Regular ArticleEvent-Related fMRI: Characterizing Differential Responses☆
Abstract
We present an approach to characterizing the differences among event-related hemodynamic responses in functional magnetic resonance imaging that are evoked by different sorts of stimuli. This approach is predicated on a linear convolution model and standard inferential statistics as employed by statistical parametric mapping. In particular we model evoked responses, and their differences, in terms of basis functions of the peri-stimulus time. This facilitates a characterization of the temporal response profiles that has a high effective temporal resolution relative to the repetition time. To demonstrate the technique we examined differential responses to visually presented words that had been seen prior to scanning or that were novel. The form of these differences involved both the magnitude and the latency of the response components. In this paper we focus on bilateral ventrolateral prefrontal responses that show deactivations for previously seen words and activations for novel words.
References (17)
- K.J. Friston et al.
Analysis of fMRI time-series revisited
NeuroImage
(1995) - K.J. Friston et al.
Characterising evoked hemodynamics with fMRI
NeuroImage
(1995) - K.J. Worsley et al.
Analysis of fMRI time-series revisited—again
NeuroImage
(1995) - E.A. DeYoe et al.
Time course of event-related MR: Signal enhancement in visual and motor cortex
Proc. Soc. Mag. Res. Med.
(1992) - G. Boynton et al.
Linear systems analysis of functional magnetic resonance imaging in human V1
J. Neurosci.
(1996) - R. Buckner et al.
Detection of transient and distributed cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging
Proc. Natl. Acad. Sci. USA
(1996) - K.J. Friston et al.
Analysis of functional MRI time series
Hum. Brain Mapping
(1994) - K.J. Friston et al.
Spatial registration and normalisation of images
Hum. Brain Mapping
(1995)
Cited by (1723)
BOLD fMRI signal has been used in conjunction with vasodilatory stimulation as a marker of cerebrovascular reactivity (CVR): the relative change in cerebral blood flow (CBF) arising from a unit change in the vasodilatory stimulus. Using numerical simulations, we demonstrate that the variability in the relative BOLD signal change induced by vasodilation is strongly influenced by the variability in deoxyhemoglobin-containing cerebral blood volume (CBV), as this source of variability is likely to be more prominent than that of CVR. It may, therefore, be more appropriate to describe the relative BOLD signal change induced by an isometabolic vasodilation as a proxy of deoxygenated CBV (CBVdHb) rather than CVR. With this in mind, a new method was implemented to map a marker of CBVdHb, termed BOLD-CBV, based on the normalization of voxel-wise BOLD signal variation by an estimate of the intravascular venous BOLD signal from voxels filled with venous blood. The intravascular venous BOLD signal variation, recorded during repeated breath-holding, was extracted from the superior sagittal sinus in a cohort of 27 healthy volunteers and used as a regressor across the whole brain, yielding maps of BOLD-CBV. In the same cohort, we demonstrated the potential use of BOLD-CBV for the normalization of stimulus-evoked BOLD fMRI by comparing group-level BOLD fMRI responses to a visuomotor learning task with and without the inclusion of voxel-wise vascular covariates of BOLD-CBV and the BOLD signal change per mmHg variation in end-tidal carbon dioxide (BOLD-CVR). The empirical measure of BOLD-CBV accounted for more between-subject variability in the motor task-induced BOLD responses than BOLD-CVR estimated from end-tidal carbon dioxide recordings. The new method can potentially increase the power of group fMRI studies by including a measure of vascular characteristics and has the strong practical advantage of not requiring experimental measurement of end-tidal carbon dioxide, unlike traditional methods to estimate BOLD-CVR. It also more closely represents a specific physiological characteristic of brain vasculature than BOLD-CVR, namely blood volume.
Medial Prefrontal Cortex Dysfunction Mediates Working Memory Deficits in Patients With Schizophrenia
2023, Biological Psychiatry Global Open ScienceSchizophrenia (SCZ) is marked by working memory (WM) deficits, which predict poor functional outcome. While most functional magnetic resonance imaging studies of WM in SCZ have focused on the dorsolateral prefrontal cortex (PFC), some recent work suggests that the medial PFC (mPFC) may play a role. We investigated whether task-evoked mPFC deactivation is associated with WM performance and whether it mediates deficits in SCZ. In addition, we investigated associations between mPFC deactivation and cortical dopamine release.
Patients with SCZ (n = 41) and healthy control participants (HCs) (n = 40) performed a visual object n-back task during functional magnetic resonance imaging. Dopamine release capacity in mPFC was quantified with [11C]FLB457 in a subset of participants (9 SCZ, 14 HCs) using an amphetamine challenge. Correlations between task-evoked deactivation and performance were assessed in mPFC and dorsolateral PFC masks and were further examined for relationships with diagnosis and dopamine release.
mPFC deactivation was associated with WM task performance, but dorsolateral PFC activation was not. Deactivation in the mPFC was reduced in patients with SCZ relative to HCs and mediated the relationship between diagnosis and WM performance. In addition, mPFC deactivation was significantly and inversely associated with dopamine release capacity across groups and in HCs alone, but not in patients.
Reduced WM task-evoked mPFC deactivation is a mediator of, and potential substrate for, WM impairment in SCZ, although our study design does not rule out the possibility that these findings could relate to cognition in general rather than WM specifically. We further present preliminary evidence of an inverse association between deactivation during WM tasks and dopamine release capacity in the mPFC.
Brain hubs defined in the group do not overlap with regions of high inter-individual variability
2023, NeuroImageConnector ‘hubs’ are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed ‘variants’ in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.
It has been shown that manipulating the proportion of congruent to incongruent trials in conflict tasks (e.g., Stroop, Simon, and flanker tasks) can vary the size of conflict effects, however, by two different mechanisms. One theory is the control learning account (the brain learns the probability of conflict and uses it to proactively adjust the control demand for future trials). The other is the irrelevant stimulus-response learning account (the brain learns the probability of irrelevant stimulus-response associations and uses it to prepare responses). Previous fMRI studies have detected the brain regions that contribute to the control-learning-modulated conflict effects, but it is less known what neural substrates underlie the conflict effects modulated by irrelevant S-R learning. We here investigated this question with a model-based fMRI study, in which the proportion of congruent to incongruent trials changed dynamically in the Simon task and the models learned the probability of irrelevant S-R associations quantitatively. Behavioral analyses showed that the unsigned prediction errors (PEs) of responses generated by the learning models correlated with reaction times irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with slow responses. The fMRI results showed that the regions of fronto-parietal and cingulo-opercular network involved in cognitive control were significantly modulated by the unsigned PEs, also irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with transiently increased activity in these regions. These results together suggest that learning of irrelevant S-R associations modulates reactive control, which demonstrates a new way to modulate cognitive control compared to the control learning account.
Hemodynamic timing in resting-state and breathing-task BOLD fMRI
2023, NeuroImageThe blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology.
Cognitive control is a capacity-limited function responsible for the resolution of conflict among competing cognitive processes. However, whether cognitive control handles multiple concurrent requests through a single bottleneck or a resource sharing mechanism remains elusive. In this functional magnetic resonance imaging study, we examined the effect of dual flanker conflict processing on behavioral performance and on activation in regions of the cognitive control network (CCN). In each trial, participants completed two flanker conflict tasks (T1 and T2) sequentially, with the stimulus onset asynchrony (SOA) varied as short (100 ms) and long (1000 ms). We found a significant conflict effect (indexed by the difference between incongruent and congruent flanker conditions) in reaction time (RT) for both T1 and T2, together with a significant interaction between SOA and T1-conflict on RT for T2 with an additive effect. Importantly, there was a small but significant SOA effect on T1 with a prolonged RT under the short SOA compared to the long SOA. Increased activation in the CCN was associated with conflict processing and the main effect of SOA. The anterior cingulate cortex and anterior insular cortex showed a significant interaction effect between SOA and T1-conflict in activation parallel with the behavioral results. The behavioral and brain activation patterns support a central resource sharing model, in which the core resources for cognitive control are shared when multiple simultaneous conflicting processes are required.
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M. D. RuggM. G. H. Coles