Journal of Psychiatry & Neuroscience最新文献

Background: Late-life depression (LLD) is the major risk factor for elderly suicide, and suicidal ideation (SI) is a crucial stage for prevention. However, LLD are less likely openly express SI. Gamma oscillations, closely linked to cognition and mental processes, may contribute to the pathophysiology of LLD and suicidal behavior through their dysregulation within large-scale brain networks. The aim of our research was to investigate the cortical functional networks in the gamma band to better understand the neurobiological mechanisms underlying SI in LLD.

Methods: Electroencephalography (EEG) was recorded from 30 LLD with SI (LLD-SI), 32 LLD without SI (LLD-NSI), and 34 normal controls. We applied source-level graph theory based on functional connectivity in gamma band and utilized machine learning to differentiate between LLD-SI and LLD-NSI groups using network features.

Results: Significant diminished gamma functional connectivity, particularly involving the orbitofrontal cortex, was observed in both subtypes of the LLD group. In graph theory analysis, LLD-SI showed decreased average clustering coefficient (p < 0.001) and characteristic path length (p = 0.021), along with increased global efficiency (p = 0.015) compared to LLD-NSI. Compared to NC, LLD-SI also demonstrated reduced average clustering coefficient (p = 0.004), characteristic path length (p = 0.004), and higher global efficiency (p = 0.004). We also found several nodal metrics, which suggested potential hubs related to SI. The graph theorical method effectively distinguished SI in LLD, with an accuracy of 69.35%, sensitivity of 73.33%, and specificity of 65.63% based on gamma-band network features.

Limitations: The sample sizes are relatively small. Higher-density EEG systems and interventional study designs should be included in future research. Future studies should incorporate external validation datasets to confirm the clinical utility of the proposed classification framework.

Conclusion: Our research provides valuable insights into the brain connectome in gamma band of SI in LLD. Gamma-band network indices may serve as potential biomarkers for detecting SI and offer frequency-specific targets for neuromodulation in suicide prevention and treatment strategies for LLD patients.

Sex and gender can impact cannabis use and related harms, yet the field has historically centered men and male bodies. In our recent systematic review of sex differences in the acute cognitive effects of cannabis, just six of 29 human studies found evidence that female and male participants differed in cognitive responses to cannabis. The goal of this Commentary is to discuss methodological limitations of published studies that complicate interpretation of data and to suggest priorities for future research to move this topic forward. We highlight inadequate statistical power, poor definition and measurement of sex, lack of consideration of sex- or gender-related characteristics, and no inclusion of transgender and gender-diverse individuals in prior studies. Future research should take an intersectional perspective, incorporate hypothesis-driven sex- and gender-informed designs, ensure adequate power for interaction analyses, and consider sex-related variables across the lifespan. This approach is necessary to advance scientific rigor and promote equitable health outcomes related to cannabis use.

Background: Brain network dynamics are responsive to task induced fluctuations, but such responsivity may not hold in schizophrenia (SCZ). We introduce and implement Centrality Dynamics (CD), a method developed specifically to capture task-driven dynamic changes in graph theoretic measures of centrality. We applied CD to functional MRI (fMRI) data in SCZ and Healthy Controls (HC) acquired during associative learning.

Methods: fMRI (3T Siemens Verio) was acquired in 88 participants (49 SCZ). Time series were extracted from 246 functionally defined cerebral nodes. We applied a dynamic windowing technique to estimate 280 partially overlapping connectomes (with 30 135 unique region-pairs per connectome). In each connectome, we calculated every node's Betweenness Centrality (BC) following which we built 246 unique time series from a node's BC in successive connectomes (where each such time series represents a node's CD). Next, in each group similarities in CD were used to cluster nodes.

Results: Clustering revealed fewer sub-networks in SCZ, and these sub-networks were formed by nodes with greater functional heterogeneity. The averaged CD of nodes in these sub-networks also showed greater Approximate Entropy (ApEn) (indicating greater stochasticity) but lower amplitude variability (suggesting less adaptability to task-induced dynamics). Finally, higher ApEn was associated with worse clinical symptoms and poorer task performance.

Limitations: Centrality Dynamics is a new method for network discovery in health and schizophrenia. Further extensions to other task-driven and resting data in other psychiatric conditions will provide fuller understanding of its promise.

Conclusion: The brain's functional connectome under task-driven conditions is not static. Characterizing these task-driven dynamics will provide new insight on the dysconnection syndrome that is schizophrenia. Centrality Dynamics provides novel characterization of task-induced changes in the brain's connectome and shows that in the schizophrenia brain, learning-evoked sub-network dynamics were (a) less responsive to learning evoked changes and (b) showed greater stochasticity.