Brain imaging genetics: integrated analysis and machine learning

2021 ◽  
Author(s):  
Li Shen
Keyword(s):  
Machine Learning ◽  
Brain Imaging ◽  
Imaging Genetics ◽  
Integrated Analysis

scholarly journals Genetic variations influence brain changes in patients with attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Santosh K. Yadav ◽  
Ajaz A. Bhat ◽  
Sheema Hashem ◽  
Sabah Nisar ◽  
Madeeha Kamal ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Brain Imaging ◽  
Attention Deficit ◽  
Genetic Variations ◽  
Imaging Genetics ◽  
Treatment Modalities ◽  
Genetic Studies ◽  
Hyperactivity Disorder ◽  
Clinical Biomarkers ◽  
The Impact

AbstractAttention-deficit hyperactivity disorder (ADHD) is a neurological and neurodevelopmental childhood-onset disorder characterized by a persistent pattern of inattentiveness, impulsiveness, restlessness, and hyperactivity. These symptoms may continue in 55–66% of cases from childhood into adulthood. Even though the precise etiology of ADHD is not fully understood, it is considered as a multifactorial and heterogeneous disorder with several contributing factors such as heritability, auxiliary to neurodevelopmental issues, severe brain injuries, neuroinflammation, consanguineous marriages, premature birth, and exposure to environmental toxins. Neuroimaging and neurodevelopmental assessments may help to explore the possible role of genetic variations on ADHD neuropsychobiology. Multiple genetic studies have observed a strong genetic association with various aspects of neuropsychobiological functions, including neural abnormalities and delayed neurodevelopment in ADHD. The advancement in neuroimaging and molecular genomics offers the opportunity to analyze the impact of genetic variations alongside its dysregulated pathways on structural and functional derived brain imaging phenotypes in various neurological and psychiatric disorders, including ADHD. Recently, neuroimaging genomic studies observed a significant association of brain imaging phenotypes with genetic susceptibility in ADHD. Integrating the neuroimaging-derived phenotypes with genomics deciphers various neurobiological pathways that can be leveraged for the development of novel clinical biomarkers, new treatment modalities as well as therapeutic interventions for ADHD patients. In this review, we discuss the neurobiology of ADHD with particular emphasis on structural and functional changes in the ADHD brain and their interactions with complex genomic variations utilizing imaging genetics methodologies. We also highlight the genetic variants supposedly allied with the development of ADHD and how these, in turn, may affect the brain circuit function and related behaviors. In addition to reviewing imaging genetic studies, we also examine the need for complementary approaches at various levels of biological complexity and emphasize the importance of combining and integrating results to explore biological pathways involved in ADHD disorder. These approaches include animal models, computational biology, bioinformatics analyses, and multimodal imaging genetics studies.

scholarly journals KEEP YOUR BRAIN YOUNG WITH MUSIC: INSIGHTS FROM MACHINE LEARNING AND BRAIN IMAGING

2018 ◽  
Vol 2 (suppl_1) ◽  
pp. 849-849
Author(s):  
J Kernbach ◽  
L Rogenmoser ◽  
G Schlaug ◽  
C Gaser
Keyword(s):  
Machine Learning ◽  
Brain Imaging

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

2020 ◽  
Author(s):  
Ninon Burgos ◽  
Simona Bottani ◽  
Johann Faouzi ◽  
Elina Thibeau-Sutre ◽  
Olivier Colliot
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Imaging Genetics ◽  
Environmental Data ◽  
Brain Disorders ◽  
Disease Treatment ◽  
Clinical Routine

Abstract 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.

Identification of diagnostic and prognostic lncRNA biomarkers in oral squamous carcinoma by integrated analysis and machine learning

2020 ◽  
Vol 29 (2) ◽  
pp. 265-275
Author(s):  
Sen Yang ◽  
Yingshu Wang ◽  
Jun Ren ◽  
Xueqin Zhou ◽  
Kaizhi Cai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Expression Profiles ◽  
Squamous Carcinoma ◽  
Prognostic Indicators ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Oral Squamous Carcinoma ◽  
Precise Diagnosis

BACKGROUND: Patients with oral squamous carcinoma (OSCC) present difficulty in precise diagnosis and poor prognosis. OBJECTIVE: We aimed to identify the diagnostic and prognostic indicators in OSCC and provide basis for molecular mechanism investigation of OSCC. METHODS: We collected sequencing data and clinical data from TCGA database and screened the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in OSCC. Machine learning and modeling were performed to identify the optimal diagnostic markers. In order to determine lncRNAs with prognostic value, survival analysis was performed through combing the expression profiles with the clinical data. Finally, co-expressed DEmRNAs of lncRNAs were identified by interacted network construction and functional annotated by GO and KEGG analysis. RESULTS: A total of 1114 (345 up- and 769 down-regulated) DEmRNAs and 156 (86 up- and 70 down-regulated) DElncRNAs were obtained in OSCC. Following the machine learning and modeling, 15 lncRNAs were identified to be the optimal diagnostic indicators of OSCC. Among them, FOXD2.AS1 was significantly associated with survival rate of patients with OSCC. In addition, Focal adhesion and ECM-receptor interaction pathways were found to be involved in OSCC. CONCLUSIONS : FOXD2.AS1 might be a prognostic marker for OSCC and our study may provide more information to the further study in OSCC.

Introduction to machine learning for brain imaging

NeuroImage ◽  
2011 ◽  
Vol 56 (2) ◽  
pp. 387-399 ◽  
Author(s):  
Steven Lemm ◽  
Benjamin Blankertz ◽  
Thorsten Dickhaus ◽  
Klaus-Robert Müller
Keyword(s):  
Machine Learning ◽  
Brain Imaging

scholarly journals Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

2016 ◽  
Vol 17 (S13) ◽  
Author(s):  
Mutlu Mete ◽  
Unal Sakoglu ◽  
Jeffrey S. Spence ◽  
Michael D. Devous ◽  
Thomas S. Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Imaging ◽  
Cocaine Dependence ◽  
Learning Approach ◽  
Machine Learning Approach

Application of multi-sensor unmanned aerial system for identification of hydrothermal alteration zones

2020 ◽  
Author(s):  
Yosoon Choi ◽  
Jieun Baek ◽  
Jangwon Suh ◽  
Sung-Min Kim
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Training Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Integrated Analysis ◽  
Unmanned Aerial System ◽  
Data Sets ◽  
Learning Techniques ◽  
Hydrothermal Alteration Zones

<p>In this study, we proposed a method to utilize a multi-sensor Unmanned Aerial System (UAS) for exploration of hydrothermal alteration zones. This study selected an area (10m × 20m) composed mainly of the andesite and located on the coast, with wide outcrops and well-developed structural and mineralization elements. Multi-sensor (visible, multispectral, thermal, magnetic) data were acquired in the study area using UAS, and were studied using machine learning techniques. For utilizing the machine learning techniques, we applied the stratified random method to sample 1000 training data in the hydrothermal zone and 1000 training data in the non-hydrothermal zone identified through the field survey. The 2000 training data sets created for supervised learning were first classified into 1500 for training and 500 for testing. Then, 1500 for training were classified into 1200 for training and 300 for validation. The training and validation data for machine learning were generated in five sets to enable cross-validation. Five types of machine learning techniques were applied to the training data sets: k-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). As a result of integrated analysis of multi-sensor data using five types of machine learning techniques, RF and SVM techniques showed high classification accuracy of about 90%. Moreover, performing integrated analysis using multi-sensor data showed relatively higher classification accuracy in all five machine learning techniques than analyzing magnetic sensing data or single optical sensing data only.</p>

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