Improving 3D edge detection for visual inspection of MRI coregistration and alignment

BACKGROUND

Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another.

NEW METHOD

We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative).

RESULTS

We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity.

COMPARISON WITH EXISTING METHODS

Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image.

CONCLUSION

The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

Lower resting state functional connectivity partially mediates adverse effects of prenatal alcohol exposure on arithmetic performance in children

BACKGROUND

Fetal alcohol spectrum disorders (FASD) include a range of neurocognitive and behavioral impairments resulting from prenatal alcohol exposure (PAE). Among the PAE-related cognitive deficits, number processing is particularly affected. This study examines alterations in number processing networks and whether changes in functional connectivity mediate the adverse effects of PAE on arithmetic performance.

METHODS

Magnetic resonance imaging (MRI) was acquired in 57 children (mean (SD) age = 11.3 (+0.9) yr), 38 with FASD (19 fetal alcohol syndrome (FAS) or partial FAS (PFAS), 19 heavily exposed (HE)) and 19 controls. Whole-brain correlation analyses were performed from five seeds located in regions involved in number processing.

RESULTS

Children with FAS/PFAS showed dose-dependent reductions in resting state functional connectivity between the seed in the right (R) posterior superior parietal lobule and a cluster in the left (L) inferior frontal gyrus, and between a seed in the R horizontal intraparietal sulcus and clusters in the R precentral gyrus and L cerebellar lobule VI. HE children showed lower resting state functional connectivity in a subset of these regions. Lower functional connectivity in the two fronto-parietal connections partially mediated the adverse effects of PAE on arithmetic performance.

CONCLUSION

This study demonstrates PAE-related functional connectivity impairments in functional networks involved in number processing. The weaker connectivity between the R posterior superior parietal lobule and the L inferior frontal gyrus suggests that impaired verbal processing and visuospatial working memory may play a role in number processing deficits, while weaker connectivity between the R intraparietal sulcus and the R precentral gyrus points to poorer finger-based numerical representation, which has been linked to arithmetic computational skills.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include: a modular HTML document that covers full single subject processing from the raw data through statistical modeling; several review scripts in the results directory of processed data; and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block", as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Typical fMRI analyses often assume a canonical hemodynamic response function (HRF) that primarily focuses on the peak height of the overshoot, neglecting other morphological aspects. Consequently, reported analyses often reduce the overall response curve to a single scalar value. In this study, we take a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. We then employ a roughness penalty at the population level to estimate the response curve, aiming to enhance predictive accuracy, inferential efficiency, and cross-study reproducibility. By examining a fast event-related FMRI dataset, we demonstrate the shortcomings and information loss associated with adopting the canonical approach. Furthermore, we address the following key questions: 1) To what extent does the HRF shape vary across different regions, conditions, and participant groups? 2) Does the data-driven approach improve detection sensitivity compared to the canonical approach? 3) Can analyzing the HRF shape help validate the presence of an effect in conjunction with statistical evidence? 4) Does analyzing the HRF shape offer evidence for whole-brain response during a simple task?

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.

Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI

Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, "Demonstrating Quality Control (QC) Procedures in fMRI." We used a sequential, hierarchical approach that contained the following major stages: (1) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject's dataset was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the detailed description of QC procedures: How to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available.

Hyperbolic trade-off: The importance of balancing trial and subject sample sizes in neuroimaging

Here we investigate the crucial role of trials in task-based neuroimaging from the perspectives of statistical efficiency and condition-level generalizability. Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most task-based FMRI designs feature a relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision. Nevertheless, little attention has been given to another important dimension of experimental design, which can equally boost a study's statistical efficiency: the trial sample size. The common practice of condition-level modeling implicitly assumes no cross-trial variability. Here, we systematically explore the different factors that impact effect uncertainty, drawing on evidence from hierarchical modeling, simulations and an FMRI dataset of 42 subjects who completed a large number of trials of cognitive control task. We find that, due to an approximately symmetic hyperbola-relationship between trial and subject sample sizes in the presence of relatively large cross-trial variability, 1) trial sample size has nearly the same impact as subject sample size on statistical efficiency; 2) increasing both the number of trials and subjects improves statistical efficiency more effectively than focusing on subjects alone; 3) trial sample size can be leveraged alongside subject sample size to improve the cost-effectiveness of an experimental design; 4) for small trial sample sizes, trial-level modeling, rather than condition-level modeling through summary statistics, may be necessary to accurately assess the standard error of an effect estimate. We close by making practical suggestions for improving experimental designs across neuroimaging and behavioral studies.

Multiple parietal pathways are associated with rTMS-induced hippocampal network enhancement and episodic memory changes

Repetitive transcranial magnetic stimulation (rTMS) of the inferior parietal cortex (IPC) increases resting-state functional connectivity (rsFC) of the hippocampus with the precuneus and other posterior cortical areas and causes proportional improvement of episodic memory. The anatomical pathway(s) responsible for the propagation of these effects from the IPC is unknown and may not be direct. In order to assess the relative contributions of candidate pathways from the IPC to the MTL via the parahippocampal cortex and precuneus, to the effects of rTMS on rsFC and memory improvement, we used diffusion tensor imaging to measure the extent to which individual differences in fractional anisotropy (FA) in these pathways accounted for individual differences in response. FA in the IPC-parahippocampal pathway and several MTL pathways predicted changes in rsFC. FA in both parahippocampal and hippocampal pathways was related to changes in episodic, but not procedural, memory. These results implicate pathways to the MTL in the enhancing effect of parietal rTMS on hippocampal rsFC and memory.

A comprehensive macaque fMRI pipeline and hierarchical atlas

Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging . Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI's new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category ("face"), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.

Executive function network's white matter alterations relate to Parkinson's disease motor phenotype

Parkinson's disease (PD) patients with postural instability and gait disorder phenotype (PIGD) are at high risk of cognitive deficits compared to those with tremor dominant phenotype (TD). Alterations of white matter (WM) integrity can occur in patients with normal cognitive functions (PD-N). However, the alterations of WM integrity related to cognitive functions in PD-N, especially in these two motor phenotypes, remain unclear. Diffusion tensor imaging (DTI) is a non-invasive neuroimaging method to evaluate WM properties and by applying DTI tractography, one can identify WM tracts connecting functional regions. Here, we 1) compared the executive function (EF) in PIGD phenotype with normal cognitive functions (PIGD-N) and TD phenotype with normal cognitive functions (TD-N) phenotypes; 2) used DTI tractography to evaluated differences in WM alterations between these two phenotypes within a task-based functional network; and 3) examined the WM integrity alterations related to EF in a whole brain network for PD-N patients regardless of phenotypes. Thirty-four idiopathic PD-N patients were classified into two groups based on phenotypes: TD-N and PIGD-N, using an algorithm based on UPDRS part III. Neuropsychological tests were used to evaluate patients' EF, including the Trail making test part A and B, the Stroop color naming, the Stroop word naming, the Stroop color-word interference task, as well as the FAS verbal fluency task and the animal category fluency tasks. DTI measures were calculated among WM regions associated with the verbal fluency network defined from previous task fMRI studies and compared between PIGD-N and TD-N groups. In addition, the relationship of DTI measures and verbal fluency scores were evaluated for our full cohort of PD-N patients within the whole brain network. These values were also correlated with the scores of the FAS verbal fluency task. Only the FAS verbal fluency test showed significant group differences, having lower scores in PIGD-N when compared to TD-N phenotype (p < 0.05). Compared to the TD-N, PIGD-N group exhibited significantly higher MD and RD in the tracts connecting the left superior temporal gyrus and left insula, and those connecting the right pars opercularis and right insula. Moreover, compared to TD-N, PIGD-N group had significantly higher RD in the tracts connecting right pars opercularis and right pars triangularis, and the tracts connecting right inferior temporal gyrus and right middle temporal gyrus. For the entire PD-N cohort, FAS verbal fluency scores positively correlated with MD in the superior longitudinal fasciculus (SLF). This study confirmed that PIGD-N phenotype has more deficits in verbal fluency task than TD-N phenotype. Additionally, our findings suggest: (1) PIGD-N shows more microstructural changes related to FAS verbal fluency task when compared to TD-N phenotype; (2) SLF plays an important role in FAS verbal fluency task in PD-N patients regardless of motor phenotypes.

A series of five population-specific Indian brain templates and atlases spanning ages 6-60 years

Anatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group‐level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative‐template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n = 466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age‐specific IBTs (ages 6–60 years) as well as maximum probability map (MPM) atlases for each template; for each age‐group's template–atlas pair, there is both a “population‐average” and a “typical” version. Validation experiments on an independent Indian structural and functional‐MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior–Posterior and Inferior–Superior axes, but minimal Left–Right. For each age‐group, the MPM brain atlases provide reasonably good representation of the native‐space volumes in the IBT space, except in a few regions with high intersubject variability. These findings provide evidence to support the use of age and population‐specific templates in human brain mapping studies.

Reduced fractional anisotropy in projection, association, and commissural fiber networks in neonates with prenatal methamphetamine exposure

Prenatal exposure to methamphetamine is associated with neurostructural changes, including alterations in white matter microstructure. This study investigated the effects of methamphetamine exposure on microstructure of global white matter networks in neonates. Pregnant women were interviewed beginning in mid-pregnancy regarding their methamphetamine use. Diffusion weighted imaging sets were acquired for 23 non-sedated neonates. White matter bundles associated with pairs of target regions within five networks (commissural fibers, left and right projection fibers, and left and right association fibers) were estimated using probabilistic tractography, and fractional anisotropy (FA) and diffusion measures determined within each connection. Multiple regression analyses showed that increasing methamphetamine exposure was significantly associated with reduced FA in all five networks, after control for potential confounders. Increased exposure was associated with lower axial diffusivity in the right association fiber network and with increased radial diffusivity in the right projection and left and right association fiber networks. Within the projection and association networks a subset of individual connections showed a negative correlation between FA and methamphetamine exposure. These findings are consistent with previous reports in older children and demonstrate that microstructural changes associated with methamphetamine exposure are already detectable in neonates.

Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as handling the multiple testing issue. Although the linear mixed-effects (LME) modeling approach (Chen et al., 2017a) is capable of capturing the relatedness in the data and incorporating explanatory variables, there are a few challenging issues: 1) it is difficult to assign accurate degrees of freedom for each testing statistic, 2) multiple testing correction is potentially over-penalizing due to model inefficiency, and 3) thresholding necessitates arbitrary dichotomous decisions. Here we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model. By loosely constraining the regions through a weakly informative prior, BML dissolves multiplicity through conservatively pooling the effect of each region toward the center and improves collective fitting and overall model performance. In addition to potentially achieving a higher inference efficiency, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in results reporting. The associated program will be available as part of the AFNI suite for general use.

Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI

Knowing the difference between left and right is generally assumed throughout the brain MRI research community. However, we note widespread occurrences of left-right orientation errors in MRI open database repositories where volumes have contained systematic left-right flips between subject EPIs and anatomicals, due to having incorrect or missing file header information. Here we present a simple method in AFNI for determining the consistency of left and right within a pair of acquired volumes for a particular subject; the presence of EPI-anatomical inconsistency, for example, is a sign that dataset header information likely requires correction. The method contains both a quantitative evaluation as well as a visualizable verification. We test the functionality using publicly available datasets. Left-right flipping is not immediately obvious in most cases, so we also present visualization methods for looking at this problem (and other potential problems), using examples from both FMRI and DTI datasets.

Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration

Neuroimaging faces the daunting challenge of multiple testing - an instance of multiplicity - that is associated with two other issues to some extent: low inference efficiency and poor reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local "unbiased" effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing "correction" by taking into consideration spatial relatedness, or neighborhood leverage, can only partly recoup statistical efficiency, resulting in potentially excessive penalization as well as arbitrariness due to thresholding procedures. Moreover, the assigned statistical evidence at the global level heavily relies on the data space (whole brain or a small volume). The present paper reviews how Stein's paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embraces it to our advantage through a global calibration process among spatial units. Global calibration is accomplished via a Gaussian distribution for the cross-region effects whose properties are not a priori specified, but a posteriori determined by the data at hand through the BML model. Our framework therefore incorporates multiplicity as integral to the modeling structure, not a separate correction step. By turning multiplicity into a strength, we aim to achieve five goals: 1) improve the model efficiency with a higher predictive accuracy, 2) control the errors of incorrect magnitude and incorrect sign, 3) validate each model relative to competing candidates, 4) reduce the reliance and sensitivity on the choice of data space, and 5) encourage full results reporting. Our modeling proposal reverberates with recent proposals to eliminate the dichotomization of statistical evidence ("significant" vs. "non-significant"), to improve the interpretability of study findings, as well as to promote reporting the full gamut of results (not only "significant" ones), thereby enhancing research transparency and reproducibility.

Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

An integrative Bayesian approach to matrix-based analysis in neuroimaging

Understanding the correlation structure associated with brain regions is a central goal in neuroscience, as it informs about interregional relationships and network organization. Correlation structure can be conveniently captured in a matrix that indicates the relationships among brain regions, which could involve electroencephalogram sensors, electrophysiology recordings, calcium imaging data, or functional magnetic resonance imaging (FMRI) data-We call this type of analysis matrix-based analysis, or MBA. Although different methods have been developed to summarize such matrices across subjects, including univariate general linear models (GLMs), the available modeling strategies tend to disregard the interrelationships among the regions, leading to "inefficient" statistical inference. Here, we develop a Bayesian multilevel (BML) modeling framework that simultaneously integrates the analyses of all regions, region pairs (RPs), and subjects. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. The adoption of the Bayesian framework allows us to achieve three goals: (a) dissolve the multiple testing issue typically associated with seeking evidence for the effect of each RP under the conventional univariate GLM; (b) make inferences on effects that would be treated as "random" under the conventional linear mixed-effects framework; and (c) estimate the effect of each brain region in a manner that indexes their relative "importance". We demonstrate the BML methodology with an FMRI dataset involving a cognitive-emotional task and compare it to the conventional GLM approach in terms of model efficiency, performance, and inferences. The associated program MBA is available as part of the AFNI suite for general use.

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.

Prenatal methamphetamine exposure is associated with corticostriatal white matter changes in neonates

Diffusion tensor imaging (DTI) studies have shown that prenatal exposure to methamphetamine is associated with alterations in white matter microstructure, but to date no tractography studies have been performed in neonates. The striato-thalamo-orbitofrontal circuit and its associated limbic-striatal areas, the primary circuit responsible for reinforcement, has been postulated to be dysfunctional in drug addiction. This study investigated potential white matter changes in the striatal-orbitofrontal circuit in neonates with prenatal methamphetamine exposure. Mothers were recruited antenatally and interviewed regarding methamphetamine use during pregnancy, and DTI sequences were acquired in the first postnatal month. Target regions of interest were manually delineated, white matter bundles connecting pairs of targets were determined using probabilistic tractography in AFNI-FATCAT, and fractional anisotropy (FA) and diffusion measures were determined in white matter connections. Regression analysis showed that increasing methamphetamine exposure was associated with reduced FA in several connections between the striatum and midbrain, orbitofrontal cortex, and associated limbic structures, following adjustment for potential confounding variables. Our results are consistent with previous findings in older children and extend them to show that these changes are already evident in neonates. The observed alterations are likely to play a role in the deficits in attention and inhibitory control frequently seen in children with prenatal methamphetamine exposure.

Intraclass correlation: Improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

Functional Connectivity Alterations between Networks and Associations with Infant Immune Health within Networks in HIV Infected Children on Early Treatment: A Study at 7 Years

Although HIV has been shown to impact brain connectivity in adults and youth, it is not yet known to what extent long-term early antiretroviral therapy (ART) may alter these effects, especially during rapid brain development in early childhood. Using both independent component analysis (ICA) and seed-based correlation analysis (SCA), we examine the effects of HIV infection in conjunction with early ART on resting state functional connectivity (FC) in 7 year old children. HIV infected (HIV+) children were from the Children with HIV Early Antiretroviral Therapy (CHER) trial and all initiated ART before 18 months; uninfected children were recruited from an interlinking vaccine trial. To better understand the effects of current and early immune health on the developing brain, we also investigated among HIV+ children the association of FC at 7 years with CD4 count and CD4%, both in infancy (6–8 weeks) and at scan. Although we found no differences within any ICA-generated resting state networks (RSNs) between HIV+ and uninfected children (27 HIV+, 18 uninfected), whole brain connectivity to seeds located at RSN connectivity peaks revealed several loci of FC differences, predominantly from seeds in midline regions (posterior cingulate cortex, paracentral lobule, cuneus, and anterior cingulate). Reduced long-range connectivity and increased short-range connectivity suggest developmental delay. Within the HIV+ children, clinical measures at age 7 years were not associated with FC values in any of the RSNs; however, poor immune health during infancy was associated with localized FC increases in the somatosensory, salience and basal ganglia networks. Together these findings suggest that HIV may affect brain development from its earliest stages and persist into childhood, despite early ART.

White Matter Abnormalities in Children with HIV Infection and Exposure

Background: Due to changes in guidelines and access to treatment, more children start combination antiretroviral therapy (ART) in infancy. With few studies examining the long-term effects of perinatal HIV infection and early ART on neurodevelopment, much is still unknown about brain maturation in the presence of HIV and ART. Follow-up studies of HIV infected (HIV+) children are important for monitoring brain development in the presence of HIV infection and ART.

Methods: We use diffusion tensor imaging (DTI) to examine white matter (WM) in 65 HIV+ and 46 control (HIV exposed uninfected (HEU) and HIV unexposed uninfected (HU)) 7-year-old children. This is a follow up of a cohort studied at 5 years, where we previously reported lower fractional anisotropy (FA) in corticospinal tract (CST) and mean diffusivity (MD) increases in inferior/superior longitudinal fasciculi (ILF/SLF), inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UF) in HIV+ children compared to uninfected controls. In addition, we also found a difference in FA related to age at which ART was initiated.

Results: At 7 years, we found two regions in the left IFOF and left ILF with lower FA in HIV+ children compared to controls. Higher MD was observed in a similar region in the IFOF, albeit bilaterally, as well as multiple clusters bilaterally in the superior corona radiata (SCR), the anterior thalamic radiation (ATR) and the right forceps minor. Unlike at 5 years, we found no impact on WM of ART initiation. In HEU children, we found a cluster in the right posterior corona radiata with higher FA compared to HU children, while bilateral regions in the CST demonstrated reduced MD.

Conclusions: At age 7, despite early ART and viral load (VL) suppression, we continue to observe differences in WM integrity. WM damage observed at age 5 years persists, and new damage is evident. The continued observation of regions with lower FA and higher MD in HIV+ children point to disruptions in ongoing white matter development regardless of early ART. Lastly, in HEU children we find higher FA and lower MD in clusters in the CST tract suggesting that perinatal HIV/ART exposure has a long-term impact on WM development.

Localized reductions in resting-state functional connectivity in children with prenatal alcohol exposure

Fetal alcohol spectrum disorders (FASD) are characterized by impairment in cognitive function that may or may not be accompanied by craniofacial anomalies, microcephaly, and/or growth retardation. Resting-state functional MRI (rs-fMRI), which examines the low-frequency component of the blood oxygen level dependent (BOLD) signal in the absence of an explicit task, provides an efficient and powerful mechanism for studying functional brain networks even in low-functioning and young subjects. Studies using independent component analysis (ICA) have identified a set of resting-state networks (RSNs) that have been linked to distinct domains of cognitive and perceptual function, which are believed to reflect the intrinsic functional architecture of the brain. This study is the first to examine resting-state functional connectivity within these RSNs in FASD. Rs-fMRI scans were performed on 38 children with FASD (19 with either full fetal alcohol syndrome (FAS) or partial FAS (PFAS), 19 nonsyndromal heavily exposed (HE)), and 19 controls, mean age 11.3 ± 0.9 years, from the Cape Town Longitudinal Cohort. Nine resting-state networks were generated by ICA. Voxelwise group comparison between a combined FAS/PFAS group and controls revealed localized dose-dependent functional connectivity reductions in five regions in separate networks: anterior default mode, salience, ventral and dorsal attention, and R executive control. The former three also showed lower connectivity in the HE group. Gray matter connectivity deficits in four of the five networks appear to be related to deficits in white matter tracts that provide intra-RSN connections. Hum Brain Mapp 38:5217-5233, 2017. © 2017 Wiley Periodicals, Inc.

MAPBOT: Meta-analytic parcellation based on text, and its application to the human thalamus

Meta-analysis of neuroimaging results has proven to be a popular and valuable method to study human brain functions. A number of studies have used meta-analysis to parcellate distinct brain regions. A popular way to perform meta-analysis is typically based on the reported activation coordinates from a number of published papers. However, in addition to the coordinates associated with the different brain regions, the text itself contains considerably amount of additional information. This textual information has been largely ignored in meta-analyses where it may be useful for simultaneously parcellating brain regions and studying their characteristics. By leveraging recent advances in document clustering techniques, we introduce an approach to parcellate the brain into meaningful regions primarily based on the text features present in a document from a large number of studies. This new method is called MAPBOT (Meta-Analytic Parcellation Based On Text). Here, we first describe how the method works and then the application case of understanding the sub-divisions of the thalamus. The thalamus was chosen because of the substantial body of research that has been reported studying this functional and structural structure for both healthy and clinical populations. However, MAPBOT is a general-purpose method that is applicable to parcellating any region(s) of the brain. The present study demonstrates the powerful utility of using text information from neuroimaging studies to parcellate brain regions.

Evaluation of A 12-Month Pilot of Long-Term and Temporary Assisted Peritoneal Dialysis

Background

Peritoneal dialysis (PD) is challenging for patients with functional limitations, and assisted PD can support these patients, but previous reports of assisted PD have not examined the role of temporary assisted PD and had difficulty identifying adequate comparator cohorts.

Methods

Peritoneal Dialysis Assist (PDA), a 12-month pilot of long-term and temporary assisted PD was completed in multiple PD centers in British Columbia, Canada. Continuous cycler PD (CCPD) patients were identified for PDA by standardized criteria, and service could be long-term or temporary/respite. The PDA program provided daily assistance with cycler dismantle and setup, but patients remained responsible for cycler connections and treatment decisions. Outcomes were compared against both the general CCPD population and patients who met PDA criteria but were not enrolled (PDA-eligible).

Results

Fifty-three PDA patients had an 88% 1-year death- and transplant-censored technique survival that was similar to the general CCPD cohort (84%) and PDA-eligible cohort (86%). The PDA cohort had lower peritonitis rates (0.18 episodes/patient-year vs 0.22 and 0.36, respectively), but higher hospitalization (55% vs 34% and 35%, respectively). Long-term PDA cost approximately CDN$15,000/year in addition to existing dialysis costs. A total of 8/11 respite PDA patients (73%) returned to self-care PD after a median PDA use of 29 days, which costs $1,250/patient.

Conclusions

Peritoneal Dialysis Assist provides effective support to functionally-limited CCPD patients and yields acceptable clinical outcomes. The program costs less than transfer to HD or long-term care, which represents cost minimization for failing self-care PD patients. Respite PDA provides effective temporary support; most patients returned to self-care PD and service was cost-effective compared with alternatives of hospitalization or transfer to HD.

FMRI Clustering in AFNI: False-Positive Rates Redux

Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 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 Gaussian shaped 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 depends 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, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.

Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling

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 publicly available in AFNI (http://afni.nimh.nih.gov).

Is the statistic value all we should care about in neuroimaging?

Here we address an important issue that has been embedded within the neuroimaging community for a long time: the absence of effect estimates in results reporting in the literature. The statistic value itself, as a dimensionless measure, does not provide information on the biophysical interpretation of a study, and it certainly does not represent the whole picture of a study. Unfortunately, in contrast to standard practice in most scientific fields, effect (or amplitude) estimates are usually not provided in most results reporting in the current neuroimaging publications and presentations. Possible reasons underlying this general trend include (1) lack of general awareness, (2) software limitations, (3) inaccurate estimation of the BOLD response, and (4) poor modeling due to our relatively limited understanding of FMRI signal components. However, as we discuss here, such reporting damages the reliability and interpretability of the scientific findings themselves, and there is in fact no overwhelming reason for such a practice to persist. In order to promote meaningful interpretation, cross validation, reproducibility, meta and power analyses in neuroimaging, we strongly suggest that, as part of good scientific practice, effect estimates should be reported together with their corresponding statistic values. We provide several easily adaptable recommendations for facilitating this process.

Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction

Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter (WM) maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical WM maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion. Hum Brain Mapp 37:4405-4424, 2016. © 2016 Wiley Periodicals, Inc.

White matter deficits mediate effects of prenatal alcohol exposure on cognitive development in childhood

Fetal alcohol spectrum disorders comprise the spectrum of cognitive, behavioral, and neurological impairments caused by prenatal alcohol exposure (PAE). Diffusion tensor imaging (DTI) was performed on 54 children (age 10.1 ± 1.0 years) from the Cape Town Longitudinal Cohort, for whom detailed drinking histories obtained during pregnancy are available: 26 with full fetal alcohol syndrome (FAS) or partial FAS (PFAS), 15 nonsyndromal heavily exposed (HE), and 13 controls. Using voxelwise analyses, children with FAS/PFAS showed significantly lower fractional anisotropy (FA) in four white matter (WM) regions and higher mean diffusivity (MD) in seven; three regions of FA and MD differences (left inferior longitudinal fasciculus (ILF), splenium, and isthmus) overlapped, and the fourth FA cluster was located in the same WM bundle (right ILF) as an MD cluster. HE children showed lower FA and higher MD in a subset of these regions. Significant correlations were observed between three continuous alcohol measures and DTI values at cluster peaks, indicating that WM damage in several regions is dose dependent. Lower FA in the regions of interest was attributable primarily to increased radial diffusivity rather than decreased axonal diffusivity, suggesting poorer axon packing density and/or myelination. Multiple regression models indicated that this cortical WM impairment partially mediated adverse effects of PAE on information processing speed and eyeblink conditioning. Hum Brain Mapp 37:2943-2958, 2016. © 2016 Wiley Periodicals, Inc.

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

FMRI data acquisition under naturalistic and continuous stimuli (e.g., watching a video or listening to music) has become popular recently due to the fact that it entails less manipulation and more realistic/complex contexts involved in the task, compared to the conventional task-based experimental designs. The synchronization or response similarities among subjects are typically measured through inter-subject correlation (ISC) between any pair of subjects. At the group level, summarizing the collection of ISC values is complicated by their intercorrelations, which necessarily lead to the violation of independence assumed in typical parametric approaches such as Student's t-test. Nonparametric methods, such as bootstrapping and permutation testing, have previously been adopted for testing purposes by resampling the time series of each subject, but the quantitative validity of these specific approaches in terms of controllability of false positive rate (FPR) has never been explored before. Here we survey the methods of ISC group analysis that have been employed in the literature, and discuss the issues involved in those methods. We then propose less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level. As part of the new approaches, subject-wise (SW) resampling is adopted instead of element-wise (EW) resampling, so that exchangeability and independence assumptions are satisfied, and the patterned correlation structure among the ISC values can be more accurately captured. We examine the FPR controllability and power achievement of all the methods through simulations, as well as their performance when applied to a real experimental dataset.

Real-time measurement and correction of both B0 changes and subject motion in diffusion tensor imaging using a double volumetric navigated (DvNav) sequence

Diffusion tensor imaging (DTI) requires a set of diffusion weighted measurements in order to acquire enough information to characterize local structure. The MRI scanner automatically performs a shimming process by acquiring a field map before the start of a DTI scan. Changes in B0, which can occur throughout the DTI acquisition due to several factors (including heating of the iron shim coils or subject motion), cause significant signal distortions that result in warped diffusion tensor (DT) parameter estimates. In this work we introduce a novel technique to simultaneously measure, report and correct in real time subject motion and changes in B0 field homogeneity, both in and through the imaging plane. This is achieved using double volumetric navigators (DvNav), i.e. a pair of 3D EPI acquisitions, interleaved with the DTI pulse sequence. Changes in the B0 field are evaluated in terms of zero-order (frequency) and first-order (linear gradients) shim. The ability of the DvNav to accurately estimate the shim parameters was first validated in a water phantom. Two healthy subjects were scanned both in the presence and absence of motion using standard, motion corrected (single navigator, vNav), and DvNav DTI sequences. The difference in performance between the proposed 3D EPI field maps and the standard 3D gradient echo field maps of the MRI scanner was also evaluated in a phantom and two healthy subjects. The DvNav sequence was shown to accurately measure and correct changes in B0 following manual adjustments of the scanner's central frequency and the linear shim gradients. Compared to other methods, the DvNav produced DTI results that showed greater spatial overlap with anatomical references, particularly in scans with subject motion. This is largely due to the ability of the DvNav system to correct shim changes and subject motion between each volume acquisition, thus reducing shear distortion.

Open Environment for Multimodal Interactive Connectivity Visualization and Analysis

Brain connectivity investigations are becoming increasingly multimodal and they present challenges for quantitatively characterizing and interactively visualizing data. In this study, we present a new set of network-based software tools for combining functional and anatomical connectivity from magnetic resonance imaging (MRI) data. The computational tools are available as part of Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), a toolbox that interfaces with Analysis of Functional NeuroImages (AFNI) and SUrface MApping (SUMA) for interactive queries and visualization. This includes a novel, tractographic mini-probabilistic approach to improve streamline tracking in networks. We show how one obtains more robust tracking results for determining white matter connections by utilizing the uncertainty of the estimated diffusion tensor imaging (DTI) parameters and a few Monte Carlo iterations. This allows for thresholding based on the number of connections between target pairs to reduce the presence of tracts likely due to noise. To assist users in combining data, we describe an interface for navigating and performing queries in two-dimensional and three-dimensional data defined over voxel, surface, tract, and graph domains. These varied types of information can be visualized simultaneously and the queries performed interactively using SUMA and AFNI. The methods have been designed to increase the user's ability to visualize and combine functional MRI and DTI modalities, particularly in the context of single-subject inferences (e.g., in deep brain stimulation studies). Finally, we present a multivariate framework for statistically modeling network-based features in group analysis, which can be implemented for both functional and structural studies.

DBSproc: An open source process for DBS electrode localization and tractographic analysis

Deep brain stimulation (DBS) is an effective surgical treatment for movement disorders. Although stimulation sites for movement disorders such as Parkinson's disease are established, the therapeutic mechanisms of DBS remain controversial. Recent research suggests that specific white-matter tract and circuit activation mediates symptom relief. To investigate these questions, we have developed a patient-specific open-source software pipeline called 'DBSproc' for (1) localizing DBS electrodes and contacts from postoperative CT images, (2) processing structural and diffusion MRI data, (3) registering all images to a common space, (4) estimating DBS activation volume from patient-specific voltage and impedance, and (5) understanding the DBS contact-brain connectivity through probabilistic tractography. In this paper, we explain our methodology and provide validation with anatomical and tractographic data. This method can be used to help investigate mechanisms of action of DBS, inform surgical and clinical assessments, and define new therapeutic targets.

Functional topography of the thalamocortical system in human

Various studies have indicated that the thalamus is involved in controlling both cortico-cortical information flow and cortical communication with the rest of the brain. Detailed anatomy and functional connectivity patterns of the thalamocortical system are essential to understanding the cortical organization and pathophysiology of a wide range of thalamus-related neurological and neuropsychiatric diseases. The current study used resting-state fMRI to investigate the topography of the human thalamocortical system from a functional perspective. The thalamus-related cortical networks were identified by performing independent component analysis on voxel-based thalamic functional connectivity maps across a large group of subjects. The resulting functional brain networks were very similar to well-established resting-state network maps. Using these brain network components in a spatial regression model with each thalamic voxel's functional connectivity map, we localized the thalamic subdivisions related to each brain network. For instance, the medial dorsal nucleus was shown to be associated with the default mode, the bilateral executive, the medial visual networks; and the pulvinar nucleus was involved in both the dorsal attention and the visual networks. These results revealed that a single nucleus may have functional connections with multiple cortical regions or even multiple functional networks, and may be potentially related to the function of mediation or modulation of multiple cortical networks. This observed organization of thalamocortical system provided a reference for studying the functions of thalamic sub-regions. The importance of intrinsic connectivity-based mapping of the thalamocortical relationship is discussed, as well as the applicability of the approach for future studies.

A DTI-based tractography study of effects on brain structure associated with prenatal alcohol exposure in newborns

Prenatal alcohol exposure (PAE) is known to have severe, long-term consequences for brain and behavioral development already detectable in infancy and childhood. Resulting features of fetal alcohol spectrum disorders include cognitive and behavioral effects, as well as facial anomalies and growth deficits. Diffusion tensor imaging (DTI) and tractography were used to analyze white matter (WM) development in 11 newborns (age since conception <45 weeks) whose mothers were recruited during pregnancy. Comparisons were made with nine age-matched controls born to abstainers or light drinkers from the same Cape Coloured (mixed ancestry) community near Cape Town, South Africa. DTI parameters, T1 relaxation time, proton density and volumes were used to quantify and investigate group differences in WM in the newborn brains. Probabilistic tractography was used to estimate and to delineate similar tract locations among the subjects for transcallosal pathways, cortico-spinal projection fibers, and cortico-cortical association fibers. In each of these WM networks, the axial diffusivity was the parameter that showed the strongest association with maternal drinking. The strongest relations were observed in medial and inferior WM, regions in which the myelination process typically begins. In contrast to studies of older individuals with PAE, fractional anisotropy did not exhibit a consistent and significant relation with alcohol exposure. To our knowledge, this is the first DTI-tractography study of prenatally alcohol exposed newborns.

Motion artifact reduction in pediatric diffusion tensor imaging using fast prospective correction

PURPOSE

To evaluate the patterns of head motion in scans of young children and to examine the influence of corrective techniques, both qualitatively and quantitatively. We investigate changes that both retrospective (with and without diffusion table reorientation) and prospective (implemented with a short navigator sequence) motion correction induce in the resulting diffusion tensor measures.

MATERIALS AND METHODS

Eighteen pediatric subjects (aged 5-6 years) were scanned using 1) a twice-refocused, 2D diffusion pulse sequence, 2) a prospectively motion-corrected, navigated diffusion sequence with reacquisition of a maximum of five corrupted diffusion volumes, and 3) a T1 -weighted structural image. Mean fractional anisotropy (FA) values in white and gray matter regions, as well as tractography in the brainstem and projection fibers, were evaluated to assess differences arising from retrospective (via FLIRT in FSL) and prospective motion correction. In addition to human scans, a stationary phantom was also used for further evaluation.

RESULTS

In several white and gray matter regions retrospective correction led to significantly (P < 0.05) reduced FA means and altered distributions compared to the navigated sequence. Spurious tractographic changes in the retrospectively corrected data were also observed in subject data, as well as in phantom and simulated data.

CONCLUSION

Due to the heterogeneity of brain structures and the comparatively low resolution (∼2 mm) of diffusion data using 2D single shot sequencing, retrospective motion correction is susceptible to distortion from partial voluming. These changes often negatively bias diffusion tensor imaging parameters. Prospective motion correction was shown to produce smaller changes.

FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox

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.

Changes in intrinsic functional brain networks following blast-induced mild traumatic brain injury

OBJECTIVE

Blast-induced mild traumatic brain injuries (mTBI) commonly go undetected by computed tomography and conventional magnetic resonance imaging (MRI). This study was used to investigate functional brain network abnormalities in a group of blast-induced mTBI subjects using independent component analysis (ICA) of resting state functional MRI (fMRI) data.

METHODS

Twenty-eight resting state networks of 13 veterans who sustained blast-induced mTBI were compared with healthy controls across three fMRI domains: blood oxygenation level-dependent spatial maps, time course spectra and functional connectivity.

RESULTS

The mTBI group exhibited hyperactivity in the temporo-parietal junctions and hypoactivity in the left inferior temporal gyrus. Abnormal frequencies in default-mode (DMN), sensorimotor, attentional and frontal networks were detected. In addition, functional connectivity was disrupted in six network pairs: DMN-basal ganglia, attention-sensorimotor, frontal-DMN, attention-sensorimotor, attention-frontal and sensorimotor-sensorimotor.

CONCLUSIONS

The results suggest white matter disruption across certain attentional networks. Additionally, given their elevated activity relative to controls', the temporo-parietal junctions of blast mTBI subjects may be compensating for diffuse axonal injury in other cortical regions.

Investigation of blast-induced traumatic brain injury

Objective

Many troops deployed in Iraq and Afghanistan have sustained blast-related, closed-head injuries from being within non-lethal distance of detonated explosive devices. Little is known, however, about the mechanisms associated with blast exposure that give rise to traumatic brain injury (TBI). This study attempts to identify the precise conditions of focused stress wave energy within the brain, resulting from blast exposure, which will correlate with a threshold for persistent brain injury.

Methods

This study developed and validated a set of modelling tools to simulate blast loading to the human head. Using these tools, the blast-induced, early-time intracranial wave motions that lead to focal brain damage were simulated.

Results

The simulations predict the deposition of three distinct wave energy components, two of which can be related to injury-inducing mechanisms, namely cavitation and shear. Furthermore, the results suggest that the spatial distributions of these damaging energy components are independent of blast direction.

Conclusions

The predictions reported herein will simplify efforts to correlate simulation predictions with clinical measures of TBI and aid in the development of protective headwear.

Baseline bone mineral density and boneturnover in pre-operative hip and knee arthroplasty patients

Aims

Osteoporosis and abnormal bone metabolism may prove to be significant factors influencing the outcome of arthroplasty surgery, predisposing to complications of aseptic loosening and peri-prosthetic fracture. We aimed to investigate baseline bone mineral density (BMD) and bone turnover in patients about to undergo arthroplasty of the hip and knee.

Methods

We prospectively measured bone mineral density of the hip and lumbar spine using dual-energy X-ray absorptiometry (DEXA) scans in a cohort of 194 patients awaiting hip or knee arthroplasty. We also assessed bone turnover using urinary deoxypyridinoline (DPD), a type I collagen crosslink, normalised to creatinine.

Results

The prevalence of DEXA proven hip osteoporosis (T-score ≤ -2.5) among hip and knee arthroplasty patients was found to be low at 2.8% (4 of 143). Spinal osteoporosis prevalence was higher at 6.9% (12 of 175). Sixty patients (42% (60 of 143)) had osteopenia or osteoporosis of either the hip or spine. The mean T-score for the hip was -0.34 (sd 1.23), which is within normal limits, and the mean hip Z-score was positive at 0.87 (sd 1.17), signifying higher-than-average BMD for age. The median urinary DPD/creatinine was raised in both female patients at 8.1 (interquartile range (IQR) 6.6 to 9.9) and male patients at 6.2 (IQR 4.8 to 7.5).

Conclusions

Our results indicate hip and knee arthroplasty patients have higher BMD of the hip and spine compared with an age-matched general population, and a lower prevalence of osteoporosis. However, untreated osteoporotic patients are undergoing arthroplasty, which may negatively impact their outcome. Raised DPD levels suggest abnormal bone turnover, requiring further investigation.

Cite this article: Bone Joint Res 2014;3:14–19.

Selective reduction in trabecular volumetric bone mineral density during treatment for childhood acute lymphoblastic leukemia

During treatment of childhood acute lymphoblastic leukemia (ALL) fracture incidence is increased. Studies using DXA, which measures a composite of both trabecular and cortical BMD, have shown reduced BMD during treatment. We investigated changes in compartmental (cortical and trabecular) volumetric BMD (vBMD) and bone geometry using peripheral quantitative computed tomography. These outcomes were also analysed in relation to adiposity and treatment factors. Thirty nine patients with ALL (64% male, median age 7.2 years (4.1-16.9)) were compared to 34 healthy controls (50% male, median age 9.1 years (4.4-18.7)). DXA-derived age-specific standard deviation scores (SDS) of the lumbar spine (LS) and femoral neck (FN) were reduced in subjects with ALL compared to controls (p ≤ 0.01). This persisted following adjustment for body size using height-specific SDS (LS -0.72 ± 1.02 vs -0.18 ± 0.72, p=0.01; FN -1.53 ± 0.96 vs -0.74 ± 0.74, p=0.001) and bone mineral apparent density (BMAD) SDS (LS -0.76 ± 1.14 vs 0.04 ± 1.08, p=0.01; FN -1.63 ± 1.38 vs -0.16 ± 1.20, p<0.001). Radial and tibial trabecular vBMD was also reduced (196.5 ± 54.9 mg/cm(3) vs 215.2 ± 39.9 mg/cm(3), p=0.03 and 232.8 ± 60.3mg/cm(3) vs 267.5 ± 60.2mg/cm(3), p=0.002, respectively), but cortical vBMD at the radius and tibia was similar in patients and controls. A lowered tibial bone strength index (BSI) was identified in patients with ALL (53.9 ± 23.1mg/mm(4) vs 82.5 ± 27.8 mg/mm(4), p<0.001) suggesting lower fracture threshold from compressive forces. No relationships with measures of adiposity, duration of treatment or cumulative corticosteroid dose were identified. Our findings therefore suggest that reduction in trabecular vBMD during childhood ALL treatment may contribute to the observed increased fracture incidence and bony morbidity in this group.

Improving DTI tractography by including diagonal tract propagation

Tractography algorithms have been developed to reconstruct likely WM pathways in the brain from diffusion tensor imaging (DTI) data. In this study, an elegant and simple means for improving existing tractography algorithms is proposed by allowing tracts to propagate through diagonal trajectories between voxels, instead of only rectilinearly to their facewise neighbors. A series of tests (using both real and simulated data sets) are utilized to show several benefits of this new approach. First, the inclusion of diagonal tract propagation decreases the dependence of an algorithm on the arbitrary orientation of coordinate axes and therefore reduces numerical errors associated with that bias (which are also demonstrated here). Moreover, both quantitatively and qualitatively, including diagonals decreases overall noise sensitivity of results and leads to significantly greater efficiency in scanning protocols; that is, the obtained tracts converge much more quickly (i.e., in a smaller amount of scanning time) to those of data sets with high SNR and spatial resolution. Importantly, the inclusion of diagonal propagation adds essentially no appreciable time of calculation or computational costs to standard methods. This study focuses on the widely-used streamline tracking method, FACT (fiber assessment by continuous tracking), and the modified method is termed "FACTID" (FACT including diagonals).

Functional covariance networks: obtaining resting-state networks from intersubject variability

In this study, we investigate a new approach for examining the separation of the brain into resting-state networks (RSNs) on a group level using resting-state parameters (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [fALFF], the Hurst exponent, and signal standard deviation). Spatial independent component analysis is used to reveal covariance patterns of the relevant resting-state parameters (not the time series) across subjects that are shown to be related to known, standard RSNs. As part of the analysis, nonresting state parameters are also investigated, such as mean of the blood oxygen level-dependent time series and gray matter volume from anatomical scans. We hypothesize that meaningful RSNs will primarily be elucidated by analysis of the resting-state functional connectivity (RSFC) parameters and not by non-RSFC parameters. First, this shows the presence of a common influence underlying individual RSFC networks revealed through low-frequency fluctation (LFF) parameter properties. Second, this suggests that the LFFs and RSFC networks have neurophysiological origins. Several of the components determined from resting-state parameters in this manner correlate strongly with known resting-state functional maps, and we term these "functional covariance networks".

Functional connectivity in vergence and saccade eye movement tasks assessed using Granger Causality analysis

Throughout the day, the human visual system acquires information using saccade and vergence eye movements. Previously, functional MRI (fMRI) experiments have shown both shared neural resources and spatial differentiation between these two systems. FMRI experiments can reveal which regions are activated within an experimental task but do not yield insight into how regions of interest (ROIs) interact with each other. This study investigated the number and direction of influences among ROIs using a Granger Causality Analysis (GCA)--a statistical technique used to identify if an ROI is significantly influencing or 'connected' to another ROI. Two stimulus protocols were used: first, a simple block design of fixation versus random eye movements; and second, a more cognitively demanding task using random versus predictable movements. Each protocol used saccadic movements and was then repeated using vergence movements. Eight subjects participated in each of the four experiments. Results show that when prediction was evoked, more connections between ROIs were observed compared to the simple tracking experiment. More connections were also observed during the vergence prediction task compared to the saccade prediction task. Differences within the number of connections may be due to the type of oculomotor eye movements, as well as to the amount of higher-level executive cognitive demand.

Clinical working postures of bachelor of oral health students

OBJECTIVE

To observe and describe the clinical working postures of final-year Bachelor of Oral Health (BOH) students.

DESIGN

Pilot observational study.

SETTING

The University of Otago Faculty of Dentistry and School of Physiotherapy.

METHODS

Eight final-year BOH students voluntarily participated in this study, where postural data were collected using a digital video camera during a standard clinical treatment session. The postural data were analysed using 3D Match biomechanical software.

RESULTS

Final-year BOH students who work in the seated position are exposed to neck flexion of greater than 35 degrees, together with trunk flexion greater than 20 degrees and bilateral elbow flexion greater than 90 degrees.

CONCLUSIONS

The findings of this study agree with the findings of previous postural studies of dental professionals. Dental hygiene students, together with their clinical supervisors, need to be aware of the importance of good working posture early in their careers, and pay particular attention to the degree of neck flexion occurring for prolonged periods.