Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

BACKGROUND: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. METHODS: We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. RESULTS: Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. LIMITATIONS: The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. CONCLUSIONS: DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.

A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties.

OBJECTIVE: In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. METHODS: We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. RESULTS: ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. CONCLUSION: Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. SIGNIFICANCE: eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.

Effect of prolonged direct and indirect peroxide bleaching on fracture toughness of human dentin.

STATEMENT OF THE PROBLEM: The effects of prolonged exposure to peroxide bleaching agents on dentin structural integrity are uncertain. PURPOSE: To evaluate the effect of in vitro prolonged tooth bleaching on the fracture toughness (K(1C)) of human dentin. MATERIALS AND METHODS: Dentin from recently extracted molar teeth was directly or indirectly treated to simulate a prolonged at-home (10% carbamide peroxide or 3% hydrogen peroxide, 6 hours/day, 5 days/week for 8 weeks) or in-office (30% hydrogen peroxide, 1 hour/week for 8 weeks) bleaching regimen (N=8/group). Placebo gel and distilled water acted as control materials. Compact tension test specimens (approximately 4.60 x 4.50 x 1.60 mm) were prepared from coronal dentin and tensile loading was applied at a rate of 10 mm/min 24 hours after the last bleaching session. Results were analyzed using analysis of variance and Tukey's test (p < 0.05). For direct bleach application, the treatment materials were applied onto dentin that was already prepared as compact tension specimens. For indirect bleach application, bleach was applied to the enamel of intact teeth prior to specimen preparation. RESULTS: There was a significant decrease in dentin K(1C) after 8 weeks of direct bleach treatment (p < 0.05). There were no significant differences between the bleach and control groups after 8 weeks of indirect bleach treatment (p=0.19). CONCLUSIONS: The in vitro fracture resistance of dentin was reduced after the prolonged use of bleach products that were applied directly to dentin. CLINICAL SIGNIFICANCE: Caution should be considered when using bleach for prolonged treatment times in clinical cases where there is dentin exposure such as occlusal attrition or gingival recession.