scholarly journals Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging

2019 ◽  
Author(s):  
Carlos Sevilla-Salcedo ◽  
Vanessa Gómez-Verdejo ◽  
Jussi Tohka ◽  
Keyword(s):  
Brain Imaging ◽  
Fundamental Problem ◽  
Hypothesis Test ◽  
The Elderly ◽  
Elderly Subjects ◽  
Imaging Data ◽  
Classification Rate ◽  
Supervised Learning Algorithms ◽  
Relevance Measure

AbstractA fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description.We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer’s disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30 – 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods.

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Min Song ◽  
Minseok Kang ◽  
Hyeonsu Lee ◽  
Yong Jeong ◽  
Se-Bum Paik
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Activity Patterns ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Sign-consistency based variable importance for machine learning in brain imaging

2017 ◽  
Author(s):  
Vanessa Gómez-Verdejo ◽  
Emilio Parrado-Hernández ◽  
Jussi Tohka ◽  
Keyword(s):  
Brain Imaging ◽  
Hypothesis Test ◽  
Variable Importance ◽  
Support Vector ◽  
Single Subject ◽  
Imaging Data ◽  
Magnetic Resonance Imaging Data ◽  
Extra Step ◽  
Sign Consistency ◽  
Variable Importance Measures

AbstractAn important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.

scholarly journals Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Laura Tomaz Da Silva ◽  
Nathalia Bianchini Esper ◽  
Duncan D. Ruiz ◽  
Felipe Meneguzzi ◽  
Augusto Buchweitz
Keyword(s):  
Deep Learning ◽  
Brain Imaging ◽  
Developmental Dyslexia ◽  
Brain Regions ◽  
Data Sets ◽  
Learning Approaches ◽  
Imaging Data ◽  
Learning Techniques ◽  
Brain Imaging Data

Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification.Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children.Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group).Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.

A semi-structured clinical interview for the assessment of diagnosis and mental state in the elderly: the Geriatric Mental State Schedule: I. Development and reliability

1976 ◽  
Vol 6 (3) ◽  
pp. 439-449 ◽  
Author(s):  
J. R. M. Copeland ◽  
M. J. Kelleher ◽  
J. M. Kellett ◽  
A. J. Gourlay ◽  
B. J. Gurland ◽  
...  
Keyword(s):  
Mental State ◽  
Psychiatric Diagnosis ◽  
The Elderly ◽  
Structured Interview ◽  
Elderly Subjects ◽  
Good Reliability ◽  
Symptom Profile ◽  
Eighth Edition ◽  
Over Time

SynopsisA standardized, semi-structured interview for examining and recording the mental state in elderly subjects is described. It allows the classification of patients by symptom profile and can demonstrate changes in that profile over time. It is believed that good reliability is demonstrated between psychiatric raters both for psychiatric diagnosis made on the basis of the schedule findings and for individual items. The Geriatric Mental State Schedule (GMS) consists mainly of items from the eighth edition of the PSE (Wing et al. 1967), together with additional items from the PSS (Spitzer et al. 1964), and extra sections dealing with disorientation and other cognitive abnormalities. Modifications have been introduced to facilitate interviewing elderly subjects.

Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

2017 ◽  
pp. 185-202 ◽  
Author(s):  
Annamária Szenkovits ◽  
Regina Meszlényi ◽  
Krisztian Buza ◽  
Noémi Gaskó ◽  
Rodica Ioana Lung ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0250755
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Pablo de Oliveira Castro ◽  
Eric Petit ◽  
Ariel Rokem ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Connectivity ◽  
Random Noise ◽  
Imaging Data ◽  
Brain Organization ◽  
Computational Sciences ◽  
Extreme Variability ◽  
Brain Imaging Data ◽  
The Stability

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.

scholarly journals Classification of Daily Activities for the Elderly Using Wearable Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Liu ◽  
Jeehoon Sohn ◽  
Sukwon Kim
Keyword(s):  
Daily Living ◽  
Wearable Sensors ◽  
The Elderly ◽  
Daily Activities ◽  
Elderly Subjects ◽  
Activity Levels ◽  
Inertial Measurement ◽  
Healthy Elderly ◽  
Measurement Units

Monitoring of activities of daily living (ADL) using wearable sensors can provide an objective indication of the activity levels or restrictions experienced by patients or elderly. The current study presented a two-sensor ADL classification method designed and tested specifically with elderly subjects. Ten healthy elderly were involved in a laboratory testing with 6 types of daily activities. Two inertial measurement units were attached to the thigh and the trunk of each subject. The results indicated an overall rate of misdetection being 2.8%. The findings of the current study can be used as the first step towards a more comprehensive activity monitoring technology specifically designed for the aging population.

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

2021 ◽  
pp. 1-15
Author(s):  
Phawis Thammasorn ◽  
Wanpracha A. Chaovalitwongse ◽  
Daniel S. Hippe ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data
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