Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks

Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents

AIM

Adolescents born very preterm (VPT; <32 weeks of gestation) face an elevated risk of executive, behavioral, and socioemotional difficulties. Evidence suggests beneficial effects of mindfulness-based intervention (MBI) on these abilities. This study seeks to investigate the association between the effects of MBI on executive, behavioral, and socioemotional functioning and reliable changes in large-scale brain networks dynamics during rest in VPT young adolescents who completed an 8-week MBI program.

METHODS

Neurobehavioral assessments and resting-state functional magnetic resonance imaging were performed before and after MBI in 32 VPT young adolescents. Neurobehavioral abilities in VPT participants were compared with full-term controls. In the VPT group, dynamic functional connectivity was extracted by using the innovation-driven coactivation patterns framework. The reliable change index was used to quantify change after MBI. A multivariate data-driven approach was used to explore associations between MBI-related changes on neurobehavioral measures and temporal brain dynamics.

RESULTS

Compared with term-born controls, VPT adolescents showed reduced executive and socioemotional functioning before MBI. After MBI, a significant improvement was observed for all measures that were previously reduced in the VPT group. The increase in executive functioning, only, was associated with reliable changes in the duration of activation of large-scale brain networks, including frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks.

CONCLUSION

The improvement in executive functioning after an MBI was associated with reliable changes in large-scale brain network dynamics during rest. These changes encompassed frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks that are related to different executive processes including self-regulation, attentional control, and attentional awareness of relevant sensory stimuli.

Proton magnetic resonance spectroscopy of N-acetyl aspartate in first depressive episode and chronic major depressive disorder: A systematic review and meta-analysis

N-acetyl aspartate (NAA) is a marker of neuronal integrity and metabolism. Deficiency in neuronal plasticity and hypometabolism are implicated in Major Depressive Disorder (MDD) pathophysiology. To test if cerebral NAA concentrations decrease progressively over the MDD course, we conducted a pre-registered meta-analysis of Proton Magnetic Resonance Spectroscopy (1H-MRS) studies comparing NAA concentrations in chronic MDD (n = 1308) and first episode of depression (n = 242) patients to healthy controls (HC, n = 1242). Sixty-two studies were meta-analyzed using a random-effect model for each brain region. NAA concentrations were significantly reduced in chronic MDD compared to HC within the frontal lobe (Hedges' g = -0.330; p = 0.018), the occipital lobe (Hedges' g = -0.677; p = 0.007), thalamus (Hedges' g = -0.673; p = 0.016), and frontal (Hedges' g = -0.471; p = 0.034) and periventricular white matter (Hedges' g = -0.478; p = 0.047). We highlighted a gap of knowledge regarding NAA levels in first episode of depression patients. Sensitivity analyses indicated that antidepressant treatment may reverse NAA alterations in the frontal lobe. We highlighted field strength and correction for voxel grey matter as moderators of NAA levels detection. Future studies should assess NAA alterations in the early stages of the illness and their longitudinal progression.

Human brain state dynamics reflect individual neuro-phenotypes

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression

Background

Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear.

Methods

Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed.

Results

Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items.

Conclusion

These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

A three-dimensional model of neural activity and phenomenal-behavioral patterns

How phenomenal experience and behavior are related to neural activity in physiology and psychopathology represents a fundamental question in neuroscience and psychiatry. The phenomenal-behavior patterns may be deconstructed into basic dimensions, i.e., psychomotricity, affectivity, and thought, which might have distinct neural correlates. This work provides a data overview on the relationship of these phenomenal-behavioral dimensions with brain activity across physiological and pathological conditions (including major depressive disorder, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, anxiety disorders, addictive disorders, Parkinson's disease, Tourette syndrome, Alzheimer's disease, and frontotemporal dementia). Accordingly, we propose a three-dimensional model of neural activity and phenomenal-behavioral patterns. In this model, neural activity is organized into distinct units in accordance with connectivity patterns and related input/output processing, manifesting in the different phenomenal-behavioral dimensions. (1) An external neural unit, which involves the sensorimotor circuit/brain's sensorimotor network and is connected with the external environment, processes external inputs/outputs, manifesting in the psychomotor dimension (processing of exteroception/somatomotor activity). External unit hyperactivity manifests in psychomotor excitation (hyperactivity/hyperkinesia/catatonia), while external unit hypoactivity manifests in psychomotor inhibition (retardation/hypokinesia/catatonia). (2) An internal neural unit, which involves the interoceptive-autonomic circuit/brain's salience network and is connected with the internal/body environment, processes internal inputs/outputs, manifesting in the affective dimension (processing of interoception/autonomic activity). Internal unit hyperactivity manifests in affective excitation (anxiety/dysphoria-euphoria/panic), while internal unit hypoactivity manifests in affective inhibition (anhedonia/apathy/depersonalization). (3) An associative neural unit, which involves the brain's associative areas/default-mode network and is connected with the external/internal units (but not with the environment), processes associative inputs/outputs, manifesting in the thought dimension (processing of ideas). Associative unit hyperactivity manifests in thought excitation (mind-wandering/repetitive thinking/psychosis), while associative unit hypoactivity manifests in thought inhibition (inattention/cognitive deficit/consciousness loss). Finally, these neural units interplay and dynamically combine into various neural states, resulting in the complex phenomenal experience and behavior across physiology and neuropsychiatric disorders.

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.

Inflammation and emotion regulation: a narrative review of evidence and mechanisms in emotion dysregulation disorders

Emotion dysregulation (ED) describes a difficulty with the modulation of which emotions are felt, as well as when and how these emotions are experienced or expressed. It is a focal overarching symptom in many severe and prevalent neuropsychiatric diseases, including bipolar disorders (BD), attention deficit/hyperactivity disorder (ADHD), and borderline personality disorder (BPD). In all these disorders, ED can manifest through symptoms of depression, anxiety, or affective lability. Considering the many symptomatic similarities between BD, ADHD, and BPD, a transdiagnostic approach is a promising lens of investigation. Mounting evidence supports the role of peripheral inflammatory markers and stress in the multifactorial aetiology and physiopathology of BD, ADHD, and BPD. Of note, neural circuits that regulate emotions appear particularly vulnerable to inflammatory insults and peripheral inflammation, which can impact the neuroimmune milieu of the central nervous system. Thus far, few studies have examined the link between ED and inflammation in BD, ADHD, and BPD. To our knowledge, no specific work has provided a critical comparison of the results from these disorders. To fill this gap in the literature, we review the known associations and mechanisms linking ED and inflammation in general, and clinically, in BD, ADHD, and BD. Our narrative review begins with an examination of the routes linking ED and inflammation, followed by a discussion of disorder-specific results accounting for methodological limitations and relevant confounding factors. Finally, we critically discuss both correspondences and discrepancies in the results and comment on potential vulnerability markers and promising therapeutic interventions.

Brain-derived subgroups of bipolar II depression associate with inflammation and choroid plexus morphology

AIM

Elevated inflammation and larger choroid plexus (ChP) volume has been previously identified in mood disorders. Connections between inflammation, ChP, and clinical symptoms in bipolar II depression (BDII-D) are unclear. Data-driven clustering based on neuroanatomical phenotypes may help to elucidate neurobiological associations in BDII-D.

METHODS

Inflammatory cytokines, clinical symptoms, and neuroanatomical features were assessed in 150 BDII-D patients. Sixty-eight cortical surface area (SA) and 19 subcortical volumes were extracted using FreeSurfer. The ChP volume was segmented manually using 3D Slicer. Regularized canonical correlation analysis was used to identify significantly correlated components between cortical SA and subcortical volumes (excluding the ChP), followed by k-means clustering to define brain-derived subgroups of BDII-D. Low-grade inflammation was derived by averaging the standardized z scores of interleukin (IL)-6, IL-1β, and tumor necrosis factor-α (TNF-α), which were computed to create a composite z-value score. Partial Pearson correlations followed by multiple comparison correction were conducted to explore associations between inflammation, clinical symptoms, and ChP volume.

RESULTS

Subgroup I demonstrated smaller subcortical volume and cortical SA, higher inflammation, and larger ChP volume compared with subgroup II. Greater ChP volume was associated with a higher low-grade inflammation (mean r = 0.289, q = 0.003), CRP (mean r = 0.249, q = 0.007), IL-6 (left r = 0.200, q = 0.03), and TNF-α (right r = 0.226, q = 0.01), while greater IL-1β was significantly associated with severe depressive symptoms in BDII-D (r = 0.218, q = 0.045).

CONCLUSIONS

Neuroanatomically-derived subgroups of BDII-D differed in their inflammation levels and ChP volume. These findings suggest an important role of elevated peripheral inflammation and larger ChP in BDII-D.

Arousal modulates the amygdala-insula reciprocal connectivity during naturalistic emotional movie watching

Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.

Volumes of hippocampal subfields suggest a continuum between schizophrenia, major depressive disorder and bipolar disorder

Objective

There is considerable debate as to whether the continuum of major psychiatric disorders exists and to what extent the boundaries extend. Converging evidence suggests that alterations in hippocampal volume are a common sign in psychiatric disorders; however, there is still no consensus on the nature and extent of hippocampal atrophy in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to verify the continuum of SZ - BD - MDD at the level of hippocampal subfield volume and to compare the volume differences in hippocampal subfields in the continuum.

Methods

A total of 412 participants (204 SZ, 98 MDD, and 110 BD) underwent 3 T MRI scans, structured clinical interviews, and clinical scales. We segmented the hippocampal subfields with FreeSurfer 7.1.1 and compared subfields volumes across the three diagnostic groups by controlling for age, gender, education, and intracranial volumes.

Results

The results showed a gradual increase in hippocampal subfield volumes from SZ to MDD to BD. Significant volume differences in the total hippocampus and 13 of 26 hippocampal subfields, including CA1, CA3, CA4, GC-ML-DG, molecular layer and the whole hippocampus, bilaterally, and parasubiculum in the right hemisphere, were observed among diagnostic groups. Medication treatment had the most effect on subfields of MDD compared to SZ and BD. Subfield volumes were negatively correlated with illness duration of MDD. Positive correlations were found between subfield volumes and drug dose in SZ and MDD. There was no significant difference in laterality between diagnostic groups.

Conclusion

The pattern of hippocampal volume reduction in SZ, MDD and BD suggests that there may be a continuum of the three disorders at the hippocampal level. The hippocampus represents a phenotype that is distinct from traditional diagnostic strategies. Combined with illness duration and drug intervention, it may better reflect shared pathophysiology and mechanisms across psychiatric disorders.

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

OBJECTIVES

Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms.

METHODS

We assessed hippocampus-based dFC coactivation patterns (CAPs) on resting-state fMRI data of 25 euthymic BD patients and 25 age- and sex-matched healthy controls (HC).

RESULTS

Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while at the same time dFC between the left hippocampus and midcingulo-insular salience system (SN) was higher in BD. Correlational analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default-like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD.

CONCLUSIONS

Overall, we observed alterations of large-scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self-centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow-up of BD patients and of disabling residual depressive symptoms in particular.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

Decoding cerebro-spinal signatures of human behavior: application to motor sequence learning

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most neuroimaging research has focused on investigating cerebral mechanisms, while the way the spinal cord accompanies the brain in shaping human behavior has been largely overlooked. Although the recent advent of functional magnetic resonance imaging (fMRI) sequences that can simultaneously target the brain and spinal cord has opened up new avenues for studying these mechanisms at multiple levels of the CNS, research to date has been limited to inferential univariate techniques that cannot fully unveil the intricacies of the underlying neural states. To address this, we propose to go beyond traditional analyses and instead use a data-driven multivariate approach leveraging the dynamic content of cerebro-spinal signals using innovation-driven coactivation patterns (iCAPs). We demonstrate the relevance of this approach in a simultaneous brain-spinal cord fMRI dataset acquired during motor sequence learning (MSL), to highlight how large-scale CNS plasticity underpins rapid improvements in early skill acquisition and slower consolidation after extended practice. Specifically, we uncovered cortical, subcortical and spinal functional networks, which were used to decode the different stages of learning with a high accuracy and, thus, delineate meaningful cerebro-spinal signatures of learning progression. Our results provide compelling evidence that the dynamics of neural signals, paired with a data-driven approach, can be used to disentangle the modular organization of the CNS. While we outline the potential of this framework to probe the neural correlates of motor learning, its versatility makes it broadly applicable to explore the functioning of cerebro-spinal networks in other experimental or pathological conditions.

Structural and functional MRI correlates of inflammation in bipolar disorder: A systematic review

BACKGROUND

Bipolar disorder (BD) is a common affective disorder characterized by recurrent oscillations between mood states and associated with inflammatory diseases and chronic inflammation. However, data on MRI abnormalities in BD and their relationship with inflammation are heterogeneous and no review has recapitulated them.

METHODS

In this pre-registered (PROSPERO: CRD42022308461) systematic review we searched Web of Science Core Collection and PubMed for articles correlating functional or structural MRI measures with immune-related markers in BD.

RESULTS

We included 23 studies (6 on functional, 16 on structural MRI findings, 1 on both, including 1'233 BD patients). Overall, the quality of the studies included was fair, with a low risk of bias.

LIMITATIONS

Heterogeneity in the methods and results of the studies and small sample sizes limit the generalizability of the conclusions.

CONCLUSIONS

A qualitative synthesis suggests that the links between immune traits and functional or structural MRI alterations point toward brain areas involved in affective and somatomotor processing, with a trend toward a negative correlation between peripheral inflammatory markers and brain regions volume. We discuss how disentangling the complex relationship between the immune system and MRI alterations in BD may unveil mechanisms underlying symptoms pathophysiology, potentially with quickly translatable diagnostic, prognostic, and therapeutic implications.

The functional connectivity between left insula and left medial superior frontal gyrus underlying the relationship between rumination and procrastination

Procrastination is regarded as a prevalent problematic behavior that impairs people's physical and mental health. Although previous studies have indicated that trait rumination is robustly positively correlated with procrastination, it remains unknown about the neural substrates underlying the relationship between trait rumination and procrastination. To address this issue, we used voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) approaches to explore the neural basis of the relationship between trait rumination and procrastination. Our behavior results found that trait rumination was significantly positively correlated to procrastination, while the VBM analysis showed that trait rumination was negatively correlated with gray matter volume of the insula. Furthermore, the RSFC results revealed a negative association of the left insula-lmSFG (left medial superior frontal gyrus) functional connectivity with trait rumination. More importantly, the mediation analysis showed that trait rumination could completely mediate the relationship between left insula-lmSFG functional connectivity and procrastination. These results suggest that the left insula-lmSFG functional connectivity involved in emotion regulation modulates the association between trait rumination and procrastination, which provides neural evidence for the relationship between trait rumination and procrastination.

Functional connectivity of the default mode network subsystems in patients with major depressive episodes with mixed features

Background

The neuroimaging mechanism of major depressive episodes with mixed features (MMF) is not clear.

Aims

This study aimed to investigate the functional connectivity of the default mode network (DMN) subsystems among patients with MMF and patients with major depressive disorder without mixed features (MDD_noMF).

Methods

This study recruited 47 patients with MDD_noMF and 27 patients with MMF from Beijing Anding Hospital, Capital Medical University, between April 2021 and June 2022. Forty-five healthy controls (HCs) were recruited. All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments. Intranetwork and internetwork functional connectivity were computed in the DMN core subsystem, dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDD_noMF, MMF and HC groups.

Results

The functional connectivity within the DMN core (F=6.32, p_FDR=0.008) and MTL subsystems (F=4.45, p_FDR=0.021) showed significant differences among the MDD_noMF, MMF and HC groups. Compared with the HC group, the patients with MDD_noMF and MMF had increased functional connectivity within the DMN MTL subsystem, and the patients with MMF also showed increased functional connectivity within the DMN core subsystem. Meanwhile, compared with the MDD_noMF, the patients with MMF had increased functional connectivity within the DMN core subsystem (mean difference (MDD_noMF-MMF)=-0.08, SE=0.04, p=0.048). However, no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.

Conclusions

Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF, findings potentially beneficial to deepen our understanding of MMF's neural basis.

From Low-Grade Inflammation in Osteoarthritis to Neuropsychiatric Sequelae: A Narrative Review

Nowadays, osteoarthritis (OA), a common, multifactorial musculoskeletal disease, is considered to have a low-grade inflammatory pathogenetic component. Lately, neuropsychiatric sequelae of the disease have gained recognition. However, a link between the peripheral inflammatory process of OA and the development of neuropsychiatric pathology is not completely understood. In this review, we provide a narrative that explores the development of neuropsychiatric disease in the presence of chronic peripheral low-grade inflammation with a focus on its signaling to the brain. We describe the development of a pro-inflammatory environment in the OA-affected joint. We discuss inflammation-signaling pathways that link the affected joint to the central nervous system, mainly using primary sensory afferents and blood circulation via circumventricular organs and cerebral endothelium. The review describes molecular and cellular changes in the brain, recognized in the presence of chronic peripheral inflammation. In addition, changes in the volume of gray matter and alterations of connectivity important for the assessment of the efficacy of treatment in OA are discussed in the given review. Finally, the narrative considers the importance of the use of neuropsychiatric diagnostic tools for a disease with an inflammatory component in the clinical setting.

Large-scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes: an exploratory study

BACKGROUND

Children born very preterm (VPT; <32 weeks' gestation) are at high risk of neurodevelopmental and behavioural difficulties associated with atypical brain maturation, including socio-emotional difficulties. The analysis of large-scale brain network dynamics during rest allows us to investigate brain functional connectivity and its association with behavioural outcomes.

METHODS

Dynamic functional connectivity was extracted by using the innovation-driven co-activation patterns framework in VPT and full-term children aged 6-9 to explore changes in spatial organisation, laterality and temporal dynamics of spontaneous large-scale brain activity (VPT, n = 28; full-term, n = 12). Multivariate analysis was used to explore potential biomarkers for socio-emotional difficulties in VPT children.

RESULTS

The spatial organisation of the 13 retrieved functional networks was comparable across groups. Dynamic features and lateralisation of network brain activity were also comparable for all brain networks. Multivariate analysis unveiled group differences in associations between dynamical functional connectivity parameters with socio-emotional abilities.

CONCLUSION

In this exploratory study, the group differences observed might reflect reduced degrees of maturation of functional architecture in the VPT group in regard to socio-emotional abilities. Dynamic features of functional connectivity could represent relevant neuroimaging markers and inform on potential mechanisms through which preterm birth leads to neurodevelopmental and behavioural disorders.

IMPACT

Spatial organisation of the retrieved resting-state networks was comparable between school-aged very preterm and full-term children. Dynamic features and lateralisation of network brain activity were also comparable across groups. Multivariate pattern analysis revealed different patterns of association between dynamical functional connectivity parameters and socio-emotional abilities in the very preterm and full-term groups. Findings suggest a reduced degree of maturation of the functional architecture in the very preterm group in association with socio-emotional abilities.

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder

Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.