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Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
2018 arXiv   pre-print
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data.  ...  Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI.  ...  Clustering is an alternative unsupervised learning approach for analysis of rs-fMRI data.  ... 
arXiv:1812.11477v1 fatcat:nd6j5jbspzh2rmxyyufdyesxom

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code

Xinyu Zhao, D. Rangaprakash, Thomas S. Denney, Jeffrey S. Katz, Michael N. Dretsch, Gopikrishna Deshpande
2018 Data in Brief  
This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic  ...  The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals.  ...  and phenotypic clusters using unsupervised learning" [1] .  ... 
doi:10.1016/j.dib.2018.01.080 pmid:30627610 pmcid:PMC6321965 fatcat:em5b2cycwvhvnime4t3dxuojt4

Functional MRI applications for psychiatric disease subtyping: a review

Lucas Miranda, Riya Paul, Benno Pütz, Bertram Müller-Myhsok
2020 Zenodo  
data other than fMRI itself, and 11 applied clustering techniques to fMRI directly.  ...  learning techniques applied to symptom or biomarker data.  ...  used for validation of subtypes obtained via unsupervised learning of symptom-related data , (b) fMRI used for validation of subtypes obtained via unsupervised learning of biomarkers other than fMRI (  ... 
doi:10.5281/zenodo.3923918 fatcat:y33tncjqtbfpphlmkceg3hnd7y

Comparison of Unsupervised Learning Algorithms for Identifying Disease Clusters in Cognitive Impairment Using Functional MRI Connectivity Features

Rishab Satyakaal, Rangaprakash D.
2019 International Journal of Neuroscience and Behavioral Science  
However, most fMRI studies using unsupervised learning offer no justification for selecting one unsupervised clustering algorithm over another and normally default to the popular K-Means algorithm.  ...  To reach the true potential benefit of unsupervised learning techniques when applied to fMRI data, we examine and compare 12 unsupervised learning algorithms in identifying Alzheimer's disease clusters  ...  Acknowledgments The data used in this paper came from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We thank ADNI for the collection and sharing of the dataset used in this article.  ... 
doi:10.13189/ijnbs.2019.070301 fatcat:sp6suuhrd5dqfffgvqd4wukoyu

Machine learning for brain signal analysis

Ainur S. Makhmet, Maxim G. Sharaev, Anuar E. Dyusembaev, Almira M Kustubayeva
2021 International Journal of Biology and Chemistry  
The new machine learning methods invented for analysis of brain signals in the re ing ate and during the performance of the different cognitive tasks would be useful and worth considering in other domains  ...  The article provides a brief overview of the theoretical concept of machine learning and its types: supervised, unsupervised and reinforcement learning.  ...  algorithms in fMRI analysis is SVM [17] .  ... 
doi:10.26577/ijbch.2021.v14.i2.01 fatcat:z5qmxy5ssvbvtmafalknxokhly

Exploration of LICA Detections in Resting State fMRI [chapter]

Darya Chyzhyk, Ann K. Shinn, Manuel Graña
2011 Lecture Notes in Computer Science  
In this paper we explore the network detections obtained with LICA in resting state fMRI data from healthy controls and schizophrenic patients.  ...  We compare with the findings of a standard Independent Component Analysis (ICA) algorithm. We do not find agreement between LICA and ICA.  ...  Resting State fMRI Background Resting state fMRI data has been used to study the connectivity of brain activations [4, 13, 17] .  ... 
doi:10.1007/978-3-642-21326-7_12 fatcat:2zilack3lvhvxghod6ikmyquxu

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity

Amicie de Pierrefeu, Thomas Fovet, Fouad Hadj-Selem, Tommy Löfstedt, Philippe Ciuciu, Stephanie Lefebvre, Pierre Thomas, Renaud Lopes, Renaud Jardri, Edouard Duchesnay
2018 Human Brain Mapping  
This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not.  ...  K E Y W O R D S hallucinations, machine learning, real-time fMRI, resting-state networks, schizophrenia  ...  the emergence of hallucinations using unsupervised analysis.  ... 
doi:10.1002/hbm.23953 pmid:29341341 fatcat:armrjac3zvahfkn7agylnc5yty

Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment

D. Rangaprakash, Alzheimer's Disease Neuroimaging Initiative, Toluwanimi Odemuyiwa, D. Narayana Dutt, Gopikrishna Deshpande
2020 Brain Informatics  
AbstractVarious machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI).  ...  Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated.  ...  In this work, we demonstrate the utility and feasibility of the unsupervised learning approach of density-based clustering, applied on resting-state fMRI (rs-fMRI) data to cluster disease states in a noise-robust  ... 
doi:10.1186/s40708-020-00120-2 pmid:33242116 fatcat:ciw3vscdjzf7jflpm2r4snn54q

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Lucas Miranda, Riya Paul, Benno Pütz, Nikolaos Koutsouleris, Bertram Müller-Myhsok
2021 Frontiers in Psychiatry  
) from the perspective of unsupervised machine learning applications for disease subtyping.  ...  We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way.Results: From the 20 studies that met the inclusion criteria, five used functional MRI  ...  For details on machine learning methods for resting-state fMRI data, refer to Khosla et al. (26) .  ... 
doi:10.3389/fpsyt.2021.665536 pmid:34744805 pmcid:PMC8569315 fatcat:jmcxqcdzjba5bhvcpcrj2zvv3i

New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modeling and Understanding of Dynamic Cognitive Processes

Nikola Kasabov, Lei Zhou, Maryam Gholami Doborjeh, Zohreh Gholami Doborjeh, Jie Yang
2017 IEEE Transactions on Cognitive and Developmental Systems  
The paper proposes a novel method based on the NeuCube SNN architecture for which the following new algorithms are introduced: fMRI data encoding into spike sequences; deep unsupervised learning of fMRI  ...  data in a 3D SNN reservoir; classification of cognitive states; connectivity visualization and analysis for the purpose of understanding cognitive dynamics.  ...  The material of the paper presents the following novel algorithms for STBD modelling, in particular -fMRI data, namely: -Data encoding into spikes; -Deep unsupervised learning in a 3D SNN cube; -Classification  ... 
doi:10.1109/tcds.2016.2636291 fatcat:d65yarmtwvcltkktgawoxm7kvy

Machine Learning for Neuroimaging with Scikit-Learn [article]

Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg (LNAO, INRIA Saclay - Ile de France), Philippe Gervais (NEUROSPIN, INRIA Saclay - Ile de France, LNAO), Andreas Muller, Jean Kossaifi, Alexandre Gramfort (NEUROSPIN, LTCI), Bertrand Thirion, Gäel Varoquaux
2014 arXiv   pre-print
Statistical machine learning methods are increasingly used for neuroimaging data analysis.  ...  Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series.  ...  method to extract networks from resting state fMRI (Kiviniemi et al., 2003) .  ... 
arXiv:1412.3919v1 fatcat:fiepevd7gzbl3ecit47e7onl6m

A Tour of Unsupervised Deep Learning for Medical Image Analysis [article]

Khalid Raza, Nripendra Kumar Singh
2018 arXiv   pre-print
Unlike supervised learning which is biased towards how it is being supervised and manual efforts to create class label for the algorithm, unsupervised learning derive insights directly from the data itself  ...  In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis.  ...  To intelligently solve these issues, unsupervised machine learning algorithm can be used.  ... 
arXiv:1812.07715v1 fatcat:4dd75wfhvnf7db3v72575tikoi

Resting-State Blood Oxygen Level-Dependent Functional MRI: A Paradigm Shift in Preoperative Brain Mapping

Eric C. Leuthardt, Monica Allen, Mudassar Kamran, Ammar H. Hawasli, Abraham Z. Snyder, Carl D. Hacker, Timothy J. Mitchell, Joshua S. Shimony
2015 Stereotactic and Functional Neurosurgery  
This problem is overcome by using resting-state fMRI (rs-fMRI) to localize function. rs-fMRI measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, representing the brain's  ...  Compared with task-related fMRI, rs-fMRI provides more comprehensive information on the functional architecture of the brain and is applicable in settings where task-related fMRI may provide inadequate  ...  The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the paper. Disclosure Statement The authors have no relevant disclosures to report.  ... 
doi:10.1159/000442424 pmid:26784290 fatcat:v4aisfh2sfaqlbrmzmlao7frzm

Integration of resting state functional MRI into clinical practice - A large single institution experience

Eric C. Leuthardt, Gloria Guzman, S. Kathleen Bandt, Carl Hacker, Ananth K. Vellimana, David Limbrick, Mikhail Milchenko, Pamela Lamontagne, Benjamin Speidel, Jarod Roland, Michelle Miller-Thomas, Abraham Z. Snyder (+4 others)
2018 PLoS ONE  
Resting state fMRI can be used in all patients, and due to its lower failure rate than task-based fMRI, it is useful for patients who are unable to cooperate with task-based studies. Fig 6.  ...  To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use.  ...  Advantages of a supervised machine learning for resting state MRI Resting state fMRI analysis methodology historically was dominated by two complementary strategies: spatial Independent Components Analysis  ... 
doi:10.1371/journal.pone.0198349 pmid:29933375 pmcid:PMC6014724 fatcat:fa7vrennjnfnfjeudj7okhj7vq


Rishi Yadav, Ankit Gautam, Ravi Bhushan Mishra
2018 Figshare  
In this paper we used OASIS fMRI dataset affected with Alzheimer's disease and normal patient's dataset.  ...  After proper processing the fMRI data we use the processed data to form classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve Bayes.  ...  We used state-of-the-art resting state functional magnetic resonance imaging (rs-fMRI) dataset from OASIS (Open Access Series of Imaging Studies). Dataset consists MRI slices of 416 subjects.  ... 
doi:10.6084/m9.figshare.6860021.v1 fatcat:nb6uqfielfd3xosvj3ekr63qee
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