scholarly journals MRI morphometric data as predictor of PET amyloid positivity: New insights using brain connectivity metrics and machine learning

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Helena Rico Pereira ◽  
Hugo Alexandre Ferreira
Keyword(s):  
Machine Learning ◽  
Brain Connectivity ◽  
Morphometric Data ◽  
Pet Amyloid

scholarly journals Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data

2021 ◽  
Author(s):  
Lea Baecker ◽  
Jessica Dafflon ◽  
Pedro F. Costa ◽  
Rafael Garcia‐Dias ◽  
Sandra Vieira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Morphometric Data ◽  
Brain Age ◽  
Machine Learning Models ◽  
Age Prediction

Classification of Autism Spectrum Disorder from EEG-based Functional Brain Connectivity Analysis

2021 ◽  
pp. 1-27
Author(s):  
Noura Alotaibi ◽  
Koushik Maharatna
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Phase Locking ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Phase Locking Value

Abstract Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.

BRAPH 2.0 : Software for the analysis of multilayer brain connectivity with graph theory and machine learning.

2021 ◽  
Author(s):  
Pablo Emiliano Gomez Ruiz ◽  
Giovanni Volpe ◽  
Joana B. Pereira ◽  
Anna Canal Garcia ◽  
Mite Mijalkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Graph Theory ◽  
Brain Connectivity

Percept-related EEG classification using machine learning approach and features of functional brain connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 093110 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vladimir Maksimenko ◽  
Alexey Koronovskii ◽  
Anastasiya E. Runnova ◽  
Maxim Zhuravlev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Connectivity ◽  
Learning Approach ◽  
Functional Brain ◽  
Eeg Classification ◽  
Machine Learning Approach

scholarly journals ML-morph: A Fast, Accurate and General Approach for Automated Detection and Landmarking of Biological Structures in Images

2019 ◽  
Author(s):  
Arthur Porto ◽  
Kjetil L. Voje
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Biological Research ◽  
List Type ◽  
Shape Variation ◽  
Morphometric Data ◽  
General Applicability ◽  
Minimal Impact ◽  
Biological Structures ◽  
Model Training

ABSTRACTMorphometrics has become an indispensable component of the statistical analysis of size and shape variation in biological structures. Morphometric data has traditionally been gathered through low-throughput manual landmark annotation, which represents a significant bottleneck for morphometric-based phenomics. Here we propose a machine-learning-based high-throughput pipeline to collect high-dimensional morphometric data in images of semi rigid biological structures.The proposed framework has four main strengths. First, it allows for dense phenotyping with minimal impact on specimens. Second, it presents landmarking accuracy comparable to manual annotators, when applied to standardized datasets. Third, it performs data collection at speeds several orders of magnitude higher than manual annotators. And finally, it is of general applicability (i.e., not tied to a specific study system).State-of-the-art validation procedures show that the method achieves low error levels when applied to three morphometric datasets of increasing complexity, with error varying from 0.5% to 2% of the structure’s length in the automated placement of landmarks. As a benchmark for the speed of the entire automated landmarking pipeline, our framework places 23 landmarks on 13,686 objects (zooids) detected in 1684 pictures of fossil bryozoans in 3.12 minutes using a personal computer.The proposed machine-learning-based phenotyping pipeline can greatly increase the scale, reproducibility and speed of data collection within biological research. To aid the use of the framework, we have developed a file conversion algorithm that can be used to leverage current morphometric datasets for automation, allowing the entire procedure, from model training all the way to prediction, to be performed in a matter of hours.

scholarly journals Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Yufen Li ◽  
Li Tao ◽  
Huiyue Chen ◽  
Hansheng Wang ◽  
Xiaoyu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prefrontal Cortex ◽  
Essential Tremor ◽  
Resting State ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern ◽  
Global Brain

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET.Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET.Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

scholarly journals Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Simon Wein ◽  
Gustavo Deco ◽  
Ana Maria Tomé ◽  
Markus Goldhacker ◽  
Wilhelm M. Malloni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Short Survey ◽  
Functional Dynamics ◽  
Axonal Fiber

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

scholarly journals Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Li ◽  
Junsong Zhang
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Computational Models ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Learning Algorithms ◽  
Theoretical Models ◽  
Machine Learning Algorithms ◽  
Computational Aesthetics ◽  
Experimental Approaches

AbstractThe mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.

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