Recent reports of inflated false positive rates (FPRs) in FMRI group analysis tools by Eklund et al. (2016) have become a large topic within (and outside) neuroimaging. They concluded that: existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussianshaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depend on "task" paradigm and voxelwise p-value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results.Regarding AFNI (which we maintain), 3dClustSim's bug-effect was greatly overstated their own -results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, though some remain >5%); additionally, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p≤0.01. †

The application of dynamic time warping (DTW) to the analysis and monitoring of batch processes is presented. This dynamic-programming-based technique has been used in the area of speech recognition for the recognition of isolated and connected words. DTW has the ability to synchronize two trajectories by appropriately translating, expanding, and contracting localized segments within both trajectories to achieve a minimum distance between the trajectories. Batch processes often are characterized by unsynchronized trajectories, due to the presence of batch-to-batch disturbances and the existence of physical constraints. To compare these batch histories and apply statistical analysis one needs to reconcile the timing differences among these trajectories. This can be achieved using DTW with only a minimal amount of process knowledge. The combination of DTW and a monitoring method based on Multiway PCA/PLS is used for both off-line and on-line implementation. Data fiom an industrial polymerization reactor are used to illustrate the implementation and the performance of this method. IntroductionBatch processes play an important role in the production of high added value products, such as specialty polymers, pharmaceuticals, and biochemical materials. Analysis and monitoring of the operation of these processes is crucial to the production of consistent, good quality products. Moreover, products from batch processes are often manufactured in a series of steps; early detection of a bad product at any of these steps will save energy, raw material, and plant capacity. Early detection will also make it easier to assign a cause to the fault and modify the process to eliminate the cause. Furthermore, there may be a chance of compensating for the fault with an appropriate control strategy if the monitoring scheme is implemented on-line.Product quality measurements in batch processes are obtained infrequently; they are often obtained after the product has been shipped to the customer, or after it has been forwarded to the next processing step. Fortunately, a multitude of process measurements, such as temperatures, pressures, flow rates, are readily available during the process of a batch. In view of this fact, MacGregor and Nomikos (1992) and MacGregor (1994, 1995a, b) proposed a method for monitoring batch processes using these readily measured process variables. Their method is based on multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS), which are extensions of PCA and PLS to handle three-dimensional matrices. The method essentially builds a statistical model for the deviations of the process variables about their average trajectories using data only from good quality batches. Then, it compares the variation of a new batch about the average trajectory with the MPCA model; any deviation that cannot be statistically attributed to the common process variation indicates that the new batch is different from the good quality batches. When quality measurements are available, one can us...

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The objective of this modeling and simulation study was to establish the role of stress wave interactions in the genesis of traumatic brain injury (TBI) from exposure to explosive blast. A high resolution (1 mm3 voxels) five material model of the human head was created by segmentation of color cryosections from the Visible Human Female data set. Tissue material properties were assigned from literature values. The model was inserted into the shock physics wave code, CTH, and subjected to a simulated blast wave of 1.3 MPa (13 bars) peak pressure from anterior, posterior, and lateral directions. Three-dimensional plots of maximum pressure, volumetric tension, and deviatoric (shear) stress demonstrated significant differences related to the incident blast geometry. In particular, the calculations revealed focal brain regions of elevated pressure and deviatoric stress within the first 2 ms of blast exposure. Calculated maximum levels of 15 KPa deviatoric, 3.3 MPa pressure, and 0.8 MPa volumetric tension were observed before the onset of significant head accelerations. Over a 2 ms time course, the head model moved only 1 mm in response to the blast loading. Doubling the blast strength changed the resulting intracranial stress magnitudes but not their distribution. We conclude that stress localization, due to early-time wave interactions, may contribute to the development of multifocal axonal injury underlying TBI. We propose that a contribution to traumatic brain injury from blast exposure, and most likely blunt impact, can occur on a time scale shorter than previous model predictions and before the onset of linear or rotational accelerations traditionally associated with the development of TBI.

We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.

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It has been argued that naturalistic conditions in FMRI studies provide a useful paradigm for investigating perception and cognition through a synchronization measure, inter-subject correlation (ISC). However, one analytical stumbling block has been the fact that the ISC values associated with each single subject are not independent, and our previous paper (Chen et al., 2016) used simulations and analyses of real data to show that the methodologies adopted in the literature do not have the proper control for false positives. In the same paper, we proposed nonparametric subject-wise bootstrapping and permutation testing techniques for one and two groups, respectively, which account for the correlation structure, and these greatly outperformed the prior methods in controlling the false positive rate (FPR); that is, subject-wise bootstrapping (SWB) worked relatively well for both cases with one and two groups, and subject-wise permutation (SWP) testing was virtually ideal for group comparisons. Here we seek to explicate and adopt a parametric approach through linear mixed-effects (LME) modeling for studying the ISC values, building on the previous correlation framework, with the benefit that the LME platform offers wider adaptability, more powerful interpretations, and quality control checking capability than nonparametric methods. We describe both theoretical and practical issues involved in the modeling and the manner in which LME with crossed random effects (CRE) modeling is applied. A data-doubling step further allows us to conveniently track the subject index, and achieve easy implementations. We pit the LME approach against the best nonparametric methods, and find that the LME framework achieves proper control for false positives. The new LME methodologies are shown to be both efficient and robust, and they will be added as an additional option and settings in an existing open source program, 3dLME, in AFNI (http://afni.nimh.nih.gov).

Recently, Eklund et al. (1) analyzed clustering methods in standard fMRI packages: AFNI (which we maintain), FSL, and SPM. They claim that (i) false-positive rates (FPRs) in traditional approaches are greatly inflated, questioning the validity of "countless published fMRI studies"; (ii) nonparametric methods produce valid, but slightly conservative, FPRs; (iii) a common flawed assumption is that the spatial autocorrelation function (ACF) of fMRI noise is Gaussian-shaped; and (iv) a 15-yold bug in AFNI's 3dClustSim significantly contributed to producing "particularly high" FPRs compared with other software. We repeated simulations from ref. AFNI and 3dClustSim SmoothnessTo test the effect of assuming a Gaussian ACF in fMRI noise, an empirical "mixed ACF" allowing for longer tails was computed from residuals (3). All FPRs (Fig. 1 E and F) decreased. Block designs remained >5%, likely reflecting dependence of the noise's spatial smoothness on temporal frequency. Heavy tails in spatial smoothness indeed have significant consequences for clustering. Nonparametric ApproachA spatial model-free, nonparametric randomization approach was added to AFNI's group-level GLM program, 3dttest++ (3). All FPRs (Fig. 1 G and H) were within the nominal confidence interval. Although this approach shows promise (as in ref . 1), it may not be feasible to generalize nonparametric permutations to complicated covariate structures and models (e.g., complex ANOVA, analysis of covariance, or linear mixed effects) (4, 5). Inflated FPRsSeveral cases showed significant FPR inflation across existing fMRI software within the testing framework of ref. 1. However, deviations from nominal FPR were not uniformly large and depended strongly on several factors. Fig. 1 and figure 1 of ref. 1 show quite good cluster results for stricter per-voxel P values (which ref. 6 found to be predominantly used in fMRI analyses) and for event-related stimuli (emphasizing the importance of good experimental design): FPR inflation was often K 10% (Beijing) or K 5% (Cambridge), affecting only clusters with marginally significant volume.We strongly disagree with Eklund et al.'s (1) summary statement: "Alarmingly, the parametric methods can give a very high degree of false positives (up to 70%, compared with the nominal 5%)." For comparison, their own nonparametric method's results actually showed up to 40% FPR. When characterizing results, medians or percentile ranges are generally more informative summary statistics than maxima. Looking backward, the typical ranges show much smaller FPR inflation than what had been highlighted, and looking forward they provide useful suggestions for experimental design and analyses (lower voxelwise P, event-related paradigms, etc.). By concentrating on the highest observed FPRs, the conclusions of Eklund et al. (1) are unnecessarily alarmist.