BOLD Variability Research Articles

Task-evoked pupil dilation and BOLD variance as indicators of locus coeruleus dysfunction

Pupillary responses during cognitive tasks are linked to functioning of the locus coeruleus (LC). The LC is an early site of abnormal tau deposition, which may contribute to key aspects of Alzheimer's disease (AD) pathophysiology. We previously found attenuation of pupillary responses to increases in cognitive load in individuals with mild cognitive impairment (MCI), suggesting pupillary responses may provide a biomarker of early risk for AD associated with LC dysfunction. The LC modulates cortical activity through two modes of operation: tonic and phasic. Early LC damage has been predicted to result in a state of persistent high tonic LC activity that may disrupt task-related phasic activity. To further examine whether pupillary responses are associated with early LC dysfunction, we measured pupil dilation during a digit span task as a measure of phasic activity, and low frequency BOLD variance (LFBV) during resting-state fMRI in key nodes of the ventral attention network (VAN) as a measure of cortical reactivity related to LC tonic activity in 358 middle-aged men. Individuals with greater LFBV in VAN nodes, i.e., higher tonic brain activity at rest, showed a smaller increase in pupil dilation from low to moderate cognitive loads. Thus, higher tonic LFBV activity at rest was related to reduced task-appropriate phasic dilation increases. The results support predictions from prominent models of LC functioning in which early LC dysfunction leads to persistent high tonic rates of activity during rest and lower signal-to-noise of phasic responses during task performance. Taken together with previous findings of early AD pathophysiology in LC and reduced phasic dilation responses to increased cognitive load in individuals with MCI, the present results suggest that pupillary responses may index early LC dysfunction and should receive further study as a potential biomarker of risk for AD.

Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation.SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan.

Impact of automated ICA-based denoising of fMRI data in acute stroke patients.

Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients.The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom.The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility (p < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity (p < 0.001). Patient-trained FIX resulted in higher RSN identifiability (p < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA.The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX with appropriate patient data proved valuable, particularly in improving the signal-to-noise ratio.

Physiological noise correction using ECG-derived respiratory signals for enhanced mapping of spontaneous neuronal activity with simultaneous EEG-fMRI

The study of spontaneous brain activity based on BOLD-fMRI may be seriously compromised by the presence of signal fluctuations of non-neuronal origin, most prominently due to cardiac and respiratory mechanisms. Methods used for modeling and correction of the so-called physiological noise usually rely on the concurrent measurement of cardiac and respiratory signals. In simultaneous EEG-fMRI recordings, which are primarily aimed at the study of spontaneous brain activity, the electrocardiogram (ECG) is typically measured as part of the EEG setup but respiratory data are not generally available. Here, we propose to use the ECG-derived respiratory (EDR) signal estimated by Empirical Mode Decomposition (EMD) as a surrogate of the respiratory signal, for retrospective physiological noise correction of typical simultaneous EEG-fMRI data. A physiological noise model based on these physiological signals (P-PNM) complemented with fMRI-derived noise regressors was generated, and evaluated, for 17 simultaneous EEG-fMRI datasets acquired from a group of seven epilepsy patients imaged at 3T. The respiratory components of P-PNM were found to explain BOLD variance significantly in addition to the cardiac components, suggesting that the EDR signal was successfully extracted from the ECG, and P-PNM outperformed an image-based model (I-PNM) in terms of total BOLD variance explained. Further, the impact of the correction using P-PNM on fMRI mapping of patient-specific epileptic networks and the resting-state default mode network (DMN) was assessed in terms of sensitivity and specificity and, when compared with an ICA-based procedure and a standard pre-processing pipeline, P-PNM achieved the best performance. Overall, our results support the feasibility and utility of extracting physiological noise models of the BOLD signal resorting to ECG data exclusively, with substantial impact on the simultaneous EEG-fMRI mapping of resting-state networks, and, most importantly, epileptic networks where sensitivity and specificity are still limited.

The suppression of scale-free fMRI brain dynamics across three different sources of effort: aging, task novelty and task difficulty.

There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P(f ) ∝ f−β, where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent (H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results were consistently observed across multiple datasets and task paradigms. We also demonstrated that estimates of H are robust across a range of time-window sizes. H was also compared to alternative metrics of BOLD variability (SDBOLD) and global connectivity (Gconn), with effort-related decreases in H producing similar decreases in SDBOLD and Gconn. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement.

Identifying Dysfunctional Cortex: Dissociable Effects of Stroke and Aging on Resting State Dynamics in MEG and fMRI.

Spontaneous signals in neuroimaging data may provide information on cortical health in disease and aging, but the relative sensitivity of different approaches is unknown. In the present study, we compared different but complementary indicators of neural dynamics in resting-state MEG and BOLD fMRI, and their relationship with blood flow. Participants included patients with post-stroke aphasia, age-matched controls, and young adults. The complexity of brain activity at rest was quantified in MEG using spectral analysis and multiscale entropy (MSE) measures, whereas BOLD variability was quantified as the standard deviation (SDBOLD), mean squared successive difference (MSSD), and sample entropy of the BOLD time series. We sought to assess the utility of signal variability and complexity measures as markers of age-related changes in healthy adults and perilesional dysfunction in chronic stroke. The results indicate that reduced BOLD variability is a robust finding in aging, whereas MEG measures are more sensitive to the cortical abnormalities associated with stroke. Furthermore, reduced complexity of MEG signals in perilesional tissue were correlated with hypoperfusion as assessed with arterial spin labeling (ASL), while no such relationship was apparent with BOLD variability. These findings suggest that MEG signal complexity offers a sensitive index of neural dysfunction in perilesional tissue in chronic stroke, and that these effects are clearly distinguishable from those associated with healthy aging.

White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain.

Decline in cognitive performance in old age is linked to both suboptimal neural processing in grey matter (GM) and reduced integrity of white matter (WM), but the whole-brain structure-function-cognition associations remain poorly understood. Here we apply a novel measure of GM processing–moment-to-moment variability in the blood oxygenation level-dependent signal (SDBOLD)—to study the associations between GM function during resting state, performance on four main cognitive domains (i.e., fluid intelligence, perceptual speed, episodic memory, vocabulary), and WM microstructural integrity in 91 healthy older adults (aged 60-80 years). We modeled the relations between whole-GM SDBOLD with cognitive performance using multivariate partial least squares analysis. We found that greater SDBOLD was associated with better fluid abilities and memory. Most of regions showing behaviorally relevant SDBOLD (e.g., precuneus and insula) were localized to inter- or intra-network “hubs” that connect and integrate segregated functional domains in the brain. Our results suggest that optimal dynamic range of neural processing in hub regions may support cognitive operations that specifically rely on the most flexible neural processing and complex cross-talk between different brain networks. Finally, we demonstrated that older adults with greater WM integrity in all major WM tracts had also greater SDBOLD and better performance on tests of memory and fluid abilities. We conclude that SDBOLD is a promising functional neural correlate of individual differences in cognition in healthy older adults and is supported by overall WM integrity.

Agreement and repeatability of vascular reactivity estimates based on a breath-hold task and a resting state scan

FMRI BOLD responses to changes in neural activity are influenced by the reactivity of the vasculature. By complementing a task-related BOLD acquisition with a vascular reactivity measure obtained through breath-holding or hypercapnia, this unwanted variance can be statistically reduced in the BOLD responses of interest. Recently, it has been suggested that vascular reactivity can also be estimated using a resting state scan.This study aimed to compare three breath-hold based analysis approaches (block design, sine–cosine regressor and CO2 regressor) and a resting state approach (CO2 regressor) to measure vascular reactivity. We tested BOLD variance explained by the model and repeatability of the measures. Fifteen healthy participants underwent a breath-hold task and a resting state scan with end-tidal CO2 being recorded during both. Vascular reactivity was defined as CO2-related BOLD percent signal change/mmHg change in CO2.Maps and regional vascular reactivity estimates showed high repeatability when the breath-hold task was used. Repeatability and variance explained by the CO2 trace regressor were lower for the resting state data based approach, which resulted in highly variable measures of vascular reactivity.We conclude that breath-hold based vascular reactivity estimations are more repeatable than resting-based estimates, and that there are limitations with replacing breath-hold scans by resting state scans for vascular reactivity assessment.

Greater BOLD variability in older compared with younger adults during audiovisual speech perception.

Older adults exhibit decreased performance and increased trial-to-trial variability on a range of cognitive tasks, including speech perception. We used blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) to search for neural correlates of these behavioral phenomena. We compared brain responses to simple speech stimuli (audiovisual syllables) in 24 healthy older adults (53 to 70 years old) and 14 younger adults (23 to 39 years old) using two independent analysis strategies: region-of-interest (ROI) and voxel-wise whole-brain analysis. While mean response amplitudes were moderately greater in younger adults, older adults had much greater within-subject variability. The greatly increased variability in older adults was observed for both individual voxels in the whole-brain analysis and for ROIs in the left superior temporal sulcus, the left auditory cortex, and the left visual cortex. Increased variability in older adults could not be attributed to differences in head movements between the groups. Increased neural variability may be related to the performance declines and increased behavioral variability that occur with aging.

Characterizing Functional Integrity: Intraindividual Brain Signal Variability Predicts Memory Performance in Patients with Medial Temporal Lobe Epilepsy

Computational modeling suggests that variability in brain signals provides important information regarding the system's capacity to adopt different network configurations that may promote optimal responding to stimuli. Although there is limited empirical work on this construct, a recent study indicates that age-related decreases in variability across the adult lifespan correlate with less efficient and less accurate performance. Here, we extend this construct to the assessment of cerebral integrity by comparing fMRI BOLD variability and fMRI BOLD amplitude in their ability to account for differences in functional capacity in patients with focal unilateral medial temporal dysfunction. We were specifically interested in whether either of these BOLD measures could identify a link between the affected medial temporal region and memory performance (as measured by a clinical test of verbal memory retention). Using partial least-squares analyses, we found that variability in a set of regions including the left hippocampus predicted verbal retention and, furthermore, this relationship was similar across a range of cognitive tasks measured during scanning (i.e., the same pattern was seen in fixation, autobiographical recall, and word generation). In contrast, signal amplitude in the hippocampus did not predict memory performance, even for a task that reliably activates the medial temporal lobes (i.e., autobiographical recall). These findings provide a powerful validation of the concept that variability in brain signals reflects functional integrity. Furthermore, this measure can be characterized as a robust biomarker in this clinical setting because it reveals the same pattern regardless of cognitive challenge or task engagement during scanning.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task.Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency.This article is part of a Special Issue entitled ‘Cognitive Enhancers’.

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging. Within a sample of healthy younger and older adults, we found that blood oxygen level-dependent signal variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared with fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions and offer a new line of inquiry in the study of rest versus task modes in the human brain.

Increased BOLD variability in the parietal cortex and enhanced parieto-occipital connectivity during tactile perception in congenitally blind individuals.

Previous studies in early blind individuals posited a possible role of parieto-occipital connections in conveying nonvisual information to the visual occipital cortex. As a consequence of blindness, parietal areas would thus become able to integrate a greater amount of multimodal information than in sighted individuals. To verify this hypothesis, we compared fMRI-measured BOLD signal temporal variability, an index of efficiency in functional information integration, in congenitally blind and sighted individuals during tactile spatial discrimination and motion perception tasks. In both tasks, the BOLD variability analysis revealed many cortical regions with a significantly greater variability in the blind as compared to sighted individuals, with an overlapping cluster located in the left inferior parietal/anterior intraparietal cortex. A functional connectivity analysis using this region as seed showed stronger correlations in both tasks with occipital areas in the blind as compared to sighted individuals. As BOLD variability reflects neural integration and processing efficiency, these cross-modal plastic changes in the parietal cortex, even if described in a limited sample, reinforce the hypothesis that this region may play an important role in processing nonvisual information in blind subjects and act as a hub in the cortico-cortical pathway from somatosensory cortex to the reorganized occipital areas.

Neuronal Dynamics Underlying High- and Low-Frequency EEG Oscillations Contribute Independently to the Human BOLD Signal

Work on animals indicates that BOLD is preferentially sensitive to local field potentials, and that it correlates most strongly with gamma band neuronal synchronization. Here we investigate how the BOLD signal in humans performing a cognitive task is related to neuronal synchronization across different frequency bands. We simultaneously recorded EEG and BOLD while subjects engaged in a visual attention task known to induce sustained changes in neuronal synchronization across a wide range of frequencies. Trial-by-trial BOLD fluctuations correlated positively with trial-by-trial fluctuations in high-EEG gamma power (60-80 Hz) and negatively with alpha and beta power. Gamma power on the one hand, and alpha and beta power on the other hand, independently contributed to explaining BOLD variance. These results indicate that the BOLD-gamma coupling observed in animals can be extrapolated to humans performing a task and that neuronal dynamics underlying high- and low-frequency synchronization contribute independently to the BOLD signal.

Baseline BOLD correlation predicts individuals' stimulus-evoked BOLD responses

To investigate whether individuals' ongoing neuronal activity at resting state can affect their response to brain stimulation, fMRI BOLD signals were imaged from the human visual cortex of fifteen healthy subjects in the absence and presence of visual stimulation. It was found that the temporal correlation strength but not amplitude of baseline BOLD signal fluctuations acquired under the eyes-fixed condition is positively correlated with the amplitude of stimulus-evoked BOLD responses across subjects. Moreover, the spatiotemporal correlations of baseline BOLD signals imply a coherent network covering the visual system, which is topographically indistinguishable from the “resting-state visual network” observed under the eyes-closed condition. The overall findings suggest that the synchronization of ongoing brain activity plays an important role in determining stimulus-evoked brain activity even at an early stage of the sensory system. The tight relationship between baseline BOLD correlation and stimulus-evoked BOLD amplitude provides an essential basis for understanding and interpreting the large inter-subject BOLD variability commonly observed in numerous fMRI studies and potentially for improving group fMRI analysis. This study highlights the importance to integrate the information from both resting-state coherent networks and task-evoked neural responses for a better understanding of how the brain functions.

Increasing measurement accuracy of age-related BOLD signal change: Minimizing vascular contributions by resting-state-fluctuation-of-amplitude scaling

In this report we demonstrate a hemodynamic scaling method with resting-state fluctuation of amplitude (RSFA) in healthy adult younger and older subject groups. We show that RSFA correlated with breath hold (BH) responses throughout the brain in groups of younger and older subjects which RSFA and BH performed comparably in accounting for age-related hemodynamic coupling changes, and yielded more veridical estimates of age-related differences in task-related neural activity. BOLD data from younger and older adults performing motor and cognitive tasks were scaled using RSFA and BH related signal changes. Scaling with RSFA and BH reduced the skew of the BOLD response amplitude distribution in each subject and reduced mean BOLD amplitude and variability in both age groups. Statistically significant differences in intrasubject amplitude variation across regions of activated cortex, and intersubject amplitude variation in regions of activated cortex were observed between younger and older subject groups. Intra- and intersubject variability differences were mitigated after scaling. RSFA, though similar to BH in minimizing skew in the unscaled BOLD amplitude distribution, attenuated the neural activity-related BOLD amplitude significantly less than BH. The amplitude and spatial extent of group activation were lower in the older than in the younger group before and after scaling. After accounting for vascular variability differences through scaling, age-related decreases in activation volume were observed during the motor and cognitive tasks. The results suggest that RSFA-scaled data yield age-related neural activity differences during task performance with negligible effects from non-neural (i.e., vascular) sources.

Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy

IntroductionEEG-correlated fMRI (EEG-fMRI) studies can reveal haemodynamic changes associated with Interictal Epileptic Discharges (IED). Methodological improvements are needed to increase sensitivity and specificity for localising the epileptogenic zone. We investigated whether the estimated EEG source activity improved models of the BOLD changes in EEG-fMRI data, compared to conventional « event-related » designs based solely on the visual identification of IED. MethodsTen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI. EEG Source Imaging (ESI) was performed on intra-fMRI averaged IED to identify the irritative zone. The continuous activity of this estimated IED source (cESI) over the entire recording was used for fMRI analysis (cESI model). The maps of BOLD signal changes explained by cESI were compared to results of the conventional IED-related model. ResultsESI was concordant with non-invasive data in 13/15 different types of IED. The cESI model explained significant additional BOLD variance in regions concordant with video-EEG, structural MRI or, when available, intracranial EEG in 10/15 IED. The cESI model allowed better detection of the BOLD cluster, concordant with intracranial EEG in 4/7 IED, compared to the IED model. In 4 IED types, cESI-related BOLD signal changes were diffuse with a pattern suggestive of contamination of the source signal by artefacts, notably incompletely corrected motion and pulse artefact. In one IED type, there was no significant BOLD change with either model. ConclusionContinuous EEG source imaging can improve the modelling of BOLD changes related to interictal epileptic activity and this may enhance the localisation of the irritative zone.

BOLD response variability due to task changes

BOLD response variability due to task changes

Preferential metabolic involvement of visual cortical areas in a subtype of Alzheimer's disease: clinical implications.

A subgroup of patients with Alzheimer's disease present with visual disturbances at onset. This study investigated whether specific cortical networks associated with visual processes are preferentially affected in this subgroup and determined the clinical implications of such abnormalities. Regional cerebral glucose metabolic rates were assessed with positron emission tomography and [18F]2-fluoro-2-deoxy-D-glucose, and general intellectual functions, memory, and visual skills were measured with cognitive tests in patients with probable Alzheimer's disease-10 with and 22 without prominent visual symptoms-and in 25 healthy comparison subjects. Both patient groups showed reduced glucose metabolism in parietal regions and in middle and superior temporal regions in comparison with the healthy subjects. The Alzheimer's disease patients without visual symptoms also showed reductions in inferior temporal, frontal, and limbic structures, as is typical of Alzheimer's disease. In contrast, the patients with visual symptoms had larger metabolic deficits than the patients without visual symptoms in the parietal and occipital cortices (including the primary visual cortex), with a relative sparing of inferior temporal, frontal, and limbic regions. Consistently, the patients with visual symptoms had significantly greater visuospatial deficits and less severe memory impairments than the patients without visual symptoms. Alzheimer's disease patients with visuospatial deficits who are studied while alive have a distinctive regional distribution of cerebral metabolic impairment that is related to specific cognitive deficits and that distinguishes them from patients with typical Alzheimer's disease. These findings imply that regional variations in brain dysfunction can occur in Alzheimer's disease, with differential involvement of cortical systems resulting in distinctive clinical subgroups.