Paul A. Taylor

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.

AFNI and Clustering: False Positive Rates Redux

In response to reports of inflated false positive rate (FPR) in FMRI group analysis tools, a series of replications, investigations, and software modifications were made to address this issue. While these investigations continue, significant progress has been made to adapt AFNI to fix such problems. Two separate lines of changes have been made. First, a long-tailed model for the spatial correlation of the FMRI noise characterized by autocorrelation function (ACF) was developed and implemented into the 3dClustSim tool for determining the cluster-size threshold to use for a given voxel-wise threshold. Second, the 3dttest++ program was modified to do randomization of the voxel-wise t-tests and then to feed those randomized t-statistic maps into 3dClustSim directly for cluster-size threshold determination-without any spatial model for the ACF. These approaches were tested with the Beijing subset of the FCON-1000 data collection. The first approach shows markedly improved (reduced) FPR, but in many cases is still above the nominal 5%. The second approach shows FPRs clustered tightly about 5% across all per-voxel p-value thresholds ≤ 0.01. If t-tests from a univariate GLM are adequate for the group analysis in question, the second approach is what the AFNI group currently recommends for thresholding. If more complex per-voxel statistical analyses are required (where permutation/randomization is impracticable), then our current recommendation is to use the new ACF modeling approach coupled with a per-voxel p-threshold of 0.001 or below. Simulations were also repeated with the now infamously “buggy” version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent).

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. Hum Brain Mapp, 36:170–186, 2015.

Is the statistic value all we should care about in neuroimaging

ABSTRACT 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.

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.

Use of jackknife resampling techniques to estimate the confidence intervals of fMRI parameters.

Purpose The objective of this study was to determine the reliability and the confidence intervals of task-activated functional MRI (fMRI) parameters using a computer-intensive resampling technique. The jackknife, a commonly used method for resampling mathematical data, was used to calculate the confidence interval of fMRI parameters for a simple bilateral finger-tapping paradigm. Method Four healthy test subjects (three men, one woman) were used to test the correlation coefficient and variability in the data. Each subject performed 4.5 cycles, each cycle having 20 s of bilateral finger tapping alternating with rest periods of equal time, producing 90 images. One additional scan of 10 cycles (200 images) was used to test the stability of the method itself. One thousand jackknifed resampled data sets of 85 elements each (from 90 original points) were generated, and the correlation coefficient was determined using an idealized “on/off” box-car reference waveform. Results Activation maps were generated that had the same confidence intervals at each pixel. These maps were more localized with less extraneous activated pixels than the maps generated with a fixed correlation coefficient threshold. There was no significant difference in the distribution of correlation coefficients between the 85, 90, and 95 element, jackknifed data sets; similar robustness was seen, as well. Conclusion The jackknife resampling technique for data analysis produced reliable distributions and statistical parameters. The jackknife estimates were shown to be stable, even from a small initial sample size. This method may be used in lieu of test-retest analysis.

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 Parkinsons 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. Hum Brain Mapp 37:422–433, 2016. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Segregation of frontoparietal and cerebellar components within saccade and vergence networks using hierarchical independent component analysis of fMRI

PURPOSE Cortical and subcortical functional activity stimulated via saccade and vergence eye movements were investigated to examine the similarities and differences between networks and regions of interest (ROIs). METHODS Blood oxygenation level-dependent (BOLD) signals from stimulus-induced functional Magnetic Resonance Imaging (MRI) experiments were analyzed studying 16 healthy subjects. Six types of oculomotor experiments were conducted using a block design to study both saccade and vergence circuits. The experiments included a simple eye movement task and a more cognitively demanding prediction task. A hierarchical independent component analysis (ICA) process began by analyzing individual subject data sets with spatial ICA to extract spatial independent components (sIC), which resulted in three ROIs. Using the time series from each of the three ROIs per subject, per oculomotor experiment, a temporal ICA was used to compute individual temporal independent components (tICs). For each of the three ROIs, the individual tICs from multiple subjects were entered into a second temporal ICA to compute group-level tICs for comparison. RESULTS Two independent spatial maps were observed for each subject (one sIC showing activity in the frontoparietal regions and another sIC in the cerebellum) during the six oculomotor tasks. Analysis of group-level tICs revealed an increased latency in the cerebellar region when compared to the frontoparietal region. CONCLUSION Shared neuronal behavior has been reported in the frontal and parietal lobes, which may in part explain the segregation of frontoparietal functional activity into one sIC. The cerebellum uses multiple time scales for motor learning. This may result in an increased latency observed in the BOLD signal of the cerebellar group-level tIC when compared to the frontal and parietal group-level tICs. The increased latency offers a possible explanation to why ICA dissects the cerebellar activity into an sIC. The hierarchical ICA process used to calculate group-level tICs can yield insight into functional connectivity within complex neural networks.

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 scanners 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.