Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity.

Characterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual's pain sensitivity, but existing studies are limited by using one single imaging modality (fMRI or DTI) and/or using one type of metrics (regional or connectivity features). As a result, pain-relevant information in MRI has not been fully revealed and the associations among different imaging modalities and different features have not been fully explored for elucidating pain sensitivity. In this study, we investigated the predictive capability of multi-features (regional and connectivity metrics) of multimodal MRI (fMRI and DTI) in the prediction of pain sensitivity using data from 210 healthy subjects. We found that fusing fMRI-DTI and regional-connectivity features are capable of more accurately predicting an individual's pain sensitivity than only using one type of feature or using one imaging modality. These results revealed rich information regarding individual pain sensitivity from the brain's both structural and functional perspectives as well as from both regional and connectivity metrics. Hence, this study provided a more comprehensive characterization of the neural correlates of individual pain sensitivity, which holds a great potential for clinical pain management.

The brain's structural differences between postherpetic neuralgia and lower back pain.

The purpose is to explore the brain's structural difference in local morphology and between-region networks between two types of peripheral neuropathic pain (PNP): postherpetic neuralgia (PHN) and lower back pain (LBP). A total of 54 participants including 38 LBP and 16 PHN patients were enrolled. The average pain scores were 7.6 and 7.5 for LBP and PHN. High-resolution structural T1 weighted images were obtained. Both grey matter volume (GMV) and morphological connectivity (MC) were extracted. An independent two-sample t-test with false discovery rate (FDR) correction was used to identify the brain regions where LBP and PHN patients showed significant GMV difference. Next, we explored the differences of MC network between LBP and PHN patients and detected the group differences in network properties by using the two-sample t-test and FDR correction. Compared with PHN, LBP patients had significantly larger GMV in temporal gyrus, insula and fusiform gyrus (p < 0.05). The LBP cohort had significantly stronger MC in the connection between right precuneus and left opercular part of inferior frontal gyrus (p < 0.05). LBP patients had significantly stronger degree in left anterior cingulate gyrus and left rectus gyrus (p < 0.05) while had significantly weaker degree than PHN patients in left orbital part of middle frontal gyrus, left supplementary motor area and left superior parietal lobule (p < 0.05). LBP and PHN patients had significant differences in the brain's GMV, MC, and network properties, which implies that different PNPs have different neural mechanisms concerning pain modulation.

Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.

BACKGROUND: Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases. METHODS: Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO2/FiO2, and plateau pressure), Simplified Acute Physiology Score II (SAPS-II), Sepsis-related Organ Failure Assessment (SOFA), OSI, and OI were calculated. With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. The performance of the proposed RF method was also validated with the combined MIMIC-III and eICU-CRD data. The performance of mortality prediction was evaluated by using the area under the receiver operating characteristics curve (AUROC) and performing calibration using the Hosmer-Lemeshow test. RESULTS: With the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality. CONCLUSIONS: Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.

Predicting Individual Pain Thresholds From Morphological Connectivity Using Structural MRI: A Multivariate Analysis Study.

Pain sensitivity is highly variable among individuals, and it is clinically important to predict an individual's pain sensitivity for individualized diagnosis and management of pain. Literature has shown that pain sensitivity is associated with regional structural features of the brain, but it remains unclear whether pain sensitivity is also related to structural brain connectivity. In the present study, we investigated the relationship between pain thresholds and morphological connectivity (MC) inferred from structural MRI based on data of 221 healthy participants. We found that MC was highly predictive of an individual's pain thresholds and, importantly, it had a better prediction performance than regional structural features. We also identified a number of most predictive MC features and confirmed the crucial role of the prefrontal cortex in the determination of pain sensitivity. These results suggest the potential of using structural MRI-based MC to predict an individual's pain sensitivity in clinical settings, and hence this study has important implications for diagnosis and treatment of pain.

Abnormalities of Cortical Thickness in Pediatric Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis.

OBJECTIVE: Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is the most common intractable seizure type of pediatric epilepsy, with alterations in the cortex across the whole brain. The aim of this study is to investigate the abnormalities of cortical thickness in pediatric MTLE-HS. METHODS: Subjects were recruited from Shenzhen Children's Hospital between September 2015 and December 2016. MTLE was confirmed by the experienced neurological physician based on International League Against Epilepsy (ILAE) diagnosis criteria, and structural magnetic resonance imaging (MRI) was performed at 3T for quantitative assessment of cortical thickness. A general linear model with age and gender as covariates was used to examine the vertex-wise differences in cortical thickness between 1) left MTLE-HS (LMTLE-HS) and healthy controls (HC), and 2) right MTLE-HS (RMTLE-HS) and HC. The family-wise error corrected significance threshold was set at P < 0.05. Through a combination of probability and cluster-size thresholding, cluster-wise P values were obtained for the resulting clusters. RESULTS: 13 LMTLE-HS, 6 RMTLE-HS, and 20 age-matched HC were finally enrolled in the study. No significant difference in the mean age (LMTLE-HS vs. HC, p=0.57; RMTLE-HS vs. HC, p=0.39) and gender ratio (LMTLE-HS vs. HC, p=0.24; RMTLE-HS vs. HC, p=0.72) was found between MTLE-HS and HC. In LMTLE-HS, cortical thickness was found significantly decreased in the ipsilateral caudal middle frontal gyrus (p=0.012) and increased in the contralateral inferior temporal gyrus (p=0.020). In RMTLE-HS, cortical thickness significantly decreased in the ipsilateral posterior parietal lobe (superior, p<0.001 and inferior parietal gyrus, p=0.03), the anterior parietal lobe (postcentral gyrus, p=0.006), the posterior frontal lobe (precentral gyrus, p=0.04 and the lateral occipital gyrus, p<0.001), and the contralateral lateral occipital gyrus, middle frontal (p<0.0001) and superior frontal gyrus (p<0.001), and pericalcarine cortex (p=0.020). CONCLUSION: We detected significant cortical abnormalities in pediatric MTLE-HS patients compared with HC. These cortical abnormalities could be explained by specific pathogenesis in MTLE-HS, and may finally contribute to understanding the intrinsic mechanism of MTLE-HS.