scholarly journals Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Danilo DonGiovanni ◽  
Lucia Maria Vaina
Keyword(s):  
Functional Connectivity ◽  
A Priori ◽  
Data Driven ◽  
Fmri Data ◽  
Support Vector ◽  
Functional Networks ◽  
Compact Groups ◽  
Mutual Correlation ◽  
Pairwise Correlation ◽  
Vector Clustering

Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in detail. Its performance assessed on simulated and experimental fMRI data is compared to other methods commonly used in functional connectivity analyses, such as Independent Component Analysis (ICA) or clustering. S&C method simplifies the extraction of functional networks in fMRI by identifying automatically spatially compact groups of voxels (ROIs) involved in whole brain scale activation networks.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

2008 ◽  
Vol 172 (1) ◽  
pp. 94-104 ◽  
Author(s):  
João Ricardo Sato ◽  
Janaina Mourão-Miranda ◽  
Maria da Graça Morais Martin ◽  
Edson Amaro ◽  
Pedro Alberto Morettin ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Functional Connectivity ◽  
Fmri Data ◽  
Support Vector ◽  
Vector Machines ◽  
The Impact

scholarly journals Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning

2021 ◽  
Author(s):  
Xin Yang ◽  
Ning Zhang ◽  
Donglin Wang
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Dictionary Learning ◽  
Learning Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Functional Networks ◽  
Training Samples ◽  
Group Ica

The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain Imaging Data Exchange I (ABIDE I). Our methodology and results have five main parts. First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). Second, we utilize a dictionary learning model to extract functional networks from the ASD group and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from the two models together as the ASD functional networks. Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both. Finally, we extract ROIs for all training samples using the above three masks, and the calculated functional connectivity was used as features for ASD and TD classification. The classification results showed that the functional networks derived from ICA and dictionary learning together outperform those derived from a single ICA model or a single dictionary learning model.

scholarly journals Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

2021 ◽  
Vol 12 (1) ◽  
pp. 57
Author(s):  
Francesco Ferracuti ◽  
Sabrina Iarlori ◽  
Zahra Mansour ◽  
Andrea Monteriù ◽  
Camillo Porcaro
Keyword(s):  
Motor Imagery ◽  
Pattern Classification ◽  
Classification System ◽  
A Priori ◽  
Hand Movement ◽  
Region Of Interest ◽  
Data Driven ◽  
Support Vector ◽  
Human Beings ◽  
Human Communication

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

scholarly journals Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study

2010 ◽  
Vol 2010 ◽  
pp. 1-24 ◽  
Author(s):  
Robert E. Kelly ◽  
Zhishun Wang ◽  
George S. Alexopoulos ◽  
Faith M. Gunning ◽  
Christopher F. Murphy ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Hybrid Methods ◽  
A Priori ◽  
Temporal Correlation ◽  
Fmri Data ◽  
Single Voxel ◽  
Dual Regression ◽  
Time Courses ◽  
Group Ica ◽  
Brain Functional Connectivity

Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-relateda prioritime courses, with “back-reconstruction” from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed.

scholarly journals Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

2018 ◽  
Author(s):  
Mustafa S Salman ◽  
Yuhui Du ◽  
Dongdong Lin ◽  
Zening Fu ◽  
Eswar Damaraju ◽  
...  
Keyword(s):  
Classification Performance ◽  
Fmri Data ◽  
Group Differences ◽  
Support Vector ◽  
Functional Networks ◽  
Adaptive Networks ◽  
Brain Functional Networks ◽  
Subject Specific ◽  
Spatio Temporal ◽  
Constrained Ica

AbstractBrain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45±8.9%, sensitivity 0.74±0.11, specificity 0.79±0.11) than STR (accuracy 67.45±8.13%, sensitivity 0.65±0.11, specificity 0.71±0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

scholarly journals Characterization of young and old adult brains: An EEG functional connectivity analysis

2018 ◽  
Author(s):  
Bahar Moezzi ◽  
Latha Madhuri Pratti ◽  
Brenton Hordacre ◽  
Lynton Graetz ◽  
Carolyn Berryman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Older Age ◽  
Support Vector ◽  
Functional Networks ◽  
Svm Classifier ◽  
Age Group ◽  
Beta Band ◽  
Machine Learning Classification ◽  
Functional Connections

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age. Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.

scholarly journals A Quantitative Data-Driven Analysis (QDA) Framework for Resting-state fMRI: a Study of the Impact of Adult Age

2021 ◽  
Author(s):  
Xia Li ◽  
Håkan Fischer ◽  
Amirhossein Manzouri ◽  
Kristoffer N.T. Månsson ◽  
Tie-Qiang Li
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Quantitative Data ◽  
Resting State Fmri ◽  
Data Driven ◽  
Fmri Data ◽  
Resting State Functional Connectivity ◽  
Sensorimotor Network ◽  
Adult Age ◽  
Connectivity Strength

AbstractPurposeThe objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC).MethodsWhole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index (CSI) and connectivity density index (CDI) utilizing the convolutions of the cross-correlation (CC) histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately.ResultsWith the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyrus (MFG), posterior cingulate cortex (PCC), right insula and inferior parietal lobule (IPL) of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: 1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; 2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; 3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age.ConclusionThe proposed QDA framework can produce threshold-free, voxel-wise analysis of R-fMRI data the RFC metrics. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.HighlightsA quantitative data-driven analysis (QDA) framework was proposed to analysis resting-state fMRI data.Threshold-free resting-state functional connectivity (RFC) metrics were derived to assess brain changes with adult age.Separate assessment of the positive and negative correlations improve sensitivity of the RFC metrics.The posterior cingulate and right insula cortices are anti-correlated and tend to manifest declines in both the negative and positive connectivity strength with adult age.Negative connectivity strength enhances with adult age in sensorimotor network.

scholarly journals An edge-centric model for harmonizing multi-relational network datasets

2021 ◽  
Author(s):  
Joshua Faskowitz ◽  
Jacob Tanner ◽  
Bratislav Misic ◽  
Richard Betzel
Keyword(s):  
Functional Connectivity ◽  
Data Driven ◽  
Functional Networks ◽  
Single Edge ◽  
Structural Covariance ◽  
Clustering Techniques ◽  
Subject Specificity ◽  
Structural Connections ◽  
Structural Networks ◽  
Relational Network

Functional and structural connections vary across conditions, measurements, and time. However, how to resolve multi-relational measures of connectivity remains an open challenge. Here, we propose an extension of structural covariance and morphometric similarity methods to integrate multiple estimates of connectivity into a single edge-centric network representation. We highlight the utility of this method through two applications: an analysis of multi-task functional connectivity data and multi-measure structural networks. In these analyses, we use data-driven clustering techniques to identify collections of edges that covary across tasks and measures, reveal overlapping mesoscale architecture. We also link these features to node-level properties such as modularity and canonical descriptors of brain systems. We further demonstrate that, in the case of multi-task functional networks, edge-level features are consistent across individuals yet exhibit subject-specificity. We conclude by highlighting other instances where the edge-centric model may be useful.

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