Correlation between serum tryptophan metabolism and treatment efficacy of dapoxetine in patients with premature ejaculation: A pilot study.

INTRODUCTION: Primary premature ejaculation (PPE) is a common male neurobiological disorder. Currently, there is consensus that the impairment in central serotonin (5-HT) neurotransmission constitutes a key pathogenic factor in PPE. Selective serotonin reuptake inhibitors (SSRIs) serve as the primary pharmacological intervention; however, a comprehensive elucidation of their mechanism of action remains incomplete. Owing to significant individual variability in efficacy, SSRIs exhibit a high discontinuation rate. Hence, there is an urgent need to address the selection of SSRIs for PPE treatment. OBJECTIVE: This study aims to investigate the characteristics of tryptophan (TRP) metabolism in patients with PPE and to assess its influence on the efficacy of SSRIs. METHODS: The exploratory study included a total of 16 patients with PPE and 16 control subjects who were healthy men without any sexual dysfunction. Upon enrollment in the study, all participants underwent a thorough medical history review and physical examination. Subsequently, their serum levels of TRP, its metabolites, large neutral amino acids (LNAAs), and metabolite ratios were assessed using a liquid chromatography-mass spectrometry (LC-MS) assay. After a period of 4 weeks of dapoxetine treatment, all patients with PPE underwent reassessment using the Premature Ejaculation Diagnostic Tool (PEDT) score and intravaginal ejaculatory latency time (IELT) test. RESULTS: The ratio of serum TRP to other LNAAs (TRP/LNAAs) in patients with PPE was found to be significantly lower compared to the control group (P < 0.05). Conversely, the ratio of kynurenine to TRP (KYN/TRP) was observed to be significantly higher in the PPE patients compared to the control group (P < 0.05). Including the serum TRP/LNAAs ratio and KYN/TRP ratio in the prediction model yielded the highest prediction efficiency for PPE. There was a significant negative correlation between the ratio of TRP/LNAAs before the treatment and the IELT after 4 weeks of the treatment. Additionally, there was a significant positive correlation observed between the ratio of TRP/LNAAs before the treatment and the PEDT score after 4 weeks of the treatment. CONCLUSIONS: This study demonstrates that the reduction in the TRP/LNAAs ratio and the elevation of the KYN/TRP ratio are significant characteristics associated with PPE. These findings suggest that diminished tryptophan availability in the brain and the activation of the kynurenine (KYN) pathway may play a role in the pathogenesis of PPE. The TRP/LNAAs ratio has potential as a reliable indicator of central serotonin (5-HT) levels. Considering the TRP/LNAAs ratio when selecting SSRIs for the treatment of PPE may enhance the response rate of this medication.

The restoration ability of a short nap after sleep deprivation on the brain cognitive function: A dynamic functional connectivity analysis.

AIMS: The brain function impairment induced by sleep deprivation (SD) is temporary and can be fully reversed with sufficient sleep. However, in many cases, long-duration recovery sleep is not feasible. Thus, this study aimed to investigate whether a short nap after SD is sufficient to restore brain function. METHODS: The data of 38 subjects, including resting state functional magnetic resonance imaging data collected at three timepoints (before SD, after 30 h of SD, and after a short nap following SD) and psychomotor vigilance task (PVT) data, were collected. Dynamic functional connectivity (DFC) analysis was used to evaluate changes in brain states among three timepoints, and four DFC states were distinguished across the three timepoints. RESULTS: Before SD, state 2 (a resting-like FC matrix) was dominant (48.26%). However, after 30 h SD, the proportion of state 2 dramatically decreased, and state 3 (still resting-like, but FCs were weakened) became dominant (40.92%). The increased proportion of state 3 positively correlated with a larger PVT "lapse" time. After a nap, the proportions of states 2 and 3 significantly increased and decreased, respectively, and the change in proportion of state 2 negatively correlated with the change in PVT "lapse" time. CONCLUSIONS: Taken together, the results indicated that, after a nap, the cognitive function impairment caused by SD may be reversed to some extent. Additionally, DFC differed among timepoints, which was also associated with the extent of cognitive function impairment after SD (state 3) and the extent of recovery therefrom after a nap (state 2).

White matter structural topologic efficiency predicts individual resistance to sleep deprivation.

BACKGROUND: Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD. METHODS: The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD. DTI and graph theory approaches were used to investigate the topologic organization differences of the brain structural connectome between SD-vulnerable and -resistant individuals. We measured the level of global efficiency and clustering in rich club and non-rich club organizations. RESULTS: We demonstrated that participants vulnerable to SD had less global efficiency, network strength, and local efficiency but longer shortest path length compared with participants resistant to SD. Lower efficiency was mainly distributed in the right insula, bilateral thalamus, bilateral frontal, temporal, and temporal lobes. Furthermore, a disrupted subnetwork was observed that consisted of widespread connections. Moreover, the vulnerable group showed significantly decreased strength of the rich club compared with the resistant group. The strength of rich club connectivity was found to be correlated negatively with PVT performance (r = -0.395, p = 0.005). We further tested the reliability of the results. CONCLUSION: The findings revealed that individual differences in resistance to SD are related to disrupted topologic efficiency connectome pattern, and our study may provide potential connectome-based biomarkers for the early detection of the vulnerable degree to SD.

Structural abnormalities in the Fronto-Parietal Network: Linking white matter integrity to sustained attention deficits in Schizophrenia.

Schizophrenia is associated with a range of cognitive deficits, among which impairments in sustained attention are particularly significant. Previous research has investigated functional changes in the fronto-parietal network (FPN) related to attentional control in schizophrenia. However, the role of structural connectivity within the FPN in sustained attention deficits remains under-explored. Utilizing diffusion tensor imaging (DTI), this study investigated white matter integrity in 75 participants, comprising 37 individuals with schizophrenia (SZ) and 38 healthy controls (HC). Psychomotor vigilance task (PVT) performance was assessed to gauge sustained attention. The SZ group showed a significant reduction in fractional anisotropy (FA) and streamline counts within white matter tracts connecting frontal and parietal regions, compared to the HC group. Further, significant negative correlations were found between PVT performance and white matter integrity measures within the SZ group, specifically in the left FPN. Our findings indicate that structural abnormalities in the FPN are associated with sustained attention deficits in schizophrenia. These results contribute to our understanding of the neurobiological mechanisms underlying cognitive impairments in schizophrenia and offer potential avenues for targeted therapeutic interventions. Further research is warranted to validate these outcomes and explore their clinical implications.

Cognitive impairment after sleep deprivation: The role of precuneus related connectivity on the intra-individual variability changes.

OBJECTIVE: Intra-individual variability (IIV) in cognitive performance is thought to reflect the efficiency with which attentional resources are allocated in different circumstances requiring cognitive control. IIV in cognitive performance is associated with the strength of the negative correlation between task-positive network and default mode network (DMN) activity. In this study, we investigated the impact of sleep deprivation (SD) on functional connectivity (FC) between the DMN and psychomotor vigilance task-related network (PVT-RN), and its relationship with IIV in cognitive performance. METHODS: Two analyses, network-level independent component analysis (NL-ICA) and region-level (RL)-ICA, were employed to compare the coefficient of variation (CV) of the PVT between normal sleep and SD conditions across 67 healthy participants. RESULTS: After SD, in NL-ICA, the FC between the PVT-RN and DMN was positively correlated with the CV of the PVT, as well as the changes therein, compared with normal sleep. Using a mask derived from the DMN and PVT-RN, the RL-ICA revealed that 12 edges/connections between DMN and PVT independent components were associated with the CV of the PVT, with nine of these connections involving the precuneus. CONCLUSIONS: These findings suggest that the precuneus may play a crucial role in the interactions of various brain functions during the PVT, with the connections between the precuneus and frontoparietal and somatosensory networks being significantly altered after SD. Moreover, following SD, weakened negative FC between the precuneus and bilateral inferior parietal lobule may disrupt the balance between cognitive and executive control functions, leading to a decline in cognitive performance.

Association of Objective Sleep Characteristics and Incident Angina Pectoris: A Longitudinal Analysis from the Sleep Heart Health Study.

Purpose: While prior research has highlighted a significant association between sleep characteristics and angina pectoris (AP) incidence, the link between sleep efficiency (SE) and angina remains unexplored. This study seeks to elucidate the relationship between AP and objectively quantified SE. Patients and Methods: We examined a cohort of 2990 participants (1320 males and 1670 females; mean age 63.69 ± 13.2 years) from the Sleep Heart Health Study. The main exposure variable was SE, as determined by baseline home polysomnography, while the primary outcome was the first incidence of angina pectoris (AP) during the period between the baseline polysomnography and the end of follow-up. A multivariate Cox regression model was utilized, controlling for factors such as age, gender, BMI, smoking and alcohol consumption habits, diabetes, hypertension, sleep duration, triglycerides, cholesterol, high-density lipoprotein, apnea-hypopnea index, nocturnal oxygen saturation, to analyze the relationship between SE and AP. Results: During an average follow-up of 11 years, 284 patients developed AP. The unadjusted Kaplan-Meier analysis identified the 2nd quartile of SE as having the lowest AP risk. The multivariate Cox proportional hazards model demonstrated a higher risk of AP in quartile 1 (HR, 1.679; 95% CI, 1.109-2.542; P <0.014) and quartile 3 (HR, 1.503; 95% CI, 1.037-2.179; P <0.031), compared to quartile 2 of SE. Upon stratified analysis, this relationship was particularly pronounced in hypertensive individuals. Conclusion: Our results highlight the critical role of optimal sleep efficiency in mitigating the risk of angina pectoris, especially among hypertensive individuals.

Continuous blood purification in patients with pheochromocytoma crisis: A case report.

Key Clinical Message: Pheochromocytoma crisis accompanied by multi-organ failure necessitates prompt and comprehensive interventions, including VA-ECMO, CRRT, and others. Successful laparoscopic tumor resection promotes favorable outcomes and recovery. Abstract: Pheochromocytoma crisis is commonly associated with high mortality, high surgical risk, and rapidly fatal complications. This article presented successful treatments and nursing experiences in a patient with pheochromocytoma who developed cardiogenic shock and multiple organ failure. We report a case study of a 32-year-old female patient who experienced pheochromocytoma crisis accompanied by multiple organ failure. Initial assessment of bedside echocardiography revealed an extremely low left ventricular ejection fraction of 8%. The patient was promptly resuscitated though tracheal intubation and venoarterial extracorporeal membrane oxygenation (VA-ECMO), in conjunction with continuous renal replacement therapy (CRRT), alpha-blockers, beta-blockers, and other pharmacological interventions to manage blood pressure and heart rate. These interventions resulted in a remarkable increase in the left ventricular ejection fraction of 67%. However, the patient subsequently developed severe sepsis, which may have been caused by the intubation procedure, necessitating the discontinuation of VA-ECMO while maintaining CRRT. Close monitoring of plasma catecholamine metabolite level, hemodynamic index, inflammatory marker, liver and kidney functions, and electrolytes during CRRT support allows for evaluating the efficacy of these measures and assessing the impact of CRRT on pheochromocytoma crisis. Eventually, the patient successfully underwent laparoscopic resection of a large pheochromocytoma, leading to favorable prognosis and a successful recovery. Continuous blood purification therapy can effectively eliminate catecholamines and their byproducts from the plasma, stabilize hemodynamics, improve heart, liver, and kidney functions, significantly reduce inflammatory cytokine levels significantly, and extend the surgical window for patients.

Efficient removal of Cr from aqueous solution by catechol/m-phenylenediamine nanospheres combined with Fe(II).

The discharge of chromium-containing wastewater in industrial production causes resource loss and damage to the ecological environment. Currently, various phenolamine materials have been used to remove chromium, but their harsh adsorption conditions bring many difficulties. For example, ideal chromium removal is only achieved at low pH. In this study, we synthesized catechol/m-phenylenediamine nanospheres (CMN) and combined CMN with Fe(II) for Cr removal from aqueous solutions, and Fe(II) comes from FeSO4·7H2O. CMN was characterized and analyzed by field-emission scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), transmission electron microscopy (TEM), Fourier transformed infrared (FTIR), X-ray diffraction (XRD), X-ray photoelectron (XPS). The adsorption performance was studied through a series of adsorption experiments. When C0 = 900 mg/L and pH = 6, the maximum adsorption capacity obtained in the experiment was 977.1 mg/g. It maintains excellent adsorption properties in acidic, neutral and alkaline environments. The results of the adsorption mechanism showed that the ultra-high adsorption capacity of CMN and Fe(II) for Cr was the result of the synergistic effect of adsorption and reduction, including electrostatic attraction, reduction and coprecipitation. CMN is expected to be an ideal adsorbent for Cr removal in aqueous solution due to its low cost, high biocompatibility and high efficiency in Cr removal.

Effects on resting-state EEG phase-amplitude coupling in insomnia disorder patients following 1 Hz left dorsolateral prefrontal cortex rTMS.

Despite burgeoning evidence for cortical hyperarousal in insomnia disorder, the existing results on electroencephalography spectral features are highly heterogeneous. Phase-amplitude coupling, which refers to the modulation of the low-frequency phase to a high-frequency amplitude, is probably a more sensitive quantitative measure for characterizing abnormal neural oscillations and explaining the therapeutic effect of repetitive transcranial magnetic stimulation in the treatment of patients with insomnia disorder. Sixty insomnia disorder patients were randomly divided into the active and sham treatment groups to receive 4 weeks of repetitive transcranial magnetic stimulation treatment. Behavioral assessments, resting-state electroencephalography recordings, and sleep polysomnography recordings were performed before and after repetitive transcranial magnetic stimulation treatment. Forty good sleeper controls underwent the same assessment. We demonstrated that phase-amplitude coupling values in the frontal and temporal lobes were weaker in Insomnia disorder patients than in those with good sleeper controls at baseline and that phase-amplitude coupling values near the intervention area were significantly enhanced after active repetitive transcranial magnetic stimulation treatment. Furthermore, the enhancement of phase-amplitude coupling values was significantly correlated with the improvement of sleep quality. This study revealed the potential of phase-amplitude coupling in assessing the severity of insomnia disorder and the efficacy of repetitive transcranial magnetic stimulation treatment, providing new insights on the abnormal physiological mechanisms and future treatments for insomnia disorder.

Sparse non-convex regularization based explainable DBN in the analysis of brain abnormalities in schizophrenia.

Deep belief networks have been widely used in medical image analysis. However, the high-dimensional but small-sample-size characteristic of medical image data makes the model prone to dimensional disaster and overfitting. Meanwhile, the traditional DBN is driven by performance and ignores the explainability which is important for medical image analysis. In this paper, a sparse non-convex based explainable deep belief network is proposed by combining DBN with non-convex sparsity learning. For sparsity, the non-convex regularization and Kullback-Leibler divergence penalty are embedded into DBN to obtain the sparse connection and sparse response representation of the network. It effectively reduces the complexity of the model and improves the generalization ability of the model. Considering explainability, the crucial features for decision-making are selected through the feature back-selection based on the row norm of each layer's weight after network training. We apply the model to schizophrenia data and demonstrate it achieves the best performance among several typical feature selection models. It reveals 28 functional connections highly correlated with schizophrenia, which provides an effective foundation for the treatment and prevention of schizophrenia and methodological assurance for similar brain disorders.

The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder.

BACKGROUND: It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS: In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS: In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION: rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Functional Connectivity Alterations During Sleep Deprivation: Investigating Key Brain Regions and Networks.

BACKGROUND: Sleep deprivation (SD) has emerged as a significant public health concern because of its adverse effects on cognition and behavior. However, the influence of circadian rhythms on SD and brain activities has been less studied. This study employed functional magnetic resonance imaging (fMRI) and functional connectivity density (FCD) metrics to investigate the interaction between sleep pressure and circadian rhythms during SD. METHODS: Thirty-six volunteers with good sleep habits underwent a sleep deprivation trial. Sleepiness was assessed using the Stanford Sleepiness Scale (SSS) at multiple time points, and fMRI scans were conducted to derive global and local FCD (gFCD and iFCD) values. This study focused on specific brain regions and networks, including the thalamus, the frontoparietal network (FPN), and the default mode network (DMN). RESULTS: Analysis indicated significant changes in gFCD and iFCD values in several key brain regions. A strong correlation was found between sleepiness and both gFCD and iFCD values in certain areas, such as the left superior temporal gyrus and left thalamus. The gFCD values in these regions showed a gradual increase across sessions, while iFCD values in the right superior frontal gyrus decreased. CONCLUSIONS: This study revealed that SD leads to enhanced functional activities in the DMN and thalamus and decreased activity in the FPN. These changes in brain activity were significantly correlated with increases in sleepiness, as measured by the SSS. Our findings underscore the importance of understanding the neural underpinnings of SD and could guide future clinical interventions aimed at mitigating its effects.

Corrosion inhibition mechanism of imidazole ionic liquids with high temperature in 20% HCl solution.

BACKGROUND: This paper focuses on the chemical and physical adsorption of 1-hexyl-2,3-dimethyl imidazolium bromide (HDMIMBr), 1-decyl-2,3-dimethyl imidazolium bromide (DDMIMBr), and 1-hexadecyl-2,3-dimethyl imidazolium bromide (C16DMIMBr) on the surface of mild steel at high temperature in order to explore the mechanism of a corrosion inhibitor in a complex environment. METHODS: Gravimetric, scanning electron microscope, X-ray photoelectron spectroscopy, and electrochemical tests explored the corrosion inhibition performance from the experimental level. Quantum chemical calculations and molecular dynamics simulations reveal the corrosion inhibition mechanism from the molecular scale. RESULTS: The results show that the longer the alkyl chain of the three corrosion inhibitors studied, the better the corrosion inhibition performance. This is due to the hydrophobic effect of the long alkyl chain, which has its own synergistic effect and then self-assembles to form an adsorption film with a multilayer structure. CONCLUSION: This dense adsorption film makes corrosion inhibitors a good application prospect in complex corrosive environments.

Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia.

Objectives: Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications. Methods: The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software. Results: SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment. Conclusions: Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

Corrigendum: Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRI Graph Theory Metrics.

[This corrects the article DOI: 10.3389/fnins.2021.660365.].

Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors.

Objectives: This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods: This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results: Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions: Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective.

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (ß = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data.

Background: Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking. Methods: Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants. Results: LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value. Conclusion: These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.