Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments

Neural substrates of affective temperaments: An intersubject representational similarity analysis to resting-state functional magnetic resonance imaging in nonclinical subjects

Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting-state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders.

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.

T-cell immunophenotype correlations with cortical thickness and white matter microstructure in bipolar disorder

BACKGROUND

Inflammation and immunological alterations, such as T-cell and cytokine changes, are implicated in bipolar disorder (BD), with some evidence linking them to brain structural changes (e.g., cortical thickness (CT), gray matter (GM) volume and white matter (WM) microstructure). However, the connection between specific peripheral cell types, such as T-cells, and neuroimaging in BD remains scarcely investigated.

AIMS OF THE STUDY

This study aims to explore the link between T-cell immunophenotype and neuroradiological findings in BD.

METHODS

Our study investigated 43 type I BD subjects (22 depressive, 21 manic) and 26 healthy controls (HC), analyzing T lymphocyte immunophenotype and employing neuroimaging to assess CT for GM and fractional anisotropy (FA) for WM.

RESULTS

In lymphocyte populations, BD patients exhibited elevated CD4+ and CD4+ central memory (TCM) cells frequencies, but lower CD8+ effector memory (TEM) and terminal effector memory (TTEM) cells. Neuroimaging analysis revealed reduced CT in multiple brain regions in BD patients; and significant negative correlations between CD4 + TCM levels and CT of precuneus and fusiform gyrus. Tract-based spatial statistics (TBSS) analysis showed widespread alteration in WM microstructure in BD patients, with negative and positive correlations respectively between FA and radial diffusivity (RD) and CD4 + TCM. Additionally, positive and negative correlations were found respectively between FA and RD and the CD8 + TEM and CD8 + TTEM subsets.

CONCLUSIONS

Our research revealed distinct T lymphocyte changes and brain structure alterations in BD, underscoring possible immune-brain interactions, warranting further study and therapeutic exploration.

Internalizing Symptoms and Adverse Childhood Experiences Associated With Functional Connectivity in a Middle Childhood Sample

BACKGROUND

Research has found overlapping associations in adults of resting-state functional connectivity (RSFC) to both internalizing disorders (e.g., depression, anxiety) and a history of traumatic events. The present study aimed to extend this previous research to a younger sample by examining RSFC associations with both internalizing symptoms and adverse childhood experiences (ACEs) in middle childhood.

METHODS

We used generalized linear mixed models to examine associations between a priori within- and between-network RSFC with child-reported internalizing symptoms and ACEs using the Adolescent Brain Cognitive Development dataset (N = 10,168, mean age = 9.95 years, SD = 0.627).

RESULTS

We found that internalizing symptoms and ACEs were associated with both multiple overlapping and unique RSFC network patterns. Both ACEs and internalizing symptoms were associated with a reduced anticorrelation between the default mode network and the dorsal attention network. However, internalizing symptoms were uniquely associated with lower within-network default mode network connectivity, while ACEs were uniquely associated with both lower between-network connectivity of the auditory network and cingulo-opercular network, and higher within-network frontoparietal network connectivity.

CONCLUSIONS

The present study points to overlap in the RSFC associations with internalizing symptoms and ACEs, as well as important areas of specificity in RSFC associations. Many of the RSFC associations found have been previously implicated in attentional control functions, including modulation of attention to sensory stimuli. This may have critical importance in understanding internalizing symptoms and outcomes of ACEs.

Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder

An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas-Fernandez-Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects' laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality.

Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder

RATIONAL

With no available response biomarkers, matching an appropriate antidepressant to an individual can be a lengthy process. Improving understanding of processes underlying treatment responsivity in depression is crucial for facilitating work on response biomarkers.

OBJECTIVES

To identify differences in patterns of pre-treatment resting-state functional connectivity (rsFC) that may underlie response to antidepressant treatment.

METHODS

After a baseline MRI scan, thirty-four drug-free patients with depression were treated with an SSRI escitalopram 10 mg daily for 6 weeks; response was defined as ≥ 50% decrease in Hamilton Depression Rating Scale (HAMD) score. Thirty-one healthy controls had a baseline clinical assessment and scan. Healthy participants did not receive treatment.

RESULTS

Twenty-one (62%) of patients responded to escitalopram. Treatment responsivity was associated with enhanced rsFC of the right fronto-parietal network (FPN)-with the posterior DMN, somatomotor network (SMN) and somatosensory association cortex. The lack of treatment response was characterized by reduced rsFC: of the bilateral FPN with the contralateral SMN, of the right FPN with the posterior DMN, and of the extended sensorimotor auditory area with the inferior parietal lobule (IPL) and posterior DMN. Reduced rsFC of the posterior DMN with IPL was seen in treatment responders, although only when compared with HC.

CONCLUSIONS

The study supports the role of resting-state networks in response to antidepressant treatment, and in particular the central role of the frontoparietal and default mode networks.

Resting-state neural signal variability in women with depressive disorders

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

Neural network of bipolar disorder: Toward integration of neuroimaging and neurocircuit-based treatment strategies

Bipolar disorder (BD) is a complex psychiatric disorder characterized by dysfunctions in three domains including emotional processing, cognitive processing, and psychomotor dimensions. However, the neural underpinnings underlying these clinical profiles are not well understood. Based on the reported data, we hypothesized that (i) the core neuropathology in BD is damage in fronto-limbic network, which is associated with emotional dysfunction; (ii) changes in intrinsic brain network, such as sensorimotor network, salience network, default-mode network, central executive network are associated with impaired cognition function; and (iii) beyond the dopaminergic-driven basal ganglia-thalamo-cortical motor circuit modulated by other neurotransmitter systems, such as serotonin (subcortical-cortical modulation), the sensorimotor network and related motor function modulated by other non-motor networks such as the default-mode network are involved in psychomotor function. In this review, we propose a neurocircuit-based clinical characteristics and taxonomy to guide the treatment of BD. We draw on findings from neuropsychological and neuroimaging studies in BD and link variations in these clinical profiles to underlying neurocircuit dysfunctions. We consider pharmacological, psychotherapy, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions in BD. Finally, it is suggested that the methods of testing the neurocircuit-based taxonomy and important limitations to this approach should be considered in future.

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

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

Dynamics of amygdala connectivity in bipolar disorders: a longitudinal study across mood states

Alterations in activity and connectivity of brain circuits implicated in emotion processing and emotion regulation have been observed during resting-state for different clinical phases of bipolar disorders (BD), but longitudinal investigations across different mood states in the same patients are still rare. Furthermore, measuring dynamics of functional connectivity patterns offers a powerful method to explore changes in the brain's intrinsic functional organization across mood states. We used a novel co-activation pattern (CAP) analysis to explore the dynamics of amygdala connectivity at rest in a cohort of 20 BD patients prospectively followed-up and scanned across distinct mood states: euthymia (20 patients; 39 sessions), depression (12 patients; 18 sessions), or mania/hypomania (14 patients; 18 sessions). We compared them to 41 healthy controls scanned once or twice (55 sessions). We characterized temporal aspects of dynamic fluctuations in amygdala connectivity over the whole brain as a function of current mood. We identified six distinct networks describing amygdala connectivity, among which an interoceptive-sensorimotor CAP exhibited more frequent occurrences during hypomania compared to other mood states, and predicted more severe symptoms of irritability and motor agitation. In contrast, a default-mode CAP exhibited more frequent occurrences during depression compared to other mood states and compared to controls, with a positive association with depression severity. Our results reveal distinctive interactions between amygdala and distributed brain networks in different mood states, and foster research on interoception and default-mode systems especially during the manic and depressive phase, respectively. Our study also demonstrates the benefits of assessing brain dynamics in BD.

Affective Temperament Traits Measured by TEMPS-A and Their Associations with Cognitive Functions among Offspring of Parents with Bipolar Disorder with and without Subthreshold Symptoms

BACKGROUND

To our knowledge, there have been no studies that have examined affective temperament traits in offspring of parents with bipolar disorder (BD). The aim of this study was to identify affective temperamental characteristics and their relationships with cognitive functions in BD offspring.

METHODS

A group of BD offspring were enrolled in this study. Subthreshold symptoms were used to categorize participants as either symptomatic offspring (SO) (n=60) or asymptomatic offspring (AO) (n=52). Healthy controls (HCs; n=48) were also enrolled for comparison. We used the Chinese Short Version of Temperament Evaluation of Memphis, Pisa, Paris, and San Diego, Auto-questionnaire (TEMPS-A) to measure temperament traits, and MATRICS Consensus Cognitive Battery (MCCB) to measure cognitive functions.

RESULTS

We observed higher cyclothymic, irritable, depressive and anxious temperament scores in SO than AO when compared to HCs. In BD offspring (SO and AO), cyclothymic individuals performed better in processing speed and verbal learning than depressive individuals and better in attention/vigilance than irritable and anxious individuals; hyperthymic individuals performed better in processing speed than depressive individuals. We also observed that a higher cyclothymic score was associated with better verbal learning and verbal fluency, a higher hyperthymic score was associated with better processing speed and verbal learning; while a higher depressive score was associated with worse processing speed, verbal learning and verbal fluency and a higher irritable score was associated with worse attention/vigilance.

CONCLUSIONS

The relationships between cognitive functions and measures of temperament suggest that these features may share neurobiological substrates and appear to be heritable.

Exploring the Effects of Temperament on Gray Matter Volume of Frontal Cortex in Patients with Mood Disorders

BACKGROUND

Patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) have relatively specific temperament and structural abnormalities of brain regions related to emotion and cognition. However, the effects of temperament factors on the structure of frontal and temporal cortex is still unclear. The aims of this study were to explore the differences and relationships between temperament characteristics and the gray matter volume of frontal and temporal cortex in patients with BD or MDD.

METHODS

T1-weighted magnetic resonance imaging (MRI) data, demographic and clinical information were obtained from 279 depressed patients (90 patients with BD, 189 patients with MDD) and 162 healthy controls (HC). Temperament was assessed with the Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego - Auto questionnaire (TEMPS-A). The Desikan-Killiany atlas was used for yielding gray matter volume by FreeSurfer 6.0 software suite. A total of 22 frontal and temporal regions were chosen as regions of interest (ROIs).

RESULTS

Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and scores on cyclothymic, irritable and hyperthymic subscales. The gray matter volume in bilateral rostral middle frontal gyrus (RMFG), left temporal pole and right superior frontal gyrus were reduced in patients with BD. Patients with MDD only had lower gray matter volume in bilateral temporal pole. In the pooled patients, there were negative associations between hyperthymia and gray matter volume in right RMFG.

CONCLUSION

Patients with BD and MDD had different temperament characteristics. The prominent temperament subscales in patients with BD were cyclothymia, irritable and hyperthymia. Patients with greater hyperthymia had lower gray matter volume in right frontal gyrus. Temperament may reflect an endophenotype in patients with mood disorders, especially in BD.

Altered dynamic functional connectivity across mood states in bipolar disorder

BACKGROUND

This study aims to identify how the large-scale brain dynamic functional connectivity (dFC) differs between mood states in bipolar disorder (BD). The authors analyzed dFC in subjects with BD in depressed and euthymic states using resting-state functional magnetic resonance imaging (rsfMRI) data, and compared these states to healthy controls (HCs).

METHOD

20 subjects with BD in a depressive episode, 23 euthymic BD subjects, and 31 matched HCs underwent rsfMRI scans. Using an existing parcellation of the whole brain, we measured dFC between brain regions and identified the different patterns of brain network connections between groups.

RESULTS

In the analysis of whole brain dFC, the connectivity between the left Superior Temporal Gyrus (STG) in the somatomotor network (SMN), the right Middle Temporal Gyrus (MTG) in the default mode network (DMN) and the bilateral Postcentral Gyrus (PoG) in the DMN of depressed BD was greater than that of euthymic BD, while there was no significant difference between euthymic BD and HCs in these brain regions. Euthymic BD patients had abnormalities in the frontal-striatal-thalamic (FST) circuit compared to HCs.

CONCLUSIONS

Differences in dFC within and between DMN and SMN can be used to distinguish depressed and euthymic states in bipolar patients. The hyperconnectivity within and between DMN and SMN may be a state feature of depressed BD. The abnormal connectivity of the FST circuit can help identify euthymic BD from HCs.

Covariation between Childhood-Trauma Related Resting-State Functional Connectivity and Affective Temperaments is Impaired in Individuals with Major Depressive Disorder

Affective temperaments and childhood-trauma experiences are associated with major depressive disorder (MDD). So far, how the covariation between distinct affective temperaments and childhood-trauma insulted brain functional connectivities (FCs) contribute to MDD remains unclear. Here, we aimed to investigate whether certain brain FC patterns are related to certain affective temperaments and whether the FCs contribute to depressive symptom dimensions of MDD patients. Twenty-nine medication-free MDD patients and 58 healthy controls underwent magnetic resonance imaging scanning and completed the Hamilton Depression Rating Scale (HDRS), the Hamilton Anxiety Rating Scale (HARS), the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and the Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS). Two multivariate analyses of partial least squares (PLS) regression were used to explore the associations among childhood-trauma related resting-state FCs, affective temperaments and depressive symptom dimensions. In all participants, a linear combination of 81 FCs (involving parahippocampus, amygdala, cingulate cortex, insula, frontal-temporal-parietal-occipital cortex, pallidum, and cerebellum) were associated with a linear combination of increased depressive, irritable, anxious, and cyclothymic temperaments. Moreover, the covariation between the PLS FC profile and the PLS affective-temperament profile were enhanced in the MDD patients compared to healthy controls. In MDD participants alone, the affective-temperament modulated FC profile (mainly of the lingual and temporal cortex) was associated with the somatization symptom dimension when age, sex, ill-duration, age-of-onset, and HARS scores were adjusted. The findings imply possible neural correlates of affective temperaments and may find applications in intervention of the somatization-depression symptoms by stimulation of the related neural correlates.

The role of attention in the relationship between early life stress and depression

Early life stress (ELS) can be very harmful to an individual's wellbeing and brain development. It is well established that childhood maltreatment is a significant risk factor for depression. ELS is positively correlated with depressive symptoms both in major depression disorder patients and healthy individuals, but the cognitive and neural mechanisms underlying this association are still unclear. In the present study, we calculate the within/between-network connectivity in 528 college students, and Pearson correlation was performed to investigate the relationship between network measures and ELS. Additionally, the same method was applied to verify these results in another sample. Finally, mediation analysis was performed to explore the cognitive and neural mechanisms regarding the association between ELS and depression. Correlation analysis indicated that ELS was positively correlated with the within-network connectivity of the ventral attention network (VAN), the dorsal attention network (DAN), the salience network (SN), the somatosensory network (SMN) and the between-network connectivity of ventral attention network-dorsal attention network (VAN-DAN), ventral attention network- somatosensory network (VAN-SMN), and ventral attention network-visual network (VAN-VN). Validation results indicated that ELS is associated with the within-network connectivity of VAN and DAN. Mediation analysis revealed that attention bias and the within-network connectivity of VAN could mediated the relationship between ELS and depression. Both behavioral and neural evidence emphasize ELS's influence on individual's emotion attention. Furthermore, the present study also provides two possible mediation models to explain the potential mechanisms behind the relationship between ELS and depression.

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI

OBJECTIVE

Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.

METHOD

The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.

RESULTS

Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN).

CONCLUSION

The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Altered Electroencephalographic Resting-State Large-Scale Brain Network Dynamics in Euthymic Bipolar Disorder Patients

Background: Neuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates. Methods: We used high-density EEG to explore between-group differences in duration, coverage, and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80. Results: Microstate analysis revealed five microstates (A-E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores. Conclusion: Our results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.

Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments

Affective temperaments have been described since the early 20th century and may play a central role in psychiatric illnesses, such as bipolar disorder (BD). However, the neuronal basis of temperament is still unclear. We investigated the relationship of temperament with neuronal variability in the resting state signal-measured by fractional standard deviation (fSD) of Blood-Oxygen-Level Dependent signal-of the different large-scale networks, that is, sensorimotor network (SMN), along with default-mode, salience and central executive networks, in standard frequency band (SFB) and its sub-frequencies slow4 and slow5, in a large sample of healthy subject (HC, n = 109), as well as in the various temperamental subgroups (i.e., cyclothymic, hyperthymic, depressive, and irritable). A replication study on an independent dataset of 121 HC was then performed. SMN fSD positively correlated with cyclothymic z-score and was significantly increased in the cyclothymic temperament compared to the depressive temperament subgroups, in both SFB and slow4. We replicated our findings in the independent dataset. A relationship between cyclothymic temperament and neuronal variability, an index of intrinsic neuronal activity, in the SMN was found. Cyclothymic and depressive temperaments were associated with opposite changes in the SMN variability, resembling changes previously described in manic and depressive phases of BD. These findings shed a novel light on the neural basis of affective temperament and also carry important implications for the understanding of a potential dimensional continuum between affective temperaments and BD, on both psychological and neuronal levels.