doi:10.1016/j.neuroimage.2023.120224

Cited by 12 publications

(11 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lipp, Murphy, Caseras & Wise, 2015). Similarly, in cross-sectional studies Chen et al (2023) reported significant, but very modest correlation between ALFF and CVR. Like-wise, Golestani et al (2016) reported significant average grey matter agreement between induced CVR and RSFA and ALFF across subjects.…”

Section: Introductionmentioning

confidence: 81%

Individual variability in the relationship between physiological and resting-state fMRI metrics

Moia,

Chen,

Uruñuela

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Cerebrovascular Reactivity (CVR), the brain's vascular response to a vasodilatory stimulus, can be measured using fMRI during breathing challenges that modulate arterial CO2levels. CVR is an important indicator of cerebrovascular health, although its estimation can be challenging due to the extra experimental setup and/or the subject compliance required. To overcome these limitations, summary metrics based on resting state fluctuations (RSF), such as the (fractional) amplitude of low frequency fluctuations (f/ALFF) and the resting state fluctuation amplitude (RSFA), have been proposed as alternative estimates of CVR, as they are frequently associated with vascular and physiological factors. Previous studies have reported a significant relationship between CVR estimates obtained by means of respiratory paradigms and RSF metrics. However, the total sample sizes, considering both subjects and sessions, are typically small, and not all studies agree on the degree of the relationship. Furthermore, to our knowledge, these studies have only reported cross-sectional analyses, whereas intra-subject longitudinal relationships between CVR estimates and RSF metrics are unknown. Leveraging a unique dense sampling dataset in which resting state and breath-hold multi-echo fMRI were collected in 7 subjects with 10 sessions each, we provide evidence of high individual variability in the inter-session (i.e. intra-subject) relationship between RSF metrics and CVR. These results indicate that RSF metrics might not be a suitable proxy of CVR in clinical settings or for BOLD signal calibration as they may not properly account for intra-individual physiological variations in BOLD fMRI data.

show abstract

Section: Introductionmentioning

confidence: 81%

Individual variability in the relationship between physiological and resting-state fMRI metrics

Moia,

Chen,

Uruñuela

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In these cases, the grouping has been performed if the pair Pearson’s correlation was on average greater than the mean correlation within the other clusters. This approach ensures that the hemodynamic consistency of each cluster is preserved, contributing to reflect the whole-brain vascular complexity (G. Chen et al, 2023; Taylor et al, 2018) underlying variations of regulatory neurochemical processes (Rangaprakash et al, 2018). The resulting clustering procedure provided 62 cortical clusters as displayed in Table 1, from which demeaned fMRI time courses (i.e., within-cluster mean BOLD signal) were extracted and supplied as inputs to sparse DCM together with the BOLD signals from subcortical sources.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, a failure to consider age-related changes in the neuro-vascular coupling can induce a bias in the study of the aging brain functioning, other than a misunderstanding of its cognitive relevance (Tsvetanov, Henson, & Rowe, 2021). A fully quantification of the impact of age on the HRF is then needed since a spatial- and age-invariant canonical HRF would not accurately represent the variability induced by aging (G. Chen et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Scaling methods such as the resting-state fluctuation amplitude (RSFA) have been proposed as an index of vascular reactivity, but the effects of age on RSFA cannot be fully explained by cerebral blood flow and vascular reactivity, making it dangerous to employ it as a normalization signal (Tsvetanov, Henson, Jones, et al, 2021). Additionally, many works have employed different deconvolution methods (Caballero-Gaudes et al, 2019; G. Chen et al, 2023; Karahanoǧlu et al, 2013; Wu et al, 2013). While some techniques evaluate the BOLD signal against a given threshold to identify resting pseudo-events, possibly introducing detection errors which tend to bias the recovery of HRF shape (Wu et al, 2013), others employ a fixed HRF shape throughout the brain (Karahanoǧlu et al, 2013), limiting the description of the high variability of HRF within and across subjects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM

Baron,

Silvestri,

Benozzo

et al. 2023

Preprint

View full text Add to dashboard Cite

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.

show abstract

“…Nevertheless, the majority of age-related studies have predominantly focused on examining functional connectivity (FC) within gray matter (GM) regions, with limited research investigating the effects of ageing on functional changes in white matter (WM), which plays a critical role in transmitting neural signals between GM regions. This is likely attributed to explicit removal of WM blood-oxygenation-level-dependent (BOLD) signals as noise in research, inaccuracies in modeling white matter BOLD response functions (Chen et al, 2023a), and reduced detection sensitivity caused by low signal-to-noise ratio (SNR), resulting in white matter being an understudied area in the fMRI literature (Grajauskas et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Connectivity Structure of White-Matter Functional Network across the Adult Lifespan

Linli,

Hu,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

Background. The maturation and ageing of the human brain involve intricate biological processes that result in complex changes in brain structure and function. Although the effects of aging on functional connectivity within gray matter (GM) regions have been extensively studied, the investigation of white matter (WM) functional changes remains limited. Methods. In this study, our aim was to investigate age-related trajectory in WM functional dynamics using resting-state functional magnetic resonance imaging (rs-fMRI) data from a large lifespan sample of 494 individuals. First, GM and WM functional networks (FNs) were identified by k-means clustering. Next, we performed static and dynamic analysis of WM functional network connectivity (FNC) to explore age effect on WM-FNs. Furthermore, we investigated recurrent patterns of dynamic FNC. Finally, we conducted several validation analyses to ensure replicability. Results. We identified 9 reliable WM and 12 GM FNs. The findings revealed age-related effects on WM FNC strength and WM-GM FNC dynamics, primarily including linear positive and U-shaped age trajectories in static FNC strength, as well as linear negative and inverted U-shaped age trajectories in FNC temporal variability. Additionally, we identified three distinct brain states with significant age-related differences. The aforementioned findings were largely replicated in the validation analysis. Conclusion. High integration and low temporal variability in WN-GM FNC may reflect a less efficient network system in older adults. These findings enhance our understanding of brain aging processes and provide insights into the trajectory of WM functional dynamics during normal ageing.

show abstract

BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Cited by 12 publications

References 81 publications

Individual variability in the relationship between physiological and resting-state fMRI metrics

Individual variability in the relationship between physiological and resting-state fMRI metrics

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM

Static and Dynamic Connectivity Structure of White-Matter Functional Network across the Adult Lifespan

Contact Info

Product

Resources

About