LayNii: A software suite for layer-fMRI.

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex.

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 â€‹mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits' muscles are used for different motor actions such as flexion movements, like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for non-invasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

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.

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.

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.

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.

Processing, evaluating and understanding FMRI data with afni_proc.py.

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but first outputs a fully commented processing script that the users can read, query, interpret and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting state FMRI example commands.

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.

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 underdiscussed 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 a 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.

A diffusion tensor imaging white matter atlas of the domestic canine brain.

There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion-weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts-including association, projection, commissural, brainstem, olfactory, and cerebellar tracts-were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.

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 a 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.

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.

Temporal association of polysomnographic cardiorespiratory events with GER detected by MII-pH probe in the premature infant at term.

OBJECTIVES: The aim of the study was to examine temporal association (TA) between polysomnographic cardiorespiratory (CR) events and gastroesophageal reflux (GER) in premature infants with persistent CR events at >39 weeks postmenstrual age and determine whether the use of multichannel intraluminal impedance (MII)-pH probe improves sensitivity of the TA compared with pH probe alone. PATIENTS AND METHODS: Seven infants born between 24 and 29 weeks' gestational age with persistent CR events at 39 to 48 weeks' postmenstrual age underwent a polysomnography with MII-pH probe. Symptom index (SI) and symptom-associated probability were calculated for diverse types of reflux and CR events. SI and a Fisher exact test with variable association windows were calculated for obstructive apnea (OA). Odds ratios for an OA given a reflux event and for a reflux event given an OA were determined. RESULTS: With a Fisher exact test, a subject-specific association between MII events and OA was found in the 3 patients who required a fundoplication or had the worse clinical GER. Some level of TA was found with SI and symptom-associated probability in 6 of 7 infants. Association was found for pH > 4 and pH ≤ 4 reflux events. pH-only events with no change of MII had only a limited role in generating CR events. CONCLUSIONS: TA between CR events and GER was found in a single-subject-level analysis in some infants with persistent CR events at term. This TA suggests a causal relation between CR and reflux events that was further strengthened by the clinical outcomes of each infant.

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.

A new method for improving functional-to-structural MRI alignment using local Pearson correlation.

Accurate registration of Functional Magnetic Resonance Imaging (FMRI) T2-weighted volumes to same-subject high-resolution T1-weighted structural volumes is important for Blood Oxygenation Level Dependent (BOLD) FMRI and crucial for applications such as cortical surface-based analyses and pre-surgical planning. Such registration is generally implemented by minimizing a cost functional, which measures the mismatch between two image volumes over the group of proper affine transformations. Widely used cost functionals, such as mutual information (MI) and correlation ratio (CR), appear to yield decent alignments when visually judged by matching outer brain contours. However, close inspection reveals that internal brain structures are often significantly misaligned. Poor registration is most evident in the ventricles and sulcal folds, where CSF is concentrated. This observation motivated our development of an improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2- and T1-weighted images. In the absence of an alignment gold standard, we used three human observers blinded to registration method to provide an independent assessment of the quality of the registration for each cost functional. We found that LPC performed significantly better (p<0.001) than generic cost functionals including MI and CR. Generic cost functionals were very often not minimal near the best alignment, thereby suggesting that optimization is not the cause of their failure. Lastly, we emphasize the importance of precise visual inspection of alignment quality and present an automated method for generating composite images that help capture errors of misalignment.

ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids.

BACKGROUND: Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. NEW METHOD: ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for ECoG electrode localization. RESULTS: ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. COMPARISON WITH EXISTING METHODS: A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. CONCLUSIONS: The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.

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.