Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI

Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design efforts to predict treatment response from rs-fMRI remain limited due to difficulties in understanding the current brain state and the underlying mechanisms driving the observed patterns, which limited the clinical application of rs-fMRI. To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss. This approach results in a more expressive framework that captures brain dynamic features at different scales and enables more accurate prediction of treatment response. Our experiments on the chronic pain and depersonalization disorder datasets demonstrate that our proposed method outperforms current methods in different scenarios. To the best of our knowledge, we are the first to explore the integration of distance-based and correlation-based neural similarity into graph learning for treatment response prediction.Comment: Proceedings of the 2023 International Conference on Neural Information Processing (ICONIP

Unraveling the Brain Dynamics of Depersonalization-Derealization Disorder: A Dynamic Functional Network Connectivity Analysis

Background: Depersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.Methods: In our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on braindynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.Results: We identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.Conclusions: Our study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients

Streptococcus pneumoniae Response to Repeated Moxifloxacin or Levofloxacin Exposure in a Rabbit Tissue Cage Model

The role of moxifloxacin and levofloxacin pharmacokinetics (PK) in antimicrobial efficacy and in the selection of fluoroquinolone-resistant Streptococcus pneumoniae strains was investigated using the rabbit tissue cage abscess model. A rabbit tissue cage was created by insertion of sterile Wiffle balls in the dorsal cervical area. Animals orally received a range of moxifloxacin or levofloxacin doses that simulate human PK for 7 days 48 h after the Wiffle balls were inoculated with fluoroquinolone-sensitive S. pneumoniae (10(7) CFU). Abscess fluid was collected on a daily basis over 14 days to measure bacterial density and MICs. Moxifloxacin regimens produced a range of area under the concentration-time curve (AUC)/MIC ratios ranging from 9.2 to 444 and peak/MIC ratios ranging from 1.3 to 102. Levofloxacin doses produced AUC/MIC ratios of 5.1 to 85.5 and peak/MIC ratio of 0.9 to 14.8. Moxifloxacin at 6.5, 26, and 42 mg/kg reduced the bacterial log CFU per milliliter in abscess fluid (percentage of that in a sterile animal) by 4.2 ± 2.2 (20%), 5.8 ± 0.4 (100%), and 5.4 ± 0.4 (100%), respectively, over the dosing period. Levofloxacin at 5.5, 22, and 32 mg/kg reduced the log CFU per milliliter in abscess fluid (percentage of that in a sterile animal) by 2.8 ± 0.7 (20%), 5.1 ± 1.3 (80%), and 4.6 ± 1.3 (60%), respectively. Moxifloxacin has a greater bactericidal rate as determined by regression of log CFU versus time data. The AUC/MIC and peak/MIC ratios correlated with the efficacy of both drugs (P < 0.05). Resistance to either drug did not develop with any of the doses as assessed by a change in the MIC. In conclusion, data derived from this study show that moxifloxacin and levofloxacin exhibit rapid bactericidal activity against S. pneumoniae in vivo, and moxifloxacin exhibits enhanced bactericidal activity compared to levofloxacin, with AUC/MIC and peak/MIC ratios correlated with antimicrobial efficacy for both drugs. The development of fluoroquinolone-resistant S. pneumoniae was not observed with either drug in this model