Changes in energy and motor activity: core symptoms of bipolar mania and depression?

The Temporal Relationship between Depressive Symptoms and Loneliness: The Moderating Role of Self-Compassion

Loneliness and depression are significant mental health challenges among college students; however, the intricate relationship between these phenomena remains unclear, particularly in the context of self-compassion. In this comprehensive study, we employ a cross-lagged panel network (CLPN) analysis to investigate the symptom-level association between depression and loneliness while exploring the potential moderating influence of self-compassion. Our sample consisted of 2785 college students, who were categorized into high- and low-self-compassion groups based on scores from the Self-Compassion Scale. Depressive symptoms were assessed using the Patient Health Questionnaire-9, while the UCLA Loneliness Scale-8 measured loneliness expressions. Our findings indicate that self-compassion plays a crucial role in the relationship between depression and loneliness. Specifically, we observed distinctive patterns within the high and low-self-compassion groups. In the low-self-compassion group, "energy" emerged as the most influential symptom, whereas "motor function" exhibited the highest influence in the high-self-compassion group. Furthermore, among individuals with high self-compassion, the pathway from depression to loneliness was characterized by "guilt-being alone when desired," while the reverse path from loneliness to depression encompassed "left out-feeling sad" and "left out-anhedonia." Conversely, in the low-self-compassion group, depression and loneliness demonstrated a more intricate mutual triggering relationship, suggesting that self-compassion effectively moderates the association between these variables. This study provides valuable insights into the underlying mechanisms driving the interplay between depression and loneliness, shedding light on the pivotal role of self-compassion in this intricate dynamic.

Segregation, integration and balance in resting-state brain functional networks associated with bipolar disorder symptoms

Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested-spectral partition (NSP) method to study the segregation, integration, and balance in resting-state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine-learning approach, we identified different system-level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention-deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

Grey and white matter alteration in euthymic children with bipolar disorder: a combined source-based morphometry (SBM) and voxel-based morphometry (VBM) study

Bipolar disorder (BPD) is a psychiatric condition driving frequent mood swings between periodic extremes of happiness and depression in patients. In this study, a source-based morphometry (SBM) and voxel-based morphometry (VBM) analysis was utilized to measure the differences in the white matter (WM) and grey matter (GM) between euthymic children with BPD and typically developing (TD) children. We adapted both multivariate (SBM) and univariate (VBM) analysis in 20 children with BPD euthymia /remission and compared to the same number of TD age-matched children. The VBM did not reveal any increase in GM and WM voxel values in children with BPD. However, a decrease in the GM voxel values in the bilateral middle frontal and WM voxels in the left hippocampus, left caudate, left orbitofrontal and right inferior parietal cortices was identified. Conversely, SBM analysis in BPD displayed a high GM value in bilateral angular gyrus, bilateral inferior temporal, left supplementary motor area and left middle temporal region, while a low value was observed in left inferior and middle occipital, cerebellum, thalamus, left premotor area and left lingual gyrus. These findings suggested a crucial GM and WM alteration in multiple neural regions in BPD children even during sustained and substantial remission.

Intrinsic brain activity of subcortical-cortical sensorimotor system and psychomotor alterations in schizophrenia and bipolar disorder: A preliminary study

OBJECTIVE

Alterations in psychomotor dimension cut across different psychiatric disorders, such as schizophrenia (SCZ) and bipolar disorder (BD). This preliminary study aimed to investigate the organization of intrinsic brain activity in the subcortical-cortical sensorimotor system in SCZ (and BD) as characterized according to psychomotor dimension.

METHOD

In this resting-state functional magnetic resonance imaging (fMRI) study, functional connectivity (FC) between thalamus and sensorimotor network (SMN), along with FC from substantia nigra (SN) and raphe nuclei (RN) to basal ganglia (BG) and thalamic regions, were investigated by using an a-priori-driven and dimensional approach. This was done in two datasets: SCZ patients showing inhibited psychomotricity (n = 18) vs. controls (n = 19); SCZ patients showing excited psychomotricity (n = 20) vs. controls (n = 108). Data from a third dataset of BD in inhibited depressive or manic phases (reflecting inhibited or excited psychomotricity) were used as control.

RESULTS

SCZ patients suffering from psychomotor inhibition showed decreased thalamus-SMN FC toward around-zero values paralleled by a concomitant reduction of SN-BG/thalamus FC and RN-BG/thalamus FC (as BD patients in inhibited depression). By contrast, SCZ patients suffering from psychomotor excitation exhibited increased thalamus-SMN FC toward positive values paralleled by a concomitant reduction of RN-BG/thalamus FC (as BD patients in mania).

CONCLUSIONS

These findings suggest that patients exhibiting low or high levels of psychomotor activity show distinct patterns of thalamus-SMN coupling, which could be traced to specific deficit in SN- or RN-related connectivity. Notably, this was independent from the diagnosis of SCZ or BD, supporting an RDoC-like dimensional approach to psychomotricity.

Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

Network analysis of mood symptoms in adolescents with or at high risk for bipolar disorder

OBJECTIVES

Network analyses of psychopathology examine the relationships between individual symptoms in an attempt to establish the causal interactions between symptoms that may give rise to episodes of psychiatric disorders. We conducted a network analysis of mood symptoms in adolescents with or at risk for bipolar spectrum disorders.

METHODS

The sample consisted of 272 treatment-seeking adolescents with or at high risk for bipolar disorder who had at least subsyndromal depressive or (hypo)manic symptoms. Based on symptom scores assessed via semi-structured interviews, we constructed the network of depressive and manic symptoms and identified the most central symptoms and symptom communities within the network. We used bootstrapping analyses to determine the reliability of network parameters.

RESULTS

Symptoms within the depressive and manic mood poles were more related to each other than to symptoms of the opposing mood pole. Four communities were identified, including a depressive symptom community and three manic symptom communities. Fatigue and depressed mood were the strongest individual symptoms within the overall network (ie the most highly correlated with other symptoms), followed by motor hyperactivity. Mood lability and irritability were found to be "bridge" symptoms that connected the two mood poles.

CONCLUSIONS

Symptoms of activity/energy (ie fatigue and hyperactivity) and depressed mood are the most prominent mood symptoms among youth with bipolar spectrum disorders. Mood lability and irritability represent potential warning signs of emergent episodes of either polarity. Targeting these central and bridge symptoms would lead to more efficient assessments and therapeutic interventions for bipolar disorder.

The utility of an RDoC motor domain to understand psychomotor symptoms in depression

Despite the clinical impact of motor symptoms such as agitation or retardation on the course of depression, these symptoms are poorly understood. Novel developments in the field of instrumentation and mobile devices allow for dimensional and continuous recording of motor behavior in various settings, particularly outside the laboratory. Likewise, the use of novel assessments enables to combine multimodal neuroimaging with behavioral measures in order to investigate the neural correlates of motor dysfunction in depression. The research domain criteria (RDoC) framework will soon include a motor domain that will provide a framework for studying motor dysfunction in mood disorders. In addition, new studies within this framework will allow investigators to study motor symptoms across different stages of depression as well as other psychiatric diagnoses. Finally, the introduction of the RDoC motor domain will help test how motor symptoms integrate with the original five RDoC domains (negative valence, positive valence, cognitive, social processes, and arousal/regulation).