Longitudinal Assessment of Glymphatic Changes Following Mild Traumatic Brain Injury: Insights from PVS burden and DTI-ALPS Imaging.

Traumatic brain injury (TBI) even in the mild form may result in long-lasting post-concussion symptoms. TBI is also a known risk to late-life neurodegeneration. Recent studies suggest that dysfunction in the glymphatic system, responsible for clearing protein waste from the brain, may play a pivotal role in the development of dementia following TBI. Given the diverse nature of TBI, longitudinal investigations are essential to comprehending the dynamic changes in the glymphatic system and its implications for recovery. In this prospective study, we evaluated two promising glymphatic imaging markers, namely the enlarged perivascular space (ePVS) burden and Diffusion Tensor Imaging-based ALPS index, in 44 patients with mTBI at two early post-injury time points: approximately 14 days (14Day) and 6-12 months (6-12Mon) post-injury, while also examining their associations with post-concussion symptoms. Additionally, 37 controls, comprising both orthopedic patients and healthy individuals, were included for comparative analysis. Our key findings include: 1) White matter ePVS burden (WM-ePVS) and ALPS index exhibit significant correlations with age. 2) Elevated WM-ePVS burden in acute mTBI (14Day) is significantly linked to a higher number of post-concussion symptoms, particularly memory problems. 3) The increase in the ALPS index from acute (14Day) to the chronic (6-12Mon) phases in mTBI patients correlates with improvement in sleep measures. Furthermore, incorporating WM-ePVS burden and the ALPS index from acute phase enhances the prediction of chronic memory problems beyond socio-demographic and basic clinical information, highlighting their distinct roles in assessing glymphatic structure and activity. Early evaluation of glymphatic function could be crucial for understanding TBI recovery and developing targeted interventions to improve patient outcomes.

Meningeal contrast enhancement in multiple sclerosis: Assessment of field strength, acquisition delay, and clinical relevance.

BACKGROUND/PURPOSE: Leptomeningeal enhancement (LME) on post-contrast FLAIR is described as a potential biomarker of meningeal inflammation in multiple sclerosis (MS). Here we report an assessment of the impact of MRI field strength and acquisition timing on meningeal contrast enhancement (MCE). METHODS: This was a cross-sectional, observational study of 95 participants with MS and 17 healthy controls (HC) subjects. Each participant underwent an MRI of the brain on both a 7 Tesla (7T) and 3 Tesla (3T) MRI scanner. 7T protocols included a FLAIR image before, soon after (Gd+ Early 7T FLAIR), and 23 minutes after gadolinium (Gd+ Delayed 7T FLAIR). 3T protocol included FLAIR before and 21 minutes after gadolinium (Gd+ Delayed 3T FLAIR). RESULTS: LME was seen in 23.3% of participants with MS on Gd+ Delayed 3T FLAIR, 47.4% on Gd+ Early 7T FLAIR (p = 0.002) and 57.9% on Gd+ Delayed 7T FLAIR (p < 0.001 and p = 0.008, respectively). The count and volume of LME, leptomeningeal and paravascular enhancement (LMPE), and paravascular and dural enhancement (PDE) were all highest for Gd+ Delayed 7T FLAIR and lowest for Gd+ Delayed 3T FLAIR. Non-significant trends were seen for higher proportion, counts, and volumes for LME and PDE in MS compared to HCs. The rate of LMPE was different between MS and HCs on Gd+ Delayed 7T FLAIR (98.9% vs 82.4%, p = 0.003). MS participants with LME on Gd+ Delayed 7T FLAIR were older (47.6 (10.6) years) than those without (42.0 (9.7), p = 0.008). CONCLUSION: 7T MRI and a delay after contrast injection increased sensitivity for all forms of MCE. However, the lack of difference between groups for LME and its association with age calls into question its relevance as a biomarker of meningeal inflammation in MS.

Meningeal contrast enhancement in multiple sclerosis: assessment of field strength, acquisition delay, and clinical relevance.

Background/Purpose: Leptomeningeal enhancement (LME) on post-contrast FLAIR is described as a potential biomarker of meningeal inflammation in multiple sclerosis (MS). Here we report a comprehensive assessment of the impact of MRI field strength and acquisition timing on meningeal contrast enhancement (MCE). Methods: This was a cross-sectional, observational study of 95 participants with MS and 17 healthy controls (HC) subjects. Each participant underwent an MRI of the brain on both a 7 Tesla (7T) and 3 Tesla (3T) MRI scanner. 7T protocols included a FLAIR image before, soon after (Gd+ Early 7T FLAIR), and 23 minutes after gadolinium (Gd+ Delayed 7T FLAIR). 3T protocol included FLAIR before and 21 minutes after gadolinium (Gd+ Delayed 3T FLAIR). Results: LME was seen in 23.3% of participants with MS on Gd+ Delayed 3T FLAIR, 47.4% on Gd+ Early 7T FLAIR (p = 0.002) and 57.9% on Gd+ Delayed 7T FLAIR (p < 0.001 and p = 0.008, respectively). The count and volume of LME, leptomeningeal and paravascular enhancement (LMPE), and paravascular and dural enhancement (PDE) were all highest for Gd+ Delayed 7T FLAIR and lowest for Gd+ Delayed 3T FLAIR. Non-significant trends were seen for higher proportion, counts, and volumes for LME and PDE in MS compared to HCs. The rate of LMPE was different between MS and HCs on Gd+ Delayed 7T FLAIR (98.9% vs 82.4%, p = 0.003). MS participants with LME on Gd+ Delayed 7T FLAIR were older (47.6 (10.6) years) than those without (42.0 (9.7), p = 0.008). Conclusion: 7T MRI and a delay after contrast injection increased sensitivity for all forms of MCE. However, the lack of difference between groups for LME and its association with age calls into question its relevance as a biomarker of meningeal inflammation in MS.

Simultaneous Measurement of GABA, Glutathione, and Glutamate-Glutamine in the Thalamus using Edited MR Spectroscopy: Feasibility and Applications in Traumatic Brain Injury.

BACKGROUND: MR spectroscopy (MRS) is a noninvasive tool for evaluating biochemical alterations, such as glutamate (Glu)/gamma-aminobutyric acid (GABA) imbalance and depletion of antioxidative glutathione (GSH) after traumatic brain injury (TBI). Thalamus, a critical and vulnerable region post-TBI, is challenging for MRS acquisitions, necessitating optimization to simultaneously measure GABA/Glu and GSH. PURPOSE: To assess the feasibility and optimize acquisition and processing approaches for simultaneously measuring GABA, Glx (Glu + glutamine (Gln)), and GSH in the thalamus, employing Hadamard encoding and reconstruction of MEscher-GArwood (MEGA)-edited spectroscopy (HERMES). STUDY TYPE: Prospective. SUBJECTS: 28 control subjects (age: 35.9 ± 15.1 years), and 17 mild TBI (mTBI) patients (age: 32.4 ± 11.3 years). FIELD STRENGTH/SEQUENCE: 3T/T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE), HERMES. ASSESSMENT: We evaluated the impact of acquisition with spatial saturation bands and post-processing with spectral alignment on HERMES performance in the thalamus among controls. Within-subject variability was examined in five controls through repeated scans within a week. The HERMES spectra in the posterior cingulate cortex (PCC) of controls were used as a reference for assessing HERMES performance in a reliable target. Furthermore, we compared metabolite levels and fitting quality in the thalamus between mTBI patients and controls. STATISTICAL TESTS: Unpaired t-tests and within-subject coefficient-of-variation (CV). A P-value <0.05 was deemed significant. RESULTS: HERMES spectra, acquired with saturation bands and processed with spectral alignment, yielded reliable metabolite measurements in the thalamus. The mean within-subject CV for GABA, Glx, and GSH levels were 18%, 10%, and 16% in the thalamus (7%, 9%, and 16% in the PCC). GABA (3.20 ± 0.60 vs 2.51 ± 0.55, P < 0.01) and Glx (8.69 ± 1.23 vs 7.72 ± 1.19, P = 0.03) levels in the thalamus were significantly higher in mTBI patients than in controls, with GSH (1.27 ± 0.35 vs 1.22 ± 0.28, P = 0.65) levels showing no significant difference. DATA CONCLUSION: Simultaneous measuring GABA/Glx and GSH using HERMES is feasible in the thalamus, providing valuable insight into TBI. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Focused Ultrasound Central Lateral Thalamotomy for the Treatment of Refractory Neuropathic Pain: Phase I Trial.

BACKGROUND AND OBJECTIVES: Magnetic resonance-guided focused ultrasound (MRgFUS) central lateral thalamotomy (CLT) has not yet been validated for treating refractory neuropathic pain (NP). Our aim was to assess the safety and potential efficacy of MRgFUS CLT for refractory NP. METHODS: In this prospective, nonrandomized, single-arm, investigator-initiated phase I trial, patients with NP for more than 6 months related to phantom limb pain, spinal cord injury, or radiculopathy/radicular injury and who had undergone at least one previous failed intervention were eligible. The main outcomes were safety profile and pain as assessed using the brief pain inventory, the pain disability index, and the numeric rating scale. Medication use and the functional connectivity of the default mode network (DMN) were also assessed. RESULTS: Ten patients were enrolled, with nine achieving successful ablation. There were no serious adverse events and 12 mild/moderate severity events. The mean age was 50.9 years (SD: 12.7), and the mean symptom duration was 12.3 years (SD: 9.7). Among eight patients with a 1-year follow-up, the brief pain inventory decreased from 7.6 (SD: 1.1) to 3.8 (SD: 2.8), with a mean percent decrease of 46.3 (SD: 40.6) (paired t -test, P = .017). The mean pain disability index decreased from 43.0 (SD: 7.5) to 25.8 (SD: 16.8), with a mean percent decrease of 39.3 (SD: 41.6) ( P = .034). Numeric rating scale scores decreased from a mean of 7.2 (SD: 1.8) to 4.0 (SD: 2.8), with a mean percent decrease of 42.8 (SD: 37.8) ( P = .024). Patients with predominantly intermittent pain or with allodynia responded better than patients with continuous pain or without allodynia, respectively. Some patients decreased medication use. Resting-state functional connectivity changes were noted, from disruption of the DMN at baseline to reactivation of connectivity between DMN nodes at 3 months. CONCLUSION: MRgFUS CLT is feasible and safe for refractory NP and has potential utility in reducing symptoms as measured by validated pain scales.

Automatic Quantification of Enlarged Perivascular Space in Patients With Traumatic Brain Injury Using Super-Resolution of T2-Weighted Images.

The perivascular space (PVS) is important to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) becomes visible on magnetic resonance imaging (MRI) and is best appreciated on T2-weighted (T2w) images. However, quantification of ePVS is challenging because standard-of-care T1-weighted (T1w) and T2w images are often obtained via two-dimensional (2D) acquisition, whereas accurate quantification of ePVS normally requires high-resolution volumetric three-dimensional (3D) T1w and T2w images. The purpose of this study was to investigate the use of a deep-learning-based super-resolution (SR) technique to improve ePVS quantification from 2D T2w images for application in patients with traumatic brain injury (TBI). We prospectively recruited 26 volunteers (age: 31 ± 12 years, 12 male/14 female) where both 2D T2w and 3D T2w images were acquired along with 3D T1w images to validate the ePVS quantification using SR T2w images. We then applied the SR method to retrospectively acquired 2D T2w images in 41 patients with chronic TBI (age: 41 ± 16 years, 32 male/9 female). ePVS volumes were automatically quantified within the whole-brain white matter and major brain lobes (temporal, parietal, frontal, occipital) in all subjects. Pittsburgh Sleep Quality Index (PSQI) scores were obtained on all patients with TBI. Compared with the silver standard (3D T2w), in the validation study, the SR T2w provided similar whole-brain white matter ePVS volume (r = 0.98, p < 0.0001), and similar age-related ePVS burden increase (r = 0.80, p < 0.0001). In the patient study, patients with TBI with poor sleep showed a higher age-related ePVS burden increase than those with good sleep. Sleep status is a significant interaction factor in the whole brain (p = 0.047) and the frontal lobe (p = 0.027). We demonstrate that images produced by SR of 2D T2w images can be automatically analyzed to produce results comparable to those obtained by 3D T2 volumes. Reliable age-related ePVS burden across the whole-brain white matter was observed in all subjects. Poor sleep, affecting the glymphatic function, may contribute to the accelerated increase of ePVS burden following TBI.

Cerebellar functional connectivity change is associated with motor and neuropsychological function in early stage drug-naïve patients with Parkinson's disease.

Introduction: Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting both motor and cognitive function. Previous neuroimaging studies have reported altered functional connectivity (FC) in distributed functional networks. However, most neuroimaging studies focused on patients at an advanced stage and with antiparkinsonian medication. This study aims to conduct a cross-sectional study on cerebellar FC changes in early-stage drug-naïve PD patients and its association with motor and cognitive function. Methods: Twenty-nine early-stage drug-naïve PD patients and 20 healthy controls (HCs) with resting-state fMRI data and motor UPDRS and neuropsychological cognitive data were extracted from the Parkinson's Progression Markers Initiative (PPMI) archives. We used seed-based resting-state fMRI (rs-fMRI) FC analysis and the cerebellar seeds were defined based on the hierarchical parcellation of the cerebellum (AAL atlas) and its topological function mapping (motor cerebellum and non-motor cerebellum). Results: The early stage drug-naïve PD patients had significant differences in cerebellar FC when compared with HCs. Our findings include: (1) Increased intra-cerebellar FC within motor cerebellum, (2) increase motor cerebellar FC in inferior temporal gyrus and lateral occipital gyrus within ventral visual pathway and decreased motor-cerebellar FC in cuneus and dorsal posterior precuneus within dorsal visual pathway, (3) increased non-motor cerebellar FC in attention, language, and visual cortical networks, (4) increased vermal FC in somatomotor cortical network, and (5) decreased non-motor and vermal FC within brainstem, thalamus and hippocampus. Enhanced FC within motor cerebellum is positively associated with the MDS-UPDRS motor score and enhanced non-motor FC and vermal FC is negatively associated with cognitive function test scores of SDM and SFT. Conclusion: These findings provide support for the involvement of cerebellum at an early stage and prior to clinical presentation of non-motor features of the disease in PD patients.

Toward vendor-independent measurement of cerebral venous oxygenation: Comparison of TRUST MRI across three major MRI manufacturers and association with end-tidal CO2.

Cerebral venous oxygenation (Yv ) is a valuable biomarker for a variety of brain diseases. T2 relaxation under spin tagging (TRUST) MRI is a widely used method for Yv quantification. In this work, there were two main objectives. The first was to evaluate the reproducibility of TRUST Yv measurements across MRI scanners from different vendors. The second was to examine the correlation between Yv and end-tidal CO2 (EtCO2 ) in a multisite, multivendor setting and determine the usefulness of this correlation to account for variations in Yv caused by normal variations and physiological fluctuations. Standardized TRUST pulse sequences were implemented on three scanners from major MRI vendors (GE, Siemens, Philips). These scanners were located at two research institutions. Ten healthy subjects were scanned. On each scanner, the subject underwent two scan sessions, each of which included three TRUST scans, to evaluate the intrasession and intersession reproducibility of Yv . Each scanner was also equipped with a capnograph device to record the EtCO2 of the subject during the MRI scan. We found no significant bias in Yv measurements across the three scanners (P = 0.18). The measured Yv values on the three scanners were also strongly correlated with each other (intraclass correlation coefficients > 0.85, P < 0.001). The intrasession and intersession coefficients of variation of Yv were less than 4% and showed no significant difference among the scanners. In addition, our results revealed that (1) within the same subject, Yv increased with EtCO2 at a rate of 1.24 ± 0.17%/mmHg (P < 0.0001), and (2) across different subjects, individuals with a higher EtCO2 had a higher Yv , at a rate of 0.94 ± 0.36%/mmHg (P = 0.01). These results suggest that (1) the standardized TRUST sequences had similar accuracies and reproducibilities for the quantification of Yv across the scanners, and (2) recording of EtCO2 may be a useful complement to Yv measurement to account for CO2 -related physiological fluctuations in Yv in multisite, multivendor studies.

Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations.

Data-driven thalamic nuclei parcellation depends on high-quality manual annotations. However, the small size and low contrast changes among thalamic nuclei, yield annotations that are often incomplete, noisy, or ambiguously labelled. To train a robust thalamic nuclei parcellation model with noisy annotations, we propose a label propagation algorithm based on random walker to refine the annotations before model training. A two-step model was trained to generate first the whole thalamus and then the nuclei masks. We conducted experiments on a mild traumatic brain injury~(mTBI) dataset with noisy thalamic nuclei annotations. Our model outperforms current state-of-the-art thalamic nuclei parcellations by a clear margin. We believe our method can also facilitate the training of other parcellation models with noisy labels.

Analysis of Tongue Muscle Strain During Speech From Multimodal Magnetic Resonance Imaging.

PURPOSE: Muscle groups within the tongue in healthy and diseased populations show different behaviors during speech. Visualizing and quantifying strain patterns of these muscle groups during tongue motion can provide insights into tongue motor control and adaptive behaviors of a patient. METHOD: We present a pipeline to estimate the strain along the muscle fiber directions in the deforming tongue during speech production. A deep convolutional network estimates the crossing muscle fiber directions in the tongue using diffusion-weighted magnetic resonance imaging (MRI) data acquired at rest. A phase-based registration algorithm is used to estimate motion of the tongue muscles from tagged MRI acquired during speech. After transforming both muscle fiber directions and motion fields into a common atlas space, strain tensors are computed and projected onto the muscle fiber directions, forming so-called strains in the line of actions (SLAs) throughout the tongue. SLAs are then averaged over individual muscles that have been manually labeled in the atlas space using high-resolution T2-weighted MRI. Data were acquired, and this pipeline was run on a cohort of eight healthy controls and two glossectomy patients. RESULTS: The crossing muscle fibers reconstructed by the deep network show orthogonal patterns. The strain analysis results demonstrate consistency of muscle behaviors among some healthy controls during speech production. The patients show irregular muscle patterns, and their tongue muscles tend to show more extension than the healthy controls. CONCLUSIONS: The study showed visual evidence of correlation between two muscle groups during speech production. Patients tend to have different strain patterns compared to the controls. Analysis of variations in muscle strains can potentially help develop treatment strategies in oral diseases. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21957011.

Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer.

The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map. Investigating the link between weighting maps and speech acoustics can offer significant insights into the intricate process of speech production. To this end, in this work, we utilize two-dimensional spectrograms as a proxy representation, and develop an end-to-end deep learning framework for translating weighting maps to their corresponding audio waveforms. Our proposed plastic light transformer (PLT) framework is based on directional product relative position bias and single-level spatial pyramid pooling, thus enabling flexible processing of weighting maps with variable size to fixed-size spectrograms, without input information loss or dimension expansion. Additionally, our PLT framework efficiently models the global correlation of wide matrix input. To improve the realism of our generated spectrograms with relatively limited training samples, we apply pair-wise utterance consistency with Maximum Mean Discrepancy constraint and adversarial training. Experimental results on a dataset of 29 subjects speaking two utterances demonstrated that our framework is able to synthesize speech audio waveforms from weighting maps, outperforming conventional convolution and transformer models.

Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification.

PURPOSE: The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue. METHOD: dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue. RESULTS: Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue. CONCLUSION: The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Association between Sleep Disturbances at Subacute Stage of Mild Traumatic Brain Injury and Long-Term Outcomes.

Mild (mTBI) traumatic brain injury (TBI) accounts for the majority of all TBI cases. Evidence has suggested that patients with mTBI can suffer from long-lasting cognitive deficits, persistent symptoms, and decreased quality of life. Sleep disorders are commonly observed after TBI, with the prevalence rate of sleep disturbances in persons with TBI being much higher than that in the general population. Poor sleep quality can impair cognitive functions in the general population. This effect of sleep disturbances may impede the recovery processes in the population with TBI. The objective of this study is to add to our understanding of the relationship between self-reported sleep problems and other post-concussion symptoms and look at the association between early sleep problems and long-term outcomes in mTBI. Post-concussion symptoms, neurocognitive functions, level of global outcomes, and rating of satisfaction of life were assessed in 64 patients with mTBI. The results revealed that the presence of sleep disturbances co-occur with an increased level of overall post-concussion symptoms at the subacute stage of mTBI, particularly with symptoms including poor concentration, memory problems, and irritability. In addition, sleep disturbance at the subacute stage is associated with persistent poor concentration and memory problems, as well as worse neurocognitive function, slower overall recovery, and lower satisfactory of life at the long term. Our findings suggest that sleep disturbance can be a prognostic factor of long-term outcomes after mTBI. Early interventions to improve sleep quality can have potential benefits to facilitate the recovery process from mTBI.

Evaluating the impact of MR image harmonization on thalamus deep network segmentation.

Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain's anatomical structures that may occur due to normal aging or disease. Machine learning techniques are widely used in automatic structure segmentation. However, the contrast variation between the training and testing data makes it difficult for segmentation algorithms to generate consistent results. To address this problem, an image-to-image translation technique called MR image harmonization can be used to match the contrast between different data sets. It is important for the harmonization to transform image intensity while maintaining the underlying anatomy. In this paper, we present a 3D U-Net algorithm to segment the thalamus from multiple MR image modalities and investigate the impact of harmonization on the segmentation algorithm. Manual delineations of thalamic nuclei on two data sets are available. However, we aim to analyze the thalamus in another large data set where ground truth labels are lacking. We trained two segmentation networks, one with unharmonized images and the other with harmonized images, on one data set with manual labels, and compared their performances on the other data set with manual labels. These two data groups were diagnosed with two brain disorders and were acquired with similar imaging protocols. The harmonization target is the large data set without manual labels, which also has a different imaging protocol. The networks trained on unharmonized and harmonized data showed no significant difference when evaluating on the other data set; demonstrating that image harmonization can maintain the anatomy and does not affect the segmentation task. The two networks were evaluated on the harmonization target data set and the network trained on harmonized data showed significant improvement over the network trained on unharmonized data. Therefore, the network trained on harmonized data provides the potential to process large amounts of data from other sites, even in the absence of site-specific training data.

Alterations in motor functional connectivity in Neonatal Hypoxic Ischemic Encephalopathy.

BACKGROUND: Neonatal hypoxic-ischemic encephalopathy (HIE) is the result of global hypoxic-ischemic brain injury in neonates due to asphyxia during birth and is one of the most common causes of severe, long-term neurologic deficits in children. Methods: Resting state fMRI (rs-fMRI) was used to assess potential functional disruptions in the primary and association motor areas in HIE neonates (n = 16) compared to healthy controls (n = 11). RESULTS: Results demonstrate reduced intra-hemispheric resting state functional connectivity (rs-FC) between primary motor regions (upper extremity and facial motor regions) as well as reduced inter-hemispheric rs-FC in the HIE group. In addition, HIE neonates demonstrated increased rs-FC between motor regions and frontal, temporal and parietal cortices but decreased rs-FC with the cerebellum. DISCUSSION: These preliminary results provide initial evidence for the disruption of functional communication with the motor network in neonates with HIE. Further studies are necessary to both validate these findings in a larger dataset as well as to determine if rs-fMRI measurements collected at birth may have the potential to serve as a prognostic marker in addition to the traditional combination of clinical measurements and conventional MRI.

Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator.

Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders. Because of their heterogeneous representations, however, direct mapping between the two modalities-i.e., two-dimensional (mid-sagittal slice) plus time tagged-MRI sequence and its corresponding one-dimensional waveform-is not straightforward. Instead, we resort to two-dimensional spectrograms as an intermediate representation, which contains both pitch and resonance, from which to develop an end-to-end deep learning framework to translate from a sequence of tagged-MRI to its corresponding audio waveform with limited dataset size. Our framework is based on a novel fully convolutional asymmetry translator with guidance of a self residual attention strategy to specifically exploit the moving muscular structures during speech. In addition, we leverage a pairwise correlation of the samples with the same utterances with a latent space representation disentanglement strategy. Furthermore, we incorporate an adversarial training approach with generative adversarial networks to offer improved realism on our generated spectrograms. Our experimental results, carried out with a total of 63 tagged-MRI sequences alongside speech acoustics, showed that our framework enabled the generation of clear audio waveforms from a sequence of tagged-MRI, surpassing competing methods. Thus, our framework provides the great potential to help better understand the relationship between the two modalities.

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis.

Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-training UDA for generative tasks, such as image synthesis, is not fully investigated. In this work, we propose a novel generative self-training (GST) UDA framework with continuous value prediction and regression objective for cross-domain image synthesis. Specifically, we propose to filter the pseudo-label with an uncertainty mask, and quantify the predictive confidence of generated images with practical variational Bayes learning. The fast test-time adaptation is achieved by a round-based alternative optimization scheme. We validated our framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis problem, where datasets in the source and target domains were acquired from different scanners or centers. Extensive validations were carried out to verify our framework against popular adversarial training UDA methods. Results show that our GST, with tagged MRI of test subjects in new target domains, improved the synthesis quality by a large margin, compared with the adversarial training UDA methods.

Circumferential segmental vessel-wall enhancement on black blood MRI in patients referred for the evaluation of vasculopathy.

BACKGROUND: High resolution intracranial vessel wall magnetic resonance imaging, or black blood MRI, has recently gained traction as an adjunct to computed tomography angiography, magnetic resonance angiography, and digital subtraction angiography in the characterization of atherosclerosis, vasculitides, and inflammatory changes in the aneurysm wall. However, the occurrence of uniform circumferential segmental arterial vessel wall enhancement (CSWE) in patients without these diagnoses has not previously been studied. The purpose of this study is twofold: 1) to evaluate the prevalence of CSWE in the major intracranial arteries in patients without vasculitides, symptomatic atherosclerosis, or aneurysmal subarachnoid hemorrhage and 2) to determine the association, if any, between such enhancement and risk factors for cerebrovascular atherosclerotic disease. MATERIALS & METHODS: A retrospective study of vessel wall magnetic resonance imaging examinations was performed to evaluate for CSWE in 26 patients without known vessel wall pathology such as aneurysms or vasculitides and intracranial hemorrhage. Further evaluation of CSWE association with major intracranial atherosclerotic disease risk factors including hypertension, hyperlipidemia, diabetes mellitus and cigarette smoking was performed. RESULTS AND CONCLUSION: 46% of the cohort of patients demonstrated CSWE. Among the patients with CSWE, there was increased prevalence of CSWE in the posterior circulation vasculature with particular predilection to the V4 vertebral artery segments (92%), although there was greater association of anterior circulation CSWE with risk factors for atherosclerosis. Patients with anterior circulation CSWE also demonstrated the most number of segments with CSWE. We therefore propose that CSWE, particularly in the anterior circulation, may portend early atherosclerosis.