Blood Oxygen Level-dependent Signal Variability Research Articles

55 Within-Individual BOLD Signal Variability During a Letter N-Back Task: Implications for a Verbal Fluency Network

Objective:Functional magnetic resonance imaging (fMRI) research has generally focused on drawing conclusions from average brain activation patterns. Importantly, the brain is inherently variable; growing literature has found that within-individual blood oxygen level-dependent (BOLD) signal variability may be meaningful, and not just “noise.” For example, recent research has identified increased BOLD signal variability in healthy younger and older adults during more effortful/complex task loads of n-back paradigms, commonly used tasks that involve important elements of executive function (e.g., attention, working memory, planning, inhibition, etc.). Verbal fluency is a complex cognitive domain that also involves similar processes to generate words given certain rules. As a result, the current study builds on existing literature to investigate within-individual BOLD signal variability patterns in peak coordinates of a verbal fluency network during different loads of a letter n-back task. Due to greater executive demands, greater variability was expected during more effortful/complex n-back task loads in regions of a verbal fluency network.Participants and Methods:Forty-eight healthy young adults (Mage(SD) = 22.41(4.47), 25 females) from the Atlanta area completed a letter n-back task in an MRI scanner. After standard processing in AFNI, images were corrected for motion and physiological artifacts, which may be confounding sources of variability. Volumes associated with each load of the letter n-back task (0-back, 1-back, 2-back, 3-back, crosshair) were identified. Task runs were normalized and respective run means were subtracted prior to concatenating all runs for each load type. Standard deviations were calculated across this mean-run corrected time series. Ten peak regions of interest (ROIs) were identified from a verbal fluency network generated from 84 peer-reviewed publications for this domain gathered on NeuroSynth. Paired samples t-tests with Benjamini-Hochberg correction for multiple comparisons were conducted to explore differences in variability during n-back task loads.Results:In several of the verbal fluency network ROIs, within-individual BOLD signal variability was significantly greater for 2-back versus 0-back loads with medium to large effect sizes (p’s < .001 - < .01, Cohen’s d range: .53-.93). Variability was also significantly greater for 3-back versus 0-back loads with small to medium effect sizes (p’s < .001 - < .01, Cohen’s d range: .48-.74). Specific regions that evidenced this pattern included ROIs in the left inferior frontal gyrus, left cingulate, right inferior frontal gyrus, left middle frontal gyrus, and left superior parietal lobule. Only two regions demonstrated increased variability in the 1-back load versus crosshair (left middle frontal gyrus, p < .001, d = .63; left lentiform nucleus, p < .05, d = .42). No regions demonstrated a significant difference in variability in the 0-back load versus crosshair.Conclusions:This study contributes to growing literature examining within-individual BOLD signal variability in healthy individuals by exploring variability patterns in a verbal fluency network. The observed pattern of results supports the hypothesis and is in line with previous research, demonstrating that greater variability occurs with greater executive task demands. Future research can use an inscanner task of verbal fluency and can extend variability findings during this in-scanner task to out-of-scanner measures of verbal fluency.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review.

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

Abnormal preoperative fMRI signal variability in the pain ascending pathway is associated with the postoperative axial pain intensity in degenerative cervical myelopathy patients

BACKGROUND CONTEXTThe moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. PURPOSETo investigate preoperative alterations in regional blood-oxygen-level-dependent signal variability (BOLDsv) and inter-regional dynamic functional connectivity (dFC) in individuals with degenerative cervical myelopathy (DCM), and their potential association with postoperative axial pain severity. STUDY DESIGNCross-sectional study. PATIENT SAMPLEResting-state functional magnetic resonance imaging was obtained in 42 migraine individuals and 40 healthy controls (HCs). OUTCOME MEASURESWe calculated the standard deviation (SD) of the BOLD time-series at each voxel and the SD and mean of the dynamic conditional correlation between the brain regions which showed significant group differences in BOLDsv. METHODSA group comparison was conducted using whole-brain voxel-wise analysis of the standard deviation (SD) of the BOLD time-series which was a measure of the BOLDsv. The brain areas displaying notable group discrepancies in BOLDsv were utilized to outline regions of interest (ROIs). To determine the strength/variability of the dFC, the mean and SD of the dynamic conditional correlation were calculated within these ROIs. Moreover, the postoperative axial pain (PAP) severity of patients was evaluated. RESULTSOur results revealed that DCM patients with postoperative axial pain (PAP) demonstrated considerably increased BOLDsv in the bilateral thalamus and right insular, but significantly lower BOLDsv in the right S1. By applying dynamic functional connectivity (dFC) analysis, we found that DCM patients with PAP exhibited greater fluctuation of dFC in the thalamo-cortical pathway (specifically, thalamus-S1), when compared to HCs and patients without PAP (nPAP). Lastly, we established that dysfunctional BOLDsv and dFC in the ascending pain pathway were positively associated with the severity of PAP in DCM patients. CONCLUSIONOur results indicate a potential correlation between impaired pain ascending pathway and postoperative axial pain in DCM patients. These findings could potentially spark novel treatment approaches for individuals experiencing preoperative axial pain.

Assessing blood oxygen level-dependent signal variability as a biomarker of brain injury in sport-related concussion.

Mild traumatic brain injury is a complex neurological disorder of significant concern among athletes who play contact sports. Athletes who sustain sport-related concussion typically undergo physical examination and neurocognitive evaluation to determine injury severity and return-to-play status. However, traumatic disruption to neurometabolic processes can occur with minimal detectable anatomic pathology or neurocognitive alteration, increasing the risk that athletes may be cleared for return-to-play during a vulnerable period and receive a repetitive injury. This underscores the need for sensitive functional neuroimaging methods to detect altered cerebral physiology in concussed athletes. The present study compared the efficacy of Immediate Post-concussion Assessment and Cognitive Testing composite scores and whole-brain measures of blood oxygen level-dependent signal variability for classifying concussion status and predicting concussion symptomatology in healthy, concussed and repetitively concussed athletes, assessing blood oxygen level-dependent signal variability as a potential diagnostic tool for characterizing functional alterations to cerebral physiology and assisting in the detection of sport-related concussion. We observed significant differences in regional blood oxygen level-dependent signal variability measures for concussed athletes but did not observe significant differences in Immediate Post-concussion Assessment and Cognitive Testing scores of concussed athletes. We further demonstrate that incorporating measures of functional brain alteration alongside Immediate Post-concussion Assessment and Cognitive Testing scores enhances the sensitivity and specificity of supervised random forest machine learning methods when classifying and predicting concussion status and post-concussion symptoms, suggesting that alterations to cerebrovascular status characterize unique variance that may aid in the detection of sport-related concussion and repetitive mild traumatic brain injury. These results indicate that altered blood oxygen level-dependent variability holds promise as a novel neurobiological marker for detecting alterations in cerebral perfusion and neuronal functioning in sport-related concussion, motivating future research to establish and validate clinical assessment protocols that can incorporate advanced neuroimaging methods to characterize altered cerebral physiology following mild traumatic brain injury.

Neural signal variability relates to maladaptive rumination in depression

Rumination is a common feature of depression and predicts the onset and maintenance of depressive episodes. Maladaptive and adaptive subtypes of rumination contribute to distinct outcomes, with brooding worsening negative mood and reflection related to fewer depression symptoms in healthy populations. Neuroimaging studies have implicated several cortical midline and lateral prefrontal brain regions in rumination. Recent research indicates that blood oxygen level-dependent (BOLD) signal variability may be a novel predictor of cognitive flexibility. However, no prior studies have investigated whether brooding and reflection are associated with distinct patterns of BOLD signal variability in depression. We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depression. We examined differences in BOLD signal variability (BOLDSD) related to rumination subtypes for the following regions of interest previously implicated in rumination: amygdala, medial prefrontal, anterior cingulate, posterior cingulate, and dorsolateral prefrontal cortices (dlPFC). Rumination subtype was associated with BOLDSD in the dlPFC, with greater levels of brooding associated with lower BOLDSD in the dlPFC, even after controlling for depression severity. Depression history was related to BOLDSD in the dlPFC, with reduced BOLDSD in those with current depression versus no history of depression. These findings provide a novel demonstration of the neural circuitry associated with maladaptive rumination in depression and implicate decreased prefrontal neural signal variability in the pathophysiology of depression.

Resting-State Brain Variability in Youth With Attention-Deficit/Hyperactivity Disorder.

In this study, we sought to determine the nature of the abnormality in resting-state default mode network (DMN) activation and explore its correlation with functional connectivity in attention-deficit/hyperactivity disorder (ADHD). We obtained resting-state functional magnetic resonance images of youth with ADHD and typically developing counterparts from the publicly available ADHD-200 database. We used data from Peking University (232 scans) and New York University (172 scans); the scan repetition time was 2 s for both data collection sites. We applied generalized estimating equations to estimate the variability of the averaged blood-oxygen-level-dependent (BOLD) time series extracted from the DMN at rest. We performed network-based statistics to determine the association between the observed differences in BOLD signal variability and altered functional connectivity. We analyzed data from 105 youth with ADHD (age: mean 12.17, standard deviation 2.31, median 12.25; 15.2% female, 84.8% male) and 140 typically developing youth (age: mean 11.99, standard deviation 2.28, median 11.85; 47.1% female, 52.9% male), who aged 7–17 years. The imaging data were cross-sectionally collected for each participant at one time point. We observed a greater number of significant BOLD signal changes and higher-order polynomial significant associations in youth with ADHD. Moreover, there were significant between-group differences in BOLD signal change after the first 140 s, which coincided with decreased resting-state functional connectivity within the DMN in youth with ADHD. Increased variability of neural signaling was intermittently observed in the brains of youth with ADHD at rest, thereby indicating their default mode state was more unstable than that of typically developing youth.

Resting-state neural signal variability in women with depressive disorders

Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (pFWE <0.05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression.

Within-individual BOLD signal variability in the N-back task and its associations with vigilance and working memory

In a group of healthy adults (N = 48), this study evaluated how fMRI Blood Oxygen Level-Dependent (BOLD) signal variability differed across letter n-back task load and quantified the extent to which BOLD signal variability was associated with in-scanner accuracy and reaction time as well as out-of-scanner measures of vigilance and working memory (WM). Within-individual BOLD signal variability in regions of interest (ROIs, identified as peak coordinates in an attention/vigilance and WM network using Neurosynth) was differentially modulated across vigilance and WM trials. Within-individual BOLD signal variability was significantly greater across the majority of the ROIs in the working memory trials (2- and 3-back trials) compared to 0-back trials. Notably, this increased variability across the network was accompanied by significantly less variability in the left cingulate gyrus and left inferior temporal lobe during the working memory trials. Significantly fewer differences in within-individual BOLD signal variability were identified for vigilance trials (0- and 1-back trials) compared to crosshair. We hypothesized that increased BOLD signal variability would be associated with n-back task performance and with out-of-scanner measures of vigilance (Digit Span Forward) and WM (Auditory Consonant Trigrams and Digit Span Backward). These results were non-significant after correcting for multiple comparisons. Furthermore, using multivariate analyses (partial least squares regression; PLS-R), within-individual BOLD signal variability in regions associated with a WM-vigilance network did not significantly predict out-of-scanner test performance after appropriate cross validation, yet provided a promising trend for WM trials; greater within-individual BOLD signal variability during WM n-back trials was associated with decreased performance on all included neuropsychological measures, which provides partial support for previous findings. This study demonstrates that patterns of variability differ based on task load in the scanner and illustrates an intriguing association between within-individual BOLD signal variability and out-of-scanner behavioral performance that may be better explored in future studies with a larger sample size.

Aberrant Brain Signal Variability and COMT Genotype in Chronic TMD Patients

The analysis of brain signal variability is a promising approach to understand pathological brain function related to chronic pain. This study investigates whether blood-oxygen-level–dependent signal variability (BOLDSV) in specific frequency bands is altered in temporomandibular disorder (TMD) and correlated to its clinical features. Twelve patients with chronic myofascial TMD and 24 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. The BOLDSV was measured as the standard deviation of the BOLD time series at each voxel and compared between groups. We also examined the potential relationship between the BOLDSV and the catechol-O-methyltransferase (COMT) Val158Met polymorphism. We assessed sensory-discriminative pain in the craniofacial region, pain sensitivity to sustained masseteric pain challenge, and TMD pain frequency for clinical correlation. Patients displayed reduced BOLDSV in the dorsolateral prefrontal cortex (dlPFC) as compared with HC in all frequency bands. In the slow-3 band, patients also showed reduced BOLDSV in the medial dorsal thalamus, primary motor cortex (M1), and primary somatosensory cortex (S1) and heightened BOLDSV in the temporal pole. Notably, we found a significant correlation between lower BOLDSV (slow-3) in the orofacial M1/S1 regions and higher clinical pain (intensity/area) and higher sensitivity of the masseter muscle pain. Moreover, lower BOLDSV (slow-3) in the dlPFC and ventrolateral PFC was associated with a higher TMD pain frequency. Participants who had the COMT 158Met substitution exhibited lower BOLDSV in the dlPFC and higher BOLDSV in the temporal pole as compared with participants without the COMT 158Met substitution. An increasing number of Met alleles was associated with lower dlPFC and greater temporal pole BOLDSV in both HC and TMD groups. Together, we demonstrated that chronic TMD patients exhibit aberrant BOLDSV in the top-down pain modulatory and sensorimotor circuits associated with their pain frequency and severity. COMT Val158Met polymorphism might affect clinical symptoms in association with regional brain signal variability, specifically involved in cognitive and emotional regulation of pain.

Differential alteration of fMRI signal variability in the ascending trigeminal somatosensory and pain modulatory pathways in migraine

BackgroundThe moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. Here, we investigated the regional blood-oxygen-level-dependent signal variability (BOLDSV) and inter-regional dynamic functional connectivity (dFC) in the interictal phase of migraine and its relationship with the attack severity.MethodsWe acquired resting-state functional magnetic resonance imaging from 20 migraine patients and 26 healthy controls (HC). We calculated the standard deviation (SD) of the BOLD time-series at each voxel as a measure of the BOLD signal variability (BOLDSV) and performed a whole-brain voxel-wise group comparison. The brain regions showing significant group differences in BOLDSV were used to define the regions of interest (ROIs). The SD and mean of the dynamic conditional correlation between those ROIs were calculated to measure the variability and strength of the dFC. Furthermore, patients’ experimental pain thresholds and headache pain area/intensity levels during the migraine ictal-phase were assessed for clinical correlations.ResultsWe found that migraineurs, compared to HCs, displayed greater BOLDSV in the ascending trigeminal spinal-thalamo-cortical pathways, including the spinal trigeminal nucleus, pulvinar/ventral posteromedial (VPM) nuclei of the thalamus, primary somatosensory cortex (S1), and posterior insula. Conversely, migraine patients exhibited lower BOLDSV in the top-down modulatory pathways, including the dorsolateral prefrontal (dlPFC) and inferior parietal (IPC) cortices compared to HCs. Importantly, abnormal interictal BOLDSV in the ascending trigeminal spinal-thalamo-cortical and frontoparietal pathways were associated with the patient’s headache severity and thermal pain sensitivity during the migraine attack. Migraineurs also had significantly lower variability and greater strength of dFC within the thalamo-cortical pathway (VPM-S1) than HCs. In contrast, migraine patients showed greater variability and lower strength of dFC within the frontoparietal pathway (dlPFC-IPC).ConclusionsMigraine is associated with alterations in temporal signal variability in the ascending trigeminal somatosensory and top-down modulatory pathways, which may explain migraine-related pain and allodynia. Contrasting patterns of time-varying connectivity within the thalamo-cortical and frontoparietal pathways could be linked to abnormal network integrity and instability for pain transmission and modulation.

The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer’s disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors.

Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well‐being. Many studies of neurocognitive aging have investigated brain function with the blood‐oxygen level‐dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath‐holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age‐related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age‐related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population‐based, lifespan Cam‐CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel‐level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

Spontaneous brain state oscillation is associated with self-reported anxiety in a non-clinical sample

The anti-correlation relationship between the default-mode network (DMN) and task-positive network (TPN) may provide valuable information on cognitive functions and mental disorders. Moreover, maintaining a specific brain state and efficaciously switching between different states are considered important for self-regulation and adaptation to changing environments. However, it is currently unclear whether competitions between the DMN and TPN are associated with negative affect (here, anxiety and depression) in non-clinical samples. We measured the average dwell time of DMN dominance over the TPN (i.e., the average state duration before transition to another state, indicating persistent DMN dominance) with a sample of 302 non-clinical young adults. Subsequently, we explored individual differences in this persistent DMN dominance by examining its correlations with subjective depression and anxiety feelings. Moreover, we linked state transition between DMN/TPN dominance with right fronto-insular cortex (RFIC) blood oxygen-level dependent signal variability. We found that the average dwell time of DMN dominance was positively associated with self-reported anxiety. Furthermore, state transition between DMN or TPN dominance was positively linked to RFIC activity. These findings highlight the importance of investigating the complex and dynamic reciprocal inhibition patterns of the DMN and TPN and the important role of the RFIC in the association between these networks.

Resting State BOLD Variability Is Linked to White Matter Vascular Burden in Healthy Aging but Not in Older Adults With Subjective Cognitive Decline.

Background: Alzheimer’s disease (AD) is the leading cause of dementia. A lack of curative treatments and a rapidly aging global population have amplified the need for early biomarkers of the disease process. Recent advances suggest that subjective cognitive decline (SCD) may be one of the earliest symptomatic markers of the AD cascade. Previous studies have identified changes in variability in the blood-oxygen-level-dependent (BOLD) signal in patients with AD, with a possible association between BOLD variability and cerebrovascular factors in the aging brain. The objective of the current study was to determine whether changes in BOLD variability can be identified in individuals with SCD, and whether this signal may be associated with markers of cerebrovascular integrity in SCD and older adults without memory complaints.Method: Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database from 19 participants with SCD and 19 similarly-aged controls. For each participant, a map of BOLD signal variability (SDBOLD) was computed as the standard deviation of the BOLD time-series at each voxel. Group comparisons were performed to examine differences in resting-state SDBOLD in SCD vs. healthy controls. Relationships were then examined between participant SDBOLD maps and neuroimaging markers of white matter vascular infarcts in each group separately.Results: Between-group comparisons showed no significant differences in whole-brain SDBOLD in individuals with SCD and controls. In the healthy aging group, higher white matter hyperintensity (WMH) burden was associated with greater SDBOLD in right temporal regions (p < 0.05), and lower scores on a measure of global executive functioning. These associations were not identified in individuals with SCD.Conclusion: The current study underscores previous evidence for a relationship between SDBOLD and white matter vascular infarcts in the healthy aging brain. The findings also provide evidence for a dissociable relationship between healthy aging and SCD, such that in healthy controls, increased WMH is associated with declines in executive function that is not observed in older adults who present with memory complaints. Further multimodal work is needed to better understand the contributions of vascular pathology to the BOLD signal, and its potential relationship with pathological aging.

Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes.

The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or “BOLDSD,” is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLDSD is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLDSD changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLDSD changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLDSD is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLDSD alterations. Our study highlights that CT changes should be considered when evaluating BOLDSD alternations in the lifespan.

Resting State BOLD Variability in Alzheimer's Disease: A Marker of Cognitive Decline or Cerebrovascular Status?

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder that may benefit from early diagnosis and intervention. Therefore, there is a need to identify early biomarkers of AD using non-invasive techniques such as functional magnetic resonance imaging (fMRI). Recently, novel approaches to the analysis of resting-state fMRI data have been developed that focus on the moment-to-moment variability in the blood oxygen level dependent (BOLD) signal. The objective of the current study was to investigate BOLD variability as a novel early biomarker of AD and its associated psychophysiological correlates.Method: Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database from 19 participants with AD and 19 similarly aged controls. For each participant, a map of BOLD signal variability (SDBOLD) was computed as the standard deviation of the BOLD timeseries at each voxel. Group comparisons were performed to examine global differences in resting state SDBOLD in AD versus healthy controls. Correlations were then examined between participant SDBOLD maps and (1) ADNI-derived composite scores of memory and executive function and (2) neuroimaging markers of cerebrovascular status.Results: Between-group comparisons revealed significant (p < 0.05) increases in SDBOLD in patients with AD relative to healthy controls in right-lateralized frontal regions. Lower memory scores and higher WMH burden were associated with greater SDBOLD in the healthy control group (p < 0.1), but not individuals with AD.Conclusion: The current study provides proof of concept of a novel resting state fMRI analysis technique that is non-invasive, easily accessible, and clinically compatible. To further explore the potential of SDBOLD as a biomarker of AD, additional studies in larger, longitudinal samples are needed to better understand the changes in SDBOLD that characterize earlier stages of disease progression and their underlying psychophysiological correlates.

Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners, we investigated the effect of levodopa (l-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in l-dopa-induced behavioral benefits. Relative to placebo, l-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, l-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, l-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual's l-dopa-induced benefits in behavioral performance, namely response speed and perceptual sensitivity. Lastly, l-dopa-induced modulations of BOLD signal variability were correlated with l-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.

Age differences in brain signal variability are robust to multiple vascular controls

A host of studies support that younger, better performing adults express greater moment-to-moment blood oxygen level-dependent (BOLD) signal variability (SDBOLD) in various cortical regions, supporting an emerging view that the aging brain may undergo a generalized reduction in dynamic range. However, the exact physiological nature of age differences in SDBOLD remains understudied. In a sample of 29 younger and 45 older adults, we examined the contribution of vascular factors to age group differences in fixation-based SDBOLD using (1) a dual-echo BOLD/pseudo-continuous arterial spin labeling (pCASL) sequence, and (2) hypercapnia via a computer-controlled gas delivery system. We tested the hypothesis that, although SDBOLD may relate to individual differences in absolute cerebral blood flow (CBF), BOLD cerebrovascular reactivity (CVR), or maximum BOLD signal change (M), robust age differences in SDBOLD would remain after multiple statistical controls for these vascular factors. As expected, our results demonstrated that brain regions in which younger adults expressed higher SDBOLD persisted after comprehensive control of vascular effects. Our findings thus further establish BOLD signal variability as an important marker of the aging brain.

Regional brain signal variability: a novel indicator of pain sensitivity and coping.

Variability in blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals reflects the moment-by-moment fluctuations in resting-state fMRI (rs-fMRI) activity within specific areas of the brain. Regional BOLD signal variability was recently proposed to serve an important functional role in the efficacy of neural systems because of its relationship to behavioural performance in aging and cognition studies. We previously showed that individuals who better cope with pain have greater fluctuations in interregional functional connectivity, but it is not known whether regional brain signal variability is a mechanism underlying pain coping. We tested the hypothesis that individual pain sensitivity and coping is reflected by regional fMRI BOLD signal variability within dynamic pain connectome-brain systems implicated in the pain experience. We acquired resting-state fMRI and assessed pain threshold, suprathreshold temporal summation of pain, and the impact of pain on cognition in 80 healthy right-handed individuals. We found that regional BOLD signal variability: (1) inversely correlated with an individual's temporal summation of pain within the ascending nociceptive pathway (primary and secondary somatosensory cortex), default mode network, and salience network; (2) was correlated with an individual's ability to cope with pain during a cognitive interference task within the periaqueductal gray, a key opiate-rich brainstem structure for descending pain modulation; and (3) provided information not captured from interregional functional connectivity. Therefore, regional BOLD variability represents a pain metric with potential implications for prediction of chronic pain resilience vs vulnerability.

Altered blood oxygen level-dependent signal variability in chronic post-traumatic stress disorder during symptom provocation.

BackgroundRecent research suggests that variability in brain signal provides important information about brain function in health and disease. However, it is unknown whether blood oxygen level-dependent (BOLD) signal variability is altered in post-traumatic stress disorder (PTSD). We aimed to identify the BOLD signal variability changes of PTSD patients during symptom provocation and compare the brain patterns of BOLD signal variability with those of brain activation.MethodsTwelve PTSD patients and 14 age-matched controls, who all experienced a mining accident, underwent clinical assessment as well as fMRI scanning while viewing trauma-related and neutral pictures. BOLD signal variability and brain activation were respectively examined with standard deviation (SD) and general linear model analysis, and compared between the PTSD and control groups. Multiple regression analyses were conducted to explore the association between PTSD symptom severity and these two brain measures across all subjects as well as in the PTSD group.ResultsPTSD patients showed increased activation in the middle occipital gyrus compared with controls, and an inverse correlation was found between PTSD symptom severity and brain activation in the hippocampus and anterior cingulate cortex/medial prefrontal cortex. Brain variability analysis revealed increased SD in the insula, anterior cingulate cortex/medial prefrontal cortex, and vermis, and decreased SD in the parahippocapal gyrus, dorsolateral prefrontal cortex, somatosensory cortex, and striatum. Importantly, SD alterations in several regions were found in both traumatic and neutral conditions and were stratified by PTSD symptom severity.ConclusionBOLD signal variability may be a reliable and sensitive biomarker of PTSD, and combining brain activation and brain variability analysis may provide complementary insight into the neural basis of this disorder.