Search

Background: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain. Methods: We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set. Results: Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the '2-back versus 0-back' contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%. Limitations: We used a limited n-back paradigm that precluded the examination of higher working-memory loads. Conclusion: Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia., Introduction Working-memory deficit--the failure to represent, maintain and update information in a short period of time--is a key feature of neurocognitive deficit in schizophrenia. (1) It emerges in the prodromal [...]

Glaser‐coupled synthesized 1,3‐diyne can participate in the generation of conjugated polymers and is now widely used in organic and polymer synthesis. Currently, the catalyst in coupling are mostly based on the complexation of noble metals with complex organic ligands, which inevitably increases the research cost and storage difficulty. However, polyoxometalates (POMs), with acidic and redox properties, can be used as an excellent inorganic ligands with low cost and easy storage. In this work, we have synthesized copper catalyst 1, (NH4)4[CuMo6O18(OH)6] with polyoxometalates as inorganic ligands in a facile way and applied them in the coupling of alkynes to obtain (non)symmetrical conjugated 1,3‐diynes in high yields without bases, finally proposing a reasonable reaction path. In addition, the catalyst showed good tolerance to a variety of different groups, further demonstrating its excellent catalytic activity. It is worth mentioning that the catalyst maintained its good structure and activity after six cycles, demonstrating its promising potential for industrial applications. Thus, this paper provides a new strategy for the preparation of (un)symmetrical diynes using polyoxometalates of Cu as catalyst, and also demonstrates the potential of multi‐acid catalyst for organocatalytic applications. [ABSTRACT FROM AUTHOR]

Background and Hypothesis The integration of information that typifies working memory (WM) operation requires a flexible, dynamic functional relationship among brain regions. In schizophrenia, though WM capacity is prominently impaired at higher loads, the mechanistic underpinnings are unclear. As a result, we lack convincing cognitive remediation of load-dependent deficits. We hypothesize that reduced WM capacity arises from a disruption in dynamic functional connectivity when patients face cognitive demands. Study Design We calculate the dynamic voxel-wise degree centrality (dDC) across the functional connectome in 142 patients with schizophrenia and 88 healthy controls (HCs) facing different WM loads during an n-back task. We tested associations of the altered variability in dDC and clinical symptoms and identified intermediate connectivity configurations (clustered states) across time during WM operation. These analyses were repeated in another independent dataset of 169 subjects (102 with schizophrenia). Study Results Compared with HCs, patients showed an increased dDC variability of supplementary motor area (SMA) for the "2back vs. 0back" contrast. This instability at the SMA seen in patients correlated with increased positive symptoms and followed a limited "U-shape" pattern at rest-condition and 2 loads. In the clustering analysis, patients showed reduced centrality in the SMA, superior temporal gyrus, and putamen. These results were replicated in a constrained search in the second independent dataset. Conclusions Schizophrenia is characterized by a load-dependent reduction of stable centrality in SMA; this relates to the severity of positive symptoms, especially disorganized behaviour. Restoring SMA stability in the presence of cognitive demands may have a therapeutic effect in schizophrenia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]