Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.
UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging. Firstly, neuroimage collection and imaging-derived phenotypes are summarized. Secondly, typical studies of UKB in neuroimaging areas are introduced, which include cardiovascular risk factors, regulatory factors, brain age prediction, normality, successful and morbid brain aging, environmental and genetic factors, cognitive ability and gender. Lastly, the open challenges and future directions of UKB are discussed. This article has the potential to open up a new research field for the prevention and treatment of neurological diseases.
ObjectiveTo explore the clinical characteristics, neuroimaging, diagnosis and treatment process of inpatients with Juvenile Myoclonic Epilepsy (JME).Methods83 inpatients with JME in the Epilepsy Center of Lanzhou University Second Hospital from January 2016 to August 2020 were analyzed retrospectively. Their clinical features, seizure types, inducing factors, MRI and EEG, first consultation hospital, reason for visit and drug treatment were summarized.ResultsAmong the 83 patients, there were 43 males and 40 females, with an average age of (18±5.6) years. 21 patients had family history of epilepsy or history of febrile convulsion. the average age of onset was 11.5 years old, which was earlier than those without family history and history of febrile convulsion (P<0.05). The results of cranial nuclear magnetic resonance examination were abnormal in 14 patients, including hippocampal sclerosis and local small cysts. The first symptom of 62.7% JME patients is myoclonic seizures, followed by tonic-clonic seizures, sleep deprivation was the most common inducing factor, and tonic-clonic seizures was the most common cause of treatment in JME patients, accounting for 78.3%. 80.7% of patients choose local primary hospitals for their first consultation, and there was a non-standard use of ASMs in treatment, and the seizure free rate of epilepsy after ASMs treatment was 6%, which was lower than that in provincial hospitals (P<0.05). 88% of JME inpatients can effectively control their seizures through monotherapy, among which valproic acid is the most commonly used monotherapy and combination therapy. The new oral ASMs lamotrigine and levetiracetam tablets were mostly used in female patients.ConclusionA family history of epilepsy and history of febrile convulsion may be associated with an earlier age of onset in patients with JME. Neuroimaging abnormalities can be found in a small number of patients with JME, including hippocampal sclerosis and local small cysts. Tonic-clonic seizures is the main treatment cause of JME patients, and most of them are first diagnosed in local hospitals, but the seizure free rate of epilepsy in local hospitals after ASMs treatment is low, so the training of epilepsy related knowledge for doctors in primary hospitals is helpful to the diagnosis of clinical JME and improve its control rate.
Hemorrhagic transformation is one of the most serious complications after endovascular treatment in patients with acute ischemic stroke, which is closely related to neurological deterioration and poor functional prognosis. Therefore, early detection and treatment of hemorrhagic transformation are of great significance for improving patient prognosis. Brain CT, CT angiography, CT perfusion imaging, MRI, diffusion weighted imaging, and susceptibility weighted imaging are relatively commonly used imaging methods in clinical practice. Reasonable use of imaging methods can reduce the risk of hemorrhagic transformation and improve patient prognosis. This article reviews common imaging evaluation techniques for hemorrhagic transformation in clinical practice in order to provide ideas for clinical diagnosis and treatment.