A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

Önal, Itır; Özay, Mete; Mızrak, Eda; Öztekin, Ilke; Vural, Fatoş T. Yarman

doi:10.1109/tsipn.2017.2679491

Cited by 11 publications

(12 citation statements)

References 32 publications

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“…Specifically, networks built from second-order RSA and multivariate functional connectivity could also be compared (Coutanche and Thompson-Schill, 2014;Anzellotti and Coutanche, 2018). Other analyses than RSA exist that can pick up on more complex spatial relations between ROIs (e.g., Haxby et al, 2011;Ozay et al, 2012;Firat et al, 2013;Onal et al, 2017). Although RSA is one of the most commonly used approaches in cognitive neuroscience in comparing neural spaces across different brain regions, uncovering the complexity of the spatial relationships captured through more advanced approaches may help to compare the three methodologies in depth.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

2020

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The invention of representational similarity analysis [RSA, following multi-voxel pattern analysis (MVPA)] has allowed cognitive neuroscientists to identify the representational structure of multiple brain regions, moving beyond functional localization. By comparing these structures, cognitive neuroscientists can characterize how brain areas form functional networks. Univariate analysis (UNIVAR) and functional connectivity analysis (FCA) are two other popular methods to identify functional networks. Despite their popularity, few studies have examined the relationship between networks from RSA with those from UNIVAR and FCA. Thus, the aim of the current study is to examine the similarities between neural networks derived from RSA with those from UNIVAR and FCA to explore how these methods relate to each other. We analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from RSA, UNIVAR, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows unique organization that cannot be explained by any of the other methods. Thus, we conclude that the RSA, UNIVAR and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

2020

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“…In the work done by Ertugrul et al [42], functionally local meshes are constructed around each voxel of the brain volume using a functional neighborhood system. They use Pearson correlation to specify the functional neighbors.…”

Section: The Results Of Other Studies Conducted On This Datasetmentioning

confidence: 99%

Decoding cognitive states using the bag of words model on fMRI time series

Sucu

Akbaş

Öztekin

et al. 2016

2016 24th Signal Processing and Communication Application Conference (SIU)

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Bag-of-words (BoW) modeling has yielded successful results in document and image classification tasks. In this study, we explore the use of BoW for cognitive state classification. We estimate a set of common patterns embedded in the Functional Magnetic Resonance Imaging (fMRI) time series recorded in three dimensional voxel coordinates by clustering the Blood Oxygen Level Dependent (BOLD) responses. We use these common patterns, called the code-words, to encode activities of both individual voxels and group of voxels, and obtain BoW representations on which we train linear classifiers. We experimented with a number of different BoW representations such as encoding spatial and functional neighbors, spatial pooling, and soft and hard encoding. Our experimental results show that, on a multiclass fMRI dataset, the hard BoW encoding, when applied to individual voxels, significantly improves the classification accuracy (an average 7.22% increase when applied on average intensity per voxel and an average 15.52% increase when applied to raw intensity time series per voxel) compared to a classical multi voxel pattern analysis (MVPA) method. This v preliminary result gives us a clue to generate a code-book for fMRI data which may be used to represent a variety of cognitive states to study the human brain.

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“…Numerous studies have proposed various computational models in order to build brain networks from fMRI measurements, both during cognitive tasks or during resting state. These studies represent a shift in the literature toward brain decoding algorithms that are based on the connectivity patterns in the brain motivated by the findings that these patterns provide more information about cognitive tasks than the isolated behavior of individual groups of voxels or anatomical regions (Lindquist, 2008;Ekman et al, 2012;Shirer et al, 2012;Richiardi et al, 2013;Onal et al, 2017). Some of these studies focused on the pairwise relationships between voxels or brain regions.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Complex Problem Solving by Dynamic Brain Networks

Alchihabi

Ekmekci

Kivilcim

et al. 2021

Front. Neuroinform.

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Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.

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A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

Cited by 11 publications

References 32 publications

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

Decoding cognitive states using the bag of words model on fMRI time series

Analyzing Complex Problem Solving by Dynamic Brain Networks

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