Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity

Abstract

Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.

Hemodynamic timing in resting-state and breathing-task BOLD fMRI

“…We assess the agreement of hemodynamic timing estimates derived from data that contained a breathhold task and from data that only contained resting-state (spontaneous) fluctuations, to understand how similar these timing estimates are in an individual. To further interpret our assessment of the agreement, we applied amplitude thresholds (Chang et al, 2008; Fesharaki et al, 2021) and different levels of smoothing of the input data, hypothesizing that signal-to-noise factors would modulate the absolute agreement of hemodynamic timing estimates. We also investigated which areas of the brain (if any) had the highest agreement, to help provide anatomical insight into what may be driving agreement or disagreement.…”

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

“…Biswal , 2008;Kannurpatti et al, 2014); in fact, the simplicity of RS fMRI makes it particularly suitable in clinical settings for more vulnerable populations (Fesharaki et al, 2021) and extreme cases such as loss of consciousness (Golestani et al, 2016). Previous studies have even suggested that RSF maps would be better estimators of physiological changes given the high regional agreement between the two measures (De Vis et al, 2018;Kannurpatti & Biswal, 2008;Kannurpatti et al, 2011).…”

“…An easier setup and better patient tolerance of RS fMRI, compared to studies evoking vasodilatory responses using breathing or gas-inhaled challenges, are often cited as reasons to adopt RSF metrics as a proxy for CVR (Fesharaki et al, 2021; Golestani et al, 2016; Kannurpatti & Biswal, 2008; Kannurpatti et al, 2014); in fact, the simplicity of RS fMRI makes it particularly suitable in clinical settings for more vulnerable populations (Fesharaki et al, 2021) and extreme cases such as loss of consciousness (Golestani et al, 2016). Previous studies have even suggested that RSF maps would be better estimators of physiological changes given the high regional agreement between the two measures (De Vis et al, 2018; Kannurpatti & Biswal, 2008; Kannurpatti et al, 2011).…”

“…Other cross-sectional studies reported less agreement between RSF metrics and induced CVR between-subjects (Lipp et al, 2015), a variable agreement depending on age and task domains (Kannurpatti et al, 2014), or a lower sensitivity to haemodynamic impairment of RSF compared to CVR (De Vis et al, 2018), thereby inviting carefulness in interpreting the relationship between CVR and RSF. Fesharaki et al (2021) suggest that lower agreement between RSF measures and BH-induced CVR might be attributed to a lack of optimal thresholding of the corresponding maps. More recently, Huck et al (2023) showed that ALFF has a bias to the properties of venous vasculature, which might confirm the hypothesis that these RSF metrics are biased by resting cerebral blood volume (De Vis et al, 2018), although Bailes, Gomez, Setzer and Lewis (2023) attributes such bias more to the neurovascular coupling than to the CVR mechanism, since the latter does not include the same metabolic factors driving the former (D’Esposito, Deouell & Gazzaley, 2003; Iadecola, 2017; Pinto et al, 2021).…”

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas

“…The use of variability in respiration and its effect on BOLD fMRI as a CVR biomarker is a new discovery [ 107 , 111 ]. However, given that the outcomes of resting-state approaches are unpredictable, further research is necessary before they can completely replace BH procedures [ 112 ].…”