Transdiagnostic and Illness-Specific Functional Dysconnectivity Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to HC. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. K-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions.

Acceleration of brain aging after small-volume infarcts

Introduction

Previous studies have shown that stroke patients exhibit greater neuroimaging-derived biological "brain age" than control subjects. This difference, known as the brain age gap (BAG), is calculated by comparing the chronological age with predicted brain age and is used as an indicator of brain health and aging. However, whether stroke accelerates the process of brain aging in patients with small-volume infarcts has not been established. By utilizing longitudinal data, we aimed to investigate whether small-volume infarctions can significantly increase the BAG, indicating accelerated brain aging.

Methods

A total of 123 stroke patients presenting with small-volume infarcts were included in this retrospective study. The brain age model was trained via established protocols within the field of machine learning and the structural features of the brain from our previous study. We used t-tests and regression analyses to assess longitudinal brain age changes after stroke and the associations between brain age, acute stroke severity, and poststroke outcome factors.

Results

Significant brain aging occurred between the initial and 6-month follow-ups, with a mean increase in brain age of 1.04 years (t = 3.066, p < 0.05). Patients under 50 years of age experienced less aging after stroke than those over 50 years of age (p = 0.245). Additionally, patients with a National Institute of Health Stroke Scale score >3 at admission presented more pronounced adverse effects on brain aging, even after adjusting for confounders such as chronological age, sex, and total intracranial volume (F 1,117 = 7.339, p = 0.008, η 2 = 0.059). There were significant differences in the proportional brain age difference at 6 months among the different functional outcome groups defined by the Barthel Index (F 2,118 = 4.637, p = 0.012, η 2 = 0.073).

Conclusion

Stroke accelerates the brain aging process, even in patients with relatively small-volume infarcts. This phenomenon is particularly accentuated in elderly patients, and both stroke severity and poststroke functional outcomes are closely associated with accelerated brain aging. Further studies are needed to explore the mechanisms underlying the accelerated brain aging observed in stroke patients, with a particular focus on the structural alterations and plasticity of the brain following minor strokes.

Dynamic structure-function coupling across three major psychiatric disorders

BACKGROUND

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

METHODS

We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time.

RESULTS

The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

CONCLUSIONS

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Carepath for overcoming psychosis early (COPE): first 5 years of clinical operation and prospective research in the Cavan-Monaghan early intervention service

OBJECTIVES

As Ireland confronts the many challenges of broadening the introduction of early intervention services (EIS) for first episode psychosis (FEP) as national policy, this article describes Carepath for Overcoming Psychosis Early (COPE), the EIS of Cavan-Monaghan Mental Health Service, and presents prospective research findings during its first 5 years of operation.

METHODS

COPE was launched as a rural EIS with an embedded research protocol in early 2012, following an education programme for general practitioners (GPs). Here, operational activities are documented and research findings presented through to late 2016.

RESULTS

During this period, 115 instances of FEP were incepted into COPE, 70.4% via their GP and 29.6% via the Emergency Department. The annual rate of inception was 24.8/100,000 of population aged > 15 years and was 2.1-fold more common among men than women. Mean duration of untreated psychosis was 5.7 months and median time from first psychotic presentation to initiation of antipsychotic treatment was zero days. Assessments of psychopathology, neuropsychology, neurology, premorbid functioning, quality of life, insight, and functionality compared across 10 DSM-IV psychotic diagnoses made at six months following presentation indicated minimal differences between them, other than more prominent negative symptoms in schizophrenia and more prominent mania in bipolar disorder.

CONCLUSIONS

COPE illustrates the actuality of introducing and the challenges of operating a rural EIS for FEP. Prospective follow-up studies of the 5-year COPE cohort should inform on the effectiveness of this EIS model in relation to long-term outcome in psychotic illness across what appear to be arbitrary diagnostic boundaries at FEP.

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan

Introduction

The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive.

Methods

This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan.

Results

The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect.

Discussion

Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study

Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.

Visual system assessment for predicting a transition to psychosis

The field of psychiatry is far from perfect in predicting which individuals will transition to a psychotic disorder. Here, we argue that visual system assessment can help in this regard. Such assessments have generated medium-to-large group differences with individuals prior to or near the first psychotic episode or have shown little influence of illness duration in larger samples of more chronic patients. For example, self-reported visual perceptual distortions-so-called visual basic symptoms-occur in up to 2/3rds of those with non-affective psychosis and have already longitudinally predicted an impending onset of schizophrenia. Possibly predictive psychophysical markers include enhanced contrast sensitivity, prolonged backward masking, muted collinear facilitation, reduced stereoscopic depth perception, impaired contour and shape integration, and spatially restricted exploratory eye movements. Promising brain-based markers include visual thalamo-cortical hyperconnectivity, decreased occipital gamma band power during visual detection (MEG), and reduced visually evoked occipital P1 amplitudes (EEG). Potentially predictive retinal markers include diminished cone a- and b-wave amplitudes and an attenuated photopic flicker response during electroretinography. The foregoing assessments are often well-described mechanistically, implying that their findings could readily shed light on the underlying pathophysiological changes that precede or accompany a transition to psychosis. The retinal and psychophysical assessments in particular are inexpensive, well-tolerated, easy to administer, and brief, with few inclusion/exclusion criteria. Therefore, across all major levels of analysis-from phenomenology to behavior to brain and retinal functioning-visual system assessment could complement and improve upon existing methods for predicting which individuals go on to develop a psychotic disorder.

Development of the Korea-Polyenvironmental Risk Score for Psychosis

OBJECTIVE

Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs).

METHODS

We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the "population proportion" (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We first constructed the "K-PERS-I," comprising five factors based on the PPS, and then the "K-PERS-II" comprising six factors based on the ERS. The instruments accurately predicted participants' status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS.

CONCLUSION

The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.

Decreased integration of the frontoparietal network during a working memory task in major depressive disorder

BACKGROUND

Working memory deficits are a common feature in major depressive disorder and are associated with poor functional outcomes. Intact working memory performance requires the recruitment of large-scale brain networks. However, it is unknown how the disrupted recruitment of distributed regions belonging to these large-scale networks at the whole-brain level brings about working memory impairment seen in major depressive disorder.

METHODS

We used graph theory to examine the functional connectomic metrics (local and global efficiency) at the whole-brain and large-scale network levels in 38 patients with major depressive disorder and 41 healthy controls during a working memory task. Altered connectomic metrics were studied in a moderation model relating to clinical symptoms and working memory accuracy in patients, and a machine learning method was employed to assess whether these metrics carry enough illness-specific information to discriminate patients from controls.

RESULTS

Global efficiency of the frontoparietal network was reduced in major depressive disorder (false discovery rate corrected, p = 0.014); this reduction predicted worse working memory performance in patients with less severe illness burden indexed by Brief Psychiatric Rating Scale (β =-0.43, p = 0.035, t =-2.2, 95% confidence interval = [-0.043,-0.002]). We achieved a classification accuracy and area under the curve of 73.42% and 0.734, respectively, to discriminate patients from controls based on connectomic metrics, and the global efficiency of the frontoparietal network contributed most to the diagnostic classification.

CONCLUSIONS

We report a putative mechanistic link between the global efficiency of the frontoparietal network and impaired n-back performance in major depressive disorder. This relationship is more pronounced at lower levels of symptom burden, indicating the possibility of multiple pathways to cognitive deficits in severe major depressive disorder.

Anomalous brain gyrification patterns in major psychiatric disorders: a systematic review and transdiagnostic integration

Anomalous patterns of brain gyrification have been reported in major psychiatric disorders, presumably reflecting their neurodevelopmental pathology. However, previous reports presented conflicting results of patients having hyper-, hypo-, or normal gyrification patterns and lacking in transdiagnostic consideration. In this article, we systematically review previous magnetic resonance imaging studies of brain gyrification in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder at varying illness stages, highlighting the gyral pattern trajectory for each disorder. Patients with each psychiatric disorder may exhibit deviated primary gyri formation under neurodevelopmental genetic control in their fetal life and infancy, and then exhibit higher-order gyral changes due to mechanical stress from active brain changes (e.g., progressive reduction of gray matter volume and white matter integrity) thereafter, representing diversely altered pattern trajectories from those of healthy controls. Based on the patterns of local connectivity and changes in neurodevelopmental gene expression in major psychiatric disorders, we propose an overarching model that spans the diagnoses to explain how deviated gyral pattern trajectories map onto clinical manifestations (e.g., psychosis, mood dysregulation, and cognitive impairments), focusing on the common and distinct gyral pattern changes across the disorders in addition to their correlations with specific clinical features. This comprehensive understanding of the role of brain gyrification pattern on the pathophysiology may help to optimize the prediction and diagnosis of psychiatric disorders using objective biomarkers, as well as provide a novel nosology informed by neural circuits beyond the current descriptive diagnostics.