Yonggui Yuan

Aim: Bipolar depression (BDD) and major depressive disorder (MDD) share similar depressive symptoms but differ in diagnosis, treatment, and prognosis. Clarifying their neurobiological distinctions may improve diagnostic characterization and mechanistic understanding. Methods: We studied 453 participants, including 85 patients with BDD, 190 with MDD, and 178 healthy controls. Individual morphometric similarity networks (MSN) were constructed using seven structural and diffusion MRI features. Regional MSN differences between BDD and MDD were examined at the parcel level. To characterize the biological relevance of the distinct MSN pattern, we performed imaging‐transcriptomic analysis using Allen Human Brain Atlas data, followed by gene enrichment, cell‐type enrichment, receptor/transporter dominance analysis, and cognitive meta‐analytic decoding. Sensitivity analyses evaluated the effects of medication exposure and demographic imbalance. Results: Compared with MDD, BDD showed distinct MSN alterations mainly in parietal and occipital regions, including higher MSN in the left lateral occipital cortex and lower MSN in the right inferior and superior parietal cortices. The spatial transcriptomic pattern was enriched for ribosomal and protein synthesis/modification pathways. Cell‐type enrichment analyses showed that the transcriptomic pattern associated with the MSN differences was enriched for microglial and inhibitory‐neuron gene signatures, while receptor dominance analysis identified the 5‐HT _2A density map as having the largest relative contribution to the explained spatial variance. Conclusion: Our findings identify distinct cortical morphometric organization in BDD and MDD, and these differences are linked to transcriptomic, cellular, and neurotransmitter‐related annotations, providing multiscale insights into the neurobiological divergence between the two disorders.

Fault detection from 3D seismic data is commonly performed using scalar fault attributes, such as fault likelihood, coherence, and related geometric measures. These attributes can highlight discontinuities, but the resulting fault traces often suffer from fragmentation, unstable responses near intersections, and ambiguous lateral continuity on time slices. This study proposes a graph-clustering workflow for seismic fault enhancement. The method first applies multidirectional component analysis to decompose a fault-attribute slice into local directional components, and then builds a sparse weighted graph from high-response candidate points. Edge weights integrate attribute strength, spatial distance, axial-direction consistency, multidirectional component labels, and local linear-extension compatibility. Strong-edge connected components are interpreted as fault-trace instances. A proof-of-concept test on the open F3 dataset shows that the proposed workflow suppresses isolated responses, improves trace continuity, and preserves intersecting fault trends after component-wise clustering. Application to a Tarim Basin field dataset further indicates that the enhanced results provide clearer fault patterns on time and horizon slices than the original thinned fault likelihood. The workflow provides an interpretable intermediate step between local directional fault enhancement and 3D fault surface interpretation.

Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single‐subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi‐feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual‐level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph‐theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT‐based networks exhibited reduced MC primarily within and between higher‐order networks involving the default mode and frontoparietal networks, whereas CV‐based networks showed increased MC predominantly within the default mode network. By contrast, both SA‐ and SD‐based networks demonstrated enhanced MC mainly within and between lower‐order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first‐episode, drug‐naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate‐to‐good classification performance in distinguishing patients from controls. Overall, our findings of individual‐level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.

Background: Previous studies have highlighted the crucial role of the precuneus in the hyperarousal state of insomnia. However, the causal effects between its subregions and other regions remain unclear. Both traditional Chinese medicines (TCMs) and benzodiazepines are effective in insomnia treatment, but limited studies have compared the therapeutic mechanisms of these two treatments. Methods: This study included 82 chronic insomnia disorder (CID) patients and 80 healthy controls (HCs). CID patients were assigned to receive either modified suanzaoren decoction, a TCM for treating insomnia, or estazolam treatment for 6 weeks. Granger causality analysis seeded on the precuneus subregions was employed to detect effective connectivity (EC) alterations at pre‐ and post‐treatment. Moreover, a neuroimaging–transcription association analysis was conducted. Results: Compared to HCs, CID patients showed increased EC from the precuneus to the right postcentral/precentral gyrus (PoCG/PrCG) and left lingual gyrus. Meanwhile, decreased ECs from the bilateral PoCG/PrCG, right cerebellum crus VIIB, and right calcarine cortex to the precuneus were observed. After 6 weeks of treatment, significant group‐by‐time interactions on ECs from the bilateral midcingulate cortex to precuneus, as well as from the bilateral midcingulate cortex, left PrCG, and right supramarginal gyrus to precuneus were observed. Genes associated with these ECs were identified. Conclusions: The bidirectional ECs between the precuneus and regions in the sensory‐motor and visual networks in CID may reflect both hyperarousal states and compensatory responses to counteract abnormal hyperarousal. TCM tended to alleviate the hyperarousal state in CID while benzodiazepines induced an adaptive regulation due to their rapid sedative effects. Trial Registration: Clinical trial registration number: NCT06452953

Aims: Electroconvulsive therapy (ECT) is an effective intervention for treatment‐resistant depression (TRD). Although prior studies have identified cerebellar functional alterations following ECT, cerebellar–cerebral functional connectivity (FC) changes remain insufficiently explored. Methods: Cerebellar seeds were defined based on prior connectivity studies, targeting cerebellar regions functionally coupled with executive, default‐mode, and affective‐limbic networks. Seed‐based cerebellar–cerebral FC analyses were conducted using neuroimaging data from 72 patients with TRD (52 of whom completed post‐ECT assessment) and 63 healthy controls (HCs). Correlation analyses assessed clinical associations, and support vector regression was applied to predict treatment response using baseline FC. Neuroimaging–transcriptomic association analyses were conducted to identify gene expression patterns correlated with post‐ECT FC alterations. Results: Compared to HCs, patients with TRD exhibited decreased cerebellar connectivity with the sensorimotor network (SMN) and the default‐mode network (DMN). Following ECT, TRD patients showed significant alleviation of depressive symptoms, which was accompanied by a marked increase in cerebellar–DMN connectivity. Enhanced FCs between the left Crus II _Exec2 and the bilateral medial superior frontal gyrus were positively correlated with Hamilton Depression Rating Scale (HAMD) score reduction ( r = 0.343, P = 0.026). Additionally, baseline FC of the left Crus II _Exec2 significantly predicted HAMD reduction ( r = 0.412, P < 0.001). Neuroimaging–transcription association analysis revealed these FC alterations were associated with normative gene patterns enriched in synaptic signaling, ion channel, and chromatin remodeling. Conclusions: TRD is associated with reduced cerebellar–SMN and cerebellar–DMN connectivity. ECT may enhance cerebellar–DMN connectivity, potentially contributing to antidepressant effects.