scholarly journals BrainNET: Inference of brain network topology using Machine Learning

2019 ◽  
Author(s):  
Gowtham Krishnan Murugesan ◽  
Chandan Ganesh ◽  
Sahil Nalawade ◽  
Elizabeth M Davenport ◽  
Ben Wagner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Topology ◽  
Network Inference ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Brain Network ◽  
Fmri Data ◽  
Method Performance ◽  
Graph Metrics ◽  
Network Topologies

AbstractObjectiveTo develop a new fMRI network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI.MethodsBrainNET is based on Extremely Randomized Trees (ERT) to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open source simulated fMRI data of fifty subjects in twenty-eight different simulations under various confounding conditions with known ground truth was used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available Attention-deficit/hyperactivity disorder (ADHD) dataset including 134 Typically Developing Children (mean age: 12.03, males: 83), 75 ADHD Inattentive (mean age: 11.46, males: 56) and 93 ADHD Combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups.ResultsBrainNET demonstrated excellent performance across all simulations and varying confounders in identifying true presence of connections. In the ADHD dataset, BrainNET was able to identify significant changes (p< 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups was identified using correlation and PC.

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

scholarly journals Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

2020 ◽  
Vol 10 (11) ◽  
pp. 777
Author(s):  
Nicholas John Simos ◽  
Stavros I. Dimitriadis ◽  
Eleftherios Kavroulakis ◽  
Georgios C. Manikis ◽  
George Bertsias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Lupus Erythematosus ◽  
Statistical Power ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain ◽  
Graph Metrics ◽  
Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

scholarly journals Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

2019 ◽  
Author(s):  
Paria Rezaeinia ◽  
Kim Fairley ◽  
Piya Pal ◽  
François G. Meyer ◽  
R. McKell Carter
Keyword(s):  
Psychiatric Disorders ◽  
Task Performance ◽  
Network Topology ◽  
Brain Network ◽  
Imaging Data ◽  
Hierarchical Processing ◽  
Functional Brain Network ◽  
Topology Changes ◽  
The World ◽  
Network Topologies

ABSTRACTA central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting-state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting-scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.AUTHOR SUMMARYOur mental lives are made up of a series of predictions about the world calculated by our brains. The calculations that produce these predictions are a result of how areas in our brain interact. Measures based on graph representations can make it clear what information can be combined and therefore help us better understand the computations the brain is performing. We make use of cutting-edge techniques that overcome a number of previous limitations to identify specific shapes in the functional brain network. These shapes are similar to hierarchical processing streams which play a fundamental role in cognitive neuroscience. The importance of these structures and the technique is highlighted by how they change under different task constraints and in individuals diagnosed with psychiatric disorders.

scholarly journals Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0241696
Author(s):  
Xubo Leng ◽  
Margot Wohl ◽  
Kenichi Ishii ◽  
Pavan Nayak ◽  
Kenta Asahina
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Confidence Levels ◽  
Training Images ◽  
Neuroscience Research ◽  
Automated Quantification

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

scholarly journals UNSUPERVISED METHODOLOGY TO IN-SEASON MAPPING OF SUMMER CROPS IN URUGUAY WITH MODIS EVI’S TEMPORAL SERIES AND MACHINE LEARNING

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 171-176
Author(s):  
A. Cal ◽  
G. Tiscornia
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Seasonal Pattern ◽  
Gaussian Function ◽  
Ground Truth ◽  
Agricultural Crop ◽  
Kappa Index ◽  
Nonlinear Smoothing

Abstract. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of 1,667,400 hectares for the 2014–15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.

scholarly journals Quantitative comparison of Drosophila behavior annotations by human observers and a machine learning algorithm

2020 ◽  
Author(s):  
Xubo Leng ◽  
Margot Wohl ◽  
Kenichi Ishii ◽  
Pavan Nayak ◽  
Kenta Asahina
Keyword(s):  
Machine Learning ◽  
Animal Behavior ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Machine Learning Algorithm ◽  
Experimental Conditions ◽  
Systematic Assessment ◽  
Training Images ◽  
Automated Quantification ◽  
Computer Based

AbstractAutomated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.Significance StatementAccurate quantification of animal behaviors is fundamental to neuroscience. Here, we quantitatively assess how human choices influence the performance of automated classifiers trained by a machine-learning algorithm. We found that human decisions about the computational tracking method, the training images, and the images used for performance evaluation impact both the classifier outputs and how human observers interpret the results. These factors are sometimes overlooked but are critical, especially because animal behavior is itself inherently variable. Automated quantification of animal behavior is becoming increasingly prevalent: our results provide a model for bridging the gap between traditional human annotations and computer-based annotations. Systematic assessment of human choices is important for developing behavior classifiers that perform robustly in a variety of experimental conditions.

scholarly journals BrainNET: Inference of Brain Network Topology Using Machine Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 422-435
Author(s):  
Gowtham Krishnan Murugesan ◽  
Chandan Ganesh ◽  
Sahil Nalawade ◽  
Elizabeth M. Davenport ◽  
Ben Wagner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Topology ◽  
Brain Network

scholarly journals GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

2019 ◽  
Vol 11 (14) ◽  
pp. 1655 ◽  
Author(s):  
Yan Jia ◽  
Shuanggen Jin ◽  
Patrizia Savi ◽  
Yun Gao ◽  
Jing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Ground Truth ◽  
Satellite System ◽  
Soil Conditions ◽  
Retrieval Process ◽  
Method Performance ◽  
Ensemble Machine Learning ◽  
Input Variables

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

Neuroimaging Biomarker of Major Depressive Disorder

2016 ◽  
Vol 33 (S1) ◽  
pp. S492-S493
Author(s):  
N. Ichikawa ◽  
Y. Okamoto ◽  
G. Okada ◽  
G. Lisi ◽  
N. Yahata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Learning Algorithm ◽  
Training Data ◽  
Fmri Data ◽  
Machine Learning Algorithm ◽  
Major Depressive ◽  
The Brain

IntroductionRecent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.ObjectivesTo specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.AimsThe aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.MethodsThe neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.ResultsAs results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.ConclusionsThe selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 170
Author(s):  
Michael Hopwood ◽  
Phuong Pho ◽  
Alexander V. Mantzaris
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Topology ◽  
Sampling Method ◽  
Sampling Methods ◽  
Weak Ties ◽  
Multiple Sampling ◽  
Strength Of Weak Ties ◽  
Network Topologies ◽  
Graph Neural Networks

Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the context of graph neural networks. In this paper, we evaluate multiple sampling methods (i.e., ascending and descending) that sample based off different definitions of centrality (i.e., Voterank, Pagerank, degree) to observe its relation with network topology. We find that no sampling method is superior across all network topologies. Additionally, we find situations where ascending sampling provides better classification scores, showing the strength of weak ties. Two strategies are then created to predict the best sampling method, one that observes the homogeneous connectivity of the nodes, and one that observes the network topology. In both methods, we are able to evaluate the best sampling direction consistently.

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