Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms.

Objectives: Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures. Methods: We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD. Results: Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present. Conclusions: These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Altered extracellular matrix dynamics is associated with insulin resistance in adolescent children with obesity.

OBJECTIVE: The objective of this study was to examine the hypothesis that abdominal and gluteal adipocyte turnover, lipid dynamics, and fibrogenesis are dysregulated among insulin-resistant (IR) compared with insulin-sensitive (IS) adolescents with obesity. METHODS: Seven IS and seven IR adolescents with obesity participated in a 3-h oral glucose tolerance test and a multi-section magnetic resonance imaging scan of the abdominal region to examine body fat distribution patterns and liver fat content. An 8-week 70% deuterated water (2 H2 O) labeling protocol examined adipocyte turnover, lipid dynamics, and fibrogenesis in vivo from biopsied abdominal and gluteal fat. RESULTS: Abdominal and gluteal subcutaneous adipose tissue (SAT) turnover rates of lipid components were similar among IS and IR adolescents with obesity. However, the insoluble collagen (type I, subunit α2) isoform measured from abdominal, but not gluteal, SAT was elevated in IR compared with IS individuals. In addition, abdominal insoluble collagen Iα2 was associated with ratios of visceral-to-total (visceral adipose tissue + SAT) abdominal fat and whole-body and adipose tissue insulin signaling, and it trended toward a positive association with liver fat content. CONCLUSIONS: Altered extracellular matrix dynamics, but not expandability, potentially decreases abdominal SAT lipid storage capacity, contributing to the pathophysiological pathways linking adipose tissue and whole-body IR with altered ectopic storage of lipids within the liver among IR adolescents with obesity.

Rising NAFLD and metabolic severity during the Sars-CoV-2 pandemic among children with obesity in the United States.

OBJECTIVE: Nonalcoholic fatty liver disease (NAFLD), the most common liver disease among youth with obesity, precedes more severe metabolic and liver diseases. However, the impact of the Sars-CoV-2 global pandemic on the prevalence and severity of NAFLD and the associated metabolic phenotype among youth with obesity is unknown. METHODS: Participants were recruited from the Yale Pediatric Obesity Clinic during the Sars-CoV-2 global pandemic (August 2020 to May 2022) and were compared with a frequency-matched control group of youth with obesity studied before the Sars-CoV-2 global pandemic (January 2017 to November 2019). Glucose metabolism differences were assessed during an extended 180-minute oral glucose tolerance test. Magnetic resonance imaging-derived proton density fat fraction (PDFF) was used to determine intrahepatic fat content in those with NAFLD (PDFF ≥ 5.5). RESULTS: NAFLD prevalence increased in participants prior to (36.2%) versus during the Sars-CoV-2 pandemic (60.9%), with higher PDFF values observed in participants with NAFLD (PDFF ≥ 5.5%) during versus before the pandemic. An increase in visceral adipose tissue and a hyperresponsiveness in insulin secretion during the oral glucose tolerance test were also observed. CONCLUSIONS: Hepatic health differences were likely exacerbated by environmental and behavioral changes associated with the pandemic, which are critically important for clinicians to consider when engaging in patient care to help minimize the future risk for metabolic perturbations.

Brain-phenotype models fail for individuals who defy sample stereotypes.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity.

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

Dynamic-flip-angle ECG-gating with nuisance signal regression improves resting-state BOLD functional connectivity mapping by reducing cardiogenic noise.

PURPOSE: To investigate an ECG-gated dynamic-flip-angle BOLD sequence with improved robustness against cardiogenic noise in resting-state fMRI. METHODS: ECG-gating minimizes the cardiogenic noise but introduces T1 -dependent signal variation, which is minimized by combination of a dynamic-flip-angle technique and retrospective nuisance signal regression (NSR) using signals of white matter, CSF, and global average. The technique was studied with simulations in a wide range of T1 and B1 fields and phantom imaging with pre-programmed TR variations. Resting-state fMRI of 20 healthy subjects was acquired with non-gated BOLD (NG), ECG-gated constant-flip-angle BOLD (GCFA), ECG-gated BOLD with retrospective T1 -correction (GRC), and ECG-gated dynamic-flip-angle BOLD (GDFA), all processed by the same NSR method. GDFA was compared to alternative methods over temporal SNR (tSNR), seed-based connectivity, and whole-brain voxelwise connectivity based on intrinsic connectivity distribution (ICD). A previous large-cohort data set (N = 100) was used as a connectivity gold standard. RESULTS: Simulations and phantom imaging show substantial reduction of the T1 -dependent signal variation with GDFA alone, and further reduction with NSR. The resting-state study shows improved tSNR in the basal brain, comparing GDFA to NG, after both processed with NSR. Furthermore, GDFA significantly improved subcortical-subcortical and cortical-subcortical connectivity for several representative seeds and significantly improved ICD in the brainstem, thalamus, striatum, and prefrontal cortex, compared to the other 3 approaches. CONCLUSION: GDFA with NSR improves mapping of the resting-state functional connectivity of the basal-brain regions by reducing cardiogenic noise.

Data-Driven Analysis of Functional Connectivity Reveals a Potential Auditory Verbal Hallucination Network.

Schizophrenia is a severe global health problem, with over half of such patients experiencing auditory verbal hallucinations (AVHs). A better understanding of the neural correlates differentiating patients experiencing AVHs from patients not experiencing AVHs and healthy controls may identify targets that lead to better treatment strategies for AVHs. Employing 2 data-driven, voxel-based measure of functional connectivity, we studied 46 patients with schizophrenia or schizoaffective disorder (28 experiencing AVHs and 18 not experiencing AVHs). Twenty healthy controls matched for age, gender, ethnicity, education level, handedness, and estimated verbal intelligence were included for comparison. The intrinsic connectivity distribution (ICD) was used to model each voxel's connectivity to the rest of the brain using a Weibull distribution. To investigate lateralization of connectivity, we used cross-hemisphere ICD, a method that separates the contribution of each hemisphere to interrogate connectivity laterality. Patients with AVHs compared with patients without AVHs exhibited significantly decreased whole-brain connectivity in the medial prefrontal cortex and posterior cingulate cortex, less lateralized connectivity in left putamen, and more lateralized connectivity in left interior frontal gyrus. Correlations with Auditory Hallucination Rating Scale (AHRS) and post hoc seed connectivity analyses revealed significantly altered network connectivity. Using the results from all analyses comparing the patient groups and correlations with AHRS, we identified a potential AVH network, consisting of 25 nodes, showing substantial overlap with the default mode network and language processing networks. This network as a whole, instead of individual nodes, may represent actionable targets for interventions.

Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility.

Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization-or "functional connectivity." Standards have been proposed based on test-retest reliability, but open questions remain. These include how amount of data per subject influences whole-brain reliability, the influence of increasing runs versus sessions, the spatial distribution of reliability, the reliability of multivariate methods, and, crucially, how reliability maps onto prediction of behavior. We collected a dataset of 12 extensively sampled individuals (144 min data each across 2 identically configured scanners) to assess test-retest reliability of whole-brain connectivity within the generalizability theory framework. We used Human Connectome Project data to replicate these analyses and relate reliability to behavioral prediction. Overall, the historical 5-min scan produced poor reliability averaged across connections. Increasing the number of sessions was more beneficial than increasing runs. Reliability was lowest for subcortical connections and highest for within-network cortical connections. Multivariate reliability was greater than univariate. Finally, reliability could not be used to improve prediction; these findings are among the first to underscore this distinction for functional connectivity. A comprehensive understanding of test-retest reliability, including its limitations, supports the development of best practices in the field.

Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks.

OBJECTIVE: The aim of this study was to explore the relationship between global brain activity, changes in whole-brain connectivity, and changes in brain states across subjects using resting-state functional magnetic resonance imaging. METHODS: We extended current methods that use a sparse set of coactivation patterns to extract critical time points in global brain activity. Critical activity time points were defined as points where the global signal is greater than one standard deviation above or below the average global signal. Four categories of critical points were defined along dimensions of global signal intensity and trajectory. Voxel-based methods were used to interrogate differences in connectivity between these critical points. RESULTS: Several differences in connectivity were found in functional resting-state networks (RSNs) as a function of global activity. RSNs associated with cognitive functions in frontal, parietal, and subcortical regions exhibited greater whole-brain connectivity during lower global activity states. Meanwhile, RSNs associated with sensory functions exhibited greater whole-brain connectivity during the higher global activity states. Moreover, we present evidence that these results depend in part upon the standard deviation threshold used to define the critical points, suggesting critical points at different thresholds represent unique brain states. CONCLUSION: Overall, the findings support the hypothesis that the brain oscillates through different states over the course of a resting-state study reflecting differences in RSN connectivity associated with global brain activity. SIGNIFICANCE: Increased understanding of brain dynamics may help to elucidate individual differences in behavior and dysfunction.

Sex differences in normal age trajectories of functional brain networks.

Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain.

Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity.

OBJECTIVE: The purpose of this study was to investigate functional connectivity (FC) changes in epileptogenic networks in intractable partial epilepsy obtained from resting-state fMRI by using intrinsic connectivity contrast (ICC), a voxel-based network measure of degree that reflects the number of connections to each voxel. METHODS: We measured differences between intrahemispheric- and interhemispheric-ICC (ICCintra-inter) that could reveal localized connectivity abnormalities in epileptogenic zones while more global network changes would be eliminated when subtracting these values. The ICCintra-inter map was compared with the seizure onset zone (SOZ) based on intracranial EEG (icEEG) recordings in 29 patients with at least 1 year of postsurgical follow-up. Two independent reviewers blindly interpreted the icEEG and fMRI data, and the concordance rates were compared for various clinical factors. RESULTS: Concordance between the icEEG SOZ and ICCintra-inter map was observed in 72.4% (21/29) of the patients, which was higher in patients with good surgical outcome, especially in those patients with temporal lobe epilepsy (TLE) or lateral temporal seizure localization. Concordance was also better in the extratemporal lobe epilepsy than the TLE group. In 85.7% (18/21) of the cases, the ICCintra-inter values were negative in the SOZ, indicating decreased FC within the epileptic hemisphere relative to between hemispheres. CONCLUSIONS: Assessing alterations in FC using fMRI-ICC map can help localize the SOZ, which has potential as a noninvasive presurgical diagnostic tool to improve surgical outcome. In addition, the method reveals that, in focal epilepsy, both intrahemispheric- and interhemispheric-FC may be altered, in the presence of both regional as well as global network abnormalities.

Modulating intrinsic connectivity: adjacent subregions within supplementary motor cortex, dorsolateral prefrontal cortex, and parietal cortex connect to separate functional networks during task and also connect during rest.

Supplementary motor area (SMA), the inferior frontal junction (IFJ), superior frontal junction (SFJ) and parietal cortex are active in many cognitive tasks. In a previous study, we found that subregions of each of these major areas were differentially active in component processes of executive function during working memory tasks. In the present study, each of these subregions was used as a seed in a whole brain functional connectivity analysis of working memory and resting state data. These regions show functional connectivity to different networks, thus supporting the parcellation of these major regions into functional subregions. Many regions showing significant connectivity during the working memory residual data (with task events regressed from the data) were also significantly connected during rest suggesting that these network connections to subregions within major regions of cortex are intrinsic. For some of these connections, task demands modulate activity in these intrinsic networks. Approximately half of the connections significant during task were significant during rest, indicating that some of the connections are intrinsic while others are recruited only in the service of the task. Furthermore, the network connections to traditional 'task positive' and 'task negative' (a.k.a 'default mode') regions shift from positive connectivity to negative connectivity depending on task demands. These findings demonstrate that such task-identified subregions are part of distinct networks, and that these networks have different patterns of connectivity for task as they do during rest, engaging connections both to task positive and task negative regions. These results have implications for understanding the parcellation of commonly active regions into more specific functional networks.

Intrinsic brain connectivity related to age in young and middle aged adults.

Age-related variations in resting state connectivity of the human brain were examined from young adulthood through middle age. A voxel-based network measure, degree, was used to assess age-related differences in tissue connectivity throughout the brain. Increases in connectivity with age were found in paralimbic cortical and subcortical regions. Decreases in connectivity were found in cortical regions, including visual areas and the default mode network. These findings differ from those of recent developmental studies examining earlier growth trajectories, and are consistent with known changes in cognitive function and emotional processing during mature aging. The results support and extend previous findings that relied on a priori definitions of regions of interest for their analyses. This approach of applying a voxel-based measure to examine the functional connectivity of individual tissue elements over time, without the need for a priori region of interest definitions, provides an important new tool in brain science.

Social network theory applied to resting-state fMRI connectivity data in the identification of epilepsy networks with iterative feature selection.

Epilepsy is a brain disorder usually associated with abnormal cortical and/or subcortical functional networks. Exploration of the abnormal network properties and localization of the brain regions involved in human epilepsy networks are critical for both the understanding of the epilepsy networks and planning therapeutic strategies. Currently, most localization of seizure networks come from ictal EEG observations. Functional MRI provides high spatial resolution together with more complete anatomical coverage compared with EEG and may have advantages if it can be used to identify the network(s) associated with seizure onset and propagation. Epilepsy networks are believed to be present with detectable abnormal signatures even during the interictal state. In this study, epilepsy networks were investigated using resting-state fMRI acquired with the subjects in the interictal state. We tested the hypothesis that social network theory applied to resting-state fMRI data could reveal abnormal network properties at the group level. Using network data as input to a classification algorithm allowed separation of medial temporal lobe epilepsy (MTLE) patients from normal control subjects indicating the potential value of such network analyses in epilepsy. Five local network properties obtained from 36 anatomically defined ROIs were input as features to the classifier. An iterative feature selection strategy based on the classification efficiency that can avoid 'over-fitting' is proposed to further improve the classification accuracy. An average sensitivity of 77.2% and specificity of 83.86% were achieved via 'leave one out' cross validation. This finding of significantly abnormal network properties in group level data confirmed our initial hypothesis and provides motivation for further investigation of the epilepsy process at the network level.

Brain areas coactivating with motor cortex during chronic motor tics and intentional movements.

BACKGROUND: Chronic tic disorders are characterized by motor tics that are often preceded by premonitory urges to tic. Functional neuroimaging studies have documented brain activity patterns prior to and during tics, but these studies have not examined whether the activation patterns differ from those seen in normal control subjects performing similar acts. METHODS: A novel method was used to compare brain patterns during tics and intentional movements. First, the part of motor cortex specific to each patient's tic movement was identified. The brain areas activating prior to, during, and after that part of motor cortex during tics were then identified by temporally cross-correlating the time course of the localized motor region with activity in other brain areas. Given that motor cortex was active during tic execution, this yielded information regarding the brain areas active prior to, during, and after the movements. The spatiotemporal pattern of coactivation with motor cortex during tics was contrasted with that seen in healthy control subjects during intentional tic-like movements. RESULTS: Data from 16 adult subjects with tic disorders and 16 matched control subjects, who performed intentional movements similar to the patients' tics, revealed nearly identical patterns of cross-correlation to motor cortex throughout the brain in the two groups. However, the supplementary motor area showed a significantly broader profile of cross-correlation to motor cortex during tics than during intentional movements. CONCLUSIONS: These findings highlight the importance of the supplementary motor area in tic generation and may point toward novel intervention strategies for individuals suffering with severe tics.

Acupuncture-induced blood oxygenation level-dependent signals in awake and anesthetized volunteers: a pilot study.

BACKGROUND: There are conflicting data regarding clinical efficacy of acupuncture applied while patients are under general anesthesia. We hypothesize that these conflicting data are a result of the inhibitory effect of anesthesia on acupuncture-induced central nervous system activity that can be demonstrated using magnetic resonance imaging. METHODS: Using a crossover study design, volunteers received standardized Stomach 36 manual acupuncture in two experimental conditions: while undergoing a propofol-based general anesthetic, and while awake. Functional magnetic resonance imaging was conducted during both experimental sessions. Paired-t-test analyses were performed to examine the differences in acupuncture-induced blood oxygenation level-dependent (BOLD) signals between awake and anesthesia conditions. A secondary analysis was performed to account for the changes in regional cerebral blood flow at six regions of interest (thalamus, red nucleus, insula, periaqueductal gray, retrosplenial cingular gyri, and the inferior temporal region). RESULTS: Using BOLD, we found significant differences between the two experimental sessions in brain areas, including postcentral gyri, retrosplenial cingular area, left posterior insula, bilateral precuneus, thalamus, red nuclei, and substantia nigra (cluster 100, P < 0.01). A secondary analysis correcting for background cerebral blood flow found that BOLD signal differences between experimental conditions were not directly caused by changes in regional blood flow. DISCUSSION: Propofol-based anesthesia reduces the neurophysiological response to acupuncture stimulation as measured by acupuncture-induced BOLD signals. Further work should be conducted to determine the clinical significance of these findings.

Connectivity-behavior analysis reveals that functional connectivity between left BA39 and Broca's area varies with reading ability.

Correlations between temporal fluctuations in MRI signals may reveal functional connectivity between brain regions within individual subjects. Such correlations would be especially useful indices of functional connectivity if they covary with behavioral performance or other subject variables. This study investigated whether such a relationship could be demonstrated in the context of the reading circuit in the brain. The method proved sufficiently powerful to reveal significant correlations between the reading abilities of subjects and the strength of their functional connection between left Brodmann's area 39 and Broca's area during reading. This suggests that the disconnection of the angular gyrus previously reported for dyslexic readers is part of a larger continuum in which poor (but nonimpaired readers) also show reduced connectivity to the region. In addition, it illustrates the potential power of paradigms that examine correlations between behavior and functional brain connections.