Free-water correction DTI-based tractography in brain tumor surgery: assessment with functional and electrophysiological mapping of the white matter.

Peritumoral edema prevents fiber tracking from diffusion tensor imaging (DTI). A free-water correction may overcome this drawback, as illustrated in the case of a patient undergoing awake surgery for brain metastasis. The anatomical plausibility and accuracy of tractography with and without free-water correction were assessed with functional mapping and axono-cortical evoked-potentials (ACEPs) as reference methods. The results suggest a potential synergy between corrected DTI-based tractography and ACEPs to reliably identify and preserve white matter tracts during brain tumor surgery.

Connectomic assessment of injury burden and longitudinal structural network alterations in moderate-to-severe traumatic brain injury.

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Distance-based analysis of variance for brain connectivity.

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.

System-level matching of structural and functional connectomes in the human brain.

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.

Neuroimaging Findings in US Government Personnel With Possible Exposure to Directional Phenomena in Havana, Cuba.

IMPORTANCE: United States government personnel experienced potential exposures to uncharacterized directional phenomena while serving in Havana, Cuba, from late 2016 through May 2018. The underlying neuroanatomical findings have not been described. OBJECTIVE: To examine potential differences in brain tissue volume, microstructure, and functional connectivity in government personnel compared with individuals not exposed to directional phenomena. DESIGN, SETTING, AND PARTICIPANTS: Forty government personnel (patients) who were potentially exposed and experienced neurological symptoms underwent evaluation at a US academic medical center from August 21, 2017, to June 8, 2018, including advanced structural and functional magnetic resonance imaging analytics. Findings were compared with imaging findings of 48 demographically similar healthy controls. EXPOSURES: Potential exposure to uncharacterized directional phenomena of unknown etiology, manifesting as pressure, vibration, or sound. MAIN OUTCOMES AND MEASURES: Potential imaging-based differences between patients and controls with regard to (1) white matter and gray matter total and regional brain volumes, (2) cerebellar tissue microstructure metrics (eg, mean diffusivity), and (3) functional connectivity in the visuospatial, auditory, and executive control subnetworks. RESULTS: Imaging studies were completed for 40 patients (mean age, 40.4 years; 23 [57.5%] men; imaging performed a median of 188 [range, 4-403] days after initial exposure) and 48 controls (mean age, 37.6 years; 33 [68.8%] men). Mean whole brain white matter volume was significantly smaller in patients compared with controls (patients: 542.22 cm3; controls: 569.61 cm3; difference, -27.39 [95% CI, -37.93 to -16.84] cm3; P < .001), with no significant difference in the whole brain gray matter volume (patients: 698.55 cm3; controls: 691.83 cm3; difference, 6.72 [95% CI, -4.83 to 18.27] cm3; P = .25). Among patients compared with controls, there were significantly greater ventral diencephalon and cerebellar gray matter volumes and significantly smaller frontal, occipital, and parietal lobe white matter volumes; significantly lower mean diffusivity in the inferior vermis of the cerebellum (patients: 7.71 × 10-4 mm2/s; controls: 8.98 × 10-4 mm2/s; difference, -1.27 × 10-4 [95% CI, -1.93 × 10-4 to -6.17 × 10-5] mm2/s; P < .001); and significantly lower mean functional connectivity in the auditory subnetwork (patients: 0.45; controls: 0.61; difference, -0.16 [95% CI, -0.26 to -0.05]; P = .003) and visuospatial subnetwork (patients: 0.30; controls: 0.40; difference, -0.10 [95% CI, -0.16 to -0.04]; P = .002) but not in the executive control subnetwork (patients: 0.24; controls: 0.25; difference: -0.016 [95% CI, -0.04 to 0.01]; P = .23). CONCLUSIONS AND RELEVANCE: Among US government personnel in Havana, Cuba, with potential exposure to directional phenomena, compared with healthy controls, advanced brain magnetic resonance imaging revealed significant differences in whole brain white matter volume, regional gray and white matter volumes, cerebellar tissue microstructural integrity, and functional connectivity in the auditory and visuospatial subnetworks but not in the executive control subnetwork. The clinical importance of these differences is uncertain and may require further study.

Whole brain white matter connectivity analysis using machine learning: An application to autism.

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

Harmonization of multi-site diffusion tensor imaging data.

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

Assessing connectivity related injury burden in diffuse traumatic brain injury.

Many of the clinical and behavioral manifestations of traumatic brain injury (TBI) are thought to arise from disruption to the structural network of the brain due to diffuse axonal injury (DAI). However, a principled way of summarizing diffuse connectivity alterations to quantify injury burden is lacking. In this study, we developed a connectome injury score, Disruption Index of the Structural Connectome (DISC), which summarizes the cumulative effects of TBI-induced connectivity abnormalities across the entire brain. Forty patients with moderate-to-severe TBI examined at 3 months postinjury and 35 uninjured healthy controls underwent magnetic resonance imaging with diffusion tensor imaging, and completed behavioral assessment including global clinical outcome measures and neuropsychological tests. TBI patients were selected to maximize the likelihood of DAI in the absence of large focal brain lesions. We found that hub-like regions, with high betweenness centrality, were most likely to be impaired as a result of diffuse TBI. Clustering of participants revealed a subgroup of TBI patients with similar connectivity abnormality profiles who exhibited relatively poor cognitive performance. Among TBI patients, DISC was significantly correlated with post-traumatic amnesia, verbal learning, executive function, and processing speed. Our experiments jointly demonstrated that assessing structural connectivity alterations may be useful in development of patient-oriented diagnostic and prognostic tools. Hum Brain Mapp 38:2913-2922, 2017. © 2017 Wiley Periodicals, Inc.

Sex differences in the structural connectome of the human brain.

Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes.

Connectome and Maturation Profiles of the Developing Mouse Brain Using Diffusion Tensor Imaging.

This paper presents a comprehensive effort to establish a structural mouse connectome using diffusion tensor magnetic resonance imaging coupled with connectivity analysis tools. This work lays the foundation for imaging-based structural connectomics of the mouse brain, potentially facilitating a whole-brain network analysis to quantify brain changes in connectivity during development, as well as deviations from it related to genetic effects. A connectomic trajectory of maturation during postnatal ages 2-80 days is presented in the C57BL/6J mouse strain, using a whole-brain connectivity analysis, followed by investigations based on local and global network features. The global network measures of density, global efficiency, and modularity demonstrated a nonlinear relationship with age. The regional network metrics, namely degree and local efficiency, displayed a differential change in the major subcortical structures such as the thalamus and hippocampus, and cortical regions such as visual and motor cortex. Finally, the connectomes were used to derive an index of "brain connectivity index," which demonstrated a high correlation (r = 0.95) with the chronological age, indicating that brain connectivity is a good marker of normal age progression, hence valuable in detecting subtle deviations from normality caused by genetic, environmental, or pharmacological manipulations.

Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury.

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment.

Improvement of diffusion tensor imaging-based tractography by free-water correction in nonedematous gliomas: assessment with brain mapping.

OBJECTIVE: The free-water correction algorithm (Freewater Estimator Using Interpolated Initialization [FERNET]) can be applied to standard diffusion tensor imaging (DTI) tractography to improve visualization of subcortical bundles in the peritumoral area of highly edematous brain tumors. Interest in its use for presurgical planning in purely infiltrative gliomas without peritumoral edema has never been evaluated. Using subcortical maps obtained with direct electrostimulation (DES) in awake surgery as a reference standard, the authors sought to 1) assess the accuracy of preoperative DTI-based tractography with FERNET in a series of nonedematous glioma patients, and 2) determine its potential usefulness in presurgical planning. METHODS: Based on DES-induced functional disturbances and tumor topography, the authors retrospectively reconstructed the putatively stimulated bundles and the peritumoral tracts of interest (various associative and projection pathways) of 12 patients. The tractography data obtained with and without FERNET were compared. RESULTS: The authors identified 21 putative tracts from 24 stimulation sites and reconstituted 49 tracts of interest. The number of streamlines of the putative tracts crossing the DES area was 26.8% higher (96.04 vs 75.75, p = 0.016) and their volume 20.4% higher (13.99 cm3 vs 11.62 cm3, p < 0.0001) with FERNET than with standard DTI. Additionally, the volume of the tracts of interest was 22.1% higher (9.69 cm3 vs 7.93 cm3, p < 0.0001). CONCLUSIONS: Free-water correction significantly increased the anatomical plausibility of the stimulated fascicles and the volume of tracts of interest in the peritumoral area of purely infiltrative nonedematous gliomas. Because of the functional importance of the peritumoral zone, applying FERNET to DTI could have potential implications on surgical planning and the safety of glioma resection.

CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI).

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

3D-QCNet - A pipeline for automated artifact detection in diffusion MRI images.

Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post-processing carried out on these scans. This makes quality control (QC) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is validated on 9000 volumes sourced from 7 large clinical datasets spanning different acquisition protocols (with different gradient directions, high and low b-values, single-shell and multi-shell acquisitions) from multiple scanners. Additionally, they represent diverse subject demographics including age, sex and the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. Thus, 3D-QCNet can be integrated into diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment.

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

Longitudinal Abnormalities in White Matter Extracellular Free Water Volume Fraction and Neuropsychological Functioning in Patients with Traumatic Brain Injury.

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes potentially associated with increases in brain extracellular water content, but the nature of extracellular free water abnormalities after neurotrauma and its relationship to measures typically thought to reflect traumatic axonal injury are not well characterized. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of a cohort with primarily complicated mild traumatic brain injury. We analyzed a cohort of 59 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 36 healthy controls. Patients underwent brain magnetic resonance imaging (MRI) at 2 weeks (n = 59) and 6 months (n = 29) post-injury, and controls underwent a single MRI. Of the participants with TBI, 50 underwent clinical neuropsychological assessment at 2 weeks and 28 at 6 months. For each subject, we derived a summary score representing deviations in whole brain white matter extracellular free water volume fraction (VF) and free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients at 2 weeks and 6 months post-injury relative to controls. SAS for VF exhibited moderate correlation with neuropsychological functioning, particularly on measures of executive function. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts.

Introduction: The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway. Methods: Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target. Results: We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04). Conclusion: We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

Tractography-Based Surgical Targeting for Thalamic Deep Brain Stimulation: A Comparison of Probabilistic vs Deterministic Fiber Tracking of the Dentato-Rubro-Thalamic Tract.

BACKGROUND: The ventral intermediate (VIM) thalamic nucleus is the main target for the surgical treatment of refractory tremor. Initial targeting traditionally relies on atlas-based stereotactic targeting formulas, which only minimally account for individual anatomy. Alternative approaches have been proposed, including direct targeting of the dentato-rubro-thalamic tract (DRTT), which, in clinical settings, is generally reconstructed with deterministic tracking. Whether more advanced probabilistic techniques are feasible on clinical-grade magnetic resonance acquisitions and lead to enhanced reconstructions is poorly understood. OBJECTIVE: To compare DRTT reconstructed with deterministic vs probabilistic tracking. METHODS: This is a retrospective study of 19 patients with essential tremor who underwent deep brain stimulation (DBS) with intraoperative neurophysiology and stimulation testing. We assessed the proximity of the DRTT to the DBS lead and to the active contact chosen based on clinical response. RESULTS: In the commissural plane, the deterministic DRTT was anterior (P < 10-4) and lateral (P < 10-4) to the DBS lead. By contrast, although the probabilistic DRTT was also anterior to the lead (P < 10-4), there was no difference in the mediolateral dimension (P = .5). Moreover, the 3-dimensional Euclidean distance from the active contact to the probabilistic DRTT was smaller vs the distance to the deterministic DRTT (3.32 ± 1.70 mm vs 5.01 ± 2.12 mm; P < 10-4). CONCLUSION: DRTT reconstructed with probabilistic fiber tracking was superior in spatial proximity to the physiology-guided DBS lead and to the empirically chosen active contact. These data inform strategies for surgical targeting of the VIM.