Brain/MINDS Beyond Human Brain MRI Project: A Protocol for Multi-Site Harmonization across Brain Disorders Throughout the Lifespan

AbstractPsychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (FY2018 ∼ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. The Harmonization protocol (HARP) was established for five high-quality 3T scanners to obtain multimodal brain images including T1 and T2-weighted, resting state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, providing high reproducibility and sensitivity to subject-specific connectome. A total of 75 TS, as well as patients with various psychiatric and neurological disorders, are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a rigorous, prospective harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

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N-Acetylaspartate Is an Important Brain Osmolyte

Biomolecules ◽

10.3390/biom10020286 ◽

2020 ◽

Vol 10 (2) ◽

pp. 286 ◽

Cited By ~ 1

Author(s):

Marina Warepam ◽

Khurshid Ahmad ◽

Safikur Rahman ◽

Hamidur Rahaman ◽

Kritika Kumari ◽

...

Keyword(s):

Human Brain ◽

Protein Stability ◽

Neurological Disorders ◽

Thermal Denaturation ◽

Dependent Manner ◽

Brain Disorders ◽

Ph Values ◽

Neurological Conditions ◽

The Brain ◽

Insight Into

Most of the human diseases related to various proteopathies are confined to the brain, which leads to the development of various forms of neurological disorders. The human brain consists of several osmolytic compounds, such as N-Acetylaspartate (NAA), myo-inositol (mI), glutamate (Glu), glutamine (Gln), creatine (Cr), and choline-containing compounds (Cho). Among these osmolytes, the level of NAA drastically decreases under neurological conditions, and, hence, NAA is considered to be one of the most widely accepted neuronal biomarkers in several human brain disorders. To date, no data are available regarding the effect of NAA on protein stability, and, therefore, the possible effect of NAA under proteopathic conditions has not been fully uncovered. To gain an insight into the effect of NAA on protein stability, thermal denaturation and structural measurements were carried out using two model proteins at different pH values. The results indicate that NAA increases the protein stability with an enhancement of structure formation. We also observed that the stabilizing ability of NAA decreases in a pH-dependent manner. Our study indicates that NAA is an efficient protein stabilizer at a physiological pH.

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Prediction of Neurological Disorders Using Visual Saliency

Advances in Medical Technologies and Clinical Practice - Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease ◽

10.4018/978-1-7998-7188-0.ch001 ◽

2021 ◽

pp. 1-11

Author(s):

Sreelakshmi S. ◽

Anoop V. S.

Keyword(s):

Neurological Disorders ◽

Early Stage ◽

Brain Magnetic Resonance Imaging ◽

Visual Saliency ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Magnetic Resonance Imaging Mri ◽

Processing Techniques ◽

The Brain ◽

Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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Cost of Brain Disorders in Italy – A Review

European Neurological Review ◽

10.17925/enr.2009.04.02.120 ◽

2009 ◽

Vol 4 (2) ◽

pp. 120

Author(s):

Maura Pugliatti ◽

Paola Cossu ◽

Patrik Sobocki ◽

Ettore Beghi ◽

◽

...

Keyword(s):

Neurological Disorders ◽

Disease Burden ◽

Psychotic Disorders ◽

Ad Hoc ◽

Cost Model ◽

Adult Mortality ◽

Brain Disorders ◽

European Brain ◽

Socioeconomic Burden ◽

The Brain

Brain disorders represent 35% of the total disease burden in Europe and 37% of the total disease burden in European regions with very low child mortality and low adult mortality; the latter group includes Italy. The negative socioeconomic impact of this burden is reflected in two fundamental issues: consumption of resources and state of health. In recent years, the European Brain Council (EBC), a co-ordinating council formed by European organisations and patient associations in neurological disorders, has encouraged and supported projects aimed at analysing the socioeconomic burden of brain disorders in Europe. Within the EBC, the pan-European study on Cost of Disorders of the Brain in Europe (CDBE) aimed at reporting the best possible estimates of the societal cost of 12 brain disorders (addiction, affective disorders, anxiety disorders, tumours, dementia, epilepsy, migraine and other headaches, multiple sclerosis, Parkinson's disease, psychotic disorders, stroke and trauma) based on the existing literature, using an ad hoc cost model. The aggregated results for Italy from the CDBE study are reviewed in this paper.

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Analysis of shared heritability in common disorders of the brain

Science ◽

10.1126/science.aap8757 ◽

2018 ◽

Vol 360 (6395) ◽

pp. eaap8757 ◽

Cited By ~ 362

Author(s):

◽

Verneri Anttila ◽

Brendan Bulik-Sullivan ◽

Hilary K. Finucane ◽

Raymond K. Walters ◽

...

Keyword(s):

Psychiatric Disorders ◽

Neurological Disorders ◽

Statistical Power ◽

Association Studies ◽

Genetic Correlations ◽

Genome Wide Association Studies ◽

Brain Disorders ◽

Cognitive Measures ◽

Genome Wide ◽

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.

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Pelizaeus-Merzbacher Disease: A Case Report

Case Reports in Clinical Practice ◽

10.18502/crcp.v5i2.3841 ◽

2020 ◽

Author(s):

Ghazaleh Jamalipour Soufi ◽

Siavash Iravan

Keyword(s):

Case Report ◽

White Matter ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

Proteolipid Protein ◽

Radiology Department ◽

Magnetic Resonance Imaging Mri ◽

Normal White Matter ◽

Pelizaeus Merzbacher Disease ◽

The Brain

Pelizaeus-Merzbacher Disease (PMD), as a rare genetically x-linked leukodystrophy, is a disorder of proteolipid protein expression in myelin formation. This disorder is clinically presented by neurodevelopmental delay and abnormal pendular eye movements. The responsible gene for this disorder is the proteolipid protein gene (PLP1). Our case was a oneyear-old boy referred to the radiology department for evaluating the Central Nervous System (CNS) development by brain Magnetic Resonance Imaging (MRI). Clinically, he demonstrated neuro-developmental delay symptoms. The brain MRI results indicated a diffuse lack of normal white matter myelination. This case report should be considered about the possibilityof PMD in the brain MRI of patients who present a diffuse arrest of normal white matter myelination.

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Identifying Type 2 Diabetic Brains by Investigating Disease-Related Structural Changes in Magnetic Resonance Imaging

Frontiers in Neuroscience ◽

10.3389/fnins.2021.728874 ◽

2021 ◽

Vol 15 ◽

Author(s):

Yuna Chen ◽

Yongsheng Pan ◽

Shangyu Kang ◽

Junshen Lu ◽

Xin Tan ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Brain Structure ◽

Brain Mri ◽

Brain Diseases ◽

Imaging Biomarkers ◽

Resonance Imaging ◽

High Blood Glucose ◽

The Brain

Diabetes with high blood glucose levels may damage the brain nerves and thus increase the risk of dementia. Previous studies have shown that dementia can be reflected in altered brain structure, facilitating computer-aided diagnosis of brain diseases based on structural magnetic resonance imaging (MRI). However, type 2 diabetes mellitus (T2DM)-mediated changes in the brain structures have not yet been studied, and only a few studies have focused on the use of brain MRI for automated diagnosis of T2DM. Hence, identifying MRI biomarkers is essential to evaluate the association between changes in brain structure and T2DM as well as cognitive impairment (CI). The present study aims to investigate four methods to extract features from MRI, characterize imaging biomarkers, as well as identify subjects with T2DM and CI.

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3D U-Net for Skull Stripping in Brain MRI

Applied Sciences ◽

10.3390/app9030569 ◽

2019 ◽

Vol 9 (3) ◽

pp. 569 ◽

Cited By ~ 14

Author(s):

Hyunho Hwang ◽

Hafiz Zia Ur Rehman ◽

Sungon Lee

Keyword(s):

Deep Learning ◽

Automatic Segmentation ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

Brain Extraction ◽

Skull Stripping ◽

Segmentation Algorithms ◽

Magnetic Resonance Imaging Mri ◽

Deep Learning Network ◽

The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

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The association between aortic regurgitation and undetermined embolic infarction with aortic complex plaque

International Journal of Stroke ◽

10.1177/1747493017729549 ◽

2017 ◽

Vol 13 (4) ◽

pp. 391-399 ◽

Cited By ~ 1

Author(s):

Dae-Won Kim ◽

Jung Sun Cho ◽

Jae Yeong Cho ◽

Kye Hun Kim ◽

Byung Joo Sun ◽

...

Keyword(s):

Transesophageal Echocardiography ◽

Aortic Regurgitation ◽

Thoracic Aorta ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

High Mobility ◽

Embolic Stroke ◽

Bubble Test ◽

Aortic Plaques ◽

The Brain

Background Retrograde embolism from the descending thoracic aorta is one possible cause of undetermined ischemic stroke. Significant aortic regurgitation can increase the amount of reversed flow in the thoracic aorta and thus is associated with an increased incidence of stroke. Aims This study aimed to examine the association between significant aortic regurgitation and undetermined embolic infarction with aortic complex plaques. Methods This study included 380 patients with undetermined embolic stroke who did not have abnormal flow such as atrial septal defect, patent foramen ovale determined by agitated saline bubble test, intracardiac thrombi on transesophageal echocardiography, atrial fibrillation, or small vessel stroke, cerebral artery, and carotid stenosis on the brain magnetic resonance imaging. The patients were divided into the complex aortic plaques group (n = 63), which was defined as having plaque with >4 mm in thickness, ulceration, or high mobility, and the no complex aortic plaques group (n = 317). Results Transesophageal echocardiography with a bubble study, brain MRI, and laboratory tests were performed for all subjects. Significant aortic regurgitation was more prevalent in patients with undetermined embolic stroke and complex aortic plaques than in patients without complex aortic plaques (adjusted OR = 4.981; 95% CI = 1.323–18.876, P = 0.028). In addition, the distribution of complex aortic plaques according to the severity of aortic regurgitation in patients with undetermined embolic stroke had a tendency toward the ascending thoracic aorta and proximal aortic arch. Conclusions Significant aortic regurgitation may affect undetermined embolic stroke in patients with complex aortic plaques.

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A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/07177 ◽

2021 ◽

Vol 23 (07) ◽

pp. 516-529

Author(s):

Reshma L ◽

◽

Sai Priya Nalluri ◽

Priya R Sankpal ◽

◽

...

Keyword(s):

Deep Learning ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

Support Vector ◽

Imaging Data ◽

Diagnosis Of Dementia ◽

User Friendly ◽

Clustering Pattern ◽

Medical Analysis ◽

The Brain

In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

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Heart-brain connections: phenotypic and genetic insights from 40,000 cardiac and brain magnetic resonance images

10.1101/2021.11.01.21265779 ◽

2021 ◽

Author(s):

Bingxin Zhao ◽

Tengfei Li ◽

Zirui Fan ◽

Yue Yang ◽

Xifeng Wang ◽

...

Keyword(s):

Magnetic Resonance ◽

Psychological Health ◽

Complex Traits ◽

Brain Mri ◽

Genetic Correlations ◽

Brain Magnetic Resonance Imaging ◽

Magnetic Resonance Images ◽

Brain Diseases ◽

Functional Connections ◽

The Brain

Cardiovascular health interacts with cognitive and psychological health in complex ways. Yet, little is known about the phenotypic and genetic links of heart-brain systems. Using cardiac and brain magnetic resonance imaging (CMR and brain MRI) data from over 40,000 UK Biobank subjects, we developed detailed analyses of the structural and functional connections between the heart and the brain. CMR measures of the cardiovascular system were strongly correlated with brain basic morphometry, structural connectivity, and functional connectivity after controlling for body size and body mass index. The effects of cardiovascular risk factors on the brain were partially mediated by cardiac structures and functions. Using 82 CMR traits, genome-wide association study identified 80 CMR-associated genomic loci (P < 6.09 * 10^{-10}), which were colocalized with a wide spectrum of heart and brain diseases. Genetic correlations were observed between CMR traits and brain-related complex traits and disorders, including schizophrenia, bipolar disorder, anorexia nervosa, stroke, cognitive function, and neuroticism. Our results reveal a strong heart-brain connection and the shared genetic influences at play, advancing a multi-organ perspective on human health and clinical outcomes.

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