METHODS: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).
RESULTS: PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).
DISCUSSION: Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.
MATERIALS & METHODS: Data were obtained retrospectively from all patients who underwent both CT examinations - brain (frontal bone), thorax (T7), abdomen (L3), spine (T7 & L3) or pelvis (left hip) - and DXA between 2014 and 2018 in our centre. To ensure comparability, the period between CT and DXA studies must not exceed one year. Correlations between HU values and t-scores were calculated using Pearson's correlation. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was used to determine threshold HU values for predicting osteoporosis.
RESULTS: The inclusion criteria were met by 1043 CT examinations (136 head, 537 thorax, 159 lumbar and 151 left hip). The left hip consistently provided the most robust correlations (r = 0.664-0.708, p 0.05.
CONCLUSION: HU values derived from the hip, T7 and L3 provided a good to moderate correlation to t-scores with a good prediction for osteoporosis. The suggested optimal thresholds may be used in clinical settings after external validations are performed.
METHOD: Cross-sectional study on 68 parents of Malaysian children aged 2-18 years with TSC. QOL was assessed using proxy-report Paediatric Quality of Life Inventory (PedsQL) V.4.0, and scores compared with those from a previous cohort of healthy children. Parents also completed questionnaires on child behaviour (child behaviour checklist (CBCL)) and parenting stress (parenting stress index-short form). Multiple regression analysis was used to determine sociodemographic, medical, parenting stress and behavioural factors that impacted on QOL.
RESULTS: The mean proxy-report PedsQL V.4.0 total scale score, physical health summary score and psychosocial health summary score of the patients were 60.6 (SD 20.11), 65.9 (SD 28.05) and 57.8 (SD 19.48), respectively. Compared with healthy children, TSC patients had significantly lower mean PedsQL V.4.0 total scale, physical health and psychosocial health summary scores (mean difference (95% CI): 24 (18-29), 20 (12-27) and 26 (21-31) respectively). Lower total scale scores were associated with clinically significant CBCL internalising behaviour scores, age 8-18 years and Chinese ethnicity. Lower psychosocial health summary scale scores were associated with clinically significant CBCL internalising behaviour scores, Chinese ethnicity or >1 antiepileptic drug (AED).
CONCLUSION: Parents of children with TSC reported lower PedsQL V.4.0 QOL scores in all domains, with psychosocial health most affected. Older children, those with internalising behaviour problems, of Chinese ethnicity or on >1 AED was at higher risk of lower QOL. Clinicians need to be vigilant of QOL needs among children with TSC particularly with these additional risk factors.
METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.
RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.
CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
Materials and methods: Eighty-four patients were randomly divided into two groups receiving either study drug infusion. Anxiety
score, level of sedation using the Bispectral Index and Observer’s Assessment of Alertness and Sedation, hemodynamic stability, and
overall patient’s feedback on anxiolysis were assessed.
Results: Both groups showed a significant drop in mean anxiety score at 10 and 30 min after starting surgery. Difference in median
anxiety scores showed a significant reduction in anxiety score at the end of the surgery in the dexmedetomidine group compared to the
propofol group. Dexmedetomidine and propofol showed a significant drop in mean arterial pressure in the first 30 min and first 10 min
respectively. Both drugs demonstrated a significant drop in heart rate in the first 20 min from baseline after starting the drug infusion.
Patients in the dexmedetomidine group (76.20%) expressed statistically excellent feedback on anxiolysis compared to patients in the
propofol group (45.20%).
Conclusion: Dexmedetomidine infusion was found to significantly reduce anxiety levels at the end of surgery compared to propofol
during regional anesthesia.