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Regional BOLD variability relative to the mean for each of the n-back conditions. For each level of WM load, BOLD variability is displayed relative to the mean of MSSD within condition. Warm-colored voxels evidence greater BOLD variability for that condition, while cool-colored regions show lesser variability than the mean for that condition. Regions at the mean MSSD are shown in black.
Source publication
Moment-to-moment fluctuations in brain signal assessed by functional magnetic resonance imaging blood oxygenation level dependent (BOLD) variability is increasingly thought to represent important "signal" rather than measurement-related "noise." Efforts to characterize BOLD variability in healthy aging have yielded mixed outcomes, demonstrating bot...
Contexts in source publication
Context 1
... a means of providing the spatial pattern of variability for each condition, voxel-wise variability was plotted relative to the condition mean across all participants, for each condition (0-, 2-, 3-, 4-back) in Figure 1. Warm color scale depicts the brain regions that demonstrate greater BOLD variability relative to the mean within the condition, while cool colors represent regions that evidence lesser BOLD variability, and voxels exhibiting the mean BOLD variability for that condition are presented in black. ...
Context 2
... voxels evidenced lower MSSD with age in any condition. Age parametric maps are provided for each n-back condition in Supplemental Figure 1 and coordinates in Supplemental Table 1. ...
Context 3
... current study demonstrated that BOLD variability, on average across all participants within condition, evidenced differential patterns across the cerebral cortex, with relatively greater BOLD variability in subcortical regions and relatively lesser BOLD variability in cortical regions (Fig. 1). However, both cortical and subcortical regions robustly demonstrated greater variability during the n-back task with increasing age, including in the superior temporal, parahippocampal and fusiform gyri, superior parietal lobule, angular, precentral, superior frontal, middle frontal, and inferior frontal gyri, amygdala, nucleus ...
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Suppression of the brain’s default mode network (DMN) during external goal-directed cognitive tasks has been consistently observed in neuroimaging studies. However, emerging insights suggest the DMN is not a monolithic “task-negative” network but is comprised of subsystems that show functional heterogeneity. Despite considerable research interest,...
Citations
... Over the last decade, a growing body of evidence has implicated intraindividual variability in the brain's BOLD response-the signal measured by fMRI-as a unique and informative metric of brain function and cognitive capacity (Boylan et al., 2021;Gaut et al., 2019;Grady & Garrett, 2018;Guitart-Masip et al., 2016;Burzynska et al., 2015;Garrett, McIntosh, & Grady, 2014;Garrett, Kovacevic, McIntosh, & Grady, 2011Protzner, Kovacevic, Cohn, & McAndrews, 2013;Garrett, Kovacevic, McIntosh, & Grady, 2010). This perspective on BOLD variability (SD BOLD ) rests on two separate claims. ...
... This perspective on BOLD variability (SD BOLD ) rests on two separate claims. First, SD BOLD is hypothesized to capture important properties of brain dynamics associated with aging and cognitive performance, with a body of research showing that BOLD variability tends to systematically decrease across the lifespan in many cortical regions (e.g., Grady & Garrett, 2018;Garrett, Samanez-Larkin, et al., 2013;Garrett, Kovacevic, et al., 2011;Garrett et al., 2010, but see Boylan et al., 2021) and that increased variability is associated with increased cognitive performance (Garrett, Kovacevic, et al., 2011Garrett, Samanez-Larkin, et al., 2013). Importantly, in these studies, the relationships of both age and cognitive performance with task-related BOLD variability are stronger than those evident with traditionally used measures of average BOLD signal (mean BOLD ), indicating that SD BOLD is a promising and informative measure of brain function. ...
... Many hypotheses in the current study were tested using partial least squares (PLS), a technique commonly used in BOLD variability studies (Boylan et al., 2021;Roberts, Grady, & Addis, 2020;Garrett et al., 2014;Garrett, Kovacevic, et al., 2011. PLS is a multivariate technique that examines the pattern of covariances across brain and design (i.e., conditions/behaviors) matrices, resulting in latent variables that comprise sets of voxels associated with contrasts of tasks and/or behaviors (Krishnan, Williams, McIntosh, & Abdi, 2011;McIntosh & Lobaugh, 2004). ...
BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to “absolute” meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.
... Alterations of BOLD variability have been reported during aging [15], and it was also linked to cognitive performance [18]. Altered BOLD variability has been reported in a wide range of neuropsychiatric and metabolic disorders, such as Alzheimer's disease [19], small vessel disease [13], drug-resistant epilepsy [20], generalized anxiety disorder [21] and chronic kidney disease [22]. ...
... Since functional connectivity between regions also fluctuates in time, and these fluctuations can be categorized into distinct temporal states [25,26], it is possible that such states also occur in the variability of the BOLD signal. Indeed, several studies reported BOLD variability changes in response to task demands [18]. In the absence of determinable states that conform to e.g., an external task, clustering techniques can be used to derive states, which have been effectively used in estimating resting-state time-varying functional connectivity [25]. ...
... This points to a potential use of time-varying methods in assessing BOLD variability during rest. Task-based studies have shown that BOLD variability is different during task performance and task free periods [18,23]. Our results suggest that periods of increased and decreased BOLD variability also alternate during rest. ...
Background
Migraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients.
Methods
Two independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman’s rank correlation was calculated to test associations with clinical parameters.
Results
Resting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort.
Conclusion
Episodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.
... Given the functional and behavioral relevance of brain signal variability and complexity and their increasing use in cognitive and clinical neuroscience to study individual differences, we here used task and rest BOLD fMRI data from the Human Connectome Project (van N = 330) to systematically investigate the reliability of several variability and complexity measures. As variability-based measures, we investigate the reliability of the standard deviation of the BOLD signal (SD BOLD ) as the most frequently used measure of variability in fMRI research, as well as the mean absolute successive difference from timestep to timestep (MASD BOLD ), reflecting the rate at which the signal varies over time, and the mean squared successive difference (MSSD BOLD ) which emphasizes large abrupt changes (Boylan et al., 2021;Mohr & Nagel, 2010;Nomi et al., 2017). ...
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between‐person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures—which is an important precondition for robust individual differences as well as longitudinal research—is not yet sufficiently studied. We examined reliability (split‐half correlations) and test–retest correlations for task‐free (resting‐state) BOLD fMRI as well as split‐half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split‐half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test–retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time‐resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region‐specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well‐suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function.
Practitioner Points
Variability and complexity measures of BOLD activation show good split‐half reliability and moderate test–retest reliability.
Measures of variability of global functional connectivity over time can robustly quantify neural dynamics.
Length of fMRI data has only a minor effect on reliability.
... Increased CV BOLD also correlated with lower MMSE values in all three datasets. This is in line with the previous finding that greater BOLD variability is associated with poorer cognitive function (Boylan et al., 2021). ...
Abstract Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are two of the most common early-onset dementia diseases, affecting millions of people worldwide. These disorders often exhibit overlapping symptoms and present diagnostic challenges. The diagnostic accuracy of current methods is not entirely satisfactory, with difficulties arising in the early or prodromal phases of the disease. For example, as many as half of the bvFTD patients might receive an incorrect psychiatric diagnosis, such as schizophrenia, before the correct diagnosis is made. This thesis project, comprising three original publications, investigated functional connectivity and blood oxygenation level-dependent (BOLD) signal variability in AD, bvFTD, and schizophrenia, utilizing five independent datasets with a total of 83 AD patients, 56 bvFTD patients, 23 schizophrenia patients, and 388 controls. The research utilizes advanced neuroimaging methods to study brain changes associated with AD and bvFTD: voxel-based morphometry for measuring brain atrophy patterns, independent component analysis to assess functional connectivity, and both standard and ultrafast functional magnetic resonance imaging, along with multimodal imaging, to investigate brain signal variability and its underlying physiological mechanisms. A new quality control method and brain signal variability measure, CVBOLD, is introduced. The results from this project show that some changes in functional connectivity previously reported within the salience and default mode networks in patients with AD and bvFTD, might only be detectable following CVBOLD based quality control and brain atrophy correction methods. The key findings include the identification of distinct patterns of increased CVBOLD in AD and bvFTD, which was not observed in the data on schizophrenia. CVBOLD efficiently differentiates AD and bvFTD patients from controls. CVBOLD is consistent in controls and increases only in AD and bvFTD patients over time as the disease progresses. Additionally, an anatomically similar increase in cardiorespiratory signal variability was observed in AD, potentially indicating increased physiological pulsations behind the observed CVBOLD increase and linking these findings to the glymphatic system. The new CVBOLD contrast developed in this project may offer a non-invasive biomarker for helping in the diagnosis of AD and bvFTD.
... In this study, we examined two composite measures of cognitive function from the NIH Toolbox, i.e. fluid intelligence (gF) that combines scores on tests that engage flexible thinking, such as the flanker task and list sorting, and crystallized intelligence (gC), which characterizes tasks relying on acquired knowledge, such as vocabulary and reading comprehension (Cattell and Kuhlen, 1963). Decreased reaction time, increased stability of responding and better accuracy on a variety of tasks requiring flexible cognition are associated with greater task-related BOLD-SD in a number of studies (Garrett et al., 2011Guitart-Masip et al., 2016;Grady and Garrett, 2018;Malins et al., 2018;Good et al., 2020), although not all (Boylan et al., 2021). Consistent with the bulk of this work, we recently reported that faster and more accurate working memory performance was associated with increased task-related BOLD-SD in attention and cognitive control brain networks, as well as reduced BOLD-SD in sensorimotor networks (Rieck et al., 2022). ...
Temporal variability of the fMRI-derived blood-oxygen level dependent (BOLD) signal during cognitive tasks shows important associations with individual differences in age and performance. Less is known about relations between spontaneous BOLD variability measured at rest and relatively stable cognitive measures, such as IQ or socioemotional function. Here, we examined associations among resting BOLD variability, cognitive/socioemotional scores from the NIH Toolbox, and optimal time-of-day for alertness (chronotype) in a sample of 157 adults from 20-86 years of age. To investigate individual differences in these associations independently of age, we regressed age out from both behavioral and BOLD variability scores. We hypothesized that greater BOLD variability would be related to higher fluid cognition scores, more positive scores on socioemotional scales, and a morningness chronotype. Consistent with this idea, we found positive correlations between resting BOLD variability, positive socioemotional scores (e.g., self-efficacy) and morning chronotype, as well as negative correlations between variability and negative emotional scores (e.g., loneliness). Unexpectedly, we found negative correlations between BOLD variability and fluid cognition. These results suggest that greater resting brain signal variability facilitates optimal socioemotional function and characterizes those with morning-type circadian rhythms, but individuals with greater fluid cognition may be more likely to show less temporal variability in spontaneous measures of BOLD activity.
... The standard deviation of the BOLD signal (SD BOLD ) is a widely used variance-based measure of neural variability. SD BOLD during task-free resting-state relates to neural development, aging, and optimal cognitive functions [24][25][26], indicating its relevance for MPDs that share prominent cognitive changes. ...
Across the major psychiatric disorders (MPDs), a shared disruption in brain physiology is suspected. Here we investigate the neural variability at rest, a well-established behavior-relevant marker of brain function, and probe its basis in gene expression and neurotransmitter receptor profiles across the MPDs. We recruited 219 healthy controls and 279 patients with schizophrenia, major depressive disorder, or bipolar disorders (manic or depressive state). The standard deviation of blood oxygenation level-dependent signal (SDBOLD) obtained from resting-state fMRI was used to characterize neural variability. Transdiagnostic disruptions in SDBOLD patterns and their relationships with clinical symptoms and cognitive functions were tested by partial least-squares correlation. Moving beyond the clinical sample, spatial correlations between the observed patterns of SDBOLD disruption and postmortem gene expressions, Neurosynth meta-analytic cognitive functions, and neurotransmitter receptor profiles were estimated. Two transdiagnostic patterns of disrupted SDBOLD were discovered. Pattern 1 is exhibited in all diagnostic groups and is most pronounced in schizophrenia, characterized by higher SDBOLD in the language/auditory networks but lower SDBOLD in the default mode/sensorimotor networks. In comparison, pattern 2 is only exhibited in unipolar and bipolar depression, characterized by higher SDBOLD in the default mode/salience networks but lower SDBOLD in the sensorimotor network. The expression of pattern 1 related to the severity of clinical symptoms and cognitive deficits across MPDs. The two disrupted patterns had distinct spatial correlations with gene expressions (e.g., neuronal projections/cellular processes), meta-analytic cognitive functions (e.g., language/memory), and neurotransmitter receptor expression profiles (e.g., D2/serotonin/opioid receptors). In conclusion, neural variability is a potential transdiagnostic biomarker of MPDs with a substantial amount of its spatial distribution explained by gene expressions and neurotransmitter receptor profiles. The pathophysiology of MPDs can be traced through the measures of neural variability at rest, with varying clinical-cognitive profiles arising from differential spatial patterns of aberrant variability.
... Traditional functional neuroimaging measurements of the hemodynamic response utilize an average signal which does not consider variability of activity within individuals (Garrett et al., 2010). While some studies show increased IIV associated with better performance (Garrett et al., 2013), other studies show negative or differential associations between neural IIV and behavior (Boylan et al., 2021;Guitart-Masip et al., 2016). The mixed findings may underscore distinctive relationships between neural IIV and cognitive domain, task difficulty, age, and disease (Armbruster-Genç et al., 2016;Garrett et al., 2013;Grady & Garrett, 2018;Guitart-Masip et al., 2016). ...
... The mixed findings may underscore distinctive relationships between neural IIV and cognitive domain, task difficulty, age, and disease (Armbruster-Genç et al., 2016;Garrett et al., 2013;Grady & Garrett, 2018;Guitart-Masip et al., 2016). For instance, increased neural IIV may be adaptive in learning, but excessive IIV may have negative associations (Boylan et al., 2021;Dinstein et al., 2015;Steinberg et al., 2022). We have previously found that increased neural IIV in the PFC from single-tasks to dual-task walking was greater in men and people with cognitive impairment . ...
... Greater IIV in the hemodynamic response has been associated with greater task difficulty, greater variability in movement, and cognitive impairments (Haar et al., 2017;Holtzer et al., 2020). Neural IIV has been suggested to be task and region-dependent (Armbruster-Genç et al., 2016;Boylan et al., 2021;Guitart-Masip et al., 2016). It has been postulated that neural IIV may follow a u-shaped curve, with some variability necessary for learning but too much variability being disadvantageous and associated with clinical disorders (Dinstein et al., 2015). ...
Objective:
Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance.
Method:
Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions.
Results:
Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02).
Conclusions:
Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.
... However, the interaction between variability, age and cognition becomes more complex. It has been reported that this measure is conversely impacted by the interaction between age and cognitive load, while brain variability decreases with the aging process with the least cognitive burden; the pattern is reversed with increased cognitive load (Boylan et al., 2021). In addition, with tasks increasing in difficulty, younger performers exhibited a pattern of decreasing variability, while older performers showed increased patterns (Boylan et al., 2021). ...
... It has been reported that this measure is conversely impacted by the interaction between age and cognitive load, while brain variability decreases with the aging process with the least cognitive burden; the pattern is reversed with increased cognitive load (Boylan et al., 2021). In addition, with tasks increasing in difficulty, younger performers exhibited a pattern of decreasing variability, while older performers showed increased patterns (Boylan et al., 2021). It seems that older neurons in aging individuals, when coming up with complicated task demands, need more fluctuations (i.e., brain variability) to meet the need of cognitive operations S. C. Li et al., 2006). ...
... In Section 2, it is apparent that IRTFs are tightly correlated to cognitive condition. But it is still hard to say that there is a linear relationship between IRTFs and cognitions as the relationship is always interacted with other factors, such as aging (Boylan et al., 2021). To present the generous viewpoint of strong correspondence between IRTFs and cognition, detailed descriptions of specific brain regions are avoided in the previous sections. ...
Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.
... Over the last decade, a growing body of evidence has implicated intra-individual variability in the brain's blood oxygen level dependent (BOLD) response-the signal measured by functional magnetic resonance imaging (fMRI)-as a unique and informative metric of brain function and cognitive capacity (Boylan et al., 2020;Burzynska et al., 2015;Garrett et al., 2010;Garrett, Kovacevic, et al., 2011;Garrett, Kovacevic, et al., 2013;Garrett et al., 2014;Gaut et al., 2019;Grady & Garrett, 2018;Guitart-Masip et al., 2016;Protzner et al., 2013). This perspective on BOLD variability (SDBOLD) rests on two separate claims. ...
... This perspective on BOLD variability (SDBOLD) rests on two separate claims. First, SDBOLD is hypothesised to capture important properties of brain dynamics associated with aging and cognitive performance, with a body of research showing that BOLD variability tends to systematically decrease across the lifespan in many cortical regions (e.g., Garrett et al., 2010Garrett et al., , 2013Garrett, Kovacevic, et al., 2011;Grady & Garrett, 2018, but see Boylan et al., 2020) and that increased variability is associated with increased cognitive performance (Garrett, Kovacevic, et al., 2011Garrett, Samanez-Larkin, et al., 2013). ...
... Many hypotheses in the current study were tested using partial least squares (PLS), a technique commonly used in BOLD variability studies (Boylan et al., 2020;Garrett et al., 2014;Garrett, Kovacevic, et al., 2011Roberts et al., 2020). PLS is a multivariate technique that examines pattern of covariances across brain and design (i.e., conditions/behaviours) matrices, resulting in latent variables that comprise sets of voxels associated with contrasts of tasks and/or behaviours (Krishnan et al., 2011;McIntosh & Lobaugh, 2004). ...
BOLD variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and a powerful predictor of behavioural performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that while SDBOLD may not be related to raw meanBOLD it will be linearly related to absolute meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than response times. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD; and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.
... High BOLD variability has been related to poorer cognitive functions, as measured by a working memory n-back task (77), although other studies have shown an increase of BOLD variability with age in some brain regions, but not the hippocampus (52). A potential explanation is that increased . ...
Background
Abnormal neurotransmitter levels have been reported in subjects at high risk for schizophrenia, leading to a shift in the excitatory/inhibitory balance. However, it is unclear if these alterations are predating the onset of clinically relevant symptoms. Our aim was to explore in vivo measures of excitatory/inhibitory balance in 22q11.2 deletion carriers, a population at high genetic risk for psychosis.
Methods
Glx (glutamate + glutamine) and GABA+ concentrations were estimated in the anterior cingulate cortex (ACC), superior temporal gyrus (STG) and hippocampus using a MEGAPRESS sequence and the Gannet toolbox in 52 deletion carriers and 42 controls. T1-weighted images were acquired longitudinally and processed with Freesurfer v.6.0 to extract hippocampal volume. Subgroup analyses were conducted in deletion carriers with psychotic symptoms identified by means of SIPS.
Results
While no differences were found in the ACC, deletion carriers had higher levels of Glx in the hippocampus and STG, and lower levels of GABA+ in the hippocampus compared to controls. We additionally found a higher Glx concentration in the hippocampus of psychotic compared to non-psychotic deletion carriers. Finally, more pronounced hippocampal atrophy and increased functional variability were both significantly associated with increased Glx levels in deletion carriers.
Conclusions
This study provides evidence for an excitatory/inhibitory imbalance in temporal brain structures of deletion carriers, with a further hippocampal Glx increase in individuals with psychotic symptoms that was associated with hippocampal atrophy and abnormal function. These results support theories proposing abnormally enhanced glutamatergic neural transmission as a mechanistic explanation for hippocampal atrophy via excitotoxicity. Overall, our results highlight a central role of glutamate in the hippocampus of individuals at genetic risk for schizophrenia.
