Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder

OBJECTIVE

Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms.

METHODS

Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups.

RESULTS

Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively.

CONCLUSION

This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.

Top-down and bottom-up alterations of connectivity patterns of the suprachiasmatic nucleus in chronic insomnia disorder

The importance of the suprachiasmatic nucleus (SCN, also called the master circadian clock) in regulating sleep and wakefulness has been confirmed by multiple animal research. However, human studies of SCN in vivo are still nascent. Recently, the development of resting-state functional magnetic resonance imaging (fMRI) has made it possible to study SCN-related connectivity changes in patients with chronic insomnia disorder (CID). Hence, this study aimed to explore whether sleep-wake circuitry (i.e., communication between the SCN and other brain regions) is disrupted in human insomnia. Forty-two patients with CID and 37 healthy controls (HCs) underwent fMRI scanning. Resting-state functional connectivity (rsFC) and Granger causality analysis (GCA) were performed to find abnormal functional and causal connectivity of the SCN in CID patients. In addition, correlation analyses were conducted to detect associations between features of disrupted connectivity and clinical symptoms. Compared to HCs, CID patients showed enhanced rsFC of the SCN-left dorsolateral prefrontal cortex (DLPFC), as well as reduced rsFC of the SCN-bilateral medial prefrontal cortex (MPFC); these altered cortical regions belong to the "top-down" circuit. Moreover, CID patients exhibited disrupted functional and causal connectivity between the SCN and the locus coeruleus (LC) and the raphe nucleus (RN); these altered subcortical regions constitute the "bottom-up" pathway. Importantly, the decreased causal connectivity from the LC-to-SCN was associated with the duration of disease in CID patients. These findings suggest that the disruption of the SCN-centered "top-down" cognitive process and "bottom-up" wake-promoting pathway may be intimately tied to the neuropathology of CID.

Group independent components underpin responses to items from a depression scale

OBJECTIVE

The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales.

METHODS

In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping.

RESULTS

The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC.

CONCLUSION

Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

AIM

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

METHODS

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

RESULTS

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

CONCLUSION

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Brooding and neuroticism are strongly interrelated manifestations of the phenome of depression

Introduction

We found that neuroticism may be identified as a subclinical manifestation of the phenome of depression, comprising depressive and anxiety symptoms, and suicidal behaviors. Rumination is positively associated with depression and neuroticism and may mediate the effects of neuroticism on depression. This study aimed to determine whether rumination or its components, including brooding or reflection, mediate the effects of neuroticism on depression or, alternatively, whether both neuroticism and rumination are manifestations of the phenome of depression.

Methods

This study recruited 74 depressed subjects and 44 healthy controls. The depression group was split into groups with high versus low brooding scores. We used partial least squares (PLS) to examine mediation effects.

Results

We found that brooding and reflection scores are significantly higher in depressed patients than in controls. Patients with higher brooding scores have increased severity of depression, anxiety, insomnia, neuroticism, and current suicidal ideation as compared with patients with lower brooding scores and controls. There is a strong positive association between rumination, and neuroticism, depression, anxiety, and lifetime and current suicidal behaviors. PLS analysis shows that brooding does not mediate the effects of neuroticism on the depression phenome because no discriminant validity could be established between neuroticism and brooding, or between neuroticism and brooding and the depression phenome. We were able to extract one validated latent vector from brooding and neuroticism, insomnia, depression, anxiety, and current suicidal behaviors.

Conclusion

Overall, this study supports the theory that rumination and neuroticism are reflective manifestations of the phenome of depression.

Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia

PURPOSE OF REVIEW

Over the last decade, evidence suggests that a combination of behavioral and neuroimaging findings can help illuminate changes in functional dysconnectivity in schizophrenia. We review the recent connectivity literature considering several vital models, considering connectivity findings, and relationships with clinical symptoms. We reviewed resting state fMRI studies from 2017 to 2023. We summarized the role of two sets of brain networks (cerebello-thalamo-cortical (CTCC) and the triple network set) across three hypothesized models of schizophrenia etiology (neurodevelopmental, vulnerability-stress, and neurotransmitter hypotheses).

RECENT FINDINGS

The neurotransmitter and neurodevelopmental models best explained CTCC-subcortical dysfunction, which was consistently connected to symptom severity and motor symptoms. Triple network dysconnectivity was linked to deficits in executive functioning, and the salience network (SN)-default mode network dysconnectivity was tied to disordered thought and attentional deficits. This paper links behavioral symptoms of schizophrenia (symptom severity, motor, executive functioning, and attentional deficits) to various hypothesized mechanisms.

The physio-affective phenome of major depression is strongly associated with biomarkers of astroglial and neuronal projection toxicity which in turn are associated with peripheral inflammation, insulin resistance and lowered calcium

BACKGROUND

Major depressive disorder (MDD) is characterized by elevated activity of peripheral neuro-immune and neuro-oxidative pathways, which may cause neuro-affective toxicity by disrupting neuronal circuits in the brain. No study has explored peripheral indicators of neuroaxis damage in MDD in relation to serum inflammatory and insulin resistance (IR) biomarkers, calcium, and the physio-affective phenome consisting of depressive, anxious, chronic fatigue, and physiosomatic symptoms.

METHODS

Serum levels of phosphorylated tau protein 217 (P-tau217), platelet-derived growth factor receptor beta (PDGFR), neurofilament light chain (NF-L), glial fibrillary acidic protein (GFAP), C-reactive protein (CRP), calcium and the HOMA2-insulin resistance (IR) index were measured in 94 MDD patients and 47 controls.

RESULTS

61.1 % of the variance in the physio-affective phenome (conceptualized as a factor extracted from depression, anxiety, fatigue and physiosomatic symptoms) is explained by the regression on GFAP, NF-L, P-tau2017, PDGFRβ and HOMA2-IR (all positively associated), and decreased calcium. In addition, CRP and HOMA2-IR predicted 28.9 % of the variance in the neuroaxis index. We observed significant indirect effects of CRP and calcium on the physio-affective phenome which were partly mediated by the four neuroaxis biomarkers. Annotation and enrichment analysis revealed that the enlarged GFAP, P-tau217, PDGFR, and NF-L network was enriched in glial cell and neuronal projections, the cytoskeleton and axonal transport, including a mitochondrion.

CONCLUSIONS

Peripheral inflammation and IR may damage the astroglial and neuronal projections thereby interfering with mitochondrial transport. This neurotoxicity, combined with inflammation, IR and lowered calcium, may, at least in part, induce the phenome of MDD.

Treatment response with ketamine in chronic suicidality: An open label functional connectivity study

BACKGROUND

Ketamine has recently been proposed as a treatment option for suicidality. Whilst its mechanism of action has been explored at molecular levels, the effect on the brain at the organ level remains unclear. Here we investigate immediate post-treatment and prolonged large-scale resting-state neural network changes to elucidate the neuronal underpinnings associated with ketamine's therapeutic effects.

METHODS

Twenty-eight adults (aged 22-72 years) participated in the Oral Ketamine Trial On Suicidality, which is an open-label trial of weekly sub-anaesthetic doses of oral ketamine over 6 weeks. MRI was acquired at baseline, post-treatment, and follow-up. Functional connectivity changes at post-treatment and follow-up were examined using seed based and independent component analysis.

RESULTS

The seed-based connectivity analysis revealed significantly reduced connectivity at post-treatment from the right hippocampus to both right and left superior frontal gyrus, from the left anterior parahippocampus to right superior frontal gyrus, left superior frontal gyrus, right middle frontal gyrus, and left frontal operculum cortex. Compared with baseline, the ICA showed reduced anterior default mode network connectivities to bilateral posterior cingulate cortex, middle and anterior cingulate cortex, lingual gyrus, and cuneus and increased connectivity of the frontoparietal network to the right superior parietal lobule at post-treatment.

LIMITATIONS

Open label pilot study.

CONCLUSIONS

We have shown sub-anaesthetic doses of ketamine alters connectivity in networks which have been shown to be aberrantly hyper-connected in numerous psychiatric conditions. These neurocircuitry changes are supported by significant reductions in suicide ideation. Our results provide support for the use of ketamine as a treatment for suicidality.

Depressive symptoms due to stroke are strongly predicted by the volume and location of the cerebral infarction, white matter hyperintensities, hypertension, and age: A precision nomothetic psychiatry analysis

OBJECTIVES

To delineate the effects of white matter hyperintensities (WMHs) as measured by Fluid-attenuated inversion recovery (FLAIR) and infarction volume as measured by Diffusion-weighted imaging (DWI) on post-stroke depression symptoms.

METHODS

Baseline National Institutes of Health Stroke Score (NIHSS) and Modified Rankin Scale (mRS) scores, and FLAIR and DWI MRIs to assess WMHs and acute infarct volumes, respectively, were assessed in 47 patients (≥55 years) with acute ischemic stroke and 17 normal controls. The Montgomery-Åsberg Depression Rating Scale (MDRS) was assessed three months after the stroke.

RESULTS

The MADRS score was significantly increased in stroke patients as compared with normal controls. The MADRS scale is not unidimensional and cannot be used as an accurate indicator of depression severity in stroke patients. Three months after stroke, key depressive (sadness and inability to feel) and concentration-tension symptoms, and lassitude are significantly predicted by the infarct volume. Right side infarction strongly predicts key depressive symptoms and left side infarction strongly predicts concentration-tension and lassitude scores. Total WMHs significantly predict key depressive and concentration-tension symptoms, and lassitude, with these effects being mediated by right and left DWI stroke volumes and associated disabilities.

CONCLUSIONS

Interactions between age, hypertension, a chronic atherosclerotic process, and acute stroke account for the onset of key depressive symptoms three months after the acute infarct. Chronic and acute neuro-immune and neuro-oxidative stress pathways associated with the formation of WMHs and acute stroke may explain the incidence of post-stroke key depressive and concentration-tension symptoms, and lassitude.

Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task

Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.