Longitudinal Alterations of Cerebral Blood Flow in High-Contact Sports.

OBJECTIVE: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects. We investigated whether exposure to head impacts causes longitudinal CBF changes. METHODS: We prospectively studied 63 American football (high-contact cohort) and 34 volleyball (low-contact controls) male collegiate athletes, tracking CBF using 3D pseudocontinuous arterial spin labeling magnetic resonance imaging for up to 4 years. Regional relative CBF (rCBF, normalized to cerebellar CBF) was computed after co-registering to T1-weighted images. A linear mixed effects model assessed the relationship of rCBF to sport, time, and their interaction. Within football players, we modeled rCBF against position-based head impact risk and baseline Standardized Concussion Assessment Tool score. Additionally, we evaluated early (1-5 days) and delayed (3-6 months) post-concussion rCBF changes (in-study concussion). RESULTS: Supratentorial gray matter rCBF declined in football compared with volleyball (sport-time interaction p = 0.012), with a strong effect in the parietal lobe (p = 0.002). Football players with higher position-based impact-risk had lower occipital rCBF over time (interaction p = 0.005), whereas players with lower baseline Standardized Concussion Assessment Tool score (worse performance) had relatively decreased rCBF in the cingulate-insula over time (interaction effect p = 0.007). Both cohorts showed a left-right rCBF asymmetry that decreased over time. Football players with an in-study concussion showed an early increase in occipital lobe rCBF (p = 0.0166). INTERPRETATION: These results suggest head impacts may result in an early increase in rCBF, but cumulatively a long-term decrease in rCBF. ANN NEUROL 2023;94:457-469.

Direct Visualization and Mapping of the Spatial Course of Fiber Tracts at Microscopic Resolution in the Human Hippocampus.

While hippocampal connectivity is essential to normal memory function, our knowledge of human hippocampal circuitry is largely inferred from animal studies. Using polarized light microscopy at 1.3 µm resolution, we have directly visualized the 3D course of key medial temporal pathways in 3 ex vivo human hemispheres and 2 ex vivo vervet monkey hemispheres. The multiple components of the perforant path system were clearly identified: Superficial sheets of fibers emanating from the entorhinal cortex project to the presubiculum and parasubiculum, intermixed transverse and longitudinal angular bundle fibers perforate the subiculum and then project to the cornu ammonis (CA) fields and dentate molecular layer, and a significant alvear component runs from the angular bundle to the CA fields. From the hilus, mossy fibers localize to regions of high kainate receptor density, and the endfolial pathway, mostly investigated in humans, merges with the Schaffer collaterals. This work defines human hippocampal pathways underlying mnemonic function at an unprecedented resolution.

The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing.

Despite the widespread use of magnetic resonance imaging (MRI) of the brain, the relative contribution of different biological components (e.g. lipids and proteins) to structural MRI contrasts (e.g., T1, T2, T2*, proton density, diffusion) remains incompletely understood. This limitation can undermine the interpretation of clinical MRI and hinder the development of new contrast mechanisms. Here, we determine the respective contribution of lipids and proteins to MRI contrast by removing lipids and preserving proteins in mouse brains using CLARITY. We monitor the temporal dynamics of tissue clearance via NMR spectroscopy, protein assays and optical emission spectroscopy. MRI of cleared brain tissue showed: 1) minimal contrast on standard MRI sequences; 2) increased relaxation times; and 3) diffusion rates close to free water. We conclude that lipids, present in myelin and membranes, are a dominant source of MRI contrast in brain tissue.

MRI and histopathologic study of a novel cholesterol-fed rabbit model of xanthogranuloma.

PURPOSE: To develop a rabbit model of xanthogranuloma based on supplementation of dietary cholesterol. The aim of this study was to analyze the xanthogranulomatous lesions using magnetic resonance imaging (MRI) and histological examination. MATERIALS AND METHODS: Rabbits were fed a low-level cholesterol (CH) diet (n = 10) or normal chow (n = 5) for 24 months. In vivo brain imaging was performed on a 3T MR system using fast imaging employing steady state acquisition, susceptibility-weighted imaging, spoiled gradient recalled, T1 -weighted inversion recovery imaging and T1 relaxometry, PD-weighted and T2 -weighted spin-echo imaging and T2 relaxometry, iterative decomposition of water and fat with echo asymmetry and least-squares estimation, ultrashort TE MRI (UTE-MRI), and T2* relaxometry. MR images were evaluated using a Likert scale for lesion presence and quantitative analysis of lesion size, ventricular volume, and T1 , T2 , and T2* values of lesions was performed. After imaging, brain specimens were examined using histological methods. RESULTS: In vivo MRI revealed that 6 of 10 CH-fed rabbits developed lesions in the choroid plexus. Region-of-interest analysis showed that for CH-fed rabbits the mean lesion volume was 8.5 ± 2.6 mm(3) and the volume of the lateral ventricle was significantly increased compared to controls (P < 0.01). The lesions showed significantly shorter mean T2 values (35 ± 12 msec, P < 0.001), longer mean T1 values (1581 ± 146 msec, P < 0.05), and shorter T2* values (22 ± 13 msec, P < 0.001) compared to adjacent brain structures. The ultrashort T2* components were visible using UTE-MRI. Histopathologic evaluation of lesions demonstrated features of human xanthogranuloma. CONCLUSION: Rabbits fed a low-level CH diet develop sizable intraventricular masses that have similar histopathological features as human xanthogranuloma. Multiparametric MRI techniques were able to provide information about the complex composition of these lesions. J. Magn. Reson. Imaging 2016;44:673-682.

Ultra-high resolution in-vivo 7.0T structural imaging of the human hippocampus reveals the endfolial pathway.

The hippocampus is a very important structure in memory formation and retrieval, as well as in various neurological disorders such as Alzheimer's disease, epilepsy and depression. It is composed of many intricate subregions making it difficult to study the anatomical changes that take place during disease. The hippocampal hilus may have a unique neuroanatomy in humans compared to that in monkeys and rodents, with field CA3h greatly enlarged in humans compared to that in rodents, and a white-matter pathway, called the endfolial pathway, possibly only present in humans. In this study we have used newly developed 7.0T whole brain imaging sequence, balanced steady-state free precession (bSSFP) that can achieve 0.4mm isotropic images to study, in vivo, the anatomy of the hippocampal hilus. A detailed hippocampal subregional segmentation was performed according to anatomic atlases segmenting the following regions: CA4, CA3, CA2, CA1, SRLM (stratum radiatum lacunosum moleculare), alveus, fornix, and subiculum along with its molecular layer. We also segmented a hypointense structure centrally within the hilus that resembled the endfolial pathway. To validate that this hypointense signal represented the endfolial pathway, we acquired 0.1mm isotropic 8-phase cycle bSSFP on an excised specimen, and then sectioned and stained the specimen for myelin using an anti-myelin basic protein antibody (SMI 94). A structure tensor analysis was calculated on the myelin-stained section to show directionality of the underlying fibers. The endfolial pathway was consistently visualized within the hippocampal body in vivo in all subjects. It is a central pathway in the hippocampus, with unknown relevance in neurodegenerative disorders, but now that it can be visualized noninvasively, we can study its function and alterations in neurodegeneration.

Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol.

OBJECTIVE: An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD: MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS: The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.

Right arcuate fasciculus abnormality in chronic fatigue syndrome.

PURPOSE: To identify whether patients with chronic fatigue syndrome (CFS) have differences in gross brain structure, microscopic structure, or brain perfusion that may explain their symptoms. MATERIALS AND METHODS: Fifteen patients with CFS were identified by means of retrospective review with an institutional review board-approved waiver of consent and waiver of authorization. Fourteen age- and sex-matched control subjects provided informed consent in accordance with the institutional review board and HIPAA. All subjects underwent 3.0-T volumetric T1-weighted magnetic resonance (MR) imaging, with two diffusion-tensor imaging (DTI) acquisitions and arterial spin labeling (ASL). Open source software was used to segment supratentorial gray and white matter and cerebrospinal fluid to compare gray and white matter volumes and cortical thickness. DTI data were processed with automated fiber quantification, which was used to compare piecewise fractional anisotropy (FA) along 20 tracks. For the volumetric analysis, a regression was performed to account for differences in age, handedness, and total intracranial volume, and for the DTI, FA was compared piecewise along tracks by using an unpaired t test. The open source software segmentation was used to compare cerebral blood flow as measured with ASL. RESULTS: In the CFS population, FA was increased in the right arcuate fasciculus (P = .0015), and in right-handers, FA was also increased in the right inferior longitudinal fasciculus (ILF) (P = .0008). In patients with CFS, right anterior arcuate FA increased with disease severity (r = 0.649, P = .026). Bilateral white matter volumes were reduced in CFS (mean ± standard deviation, 467 581 mm(3) ± 47 610 for patients vs 504 864 mm(3) ± 68 126 for control subjects, P = .0026), and cortical thickness increased in both right arcuate end points, the middle temporal (T = 4.25) and precentral (T = 6.47) gyri, and one right ILF end point, the occipital lobe (T = 5.36). ASL showed no significant differences. CONCLUSION: Bilateral white matter atrophy is present in CFS. No differences in perfusion were noted. Right hemispheric increased FA may reflect degeneration of crossing fibers or strengthening of short-range fibers. Right anterior arcuate FA may serve as a biomarker for CFS.

Seven-Tesla MRI and neuroimaging biomarkers for Alzheimer's disease.

The goal of this paper was to review the effectiveness of using 7-T MRI to study neuroimaging biomarkers for Alzheimer's disease (AD). The authors reviewed the literature for articles published to date on the use of 7-T MRI to study AD. Thus far, there are 3 neuroimaging biomarkers for AD that have been studied using 7-T MRI in AD tissue: 1) neuroanatomical atrophy; 2) molecular characterization of hypointensities; and 3) microinfarcts. Seven-Tesla MRI has had mixed results when used to study the 3 aforementioned neuroimaging biomarkers for AD. First, in the detection of neuroanatomical atrophy, 7-T MRI has exciting potential. Historically, noninvasive imaging of neuroanatomical atrophy during AD has been limited by suboptimal resolution. However, now there is compelling evidence that the high resolution of 7-T MRI may help overcome this hurdle. Second, in detecting the characterization of hypointensities, 7-T MRI has had varied success. PET scans will most likely continue to lead in the noninvasive imaging of amyloid plaques; however, there is emerging evidence that 7-T MRI can accurately detect iron deposits within activated microglia, which may help shed light on the role of the immune system in AD pathogenesis. Finally, in the detection of microinfarcts, 7-T MRI may also play a promising role, which may help further elucidate the relationship between cerebrovascular health and AD progression.

Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study.

The medial temporal lobe (MTL) is the first brain area to succumb to neurofibrillary tau pathology in Alzheimer's disease (AD). Postmortem human tissue evaluation suggests that this pathology propagates in an ordered manner, with the entorhinal cortex (ERC) and then CA1 stratum radiatum and stratum lacunosum-moleculare (CA1-SRLM)--two monosynaptically connected structures--exhibiting selective damage. Here, we hypothesized that, if ERC and CA1-SRLM share an early vulnerability to AD pathology, then atrophy should occur in a proportional manner between the two structures. We tested this hypothesis in living humans, using ultra-high field 7.0 T MRI to make fine measurements of MTL microstructure. Among a pool of age-matched healthy controls and patients with amnestic mild cognitive impairment and mild AD, we found a significant correlation between ERC and CA1-SRLM size that could not be explained by global atrophy affecting the MTL. Of the various structures that contribute axons or dendrites into the CA1-SRLM neuropil, only ERC emerged as a significant predictor of CA1-SRLM size in a linear regression analysis. In contrast, other synaptically connected elements of the MTL did not exhibit size correlations. CA1-SRLM and ERC structural covariance was significant for older controls and not patients, whereas the opposite pattern emerged for a correlation between CA1-SRLM and episodic memory performance. Interestingly, CA1-SRLM and ERC were the only MTL structures to atrophy in older controls relative to a younger comparison group. Together, these findings suggest that ERC and CA1-SRLM share vulnerability to both age and AD-associated atrophy.

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types.

OBJECTIVE: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. METHODS: To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). RESULTS: The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. CONCLUSION: The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. SIGNIFICANCE: This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

AI-based denoising of head impact kinematics measurements with convolutional neural network for traumatic brain injury prediction.

OBJECTIVE: Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring. METHODS: We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts. RESULTS: On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%). CONCLUSION: The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts. SIGNIFICANCE: This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.

Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI.

Disentangling human brain connectivity requires an accurate description of nerve fiber trajectories, unveiled via detailed mapping of axonal orientations. However, this is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer axonal connectivity because it is sensitive to axonal alignment, but it has limited spatial resolution and specificity. Scattered light imaging (SLI) and small-angle X-ray scattering (SAXS) reveal axonal orientations with microscopic resolution and high specificity, respectively. Here, we apply both scattering techniques on the same samples and cross-validate them, laying the groundwork for ground-truth axonal orientation imaging and validating dMRI. We evaluate brain regions that include unidirectional and crossing fibers in human and vervet monkey brain sections. SLI and SAXS quantitatively agree regarding in-plane fiber orientations including crossings, while dMRI agrees in the majority of voxels with small discrepancies. We further use SAXS and dMRI to confirm theoretical predictions regarding SLI determination of through-plane fiber orientations. Scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, facilitating detailed investigations of complex fiber architecture in the animal and human brain.

Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation.

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.

An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract.

Background: At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM. Methods: Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients. Results: The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts. Conclusion: We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.

Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings.

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and on the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 to 39.6%), 18% at 5.5 m/s (range: - 5.5 to 40.5%), and 10% at 7.4 m/s (range: - 6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. METHODS: Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. RESULTS: The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. CONCLUSION: The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering.

Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brain's structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. We compare results to polarized light imaging (3D-PLI), tracer experiments, and to outputs from diffusion MRI that sometimes fails to detect crossings. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS could serve as a ground truth for validating fiber orientations derived using diffusion MRI as well as microscopy-based methods. STATEMENT OF SIGNIFICANCE: To study how the nerve fibers in our brain are interconnected, scientists need to visualize their trajectories, which often cross one another. Here, we show the unique capacity of small-angle X-ray scattering (SAXS) to study these fiber crossings without use of labeling, taking advantage of SAXS's specificity to myelin - the insulating sheath that is wrapped around nerve fibers. We use SAXS to detect double and triple crossing fibers and unveil intricate crossings in mouse, pig, vervet monkey, and human brains. This non-destructive method can uncover complex fiber trajectories and validate other less specific imaging methods (e.g., MRI or microscopy), towards accurate mapping of neuronal connectivity in the animal and human brain.

Ultra-high resolution diffusion tensor imaging of the microscopic pathways of the medial temporal lobe.

Diseases involving the medial temporal lobes (MTL) such as Alzheimer's disease and mesial temporal sclerosis pose an ongoing diagnostic challenge because of the difficulty in identifying conclusive imaging features, particularly in pre-clinical states. Abnormal neuronal connectivity may be present in the circuitry of the MTL, but current techniques cannot reliably detect those abnormalities. Diffusion tensor imaging (DTI) has shown promise in defining putative abnormalities in connectivity, but DTI studies of the MTL performed to date have shown neither dramatic nor consistent differences across patient populations. Conventional DTI methodology provides an inadequate depiction of the complex microanatomy present in the medial temporal lobe because of a typically employed low isotropic resolution of 2.0-2.5 mm, a low signal-to-noise ratio (SNR), and echo-planar imaging (EPI) geometric distortions that are exacerbated by the inhomogeneous magnetic environment at the skull base. In this study, we pushed the resolving power of DTI to near-mm isotropic voxel size to achieve a detailed depiction of mesial temporal microstructure at 3 T. High image fidelity and SNR at this resolution are achieved through several mechanisms: (1) acquiring multiple repetitions of the minimum field of view required for hippocampal coverage to boost SNR; (2) utilizing a single-refocused diffusion preparation to enhance SNR further; (3) performing a phase correction to reduce Rician noise; (4) minimizing distortion and maintaining left-right distortion symmetry with axial-plane parallel imaging; and (5) retaining anatomical and quantitative accuracy through the use of motion correction coupled with a higher-order eddy-current correction scheme. We combined this high-resolution methodology with a detailed segmentation of the MTL to identify tracks in all subjects that may represent the major pathways of the MTL, including the perforant pathway. Tractography performed on a subset of the data identified similar tracks, although they were lesser in number. This detailed analysis of MTL substructure may have applications to clinical populations.

Medial Temporal Lobe Anatomy.

The medial temporal lobe (MTL) is a complex anatomic region encompassing the hippocampal formation, parahippocampal region, and amygdaloid complex. To enable the reader to understand the well-studied regional anatomic relationships and cytoarchitecture that form the basis of functional connectivity, the authors have created a detailed yet approachable anatomic reference for clinicians and scientists, with special attention to MR imaging. They have focused primarily on the hippocampal formation, discussing its gross structural features, anatomic relationships, and subfield anatomy and further discuss hippocampal terminology and development, hippocampal connectivity, normal anatomic variants, clinically relevant disease processes, and automated hippocampal segmentation software.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.

OBJECTIVE: Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). METHODS: We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). RESULTS: PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. CONCLUSION: The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. SIGNIFICANCE: The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.