Periventricular and juxtacortical characterization of UManitoba-JHU functionally defined human white matter atlas networks.

Background: The open-access UManitoba-JHU functionally defined human white matter (WM) atlas contains specific WM pathways and general WM regions underlying 12 functional brain networks in ICBM152 template space. However, it is not known whether any of these WM networks are disproportionately co-localized with periventricular and/or juxtacortical WM (PVWM and JCWM), which could potentially impact their ability to infer network-specific effects in future studies-particularly in patient populations expected to have disproportionate PVWM and/or JCWM damage. Methods: The current study therefore identified intersecting regions of PVWM and JCWM (defined as WM within 5 mm of the ventricular and cortical boundaries) and: (1) the ICBM152 global WM mask, and (2) all 12 UManitoba-JHU WM networks. Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), and proportion of volume (POV) values between PVWM (and JCWM) and each functionally defined WM network were then compared to corresponding values between PVWM (and JCWM) and global WM. Results: Between the 12 WM networks and PVWM, 8 had lower DSC, JSC, and POV; 1 had lower DSC and JSC, but higher POV; and 3 had higher DSC, JSC, and POV compared to global WM. For JCWM, all 12 WM networks had lower DSC, JSC, and POV compared to global WM. Conclusion: The majority of UManitoba-JHU functionally defined WM networks exhibited lower than average spatial similarity with PVWM, and all exhibited lower than average spatial similarity with JCWM. This suggests that they can be used to explore network-specific WM changes, even in patient populations with known predispositions toward PVWM and/or JCWM damage.

Grasping with a Twist: Dissociating Action Goals from Motor Actions in Human Frontoparietal Circuits.

In daily life, prehension is typically not the end goal of hand-object interactions but a precursor for manipulation. Nevertheless, functional MRI (fMRI) studies investigating manual manipulation have primarily relied on prehension as the end goal of an action. Here, we used slow event-related fMRI to investigate differences in neural activation patterns between prehension in isolation and prehension for object manipulation. Sixteen (seven males and nine females) participants were instructed either to simply grasp the handle of a rotatable dial (isolated prehension) or to grasp and turn it (prehension for object manipulation). We used representational similarity analysis (RSA) to investigate whether the experimental conditions could be discriminated from each other based on differences in task-related brain activation patterns. We also used temporal multivoxel pattern analysis (tMVPA) to examine the evolution of regional activation patterns over time. Importantly, we were able to differentiate isolated prehension and prehension for manipulation from activation patterns in the early visual cortex, the caudal intraparietal sulcus (cIPS), and the superior parietal lobule (SPL). Our findings indicate that object manipulation extends beyond the putative cortical grasping network (anterior intraparietal sulcus, premotor and motor cortices) to include the superior parietal lobule and early visual cortex.SIGNIFICANCE STATEMENT A simple act such as turning an oven dial requires not only that the CNS encode the initial state (starting dial orientation) of the object but also the appropriate posture to grasp it to achieve the desired end state (final dial orientation) and the motor commands to achieve that state. Using advanced temporal neuroimaging analysis techniques, we reveal how such actions unfold over time and how they differ between object manipulation (turning a dial) versus grasping alone. We find that a combination of brain areas implicated in visual processing and sensorimotor integration can distinguish between the complex and simple tasks during planning, with neural patterns that approximate those during the actual execution of the action.

Neuropsychological and Structural Neuroimaging Outcomes in LGI1-Limbic Encephalitis: A Case Study.

OBJECTIVE: Anti-leucine-rich glioma-inactivated 1 limbic encephalitis (LGI1-LE) is a rare autoimmune condition that affects the structural integrity and functioning of the brain's limbic system. Little is known about its impact on long-term neuropsychological functioning and the structural integrity of the medial temporal lobe. Here we examined the long-term neuropsychological and neuroanatomical outcomes of a 68-year-old male who acquired LGI1-LE. METHODS: Our case patient underwent standardized neuropsychological testing at two time points. Volumetric analyses of T1-weighted images were undertaken at four separate time points and qualitatively compared with a group of age-matched healthy controls. RESULTS: At the time of initial assessment, our case study exhibited focal impairments in verbal and visual episodic memory and these impairments continued to persist after undergoing a course of immunotherapy. Furthermore, in reference to an age-matched healthy control group, over the course of 11 months, volumetric brain imaging analyses revealed that areas of the medial temporal lobe including specific hippocampal subfields (e.g., CA1 and dentate gyrus) underwent a subacute period of volumetric enlargement followed by a chronic period of volumetric reduction in the same regions. CONCLUSIONS: In patients with persisting neurocognitive deficits, LGI1-LE may produce chronic volume loss in specific areas of the medial temporal lobe; however, this appears to follow a subacute period of volume enlargement possibly driven by neuro-inflammatory processes.

Effects of Vascular Comorbidity on Cognition in Multiple Sclerosis Are Partially Mediated by Changes in Brain Structure.

Objective: Vascular comorbidities are associated with reduced cognitive performance and with changes in brain structure in people with multiple sclerosis (MS). Understanding causal pathways is necessary to support the design of interventions to mitigate the impacts of comorbidities, and to monitor their effectiveness. We assessed the inter-relationships among vascular comorbidity, cognition and brain structure in people with MS. Methods: Adults with neurologist-confirmed MS reported comorbidities, and underwent assessment of their blood pressure, HbA1c, and cognitive functioning (i.e., Symbol Digit Modalities Test, California Verbal Learning Test, Brief Visuospatial Memory Test-Revised, and verbal fluency). Test scores were converted to age-, sex-, and education-adjusted z-scores. Whole brain magnetic resonance imaging (MRI) was completed, from which measures of thalamic and hippocampal volumes, and mean diffusivity of gray matter and normal-appearing white matter were converted to age and sex-adjusted z-scores. Canonical correlation analysis was used to identify linear combinations of cognitive measures (cognitive variate) and MRI measures (MRI variate) that accounted for the most correlation between the cognitive and MRI measures. Regression analyses were used to test whether MRI measures mediated the relationships between the number of vascular comorbidities and cognition measures. Results: Of 105 participants, most were women (84.8%) with a mean (SD) age of 51.8 (12.8) years and age of symptom onset of 29.4 (10.5) years. Vascular comorbidity was common, with 35.2% of participants reporting one, 15.2% reporting two, and 8.6% reporting three or more. Canonical correlation analysis of the cognitive and MRI variables identified one pair of variates (Pillai's trace = 0.45, p = 0.0035). The biggest contributors to the cognitive variate were the SDMT and CVLT-II, and to the MRI variate were gray matter MD and thalamic volume. The correlation between cognitive and MRI variates was 0.50; these variates were used in regression analyses. On regression analysis, vascular comorbidity was associated with the MRI variate, and with the cognitive variate. After adjusting for the MRI variate, vascular comorbidity was not associated with the cognitive variate. Conclusion: Vascular comorbidity is associated with lower cognitive function in people with MS and this association is partially mediated via changes in brain macrostructure and microstructure.

The comorbidity and cognition in multiple sclerosis (CCOMS) neuroimaging protocol: Study rationale, MRI acquisition, and minimal image processing pipelines.

The Comorbidity and Cognition in Multiple Sclerosis (CCOMS) study represents a coordinated effort by a team of clinicians, neuropsychologists, and neuroimaging experts to investigate the neural basis of cognitive changes and their association with comorbidities among persons with multiple sclerosis (MS). The objectives are to determine the relationships among psychiatric (e.g., depression or anxiety) and vascular (e.g., diabetes, hypertension, etc.) comorbidities, cognitive performance, and MRI measures of brain structure and function, including changes over time. Because neuroimaging forms the basis for several investigations of specific neural correlates that will be reported in future publications, the goal of the current manuscript is to briefly review the CCOMS study design and baseline characteristics for participants enrolled in the three study cohorts (MS, psychiatric control, and healthy control), and provide a detailed description of the MRI hardware, neuroimaging acquisition parameters, and image processing pipelines for the volumetric, microstructural, functional, and perfusion MRI data.

Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure.

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive "thought experiments" to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

The Aging Imageomics Study: rationale, design and baseline characteristics of the study population.

Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging's (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged ≥50 years (mean 67, range 50-96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging.

Magnetic Resonance Imaging Biomarkers of Brain Connectivity in Predicting Outcome after Mild Traumatic Brain Injury: A Systematic Review.

There is growing interest in developing magnetic resonance imaging (MRI) biomarkers of brain connectivity from resting-state functional (rs-fMRI) and diffusion tensor imaging (DTI) to aid in the diagnosis and management of patients with mild traumatic brain injury (mTBI). To determine whether early MRI biomarkers of brain connectivity are useful in predicting outcome after mTBI, we conducted a systematic review using the following inclusion criteria: 1) patients aged >16 years with mTBI, 2) MRI performed during the first month post-injury, 3) outcome measure available, 4) control group, and 5) original article published in a peer-reviewed journal. Of the 1351 citations identified, 14 studies met inclusion criteria (5 rs-fMRI and 10 DTI; 680 patients with mTBI vs. 436 controls) including those where MRI was performed from <12 h to 1 month post-injury. The most common clinical outcome measure used in these studies was symptom burden using the Rivermead Post-Concussion Questionnaire. The most frequently studied brain connectivity MRI biomarkers were global functional connectivity, default-mode network, and fractional anisotropy (FA). Despite the scant evidence and considerable methodological heterogeneity observed among studies, we conclude that brain connectivity MRI biomarkers obtained within 1 month of injury may be potentially useful in predicting outcome in mTBI. Further longitudinal studies are needed to evaluate the effect of mTBI on MRI-based brain connectivity biomarkers and examine how incorporation of these tests can inform the clinical care of individual mTBI patients.

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package.

BACKGROUND: Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI - including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms - to evaluate their performance in the presence of MS-related pathologies. METHODS: 3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests. RESULTS: All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms. CONCLUSIONS: SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Comparisons between multi-component myelin water fraction, T1w/T2w ratio, and diffusion tensor imaging measures in healthy human brain structures.

Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures [T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = -0.49, ρ = -0.62), MD vs. MWF (r = -0.22, ρ = -0.29), RD vs. FA (r = -0.62, ρ = -0.75) and MD vs. FA (r = -0.22, ρ = -0.18)]. However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues - with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).

Effect of echo time and T2-weighting on GRASE-based T1w/T2w ratio measurements at 3T.

Tissue contrast can be enhanced by dividing T1-weighted (T1w) images by T2-weighted (T2w) images to map the so-called T1w/T2w ratio, which has become an increasingly popular technique for quantifying brain tissue changes associated with neurodevelopment, aging, and a variety of neurodegenerative diseases. However, although it is self-evident that T1w/T2w ratios increase with the amount of T2-weighting in the T2w image - which is determined by the echo time (TE), all else being equal - longer TEs also reduce the signal-to-noise ratio (SNR) of the T2w images, and it is not clear how these SNR characteristics affect the reliability of T1w/T2w measurements. Therefore, the current study systematically investigated how different amounts of T2-weighting affected T1w/T2w measurements in order to determine whether there is an optimal amount of T2-weighting. T1w images were acquired from 10 neurologically healthy adults using a 3D turbo field echo (TFE) sequence, and a series of T2-weighted images were extracted from a multi-echo 3D combined gradient- and spin-echo (GRASE) sequence. Analyses of 12 anatomically defined brain regions revealed that both the mean and standard deviation of the T1w/T2w measurements increased exponentially with TE of the T2w images, and that T2w images with TE ≈ 120-160 ms yielded the most consistent/reproducible contrast between white matter ROIs and the whole-brain T1w/T2w signal. Furthermore, comparisons between T1w/T2w measurements and multi-component T2-relaxation myelin water fractions (MWFs) in the same brain regions revealed that T2w images with TE ≥ 160 ms drastically reduced the degree of correlation between T1w/T2w measurements and MWF. Overall, these findings suggest that: 1) there is a substantial trade-off between increased T1w/T2w contrast (based on longer TEs for the T2w images) and the reliability of quantitative T1w/T2w signals; and 2) the optimal TE for T2w GRASE scans is between 120 ms and 160 ms for calculating T1w/T2w ratios.

Can T1 w/T2 w ratio be used as a myelin-specific measure in subcortical structures? Comparisons between FSE-based T1 w/T2 w ratios, GRASE-based T1 w/T2 w ratios and multi-echo GRASE-based myelin water fractions.

Given the growing popularity of T1 -weighted/T2 -weighted (T1 w/T2 w) ratio measurements, the objective of the current study was to evaluate the concordance between T1 w/T2 w ratios obtained using conventional fast spin echo (FSE) versus combined gradient and spin echo (GRASE) sequences for T2 w image acquisition, and to compare the resulting T1 w/T2 w ratios with histologically validated myelin water fraction (MWF) measurements in several subcortical brain structures. In order to compare these measurements across a relatively wide range of myelin concentrations, whole-brain T1 w magnetization prepared rapid acquisition gradient echo (MPRAGE), T2 w FSE and three-dimensional multi-echo GRASE data were acquired from 10 participants with multiple sclerosis at 3 T. Then, after high-dimensional, non-linear warping, region of interest (ROI) analyses were performed to compare T1 w/T2 w ratios and MWF estimates (across participants and brain regions) in 11 bilateral white matter (WM) and four bilateral subcortical grey matter (SGM) structures extracted from the JHU_MNI_SS 'Eve' atlas. Although the GRASE sequence systematically underestimated T1 w/T2 w values compared to the FSE sequence (revealed by Bland-Altman and mountain plots), linear regressions across participants and ROIs revealed consistently high correlations between the two methods (r2 = 0.62 for all ROIs, r2 = 0.62 for WM structures and r2 = 0.73 for SGM structures). However, correlations between either FSE-based or GRASE-based T1 w/T2 w ratios and MWFs were extremely low in WM structures (FSE-based, r2 = 0.000020; GRASE-based, r2 = 0.0014), low across all ROIs (FSE-based, r2 = 0.053; GRASE-based, r2 = 0.029) and moderate in SGM structures (FSE-based, r2 = 0.20; GRASE-based, r2 = 0.17). Overall, our findings indicated a high degree of correlation (but not equivalence) between FSE-based and GRASE-based T1 w/T2 w ratios, and low correlations between T1 w/T2 w ratios and MWFs. This suggests that the two T1 w/T2 w ratio approaches measure similar facets of subcortical tissue microstructure, whereas T1 w/T2 w ratios and MWFs appear to be sensitized to different microstructural properties. On this basis, we conclude that multi-echo GRASE sequences can be used in future studies to efficiently elucidate both general (T1 w/T2 w ratio) and myelin-specific (MWF) tissue characteristics.

Human neuroimaging reveals the subcomponents of grasping, reaching and pointing actions.

Although the neural underpinnings of visually guided grasping and reaching have been well delineated within lateral and medial fronto-parietal networks (respectively), the contributions of subcomponents of visuomotor actions have not been explored in detail. Using careful subtraction logic, here we investigated which aspects of grasping, reaching, and pointing movements drive activation across key areas within visuomotor networks implicated in hand actions. For grasping tasks, we find activation differences based on the precision required (fine > coarse grip: anterior intraparietal sulcus, aIPS), the requirement to lift the object (grip + lift > grip: aIPS; dorsal premotor cortex, PMd; and supplementary motor area, SMA), and the number of digits employed (3-/5- vs. 2-digit grasps: ventral premotor cortex, PMv; motor cortex, M1, and somatosensory cortex, S1). For reaching/pointing tasks, we find activation differences based on whether the task required arm transport ((reach-to-point with index finger and reach-to-touch with knuckles) vs. point-without-reach; anterior superior parietal lobule, aSPL) and whether it required pointing to the object centre ((point-without-reach and reach-to-point) vs. reach-to-touch: anterior superior parieto-occipital cortex, aSPOC). For point-without-reach, in which the index finger is oriented towards the object centre but from a distance (point-without-reach > (reach-to-point and reach-to-touch)), we find activation differences that may be related to the communicative nature of the task (temporo-parietal junction, TPJ) and the need to precisely locate the target (lateral occipito-temporal cortex, LOTC). The present findings elucidate the different subcomponents of hand actions and the roles of specific brain regions in their computation.

Recruitment of Foveal Retinotopic Cortex During Haptic Exploration of Shapes and Actions in the Dark.

The role of the early visual cortex and higher-order occipitotemporal cortex has been studied extensively for visual recognition and to a lesser degree for haptic recognition and visually guided actions. Using a slow event-related fMRI experiment, we investigated whether tactile and visual exploration of objects recruit the same "visual" areas (and in the case of visual cortex, the same retinotopic zones) and if these areas show reactivation during delayed actions in the dark toward haptically explored objects (and if so, whether this reactivation might be due to imagery). We examined activation during visual or haptic exploration of objects and action execution (grasping or reaching) separated by an 18 s delay. Twenty-nine human volunteers (13 females) participated in this study. Participants had their eyes open and fixated on a point in the dark. The objects were placed below the fixation point and accordingly visual exploration activated the cuneus, which processes retinotopic locations in the lower visual field. Strikingly, the occipital pole (OP), representing foveal locations, showed higher activation for tactile than visual exploration, although the stimulus was unseen and location in the visual field was peripheral. Moreover, the lateral occipital tactile-visual area (LOtv) showed comparable activation for tactile and visual exploration. Psychophysiological interaction analysis indicated that the OP showed stronger functional connectivity with anterior intraparietal sulcus and LOtv during the haptic than visual exploration of shapes in the dark. After the delay, the cuneus, OP, and LOtv showed reactivation that was independent of the sensory modality used to explore the object. These results show that haptic actions not only activate "visual" areas during object touch, but also that this information appears to be used in guiding grasping actions toward targets after a delay.SIGNIFICANCE STATEMENT Visual presentation of an object activates shape-processing areas and retinotopic locations in early visual areas. Moreover, if the object is grasped in the dark after a delay, these areas show "reactivation." Here, we show that these areas are also activated and reactivated for haptic object exploration and haptically guided grasping. Touch-related activity occurs not only in the retinotopic location of the visual stimulus, but also at the occipital pole (OP), corresponding to the foveal representation, even though the stimulus was unseen and located peripherally. That is, the same "visual" regions are implicated in both visual and haptic exploration; however, touch also recruits high-acuity central representation within early visual areas during both haptic exploration of objects and subsequent actions toward them. Functional connectivity analysis shows that the OP is more strongly connected with ventral and dorsal stream areas when participants explore an object in the dark than when they view it.

Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks.

Background: Despite the popularity of functional connectivity analyses and the well-known topology of several intrinsic cortical networks, relatively little is known about the white matter regions (i.e., structural connectivity) underlying these networks. In the current study, we have therefore performed fMRI-guided diffusion tensor imaging (DTI) tractography to create probabilistic white matter atlases for eight previously identified functional brain networks, including the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Methods: Whole-brain diffusion imaging data were acquired from a cohort of 32 healthy volunteers, and were warped to the ICBM template using a two-stage, high-dimensional, non-linear spatial normalization procedure. Deterministic tractography, with fractional anisotropy (FA) ≥0.15 and deviation angle <50°, was then performed using the Fiber Association by Continuous Tracking (FACT) algorithm, and a multi-ROI approach to identify tracts of interest. Regions-of-interest (ROIs) for each of the eight networks were taken from a pre-existing atlas of functionally defined regions to explore all ROI-to-ROI connections within each network, and all resulting streamlines were saved as binary masks to create probabilistic atlases (across participants) for tracts between each ROI-to-ROI pair. Results: The resulting functionally-defined white matter atlases (i.e., for each tract and each network as a whole) were saved as NIFTI images in stereotaxic ICBM coordinates, and have been added to the UManitoba-JHU Functionally-Defined Human White Matter Atlas (http://www.nitrc.org/projects/uofm_jhu_atlas/). Conclusion: To the best of our knowledge, this work represents the first attempt to comprehensively identify and map white matter connectomes for the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Therefore, the resulting probabilistic atlases represent a unique tool for future neuroimaging studies wishing to ascribe voxel-wise or ROI-based changes (i.e., in DTI or other quantitative white matter imaging signals) to these functional brain networks.

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study.

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, "tractography") approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template-using high-dimensional, non-linear warping methods-we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.