Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework

Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. In our previous work we used generalized canonical correlation analysis to extract a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Here we focus on using a cost function that simultaneously maximizes temporal and spatial reproducibility of MRI statistical parametric map. Based on a modified version of generalized canonical correlation analysis (gCCA) we propose a method to extract a highly reproducible map by maximizing the sum of the pair-wise correlations between pairs of maps while the associated temporal response to the extracted map follows the subjects' temporal response. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations we compare the proposed approach with our previous work presented in. Our results show that the proposed modified gCCA is an efficient approach for extracting both default mode network and task mode network.

This paper proposes an iterative sparse representation-based algorithmfor voxel selection in functionalmagnetic resonance imaging (fMRI) data. The output of the algorithm is a sparse weight vector, of which the magnitude of each entry represents the significance of its corresponding voxel with respect to mental tasks or stimulus. To demonstrate the validity of our algorithm and illustrate its application, we apply this algorithm to the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007 fMRI data set for selecting the voxels which are the most relevant to the tasks of the subjects. Compared with three baseline methods, general linear model (GLM)-based statistical parametric mapping (SPM), correlation method and mutual information method, our method shows satisfactory performance for voxel selection.

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Canonical Variate Analysis (CVA) has two major problems: sampling error and accumulated rounding error. This paper describes a method designed to minimize these problems. The combined effect of the two problems was assessed for CVA and the new technique in an empirical study. The new technique appears to be superior to CVA when the ratio of variables to sampling units is greater than 1/10.

We have endeavored in the preceding chapters to draw attention to the richness and flexibility of canonical analysis. The flexibility of the underlying model, in particular, is expressed in part by the number of apparently distinct methods which prove on examination to be special cases of canonical correlation analysis. Canonical variate analysis and dual scaling were both shown abstractly to be equivalent to canonical correlation analysis. The model’s flexibility is complemented by a correspondingly diverse range of application. While, in relation to the worked examples of Chaps. 6–12, it may be that the ability of canonical analysis to have parsimoniously summarized the dependence structures in question is to be attributed to the small scale of these investigations, within the class of small-scale investigations the applicability of canonical analysis was shown to be very wide. Altogether, it seems justifiable to conclude that canonical analysis offers an unusually versatile structure by means of which statistical theory, ecological theory and empirical data can be harnessed to provide insight into the organization of ecological systems.

Independent component analysis aiming at detecting the functional connectivity among discrete cortical brain regions has been extensively used to explore the functional magnetic resonance imaging data. Although the independent components (ICs) were with relatively high quality, the noise embedding in ICs has a great impact on the true active/inactive region inference and the reproducibility, in postprocessing stage, e.g., the extraction of statistical parametrical maps (SPMs). In this paper, a novel brain network enhancement model (BNEM) is proposed, which mainly consists of two key techniques: 1) 3-D wavelet noise filter (3DWNF) for the meaningful ICs, which greatly suppresses noise and enforces the real activation inference of SPMs; and 2) a spatial reproducibility enhancement algorithm (SREA), aiming to improve the reproducibility of SPMs. The simulated experiment demonstrated that the postfiltering signals by 3DWNF were with higher correlation and less normalized mean square error to the ground truths than the prefiltering ones; SREA could further enhance the quality of most postfiltering ones, preserving the consistency with 3DWNF. The real data experiments also revealed that 1) 3DWNF could lead to more accurate preservation of the true positive voxels by correctly identifying the high proportionally misclassified voxels of the nonenhanced SPMs; 2) SREA could further improve the classification accuracy of the active/inactive voxels of SPMs corresponding to the 3DWNF denoised ICs; and 3) both 3DWNF and SREA contribute to the reproducibility enhancement of the reproduced SPMs by BNEM. Thus, BNEM is expected to have wide applicability in the neuroscience and clinical domain.

Functional near-infrared spectroscopy (fNIRS) is being increasingly utilized to visualize the brain areas involved in cognitive activity to understand the human brain better. Its portability and easy setup give it an advantage over other functional brain imaging tools. The current study utilizes fNIRS while performing a Stroop test, which is commonly used to assess the impairment of information selection in depression. To compare cortical activation during the Stroop test in depressed individuals to healthy controls. This cross-sectional study compared oxyhemoglobin (OxyHb) concentration changes in 39 individuals with depression to 40 healthy individuals. The Stroop test was construed in an event-related design with an intertrial interval of 2 seconds with jitter. A continuous wave fNIRS system was used for recording the cortical activity at 17 locations. Analysis of fNIRS data was done using statistical parametric mapping (SPM) for estimating general linear model (GLM) coefficients. Further analysis of the mean change of OxyHb concentrations during the 2 seconds after the presentation of congruent and incongruent stimuli was done between the groups using Mann-Whitney U test corrected for multiple comparisons with Bonferroni correction. While the number of errors and correct responses were similar between the groups, the reaction time for correct responses was more in the depression group in comparison to healthy individuals (t = -2.39, P = 0.01). For both healthy and depressive individuals in incongruent versus congruent task contrast, deactivation was seen in the region between the left middle frontal sulcus and frontopolar area of the brain (t = 0.41 and t = 0.21, respectively, P < 0.05). The depressed group had a higher mean change in OxyHb concentration following incongruent stimuli in comparison to the HCs (mean rank: HC = 32.63, depression = 47.56). Our findings indicate that individuals with depression exhibited prolonged reaction times and distinct activation patterns of the frontal cortex compared to healthy individuals. The observed pattern of brain activation for congruent and incongruent tasks among both healthy and depressed individuals aligns with the findings of the prior studies, emphasizing the utility of fNIRS as a valuable instrument for assessing brain activity.

Owing to the importance of rolling bearings in mechanical systems, many meaningful works have been presented for bearing degradation assessment. However, most of contributions fail to take into account the influence of input features on the performance of a degradation model. As a potential multivariable process control method considering serial correlations, canonical variate analysis (CVA) is introduced to detect abnormal behaviours of rolling bearings. This study combines the CVA and support vector data description (SVDD) to develop a robust approach to weaken or remove the negative impact of input data on bearing degradation estimation. First, the time‐domain features extracted from healthy vibration signals are fused by CVA to generate projection data for SVDD training. Second, the distance of new variables to the centre of the hypersphere is calculated as an indicator to describe the degradation of rolling bearings. To demonstrate the effectiveness and robustness of the proposed method, two different experiments on bearing run‐to‐failure tests are conducted. Experimental results validate that the proposed work is capable of detecting bearing failure under various working conditions. Besides, comparison of the proposed work with the original CVA is implemented. The robustness of the proposed work is enhanced obviously compared with the original CVA.

This paper deals with the fault detection of a pneumatic control valve using canonical variate analysis (CVA). CVA can find the optimal linear combinations of p-window and f-window data, so that the correlation between these combinations can be maximized. Based on CVA, the p-window data is considered by traditional hotelling T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> statistic and squared prediction error (SPE) indicators, the corresponding fault detection rates (FDR) are low. In order to improve the FDR, a detection indicator based on SMD (square of the Mahalanobis distance) of the residual is proposed in this paper. The proposed indicator considers not only the information in the p-window data, but also that of the f-window data, which can improve the FDRs. The proposed techniques have been validated using a Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) benchmark. It concludes that 14 out of the 19 faults can be successfully detected using the proposed method (CVA-SMD). Simulation results have shown that the CVA-SMD can improve the FDR compared with existing CVA-T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and CVA-SPE methods. Experiments based on real-world data have also demonstrated that the CVA-SMD has better performance than existing PCA-T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , PCA-SPE, PCA-SMD, CVA-T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and CVA-SPE methods.

PurposeWe developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS).MethodsHierarchical segmentation using multiple deep learning models was employed to segment brain subregions within a clinically feasible processing time. The T1WI and brain mask pairs in 486 subjects were used as training data for training of the deep learning segmentation models. Training data were generated using a multi-atlas registration-based method. The high quality of training data was confirmed through visual evaluation and manual correction by neuroradiologists. The brain 3D-T1WI scan–rescan data of the 11 healthy subjects were obtained using three MRI scanners for evaluating the repeatability and reproducibility. The volumes of the eight ROIs—including gray matter, white matter, cerebrospinal fluid, hippocampus, orbital gyrus, cerebellum posterior lobe, putamen, and thalamus—obtained using DLHBS, SPM 12 with default settings, and FS with the “recon-all” pipeline. These volumes were then used for evaluation of repeatability and reproducibility.ResultsIn the volume measurements, the bilateral thalamus showed higher repeatability with DLHBS compared with SPM. Furthermore, DLHBS demonstrated higher repeatability than FS in across all eight ROIs. Additionally, higher reproducibility was observed with DLHBS in both hemispheres of six ROIs when compared with SPM and in five ROIs compared with FS. The lower repeatability and reproducibility in DLHBS were not observed in any comparisons.ConclusionOur results showed that the best performance in both repeatability and reproducibility was found in DLHBS compared with SPM and FS.

SummaryHumans show a remarkable ability to discriminate others' gaze direction, even though a given direction can be conveyed by many physically dissimilar configurations of different eye positions and head views. For example, eye contact can be signaled by a rightward glance in a left-turned head or by direct gaze in a front-facing head. Such acute gaze discrimination implies considerable perceptual invariance. Previous human research found that superior temporal sulcus (STS) responds preferentially to gaze shifts [1], but the underlying representation that supports such general responsiveness remains poorly understood. Using multivariate pattern analysis (MVPA) of human functional magnetic resonance imaging (fMRI) data, we tested whether STS contains a higher-order, head view-invariant code for gaze direction. The results revealed a finely graded gaze direction code in right anterior STS that was invariant to head view and physical image features. Further analyses revealed similar gaze effects in left anterior STS and precuneus. Our results suggest that anterior STS codes the direction of another's attention regardless of how this information is conveyed and demonstrate how high-level face areas carry out fine-grained, perceptually relevant discrimination through invariance to other face features.

Financial liquidity and profitability are two critical phenomena present in the financial economy of a company, whose relations depend on each other and may course in different directions. At the same time, they are an example of the complexity of the problem, which demands a proper approach, allowing one to reconcile two opposing objectives of any enterprise, i.e., maximizing the benefits for the owners and minimizing the risk of losing financial liquidity. Until now, the relationship between liquidity and profitability has not been examined explicitly, using multidimensional methods in particular. Nevertheless, the links between profitability and financial liquidity maintenance ensure the sustainable development of enterprises in different branches. This paper formulates two aims: scientific and practical. The scientific one concerns adopting the canonical variate analysis method to visualize the differences and relationships between food industry companies regarding financial liquidity and profitability. The practical one relates to indicating the relationship between financial liquidity and profitability in different groups of food industry companies. To study the relationships between the selected groups of enterprises and describe them, the liquidity and profitability ratios were utilized, involving canonical variate analysis based on transformation by linear combination and singular value decomposition. The analysis found that the most important feature highlighting the group of the examined entities regarding financial liquidity was the cash conversion cycle. The research results showed the existence of multidirectional relationships between liquidity and profitability. The research indicates that they depend on indicators describing financial dependencies and the industries in which they operate. This led to a much deeper and broader interpretation of the assessment of the financial situation of companies to support their sustainable development.

A synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis

The computerized brain atlas (CBA) and statistical parametric mapping (SPM) are two procedures for voxel-based statistical evaluation of PET activation studies. Each includes spatial standardization of image volumes, computation of a statistic, and evaluation of its significance. In addition, smoothing and correcting for differences of global means are commonly performed in SPM before statistical analysis. We report a comparison of methods in an analysis of regional cerebral blood flow (rCBF) in 10 human volunteers and 10 simulated activations. For the human studies, CBA or linear SPM standarization methods were followed by smoothing and computation of a statistic with the paired t-test of CBA or general linear model of SPM. No standardization, linear, and nonlinear SPM standardization were applied to the simulations. Significance of the statistic was evaluated using the cluster-size method common to SPM and CBA. SPM employs the theory of Gaussian random fields to estimate the cluster size distributions; simulations described in the Appendix provided empirical distributions derived from t-maps. The quantities evaluated were number and size of functional regions (FRs), maximum statistic, average resting rCBF, and percentage change. For the simulations, the efficiency of signal detection and rate of false positives could be evaluated as well as the distributions of statistics and cluster size in the absence of signal. The similarity of the results yielded by similar methods of analysis for the human studies and the simulated activations substantiates the robustness of the methods for selecting functional regions. However, the analysis of simulated activations demonstrated that quantitative evaluation of significance of a functional region encounters important obstacles at every stage of the analysis.

Comparison between five pattern-based approaches for automated diagnostic classification of mature/peripheral B-cell neoplasms based on standardized EuroFlow flow cytometry immunophenotypic data.