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Background: The “central vein sign” (CVS), a linear hypointensity on T2*-weighted imaging corresponding to a central vein/venule, is associated with multiple sclerosis (MS) lesions. The effect of lesion-size exclusion criteria on MS diagnostic accuracy has not been extensively studied. Objective: Investigate the optimal lesion-size exclusion criteria for CVS use in MS diagnosis. Methods: Cross-sectional study of 163 MS and 51 non-MS, and radiological/histopathological correlation of 5 MS and 1 control autopsy cases. The effects of lesion-size exclusion on MS diagnosis using the CVS, and intralesional vein detection on histopathology were evaluated. Results: CVS+ lesions were larger compared to CVS− lesions, with effect modification by MS diagnosis (mean difference +7.7 mm3, p = 0.004). CVS percentage-based criteria with no lesion-size exclusion showed the highest diagnostic accuracy in differentiating MS cases. However, a simple count of three or more CVS+ lesions greater than 3.5 mm is highly accurate and can be rapidly implemented (sensitivity 93%; specificity 88%). On magnetic resonance imaging (MRI)-histopathological correlation, the CVS had high specificity for identifying intralesional veins (0/7 false positives). Conclusion: Lesion-size measures add important information when using CVS+ lesion counts for MS diagnosis. The CVS is a specific biomarker corresponding to intralesional veins on histopathology.

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI—at non-injurious strain levels—and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.

Susceptibility-weighted imaging (SWI) provides superior image contrast of cerebral microhemorrhages (CMBs). It is based on a three-dimensional (3D) gradient echo (GRE) sequence with a relatively long imaging time.To evaluate whether an accelerated 3D segmented echo planar imaging SWI is comparable to GRE SWI in detecting CMBs in traumatic brain injury (TBI).Prospective.Four healthy volunteers and 46 consecutive subjects (38.0 ± 14.4 years, 16 females; 12 mild, 13 moderate, and 7 severe TBI).A 3 T scanner/3D gradient echo and 3D segmented echo planar imaging (segEPI).Brain images were acquired using GRE and segEPI in a single session (imaging time = 9 minutes 47 seconds and 1 minute 30 seconds, respectively). The signal-to-noise ratio (SNR) calculated from healthy volunteer thalamus and centrum semiovale were compared. CMBs were counted by three raters blinded to diagnostic information.A t-test was used to assess SNR difference. Pearson correlation and Wilcoxon signed-rank test were performed using CMB counts. The intermethod agreement was evaluated using Bland-Altman method. Intermethod and interrater reliabilities of image-based diffuse axonal injury (DAI) diagnoses were evaluated using Cohen's kappa and percent agreement. P ≤ 0.05 was considered statistically significant.Thalamus SNRs were 16.9 ± 2.2 and 16.5 ± 3 for GRE and segEPI (P = 0.84), respectively. Centrum semiovale SNRs were 25.8 ± 4.6 and 21.1 ± 2.7 (P = 0.13). The correlation coefficient of CMBs was 0.93, and differences were not significant (P = 0.56-0.85). For DAI diagnoses, Cohen's kappa was 0.62-0.84 and percent agreement was 85%-94%.CMB counts on segEPI and GRE were highly correlated, and DAI diagnosis was made equally effectively. segEPI SWI can potentially replace GRE SWI in detecting TBI CMBs, especially when time constraints are critical.1 TECHNICAL EFFICACY: Stage 2.

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.

Background and ObjectivesThe central vein sign (CVS), a central linear hypointensity within lesions on T2*-weighted imaging, has been established as a sensitive and specific biomarker for the diagnosis of multiple sclerosis (MS). However, the CVS has not yet been comprehensively studied in newly developing MS lesions. We aimed to identify the CVS profiles of new white matter lesions in patients with MS followed over time and investigate demographic and clinical risk factors associated with new CVS+ or CVS− lesion development.MethodsIn this retrospective longitudinal cohort study, adults from the NIH MS Natural History Study were considered for inclusion. Participants with new T2 or enhancing lesions were identified through review of the radiology report and/or longitudinal subtraction imaging. Each new lesion was evaluated for the CVS. Clinical characteristics were identified through chart review.ResultsA total of 153 adults (95 relapsing-remitting MS, 27 secondary progressive MS, 16 primary progressive MS, 5 clinically isolated syndrome, and 10 healthy; 67% female) were included. Of this cohort, 96 had at least 1 new T2 or contrast-enhancing lesion during median 3.1 years (Q1–Q3: 0.7–6.3) of follow-up; lesions eligible for CVS evaluation were found in 62 (65%). Of 233 new CVS-eligible lesions, 159 (68%) were CVS+, with 30 (48%) individuals having only CVS+, 12 (19%) only CVS−, and 20 (32%) both CVS+ and CVS− lesions. In gadolinium-enhancing (Gd+) lesions, the CVS+ percentage increased from 102/152 (67%) at the first time point where the lesion was observed, to 92/114 (82%) after a median follow-up of 2.8 years. Younger age (OR = 0.5 per 10-year increase, 95% CI = 0.3–0.8) and higher CVS+ percentage at baseline (OR = 1.4 per 10% increase, 95% CI = 1.1–1.9) were associated with increased likelihood of new CVS+ lesion development.DiscussionIn a cohort of adults with MS followed over a median duration of 3 years, most newly developing T2 or enhancing lesions were CVS+ (68%), and nearly half (48%) developed new CVS+ lesions only. Importantly, the effects of edema and T2 signal changes can obscure small veins in Gd+ lesions; therefore, caution and follow-up is necessary when determining their CVS status.Trial Registration InformationClinical trial registration number NCT00001248.Classification of EvidenceThis study provides Class III evidence that younger age and higher CVS+ percentage at baseline are associated with new CVS+ lesion development.

Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain–skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo. The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s(−2)) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.

Objective: To obtain dense spatiotemporal measurements of brain deformation from two distinct but complementary head motion experiments: linear and rotational accelerations. Methods: This study introduces a strategy for integrating harmonic phase analysis of tagged magnetic resonance imaging (MRI) and finite-element models to extract mechanically representative deformation measurements. The method was calibrated using simulated as well as experimental data, demonstrated in a phantom including data with image artifacts, and used to measure brain deformation in human volunteers undergoing rotational and linear acceleration. Results: Evaluation methods yielded a displacement error of 1.1 mm compared to human observers and strain errors between ${\text{0.1}}\pm {\text{0.2}}{\% \,(\text{mean}\pm \text{std}.\,\text{dev.)}}$ for linear acceleration and ${\text{0.7}}\pm {\text{0.3}}\% $ for rotational acceleration. This study also demonstrates an approach that can reduce error by 86% in the presence of corrupted data. Analysis of results shows consistency with 2-D motion estimation, agreement with external sensors, and the expected physical behavior of the brain. Conclusion: Mechanical regularization is useful for obtaining dense spatiotemporal measurements of in vivo brain deformation under different loading regimes. Significance: The measurements suggest that the brain's 3-D response to mild accelerations includes distinct patterns observable using practical MRI resolutions. This type of measurement can provide validation data for computer models for the study of traumatic brain injury.

Magnetic resonance imaging (MRI) is a powerful tool for visualizing traumatic brain injury(TBI)-related lesions. Trauma-induced encephalomalacia is frequently identified by its hyperintense appearance on fluid-attenuated inversion recovery (FLAIR) sequences. In addition to parenchymal lesions, TBI commonly results in cerebral microvascular injury, but its anatomical relationship to parenchymal encephalomalacia is not well characterized. The current study utilized a multi-modal MRI protocol to assess microstructural tissue integrity (by mean diffusivity [MD] and fractional aniosotropy [FA]) and altered vascular function (by cerebral blood flow [CBF] and cerebral vascular reactivity [CVR]) within regions of visible encephalomalacia and normal appearing tissue in 27 chronic TBI (minimum 6 months post-injury) subjects. Fifteen subjects had visible encephalomalacias whereas 12 did not have evident lesions on MRI. Imaging from 14 age-matched healthy volunteers were used as controls. CBF was assessed by arterial spin labeling (ASL) and CVR by measuring the change in blood-oxygen-level–dependent (BOLD) MRI during a hypercapnia challenge. There was a significant reduction in FA, CBF, and CVR with a complementary increase in MD within regions of FLAIR-visible encephalomalacia (p

ObjectiveTo determine whether neurofilament light (NfL), glial fibrillary acidic protein (GFAP), tau, and ubiquitin C-terminal hydrolase-L1 (UCH-L1) measured in serum relate to traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) measures of traumatic axonal injury (TAI) in patients with TBI.MethodsPatients with TBI (n = 162) and controls (n = 68) were prospectively enrolled between 2011 and 2019. Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days, and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury.ResultsAt enrollment, patients with TBI had increased serum NfL compared to controls (p < 0.0001). Serum NfL decreased over the course of 5 years but remained significantly elevated compared to controls. Serum NfL at 30 days distinguished patients with mild, moderate, and severe TBI from controls with an area under the receiver-operating characteristic curve (AUROC) of 0.84, 0.92, and 0.92, respectively. At enrollment, serum GFAP was elevated in patients with TBI compared to controls (p < 0.001). GFAP showed a biphasic release in serum, with levels decreasing during the first 6 months of injury but increasing over the subsequent study visits. The highest AUROC for GFAP was measured at 30 days, distinguishing patients with moderate and severe TBI from controls (both 0.89). Serum tau and UCH-L1 showed weak associations with TBI severity and neuroimaging measures. Longitudinally, serum NfL was the only biomarker that was associated with the likely rate of MRI brain atrophy and DTI measures of progression of TAI.ConclusionsSerum NfL shows greater diagnostic and prognostic utility than GFAP, tau, and UCH-L1 for subacute and chronic TBI.Classification of evidenceThis study provides Class III evidence that serum NfL distinguishes patients with mild TBI from healthy controls.

Natural modes and frequencies of three-dimensional (3D) deformation of the human brain were identified from in vivo tagged magnetic resonance images (MRI) acquired dynamically during transient mild acceleration of the head. Twenty 3D strain fields, estimated from tagged MRI image volumes in 19 adult subjects, were analyzed using dynamic mode decomposition (DMD). These strain fields represented dynamic, 3D brain deformations during constrained head accelerations, either involving rotation about the vertical axis of the neck or neck extension. DMD results reveal fundamental oscillatory modes of deformation at damped frequencies near 7 Hz (in neck rotation) and 11 Hz (in neck extension). Modes at these frequencies were found consistently among all subjects. These characteristic features of 3D human brain deformation are important for understanding the response of the brain in head impacts and provide valuable quantitative criteria for the evaluation and use of computer models of brain mechanics.

Traumatic Brain Injury (TBI) is associated with both diffuse axonal injury (DAI) and diffuse vascular injury (DVI), which result from inertial shearing forces. These terms are often used interchangeably, but the spatial relationships between DAI and DVI have not been carefully studied. Multimodal magnetic resonance imaging (MRI) can help distinguish these injury mechanisms: diffusion tensor imaging (DTI) provides information about axonal integrity, while arterial spin labeling (ASL) can be used to measure cerebral blood flow (CBF), and the reactivity of the Blood Oxygen Level Dependent (BOLD) signal to a hypercapnia challenge reflects cerebrovascular reactivity (CVR). Subjects with chronic TBI (n = 27) and healthy controls (n = 14) were studied with multimodal MRI. Mean values of mean diffusivity (MD), fractional anisotropy (FA), CBF, and CVR were extracted for pre-determined regions of interest (ROIs). Normalized z-score maps were generated from the pool of healthy controls. Abnormal ROIs in one modality were not predictive of abnormalities in another. Approximately 9-10% of abnormal voxels for CVR and CBF also showed an abnormal voxel value for MD, while only 1% of abnormal CVR and CBF voxels show a concomitant abnormal FA value. These data indicate that DAI and DVI represent two distinct TBI endophenotypes that are spatially independent.

High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE 1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images. 2 This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods.

Highlights • PML has characteristic dynamic changes in brain and lesion volume on MRI. • JCnet is an automated method for brain atrophy and lesion segmentation in PML. • JCnet improves PML lesion segmentation accuracy compared to conventional methods. • JCnet can accurately track PML lesion changes over time., Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus and associated with substantial morbidity and mortality. Accurate MRI assessment of PML lesion burden and brain parenchymal atrophy is of decisive value in monitoring the disease course and response to therapy. However, there are currently no validated automatic methods for quantification of PML lesion burden or associated parenchymal volume loss. Furthermore, manual brain or lesion delineations can be tedious, require the use of valuable time resources by radiologists or trained experts, and are often subjective. In this work, we introduce JCnet (named after the causative viral agent), an end-to-end, fully automated method for brain parenchymal and lesion segmentation in PML using consecutive 3D patch-based convolutional neural networks. The network architecture consists of multi-view feature pyramid networks with hierarchical residual learning blocks containing embedded batch normalization and nonlinear activation functions. The feature maps across the bottom-up and top-down pathways of the feature pyramids are merged, and an output probability membership generated through convolutional pathways, thus rendering the method fully convolutional. Our results show that this approach outperforms and improves longitudinal consistency compared to conventional, state-of-the-art methods of healthy brain and multiple sclerosis lesion segmentation, utilized here as comparators given the lack of available methods validated for use in PML. The ability to produce robust and accurate automated measures of brain atrophy and lesion segmentation in PML is not only valuable clinically but holds promise toward including standardized quantitative MRI measures in clinical trials of targeted therapies. Code is available at: https://github.com/omarallouz/JCnet.

Traumatic brain injury (TBI) is a risk factor for the later development of neurodegenerative diseases that may have various underlying pathologies. Chronic traumatic encephalopathy (CTE) in particular is associated with repetitive mild TBI (mTBI) and is characterized pathologically by aggregation of hyperphosphorylated tau into neurofibrillary tangles (NFTs). CTE may be suspected when behavior, cognition, and/or memory deteriorate following repetitive mTBI. Exposure to blast overpressure from improvised explosive devices (IEDs) has been implicated as a potential antecedent for CTE amongst Iraq and Afghanistan Warfighters. In this study, we identified biomarker signatures in rats exposed to repetitive low-level blast that develop chronic anxiety-related traits and in human veterans exposed to IED blasts in theater with behavioral, cognitive, and/or memory complaints. Rats exposed to repetitive low-level blasts accumulated abnormal hyperphosphorylated tau in neuronal perikarya and perivascular astroglial processes. Using positron emission tomography (PET) and the [18F]AV1451 (flortaucipir) tau ligand, we found that five of 10 veterans exhibited excessive retention of [18F]AV1451 at the white/gray matter junction in frontal, parietal, and temporal brain regions, a typical localization of CTE tauopathy. We also observed elevated levels of neurofilament light (NfL) chain protein in the plasma of veterans displaying excess [18F]AV1451 retention. These findings suggest an association linking blast injury, tauopathy, and neuronal injury. Further study is required to determine whether clinical, neuroimaging, and/or fluid biomarker signatures can improve the diagnosis of long-term neuropsychiatric sequelae of mTBI.

To implement and evaluate interleaved blip-up, blip-down, non-segmented 3D echo planar imaging (EPI) with pseudo-continuous arterial spin labeling (pCASL) and post-processing for reduced susceptibility artifact cerebral blood flow (CBF) maps.3D EPI non-segmented acquisition with a pCASL labeling sequence was modified to include alternating k-space coverage along phase encoding direction (referred to as "blip-reversed") for alternating dynamic acquisitions of control and label pairs. Eight volunteers were imaged on a 3T scanner. Images were corrected for distortion using spatial shifting transformation of the underlying field map. CBF maps were calculated and compared with maps obtained without blip reversal using matching gray matter (GM) images from a high resolution 3D scan. Additional benefit of using the correction for alternating blip-up and blip-down acquisitions was assessed by comparing to corrected blip-up only and corrected blip-down only CBF maps. Matched Student t-test of overlapping voxels for the eight volunteers was done to ascertain statistical improvement in distortion.Mean CBF value in GM for the eight volunteers from distortion corrected CBF maps was 50.8±9.9ml/min/100 gm tissue. Corrected CBF maps had 6.3% and 4.1% more voxels in GM when compared with uncorrected blip up (BU) and blip down (BD) images, respectively. Student t-test showed significant reduction in distortion when compared with blip-up images and blip-down images (p0.001). When compared with corrected BU and corrected BD only CBF maps, BU and BD corrected maps had 2.3% and 1% more voxels (p=0.006 and 0.04, respectively).Pseudo-continuous arterial spin labeling with non-segmented 3D EPI acquisition using alternating blip-reversed k-space traversal and distortion correction provided significantly better matching GM CBF maps. In addition, employing alternating blip-reversed acquisitions during pCASL acquisition resulted in statistically significant improvement over corrected blip-up and blip-down CBF maps.

Being able to focus on a complex task and inhibit unwanted actions or interfering information (i.e., inhibitory control) are essential human cognitive abilities. However, it remains unknown the extent to which mild traumatic brain injury (mTBI) may impact these critical functions. In this study, seventeen patients and age-matched healthy controls (HC) performed a variant of the Stroop task and attention-demanding 4-choice response tasks (4CRT) with identical stimuli but two contexts: one required only routine responses and the other with occasional response conflicts. The results showed that mTBI patients performed equally well as the HC when the 4CRT required only routine responses. However, when the task conditions included occasional response conflicts, mTBI patients with even a single concussion showed a significant slow-down in all responses and higher error rates relative to the HC. Results from event-related functional magnetic resonance imaging (efMRI) revealed altered neural activity in the mTBI patients in the cerebellum-thalamo-cortical and the fronto-basal-ganglia networks regulating inhibitory control. These results suggest that even without apparent difficulties in performing complex attention-demanding but routine tasks, patients with mTBI may experience long-lasting deficits in regulating inhibitory control when situations call for rapid conflict resolutions.

Diffuse axonal injury (DAI) is a debilitating consequence of traumatic brain injury (TBI) attributed to abnormal stretching of axons caused by blunt head trauma or acceleration of the head. We developed an anatomically accurate, subject-specific, three-dimensional (3D) computational model of the human brain, and used it to study the dynamic deformations in the substructures of the brain when the head is subjected to rotational accelerations. The computational head models use anatomy and morphology of the white matter fibers obtained using MRI. Subject-specific full-field shearing motions in live human brains obtained through a recently developed tagged MRI imaging technique are then used to validate the models by comparing the measured and predicted heterogeneous dynamic mechanical response of the brain. These results are used to elucidate the dynamics of local shearing deformations in the brain substructures caused by rotational acceleration of the head. Our work demonstrates that the rotational dynamics of the brain has a timescale of ∼100 ms as determined by the shearing wave speeds, and thus the injuries associated with rotational accelerations likely occur over these time scales. After subject-specific validation using the live human subject data, a representative subject-specific head model is used to simulate a real life scenario that resulted in a concussive injury. Results suggest that regions of the brain, in the form of a toroid, encompassing the white matter, the cortical gray matter, and outer parts of the limbic system have a higher susceptibility to injury under axial rotations of the head.

We assessed the utility of a brief MRI protocol, appropriate for the acute setting, to detect acute traumatic brain injury (TBI) in patients with suspected mild TBI (mTBI) and distinguish traumatic from nontraumatic brain injury by comparing trauma with nontrauma patients. Twenty-two patients with suspected mTBI were included in this exploratory study over a period of 9 months. Median time from injury to MR scanning was 5.4 h (interquartile range 3.6-15.3). To determine the specificity of certain findings for TBI, 61 patients presenting with suspected minor acute stroke were included as a comparative group using the same MRI methods. A selected series of MRI sequences (diffusion-weighted imaging, fluid attenuated inversion recovery [FLAIR], and T2* weighted) were independently evaluated by two neuroradiologists blinded to clinical diagnosis, for presence of specific findings. In a separate session, all cases in which at least one MRI sequence above was positive were classified as TBI, stroke, or indeterminate. Intracranial MRI abnormalities were observed in 47 (57%) of the 83 studied patients. Based on findings on MRI, 12 (55%) of 22 suspected mTBI patients were classified as having traumatic injury. Nine (47%) of the 19 suspected mTBI patients with a negative CT had findings on MRI. Abnormalities on MRI consistent with trauma were observed most frequently on postcontrast FLAIR (83%) and T2*-weighted (58%) sequences. We demonstrated the ability of a fast MRI protocol to identify trauma-related abnormalities not seen on CT, and differentiate acute trauma from nonspecific chronic disease in a blinded cohort of mTBI patients.

It is not known whether lateralized olfactory sensitivity deficits are present in MS. Since projections from the olfactory bulb to the olfactory cortex are largely ipsilateral, and since both functional imaging and psychophysical studies suggest that the right side of the brain may be more involved in olfactory processing than the left, we addressed this issue by administering well-validated tests of odor detection, along with tests of odor identification, to each side of the nose of 73 MS patients and 73 age-, gender-, and race-matched normal controls. We also determined, in 63 of the MS patients, whether correlations were present between the olfactory test measures and MRI-determined lesions in brain regions ipsilateral and contralateral to the nose side that was tested. No significant left:right differences in either olfactory sensitivity or identification were present, although in both cases mean performance was lower in the MS than in the control subjects (ps < 0.0001). Scores on the two sides of the nose were positively correlated with one another (identification r = 0.71, p < 0.0001; threshold r = 0.56, p < 0.0001). The percent of MS patients whose bilateral test scores fell below the 10th percentile of controls did not differ between the odor identification and detection threshold tests. Both left and right odor identification and detection test scores were weakly correlated with lesion volumes in temporal and frontal lobe brain regions (r’s < 0.40). Our findings demonstrate that MS does not differentially influence odor perception on left and right sides of the nose, regardless of whether sensitivity or identification is being measured. They also indicate that tests of odor identification and detection are similarly influenced by MS and that such influences are associated with central brain lesions.

ObjectiveTo determine whether serum neurofilament light (NfL) correlates with CSF NfL, traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) estimates of traumatic axonal injury (TAI).MethodsParticipants were prospectively enrolled in Sweden and the United States between 2011 and 2019. The Swedish cohort included 45 hockey players with acute concussion sampled at 6 days, 31 with repetitive concussion with persistent postconcussive symptoms (PCS) assessed with paired CSF and serum (median 1.3 years after concussion), 28 preseason controls, and 14 nonathletic controls. Our second cohort included 230 clinic-based participants (162 with TBI and 68 controls). Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury.ResultsIn athletes with paired specimens, CSF NfL and serum NfL were correlated (r = 0.71, p < 0.0001). CSF and serum NfL distinguished players with PCS >1 year from PCS ≤1 year (area under the receiver operating characteristic curve [AUROC] 0.81 and 0.80). The AUROC for PCS >1 year vs preseason controls was 0.97. In the clinic-based cohort, NfL at enrollment distinguished patients with mild from those with moderate and severe TBI (p < 0.001 and p = 0.048). Serum NfL decreased over the course of 5 years (ß = −0.09 log pg/mL, p < 0.0001) but remained significantly elevated compared to controls. Serum NfL correlated with measures of functional outcome, MRI brain atrophy, and DTI estimates of TAI.ConclusionsSerum NfL shows promise as a biomarker for acute and repetitive sports-related concussion and patients with subacute and chronic TBI.Classification of evidenceThis study provides Class III evidence that increased concentrations of NfL distinguish patients with TBI from controls.

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sorensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

PURPOSE: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is being placed on the generalizations of these models for production deployment. With sufficiently large training datasets, models can typically avoid overfitting their data; however, for medical imaging it is often difficult to obtain enough data from a single site. Sharing data between institutions is also frequently nonviable or prohibited due to security measures and research compliance constraints, enforced to guard protected health information (PHI) and patient anonymity. METHODS: In this paper, we implement cyclic weight transfer with independent datasets from multiple geographically disparate sites without compromising PHI. We compare results between single-site learning (SSL) and multi-site learning models (MSL) on testing data drawn from each of the training sites as well as two other institutions. RESULTS: The MSL model attains an average Dice Similarity Coefficient (DSC) of 0.690 on the holdout institution datasets with a volume correlation of 0.914, respectively corresponding to a 7% and 5% statistically significant improvement over the average of both SSL models, which attained an average DSC of 0.646 and average correlation of 0.871. CONCLUSIONS: We show that a neural network can be efficiently trained on data from two physically remote sites without consolidating patient data to a single location. The resulting network improves model generalization and achieves higher average DSCs on external datasets than neural networks trained on data from a single source.

We employ an advanced 3D computational model of the head with high anatomical fidelity, together with measured tissue properties, to assess the consequences of dynamic loading to the head in two distinct modes: head rotation and head extension. We use a subject-specific computational head model, using the material point method, built from T1 magnetic resonance images, and considering the anisotropic properties of the white matter which can predict strains in the brain under large rotational accelerations. The material model now includes the shear anisotropy of the white matter. We validate the model under head rotation and head extension motions using live human data, and advance a prior version of the model to include biofidelic falx and tentorium. We then examine the consequences of incorporating the falx and tentorium in terms of the predictions from the computational head model.

Background: The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear. Objective: To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS. Methods: A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category. Results: A total of 86 DMT epochs (DMT-H: n = 32; DMT-L: n = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (−0.15% vs −0.81%; p = 0.001) and putaminal (−0.27% vs −0.73%; p = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume. Conclusion: DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS.

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.

Early intervention following combat deployment has the potential to prevent posttraumatic stress disorder (PTSD), but there is a need for greater understanding of the factors that contribute to PTSD symptom progression. This study investigated: (1) fear-potentiated startle during a fear extinction, (2) white matter microstructure, and (3) PTSD symptom severity, in 48 recently deployed service members (SMs) who did not have sufficient PTSD symptoms to meet criteria for a clinical diagnosis. Electromyography startle during a conditional discrimination paradigm, diffusion tensor imaging, and the Clinician Administered PTSD Scale were assessed in a cohort of SMs within 2 months after their return from Iraq or Afghanistan. Significant correlations were found between left uncinate fasciculus (UF) white matter tract integrity and total PTSD symptoms, r=-0.343, p=0.018; the left UF and hyperarousal symptoms, r=-0.29, p=0.047; right UF integrity and total PTSD symptoms r=-0.3371, p=0.01; right UF integrity and hyperarousal symptoms r=-0.332, p=0.023; left UF and startle during early extinction, r=.31, p=0.033. Our results indicate that compromise of UF tract frontal-limbic connections are associated with greater PTSD symptom severity and lower startle response during extinction. In a subthreshold population, such a relationship between brain structure, physiological reactivity, and behavioral expression may reveal vulnerabilities that could have significant implications for PTSD symptom development.

Empirical studies of taste function in multiple sclerosis (MS) are rare. Moreover, a detailed assessment of whether quantitative measures of taste function correlate with the punctate and patchy myelin-related lesions found throughout the CNS of MS patients has not been made. We administered a 96-trial test of sweet (sucrose), sour (citric acid), bitter (caffeine) and salty (NaCl) taste perception to the left and right anterior (CN VII) and posterior (CN IX) tongue regions of 73 MS patients and 73 matched controls. The number and volume of lesions were assessed using quantitative MRI in 52 brain regions of 63 of the MS patients. Taste identification scores were significantly lower in the MS patients for sucrose (p = 0.0002), citric acid (p = 0.0001), caffeine (p = 0.0372) and NaCl (p = 0.0004) and were present in both anterior and posterior tongue regions. The percent of MS patients with identification scores falling below the 5th percentile of controls was 15.07 % for caffeine, 21.9 % for citric acid, 24.66 % for sucrose, and 31.50 % for NaCl. Such scores were inversely correlated with lesion volumes in the temporal, medial frontal, and superior frontal lobes, and with the number of lesions in the left and right superior frontal lobes, right anterior cingulate gyrus, and left parietal operculum. Regardless of the subject group, women outperformed men on the taste measures. These findings indicate that a sizable number of MS patients exhibit taste deficits that are associated with MS-related lesions throughout the brain., U.S. National Institutes of Health ( RO1 DC 02974 -- R01 NS 37172 -- R01 NS070906)

Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis., Highlights • Longitudinal analysis of MR images of the human brain provides knowledge about brain changes during aging and diseases. • Unlike previous methods, some temporal anatomical changes in the brain has been modeled by MR intensity change. • A 4D intensity based filter is proposed as a pre-processing step to 3D segmentation methods. • The 4D filter ensures consistency in segmentation while retaining sensitivity to true anatomical changes. • Experiments show improved atrophy detection in presence of dementia, cognitive impairment and different phenotypes of multiple sclerosis.

On average, African Americans with multiple sclerosis demonstrate higher inflammatory disease activity, faster disability accumulation, greater visual dysfunction, more pronounced brain tissue damage and higher lesion volume loads compared to Caucasian Americans with multiple sclerosis. Neurodegeneration is an important component of multiple sclerosis, which in part accounts for the clinical heterogeneity of the disease. Brain atrophy appears to be widespread, although it is becoming increasingly recognized that regional substructure atrophy may be of greater clinical relevance. Patient race (within the limitations of self-identified ancestry) is regarded as an important contributing factor. However, there is a paucity of studies examining differences in neurodegeneration and brain substructure volumes over time in African Americans relative to Caucasian American patients. Optical coherence tomography is a non-invasive and reliable tool for measuring structural retinal changes. Recent studies support its utility for tracking neurodegeneration and disease progression in vivo in multiple sclerosis. Relative to Caucasian Americans, African American patients have been found to have greater retinal structural injury in the inner retinal layers. Increased thickness of the inner nuclear layer and the presence of microcystoid macular pathology at baseline predict clinical and radiological inflammatory activity, although whether race plays a role in these changes has not been investigated. Similarly, assessment of outer retinal changes according to race in multiple sclerosis remains incompletely characterized. Twenty-two African Americans and 60 matched Caucasian Americans with multiple sclerosis were evaluated with brain MRI, and 116 African Americans and 116 matched Caucasian Americans with multiple sclerosis were monitored with optical coherence tomography over a mean duration of 4.5 years. Mixed-effects linear regression models were used in statistical analyses. Grey matter (-0.9%/year versus -0.5%: P =0.02), white matter (-0.7%/year versus -0.3%: P =0.04) and nuclear thalamic (-1.5%/year versus -0.7%/year: P =0.02) atrophy rates were approximately twice as fast in African Americans. African Americans also exhibited higher proportions of microcystoid macular pathology (12.1% versus 0.9%, P =0.001). Retinal nerve fibre layer (-1.1% versus -0.8%: P =0.02) and ganglion cell+ inner plexiform layer (-0.7%/year versus -0.4%/year: P =0.01) atrophy rates were faster in African versus Caucasian Americans. African Americans on average exhibited more rapid neurodegeneration than Caucasian Americans and had significantly faster brain and retinal tissue loss. These results corroborate the more rapid clinical progression reported to occur, in general, in African Americans with multiple sclerosis and support the need for future studies involving African Americans in order to identify individual differences in treatment responses in multiple sclerosis.

Dizziness, postural instability, and ataxia are among the most debilitating symptoms of multiple sclerosis (MS), reflecting, in large part, dysfunctional integration of visual, somatosensory, and vestibular sensory cues. However, the role of MS-related supratentorial lesions in producing such symptoms is poorly understood. In this study, motor control test (MCT) and dynamic sensory organization test (SOT) scores of 58 MS patients were compared to those of 72 healthy controls; correlations were determined between the MS scores of 49 patients and lesion volumes within 26 brain regions. Depending upon platform excursion direction and magnitude, MCT latencies, which were longer in MS patients than controls (p

Objective: Pathology in both cortex and deep gray matter contribute to disability in multiple sclerosis (MS). We used the increased signal-to-noise ratio of 7-tesla (7T) MRI to visualize small lesions within the thalamus and to relate this to clinical information and cortical lesions. Methods: We obtained 7T MRI scans on 34 MS cases and 15 healthy volunteers. Thalamic lesion number and volume were related to demographic data, clinical disability measures, and lesions in cortical gray matter. Results: Thalamic lesions were found in 24/34 of MS cases. Two lesion subtypes were noted: discrete, ovoid lesions, and more diffuse lesional areas lining the periventricular surface. The number of thalamic lesions was greater in progressive MS compared to relapsing–remitting (mean ±SD, 10.7 ±0.7 vs. 3.0 ±0.7, respectively, p < 0.001). Thalamic lesion burden (count and volume) correlated with EDSS score and measures of cortical lesion burden, but not with white matter lesion burden or white matter volume. Conclusions: Using 7T MRI allows identification of thalamic lesions in MS, which are associated with disability, progressive disease, and cortical lesions. Thalamic lesion analysis may be a simpler, more rapid estimate of overall gray matter lesion burden in MS.

Integrated PET/MR systems are becoming increasingly popular in clinical and research applications. Quantitative PET reconstruction requires correction for γ-photon attenuations using an attenuation coefficient map (μ map) that is a measure of the electron density. One challenge of PET/MR, in contrast to PET/CT, lies in the accurate computation of μ maps. Unlike CT, MR imaging measures physical properties not directly related to electron density. Previous approaches have computed the attenuation coefficients using a segmentation of MR images or using deformable registration of atlas CT images to the space of the subject MR images. Methods: In this work, we propose a patch-based method to generate whole-head μ maps from ultrashort echo-time (UTE) MR imaging sequences. UTE images are preferred to other MR sequences because of the increased signal from bone. To generate a synthetic CT image, we use patches from a reference dataset, which consists of dual-echo UTE images and a coregistered CT scan from the same subject. Matching of patches between the reference and target images allows corresponding patches from the reference CT scan to be combined via a Bayesian framework. No registration or segmentation is required. Results: For evaluation, UTE, CT, and PET data acquired from 5 patients under an institutional review board–approved protocol were used. Another patient (with UTE and CT data only) was selected to be the reference to generate synthetic CT images for these 5 patients. PET reconstructions were attenuation-corrected using the original CT, our synthetic CT, Siemens Dixon-based μ maps, Siemens UTE-based μ maps, and deformable registration-based CT. Our synthetic CT–based PET reconstruction showed higher correlation (average ρ = 0.996, R2 = 0.991) to the original CT-based PET, as compared with the segmentation- and registration-based methods. Synthetic CT–based reconstruction had minimal bias (regression slope, 0.990), as compared with the segmentation-based methods (regression slope, 0.905). A peak signal-to-noise ratio of 35.98 dB in the reconstructed PET activity was observed, compared with 29.767, 29.34, and 27.43 dB for the Siemens Dixon-, UTE-, and registration-based μ maps. Conclusion: A patch-matching approach to synthesize CT images from dual-echo UTE images leads to significantly improved accuracy of PET reconstruction as compared with actual CT scans. The PET reconstruction is improved over segmentation- (Dixon and Siemens UTE) and registration-based methods, even in subjects with pathologic findings.

Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.

The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI). Multi-atlas whole brain segmentation is performed using the T1-weighted MRI and the gray matter cerebrum labels are extended into the longitudinal fissure using fast marching to find an initial estimate of the falx. To correct the falx boundaries, we register and then deform a set of SWI with manually delineated falx boundaries into the subject space. The continuous-STAPLE algorithm fuses sets of corresponding points to produce an estimate of the corrected falx boundary. Correspondence between points on the deformed falx boundaries is obtained using coherent point drift. We compare our method to manual ground truth, a multi-atlas approach without correction, and single-atlas approaches.

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain three-dimensional (3D) motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine two-dimensional motion extracted from sparse slice acquisitions into 3D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac dataset. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.

PURPOSE: To explore: (1) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1-weighted scans related to gradient nonlinearity (GNL) and subject positioning; (2) the effect of vendor-implemented GNL-corrections; and (3) easily applicable methods that can be used to retrospectively correct data. METHODS: A multiple sclerosis patient was scanned at 7 sites using 3T MRI scanners with the same 3D T1-weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on: (1) phantom data fit; (2) normalization with C2 vertebral body diameters; and (3) the Jacobian determinant of non-linear registrations to a template, were tested. RESULTS: Depending on positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1-weighted scans when no distortion corrections were utilized. 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. CONCLUSION: Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies and point the way to prospectively and retrospectively managing GNL-distortions in a variety of settings, including clinical environments.

Post-traumatic stress disorder (PTSD) is commonly associated with mild traumatic brain injury (mTBI). To better understand their relationship, we examined neuroanatomical structures and neuropsychological performance in a sample of individuals with mTBI, with and without PTSD symptoms. Thirty-nine subjects with mTBI were dichotomized into those with (n = 12) and without (n = 27) significant PTSD symptoms based on scores on the PTSD Checklist. Using a region-of-interest approach, fronto-temporal volumes, fiber bundles obtained by diffusion tensor imaging, and neuropsychological scores were compared between the two groups. After controlling for total intracranial volume and age, subjects with mTBI and PTSD symptoms exhibited volumetric differences in the entorhinal cortex, an area associated with memory networks, relative to mTBI-only patients (F = 4.28; p = 0.046). Additionally, subjects with PTSD symptoms showed reduced white matter integrity in the right cingulum bundle (axial diffusivity, F = 6.04; p = 0.020). Accompanying these structural alterations, mTBI and PTSD subjects also showed impaired performance in encoding (F = 5.98; p = 0.019) and retrieval (F = 7.32; p = 0.010) phases of list learning and in tests of processing speed (Wechsler Adult Intelligence Scale Processing Speed Index, F = 12.23; p = 0.001; Trail Making Test A, F = 5.56; p = 0.024). Increased volume and white matter disruptions in these areas, commonly associated with memory functions, may be related to functional disturbances during cognitively demanding tasks. Differences in brain volume and white matter integrity between mTBI subjects and those with mTBI and co-morbid PTSD symptoms point to neuroanatomical differences that may underlie poorer recovery of mTBI subjects who experience PTSD symptoms. These findings support theoretical models of PTSD and its relationship to learning deficits.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) may share common symptom and neuropsychological profiles in military service members (SMs) following deployment; while a connection between the two conditions is plausible, the relationship between them has been difficult to discern. The intent of this report is to enhance our understanding of the relationship between findings on structural and functional brain imaging and symptoms of PTSD. Within a cohort of SMs who did not meet criteria for PTSD but were willing to complete a comprehensive assessment within 2 months of their return from combat deployment, we conducted a nested case-control analysis comparing those with combat-related mTBI to age/gender-matched controls with diffusion tensor imaging, resting state functional magnetic resonance imaging and a range of psychological measures. We report degraded white matter integrity in those with a history of combat mTBI, and a positive correlation between the white matter microstructure and default mode network (DMN) connectivity. Higher clinician-administered and self-reported subthreshold PTSD symptoms were reported in those with combat mTBI. Our findings offer a potential mechanism through which mTBI may alter brain function, and in turn, contribute to PTSD symptoms.

Background: Susceptibility-based MRI offers a unique opportunity to study neurological diseases such as multiple sclerosis (MS). In this work, we assessed a three-dimensional segmented echo-planar-imaging (3D-EPI) sequence to rapidly acquire high-resolution T2*-weighted and phase contrast images of the whole brain. We also assessed if these images could depict important features of MS at clinical field strength, and we tested the effect of a gadolinium-based contrast agent (GBCA) on these images. Materials and methods: The 3D-EPI acquisition was performed on four healthy volunteers and 15 MS cases on a 3T scanner. The 3D sagittal images of the whole brain were acquired with a voxel size of 0.55 × 0.55 × 0.55 mm3 in less than 4 minutes. For the MS cases, the 3D-EPI acquisition was performed before, during, and after intravenous GBCA injection. Results: Both T2*-weighted and phase-contrast images from the 3D-EPI acquisition were sensitive to the presence of lesions, parenchymal veins, and tissue iron. Conspicuity of the veins was enhanced when images were obtained during injection of GBCA. Conclusions: We propose this rapid imaging sequence for investigating, in a clinical setting, the spatiotemporal relationship between small parenchymal veins, iron deposition, and lesions in MS patient brains.

Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1–C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1–T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access) In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source.

Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.

Extract of Ginkgo biloba (EGb), a dietary supplement used for a number of conditions including dementia, has been suggested to increase cerebral bloodflow (CBF). The purpose of this study was to determine if changes in CBF could be detected by dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI)in elderly human subjects taking EGb.DSC-MRI was performed in nine healthy men(mean age 61±10 years) before and after 4 weeks of 60 mg EGb taken twice daily. One subject underwent six consecutive scans to evaluate intrasubject reproducibility. CBF values were computed before and after EGb, and analyzed at three different levels of spatial resolution, using voxel-based statistical parametric mapping (SPM), and regions of interest in different lobes, and all regions combined.Normalized intrasubject CBF (nCBF) measurements had a standard deviation of 7% and 4% in gray and white matter (WM) regions, respectively. SPM using an uncorrected, voxel-level threshold of P≤0.001 showed a small CBF increase in the left parietal-occipital region.CBF in individual lobar regions did not show any significant change post-EGb, but all regions combined showed a significant increase of non-normalized CBF after EGb (15% in white and 13% in gray matter, respectively, P≤0.0001).nCBF measured by DSC-MRI has good intrasubject reproducibility. In this small cohort of normal elderly individuals, a mild increase in CBF is found in the left parietal-occipital WM after EGb, as well as a small but statistically significant increase in global CBF.

The authors present preliminary results from a pilot study on patterns of brain injury associated with incident major depression after traumatic brain injury (TBI). Brain metabolite ratios, regional brain volumes, and cognitive performance were compared between 10 subjects with incident major depression post-TBI and seven TBI patients without major depression. TBI-depressed participants performed poorly on tests of frontotemporal functioning, had lower choline/creatine and N-acetylaspartate/creatine ratios in the right basal ganglia and had lower regional brain volumes in the right frontal, left occipital, and temporal lobes. The results suggest a possible role of frontotemporal lobe and basal ganglia pathology in depression after TBI.

Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms. Developing custom tools and ensuring interoperability with existing tools is a significant hurdle. To address these limitations, we present a new framework for algorithm development that implicitly ensures tool interoperability, generates graphical user interfaces, provides advanced batch processing tools, and, most importantly, requires minimal additional programming or computational overhead. Java-based rapid prototyping with this system is an efficient and practical approach to evaluate new algorithms since the proposed system ensures that rapidly constructed prototypes are actually fully-functional processing modules with support for multiple GUI's, a broad range of file formats, and distributed computation. Herein, we demonstrate MRI image processing with the proposed system for cortical surface extraction in large cross-sectional cohorts, provide a system for fully automated diffusion tensor image analysis, and illustrate how the system can be used as a simulation framework for the development of a new image analysis method. The system is released as open source under the Lesser GNU Public License (LGPL) through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).

Atlas-based segmentation techniques are often employed to encode anatomical information for the delineation of multiple structures in magnetic resonance images of the brain. One of the primary challenges of these approaches is to efficiently model qualitative and quantitative anatomical knowledge without introducing a strong bias toward certain anatomical preferences when segmenting new images. This paper explores the use of topological information as a prior and proposes a segmentation framework based on both topological and statistical atlases of brain anatomy. Topology can be used to describe continuity of structures, as well as the relationships between structures, and is often a critical component in cortical surface reconstruction and deformation-based morphometry. Our method guarantees strict topological equivalence between the segmented image and the atlas, and relies only weakly on a statistical atlas of shape. Tissue classification and fast marching methods are used to provide a powerful and flexible framework to handle multiple image contrasts, high levels of noise, gain field inhomogeneities, and variable anatomies. The segmentation algorithm has been validated on simulated and real brain image data and made freely available to researchers. Our experiments demonstrate the accuracy and robustness of the method and the limited influence of the statistical atlas.

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that enforces any desired topology using a fast marching technique. The method has been applied successfully to the correction of the cortical gray matter / white matter interface in segmented brain images and is publicly released as a software plug-in or the MIPAV package.

This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.