Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health

The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally, the DMN plays a crucial role in brain development during childhood and adolescence, influencing fundamental cognitive and emotional processes. This literature review aims to provide a comprehensive overview of the DMN, addressing its structural, functional, and evolutionary aspects, as well as its impact from infancy to adulthood. By gaining a deeper understanding of the organization and function of the DMN, we can advance our knowledge of the neural mechanisms that underlie cognition, behavior, and mental health. This, in turn, can lead to more effective therapeutic strategies for a range of neuropsychiatric conditions.

A study of dynamic functional connectivity changes in flight trainees based on a triple network model

The time-varying functional connectivity of the Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) in flight trainees during a resting state was investigated using dynamic functional network connectivity (dFNC). The study included 39 flight trainees and 37 age- and sex-matched healthy controls. Resting-state fMRI data and behavioral test outcomes were obtained from both groups. Independent component analysis (ICA), sliding window, and K-means clustering approaches were utilized for evaluating functional network connectivity (FNC) and temporal metrics based on the triple networks. Correlation analyses were performed on the behavioral assessments and these metrics. The flight trainees demonstrated a significantly enhanced functional connection linking the CEN and DMN in state 2 (P < 0.05, FDR corrected). Additionally, flight trainees spent less time in state 5, while they exhibited a protracted mean dwell time and fractional windows in state 2, which were significantly correlated with accuracy on the Berg Card Sorting Test (BCST) and Change Detection Test (all P < 0.05). The improved connectivity of flight trainees between the CEN and DMN following the completion of rigorous flight training resulted in increased stability. This enhancement may be relevant to cognitive abilities such as decision-making, memory, and information integration. When multitasking, flight trainees displayed superior visual processing skills and enhanced cognitive flexibility. dFNC research provides a unique perspective on the sophisticated cognitive capabilities that are required in high-demand, high-stress occupations such as piloting, thereby providing significant insights into the intricate brain mechanisms that are inherent in these domains.

Beyond NMDA Receptors: A Narrative Review of Ketamine's Rapid and Multifaceted Mechanisms in Depression Treatment

The rising prevalence of depression, with its associated suicide risk, demands effective fast-acting treatments. Ketamine has emerged as promising, demonstrating rapid antidepressant effects. While early studies show swift mood improvements, its precise mechanisms remain unclear. This article aims to compile and synthesize the literature on ketamine's molecular actions. Ketamine primarily works by antagonizing NMDA receptors, reducing GABAergic inhibition, and increasing glutamate release. This enhanced glutamate activates AMPA receptors, triggering crucial downstream cascades, including BDNF-TrkB and mTOR pathways, promoting synaptic proliferation and regeneration. Moreover, neuroimaging studies have demonstrated alterations in brain networks involved in emotional regulation, including the Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN), which are frequently disrupted in depression. Despite the promising findings, the literature reveals significant inaccuracies and gaps in understanding the full scope of ketamine's therapeutic potential. For instance, ketamine engages with opioid receptors, insinuating a permissive role of the opioid system in amplifying ketamine's antidepressant effects, albeit ketamine does not operate as a direct opioid agonist. Further exploration is requisite to comprehensively ascertain its safety profile, long-term efficacy, and the impact of genetic determinants, such as BDNF polymorphisms, on treatment responsiveness.

Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback

Background

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

Methods

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

Results

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037).

Limitations

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

Conclusion

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Specific alterations of resting-state functional connectivity in the triple network related to comorbid anxiety in major depressive disorder

The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting-state functional connectivity (RSFC) within this so-called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients. Sixty-eight participants (52.9% male, mean age 35.3 years), consisting of 25 patients with comorbid MDD and ADs (MDD + AD), 20 patients with MDD only (MDD) and 23 healthy controls (HCs) were investigated clinically and with 3T resting-state fMRI. RSFC utilizing a seed-based approach within the three networks belonging to the triple network was compared between the groups. Compared with HC, MDD + AD showed significantly reduced RSFC between the ECN and the VAN, the DMN and the VAN and within the ECN. No differences could be found for the MDD group compared with both other groups. Furthermore, symptom severity and medication status did not affect RSFC values. The results of this study show a distinct set of alterations of RSFC for patients with comorbid MDD and AD compared with HCs. This set of dysfunctions might be related to less adequate switching between the DMN and the ECN as well as poorer functioning of the ECN. This might contribute to additional difficulties in engaging and utilizing consciously controlled emotional regulation strategies.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

Towards a Neurophenomenological Understanding of Self-Disorder in Schizophrenia Spectrum Disorders: A Systematic Review and Synthesis of Anatomical, Physiological, and Neurocognitive Findings

The concept of anomalous self-experience, also termed Self-Disorder, has attracted both clinical and research interest, as empirical studies suggest such experiences specifically aggregate in and are a core feature of schizophrenia spectrum disorders. A comprehensive neurophenomenological understanding of Self-Disorder may improve diagnostic and therapeutic practice. This systematic review aims to evaluate anatomical, physiological, and neurocognitive correlates of Self-Disorder (SD), considered a core feature of Schizophrenia Spectrum Disorders (SSDs), towards developing a neurophenomenological understanding. A search of the PubMed database retrieved 285 articles, which were evaluated for inclusion using PRISMA guidelines. Non-experimental studies, studies with no validated measure of Self-Disorder, or those with no physiological variable were excluded. In total, 21 articles were included in the review. Findings may be interpreted in the context of triple-network theory and support a core dysfunction of signal integration within two anatomical components of the Salience Network (SN), the anterior insula and dorsal anterior cingulate cortex, which may mediate connectivity across both the Default Mode Network (DMN) and Fronto-Parietal Network (FPN). We propose a theoretical Triple-Network Model of Self-Disorder characterized by increased connectivity between the Salience Network (SN) and the DMN, increased connectivity between the SN and FPN, decreased connectivity between the DMN and FPN, and increased connectivity within both the DMN and FPN. We go on to describe translational opportunities for clinical practice and provide suggestions for future research.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Abnormal long- and short-range functional connectivity in patients with first-episode drug-naïve melancholic and non-melancholic major depressive disorder

BACKGROUND

We attempted to explore the common and distinct long- and short-range functional connectivity (FC) patterns of melancholic and non-melancholic major depressive disorder (MDD) and their associations with clinical characteristics.

METHODS

Fifty-nine patients with first-episode drug-naïve MDD, including 31 patients with melancholic features and 28 patients with non-melancholic features, underwent resting-state functional magnetic resonance imaging (fMRI) scanning to examine long- and short-range FC. Thirty-two healthy volunteers were recruited as controls. The support vector machines (SVM) was applied to distinguish the melancholic patients from the non-melancholic patients by using the FC of abnormal brain regions.

RESULTS

Compared to healthy volunteers, patients with MDD showed increased long-range positive FC (lpFC) in the right insula/inferior frontal gyrus and left insula. Relative to non-melancholic patients, melancholic patients displayed decreased lpFC in the right lingual gyrus, decreased short-range positive FC (spFC) in the right middle temporal gyrus and right superior parietal lobule, increased lpFC in the left inferior parietal lobule, and increased spFC in the left middle occipital gyrus/inferior occipital gyrus, left cerebellum VII/IX, and bilateral cerebellum CrusII. Increased lpFC in the left inferior parietal lobule in melancholic patients was correlated with the TEPS abstract anticipatory scores. SVM results showed that FCs of five combinations within different brain regions could distinguish melancholic patients from non-melancholic patients.

CONCLUSIONS

FC abnormalities in the default mode network and parietal-occipital brain regions may underlie the neurobiology of melancholic MDD. An increased lpFC in the left inferior parietal lobule correlated with anhedonia may be a distinctive neurobiological feature of melancholic MDD.

Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

AIM

This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression.

METHOD AND SUBJECTS

We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups.

RESULTS AND DISCUSSION

Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects.

CONCLUSION

The study provides supportive evidence for impaired functional connectivity networks in MDE patients.