Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD. High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

Background and Purpose—: Mechanisms, acute management, and outcomes for patients who experience intracerebral hemorrhage may differ from patients with ischemic stroke. Studies of very early rehabilitation have been mainly undertaken in patients with ischemic stroke, and it is unknown if benefits apply to those with intracerebral hemorrhage. We hypothesized that early rehabilitation, within 48 hours of stroke, would improve survival and functional outcomes in patients with intracerebral hemorrhage. Methods—: This was a multicenter, randomized controlled study, with blinded assessment of outcome at 3 and 6 months. Eligible patients were randomized to receive standard care or standard care plus early rehabilitation. Primary outcome includes survival. Secondary outcomes includes health-related quality of life using the 36-item Short Form Questionnaire, function measured with the modified Barthel Index, and anxiety measured with the Zung Self-Rated Anxiety Scale. Results—: Two hundred forty-three of 326 patients were randomized (mean age, 59 years; 56% men). At 6 months, patients receiving standard care were more likely to have died (adjusted hazard ratio, 4.44; 95% confidence interval [CI], 1.24–15.87); for morbidity outcomes, a 6-point difference in the Physical Component Summary score of the 36-item Short Form Questionnaire (95% CI, 4.2–8.7), a 7-point difference for the Mental Component Summary score (95% CI, 4.5–9.5), a 13-point difference in Modified Barthel Index scores (95% CI, 6.8–18.3), and a 6-point difference in Self-Rating Anxiety Scale scores (95% CI, 4.4–8.3) was reported in favor of the intervention groups. Conclusions—: For the first time, we have shown that commencing rehabilitation within 48 hours of intracerebral hemorrhage improves survival and functional outcomes at 6 months after stroke in hospitalized patients in China. Clinical Trial Registration—: URL: http://www.chictr.org/en . Unique identifier: ChiCTR-TRC-13004039.

Transcranial direct current stimulation (tDCS) can improve cognitive function. However, it is not clear how high-definition tDCS (HD-tDCS) regulates the cognitive function and its neural mechanism, especially in individuals with mild cognitive impairment (MCI). This study aimed to examine whether HD-tDCS can modulate cognitive function in individuals with MCI and to determine whether the potential variety is related to spontaneous brain activity changes recorded by resting-state functional magnetic resonance imaging (rs-fMRI). Forty-three individuals with MCI were randomly assigned to receive either 10 HD-tDCS sessions or 10 sham sessions to the left dorsolateral prefrontal cortex (L-DLPFC). The fractional amplitude of low-frequency fluctuation (fALFF) and the regional homogeneity (ReHo) was computed using rs-fMRI data from all participants. The results showed that the fALFF and ReHo values changed in multiple areas following HD-tDCS. Brain regions with significant decreases in fALFF values include the Insula R, Precuneus R, Thalamus L, and Parietal Sup R, while the Temporal Inf R, Fusiform L, Occipital Sup L, Calcarine R, and Angular R showed significantly increased in their fALFF values. The brain regions with significant increases in ReHo values include the Temporal Inf R, Putamen L, Frontal Mid L, Precentral R, Frontal Sup Medial L, Frontal Sup R, and Precentral L. We found that HD-tDCS can alter the intensity and synchrony of brain activity, and our results indicate that fALFF and ReHo analysis are sensitive indicators for the detection of HD-tDCS during spontaneous brain activity. Interestingly, HD-tDCS increases the ReHo values of multiple brain regions, which may be related to the underlying mechanism of its clinical effects, these may also be related to a potential compensation mechanism involving the mobilization of more regions to complete a function following a functional decline.