Resting-state Connectivity Research Articles

Comparison of dry and wet electroencephalography for the assessment of cognitive evoked potentials and sensor-level connectivity

BackgroundMultipin dry electrodes (dry EEG) provide faster and more convenient application than wet EEG, enabling extensive data collection. This study aims to compare task-related time-frequency representations and resting-state connectivity between wet and dry EEG methods to establish a foundation for using dry EEG in investigations of brain activity in neuropsychiatric disorders.MethodsIn this counterbalanced cross-over study, we acquired wet and dry EEG in 33 healthy participants [n = 22 females, mean age (SD) = 24.3 (± 3.4) years] during resting-state and an auditory oddball paradigm. We computed mismatch negativity (MMN), theta power in task EEG, and connectivity measures from resting-state EEG using phase lag index (PLI) and minimum spanning tree (MST). Agreement between wet and dry EEG was assessed using Bland–Altman bias.ResultsMMN was detectable with both systems in time and frequency domains, but dry EEG underestimated MMN mean amplitude, peak latency, and theta power compared to wet EEG. Resting-state connectivity was reliably estimated with dry EEG using MST diameter in all except the very low frequencies (0.5–4 Hz). PLI showed larger differences between wet and dry EEG in all frequencies except theta.ConclusionDry EEG reliably detected MMN and resting-state connectivity despite a lower signal-to-noise ratio. This study provides the methodological basis for using dry EEG in studies investigating the neural processes underlying psychiatric and neurological conditions.

Connectivity Changes Following Episodic Future Thinking in Alcohol Use Disorder.

Introduction: Recent addiction and obesity-related research suggests that episodic future thinking (EFT) can serve as a promising intervention to promote healthy decision-making. We used data from a pilot study to investigate the acute neural effects of EFT in alcohol use disorder (AUD). Because of the limitations of those data, we additionally used data from a previously published functional MRI (fMRI) study in which participants had not received any intervention for their AUD. Methods: In an out-of-scanner, guided interview, participants (n = 24; median age = 37.3 years; median AUDIT = 22.5) generated scenarios and cues about their future (EFT intervention, n = 15) or recent past (control episodic thinking [CET] control intervention, n = 9). Then, they performed both resting-state and task-based (delay discounting [DD]) fMRI. We used nodes from the default mode network and salience networks as well as the hippocampus to perform seed-based analyses of the resting-state data. The results then guided psychophysiological interaction analyses in the DD task. In addition, we used data from a larger, previously reported study as a "no intervention" group of AUD participants (n = 50; median age = 43.3; median Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) alcohol dependence score = 7) to reproduce and aid in interpreting our key findings. Results: EFT, but not CET, participants showed statistically improved DD rates-a behavioral marker for addiction. Resting-state analyses of the left hippocampus revealed connectivity differences in the frontal poles. The directionality of this difference suggested that EFT may reduce a hypo-connectivity relationship between these regions in AUD. We also found resting-state connectivity differences between the salience network and the right dorsolateral prefrontal cortex (R DLPFC), which then led us to discover R-to-L DLPFC psychophysiological interaction differences during DD. Moreover, the resting-state salience-to-DLPFC functional connectivity showed an inverse relationship to DD rate while hyperconnectivity between left and right DLPFC reflected slower reaction times during DD trials. Discussion: These findings suggest that previously noted benefits of EFT such as the improved DD replicated here might coincide with changes in neural connectivity patterns in AUD. The alterations in connectivity highlight potential mechanisms underlying the effectiveness of EFT in improving decision-making in AUD. Understanding these neural effects may contribute to the further development of targeted interventions for AUD and related disorders.

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BackgroundWorking memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group. MethodsForty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic. ResultsAt the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls. ConclusionOur results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited

Neurophysiological correlates of trait and behavioral impulsivity across methamphetamine and gambling Addiction

Investigating neurophysiological markers linked to impulsivity in individuals with gambling and methamphetamine addiction using resting-state EEG data offers valuable insights into the underlying neurophysiological mechanisms associated with impulsivity in individuals with addiction. This study aims to use resting-state EEG to explore the connection between various types of addiction and different aspects of impulsivity. Participants from the methamphetamine, gambling, and healthy control groups (abbreviation: MA, GB, HC) underwent EEG recordings and completed measures of impulsivity. Group differences in trait scores and behavioral tendencies were analyzed. Abnormal connections with node linkage and importance changes were analyzed through the resting-state EEG power spectral and network analyses. Further, relationships between impulsivity scores and connectivity differences in groups were explored through correlation analysis. Finally, these abnormal connections related to impulsivity were tested for their effect of distinguishing individuals with addiction from healthy controls through the ROC analysis. Results revealed that GB displayed the highest trait impulsivity on the overall score, while MA exhibited greater attentional impulsivity. Variations in behavioral impulsivity were reflected in response times. Resting-state EEG analysis showed higher beta power in GB. Specific channel pairs demonstrated abnormal connections and altered connectivity patterns in the beta band, with MA displaying a less efficient network compared to GB. Correlation analyses uncovered associations between impulsivity scores and connectivity, which were influenced by group differences. Furthermore, resting-state EEG connections effectively differentiated individuals with addiction from healthy controls. Overall, this study contributes valuable insights into the neural mechanisms of addiction-related impulsivity, emphasizing the potential of resting-state EEG connections as an important neurophysiological correlate.

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

Altered resting-state connectivity of the auditory cortex in congenital amusia: A functional magnetic resonance imaging study in Mandarin speakers.

Brain imaging studies have reported that the neural deficits of congenital amusia in non-tonal language speakers are mainly in the connectivity between the auditory cortex and the inferior frontal gyrus (IFG) in the right hemisphere. However, the relationship between the functional connectivity (FC) in these regions and the music perception ability of amusia in tonal language speakers remains unclear. In this study, we investigated the FC characteristics of amusia in Mandarin speakers in resting-state functional magnetic resonance imaging data by voxel-wise connectivity analyses with seeds in left and right Heschl's gyri (HG) and region of interest (ROI)-to-ROI connectivity analyses. Our findings indicate increased connectivity between right HG and bilateral posterior superior temporal gyrus, as determined by voxel-wise connectivity analyses in amusia. Conversely, reduced connectivity was observed between bilateral HG and bilateral IFG (orbital part) as assessed through ROI-to-ROI connectivity analyses in amusia when compared to controls. Moreover, the music perception scores of amusia in Mandarin speakers were associated with diminished connectivity between the left HG and the right IFG. This study furnishes direct evidence for the link between music perception deficits and the aberrant frontotemporal connectivity of congenital amusia in tonal language speakers in resting state.

Functional network disruptions in youth with concussion using the Adolescent Brain Cognitive Development study

ABSTRACT Objective This study aimed to compare psychosocial outcomes and functional neuroimaging among youth with concussion, youth with anxiety, and age- and sex-matched controls. Methods Using archival data from the Adolescent Brain Cognitive DevelopmentSM Study, we analyzed between-group differences in psychosocial outcomes measured by the Child Behavior Checklist’s internalizing and externalizing problem scales, and assessed brain function using resting-state fMRI network-region connectivity (specifically frontoparietal network (FPN) and default mode network (DMN) connectivity with the amygdala). Results Significant differences in psychosocial outcomes were found across all groups, with the anxiety group reporting the most internalizing problems, followed by the concussion group which significantly differed from controls. Additionally, FPN-amygdala connectivity was significantly reduced in the concussion group only; this reduced connectivity did not predict psychosocial outcomes across groups. Conclusion This study provided preliminary findings that brain connectivity is reduced exclusively in individuals with concussion. Although disruptions were observed in the concussion group, further investigation is warranted to understand how disruptions may be associated with concussion symptoms. Studies that utilize well-defined control and study groups, and comprehensive cognitive and mental health measures will offer a deeper understanding of the relationship between brain function and psychosocial outcomes.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity.

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications

The networks observed in the brain during resting-state activity are not entirely “task-free.” Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

Neural correlates of systemic lidocaine administration in healthy adults measured by functional MRI: a single arm open label study.

Intravenous lidocaine is increasingly used as a nonopioid analgesic, but how it acts in the brain is incompletely understood. We conducted a functional MRI study of pain response, resting connectivity, and cognitive task performance in volunteers to elucidate the effects of lidocaine at the brain-systems level. We enrolled 27 adults (age 22-55 yr) in this single-arm, open-label study. Pain response task and resting-state functional MRI scans at 3 T were obtained at baseline and then with a constant effect-site concentration of lidocaine. Electric nerve stimulation, titrated in advance to 7/10 intensity, was used for the pain task (five times every 10 s). Group-level differences in pain task-evoked responses (primary outcome, focused on the insula) and in resting connectivity were compared between baseline and lidocaine conditions, using adjusted P<0.05 to account for multiple comparisons. Pain ratings and performance on a brief battery of computer-based tasks were also recorded. Lidocaine infusion was associated with decreased pain-evoked responses in the insula (left: Z=3.6, P<0.001, right: Z=3.6, P=0.004) and other brain areas including the cingulate gyrus, thalamus, and primary sensory cortex. Resting-state connectivity showed significant diffuse reductions in both region-to-region and global connectivity measures with lidocaine. Small decreases in pain intensity and unpleasantness and worse memory performance were also seen with lidocaine. Lidocaine was associated with broad reductions in functional MRI response to acute pain and modulated whole-brain functional connectivity, predominantly decreasing long-range connectivity. This was accompanied by small but significant decreases in pain perception and memory performance. NCT05501600.

Exploring the late maturation of an intrinsic episodic memory network: A resting-state fMRI study

Previous research suggests that episodic memory relies on functional neural networks,which are present even in the absence of an explicit task. The regions that integrate.these networks and the developmental changes in intrinsic functional connectivity.remain elusive. In the present study, we outlined an intrinsic episodic memory network.(iEMN) based on a systematic selection of functional connectivity studies, and.inspected network differences in resting-state fMRI between adolescents (13–17 years.old) and adults (23–27 years old) from the publicly available NKI-Rockland Sample.Through a review of brain regions commonly associated with episodic memory.networks, we identified a potential iEMN composed by 14 bilateral ROIs, distributed.across temporal, frontal and parietal lobes. Within this network, we found an increase.in resting-state connectivity from adolescents to adults between the right temporal pole.and two regions in the right lateral prefrontal cortex. We argue that the coordination of.these brain regions, connecting areas of semantic processing and areas of controlled.retrieval, arises as an important feature towards the full maturation of the episodic.memory system. The findings add to evidence suggesting that adolescence is a key.period in memory development and highlights the role of intrinsic functional.connectivity in such development.

Resting-state alterations in emotion salience and default-mode network connectivity in atypical trajectories of psychotic-like experiences.

Social cognition is commonly altered in people with psychosis. Two main brain networks have been implicated: the default-mode network (DMN), which is associated with socio-cognitive processing, and the salience network (SN) associated with socio-affective processing. Disturbances to the resting-state functional connectivity of these networks have been identified in schizophrenia and high-risk individuals, but there have been no studies in adolescents displaying distinct trajectories of subclinical psychotic-like experiences (PLEs). To address this, the present study measured SN and DMN resting-state connectivity in a unique longitudinally followed sample of youth (n = 92) presenting with typical and atypical 4-year PLE trajectories. Compared to the typically developing low PLE control group, the atypical increasing PLE trajectory displayed reduced connectivity between the SN and DMN, increased connectivity between left and right insula, and widespread dysconnectivity from the insula and amygdala. These alterations are similar to those reported in schizophrenia and clinical high-risk samples, suggesting that early detection may be useful for mapping the developmental trajectories of psychotic disorders.

Changed resting-state connectivity of anterior insular cortex affects subjective pain reduction after knee arthroplasty: A longitudinal study

The mechanism of chronic knee osteoarthritis (OA) pain and postoperative pain due to knee arthroplasty has not been elucidated. This could be involved neuroplasticity in brain connectivity. To clarify the mechanism of chronic knee OA pain and postoperative pain, we examined the relationship between resting-state functional connectivity (rs-FC) and clinical measurements in knee OA before and after knee arthroplasty, focusing on rs-FCs with the anterior insular cortex (aIC) as the key region. Fifteen patients with knee OA underwent resting-state functional magnetic resonance imaging and clinical measurements shortly before and 6 months after knee arthroplasty, and 15 age- and sex-matched control patients underwent an identical protocol. Seed-to-voxel analysis was performed to compare the clinical measurements and changed rs-FCs, using the aIC as a seed region, between the preoperative and postoperative patients, as well as between the operative and control patients. In preoperative patients, rs-FCs of the aIC to the OFC, frontal pole, subcallosal area, and medial frontal cortex increased compared with those of the control patients. The strength of rs-FC between the left aIC and right OFC decreased before and after knee arthroplasty. The decrease in rs-FC between the left aIC and right OFC was associated with decreased subjective pain score. Our study showed a correlation between longitudinally changed rs-FC and clinical measurement before and after knee arthroplasty. Rs-FC between the aIC and OFC have the potential to elucidate the mechanisms of knee OA pain and postoperative pain due to knee arthroplasty.

Interactive effect of air pollution and genetic risk of depression on processing speed by resting-state functional connectivity of occipitoparietal network

BackgroundAir pollution, a reversible environmental factor, was significantly associated with the cognitive domains that are impaired in major depressive disorder (MDD), notably processing speed. Limited evidence explores the interactive effect of air pollution and the genetic risk of depression on cognition. This cross-sectional study aims to extend the research by specifically examining how this interaction influences depression-related cognitive impairment and resting-state brain function.MethodsEligible participants were 497 healthy adult volunteers (48.7% males, mean age 24.5) living in Beijing for at least 1 year and exposed to relatively high air pollution from the local community controlling for socioeconomic and genomic. Six months’ ambient air pollution exposures were assessed based on residential addresses using monthly averages of fine particulate matter with a diameter of less than or equal to 2.5 μm (PM2.5). A cross-sectional analysis was conducted using functional magnetic resonance imaging (fMRI) and cognitive performance assessments. The polygenic risk score (PRS) of MDD was used to estimate genetic susceptibility.ResultsUsing a general linear model and partial least square regression, we observed a negative association between resting-state local connectivity in precuneus and PRS-by-PM2.5 interactive effect (PFWE = 0.028), indicating that PM2.5 exposure reduced the spontaneous activity in precuneus in individuals at high genetic risk for MDD. DNA methylation and gene expression of the SLC30A3 gene, responsible for maintaining zinc-glutamate homeostasis, was suggestively associated with this local connectivity. For the global functional connectivity, the polygenic risk for MDD augmented the neural impact of PM2.5 exposure, especially in the frontal-parietal and frontal-limbic regions of the default mode network (PFDR < 0.05). In those genetically predisposed to MDD, increased PM2.5 exposure positively correlated with resting-state functional connectivity between the left angular gyrus and left cuneus gyrus. This connectivity was negatively associated with processing speed.ConclusionsOur cross-sectional study suggests that air pollution may be associated with an increased likelihood of cognitive impairment in individuals genetically predisposed to depression, potentially through alterations in the resting-state function of the occipitoparietal and default mode network.

Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine.

Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.

A multi-modal assessment of self-knowledge in adolescents with non-suicidal self-injury: a research domains criteria (RDoC) study.

Adolescence is a critical period for brain development, consolidation of self-understanding, and onset of non-suicidal self-injury (NSSI). This study evaluated the RDoC (Research Domain Criteria) sub-construct of Self-Knowledge in relation to adolescent NSSI using multiple units of analysis. One hundred and sixty-four adolescents assigned female at birth (AFAB), ages 12-16 years with and without a history of NSSI entered a study involving clinical assessment and magnetic resonance imaging (MRI), including structural, resting-state functional MRI (fMRI), and fMRI during a self-evaluation task. For imaging analyses, we used an a priori defined Self Network (anterior cingulate, orbitofrontal, and posterior cingulate cortices; precuneus). We first examined interrelationships among multi-level Self variables. We then evaluated the individual relationships between NSSI severity and multi-level Self variables (self-report, behavior, multi-modal brain Self Network measures), then conducted model testing and multiple regression to test how Self variables (together) predicted NSSI severity. Cross-correlations revealed key links between self-reported global self-worth and self-evaluation task behavior. Individually, greater NSSI severity correlated with lower global self-worth, more frequent and faster negative self-evaluations, lower anterior Self Network activation during self-evaluation, and lower anterior and posterior Self Network resting-state connectivity. Multiple regression analysis revealed the model including multi-level Self variables explained NSSI better than a covariate-only model; the strongest predictive variables included self-worth, self-evaluation task behavior, and resting-state connectivity. Disruptions in Self-Knowledge across multiple levels of analysis relate to NSSI in adolescents. Findings suggest potential neurobiological treatment targets, potentially enhancing neuroplasticity in Self systems to facilitate greater flexibility (more frequently positive) of self-views in AFAB adolescents.

Is there a relationship between resting state connectivity within large-scale functional networks and implicit motor learning impairments in schizophrenia and bipolar disorder?

The aim of this exploratory study is to evaluate whether implicit motor learning impairments observed in schizophrenia (SZ) and bipolar disorder (BD) are associated with the resting state functional connectivity (rs-FC) within large-scale functional networks. The study involved 30 BD patients, 30 SZ patients and 30 healthy controls (HC). Implicit motor learning was evaluated with the use of serial reaction time task (SRTT). Prior to the training patients underwent resting state functional magnetic resonance imaging (rsfMRI) examination. We have measured rs-FC within salience network (SAN), default mode network (DMN), frontoparietal network (FPN), sensorimotor network (SMN), limbic network (LN) and visual network (VIN) and their associations with implicit motor learning indices. rs-FC within SAN, DMN, FPN, SMN, LN and VIN reveal no significant association with implicit motor learning indices. BD, SZ and HC groups did not differ in terms of rs-FC within abovementioned networks. We have shown that in the studied groups SRTT performance could not be predicted by rs-FC within the major large-scale functional networks, i.e., SMN, FPN, VIN, LN, SAN and DMN. The observation of the independence of implicit motor learning from the initial activity of these systems is important for proper understanding of neuronal underpinnings of this process and planning further neuroimaging research on this topic.

Differential increase of hippocampal subfield volume after socio-affective mental training relates to reductions in diurnal cortisol

The hippocampus is a central modulator of the HPA-axis, impacting the regulation of stress on brain structure, function, and behavior. The current study assessed whether three different types of 3 months mental Training Modules geared towards nurturing (a) attention-based mindfulness, (b) socio-affective, or (c) socio-cognitive skills may impact hippocampal organization by reducing stress. We evaluated mental training-induced changes in hippocampal subfield volume and intrinsic functional connectivity, by combining longitudinal structural and resting-state fMRI connectivity analysis in 332 healthy adults. We related these changes to changes in diurnal and chronic cortisol levels. We observed increases in bilateral cornu ammonis volume (CA1-3) following the 3 months compassion-based module targeting socio-affective skills (Affect module), as compared to socio-cognitive skills (Perspective module) or a waitlist cohort with no training intervention. Structural changes were paralleled by relative increases in functional connectivity of CA1-3 when fostering socio-affective as compared to socio-cognitive skills. Furthermore, training-induced changes in CA1-3 structure and function consistently correlated with reductions in cortisol output. Notably, using a multivariate approach, we found that other subfields that did not show group-level changes also contributed to changes in cortisol levels. Overall, we provide a link between a socio-emotional behavioural intervention, changes in hippocampal subfield structure and function, and reductions in cortisol in healthy adults.

Differential increase of hippocampal subfield volume after socio-affective mental training relates to reductions in diurnal cortisol.

The hippocampus is a central modulator of the HPA-axis, impacting the regulation of stress on brain structure, function, and behavior. The current study assessed whether three different types of 3 months mental Training Modules geared towards nurturing (a) attention-based mindfulness, (b) socio-affective, or (c) socio-cognitive skills may impact hippocampal organization by reducing stress. We evaluated mental training-induced changes in hippocampal subfield volume and intrinsic functional connectivity, by combining longitudinal structural and resting-state fMRI connectivity analysis in 332 healthy adults. We related these changes to changes in diurnal and chronic cortisol levels. We observed increases in bilateral cornu ammonis volume (CA1-3) following the 3 months compassion-based module targeting socio-affective skills (Affect module), as compared to socio-cognitive skills (Perspective module) or a waitlist cohort with no training intervention. Structural changes were paralleled by relative increases in functional connectivity of CA1-3 when fostering socio-affective as compared to socio-cognitive skills. Furthermore, training-induced changes in CA1-3 structure and function consistently correlated with reductions in cortisol output. Notably, using a multivariate approach, we found that other subfields that did not show group-level changes also contributed to changes in cortisol levels. Overall, we provide a link between a socio-emotional behavioural intervention, changes in hippocampal subfield structure and function, and reductions in cortisol in healthy adults.

Dissecting Heterogeneity in Functional Network Connectivity Aberrations in Antipsychotic Medication-Naïve First Episode Psychosis Patients - A Normative Modeling Study.

While there is a general consensus that functional connectome pathology is a key mechanism underlying psychosis spectrum disorders, the literature is plagued with inconsistencies and translation into clinical practice is non-existent. This is perhaps because group-level findings may not be accurate reflections of pathology at the individual patient level. To characterize inter-individual heterogeneity in functional networks and investigate if normative values can be leveraged to identify biologically less heterogeneous subgroups of patients. We used data collected in a case-control study conducted at the University of Alabama at Birmingham (UAB). We recruited antipsychotic medication-naïve first-episode psychosis patients from UAB outpatient, inpatient, and emergency room settings. Individual-level patterns of deviations from a normative reference range in resting-state functional networks using the Yeo-17 atlas for parcellations. Statistical analyses included 108 medication-naïve first-episode psychosis patients. We found that there is a high level of inter-individual heterogeneity in resting-state network connectivity deviations from the normative reference range. Interestingly 48% of patients did not have any functional connectivity deviations, and no more than 11.1% of patients shared functional deviations between the same regions of interest. In a post hoc analysis, we grouped patients based on deviations into four theoretically possible groups. We discovered that all four groups do exist in our experimental data and showed that subgroups based on deviation profiles were significantly less heterogeneous compared to the overall group (positive deviation group: z= -2.88, p = 0.002; negative deviation group: z= -3.36, p<0.001). Our findings experimentally demonstrate that there is a high level of inter-individual heterogeneity in resting-state network pathology in first-episode psychosis patients which support the idea that group-level findings are not accurate reflections of pathology at the individual level. We also demonstrated that normative functional connectivity deviations may have utility for identifying biologically less heterogeneous subgroups of patients, even though they are not distinguishable clinically. Our findings constitute a significant step towards making precision psychiatry a reality, where patients are selected for treatments based on their individual biological characteristics. Question: How heterogeneous is individual-level resting-state functional network pathology in patients suffering from a first psychotic episode? Can normative reference values in functional network connectivity be leveraged to identify biologically more homogenous subgroups of patients?Findings: We report that functional network pathology is highly heterogeneous, with no more than 11% of patients sharing functional deviations between the same regions of interest.Meaning: Normative modeling is a tool that can map individual neurobiological differences and enables the classification of a clinically heterogenous patient group into subgroups that are neurobiologically less heterogenous.