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

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.

Increased between-network connectivity: A risk factor for tau elevation and disease progression

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

Reliability of variability and complexity measures for task and task-free BOLD fMRI

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.

The neurophysiological brain-fingerprint of Parkinson's disease

BACKGROUND

Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD).

METHODS

We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s.

FINDINGS

The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology.

INTERPRETATION

The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets.

FUNDING

Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Effects of Age on the Auditory Cortex During Speech Perception in Noise: Evidence From Functional Near-Infrared Spectroscopy

OBJECTIVES

Age-related speech perception difficulties may be related to a decline in central auditory processing abilities, particularly in noisy or challenging environments. However, how the activation patterns related to speech stimulation in different noise situations change with normal aging has yet to be elucidated. In this study, we aimed to investigate the effects of noisy environments and aging on patterns of auditory cortical activation.

DESIGN

We analyzed the functional near-infrared spectroscopy signals of 20 young adults, 21 middle-aged adults, and 21 elderly adults, and evaluated their cortical response patterns to speech stimuli under five different signal to noise ratios (SNRs). In addition, we analyzed the behavior score, activation intensity, oxyhemoglobin variability, and dominant hemisphere, to investigate the effects of aging and noisy environments on auditory cortical activation.

RESULTS

Activation intensity and oxyhemoglobin variability both showed a decreasing trend with aging at an SNR of 0 dB; we also identified a strong correlation between activation intensity and age under this condition. However, we observed an inconsistent activation pattern when the SNR was 5 dB. Furthermore, our analysis revealed that the left hemisphere may be more susceptible to aging than the right hemisphere. Activation in the right hemisphere was more evident in older adults than in the left hemisphere; in contrast, younger adults showed leftward lateralization.

CONCLUSIONS

Our analysis showed that with aging, auditory cortical regions gradually become inflexible in noisy environments. Furthermore, changes in cortical activation patterns with aging may be related to SNR conditions, and that understandable speech with a low SNR ratio but still understandable may induce the highest level of activation. We also found that the left hemisphere was more affected by aging than the right hemisphere in speech perception tasks; the left-sided dominance observed in younger individuals gradually shifted to the right hemisphere with aging.

Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes

Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder

BACKGROUND

Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions.

METHODS

In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes.

RESULTS

Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively.

CONCLUSIONS

These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.

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.

The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer's disease

Over the past several years, there has been a surge in blood biomarker studies examining the value of plasma or serum neurofilament light (NfL) as a biomarker of neurodegeneration for Alzheimer's disease. However, there have been limited efforts to combine existing findings to assess the utility of blood NfL as a biomarker of neurodegeneration for Alzheimer's disease. In addition, we still need better insight into the specific aspects of neurodegeneration that are reflected by the elevated plasma or serum concentration of NfL. In this review, we survey the literature on the cross-sectional and longitudinal relationships between blood-based NfL levels and other, neuroimaging-based, indices of neurodegeneration in individuals on the Alzheimer's continuum. Then, based on the biomarker classification established by the FDA-NIH Biomarker Working group, we determine the utility of blood-based NfL as a marker for monitoring the disease status (i.e. monitoring biomarker) and predicting the severity of neurodegeneration in older adults with and without cognitive decline (i.e. a prognostic or a risk/susceptibility biomarker). The current findings suggest that blood NfL exhibits great promise as a monitoring biomarker because an increased NfL level in plasma or serum appears to reflect the current severity of atrophy, hypometabolism and the decline of white matter integrity, particularly in the brain regions typically affected by Alzheimer's disease. Longitudinal evidence indicates that blood NfL can be useful not only as a prognostic biomarker for predicting the progression of neurodegeneration in patients with Alzheimer's disease but also as a susceptibility/risk biomarker predicting the likelihood of abnormal alterations in brain structure and function in cognitively unimpaired individuals with a higher risk of developing Alzheimer's disease (e.g. those with a higher amyloid-β). There are still limitations to current research, as discussed in this review. Nevertheless, the extant literature strongly suggests that blood NfL can serve as a valuable prognostic and susceptibility biomarker for Alzheimer's disease-related neurodegeneration in clinical settings, as well as in research settings.

The neurophysiological brain-fingerprint of Parkinson's disease

In this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson's disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson's brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson's disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson's disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson's disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.

Neural variability in three major psychiatric disorders

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.

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.

The biological role of local and global fMRI BOLD signal variability in human brain organization

Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.

Profiling intra- and inter-individual differences in brain development across early adolescence

As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.

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.

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

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

Multiscale entropy of ADHD children during resting state condition

This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09869-0.

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.

Functional Magnetic Resonance Imaging Signal Variability Is Associated With Neuromodulation in Fibromyalgia

OBJECTIVES

Although primary motor cortex (M1) transcranial direct current stimulation (tDCS) has an analgesic effect in fibromyalgia (FM), its neural mechanism remains elusive. We investigated whether M1-tDCS modulates a regional temporal variability of blood-oxygenation-level-dependent (BOLD) signals, an indicator of the brain's flexibility and efficiency and if this change is associated with pain improvement.

MATERIALS AND METHODS

In a within-subjects cross-over design, 12 female FM patients underwent sham and active tDCS on five consecutive days, respectively. Each session was performed with an anode placed on the left M1 and a cathode on the contralateral supraorbital region. The subjects also participated in resting-state functional magnetic resonance imaging (fMRI) at baseline and after sham and active tDCS. We compared the BOLD signal variability (SDBOLD), defined as the standard deviation of the BOLD time-series, between the tDCS conditions. Baseline SDBOLD was compared to 15 healthy female controls.

RESULTS

At baseline, FM patients showed reduced SDBOLD in the ventromedial prefrontal cortex (vmPFC), lateral PFC, and anterior insula and increased SDBOLD in the posterior insula compared to healthy controls. After active tDCS, compared to sham, we found an increased SDBOLD in the left rostral anterior cingulate cortex (rACC), lateral PFC, and thalamus. After sham tDCS, compared to baseline, we found a decreased SDBOLD in the dorsomedial PFC and posterior cingulate cortex/precuneus. Interestingly, after active tDCS compared to sham, pain reduction was correlated with an increased SDBOLD in the rACC/vmPFC but with a decreased SDBOLD in the posterior insula.

CONCLUSION

Our findings suggest that M1-tDCS might revert temporal variability of fMRI signals in the rACC/vmPFC and posterior insula linked to FM pain. Changes in neural variability would be part of the mechanisms underlying repetitive M1-tDCS analgesia in FM.

Functional MRI responses to naturalistic stimuli are increasingly typical across early childhood

While findings show that throughout development, there are child- and age-specific patterns of brain functioning, there is also evidence for significantly greater inter-individual response variability in young children relative to adults. It is currently unclear whether this increase in functional "typicality" (i.e., inter-individual similarity) is a developmental process that occurs across early childhood, and what changes in BOLD response may be driving changes in typicality. We collected fMRI data from 81 typically developing 4-8-year-old children during passive viewing of age-appropriate television clips and asked whether there is increasing typicality of brain response across this age range. We found that the "increasing typicality" hypothesis was supported across many regions engaged by passive viewing. Post hoc analyses showed that in a priori ROIs related to language and face processing, the strength of the group-average shared component of activity increased with age, with no concomitant decline in residual signal or change in spatial extent or variability. Together, this suggests that increasing inter-individual similarity of functional responses to audiovisual stimuli is an important feature of early childhood functional brain development.

Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

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.

Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences

Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task.

Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence

The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients’ cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted，and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder

Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.

Histamine Release in the Prefrontal Cortex Excites Fast-Spiking Interneurons while GABA Released from the Same Axons Inhibits Pyramidal Cells

We studied how histamine and GABA release from axons originating from the hypothalamic tuberomammillary nucleus (TMN) and projecting to the prefrontal cortex (PFC) influence circuit processing. We optostimulated histamine/GABA from genetically defined TMN axons that express the histidine decarboxylase gene (TMN_HDC axons). Whole-cell recordings from PFC neurons in layer 2/3 of prelimbic, anterior cingulate, and infralimbic regions were used to monitor excitability before and after optostimulated histamine/GABA release in male and female mice. We found that histamine-GABA release influences the PFC through actions on distinct neuronal types: the histamine stimulates fast-spiking interneurons; and the released GABA enhances tonic (extrasynaptic) inhibition on pyramidal cells (PyrNs). For fast-spiking nonaccommodating interneurons, histamine released from TMN_HDC axons induced additive gain changes, which were blocked by histamine H1 and H2 receptor antagonists. The excitability of other fast-spiking interneurons in the PFC was not altered. In contrast, the GABA released from TMN_HDC axons predominantly produced divisive gain changes in PyrNs, increasing their resting input conductance, and decreasing the slope of the input-output relationship. This inhibitory effect on PyrNs was not blocked by histamine receptor antagonists but was blocked by GABA_A receptor antagonists. Across the adult life span (from 3 to 18 months of age), the GABA released from TMN_HDC axons in the PFC inhibited PyrN excitability significantly more in older mice. For individuals who maintain cognitive performance into later life, the increases in TMN_HDC GABA modulation of PyrNs during aging could enhance information processing and be an adaptive mechanism to buttress cognition.SIGNIFICANCE STATEMENT The hypothalamus controls arousal state by releasing chemical neurotransmitters throughout the brain to modulate neuronal excitability. Evidence is emerging that the release of multiple types of neurotransmitters may have opposing actions on neuronal populations in key cortical regions. This study demonstrates for the first time that the neurotransmitters histamine and GABA are released in the prefrontal cortex from axons originating from the tuberomammillary nucleus of the hypothalamus. This work demonstrates how hypothalamic modulation of neuronal excitability is maintained throughout adult life, highlighting an unexpected aspect of the aging process that may help maintain cognitive abilities.

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.

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.

Signal Variability and Cognitive Function in Older Long-Term Survivors of Breast Cancer with Exposure to Chemotherapy: A Prospective Longitudinal Resting-State fMRI Study

The purpose of this study was to assess the effect of chemotherapy on brain functional resting-state signal variability and cognitive function in older long-term survivors of breast cancer. This prospective longitudinal study enrolled women age ≥ 65 years of age who were breast cancer survivors after exposure to chemotherapy (CH), age-matched survivors not exposed to chemotherapy, and healthy controls. Participants completed resting-state functional brain MRI and neurocognitive testing upon enrollment (timepoint 1, TP1) and again two years later (timepoint 2, TP2). There were 20 participants in each of the three groups at TP1. The CH group showed a significant decrease in SD_BOLD (blood-oxygen-level-dependent signal variability in standard deviation) in the right middle occipital gyrus (ΔSD_BOLD = -0.0018, p = 0.0085, q (p_FDR) = 0.043 at MNI (42, -76, 17)) and right middle temporal gyrus (ΔSD_BOLD = -0.0021, p = 0.0006, q (p_FDR) = 0.001 at MNI (63, -39, -12)). There were negative correlations between the crystallized composite scores and SD_BOLD values at the right inferior occipital gyrus (correlation coefficient r = -0.84, p = 0.001, q (p_FDR) = 0.016) and right middle temporal gyrus (r = -0.88, p = 0.000, q (p_FDR) = 0.017) for the CH group at TP1. SD_BOLD could be a potentially useful neuroimaging marker for older long-term survivors of breast cancer with exposure to chemotherapy.

Functional network alterations in young brain tumor patients with radiotherapy-induced memory impairments and vascular injury

Background

Cognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.

Methods

Nineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.

Results

Compared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34–0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.

Conclusions

MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

Resting-state neural signal variability in women with depressive disorders

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

Age-Specific Activation Patterns and Inter-Subject Similarity During Verbal Working Memory Maintenance and Cognitive Reserve

Cognitive Reserve (CR), according to a recent consensus definition of the NIH-funded Reserve and Resilience collaboratory, is constituted by any mechanism contributing to cognitive performance beyond, or interacting with, brain structure in the widest sense. To identity multivariate activation patterns fulfilling this postulate, we investigated a verbal Sternberg fMRI task and imaged 181 people with age coverage in the ranges 20-30 (44 participants) and 55-70 (137 participants). Beyond task performance, participants were characterized in terms of demographics, and neuropsychological assessments of vocabulary, episodic memory, perceptual speed, and abstract fluid reasoning. Participants studied an array of either one, three, or six upper-case letters for 3 s (=encoding phase), then a blank fixation screen was presented for 7 s (=maintenance phase), to be probed with a lower-case letter to which they responded with a differential button press whether the letter was part of the studied array or not (=retrieval phase). We focused on identifying maintenance-related activation patterns showing memory load increases in pattern score on an individual participant level for both age groups. We found such a pattern that increased with memory load for all but one person in the young participants (p < 0.001), and such a pattern for all participants in the older group (p < 0.001). Both patterns showed broad topographic similarities; however, relationships to task performance and neuropsychological characteristics were markedly different and point to individual differences in Cognitive Reserve. Beyond the derivation of group-level activation patterns, we also investigated the inter-subject spatial similarity of individual working memory rehearsal patterns in the older participants' group as a function of neuropsychological and task performance, education, and mean cortical thickness. Higher task accuracy and neuropsychological function was reliably associated with higher inter-subject similarity of individual-level activation patterns in older participants.

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.

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior

In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.

Variability and task-responsiveness of electrophysiological dynamics: scale-free stability and oscillatory flexibility

Cortical oscillations and scale-free neural activity are thought to influence a variety of cognitive functions, but their differential relationships to neural stability and flexibility has never been investigated. Based on the existing literature, we hypothesize that scale-free and oscillatory processes in the brain exhibit different trade-offs between stability and flexibility; specifically, cortical oscillations may reflect variable, task-responsive aspects of brain activity, while scale-free activity is proposed to reflect a more stable and task-unresponsive aspect. We test this hypothesis using data from two large-scale MEG studies (HCP: n = 89; CamCAN: n = 195), operationalizing stability and flexibility by task-responsiveness and spontaneous intra-subject variability in resting state. We demonstrate that the power-law exponent of scale-free activity is a highly stable parameter, which responds little to external cognitive demands and shows minimal spontaneous fluctuations over time. In contrast, oscillatory power, particularly in the alpha range (8-13 Hz), responds strongly to tasks and exhibits comparatively large spontaneous fluctuations over time. In sum, our data support differential roles for oscillatory and scale-free activity in the brain with respect to neural stability and flexibility. This result carries implications for criticality-based theories of scale-free activity, state-trait models of variability, and homeostatic views of the brain with regulated variables vs. effectors.

Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females

Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.

Developmental coupling of cerebral blood flow and fMRI fluctuations in youth

The functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow (CBF) and neural activity, yet how this coupling evolves over development remains unexplored. Here, we examine the relationship between CBF, measured by arterial spin labeling, and the amplitude of low-frequency fluctuations (ALFF) from resting-state magnetic resonance imaging across a sample of 831 children (478 females, aged 8-22 years) from the Philadelphia Neurodevelopmental Cohort. We first use locally weighted regressions on the cortical surface to quantify CBF-ALFF coupling. We relate coupling to age, sex, and executive functioning with generalized additive models and assess network enrichment via spin testing. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.

The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What's more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

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

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

Intrinsic brain functional connectivity patterns in alcohol use disorder

Alcohol use disorder is associated with damaging effects to the brain. This study aimed to examine differences in static and dynamic intrinsic functional connectivity patterns in individuals with a history of alcohol use disorder in comparison to those with no history of alcohol abuse. A total of 55 participants consisting of 23 patients and 32 control individuals underwent neuropsychological assessments and resting-state functional magnetic resonance imaging on a 3 Tesla MRI scanner. Differences in functional connectivity between the two groups were determined using static and dynamic independent component analysis. Differences in static functional connectivity between the two groups were identified in the default mode network, attention network, frontoparietal network, frontal cortical network and cerebellar network. Furthermore, the analyses revealed specific differences in the dynamic temporal characteristics of functional connectivity between the two groups of participants, in a cluster involving key regions in reward, sensorimotor and frontal cortical functional networks, with some connections correlating with the length of sobriety and some others with the severity of drinking. The findings altogether suggest dysregulation in the intrinsic connectivity of cortico-basal ganglia-thalamo-cortical loops that may reflect persistent alcohol use disorder-related network abnormalities, compensatory recovery-related processes whereby additional neural resources are recruited to achieve normal levels of performance, or a predisposition toward developing alcohol use disorder.

Altered Dynamic Functional Connectivity in Subcortical Ischemic Vascular Disease With Cognitive Impairment

Subcortical ischemic vascular disease (SIVD) can cause cognitive impairment and affect the static functional connectivity of resting functional magnetic resonance imaging (fMRI). Numerous previous studies have demonstrated that functional connectivities (FCs) fluctuate dynamically over time. However, little is known about the impact of cognitive impairment on brain dynamic functional connectivity (DFC) in SIVD patients with MCI. In the present study, the DFC analysis method was applied to the resting functional magnetic resonance imaging (fMRI) data of 37 SIVD controls (SIVD-Control) without cognitive impairment, 34 SIVD patients with amnestic MCI (SIVD-aMCI) and 30 SIVD patients with nonamnestic MCI (SIVD-naMCI). The results indicated that the cognitive impairment of SIVD mainly reduced the mean dwell time of State 3 with overall strong positive connections. The reduction degree of SIVD-aMCI was larger than that of SIVD-naMCI. The memory/execution function impairment of SIVD also changed the relationship between the mean dwell time of State 3 and the behavioral performance of the memory/execution task from significant to non-significant correlation. Moreover, SIVD-aMCI showed significantly lower system segregation of FC states than SIVD-Control and SIVD-naMCI. The system segregation of State 5 with overall weak connections was significantly positive correlated with the memory performance. The results may suggest that the mean dwell time of State 3 and the system segregation of State 5 may be used as important neural measures of cognitive impairments of SIVD.

Neural Variability in the Prefrontal Cortex as a Reflection of Neural Flexibility and Stability in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

Functional Near-Infrared Spectroscopy (fNIRS) studies provide direct evidence to the important role of the prefrontal cortex (PFC) during walking in aging and Parkinson's disease (PD). Most studies mainly explored mean HbO2 levels, while moment-to-moment variability measures have been rarely investigated. Variability measures can inform on flexibility that is imperative for adaptive function. We hypothesized that patients with PD will show less variability in HbO2 signals during walking compared to healthy controls.

METHODS

206 participants, 57 healthy controls (age: 68.9±1.0 years; 27 women) and 149 idiopathic PD patients (age: 69.8±0.6 years, 50 women, disease duration: 8.27±5.51 years) performed usual walking and dual-task walking (serial 3 subtractions) with an fNIRS system placed on the forehead. HbO2 variability was calculated using the standard deviation (SD), range, and mean detrended time series of fNIRS-derived HbO2 signal evaluated during each walking task. HbO2 variability was compared between groups and between walking tasks using mixed model analyses.

RESULTS

Higher variability (SD, range, mean detrended time series) was observed during dual-task walking, compared to usual walking (p<0.025), but this was derived from the differences within the healthy control group (group X task interaction: p<0.007). On the other hand, task repetition demonstrated reduced variability in healthy controls but increased variability in patients with PD (interaction group*walk-repetition: p<0.048). The MDS-UPDRS motor score correlated with HbO2 range (r=0.142, p=0.050) and HbO2 SD (r=0.173, p=0.018) during usual walking in all participants.

DISCUSSION

In this study, we suggest a new way to interpret changes in HbO2 variability. We relate increased HbO2 variability to flexible adaptation to environmental challenges and decreased HbO2 variability to the stability of performance. Our results show that both are limited in PD however, further investigation of these concepts is required. Moreover, HbO2 variability measures are an important aspect of brain function that adds new insights into the role of PFC during walking with aging and PD.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that patients with PD have more variability within Hb02 signals during usual-walking, compared to healthy controls, but not during dual-task walking.

Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults

Aging is associated with cognitive impairment, but there are large individual differences in these declines. One neural measure that is lower in older adults and predicts these individual differences is moment-to-moment brain signal variability. Testing the assumption that GABA should heighten neural variability, we examined whether reduced brain signal variability in older, poorer performing adults could be boosted by increasing GABA pharmacologically. Brain signal variability was estimated using fMRI in 20 young and 24 older healthy human adults during placebo and GABA agonist sessions. As expected, older adults exhibited lower signal variability at placebo, and, crucially, GABA agonism boosted older adults' variability to the levels of young adults. Furthermore, poorer performing older adults experienced a greater increase in variability on drug, suggesting that those with more to gain benefit the most from GABA system potentiation. GABA may thus serve as a core neurochemical target in future work on aging- and cognition-related human brain dynamics.SIGNIFICANCE STATEMENT Prior research indicates that moment-to-moment brain signal variability is lower in older, poorer performing adults. We found that this reduced brain signal variability could be boosted through GABA agonism in older adults to the levels of young adults and that this boost was largest in the poorer performing older adults. These results provide the first evidence that brain signal variability can be restored by increasing GABAergic activity and suggest the promise of developing interventions targeting inhibitory systems to help slow cognitive declines in healthy aging.

Age-Related Intrinsic Functional Connectivity Changes of Locus Coeruleus from Childhood to Older Adults

The locus coeruleus is critical for selective information processing by modulating the brain's connectivity configuration. Increasingly, studies have suggested that LC controls sensory inputs at the sensory gating stage. Furthermore, accumulating evidence has shown that young children and older adults are more prone to distraction and filter out irrelevant information less efficiently, possibly due to the unoptimized LC connectivity. However, the LC connectivity pattern across the life span is not fully examined yet, hampering our ability to understand the relationship between LC development and the distractibility. In this study, we examined the intrinsic network connectivity of the LC using a public fMRI dataset with wide-range age samples. Based on LC-seed functional connectivity maps, we examined the age-related variation in the LC connectivity with a quadratic model. The analyses revealed two connectivity patterns explicitly. The sensory-related brain regions showed a positive quadratic age effect (u-shape), and the frontal regions for the cognitive control showed a negative quadratic age effect (inverted u-shape). Our results imply that such age-related distractibility is possibly due to the impaired sensory gating by the LC and the insufficient top-down controls by the frontal regions. We discuss the underlying neural mechanisms and limitations of our study.

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.

Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan

The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.