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

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.

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Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression

NeuroImage Clinical ◽

10.1016/j.nicl.2019.101747 ◽

2019 ◽

Vol 22 ◽

pp. 101747 ◽

Cited By ~ 26

Author(s):

Mustafa S. Salman ◽

Yuhui Du ◽

Dongdong Lin ◽

Zening Fu ◽

Alex Fedorov ◽

...

Keyword(s):

Group Differences ◽

Adaptive Networks ◽

Spatio Temporal ◽

Group Ica ◽

Constrained Ica

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Estimating dynamic brain functional networks using multi-subject fMRI data

NeuroImage ◽

10.1016/j.neuroimage.2018.07.045 ◽

2018 ◽

Vol 183 ◽

pp. 635-649 ◽

Cited By ~ 5

Author(s):

Suprateek Kundu ◽

Jin Ming ◽

Jordan Pierce ◽

Jennifer McDowell ◽

Ying Guo

Keyword(s):

Fmri Data ◽

Functional Networks ◽

Brain Functional Networks

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Multi-head GAGNN: A Multi-head Guided Attention Graph Neural Network for Modeling Spatio-temporal Patterns of Holistic Brain Functional Networks

10.1007/978-3-030-87234-2_53 ◽

2021 ◽

pp. 564-573

Author(s):

Jiadong Yan ◽

Yuzhong Chen ◽

Shimin Yang ◽

Shu Zhang ◽

Mingxin Jiang ◽

...

Keyword(s):

Neural Network ◽

Temporal Patterns ◽

Functional Networks ◽

Brain Functional Networks ◽

Spatio Temporal

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Identification of subject-specific brain functional networks using a collaborative sparse nonnegative matrix decomposition method

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2016.7493430 ◽

2016 ◽

Cited By ~ 4

Author(s):

Hongming Li ◽

Ted Satterthwaite ◽

Yong Fan

Keyword(s):

Decomposition Method ◽

Nonnegative Matrix ◽

Matrix Decomposition ◽

Functional Networks ◽

Brain Functional Networks ◽

Subject Specific

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

Computational Intelligence and Neuroscience ◽

10.1155/2016/4705162 ◽

2016 ◽

Vol 2016 ◽

pp. 1-19 ◽

Cited By ~ 3

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.

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AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-451-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 451-456 ◽

Cited By ~ 1

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

G. Bilgin

Keyword(s):

Classification Accuracy ◽

Alpha Angle ◽

Test Site ◽

Classification Performance ◽

Support Vector ◽

Observation Data ◽

Data Set ◽

Crop Classification ◽

Sar Data ◽

Spatio Temporal

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (&alpha;) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H&alpha;, (2) H&alpha;Span, (3) H&alpha;A, (4) H&alpha;ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&alpha;ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H&alpha;ASpan while lowest classification accuracy was obtained as 66.99% by NB and H&alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.

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Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain

Entropy ◽

10.3390/e21121156 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1156 ◽

Cited By ~ 1

Author(s):

Yanbing Jia ◽

Huaguang Gu

Keyword(s):

Human Brain ◽

Resting State ◽

Clustering Algorithm ◽

Resting State Fmri ◽

Brain Regions ◽

Sample Entropy ◽

Blood Oxygenation ◽

Fmri Data ◽

Functional Networks ◽

Brain Functional Networks

Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.

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Multiple graph fusion based on Riemannian geometry for motor imagery classification

Applied Intelligence ◽

10.1007/s10489-021-02975-2 ◽

2022 ◽

Author(s):

Xiaofeng Xie ◽

Xiaokun Zou ◽

Tianyou Yu ◽

Rongnian Tang ◽

Yao Hou ◽

...

Keyword(s):

Riemannian Manifold ◽

Motor Imagery ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Classification Problems ◽

Eeg Signals ◽

Spatial Covariance ◽

Bilinear Mapping ◽

Subject Specific

AbstractIn motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

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