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Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data., (© 2018 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

One-sided t-tests are commonly used in the neuroimaging field, but two-sided tests should be the default unless a researcher has a strong reason for using a one-sided test. Here we extend our previous work on cluster false positive rates, which used one-sided tests, to two-sided tests. Briefly, we found that parametric methods perform worse for two-sided t-tests, and that nonparametric methods perform equally well for one-sided and two-sided tests., (© 2018 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

A recent Editorial in Cognitive Neuroscience reconsiders the findings of our work on the accuracy of false positive rate control with cluster inference in functional magnetic resonance imaging (fMRI), in particular criticizing our use of resting-state fMRI as a source for null data in the evaluation of task fMRI methods. We defend this use of resting fMRI data, as while there is much structure in this data, we argue it is representative of task data noise and task analysis software should be able to accommodate this noise. We also discuss a potential problem with Slotnick's own method.

The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

This paper extends Jones' popular electrostatic repulsion based algorithm for distribution of single-shell Q-space samples in two fundamental ways. The first alleviates the single-shell requirement enabling full Q-space sampling. Such an extension is not immediately obvious since it requires distributing samples evenly in 3 dimensions. The extension is as elegant as it is simple: Add a container volume of the desired shape having a constant charge density and a total charge equal to the negative of the sum of the moving point charges. Results for spherical and cubic charge containers are given. The second extension concerns the way distances between sample point are measured. The Q-space samples represent orientation, rather than direction and it would seem appropriate to use a metric that reflects this fact, e.g. a tensor metric. To this end we present a means to employ a generalized metric in the optimization. Minimizing the energy will result in a 3-dimensional distribution of point charges that is uniform in the terms of the specified metric. The radically different distributions generated using different metrics pinpoints a fundamental question: Is there an inherent optimal metric for Q-space sampling? Our work provides a versatile tool to explore the role of different metrics and we believe it will be an important contribution to further the continuing debate and research on the matter.

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64×64×22voxels), all the preprocessing takes about 0.5s on the GPU, compared to 5s with an optimized CPU implementation and 120s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10,000 permutations, with smoothing in each permutation, takes about 50s if three GPUs are used, compared to 0.5-2.5h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Aim: The influences on the signal-to-noise ratio (SNR) of Displacement ENcoding with Stimulated Echoes (DENSE) MRI of field strength, receiver coil sensitivity and choice of flip angle strategy have been previously investigated individually. In this study, all of these parameters have been investigated in the same setting, and a mutual comparison of their impact on SNR is presented., Materials and Methods: Ten healthy volunteers were imaged in a 1.5 T and a 3 T MRI system, using standard five- or six-channel cardiac coils as well as 32-channel coils, with four different excitation patterns. Variation of spatial coil sensitivity was assessed by regional SNR analysis., Results: SNR ranging from 2.8 to 30.5 was found depending on the combination of excitation patterns, coil sensitivity and field strength. The SNR at 3 T was 53±26% higher than at 1.5 T (P<.001), whereas spatial differences of 59±26% were found in the ventricle (P<.001). Thirty-two-channel coils provided 52±29% higher SNR compared to standard five- or six-channel coils (P<.001). A fixed flip angle strategy provided an excess of 50% higher SNR in half of the imaged cardiac cycle compared to a sweeping flip angle strategy, and a single-phase acquisition provided a sixfold increase of SNR compared to a cine acquisition., Conclusion: The effect of field strength and receiver coil sensitivity influences the SNR with the same order of magnitude, whereas flip angle strategy can have a larger effect on SNR. Thus, careful choice of imaging hardware in combination with adaptation of the acquisition protocol is crucial in order to realize sufficient SNR in DENSE MRI., (Copyright © 2011 Elsevier Inc. All rights reserved.)

A method to acquire multiple displacement encoded slices within a single breath hold is presented. Efficiency is improved over conventional DENSE without compromising image quality by readout of multiple slices in the same cardiac cycle, thus utilizing the position-encoded stimulated echo available in the whole heart. The method was evaluated by comparing strain values obtained using the proposed method to strain values obtained by conventional separate breath-hold single-slice DENSE acquisitions. Good agreement (Lagrangian E(2) strain bias = 0.000, 95% limits of agreement +/- 0.04, root-mean-square-difference 0.02 [9.4% of mean end-systolic E(2)]) was found between the methods, indicating that the proposed method can replace a multiple breath-hold acquisition. Eliminating the need for multiple breath holds reduces the risk of changes in breath-hold positions or heart rate, results in higher patient comfort, and facilitates inclusion of DENSE in a clinical routine protocol., ((c) 2010 Wiley-Liss, Inc.)

We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

Purpose: To validate the performance of an analysis method for fMRI data based on restricted canonical correlation analysis (rCCA) and adaptive filtering, and to increase the usability of the method by introducing a new technique for significance estimation of rCCA maps., Materials and Methods: Activation data from a language task and also a resting state fMRI data were collected from eight volunteers. Data was analyzed using both the rCCA method and the General Linear Model (GLM). A modified Receiver Operating Characteristic (ROC) method was used to evaluate the performance of the different analysis methods. The area under a fraction of the ROC curve was used as a measure of performance. On resting state data the fraction of voxels above certain significance thresholds were used to evaluate the significance estimation method., Results: The rCCA method scored significantly higher on the area under the ROC curve than the GLM. The fraction of activated voxels determined by thresholding according to the introduced significance estimation technique showed good agreement with the thresholds selected., Conclusion: The rCCA method is an effective analysis tool for fMRI data and its usability is increased with the introduced significance estimation method.

Purpose: To develop a new volumetric imaging method resolved over both the cardiac and respiratory cycles, to enable future physiological and pathophysiological studies of respiratory-related cardiac motion., Materials and Methods: An acquisition scheme is proposed whereby the k-space acquisition order is controlled in real-time by the current cardiac and respiratory phases. To reduce eddy-current effects induced by sudden jumps in k-space, the acquisition order is further optimized by the use of a Hilbert curve trajectory in the k(y)-k(z) plane. A complete three-dimensional (3D) k-space is acquired for all combinations of cardiac and respiratory phases, yielding a five-dimensional (5D) data set after retrospective reconstruction., Results: Left (LV) and right ventricular (RV) wall excursion was measured in a healthy volunteer. Diastolic LV diameter was shown to increase during expiration and decrease during inspiration, as expected from previous echocardiography studies. The LV volume was estimated for all cardiac and respiratory phases with the use of a fully 3D segmentation tool. The results confirmed that the diastolic LV volume increased during expiration and decreased during inspiration., Conclusion: With its ability to measure motion anywhere in the heart, the described technique provides a promising approach for in-depth description of interventricular coupling, including 3D ventricular volumes, during both the cardiac and respiratory cycles.

Studies of myocardial motion using magnetic resonance imaging usually require multiple breath holds and several methods have been proposed in order to reduce the scan time. Rapid imaging using k-t BLAST has gained much attention with its high reduction factors and image quality. Temporal smoothing, however, may reduce the accuracy when assessing cardiac function. In the present work, a modified reconstruction filter is proposed, that preserves more of the high temporal frequencies. Artificial decimation of a fully sampled data set was used to evaluate the reconstruction filter. Compared to the conventional k-t BLAST reconstruction, the modified filter produced images with sharper temporal delineation of the myocardial walls. Quantitative analysis by means of regional velocity estimation showed that the modified reconstruction filter produced more accurate velocity estimations.

Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data., (2002 Elsevier Science (USA))

A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323-330, 2001., (Copyright 2001 Wiley-Liss, Inc.)

We present a class of adaptive filtering techniques of functional magnetic resonance imaging (fMRI) data related to bilateral filtering. This class of methods average activities in consistent regions rather than regions that maximize correlation with a BOLD model. Similarity measures based on signal similarity and anatomical similarity are discussed and compared experimentally to standard linear low pass filtering. It is demonstrated that adaptive filtering provides improved detection of activated regions. [ABSTRACT FROM PUBLISHER]