Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer’s disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method for the diagnosis of neurological disorders.

Mycophenolate mofetil (MMF) is given to children in fixed doses based either on body weight or body surface area. There are data indicating mycophenolic acid (MPA) blood levels should be monitored in the early period of transplantation. However, there is little information regarding MPA pharmacokinetics (PK) in stable pediatric recipients. We evaluated MPA-PK in 20 stable renal transplant children (11.7±1.9 years) under long-term (46±31 months) MMF (26.1±7 mg/kg per day or 785±183 mg/m 2 per day) therapy plus prednisone and cyclosporin A (n=16), tacrolimus (n=3), or MMF/prednisone (n=1). Total MPA levels were measured using the EMIT-MPA assay at 0, 1, 2, 3, 4, 6, and 8 h after an oral dose of MMF. The level at 12 h was considered equal to the trough level for AUC 0-12 calculation. Mean C 0 , C max , AUC 0-12 , and T max were 3.46±1.32, 13.5±0.58 µg/ml, 63.2±24.4 µg.h/ml, and 1.3±0.6 h, respectively. Six (30%) children were considered to have an adequate exposure (36-54 µg.h/ml) to MPA, 11 (55%) showed an AUC 0-12 >54 µg.h/ml, and 3 (15%) showed an AUC 0-12 <36 µg.h/ml. A C max ≥10 µg/ml was seen in 13 (65%) children. MMF dose did not correlate with AUC 0-12 or C max . The combination of variables C 0 , C 1 , and C 4 provided an equation to predict exposure (r 2 =0.75) where AUC 0-12 =12.62+ (7.78xC 0 )+(0.90xC 1 )+(1.30xC 2 ) (P<0.001). The use of MMF without monitoring MPA blood levels may cause unnecessary overexposure to the drug in stable pediatric recipients.

Autism spectrum disorder is a multifactorial neurodevelopmental disorder with high genetic heterogeneity. Studies of brain networks in autism can provide new insights into the dynamics of information processing in individuals who suffer from such a condition. This paper proposes a method for automatic diagnosis of autism based on fMRI time series and machine learning algorithms. We verify that the left ventral posterior cingulate cortex region reduces the functional connectivity of the brain area in patients with autism spectrum disorder. Also, the brain networks of patients with autism spectrum disorder show more segregation, lower distribution of information, and less connectivity. Our methodology accurately differentiates control and autistic subjects providing an area under the curve close to higher than 95%.