BOLD Variability is Related to Dopaminergic Neurotransmission and Cognitive Aging

Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability

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.

Differences Between Resting-State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults

Background: Resting-state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and traditional seed-based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. Methods: Thirty-four healthy participants were separated into a younger adult group (age 25-35, n = 17) and an older adult group (age 65+, n = 17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Results: Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. Conclusion: The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Greater up-modulation of intra-individual brain signal variability makes a high-load cognitive task more arduous for older adults

The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.

Brain signal variability and executive functions across the life span

Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking

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.

Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis

Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.

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.

Biphasic patterns of age-related differences in dopamine D1 receptors across the adult lifespan

Age-related alterations in D1-like dopamine receptor (D1DR) have distinct implications for human cognition and behavior during development and aging, but the timing of these periods remains undefined. Enabled by a large sample of in vivo assessments (n = 180, age 20 to 80 years of age, 50% female), we discover that age-related D1DR differences pivot at approximately 40 years of age in several brain regions. Focusing on the most age-sensitive dopamine-rich region, we observe opposing pre- and post-forties interrelations among caudate D1DR, cortico-striatal functional connectivity, and memory. Finally, particularly caudate D1DR differences in midlife and beyond, but not in early adulthood, associate with manifestation of white matter lesions. The present results support a model by which excessive dopamine modulation in early adulthood and insufficient modulation in aging are deleterious to brain function and cognition, thus challenging a prevailing view of monotonic D1DR function across the adult lifespan.

Relation of resting brain signal variability to cognitive and socioemotional measures in an adult lifespan sample

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 to 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 Role of Blood Oxygen Level Dependent Signal Variability in Pediatric Neuroscience: A Systematic Review

BACKGROUND

As pediatric BOLD Signal Variability (SV) analysis is relatively novel, there is a need to provide a foundational framework that gives researchers an entry point into engaging with the topic. This begins with clarifying the definition of BOLD signal variability by identifying and categorizing the various metrics utilized to measure BOLD SV.

METHODS

A systematic review of the literature was conducted. Inclusion criteria were restricted to studies utilizing any metric of BOLD SV and with individuals younger than 18 in the study population. The definition of BOLD SV was any measure of intra-individual variability in the BOLD signal. Five databases were searched: Psychinfo, Healthstar, MEDLINE, Embase, and Scopus.

RESULTS

A total of 17 observational studies, including male (n = 1796) and female (n = 1324) pediatric participants were included. Eight studies quantified variability as the amount of deviation from the average BOLD signal, seven used complexity-based metrics, three used correlation measures of variability, and one used the structure of the hemodynamic response function. In this study, 10 methods of quantifying signal variability were identified. Associations and trends in BOLD SV were commonly found with age, factors specific to mental and/or neurological disorders such as attention deficit disorder, epilepsy, psychotic symptoms, and performance on psychological and behavioral tasks.

CONCLUSIONS

BOLD SV is a potential biomarker of neurodevelopmental and neurological conditions and symptom severity in mental disorders for defined pediatric populations. Studies that establish clinical trends and identify the mechanisms underlying BOLD SV with a low risk of bias are needed before clinical applications can be utilized by physicians.

Dopamine D1-signaling modulates maintenance of functional network segregation in aging

Past research has shown that as individuals age, there are decreases in within-network connectivity and increases in between-network connectivity, a pattern known as functional dedifferentiation. While the mechanisms behind reduced network segregation are not fully understood, evidence suggests that age-related differences in the dopamine (DA) system may play a key role. The DA D1-receptor (D1DR) is the most abundant and age-sensitive receptor subtype in the dopaminergic system, known to modulate synaptic activity and enhance the specificity of the neuronal signals. In this study from the DyNAMiC project (N = 180, 20-79y), we set out to investigate the interplay among age, functional connectivity, and dopamine D1DR availability. Using a novel application of multivariate Partial Least squares (PLS), we found that older age, and lower D1DR availability, were simultaneously associated with a pattern of decreased within-network and increased between-network connectivity. Individuals who expressed greater distinctiveness of large-scale networks exhibited more efficient working memory. In line with the maintenance hypotheses, we found that older individuals with greater D1DR in caudate exhibited less dedifferentiation of the connectome, and greater working memory, compared to their age-matched counterparts with less D1DR. These findings suggest that dopaminergic neurotransmission plays an important role in functional dedifferentiation in aging with consequences for working memory function at older age.

Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood

Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond ("bad stock") or expected value greater than the bond ("good stock"). Five measures of behavior (choice inflexibility, risk seeking, suboptimal investment) and beliefs (absolute error, optimism) were computed and D2-like binding potential was extracted from four brain regions of interest (midbrain, amygdala, anterior cingulate, insula). We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension (r = 0.76) revealed that the strongest associations were between measures of choice inflexibility, incorrect choice, optimism, amygdala binding potential, and age. Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.

Ultra-high-field pharmacological functional MRI of dopamine D1 receptor-related interventions in anesthetized rats

The dopamine D1 receptor (D1R) is associated with schizophrenia, Parkinson's disease, and attention deficit hyperactivity disorder. Although the receptor is considered a therapeutic target for these diseases, its neurophysiological function has not been fully elucidated. Pharmacological functional MRI (phfMRI) has been used to evaluate regional brain hemodynamic changes induced by neurovascular coupling resulting from pharmacological interventions, thus phfMRI studies can be used to help understand the neurophysiological function of specific receptors. Herein, the blood oxygenation level-dependent (BOLD) signal changes associated with D1R action in anesthetized rats was investigated by using a preclinical ultra-high-field 11.7-T MRI scanner. PhfMRI was performed before and after administration of the D1-like receptor agonist (SKF82958), antagonist (SCH39166), or physiological saline subcutaneously. Compared to saline, the D1-agonist induced a BOLD signal increase in the striatum, thalamus, prefrontal cortex, and cerebellum. At the same time, the D1-antagonist reduced the BOLD signal in the striatum, thalamus, and cerebellum by evaluating temporal profiles. PhfMRI detected D1R-related BOLD signal changes in the brain regions associated with high expression of D1R. We also measured the early expression of c-fos at the mRNA level to evaluate the effects of SKF82958 and isoflurane anesthesia on neuronal activity. Regardless of the presence of isoflurane anesthesia, c-fos expression level was increased in the region where positive BOLD responses were observed with administration of SKF82958. These findings demonstrated that phfMRI could be used to identify the effects of direct D1 blockade on physiological brain functions and also for neurophysiological assessment of dopamine receptor functions in living animals.

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 (BOLD_SD) 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 BOLD_SD in the dlPFC, with greater levels of brooding associated with lower BOLD_SD in the dlPFC, even after controlling for depression severity. Depression history was related to BOLD_SD in the dlPFC, with reduced BOLD_SD 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.

Hippocampal convergence during anticipatory midbrain activation promotes subsequent memory formation

The hippocampus has been a focus of memory research since H.M's surgery abolished his ability to form new memories, yet its mechanistic role in memory remains debated. Here, we identify a candidate memory mechanism: an anticipatory hippocampal "convergence state", observed while awaiting valuable information, and which predicts subsequent learning. During fMRI, participants viewed trivia questions eliciting high or low curiosity, followed seconds later by its answer. We reasoned that encoding success requires a confluence of conditions, so that hippocampal states more conducive to memory formation should converge in state space. To operationalize convergence of neural states, we quantified the typicality of multivoxel patterns in the medial temporal lobes during anticipation and encoding of trivia answers. We found that the typicality of anticipatory hippocampal patterns increased during high curiosity. Crucially, anticipatory hippocampal pattern typicality increased with dopaminergic midbrain activation and uniquely accounted for the association between midbrain activation and subsequent recall. We propose that hippocampal convergence states may complete a cascade from motivation and midbrain activation to memory enhancement, and may be a general predictor of memory formation.

Resting-state BOLD signal variability is associated with individual differences in metacontrol

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch

Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as "fingerprints" to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.

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.

Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome

BACKGROUND

Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

METHODS

A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes.

RESULTS

Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total r_{ACTUAL,PREDICTED} = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long.

CONCLUSIONS

Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

Resting-state functional magnetic resonance imaging signal variations in aging: The role of neural activity

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.

A literature review of dopamine in binge eating

OBJECTIVE

Binge eating, a core diagnostic symptom in binge eating disorder and bulimia nervosa, increases the risk of multiple physiological and psychiatric disorders. The neurotransmitter dopamine is involved in food craving, decision making, executive functioning, and impulsivity personality trait; all of which contribute to the development and maintenance of binge eating. The objective of this paper is to review the associations of dopamine levels/activities, dopamine regulator (e.g., dopamine transporter, degrading enzymes) levels/activities, and dopamine receptor availability/affinity with binge eating.

METHODS

A literature search was conducted in PubMed and PsycINFO to obtain human and animal studies published since 2010.

RESULTS

A total of 31 studies (25 human, six animal) were included. Among the human studies, there were 12 case-control studies, eight randomized controlled trials, and five cross-sectional studies. Studies used neuroimaging (e.g., positron emission tomography), genetic, and pharmacological (e.g., dopamine transporter inhibitor) techniques to describe or compare dopamine levels/activities, dopamine transporter levels/activities, dopamine degrading enzyme (e.g., catechol-O-methyltransferase) levels/activities, and dopamine receptor (e.g., D1, D2) availability/affinity among participants with and without binge eating. Most human and animal studies supported an altered dopaminergic state in binge eating (26/31, 83.9%); however, results were divergent regarding whether the altered state was hyperdopaminergic (9/26, 34.6%) or hypodopaminergic (17/26, 65.4%). The mixed findings may be partially explained by the variability in sample characteristics, study design, diagnosis criteria, and neuroimaging/genetic/pharmacological techniques used. However, it is possible that instead of being mutually exclusive, the hyperdopaminergic and hypodopaminergic state may co-exist, but in different stages of binge eating or in different individual genotypes.

CONCLUSIONS

For future studies to clarify the inconsistent findings, a homogenous sample that controls for confounders that may influence dopamine levels (e.g., psychiatric diseases) is preferable. Longitudinal studies are needed to evaluate whether the hyper- and hypo-dopaminergic states co-exist in different stages of binge eating or co-exist in individual phenotypes. Binge eating is characterized by eating a large amount of food in a short time and a feeling of difficulty to stop while eating. Binge eating is the defining symptom of binge eating disorder and bulimia nervosa, both of which are associated with serious health consequences. Studies have identified several psychological risk factors of binge eating, including a strong desire for food, impaired cognitive skills, and distinct personality traits (e.g., quick action without careful thinking). However, the physiological markers of binge eating remain unclear. Dopamine is a neurotransmitter that is heavily involved in feeding behavior, human motivation, cognitive ability, and personality. Therefore, dopamine is believed to play a critical role in binge eating. This review synthesized study findings related to the levels and activities of dopamine, dopamine regulators, and dopamine receptors in the context of binge eating. The primary finding is that most studies that used neuroimaging, genetic, or drug techniques found an altered dopaminergic state related to binge eating. However, the literature is inconsistent concerning the direction of the alteration. Considering the mixed findings and the limitations in study design, future studies, especially those that include repeated measurements, are needed to clarify the role of dopamine in binge eating.

Altered Microstate Dynamics and Spatial Complexity in Late-Life Schizophrenia

BACKGROUND

Resting-state EEG microstate and omega complexity analyses have been widely used to explore deviant brain function in various neuropsychiatric disorders. This study aimed to investigate the features of microstate dynamics and spatial complexity in patients with late-life schizophrenia (LLS).

METHOD

Microstate and omega complexity analyses were performed on resting-state EEG data from 39 in patients with LLS and compared with 40 elderly normal controls (NCs).

RESULT

The duration of microstate classes A and D were significantly higher in patients with LLS compared with NCs. The occurrence of microstate classes A, B, and C was significantly lower in patients with LLS compared with NCs. LLS patients have a lower time coverage of microstate class A and a higher time coverage of class D than NCs. Transition probabilities from microstate class A to B and from class A to C were significantly lower in patients with LLS compared with NCs. Transition probabilities between microstate class B and D were significantly higher in patients with LLS compared with NCs. Global omega complexity and anterior omega complexity were significantly higher in patients with LLS compared with NCs.

CONCLUSION

This study revealed an altered pattern of microstate dynamics and omega complexity in patients with LLS. This may reflect the disturbed neural basis underlying LLS and enhance the understanding of the pathophysiology of LLS.

Fronto-striatal dopamine D2 receptor availability is associated with cognitive variability in older individuals with low dopamine integrity

Within-person, moment-to-moment, variability in behavior increases with advancing adult age, potentially reflecting the influence of reduced structural and neurochemical brain integrity, especially that of the dopaminergic system. We examined the role of dopamine D2 receptor (D2DR) availability, grey-, and white-matter integrity, for between-person differences in cognitive variability in a large sample of healthy older adults (n = 181; 64-68 years) from the Cognition, Brain, and Aging (COBRA) study. Intra-individual variability (IIV) in cognition was measured as across-trial variability in participants' response times for tasks assessing perceptual speed and working memory, as well as for a control task of motor speed. Across the whole sample, no associations of D2DR availability, or grey- and white-matter integrity, to IIV were observed. However, within-person variability in cognition was increased in two subgroups of individuals displaying low mean-level cognitive performance, one of which was characterized by low subcortical and cortical D2DR availability. In this latter group, fronto-striatal D2DR availability correlated negatively with within-person variability in cognition. This finding suggests that the influence of D2DR availability on cognitive variability may be more easily disclosed among individuals with low dopamine-system integrity, highlighting the benefits of large-scale studies for delineating heterogeneity in brain-behavior associations in older age.

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

Emotion dysregulation is central to the development and maintenance of psychopathology, and is common across many psychiatric disorders. Neurobiological models of emotion dysregulation involve the fronto-limbic brain network, including in particular the amygdala and prefrontal cortex (PFC). Neural variability has recently been suggested as an index of cognitive flexibility. We hypothesized that within-subject neural variability in the fronto-limbic network would be related to inter-individual variation in emotion dysregulation in the context of low affective control. In a multi-site cohort (N = 166, 93 females) of healthy individuals and individuals with emotional dysregulation (attention deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD)), we applied partial least squares (PLS), a multivariate data-driven technique, to derive latent components yielding maximal covariance between blood-oxygen level-dependent (BOLD) signal variability at rest and emotion dysregulation, as expressed by affective lability, depression and mania scores. PLS revealed one significant latent component (r = 0.62, p = 0.044), whereby greater emotion dysregulation was associated with increased neural variability in the amygdala, hippocampus, ventromedial, dorsomedial and dorsolateral PFC, insula and motor cortex, and decreased neural variability in occipital regions. This spatial pattern bears a striking resemblance to the fronto-limbic network, which is thought to subserve emotion regulation, and is impaired in individuals with ADHD, BD, and BPD. Our work supports emotion dysregulation as a transdiagnostic dimension with neurobiological underpinnings that transcend diagnostic boundaries, and adds evidence to neural variability being a relevant proxy of neural efficiency.

Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance

Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change-change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of "local" dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change-change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.

Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder

Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic fluctuations are static throughout the entire scan. However, it has recently been suggested that the dynamic alternations in functional connectivity (FC) may reflect the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior, and thus may act as biomarkers of disease. Methods: In this study, the resting-state functional MRI (fMRI) data were collected from 26 patients with GAD, 22 patients with PD, and 26 healthy controls (HCs). We investigated dynamic functional connectivity (DFC) by using the group spatial independent component analysis, a sliding window approach, and the k-means clustering methods. For group comparisons, the temporal properties of DFC states were analyzed statistically. Results: The dynamic analysis demonstrated two discrete connectivity "States" across the entire group, namely, a more segregated State I and a strongly integrated State II. Compared with HCs, patients with both GAD and PD spent more time in the weakly within-network State I, while performing fewer transitions and dwelling shorter in the integrated State II. Additionally, the analysis of DFC strength showed that connections associated with ADs were identified including the regions that belonged to default mode (DM), executive control (EC), and salience (SA) networks, especially the connections between SA and DM networks. However, no significant difference was found between the GAD and PD groups in temporal features and connection strength. Conclusions: More common but less specific alterations were detected in the GAD and PD groups, which implied that they might have similar state-dependent neurophysiological mechanisms and, in addition, could hopefully help us better understand their abnormal affective and cognitive performances in the clinic.

The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SD_BOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSD_BOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSD_BOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSD_BOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSD_BOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSD_BOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.

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 (BOLD_SV) 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 BOLD_SV 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 BOLD_SV 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 BOLD_SV in the dorsolateral prefrontal cortex (dlPFC) as compared with HC in all frequency bands. In the slow-3 band, patients also showed reduced BOLD_SV in the medial dorsal thalamus, primary motor cortex (M1), and primary somatosensory cortex (S1) and heightened BOLD_SV in the temporal pole. Notably, we found a significant correlation between lower BOLD_SV (slow-3) in the orofacial M1/S1 regions and higher clinical pain (intensity/area) and higher sensitivity of the masseter muscle pain. Moreover, lower BOLD_SV (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 BOLD_SV in the dlPFC and higher BOLD_SV 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 BOLD_SV in both HC and TMD groups. Together, we demonstrated that chronic TMD patients exhibit aberrant BOLD_SV 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.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability

Recent functional magnetic resonance imaging studies have reported that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is positively associated with task performance and, thus, may reflect a behaviorally sensitive signal. However, it is not clear whether estimates of resting-state and task-driven BOLD variability are differentially related to cognition, as they may be driven by distinct sources of variance in the BOLD signal. Moreover, other studies have suggested that age differences in resting-state BOLD variability may be particularly sensitive to individual differences in cardiovascular, rather than neural, factors. In this study, we tested relationships between measures of behavioral task performance and BOLD variability during both resting-state and task-driven runs of a Stroop and an animacy judgment task in a large, well-characterized sample of cognitively normal middle-aged to older adults. Resting-state BOLD variability was related to composite measures of global cognition and attentional control, but these relationships were eliminated after correction for age or cardiovascular estimates. In contrast, task-driven BOLD variability was related to attentional control measured both inside and outside the scanner, and importantly, these relationships persisted after correction for age and cardiovascular measures. Overall, these results suggest that BOLD variability is a behaviorally sensitive signal. However, resting-state and task-driven estimates of BOLD variability may differ in the degree to which they are sensitive to age-related, cardiovascular, and neural mechanisms.

Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study

Cervical spondylotic myelopathy (CSM) is a common disease of the elderly that is characterized by gait instability, sensorimotor deficits, etc. Recurrent symptoms including memory loss, poor attention, etc. have also been reported in recent studies. However, these have been rarely investigated in CSM patients. To investigate the cognitive deficits and their correlation with brain functional alterations, we conducted resting-state fMRI (rs-fMRI) signal variability. This is a novel indicator in the neuroimaging field for assessing the regional neural activity in CSM patients. Further, to explore the network changes in patients, functional connectivity (FC) and graph theory analyses were performed. Compared with the controls, the signal variabilities were significantly lower in the widespread brain regions especially at the default mode network (DMN), visual network, and somatosensory network. The altered inferior parietal lobule signal variability positively correlated with the cognitive function level. Moreover, the FC and the global efficiency of DMN increased in patients with CSM and positively correlated with the cognitive function level. According to the study results, (1) the cervical spondylotic myelopathy patients exhibited regional neural impairments, which correlated with the severity of cognitive deficits in the DMN brain regions, and (2) the increased FC and global efficiency of DMN can compensate for the regional impairment.

Spatial variability of low frequency brain signal differentiates brain states

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

Intraindividual variability in neural activity in the prefrontal cortex during active walking in older adults

Intraindividual variability in gait and cognitive performance is distinct from central-tendency measures and associated with clinical outcomes in aging. Knowledge concerning intraindividual variability in neural activity, however, has been relatively scarce, and no research to date has reported on such variability during active walking. The current study addressed this major gap in knowledge. Participants were community-residing older adults (n = 394; mean age = 76.29 ± 6.65 years; %female = 55). Functional near-infrared spectroscopy (fNIRS) was used to measure oxygenated hemoglobin (HbO2) in the prefrontal cortex under three experimental conditions: single-task-walk, single-task-alpha (cognitive task), and dual-task-walk, which required the participants to perform the two single tasks simultaneously. Intraindividual variability in neural activity was operationalized using the standard deviation of fNIRS-derived HbO2 observations assessed during a 30-s interval in each experimental condition. The increase in intraindividual variability in neural activity in the dual-task-walk condition compared to both single-task conditions was associated with the presence of cognitive impairments and being a male. Furthermore, measures of intraindividual variability in neural activity and gait performance were positively correlated only under the dual-task-walk condition. Intraindividual variability in the neural activity of gait may be a novel marker for age-related impairments in mobility and cognitive function. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission

Computational modeling of dopamine transmission is challenged by complex underlying mechanisms. Here we present a new computational model that (I) simultaneously regards release, diffusion and uptake of dopamine, (II) considers multiple terminal release events and (III) comprises both synaptic and volume transmission by incorporating the geometry of the synaptic cleft. We were able to validate our model in that it simulates concentration values comparable to physiological values observed in empirical studies. Further, although synaptic dopamine diffuses into extra-synaptic space, our model reflects a very localized signal occurring on the synaptic level, i.e. synaptic dopamine release is negligibly recognized by neighboring synapses. Moreover, increasing evidence suggests that cognitive performance can be predicted by signal variability of neuroimaging data (e.g. BOLD). Signal variability in target areas of dopaminergic neurons (striatum, cortex) may arise from dopamine concentration variability. On that account we compared spatio-temporal variability in a simulation mimicking normal dopamine transmission in striatum to scenarios of enhanced dopamine release and dopamine uptake inhibition. We found different variability characteristics between the three settings, which may in part account for differences in empirical observations. From a clinical perspective, differences in striatal dopaminergic signaling contribute to differential learning and reward processing, with relevant implications for addictive- and compulsive-like behavior. Specifically, dopaminergic tone is assumed to impact on phasic dopamine and hence on the integration of reward-related signals. However, in humans DA tone is classically assessed using PET, which is an indirect measure of endogenous DA availability and suffers from temporal and spatial resolution issues. We discuss how this can lead to discrepancies with observations from other methods such as microdialysis and show how computational modeling can help to refine our understanding of DA transmission.

Greater BOLD Variability is Associated With Poorer Cognitive Function in an Adult Lifespan Sample

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 both age-related increases and decreases in BOLD variability and both detrimental and beneficial associations. Utilizing BOLD mean-squared-successive-differences (MSSD) during a digit n-back working memory (WM) task in a sample of healthy adults (aged 20–94 years; n = 171), we examined effects of aging on whole-brain 1) BOLD variability during task (mean condition MSSD across 0–2–3-4 back conditions), 2) BOLD variability modulation to incrementally increasing WM difficulty (linear slope from 0–2–3-4 back), and 3) the association of age-related differences in variability with in- and out-of-scanner WM performance. Widespread cortical and subcortical regions evidenced increased mean variability with increasing age, with no regions evidencing age-related decrease in variability. Additionally, posterior cingulate/precuneus exhibited increased variability to WM difficulty. Notably, both age-related increases in BOLD variability were associated with significantly poorer WM performance in all but the oldest adults. These findings lend support to the growing corpus suggesting that brain-signal variability is altered in healthy aging; specifically, in this adult lifespan sample, BOLD-variability increased with age and was detrimental to cognitive performance.

Evaluating resting-state BOLD variability in relation to biomarkers of preclinical Alzheimer's disease

Recent functional magnetic resonance imaging studies have demonstrated that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is related to age differences, cognition, and symptomatic Alzheimer's disease (AD). However, no studies have examined BOLD variability in the context of preclinical AD. We tested relationships between resting-state BOLD variability and biomarkers of amyloidosis, tauopathy, and neurodegeneration in a large (N = 321), well-characterized sample of cognitively normal adults (age = 39-93), using multivariate machine learning techniques. Furthermore, we controlled for cardiovascular health factors, which may contaminate resting-state BOLD variability estimates. BOLD variability, particularly in the default mode network, was related to cerebrospinal fluid (CSF) amyloid-β42 but was not related to CSF phosphorylated tau-181. Furthermore, BOLD variability estimates were also related to markers of neurodegeneration, including CSF neurofilament light protein, hippocampal volume, and a cortical thickness composite. Notably, relationships with hippocampal volume and cortical thickness survived correction for cardiovascular health and also contributed to age-related differences in BOLD variability. Thus, BOLD variability may be sensitive to preclinical pathology, including amyloidosis and neurodegeneration in AD-sensitive areas.

Epigenetic tuning of brain signal entropy in emergent human social behavior

BACKGROUND

How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability-posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes-plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development.

METHODS

We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking.

RESULTS

We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior.

CONCLUSIONS

Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.

Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach

Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43-89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.

Resting State BOLD Variability of the Posterior Medial Temporal Lobe Correlates with Cognitive Performance in Older Adults with and without Risk for Cognitive Decline

Local brain signal variability [SD of the BOLD signal (SDBOLD]] correlates with age and cognitive performance, and recently differentiated Alzheimer's disease (AD) patients from healthy controls. However, it is unknown whether changes to SDBOLD precede diagnosis of AD or mild cognitive impairment. We compared ostensibly healthy older adult humans who scored below the recommended threshold on the Montreal cognitive assessment (MoCA) and who showed reduced medial temporal lobe (MTL) volume in a previous study ("at-risk" group, n = 20), with healthy older adults who scored within the normal range on the MoCA ("control" group, n = 20). Using multivariate partial least-squares analysis we assessed the correlations between SDBOLD and age, MoCA score, global fractional anisotropy, global mean diffusivity, and four cognitive factors. Greater SDBOLD in the MTL and occipital cortex positively correlated with performance on cognitive control/speed tasks but negatively correlated with memory scores in the control group. These relations were weaker in the at-risk group. A post hoc analysis assessed associations between MTL volumes and SDBOLD in both groups. This revealed a negative correlation, most robust in the at-risk group, between MTL SDBOLD and MTL subregion volumetry, particularly the entorhinal and parahippocampal regions. Together, these results suggest that the association between SDBOLD and cognition differs between the at-risk and control groups, which may be because of lower MTL volumes in the at-risk group. Our data indicate relations between MTL SDBOLD and cognition may be helpful in understanding brain differences in individuals who may be at risk for further cognitive decline.

Individual Cortical Entropy Profile: Test-Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation

The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test-retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.

Hemodynamic responses are abnormal in isolated cervical dystonia

Neuroimaging studies using functional magnetic resonance imaging (fMRI), which measures brain activity by detecting the changes in blood oxygenation levels, are advancing our understanding of the pathophysiology of dystonia. Neurobiological disturbances in dystonia, however, may affect neurovascular coupling and impact the interpretability of fMRI studies. We evaluated here whether the hemodynamic response patterns during a behaviorally matched motor task are altered in isolated cervical dystonia (CD). Twenty-five CD patients and 25 healthy controls (HCs) underwent fMRI scanning during a paced finger tapping task (nondystonic task in patients). Imaging data were analyzed using a constrained principal component analysis-a statistical method that combines regression analysis and principal component analysis and enables the extraction of task-related functional networks and determination of the spatial and temporal hemodynamic response patterns associated with the task performance. Data from three patients and two controls were removed due to excessive movement. No significant differences in demographics or motor performance were observed. Three task-associated functional brain networks were identified. During task performance, reduced hemodynamic responses were seen in a sensorimotor network and in a network that included key nodes of the default mode, executive control and visual networks. During rest, reductions in hemodynamic responses were seen in the cognitive/visual network. Lower hemodynamic responses within the primary sensorimotor network in patients were correlated with the increased dystonia severity. Pathophysiological disturbances in isolated CD, such as alterations in inhibitory signaling and dopaminergic neurotransmission, may impact neurovascular coupling. Not accounting for hemodynamic response differences in fMRI studies of dystonia could lead to inaccurate results and interpretations.

Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks

Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared with the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopamine-dependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.

Age-differential relationships among dopamine D1 binding potential, fusiform BOLD signal, and face-recognition performance

Facial recognition ability declines in adult aging, but the neural basis for this decline remains unknown. Cortical areas involved in face recognition exhibit lower dopamine (DA) receptor availability and lower blood-oxygen-level-dependent (BOLD) signal during task performance with advancing adult age. We hypothesized that changes in the relationship between these two neural systems are related to age differences in face-recognition ability. To test this hypothesis, we leveraged positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) to measure D1 receptor binding potential (BP_ND) and BOLD signal during face-recognition performance. Twenty younger and 20 older participants performed a face-recognition task during fMRI scanning. Face recognition accuracy was lower in older than in younger adults, as were D1 BP_ND and BOLD signal across the brain. Using linear regression, significant relationships between DA and BOLD were found in both age-groups in face-processing regions. Interestingly, although the relationship was positive in younger adults, it was negative in older adults (i.e., as D1 BP_ND decreased, BOLD signal increased). Ratios of BOLD:D1 BP_ND were calculated and relationships to face-recognition performance were tested. Multiple linear regression revealed a significant Group × BOLD:D1 BP_ND Ratio interaction. These results suggest that, in the healthy system, synchrony between neurotransmitter (DA) and hemodynamic (BOLD) systems optimizes the level of BOLD activation evoked for a given DA input (i.e., the gain parameter of the DA input-neural activation function), facilitating task performance. In the aged system, however, desynchronization between these brain systems would reduce the gain parameter of this function, adversely impacting task performance and contributing to reduced face recognition in older adults.

Dopamine D2 receptor stimulation modulates the balance between ignoring and updating according to baseline working memory ability

BACKGROUND

Working memory (WM) deficits in neuropsychiatric disorders have often been attributed to altered dopaminergic signalling. Specifically, D₂ receptor stimulation is thought to affect the ease with which items can be gated into and out of WM. In addition, this effect has been hypothesised to vary according to baseline WM ability, a putative index of dopamine synthesis levels. Moreover, whether D₂ stimulation affects WM vicariously through modulating relatively WM-free cognitive control processes has not been explored.

AIMS

We examined the effect of administering a dopamine agonist on the ability to ignore or update information in WM.

METHOD

A single dose of cabergoline (1 mg) was administered to healthy older adult humans in a within-subject, double-blind, placebo-controlled study. In addition, we obtained measures of baseline WM ability and relatively WM-free cognitive control (overcoming response conflict).

RESULTS

Consistent with predictions, baseline WM ability significantly modulated the effect that drug administration had on the proficiency of ignoring and updating. High-WM individuals were relatively better at ignoring compared to updating after drug administration. Whereas the opposite occurred in low-WM individuals. Although the ability to overcome response conflict was not affected by cabergoline, a negative relationship between the effect the drug had on response conflict performance and ignoring was observed. Thus, both response conflict and ignoring are coupled to dopaminergic stimulation levels.

CONCLUSIONS

Cumulatively, these results provide evidence that dopamine affects subcomponents of cognitive control in a diverse, antagonistic fashion and that the direction of these effects is dependent upon baseline WM.

Effect of Dopaminergic Medications on Blood Oxygen Level-Dependent Variability and Functional Connectivity in Parkinson's Disease and Healthy Aging

Both functional connectivity (FC) and blood oxygen level-dependent (BOLD) signal variability (SDBOLD) are methods that are used for examining the physiological state of the brain. Although they are derived from signal changes and are related, a few studies have explored their relationship. Here, we examined the relationship between SDBOLD and FC within the default mode network (DMN) in healthy aging participants and those with Parkinson's disease (PD) ON and OFF dopaminergic medications. Dopaminergic medications had profound effects on both DMN FC and SDBOLD measured separately in PD. Analyzing DMN FC and SDBOLD in a joint independent component analysis, we identified joint components of DMN FC and SDBOLD that were separately associated with measurements of motor and cognitive impairment in PD and qualitatively similar to those in healthy aging. Dopaminergic medications had a differential effect on these components depending on these measures of disease severity, "normalizing" the relationships. Importantly, we show that dopaminergic medication status matters in imaging PD, and it can affect both connectivity and SDBOLD. Imaging PD ON may lead to inflated estimates of SDBOLD and diminish the ability to measure changes associated with declining motor and cognitive capacities.