Improving DTI tractography by including diagonal tract propagation

Identifying sources of human interictal discharges with travelling wave and white matter propagation

Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.

Differences in network properties of the structural connectome in bipolar and unipolar depression

BACKGROUND

Differentiation between Bipolar Disorder Depression (BDD) and Unipolar Major Depressive Disorder (MDD) is critical to clinical practice. This study investigated machine learning classification of BDD and MDD using graph properties of Diffusion-weighted Imaging (DWI)-based structural connectome.

METHODS

This study included a large number of medication-free (N =229) subjects: 60 BDD, 95 MDD, and 74 Healthy Control (HC) subjects. DWI probabilistic tractography was performed to create Fractional Anisotropy (FA) and Total Streamline (TS)-based structural connectivity matrices. Global and nodal graph properties were computed from these matrices and tested for group differences. Next, using identified graph properties, machine learning classification (MLC) between BDD, MDD, MDD with risk factors for developing BD (MDD+), and MDD without risk factors for developing BD (MDD-) was conducted.

RESULTS

Communicability Efficiency of the left superior frontal gyrus (SFG) was significantly higher in BDD vs. MDD. In particular, Communicability Efficiency using TS-based connectivity in the left SFG as well as FA-based connectivity in the right middle anterior cingulate area was higher in the BDD vs. MDD- group. There were no significant differences in graph properties between BDD and MDD+. Direct comparison between MDD+ and MDD- showed differences in Eigenvector Centrality (TS-based connectivity) of the left middle frontal sulcus. Acceptable Area Under Curve (AUC) for classification were seen between the BDD and MDD- groups, and between the MDD+ and MDD- groups, using the differing graph properties.

CONCLUSION

Graph properties of DWI-based connectivity can discriminate between BDD and MDD subjects without risk factors for BD.

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.

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 collaborative resource platform for non-human primate neuroimaging

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.

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.

Relationship between topological efficiency in white matter structural networks with cerebral oxygen metabolism in young adults

The relationship between the topological characteristics of the white matter (WM) network have been shown to be related to neural development, intelligence, and various diseases; however, few studies have been conducted to explore the relationship between topological characteristics of the WM network and cerebral metabolism. In a recent study we investigated the relationship between WM network topological and metabolic metrics of the cerebral parenchyma in healthy volunteers using the newly developed T2-relaxation-under-spin-tagging (TRUST) magnetic resonance imaging technique and graph theory approaches. Ninety-six healthy adults (25.5 ± 1.8 years of age) were recruited as volunteers in the current study. The cerebral metabolic rate of oxygen (CMRO₂), oxygen extraction fraction, and the global topological metrics of the WM network (global efficiency [E_glob], local efficiency, and small-worldliness) were assessed. A stepwise multiple linear regression model was estimated. CMRO₂ was entered as the dependent variable. The topological and demographic parameters (age, gender, FIQ, SBP, gray matter volume, and WM volume) were entered as independent variables in the model. The final performing models were comprised of predictors of E_glob, FIQ, and age (adjusted R² values were 0.489 [L-AAL] and 0.424 [H-1024]). Our study initially revealed a relationship between E_glob and cerebral oxygen metabolism in healthy young adults.

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.

Connectivity profiles of the insular network for speech control in healthy individuals and patients with spasmodic dysphonia

The importance of insula in speech control is acknowledged but poorly understood, partly due to a variety of clinical symptoms resulting from insults to this structure. To clarify its structural organization within the speech network in healthy subjects, we used probabilistic diffusion tractography to examine insular connectivity with three cortical regions responsible for sound processing [Brodmann area (BA) 22], motor preparation (BA 44) and motor execution (laryngeal/orofacial primary motor cortex, BA 4). To assess insular reorganization in a speech disorder, we examined its structural connectivity in patients with spasmodic dysphonia (SD), a neurological condition that selectively affects speech production. We demonstrated structural segregation of insula into three non-overlapping regions, which receive distinct connections from BA 44 (anterior insula), BA 4 (mid-insula) and BA 22 (dorsal and posterior insula). There were no significant differences either in the number of streamlines connecting each insular subdivision to the cortical target or hemispheric lateralization of insular clusters and their projections between healthy subjects and SD patients. However, spatial distribution of the insular subdivisions connected to BA 4 and BA 44 was distinctly organized in healthy controls and SD patients, extending ventro-posteriorly in the former group and anterio-dorsally in the latter group. Our findings point to structural segregation of the insular sub-regions, which may be associated with the different aspects of sensorimotor and cognitive control of speech production. We suggest that distinct insular involvement may lead to different clinical manifestations when one or the other insular region and/or its connections undergo spatial reorganization.

Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation

Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

Changes in functional organization and white matter integrity in the connectome in Parkinson's disease

Parkinson's disease (PD) leads to dysfunction in multiple cortico-striatal circuits. The neurodegeneration has also been associated with impaired white matter integrity. This structural and functional “disconnection” in PD needs further characterization.

We investigated the structural and functional organization of the PD whole brain connectome consisting of 200 nodes using diffusion tensor imaging and resting-state functional MRI, respectively. Data from 20 non-demented PD patients on dopaminergic medication and 20 matched controls were analyzed using graph theory-based methods. We focused on node strength, clustering coefficient, and local efficiency as measures of local network properties; and network modularity as a measure of information flow.

PD patients showed reduced white matter connectivity in frontoparietal-striatal nodes compared to controls, but no change in modular organization of the white matter tracts. PD group also showed reduction in functional local network metrics in many nodes distributed across the connectome. There was also decreased functional modularity in the core cognitive networks including the default mode and dorsal attention networks, and sensorimotor network, as well as a lack of modular distinction in the orbitofrontal and basal ganglia nodes in the PD group compared to controls.

Our results suggest that despite subtle white matter connectivity changes, the overall structural organization of the PD connectome remains robust at relatively early disease stages. However, there is a breakdown in the functional modular organization of the PD connectome.

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DTI and rs-fMRI investigation of the connectome in Parkinson's disease (PD)

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Local network properties and modularity were examined using graph theory.

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White matter connectivity was decreased in frontoparietal-striatal nodes in PD.

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Functional modularity of cognitive and motor networks was reduced in PD.

Structural Organization of the Laryngeal Motor Cortical Network and Its Implication for Evolution of Speech Production

The laryngeal motor cortex (LMC) is essential for the production of learned vocal behaviors because bilateral damage to this area renders humans unable to speak but has no apparent effect on innate vocalizations such as human laughing and crying or monkey calls. Several hypotheses have been put forward attempting to explain the evolutionary changes from monkeys to humans that potentially led to enhanced LMC functionality for finer motor control of speech production. These views, however, remain limited to the position of the larynx area within the motor cortex, as well as its connections with the phonatory brainstem regions responsible for the direct control of laryngeal muscles. Using probabilistic diffusion tractography in healthy humans and rhesus monkeys, we show that, whereas the LMC structural network is largely comparable in both species, the LMC establishes nearly 7-fold stronger connectivity with the somatosensory and inferior parietal cortices in humans than in macaques. These findings suggest that important "hard-wired" components of the human LMC network controlling the laryngeal component of speech motor output evolved from an already existing, similar network in nonhuman primates. However, the evolution of enhanced LMC-parietal connections likely allowed for more complex synchrony of higher-order sensorimotor coordination, proprioceptive and tactile feedback, and modulation of learned voice for speech production.

SIGNIFICANCE STATEMENT

The role of the primary motor cortex in the formation of a comprehensive network controlling speech and language has been long underestimated and poorly studied. Here, we provide comparative and quantitative evidence for the significance of this region in the control of a highly learned and uniquely human behavior: speech production. From the viewpoint of structural network organization, we discuss potential evolutionary advances of enhanced temporoparietal cortical connections with the laryngeal motor cortex in humans compared with nonhuman primates that may have contributed to the development of finer vocal motor control necessary for speech production.

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.

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.

Location of brain tumor intersecting white matter tracts predicts patient prognosis

Brain tumor cells invade adjacent normal brain along white matter (WM) bundles of axons. We therefore hypothesized that the location of tumor intersecting WM tracts would be associated with differing survival. This study introduces a method, voxel-wise survival analysis (VSA), to determine the relationship between the location of brain tumor intersecting WM tracts and patient prognosis. 113 primary glioblastoma (GBM) patients were retrospectively analyzed for this study. Patient specific tumor location, defined by contrast-enhancement, was combined with diffusion tensor imaging derived tractography to determine the location of axons intersecting tumor enhancement (AXITEs). VSA was then used to determine the relationship between the AXITE location and patient survival. Tumors intersecting the right anterior thalamic radiation (ATR), right inferior fronto-occipital fasciculus (IFOF), right and left cortico-spinal tract (CST), and corpus callosum (CC) were associated with decreased overall survival. Tumors intersecting the CST, body of the CC, right ATR, posterior IFOF, and inferior longitudinal fasciculus are associated with decreased progression-free survival (PFS), while tumors intersecting the right genu of the CC and anterior IFOF are associated with increased PFS. Patients with tumors intersecting the ATR, IFOF, CST, or CC had significantly improved survival prognosis if they were additionally treated with bevacizumab. This study demonstrates the usefulness of VSA by locating AXITEs associated with poor prognosis in GBM patients. This information should be included in patient-physician conversations, therapeutic strategy, and clinical trial design.

Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution

Recent emergence of human connectome imaging has led to a high demand on angular and spatial resolutions for diffusion magnetic resonance imaging (MRI). While there have been significant growths in high angular resolution diffusion imaging, the improvement in spatial resolution is still limited due to a number of technical challenges, such as the low signal-to-noise ratio and high motion artifacts. As a result, the benefit of a high spatial resolution in the whole-brain connectome imaging has not been fully evaluated in vivo. In this brief report, the impact of spatial resolution was assessed in a newly acquired whole-brain three-dimensional diffusion tensor imaging data set with an isotropic spatial resolution of 0.85 mm. It was found that the delineation of short cortical association fibers is drastically improved as well as the definition of fiber pathway endings into the gray/white matter boundary-both of which will help construct a more accurate structural map of the human brain connectome.

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.