Rositsa Paunova

Background: Chronic insomnia (CID) is a highly prevalent sleep disorder, yet the precise mechanisms underlying it remain incompletely understood. The aim of this study is to analyze effective connectivity between key regions of the default mode network (DMN), executive control network (ECN), and salience network (SN) in patients with CID as potential neurologic correlates of the hyperarousal state. Methods: Thirty-one CID patients and 24 healthy controls (HC) were recruited. All the subjects filled out the Insomnia severity index scale (ISI), Beck depression inventory (BDI), and Epworth sleepiness scale (ESS), underwent polysomnography, and were scanned on functional magnetic resonance imaging. Statistical Parametric Mapping 12 was used to analyze the results. Spectral dynamic causal modeling was applied to the chosen regions of interest. Results: There were three significant connections present in the CID group—inhibitory from the dorsolateral prefrontal cortex (DLPFC) to the right hippocampus (Hippocamp R); excitatory from the dorsomedial prefrontal cortex to the ventromedial prefrontal cortex; and excitatory from the common medial prefrontal cortex to the right anterior insula (AIR). Two statistically significant excitatory connections were lacking in the patients’ group—from the posterior cingulate cortex (PCC) to AIR, and from precuneus to PCC. CID patients scored higher on the ISI and BDI. Significant negative correlations between DLPFC-Hippocamp R connectivity and both ISI and BDI scores were identified. Conclusions: Disruptions within the DMN and between the DMN, SN, and ECN reflect an impaired ability to appropriately shift between internally and externally directed cognitive states—an imbalance that potentially underlies the hyperarousal state of CID.

Background: Chronic insomnia (CID) is a highly prevalent sleep disorder, yet the precise mechanisms underlying it remain incompletely understood. The aim of this study is to analyse effective connectivity between key regions of the default mode network (DMN), executive control network (ECN), and salience network (SN) in patients with CID as potential neurologic correlates of the hyperarousal state. Methods: Thirty-one CID patients and 24 healthy controls (HC) were recruited. All subjects filled out Insomnia severity index scale (ISI), Beck depression inventory (BDI) and Epworth sleepiness scale (ESS), underwent polysomnography, and were scanned on functional magnetic resonance imaging. Statistical Parametric Mapping 12 was used to analyse the results. Spectral dynamic causal modeling was applied to the chosen regions of interest. Results: There were three significant connections, present in the CID group – inhibitory from the dorsolateral prefrontal cortex (DLPFC) to the right hippocampus (Hippocamp R), excitatory from the dorsomedial prefrontal cortex to the ventromedial prefrontal cortex, and excitatory from the common medial prefrontal cortex to the right anterior insula (AIR). Two statistically significant excitatory connections were lacking in the patients’ group – from the posterior cingulate cortex (PCC) to AIR, and from precuneus to PCC. CID patients scored higher on the ISI and BDI. Significant negative correlations between DLPFC- Hippocamp R connectivity and both ISI and BDI scores were identified. Conclusions: Disruptions within the DMN and between the DMN, SN and ECN reflect an impaired ability to appropriately shift between internally and externally directed cognitive states—an imbalance potentially underlying the hyperarousal state of CID.

The study aimed to assess the discriminative capacity of a machine learning algorithm in distinguishing between individuals with Major Depressive Disorder and healthy controls based on a dataset collected during the performance of a Stroop Color and Word Test combined with an n-back component in functional magnetic resonance imaging. A total of 50 participants were recruited, including 24 patients with depression and 26 healthy controls. The analysis employed a multivariate linear model, which identified two principal components characterized by their eigenvalues. The key finding of our study highlights the distinct contribution of eigenvalues, as represented in the principal components, to brain signatures with a strong capacity to differentiate between the two diagnostic groups examined for depression and healthy controls. Moreover, the results present a fresh network-level perspective, emphasizing the intricate interactions among different brain networks in major depression disorder. These findings support prior research indicating disruptions in sensory processing, cognitive control, and emotional regulation in Major Depressive Disorder. The results provide a novel, network-level perspective on these alterations, emphasizing the intricate interplay between sensory, cognitive, and emotional processes. Understanding these network dynamics may offer valuable insights into the neural mechanisms of Major Depressive Disorder and inform targeted interventions aimed at restoring functional connectivity and improving symptom management.

Objective: Based on earlier research, we hypothesized that patient response to diagnostically relevant items (paranoid (DP) and depressive (DS)) from clinical self‐assessment scale (PD‐S) could be cross‐validated when performed simultaneously with task‐based fMRI, resulting in specific and common neural circuit activations in response to the PDS items. In the current study, we tried to overcome one of the limitations of our previous research, namely, the lack of a healthy control (HC) group. Thus, our aim was to investigate the possible differences between patients with schizophrenia (SCZ) and depression and HCs using independent component analysis.Methods: A total of 72 subjects participated in this study, including 21 HCs, 26 patients with SCZ, and 25 patients with major depressive episode (DEP). Patients were scanned on a 3Т MRI system using a functional MRI task representing statements from DP‐DS scale and diagnostically neutral (DN) statements. The data were processed using group independent component analysis for fMRI toolbox (GIFT).Results: Five components were identified as task‐related, but only three of them were found to demonstrate statistical difference between SCZ, DEP, and HC, namely, components 6, 7, and 9. The contrast between SCZ and HC was presented by Component 6 (cingulate gyrus and basal ganglia), which exhibited significant difference when comparing all three active conditions. On the other hand, SCZ and DEP group differences were presented by Component 7 for DS‐DN comparison (frontoparietal network and superior temporal gyrus) and Component 9 (medial frontal gyrus, precuneus, angular gyrus, and among others) which highlighted preferential processing related to the DP‐DN and the DS‐DP comparison.Conclusion: Our results demonstrate differences in brain circuits processing preferentially diagnostic stimuli across the three groups. Those circuits involve mainly networks of cognitive and affective functioning, which provides insights in their role for pathophysiology of SCZ and DEP. This evidence fosters the theory of transdisciplinary validation, where neurobiological measure such as functional MRI is determined as external validity operation for the diagnostic assessment scales. This can facilitate the use of clinical evaluation methods as proxy measures of brain functions.

Introduction: In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).Methods: We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.Results: As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 – for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.Discussion: Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.