scholarly journals A Low Rank and Sparse Paradigm Free Mapping Algorithm For Deconvolution of FMRI Data

2021 ◽  
Author(s):  
Eneko Urunuela ◽  
Stefano Moia ◽  
Cesar Caballero-Gaudes
Keyword(s):  
Low Rank ◽  
Fmri Data ◽  
Mapping Algorithm

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

scholarly journals Alzheimer Disease Diagnosis from fMRI images Based on Latent Low Rank Features and Support Vector Machine (SVM)

2019 ◽  
Vol 14 ◽  
Author(s):  
Nastaran Shahparian ◽  
Mehran Yazdi ◽  
Mohammad Reza Khosravi
Keyword(s):  
Support Vector Machine ◽  
Alzheimer Disease ◽  
Neurological Diseases ◽  
Practical Method ◽  
Disease Diagnosis ◽  
Low Rank ◽  
Gaussian Kernel ◽  
Fmri Data ◽  
Support Vector ◽  
Svm Classifier

Purpose: In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer disease. Design/methodology/approach: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to healthy category, mild stage of the disease or Alzheimer stage. Findings: The obtained classification accuracy for the proposed method is more than 97.5%. Originality/value: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.

scholarly journals Subspace-constrained approaches to low-rank fMRI acceleration

2020 ◽  
Author(s):  
Harry T Mason ◽  
Nadine N. Graedel ◽  
Karla L. Miller ◽  
Mark Chiew
Keyword(s):  
Degrees Of Freedom ◽  
Parallel Imaging ◽  
Statistical Parameter ◽  
Computation Time ◽  
Low Rank ◽  
Fmri Data ◽  
High Fidelity ◽  
Physiological Noise ◽  
High Acceleration ◽  
Reconstruction Quality

Acceleration methods in fMRI aim to reconstruct high fidelity images from undersampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to undersampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints was found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints.

Functional Anatomy of Intensity Aspects of Attention

2003 ◽  
Vol 14 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Walter Sturm
Keyword(s):  
Right Hemisphere ◽  
Functional Anatomy ◽  
Fmri Data ◽  
Functional Networks ◽  
Top Down ◽  
Stimulus Modality ◽  
Spatial Orienting ◽  
Phasic Alertness ◽  
Co Activation ◽  
Attention System

Abstract: Behavioral and PET/fMRI-data are presented to delineate the functional networks subserving alertness, sustained attention, and vigilance as different aspects of attention intensity. The data suggest that a mostly right-hemisphere frontal, parietal, thalamic, and brainstem network plays an important role in the regulation of attention intensity, irrespective of stimulus modality. Under conditions of phasic alertness there is less right frontal activation reflecting a diminished need for top-down regulation with phasic extrinsic stimulation. Furthermore, a high overlap between the functional networks for alerting and spatial orienting of attention is demonstrated. These findings support the hypothesis of a co-activation of the posterior attention system involved in spatial orienting by the anterior alerting network. Possible implications of these findings for the therapy of neglect are proposed.

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