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Classifying instantaneous cognitive states from FMRI data

Tom M Mitchell, Rebecca Hutchinson, Marcel A Just, Radu S Niculescu, Francisco Pereira, Xuerui Wang
2003 AMIA Annual Symposium Proceedings  
We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data.  ...  We describe a machine learning approach to this problem, and report on its successful use for discriminating cognitive states such as observing a picture versus reading a sentence, and reading a word about  ...  APPROACH Our approach to classifying instantaneous cognitive states is based on a machine learning approach (see [6] ), in which we train classifiers to predict the subject's cognitive state given their  ... 
pmid:14728216 pmcid:PMC1479944 fatcat:ejak5ptfgjgmbpcxbx2lyh7si4

Classification of Cognitive States from fMRI data using Fisher Discriminant Ratio and Regions of Interest

Luu Ngoc Do, Hyung Jeong Yang
2012 International Journal of Contents  
images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods.  ...  In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative  ...  The goal of this approach is training machine learning classifiers to automatically detect the subject's cognitive state at a single time interval.  ... 
doi:10.5392/ijoc.2012.8.4.056 fatcat:xwm4kluxybbhjebip54t5xy6ea

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Gopikrishna Deshpande, Zhihao Li, Priya Santhanam, Claire D. Coles, Mary Ellen Lynch, Stephan Hamann, Xiaoping Hu, Olaf Sporns
2010 PLoS ONE  
In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously  ...  Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support  ...  Analyzed the data: GD ZL PS. Contributed reagents/materials/analysis tools: GD. Wrote the paper: GD ZL CC XH. Performed data pre-processing: ZL PS.  ... 
doi:10.1371/journal.pone.0014277 pmid:21151556 pmcid:PMC3000328 fatcat:uzz4vfuabrgbrl6l6kldmh4qh4

Mapping distinct timescales of functional interactions among brain networks

Mali Sundaresan, Arshed Nabeel, Devarajan Sridharan
2019 Advances in Neural Information Processing Systems  
We challenge this claim with simulations and a novel machine learning approach.  ...  Next, we analyze fMRI scans from 500 subjects and show that a linear classifier trained on either instantaneous or lag-based GC connectivity reliably distinguishes task versus rest brain states, with ~  ...  We would like to thank Hritik Jain for help with data analysis.  ... 
pmid:31285649 pmcid:PMC6614036 fatcat:hgzscu353vdfhkxyqbfnkos7bm

Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
2018 arXiv   pre-print
The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.  ...  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.  ...  Application of Machine Learning in rs-fMRI A vast majority of literature on machine learning for rs-fMRI is devoted to unsupervised learning approaches.  ... 
arXiv:1812.11477v1 fatcat:nd6j5jbspzh2rmxyyufdyesxom

Instantaneous fMRI based cerebral parameters for automatic Alzheimer, mild cognitive impairment and healthy subject classification

2019 Journal of Integrative Neuroscience  
J o u r n a l o f I n t e g r a t i v e N e u r o s c i e n c e  ...  Acknowledgment This research has been supported by the Cognitive Sciences and Technologies Council of Iran (COGC), under the grant number 2250.  ...  ., 2014; Lee and Ye, 2012) different machine learning and dictionary learning approaches are introduced and discussed.  ... 
doi:10.31083/j.jin.2019.03.153 pmid:31601074 fatcat:xwp273qtt5bxvjwf65jqvfwn4q

fMRI Based Cerebral Instantaneous Parameters for Automatic Alzheimer's, Mild Cognitive Impairment and Healthy Subject Classification [article]

Esmaeil Seraj, Mehran Yazdi, Nastaran Shahparian
2019 arXiv   pre-print
To this end, after performing the region-of-interest (ROI) analysis on fMRI data, different features covering power, entropy and coherency aspects of data are derived from instantaneous phase and envelope  ...  The reported performance in overall accuracy using fMRI data of 111 combined subjects, is 80.1% with 80.0% Sensitivity to both Alzheimer's and Normal categories distinction and is comparable to the state-of-the-art  ...  Acknowledgments This research has been supported by the Cognitive Sciences and Technologies Council of Iran (COGC), under the grant number 2250.  ... 
arXiv:1904.07441v1 fatcat:ecs7ijhhonh23fauagvwtd7xyy

Decoding task-specific cognitive states with slow, directed functional networks in the human brain [article]

Devarajan Sridharan, Shagun Ajmera, Hritik Jain, Mali Sundaresan
2019 bioRxiv   pre-print
With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks.  ...  Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach  ...  The authors would like to thank Lionel Barrett and Catie Chang for their comments on a preliminary version of this manuscript, and Govindan Rangarajan and Arshed Nabeel for helpful discussions.  ... 
doi:10.1101/681544 fatcat:2jtohh3efjhz7n5sad5pkqmp6a

Model-based whole-brain effective connectivity to study distributed cognition in health and disease

Matthieu Gilson, Gorka Zamora-López, Vicente Pallarés, Mohit H Adhikari, Mario Senden, Adrià Tauste Campo, Dante Mantini, Maurizio Corbetta, Gustavo Deco, Andrea Insabato
2019 Network Neuroscience  
We illustrate this approach using two applications on task-evoked fMRI data.  ...  Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition.  ...  to extract information from data in a top-down approach.  ... 
doi:10.1162/netn_a_00117 pmid:32537531 pmcid:PMC7286310 fatcat:jflwomj6vjdajfi3byxstj6niu

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model

Zhongyang Wang, Junchang Xin, Qi Chen, Zhiqiong Wang, Xinlei Wang
2022 Diagnostics  
Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes  ...  The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure  ...  [13] proposed data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participant; the results show that non-linear dynamical fluctuations  ... 
doi:10.3390/diagnostics12051298 pmid:35626453 fatcat:reeewotrbbervix2mnklzu4i4a

Decoding task-specific cognitive states with slow, directed functional networks in the human brain

Shagun Ajmera, Hritik Jain, Mali Sundaresan, Devarajan Sridharan
2020 eNeuro  
Here, we apply machine learning to fMRI data from 1000 human participants and show that directed connectivity, estimated with Granger-Geweke Causality from fMRI data, accurately predicts task-specific  ...  Here we demonstrate that, despite these widely-held caveats, GC networks estimated from fMRI recordings contain useful information for classifying task specific cognitive states.  ...  learning (Arbabshirani et al., 2017) to fMRI-GC networks.  ... 
doi:10.1523/eneuro.0512-19.2019 pmid:32265196 pmcid:PMC7358332 fatcat:na6kw4j7gbhsxja2vkdqvka2fy

Review on Early Detection of Alzheimer's Disease using Neuroimaging Techniques

Vishnu N, R Vaidya, Chaitra N, Srinidhi S P, Shreyas B
2021 Zenodo  
Early detection of AD is very crucial for further aid and treatment. This paper presents a review and analysis of the different methods employed to detect AD or mild cognitive impairment (MCI).  ...  Machine learning, neuroimaging, and deep learning neural networks are few of the techniques which are compared and analysed based on their performance and accuracy.  ...  Machine learning algorithms split the available data into test and train data and the system learns from the train data. It then uses this past learning experience to classify the given event.  ... 
doi:10.5281/zenodo.4420080 fatcat:56eec7xvc5hxbgihmqmvz7gsxu

Unsupervised Learning of Brain States from fMRI Data [chapter]

F. Janoos, R. Machiraju, S. Sammet, M. V. Knopp, I. Á. Mórocz
2010 Lecture Notes in Computer Science  
A popular approach is to learn a mapping from the data to the observed behavior.  ...  However, identifying the instantaneous cognitive state without reference to external conditions is a relatively unexplored problem and could provide important insights into mental processes.  ...  Conclusion In this paper, we presented a purely data-driven approach to learn the instantaneous state of the brain from the distribution of intensities in fMRI scans.  ... 
doi:10.1007/978-3-642-15745-5_25 fatcat:j75yt3t2wbanrfsru5u3ofeyya

Neural And Music Correlates Of Music-Evoked Emotions

Konstantinos Patlatzoglou, Dr. Rafael Ramirez
2016 Zenodo  
music using MIR (music information retreival) tools, a machine learning approach is selected for the creation of the model.  ...  Using fMRI data obtained from 17 individuals during a music listening session of 24 tracks (which belong to 3 classes of joy, fear and neutral stimuli), along with the extraction of audio descriptors from  ...  Cognitive states, such as an induced emotion, have been shown to be predicted solely from the fMRI of a subject with high degree of accuracy.  ... 
doi:10.5281/zenodo.1161286 fatcat:elnq7oi5nbakdgqaa6xphc6mne

Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features

JunHyun Kim, Minhong Jeong, Wesley R. Stiles, Hak Soo Choi
2022 International Journal of Molecular Sciences  
Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death.  ...  Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease.  ...  The recent use of fMRI imaging for AD recognition has been extended to machine learning and deep learning techniques, but different algorithms and analysis methods lead to many controversial findings.  ... 
doi:10.3390/ijms23116079 pmid:35682758 fatcat:zmlxgp7ksnfmbnfhqv4p2ow53q
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