OBJECTIVE: To address the feasibility and predictive value of multimodal image-based virtual reality in detecting and assessing features of neurovascular confliction (NVC), particularly regarding the detection of offending vessels, degree of compression exerted on the nerve root, in patients who underwent microvascular decompression for nonlesional trigeminal neuralgia and hemifacial spasm (HFS). METHODS: This prospective study includes 42 consecutive patients who underwent microvascular decompression for classic primary trigeminal neuralgia or HFS. All patients underwent preoperative 1.5-T magnetic resonance imaging (MRI) with T2-weighted three-dimensional (3D) sampling perfection with application-optimized contrasts by using different flip angle evolutions, 3D time-of-flight magnetic resonance angiography, and 3D T1-weighted gadolinium-enhanced sequences in combination, whereas 2 patients underwent extra experimental preoperative 7.0-T MRI scans with the same imaging protocol. Multimodal MRIs were then coregistered with open-source software 3D Slicer, followed by 3D image reconstruction to generate virtual reality (VR) images for detection of possible NVC in the cerebellopontine angle. Evaluations were performed by 2 reviewers and compared with the intraoperative findings. RESULTS: For detection of NVC, multimodal image-based VR sensitivity was 97.6% (40/41) and specificity was 100% (1/1). Compared with the intraoperative findings, the κ coefficients for predicting the offending vessel and the degree of compression were >0.75 (P < 0.001). The 7.0-T scans have a clearer view of vessels in the cerebellopontine angle, which may have significant impact on detection of small-caliber offending vessels with relatively slow flow speed in cases of HFS. CONCLUSIONS: Multimodal image-based VR using 3D sampling perfection with application-optimized contrasts by using different flip angle evolutions in combination with 3D time-of-flight magnetic resonance angiography sequences proved to be reliable in detecting NVC and in predicting the degree of root compression. The VR image-based simulation correlated well with the real surgical view.

OBJECTIVE Endoscopic endonasal approaches are increasingly performed for the surgical treatment of multiple skull base pathologies. Preventing postoperative CSF leaks remains a major challenge, particularly in extended approaches. In this study, the authors assessed the potential use of modern multimaterial 3D printing and neuronavigation to help model these extended defects and develop specifically tailored prostheses for reconstructive purposes. METHODS Extended endoscopic endonasal skull base approaches were performed on 3 human cadaveric heads. Preprocedure and intraprocedure CT scans were completed and were used to segment and design extended and tailored skull base models. Multimaterial models with different core/edge interfaces were 3D printed for implantation trials. A novel application of the intraoperative landmark acquisition method was used to transfer the navigation, helping to tailor the extended models. RESULTS Prostheses were created based on preoperative and intraoperative CT scans. The navigation transfer offered sufficiently accurate data to tailor the preprinted extended skull base defect prostheses. Successful implantation of the skull base prostheses was achieved in all specimens. The progressive flexibility gradient of the models’ edges offered the best compromise for easy intranasal maneuverability, anchoring, and structural stability. Prostheses printed based on intraprocedure CT scans were accurate in shape but slightly undersized. CONCLUSIONS Preoperative 3D printing of patient-specific skull base models is achievable for extended endoscopic endonasal surgery. The careful spatial modeling and the use of a flexibility gradient in the design helped achieve the most stable reconstruction. Neuronavigation can help tailor preprinted prostheses.

Diffusion tensor imaging studies report childhood adversity (CA) is associated with reduced fractional anisotropy (FA) in multiple white matter tracts in adults. Reduced FA may result from changes in tissue, suggesting myelin/axonal damage, and/or from increased levels of extracellular free-water, suggesting atrophy or neuroinflammation. Free-water imaging can separately identify FA in tissue (FA) and the fractional volume of free-water (FW). We tested whether CA was associated with altered FA, FA, and FW in seven white matter regions of interest (ROI), in which FA changes had been previously linked to CA (corona radiata, corpus callosum, fornix, cingulum bundle: hippocampal projection, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus). Tract-based spatial statistics were performed in 147 psychiatrically healthy adults who had completed a self-report questionnaire on CA primarily stemming from parental maltreatment. ROI were extracted according to the protocol provided by the ENIGMA-DTI working group. Analyses were performed both treating CA as a continuous and a categorical variable. CA was associated with reduced FA in all ROI (although categorical analyses failed to find an association in the fornix). In contrast, CA was only associated with reduced FAin the corona radiata, corpus callosum, and uncinate fasciculus (with the continuous measure of CA finding evidence of a negative relation between CA and FAin the fornix). There was no association between CA on FW in any ROI. These results provide preliminary evidence that childhood adversity is associated with changes to the microstructure of white matter itself in adulthood. However, these results should be treated with caution until they can be replicated by future studies which address the limitations of the present study.

Objectives: To determine whether or not automated FreeSurfer segmentation of brain regions considered important in repetitive head trauma can be analyzed accurately without manual correction. Materials and methods: 3 T MR neuroimaging was performed with automated FreeSurfer segmentation and manual correction of 11 brain regions in former National Football League (NFL) players with neurobehavioral symptoms and in control subjects. Automated segmentation and manually-corrected volumes were compared using an intraclass correlation coefficient (ICC). Linear mixed effects regression models were also used to estimate between-group mean volume comparisons and to correlate former NFL player brain volumes with neurobehavioral factors. Results: Eighty-six former NFL players (55.2 ± 8.0 years) and 22 control subjects (57.0 ± 6.6 years) were evaluated. ICC was highly correlated between automated and manually-corrected corpus callosum volumes (0.911), lateral ventricular volumes (right 0.980, left 0.967), and amygdala-hippocampal complex volumes (right 0.713, left 0.731), but less correlated when amygdalae (right -0.170, left -0.090) and hippocampi (right 0.539, left 0.637) volumes were separately delineated and also less correlated for cingulate gyri volumes (right 0.639, left 0.351). Statistically significant differences between former NFL player and controls were identified in 8 of 11 regions with manual correction but in only 4 of 11 regions without such correction. Within NFL players, manually corrected brain volumes were significantly associated with 3 neurobehavioral factors, but a different set of 3 brain regions and neurobehavioral factor correlations was observed for brain region volumes segmented without manual correction. Conclusions: Automated FreeSurfer segmentation of the corpus callosum, lateral ventricles, and amygdala-hippocampus complex may be appropriate for analysis without manual correction. However, FreeSurfer segmentation of the amygdala, hippocampus, and cingulate gyrus need further manual correction prior to performing group comparisons and correlations with neurobehavioral measures.

PURPOSE: Spaceflight negatively affects sensorimotor behavior; exercise mitigates some of these effects. Head down tilt bed rest (HDBR) induces body unloading and fluid shifts, and is often used to investigate spaceflight effects. Here, we examined whether exercise mitigates effects of 70 days HDBR on the brain and if fitness and brain changes with HDBR are related. METHODS: HDBR subjects were randomized to no-exercise (n = 5) or traditional aerobic and resistance exercise (n = 5). Additionally, a flywheel exercise group was included (n = 8). Exercise protocols for exercise groups were similar in intensity, therefore these groups were pooled in statistical analyses. Pre and post-HDBR MRI (structure and structural/functional connectivity) and physical fitness measures (lower body strength, muscle cross sectional area, VO2 max, body composition) were collected. Voxel-wise permutation analyses were used to test group differences in brain changes, and their associations with fitness changes. RESULTS: Comparisons of exercisers to controls revealed that exercise led to smaller fitness deterioration with HDBR but did not affect brain volume or connectivity. Group comparisons showed that exercise modulated post-HDBR recovery of brain connectivity in somatosensory regions. Posthoc analysis showed that this was related to functional connectivity decrease with HDBR in non-exercisers but not in exercisers. Correlational analyses between fitness and brain changes showed that fitness decreases were associated with functional connectivity and volumetric increases (all r >.74), potentially reflecting compensation. Modest brain changes or even decreases in connectivity and volume were observed in subjects who maintained or showed small fitness gains. These results did not survive Bonferroni correction, but can be considered meaningful because of the large effect sizes. CONCLUSION: Exercise performed during HDBR mitigates declines in fitness and strength. Associations between fitness and brain connectivity and volume changes, although unadjusted for multiple comparisons in this small sample, suggest that supine exercise reduces compensatory HDBR-induced brain changes.

Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem, where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal, and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality, and conditional causality analysis. In particular, our solution provides a more accurate estimation of the frequency-domain causality compared with the classical Geweke causality measure. Using synthetic examples and in vivo resting-state functional magnetic resonance imaging data from the human connectome project, we show that the proposed solution is more accurate at revealing frequency-domain linear dependence among high-dimensional signals.

Background: Digital Imaging and Communications in Medicine (DICOM) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.

Thalamic atrophy has been associated with exposure to repetitive head impacts (RHI) in professional fighters. The aim of this study is to investigate whether or not age at first exposure (AFE) to RHI is associated with thalamic volume in symptomatic former National Football League (NFL) players at risk for chronic traumatic encephalopathy (CTE). Eighty-six symptomatic former NFL players (mean age = 54.9 ± 7.9 years) were included. T1-weighted data were acquired on a 3T magnetic resonance imager, and thalamic volumes were derived using FreeSurfer. Mood and behavior, psychomotor speed, and visual and verbal memory were assessed. The association between thalamic volume and AFE to playing football and to number of years playing was calculated. Decreased thalamic volume was associated with more years of play (left: p = 0.03; right: p = 0.03). Younger AFE was associated with decreased right thalamic volume (p = 0.014). This association remained significant after adjusting for total years of play. Decreased left thalamic volume was associated with worse visual memory (p = 0.014), whereas increased right thalamic volume was associated with fewer mood and behavior symptoms (p = 0.003). In our sample of symptomatic former NFL players at risk for CTE, total years of play and AFE were associated with decreased thalamic volume. The effect of AFE on right thalamic volume was almost twice as strong as the effect of total years of play. Our findings confirm previous reports of an association between thalamic volume and exposure to RHI. They suggest further that younger AFE may result in smaller thalamic volume later in life.

Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI’s success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.