BrainBase: a curated knowledgebase for brain diseases.

Brain is the central organ of the nervous system and any brain disease can seriously affect human health. Here we present BrainBase (https://ngdc.cncb.ac.cn/brainbase), a curated knowledgebase for brain diseases that aims to provide a whole picture of brain diseases and associated genes. Specifically, based on manual curation of 2768 published articles along with information retrieval from several public databases, BrainBase features comprehensive collection of 7175 disease-gene associations spanning a total of 123 brain diseases and linking with 5662 genes, 16 591 drug-target interactions covering 2118 drugs/chemicals and 623 genes, and five types of specific genes in light of expression specificity in brain tissue/regions/cerebrospinal fluid/cells. In addition, considering the severity of glioma among brain tumors, the current version of BrainBase incorporates 21 multi-omics datasets, presents molecular profiles across various samples/conditions and identifies four groups of glioma featured genes with potential clinical significance. Collectively, BrainBase integrates not only valuable curated disease-gene associations and drug-target interactions but also molecular profiles through multi-omics data analysis, accordingly bearing great promise to serve as a valuable knowledgebase for brain diseases.

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In the preprocessing stage, the median filter has been used in order to remove salt-and-pepper noise because MRI images are normally affected by this type of noise, the grayscale images are also converted to RGB images in this stage. In the preprocessing stage, the histogram equalization has also been used to enhance the quality of each RGB channel. In the feature extraction stage, the three channels, namely, red, green, and blue, are extracted from the RGB images and statistical measures, namely, mean, variance, skewness, kurtosis, entropy, energy, contrast, homogeneity, and correlation, are calculated for each channel; hence, a total of 27 features, 9 for each channel, are extracted from an RGB image. After the feature extraction stage, different machine learning algorithms, such as artificial neural network, k-nearest neighbors' algorithm, decision tree, and Naïve Bayes classifiers, have been applied in the classification stage on the features extracted in the feature extraction stage. We recorded the results with all these algorithms and found that the decision tree results are better as compared to the other classification algorithms which are applied on these features. Hence, we have considered decision tree for further processing. We have also compared the results of the proposed method with some well-known algorithms in terms of simplicity and accuracy; it was noted that the proposed method outshines the existing methods.

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

The genetic architecture of the human thalamus and its overlap with ten common brain disorders.

The thalamus is a vital communication hub in the center of the brain and consists of distinct nuclei critical for consciousness and higher-order cortical functions. Structural and functional thalamic alterations are involved in the pathogenesis of common brain disorders, yet the genetic architecture of the thalamus remains largely unknown. Here, using brain scans and genotype data from 30,114 individuals, we identify 55 lead single nucleotide polymorphisms (SNPs) within 42 genetic loci and 391 genes associated with volumes of the thalamus and its nuclei. In an independent validation sample (n = 5173) 53 out of the 55 lead SNPs of the discovery sample show the same effect direction (sign test, P = 8.6e-14). We map the genetic relationship between thalamic nuclei and 180 cerebral cortical areas and find overlapping genetic architectures consistent with thalamocortical connectivity. Pleiotropy analyses between thalamic volumes and ten psychiatric and neurological disorders reveal shared variants for all disorders. Together, these analyses identify genetic loci linked to thalamic nuclei and substantiate the emerging view of the thalamus having central roles in cortical functioning and common brain disorders.

Deep learning for brain disorders: from data processing to disease treatment.

In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.

The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an important role in functional connectivity classification, among which convolutional neural network (CNN) based methods become a new hot topic since they can extract topological features in the brain network. However, the conventional CNN-based methods haven't taken sparse connectivity patterns (SCPs) of the human brain into consideration, which may lead to redundancy of the topological features, and limit their performance and generalization. To solve it, we propose a novel CNN-based model with graphical Lasso (CNNGLasso) to extract sparse topological features for brain disease classification. First, we develop a novel graphical Lasso model for revealing the SCPs at group-level. Then, the SCPs are used to guide the topological feature extraction. Finally, the obtained sparse topological features are used to classify the patients from normal controls. The experiment results on the ABIDE dataset demonstrate that the CNNGLasso outperforms the others on various performances. Besides, the abnormal brain regions derived from the trained model are consistent with the previous investigations, which further proves the application prospect of the CNNGLasso.

Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review.

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.

The Value of Corpus Callosum Measurement in the Diagnosis of Cerebral Atrophy.

OBJECTIVE: The study aimed to investigate the relationship between the corpus callosum area (CCa) and the degree of cerebral atrophy in patients with cerebral atrophy. METHODS: 119 patients with brain atrophy were grouped according to the degree of brain atrophy. Median sagittal CCa and intracranial area (ICa) were measured, and the ratio of corpus callosum to the intracranial area (CCa-ICa ratio) was calculated. The data were analyzed using ANOVA. RESULTS: CCa significantly reduced in patients with cerebral atrophy, and the degree of cerebral atrophy was found to be positively correlated with the degree of reduction in the CCa. CONCLUSION: The reduction in the CCa and the CCa-ICa ratio in the median sagittal can be used as a reference indicator for the diagnosis and grading of brain atrophy in clinical practice.

A disease category feature database construction method of brain image based on deep convolutional neural network.

BACKGROUND: Constructing a medical image feature database according to the category of disease can achieve a quick retrieval of images with similar pathological features. Therefore, this approach has important application values in the fields such as auxiliary diagnosis, teaching, research, and telemedicine. METHODS: Based on the deep convolutional neural network, an image classifier applicable to brain disease was designed to distinguish between the image features of the different brain diseases with similar anatomical structures. Through the extraction and analysis of visual features, the images were labelled with the corresponding semantic features of a specific disease category, which can establish an association between the visual features of brain images and the semantic features of the category of disease which will permit to construct a disease category feature database of brain images. RESULTS: Based on the similarity measurement and the matching strategy of high-dimensional visual feature, a high-precision retrieval of brain image with semantics category was achieved, and the constructed disease category feature database of brain image was tested and evaluated through large numbers of pathological image retrieval experiments, the accuracy and the effectiveness of the proposed approach was verified. CONCLUSION: The disease category feature database of brain image constructed by the proposed approach achieved a quick and effective retrieval of images with similar pathological features, which is beneficial to the categorization and analysis of intractable brain diseases. This provides an effective application tool such as case-based image data management, evidence-based medicine and clinical decision support.

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. OBJECTIVE: Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. METHODS: The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. RESULTS: The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. CONCLUSION: The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.

Clinical trials for deep brain stimulation: Current state of affairs.

BACKGROUND: Deep brain stimulation (DBS) is a surgical neuromodulation procedure with a historically wide range of possible therapeutic indications, including movement disorders, neuropsychiatric conditions, and cognitive disorders. Ongoing research in this field is critical to gain further insights into the mechanisms of DBS, to discover novel brain targets for new and existing indications, and to refine targeting and post-operative programming techniques for the optimization of therapeutic outcomes. OBJECTIVE: To update on the state of DBS-related clinical human research by cataloging and summarizing clinical trials that have been completed or are currently ongoing in this field worldwide. METHODS: A search was conducted for clinical trials pertaining to DBS, currently listed on the ClinicalTrials.gov database. Trials were analyzed to generate a detailed overview of ongoing DBS-related research. Specifically, trials were categorized by trial start date, study completion status, clinical phase, projected subject enrollment, disorder, brain target, country of origin, device manufacturer, funding source, and study topic. RESULTS: In total, 384 relevant clinical trials were identified. The trials spanned 28 different disorders across 26 distinct brain targets, with almost 40% of trials being for conditions other than movement disorders. The majority of DBS trials have been US-based (41.9% of studies) but many countries are becoming increasingly active. The ratio of investigator-sponsored to industry-sponsored trials was 3:1. Emphasizing the need to better understand the mechanism of action of DBS, one-third of the studies predominantly focus on imaging or electrophysiological changes associated with DBS. CONCLUSIONS: This overview of current DBS-related clinical trials provides insight into the status of DBS research and what we can anticipate in the future concerning new brain targets, indications, techniques, and developing a better understanding of the mechanisms of action of DBS.

Convolutional neural networks for multi-class brain disease detection using MRI images.

The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images, especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34, and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models, which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with the ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of the model will also help the clinicians to validate their findings after manual reading of the MRI images.

[Genetically determined epileptic encephalopathies]. / Encefalopatías epilépticas determinadas genéticamente.

Epileptic encephalopathies is a group of epileptic syndromes characterized by progressive cognitive impairment beyond the expected for the epilepsy activity. They are characterized by severe pharmaco-resistant epilepsy, severely abnormal electroencephalograms, early-age onset, neurocognitve impairment, variable phenotype and usually normal brain MRI. These syndromes are usually genetically determined. A correct and timely diagnosis could help and guide the medical counselling and the correct therapeutic approach improving the short, medium and long term outcomes. In this article we review the electroencephalographic and genetic findings along with the most recommended therapeutic options facilitating the clinical management. We include the following epileptic encephalopathy syndromes: Ohtahara, early myoclonic encephalopathy, epilepsy of infancy with migrating focal seizures, West, Dravet, non-progressive myoclonic status, Doose, Lennox-Gastaut, Landau-Kleffner and continuous spike-wave during sleep epilepsy.

Las encefalopatías epilépticas es un grupo de síndromes epilépticos caracterizados por el deterioro cognitivo más allá de lo esperado debido a la actividad epiléptica. Se caracterizan por presentar resistencia farmacológica grave, electroencefalogramas profundamente anormales, inicio en la niñez temprana, deterioro neurocognitivo, fenotipo variable y resonancia magnética de cerebro usualmente normal. Frecuentemente estos síndromes están genéticamente determinados. Su diagnóstico correcto y oportuno puede contribuir y guiar el consejo médico y terapia adecuada, influyendo así en el pronóstico a corto, mediano y largo plazo. En este artículo se revisan los hallazgos electroencefalográficos, genéticos y opciones terapéuticas más recomendadas, facilitando así la conducta clínica. Las encefalopatías epilépticas incluidas en este artículos abarcan los síndromes de Ohtahara, encefalopatia mioclónica temprana, epilepsia focal migratoria de la infancia, West, Dravet, estado mioclónico en encefalopatías no progresivas, Doose, Lennox-Gastaut, Landau-Kleffner y epilepsia con espiga-onda continuas durante el sueño de onda lenta.

Types of Cerebral Herniation and Their Imaging Features.

Cerebral herniation, defined as a shift of cerebral tissue from its normal location into an adjacent space, is a life-threatening condition that requires prompt diagnosis. The imaging spectrum can range from subtle changes to clear displacement of brain structures. For radiologists, it is fundamental to be familiar with the different imaging findings of the various subtypes of brain herniation. Brain herniation syndromes are commonly classified on the basis of their location as intracranial and extracranial hernias. Intracranial hernias can be further divided into three types: (a) subfalcine hernia; (b) transtentorial hernia, which can be ascending or descending (lateral and central); and (c) tonsillar hernia. Brain herniation may produce brain damage, compress cranial nerves and vessels causing hemorrhage or ischemia, or obstruct the normal circulation of cerebrospinal fluid, producing hydrocephalus. Owing to its location, each type of hernia may be associated with a specific neurologic syndrome. Knowledge of the clinical manifestations ensures a focused imaging analysis. To make an accurate diagnosis, the authors suggest a six-key-point approach: comprehensive analysis of a detailed history of the patient and results of clinical examination, knowledge of anatomic landmarks, direction of mass effect, recognition of displaced structures, presence of indirect radiologic findings, and possible complications. CT and MRI are the imaging modalities of choice used for establishing a correct diagnosis and guiding therapeutic decisions. They also have important prognostic implications. The preferred imaging modality is CT: the acquisition time is shorter and it is less expensive and more widely available. Patients with brain herniation are generally in critical clinical condition. Making a prompt diagnosis is fundamental for the patient's safety.©RSNA, 2019.

Subcortical heterotopic gray matter brain malformations: Classification study of 107 individuals.

OBJECTIVE: To better evaluate the imaging spectrum of subcortical heterotopic gray matter brain malformations (subcortical heterotopia [SUBH]), we systematically reviewed neuroimaging and clinical data of 107 affected individuals. METHODS: SUBH is defined as heterotopic gray matter, located within the white matter between the cortex and lateral ventricles. Four large brain malformation databases were searched for individuals with these malformations; data on imaging, clinical outcomes, and results of molecular testing were systematically reviewed and integrated with all previously published subtypes to create a single classification system. RESULTS: Review of the databases revealed 107 patients with SUBH, the large majority scanned during childhood (84%), including more than half before 4 years (59%). Although most individuals had cognitive or motor disability, 19% had normal development. Epilepsy was documented in 69%. Additional brain malformations were common and included abnormalities of the corpus callosum (65/102 [64%]), and, often, brainstem or cerebellum (47/106 [44%]). Extent of the heterotopic gray matter brain malformations (unilateral or bilateral) did not influence the presence or age at onset of seizures. Although genetic testing was not systematically performed in this group, the sporadic occurrence and frequent asymmetry suggests either postzygotic mutations or prenatal disruptive events. Several rare, bilateral forms are caused by mutations in genes associated with cell proliferation and polarity (EML1, TUBB, KATNB1, CENPJ, GPSM2). CONCLUSION: This study reveals a broad clinical and imaging spectrum of heterotopic malformations and provides a framework for their classification.

Encefalopatías epilépticas determinadas genéticamente / Genetically determined epileptic encephalopathies

Las encefalopatías epilépticas es un grupo de síndromes epilépticos caracterizados por el deterioro cognitivo más allá de lo esperado debido a la actividad epiléptica. Se caracterizan por presentar resistencia farmacológica grave, electroencefalogramas profundamente anormales, inicio en la niñez temprana, deterioro neurocognitivo, fenotipo variable y resonancia magnética de cerebro usualmente normal. Frecuentemente estos síndromes están genéticamente determinados. Su diagnóstico correcto y oportuno puede contribuir y guiar el consejo médico y terapia adecuada, influyendo así en el pronóstico a corto, mediano y largo plazo. En este artículo se revisan los hallazgos electroencefalográficos, genéticos y opciones terapéuticas más recomendadas, facilitando así la conducta clínica. Las encefalopatías epilépticas incluidas en este artículos abarcan los síndromes de Ohtahara, encefalopatia mioclónica temprana, epilepsia focal migratoria de la infancia, West, Dravet, estado mioclónico en encefalopatías no progresivas, Doose, Lennox-Gastaut, Landau-Kleffner y epilepsia con espiga-onda continuas durante el sueño de onda lenta.

Epileptic encephalopathies is a group of epileptic syndromes characterized by progressive cognitive impairment beyond the expected for the epilepsy activity. They are characterized by severe pharmaco-resistant epilepsy, severely abnormal electroencephalograms, early-age onset, neurocognitve impairment, variable phenotype and usually normal brain MRI. These syndromes are usually genetically determined. A correct and timely diagnosis could help and guide the medical counselling and the correct therapeutic approach improving the short, medium and long term outcomes. In this article we review the electroencephalographic and genetic findings along with the most recommended therapeutic options facilitating the clinical management. We include the following epileptic encephalopathy syndromes: Ohtahara, early myoclonic encephalopathy, epilepsy of infancy with migrating focal seizures, West, Dravet, non-progressive myoclonic status, Doose, Lennox-Gastaut, Landau-Kleffner and continuous spike-wave during sleep epilepsy.

Whither Research Domain Criteria?

In 2010, the National Institute of Mental Health launched the Research Diagnostic Criteria (RDoC) as a research framework aimed at advancing research into the etiology of mental disorders, the development of clinically actionable biomarkers, and the eventual development of precision medications. The foundation of RDoC in that first phase rested in the assumption that mental disorders are brain disorders that originate in aberrant neural circuitry, and that therapeutic advances could flow from alterations in that circuitry. RDoC proposed a matrix of psychological constructs with seven levels of analysis ranging from the cell to self-report, but with neural circuitry at the center. In 2016, another model was proposed in which neural circuitry became equivalent to other units of analyses. With the advent of a new Director of the NIMH, the emphasis returned to neural circuitry as a priority, along with computational psychiatry. Have these shifts undermined the RDoC project?

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the number of psychological disorders patients has been significantly raised. This paper presents a comprehensive review of some of the major human psychological disorders (stress, depression, autism, anxiety, Attention-deficit hyperactivity disorder (ADHD), Alzheimer, Parkinson, insomnia, schizophrenia and mood disorder) mined using different supervised and nature-inspired computing techniques. A systematic review methodology based on three-dimensional search space i.e. disease diagnosis, psychological disorders and classification techniques has been employed. This study reviews the discipline, models, and methodologies used to diagnose different psychological disorders. Initially, different types of human psychological disorders along with their biological and behavioural symptoms have been presented. The racial effects on these human disorders have been briefly explored. The morbidity rate of psychological disordered Indian patients has also been depicted. The significance of using different supervised learning and nature-inspired computing techniques in the diagnosis of different psychological disorders has been extensively examined and the publication trend of the related articles has also been comprehensively accessed. The brief details of the datasets used in mining these human disorders have also been shown. In addition, the effect of using feature selection on the predictive rate of accuracy of these human disorders is also presented in this study. Finally, the research gaps have been identified that witnessed that there is a full scope for diagnosis of mania, insomnia, mood disorder using emerging nature-inspired computing techniques. Moreover, there is a need to explore the use of a binary or chaotic variant of different nature-inspired computing techniques in the diagnosis of different human psychological disorders. This study will serve as a roadmap to guide the researchers who want to pursue their research work in the mining of different psychological disorders.

Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data.

In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two large, independent multi-group (ADNI and BSNIP) neuroimaging datasets. We conduct several binary classification tasks to assess the diagnostic/prognostic performance of the ResNet architecture through a rigorous, repeated and stratified k-fold cross-validation procedure for each of the classification tasks independently. We obtained better than state of the art performance for the clinically most important task in the ADNI dataset analysis, and significantly higher classification accuracies over a standard machine learning method (linear SVM) in each of the ADNI and BSNIP classification tasks. Overall, our results indicate the high potential of this architecture to learn effectual feature representations from structural brain imaging data.

Analysis of shared heritability in common disorders of the brain.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.