Psychoradiology最新文献

Background: Accumulating evidence indicates that COVID-19 may cause neurological complications detectable on brain imaging. Yet, the overall prevalence, modality-specific characteristics, and clinical implications of these neuroimaging abnormalities have not been systematically summarized through comprehensive quantitative synthesis.

Methods: We searched the PubMed, Web of Science, Scopus, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases, and Wanfang for original articles published up to August 5, 2025. The pooled proportions of brain-imaging findings on computed tomography (CT), magnetic resonance imaging (MRI), and electroencephalography (EEG), including hemorrhage, microbleeds, ischemia, stroke, encephalitis, background activity abnormality, periodic or rhythmic activity, and epileptiform discharge, were estimated using a random-effects model. This study was conducted according to PRISMA guidelines.

Results: Eighty-three eligible studies that included 9466 COVID-19 patients were included in the meta-analysis. Pooled results from 27 studies, including 3081 patients, showed that more than two-fifths (42.60%) of patients who underwent CT/MRI had objective brain abnormalities. The most frequently reported abnormalities on CT/MRI were changes in white matter and non-specific stroke. Twenty-five EEG studies, including 1273 patients, reported epileptiform discharges in one-fifth (20.54%) of cases. The systematic review of long-term brain imaging manifestations in COVID-19 survivors also found common changes in brain microstructure and function.

Conclusion: While these findings offer insights into the potential pathological mechanisms of neuroimaging abnormalities in COVID-19 patients, the high heterogeneity and variability across studies highlight the need for cautious interpretation. It will be necessary to conduct large-scale longitudinal studies with extended follow-up periods in order to validate these neuroimaging findings and clarify the long-term neuropsychiatric consequences of COVID-19.

Although the glymphatic system has been extensively investigated in neurodegenerative diseases, its potential role in psychiatric disorders is only beginning to be recognized. Conditions such as major depressive disorder, schizophrenia, and bipolar disorder frequently exhibit physiological disturbances, including disrupted sleep, neuroinflammation, vascular impairment, and altered astrocytic function, that may modulate glymphatic transport. Recent neuroimaging studies have started to explore these associations. Structural magnetic resonance imaging (MRI) has been used to quantify perivascular space enlargement in depression and schizophrenia. Diffusion-based approaches, including low-b-value diffusion and the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, have been used to estimate perivascular diffusivity, with some studies linking these measures to symptom severity. Functional MRI metrics such as blood oxygen level-dependent-cerebrospinal fluid (BOLD-CSF) coupling have provided indirect markers of CSF pulsatility, revealing associations with sleep quality and cognition. Positron emission tomography (PET) has been investigated for assessing CSF tracer dynamics and targeting neuroinflammation. While these imaging results are promising, they are often indirect, methodologically heterogeneous, and derived from small samples. This review examines current evidence on glymphatic alterations in psychiatric conditions, describes shared and disorder-specific mechanisms, and assesses how complementary MRI and PET approaches can provide a more integrated understanding of glymphatic function. It also addresses methodological challenges, identifies research gaps, and discusses opportunities to incorporate glymphatic imaging into psychiatric diagnostics, prognosis, and treatment monitoring. The article is intended for researchers and clinicians in psychiatry, neurology, and neuroimaging who are interested in the translational potential of glymphatic research.

Background: The thalamo-prefrontal white matter (WM) pathway, a core structural element of the frontal-limbic system disrupted in premenstrual syndrome (PMS), remains poorly understood.

Methods: Diffusion tensor imaging (DTI), functional MRI (fMRI), and serum cytokine levels were collected from 41 PMS participants and 51 healthy controls (HCs), all diagnosed using the Daily Record of Severity of Problems (DRSP) scale. Bilateral thalamic-frontal WM pathways-the anterior thalamic radiations (ATRs)-were reconstructed using probabilistic fiber tracking. Two-sample tests examined group differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and amplitude of low-frequency fluctuation (ALFF) within bilateral ATRs. Spearman correlations assessed associations among these MRI metrics, inflammatory cytokines, and DRSP scores. Machine learning models further evaluated the diagnostic and predictive utility of left ATR features combined with inflammatory cytokines.

Results: Compared to HCs, PMS patients exhibited increased MD, AD, RD, and ALFF values in the left ATR, as well as elevated tumor necrosis factor (TNF)-α levels. Correlation analysis revealed that these MRI alterations in the left ATR and TNF-α levels were linked to DRSP scores. Additionally, the machine learning models constructed using the optimal feature subset, involved in MD, AD and ALFF of left ATR as well as TNF-α, demonstrated robust performance in diagnosing PMS and predicting DRSP scores.

Conclusion: These findings suggest altered thalamo-frontal WM connectivity and elevated TNF-α in PMS. The left ATR may serve as a biomarker of PMS neuro-mechanisms when combined with multi-MRI and inflammation metrics.

In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.

Background: Molecular imaging plays a key role in advancing understanding of neuropsychiatric disorders. However, the conceptual structure of this interdisciplinary field remains poorly mapped from a bibliometric perspective. The objective of this study was to explore the intellectual structure and thematic development of research on molecular imaging applied to neuropsychiatric disorders using co-citation network analysis.

Methods: A bibliometric co-citation analysis was conducted using data retrieved from Scopus. A targeted search strategy identified articles from 2014 to 2023 focused on MRS, fMRI, PET, and SPECT in the context of neuropsychiatric disorders. Bibliographic data were exported, and cited references were analyzed using VOSviewer. A manually curated thesaurus was applied to unify variant citations and reduce duplication. Co-citation networks were generated, and thematic clusters were identified and interpreted based on total link strength and citation density.

Results: The co-citation network included 51 documents and revealed six major thematic clusters encompassing automated anatomical labeling and brain segmentation, functional and structural connectivity, affective neuroscience, clinical biomarkers, and methodological standardization. Notable references included foundational works on resting-state functional connectivity, motion correction, and diagnostic criteria for neuropsychiatric disorders. The clustering structure highlighted the convergence of radiology, neuroscience, and psychiatry around shared methodological tools and conceptual frameworks.

Conclusion: Co-citation analysis revealed a well-defined and maturing intellectual landscape in molecular imaging applied to neuropsychiatry. The identified clusters represent distinct yet interconnected research lines, reflecting methodological innovation and translational potential. These findings offer a roadmap for future research, emphasizing methodological rigor, interdisciplinary collaboration, and clinical applicability.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder marked by significant deficits in social interaction and restricted repetitive behaviors. Despite rigorous research efforts, the early and effective diagnosis and intervention of ASD remain challenging, due primarily to its considerable heterogeneity and complex neurobiological underpinnings. Traditional neuroimaging techniques have largely focused on individual brain responses to social stimuli, often overlooking the critical interactive dynamics that contribute to social impairments in individuals with ASD. This review explored hyperscanning, an innovative neuroimaging approach that features simultaneous recording of brain activity across multiple individuals, to enhance our understanding of the neural mechanisms underlying social difficulties in ASD. By searching published articles conducted between 2000 and 2024, we found eight empirical studies conducted between 2012 and 2024, which employed various brain imaging techniques. We analyzed and summarized participant demographics, experimental designs, and key outcomes, with a particular focus on inter-brain synchrony (IBS) as a measure of social engagement and the quality of interpersonal interactions. Our review identified specific patterns of neural synchrony that correlate with the severity of ASD symptoms. Furthermore, we critically evaluated the limitations of current studies and proposed future research directions, highlighting the need for more nuanced hyperscanning methodologies. Such advancements could significantly deepen our understanding of social impairments in ASD and inform targeted intervention strategies. This comprehensive review aimed to assess the potential of hyperscanning techniques to propel progress in ASD research and intervention, ultimately contributing to more effective clinical practices.

Magnetic resonance imaging (MRI) biomarkers have shown considerable potential in elucidating the neurobiological underpinnings of major depressive disorder (MDD). However, clinical translation of these biomarkers remains limited due to reliance on group-level analyses, which fail to capture the individual variability inherent in MDD. Precision psychiatry, which advocates for individualized approaches, offers a framework that could enhance the clinical utility of MRI biomarkers across multiple domains, including diagnostic classification, treatment response prediction, and individualized interventions. Despite this potential, current research applying MRI biomarkers to MDD within the framework of precision psychiatry remains fragmented, lacking an integrated clinical system that seamlessly combines these components. This review introduces the concept of a closed-loop clinical system, emphasizing the integration of diagnostic classification, treatment response prediction, and individualized interventions into a unified approach at the individual patient level. We summarize recent advances in these three clinical domains, highlight existing fragmentation, and discuss the challenges of achieving a cohesive system. Finally, we propose that the integration of MRI biomarkers into a closed-loop clinical system, as envisioned by precision psychiatry, holds great promise for the individualized management of MDD, improving clinical outcomes from diagnosis through recovery.

Background: Suicide attempts (SA) and non-suicidal self-injury (NSSI) are serious public health problems that frequently co-occur in adolescents females with major depressive disorder (MDD), yet their neurobiological distinctions remain unclear. Here, we sought to explore female adolescents' neural mechanisms via the local gyrification index (LGI) and resting-state functional connectivity (RS-FC) analysis.

Methods: We compared scale scores, LGI, and seed-based RS-FC among three groups of female adolescents: MDD with both NSSI and SA (SA + NSSI, n = 43), MDD with NSSI only (NSSI, n = 28), and healthy controls (HC, n = 27). Exploratory correlation analysis was applied to examine associations between the neuroimaging alterations and clinical symptom severity in depressed adolescents with SA and NSSI.

Results: Compared with the HC group, both SA + NSSI and NSSI groups showed significantly decreased LGI in the prefrontal cortex, including right rostral/caudal middle frontal gyrus (MFG), precentral gyrus and postcentral gyrus (po-CG.R), as well as left rostral MFG, precentral gyrus and opercular part of the inferior frontal gyrus. The brain regions with altered RS-FC (seeds based on po-CG.R and the rostral MFG.L) are mainly distributed in the anterior cingulate cortex, insula, postcentral gyrus, and occipital lobe (P < 0.05, FDR correction). Moreover, exploratory correlation analysis suggested no statistically significant correlations after FDR correction (α = 0.05).

Conclusion: Reduced cortical folding in postcentral and middle frontal gyri was found in both patient groups, alongside distinct functional connectivity, offering deeper neurobiological insights into SA and NSSI.

Background: Despite advances in understanding the effective connectivity (EC) of brain networks in leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis, the specific cause and underlying mechanisms of LGI1 encephalitis remain unclear.

Materials and methods: The study included 27 patients with anti-LGI1 encephalitis and 28 age- and sex-matched normal controls. Amplitude of low-frequency fluctuation (ALFF) analysis identified altered brain regions. Spectral dynamic causal modeling (spDCM) then assessed EC between these regions. Relationships between EC strength and both clinical severity and cognitive function were analyzed.

Results: Distinct EC patterns were found in patients versus controls. Specifically, inhibitory EC was observed from the hippocampus to the superior temporal gyrus, while excitatory EC was noted in the reverse direction. Patients also showed reduced inhibitory self-connections in the posterior cingulate cortex. Crucially, inhibitory EC from the right hippocampus to the left superior temporal gyrus correlated inversely with symptom severity and positively with cognitive performance. Conversely, reduced inhibitory self-connections in the posterior cingulate cortex correlated positively with symptom severity and negatively with cognitive function.

Conclusions: These findings indicate that changes in causal connections between specific brain regions significantly contribute to neurological deficits in anti-LGI1 encephalitis. The inhibitory connectivity from the hippocampus to the superior temporal gyrus may serve as a potential biomarker for personalized diagnosis, offering new insights into the underlying pathological mechanisms of this disorder.