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

Large-scale functional network connectivity alterations in adolescents with major depression and non-suicidal self-injury

Non-suicidal self-injury (NSSI) is prevalent among adolescent populations worldwide, yet its neuropathological mechanisms remain unclear. This study aimed to investigate brain functional differences in NSSI patients by utilizing large-scale functional networks and examining their correlation with clinical outcomes. Cross-sectional clinical and functional magnetic resonance imaging (fMRI) data were collected from 42 patients and 47 healthy controls. Independent component analysis (ICA) was utilized to investigate changes in both intra-network and inter-network functional connectivity. We then investigated the potential association between functional network connectivity and clinical self-injurious behavior. The results revealed significant abnormalities in intra-network functional connectivity within the left middle cingulum gyrus, right angular gyrus, and middle frontal gyrus in patients with NSSI. Additionally, we found altered inter-network connectivity patterns, particularly between higher-order cognitive networks and primary sensory networks, suggesting potential disruptions in multisensory integration and emotional regulation in these patients. This study revealed significant alterations in large-scale functional network connectivity in adolescents with depression and NSSI, particularly in networks related to emotion regulation and cognitive control. These findings provide novel perspectives on the neurobiological mechanisms of NSSI and suggest possible avenues for early intervention and treatment.

Disorganized Striatal Functional Connectivity as a Partially Shared Pathophysiological Mechanism in Both Schizophrenia and Major Depressive Disorder: A Transdiagnostic fMRI Study

Negative symptoms represent pervasive symptoms in schizophrenia (SZ) and major depressive disorder (MDD). Empirical findings suggest that disrupted striatal function contributes significantly to negative symptoms. However, the changes in striatal functional connectivity in relation to these negative symptoms, in the transdiagnostic context, remain unclear. The present study aimed to capture the shared neural mechanisms underlying negative symptoms in SZ and MDD. Resting-state functional magnetic resonance imaging data were obtained from 60 patients with SZ and MDD (33 with SZ and 27 with MDD) exhibiting predominant negative symptoms, and 52 healthy controls (HC). Negative symptoms and hedonic capacity were assessed using the Scale for Assessment of Negative Symptoms (SANS) and the Temporal Experience of Pleasure Scale (TEPS), respectively. Signal extraction for time series from 12 subregions of the striatum was carried out to examine the group differences in resting-state functional connectivity (rsFC) between striatal subregions and the whole brain. We observed significantly decreased rsFC between the right dorsal rostral putamen (DRP) and the right pallidum, the bilateral rostral putamen and the contralateral putamen, as well as between the dorsal caudal putamen and the right middle frontal gyrus in both patients with SZ and MDD. The right DRP-right pallidum rsFC was positively correlated with the level of negative symptoms in SZ. However, patients with SZ showed increased rsFC between the dorsal striatum and the left precentral gyrus, the right middle temporal gyrus, and the right lingual gyrus compared with those with MDD. Our findings expand on the understanding that reduced putaminal rsFC contributes to negative symptoms in both SZ and MDD. Abnormal functional connectivity of the putamen may represent a partially common neural substrate for negative symptoms in SZ and MDD, supporting that the comparable clinical manifestations between the two disorders are underpinned by partly shared mechanisms, as proposed by the transdiagnostic Research Domain Criteria.

Advancement in utilization of magnetic resonance imaging and biomarkers in the understanding of schizophrenia

Historically, psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders. The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal. There have been several studies that examine brain imaging in psychiatric disorders, but more work is needed to elucidate the complexities of the human brain. In this editorial, we examine two articles by Xu et al and Stoyanov et al, that show developments in the direction of using neuroimaging to examine the brains of people with schizophrenia and depression. Xu et al used magnetic resonance imaging to examine the brain structure of patients with schizophrenia, in addition to examining neurotransmitter levels as biomarkers. Stoyanov et al used functional magnetic resonance imaging to look at modulation of different neural circuits by diagnostic-specific scales in patients with schizophrenia and depression. These two studies provide crucial evidence in advancing our understanding of the brain in prevalent psychiatric disorders.

Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia's structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder's heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI's integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.

Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

Cross‐Validation of Paranoid and Depressive Scales: Results From Functional MRI Group Independent Component Analysis

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.