A 4.3-million-year-old Australopithecus anamensis mandible from Ileret, East Turkana, Kenya, and its paleoenvironmental context.

A hominin mandible, KNM-ER 63000, and associated vertebrate remains were recovered in 2011 from Area 40 in East Turkana, Kenya. Tephrostratigraphic and magnetostratigraphic analyses indicate that these fossils date to ∼4.3 Ma. KNM-ER 63000 consists of articulating but worn and weathered mandibular corpora, with a broken right M2 crown and alveoli preserved at other tooth positions. Despite extensive damage, KNM-ER 63000 preserves diagnostic anatomy permitting attribution to Australopithecus anamensis. It can be distinguished from Australopithecus afarensis by its strongly inclined symphyseal axis with a basally convex, 'cut-away' external surface, a lateral corpus that sweeps inferomedially beneath the canine-premolar row, and alignment of the canine alveolus with the postcanine axis. KNM-ER 63000 is distinguished from Ardipithecus ramidus by its thick mandibular corpus and large M2 crown. The functional trait structure and enamel's stable carbon isotopic composition of the Area 40 large-mammal community suggests an environment comparable to Kanapoi and other ∼4.5-4 Ma eastern African sites that would have offered Au. anamensis access to both C3 and C4 food resources. With an age of ∼4.3 Ma, KNM-ER 63000 is the oldest known specimen of Au. anamensis, predating the Kanapoi and Asa Issie samples by at least ∼100 kyr. This specimen extends the known temporal range of Au. anamensis and places it in temporal overlap with fossils of Ar. ramidus from Gona, Ethiopia. The morphology of KNM-ER 63000 indicates that the reconfigured masticatory system differentiating basal hominins from the earliest australopiths existed in the narrow temporal window, if any, separating the two. The very close temporal juxtaposition of these significant morphological and adaptive differences implies that Ar. ramidus was a relative rather than a direct phyletic ancestor of earliest Australopithecus.

Cognitive Motor Dissociation in Disorders of Consciousness.

BACKGROUND: Patients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness. METHODS: In this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness. We assessed the response to commands on task-based fMRI or EEG in participants without an observable response to verbal commands (i.e., those with a behavioral diagnosis of coma, vegetative state, or minimally conscious state-minus) and in participants with an observable response to verbal commands. The presence or absence of an observable response to commands was assessed with the use of the Coma Recovery Scale-Revised (CRS-R). RESULTS: Data from fMRI only or EEG only were available for 65% of the participants, and data from both fMRI and EEG were available for 35%. The median age of the participants was 37.9 years, the median time between brain injury and assessment with the CRS-R was 7.9 months (25% of the participants were assessed with the CRS-R within 28 days after injury), and brain trauma was an etiologic factor in 50%. We detected cognitive motor dissociation in 60 of the 241 participants (25%) without an observable response to commands, of whom 11 had been assessed with the use of fMRI only, 13 with the use of EEG only, and 36 with the use of both techniques. Cognitive motor dissociation was associated with younger age, longer time since injury, and brain trauma as an etiologic factor. In contrast, responses on task-based fMRI or EEG occurred in 43 of 112 participants (38%) with an observable response to verbal commands. CONCLUSIONS: Approximately one in four participants without an observable response to commands performed a cognitive task on fMRI or EEG as compared with one in three participants with an observable response to commands. (Funded by the James S. McDonnell Foundation and others.).

Recovery Potential in Patients Who Died After Withdrawal of Life-Sustaining Treatment: A TRACK-TBI Propensity Score Analysis.

Among patients with severe traumatic brain injury (TBI), there is high prognostic uncertainty but growing evidence that recovery of independence is possible. Nevertheless, families are often asked to make decisions about withdrawal of life-sustaining treatment (WLST) within days of injury. The range of potential outcomes for patients who died after WLST (WLST+) is unknown, posing a challenge for prognostic modeling and clinical counseling. We investigated the potential for survival and recovery of independence after acute TBI in patients who died after WLST. We used Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data and propensity score matching to pair participants with WLST+ to those with a similar probability of WLST (based on demographic and clinical characteristics), but for whom life-sustaining treatment was not withdrawn (WLST-). To optimize matching, we divided the WLST- cohort into tiers (Tier 1 = 0-11%, Tier 2 = 11-27%, Tier 3 = 27-70% WLST propensity). We estimated the level of recovery that could be expected in WLST+ participants by evaluating 3-, 6-, and 12-month Glasgow Outcome Scale-Extended (GOSE) and Disability Rating Scale outcomes in matched WLST- participants. Of 90 WLST+ participants (80% male, mean [standard deviation; SD] age = 59.2 [17.9] years, median [IQR] days to WLST = 5.4 [2.2, 11.7]), 80 could be matched to WLST- participants. Of 56 WLST- participants who were followed at 6 months, 31 (55%) died. Among survivors in the overall sample and survivors in Tiers 1 and 2, more than 30% recovered at least partial independence (GOSE ≥4). In Tier 3, recovery to GOSE ≥4 occurred at 12 months, but not 6 months, post-injury. These results suggest a substantial proportion of patients with TBI and WLST may have survived and achieved at least partial independence. However, death or severe disability is a common outcome when the probability of WLST is high. While further validation is needed, our findings support a more cautious clinical approach to WLST and more complete reporting on WLST in TBI studies.

Intimate Partner Violence and Other Trauma Exposures in Females With Traumatic Brain Injury.

We examined whether females with a history of traumatic brain injury (TBI) and intimate partner violence (IPV) have greater exposure to lifetime trauma relative to females with TBI but no IPV history. Further, we assessed the effects of lifetime trauma on psychological outcomes after TBI. Female participants (n = 70; age M [standard deviation-SD] = 50.5 [15.2] years) with TBI (time since injury median [interquartile range -IQR] = 10.2 [5.3-17.8] years) completed a structured assessment of lifetime history of TBI, including an IPV module to query head injuries from physical violence by an intimate partner. We characterized lifetime trauma exposure with the Adverse Childhood Experiences (ACEs) questionnaire and Survey of Exposure to Community Violence (CV). We evaluated psychological functioning with self-report questionnaires of post-traumatic stress disorder (PTSD), depression, and anxiety symptoms. Compared with those with no IPV history (n = 51), participants reporting IPV-related head injuries (n = 19; 27.1%) reported more ACEs (M[SD] IPV: 4.5[2.9]; No IPV: 1.6[1.8], p < 0.001, d = 1.08) and greater CV (IPV: 17.5[8.4]; No IPV: 7.6[6.1], p < .0001, d = 1.26). Within the full sample, ACEs (ß = 0.21, 95% confidence interval [CI] = 0.04-0.39) and CV (ß = 0.07, 95% CI = 0.01-0.13) predicted worse PTSD symptoms, while IPV alone did not. Exposure to all three sources of trauma (ACEs, CV, and IPV) was associated with worse PTSD symptoms relative to fewer traumas. The results highlight the scope of traumatic exposures among TBI survivors and the importance of considering IPV and other lifetime trauma exposure in assessing and managing TBI. Trauma-informed interventions that are modified for TBI-related impairment may offer improved outcomes in managing psychological symptoms.

Gys1 Antisense Therapy Prevents Disease-Driving Aggregates and Epileptiform Discharges in a Lafora Disease Mouse Model.

Patients with Lafora disease have a mutation in EPM2A or EPM2B, resulting in dysregulation of glycogen metabolism throughout the body and aberrant glycogen molecules that aggregate into Lafora bodies. Lafora bodies are particularly damaging in the brain, where the aggregation drives seizures with increasing severity and frequency, coupled with neurodegeneration. Previous work employed mouse genetic models to reduce glycogen synthesis by approximately 50%, and this strategy significantly reduced Lafora body formation and disease phenotypes. Therefore, an antisense oligonucleotide (ASO) was developed to reduce glycogen synthesis in the brain by targeting glycogen synthase 1 (Gys1). To test the distribution and efficacy of this drug, the Gys1-ASO was administered to Epm2b-/- mice via intracerebroventricular administration at 4, 7, and 10 months. The mice were then sacrificed at 13 months and their brains analyzed for Gys1 expression, glycogen aggregation, and neuronal excitability. The mice treated with Gys1-ASO exhibited decreased Gys1 protein levels, decreased glycogen aggregation, and reduced epileptiform discharges compared to untreated Epm2b-/- mice. This work provides proof of concept that a Gys1-ASO halts disease progression of EPM2B mutations of Lafora disease.

In situ microwave fixation provides an instantaneous snapshot of the brain metabolome.

Brain glucose metabolism is highly heterogeneous among brain regions and continues postmortem. In particular, we demonstrate exhaustion of glycogen and glucose and an increase in lactate production during conventional rapid brain resection and preservation by liquid nitrogen. In contrast, we show that these postmortem changes are not observed with simultaneous animal sacrifice and in situ fixation with focused, high-power microwave. We further employ microwave fixation to define brain glucose metabolism in the mouse model of streptozotocin-induced type 1 diabetes. Using both total pool and isotope tracing analyses, we identified global glucose hypometabolism in multiple brain regions, evidenced by reduced 13C enrichment into glycogen, glycolysis, and the tricarboxylic acid (TCA) cycle. Reduced glucose metabolism correlated with a marked decrease in GLUT2 expression and several metabolic enzymes in unique brain regions. In conclusion, our study supports the incorporation of microwave fixation for more accurate studies of brain metabolism in rodent models.

The evolution of hominoid locomotor versatility: Evidence from Moroto, a 21 Ma site in Uganda.

Living hominoids are distinguished by upright torsos and versatile locomotion. It is hypothesized that these features evolved for feeding on fruit from terminal branches in forests. To investigate the evolutionary context of hominoid adaptive origins, we analyzed multiple paleoenvironmental proxies in conjunction with hominoid fossils from the Moroto II site in Uganda. The data indicate seasonally dry woodlands with the earliest evidence of abundant C4 grasses in Africa based on a confirmed age of 21 million years ago (Ma). We demonstrate that the leaf-eating hominoid Morotopithecus consumed water-stressed vegetation, and postcrania from the site indicate ape-like locomotor adaptations. These findings suggest that the origin of hominoid locomotor versatility is associated with foraging on leaves in heterogeneous, open woodlands rather than forests.

Mycobacterium vaccae NCTC 11659, a Soil-Derived Bacterium with Stress Resilience Properties, Modulates the Proinflammatory Effects of LPS in Macrophages.

Inflammatory conditions, including allergic asthma and conditions in which chronic low-grade inflammation is a risk factor, such as stress-related psychiatric disorders, are prevalent and are a significant cause of disability worldwide. Novel approaches for the prevention and treatment of these disorders are needed. One approach is the use of immunoregulatory microorganisms, such as Mycobacterium vaccae NCTC 11659, which have anti-inflammatory, immunoregulatory, and stress-resilience properties. However, little is known about how M. vaccae NCTC 11659 affects specific immune cell targets, including monocytes, which can traffic to peripheral organs and the central nervous system and differentiate into monocyte-derived macrophages that, in turn, can drive inflammation and neuroinflammation. In this study, we investigated the effects of M. vaccae NCTC 11659 and subsequent lipopolysaccharide (LPS) challenge on gene expression in human monocyte-derived macrophages. THP-1 monocytes were differentiated into macrophages, exposed to M. vaccae NCTC 11659 (0, 10, 30, 100, 300 µg/mL), then, 24 h later, challenged with LPS (0, 0.5, 2.5, 250 ng/mL), and assessed for gene expression 24 h following challenge with LPS. Exposure to M. vaccae NCTC 11659 prior to challenge with higher concentrations of LPS (250 ng/mL) polarized human monocyte-derived macrophages with decreased IL12A, IL12B, and IL23A expression relative to IL10 and TGFB1 mRNA expression. These data identify human monocyte-derived macrophages as a direct target of M. vaccae NCTC 11659 and support the development of M. vaccae NCTC 11659 as a potential intervention to prevent stress-induced inflammation and neuroinflammation implicated in the etiology and pathophysiology of inflammatory conditions and stress-related psychiatric disorders.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

BACKGROUND: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. OBJECTIVE: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). METHODS: Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. RESULTS: The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. CONCLUSION: The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

Relationship of Aboriginal family wellbeing to social and cultural determinants, Central Australia: 'Waltja tjutangku nyakunytjaku'.

OBJECTIVE: The objective of this study was to apply a strength-based approach to examine the relation of cultural and social determinants to high family functioning for Aboriginal people in Central Australia. DESIGN: Cross-sectional study involving a quantitative analysis of survey data. Prevalence rate ratios (PRs) and 95% CIs were calculated from binomial regressions, adjusted for gender and age. Qualitative data from workshops with Aboriginal leaders in Central Australia supported the interpretation of the research findings. PARTICIPANTS: The study involved 639 Aboriginal people in Central Australia who participated in the Mayi Kuwayu Study. RESULT: Overall, 57.9% (370/639) of participants reported high/very high family functioning, 16.9% (108/639) reported moderate and 13.3% (85/639) reported low. The adjusted prevalence of family functioning was similar across gender, age groups and household sizes. Family functioning was associated with lower family financial status (aPR=0.74, 95% CI=0.60 to 0.91) and receiving welfare (0.88, 0.77 to 1.00). Family functioning was greater with high community cohesion (2.72, 1.68 to 4.39), high individual agency in community (2.15, 1.63 to 2.85); having an Aboriginal language as a first language (1.20, 1.04 to 1.37); speaking your Aboriginal language a lot (1.37, 1.12 to 1.68); high exposure to cultural practice and knowledge (1.45, 1.28 to 1.65); and multigenerational or extended family households (1.19, 1.02 to 1.38). CONCLUSION: High family functioning is a strength in Central Australia and is intrinsically connected with culture. Healthcare providers and programmes that build on the foundations of culture and family are an important approach to improving wellbeing.

Correcting cardiorespiratory noise in resting-state functional MRI data acquired in critically ill patients.

Resting-state functional MRI is being used to develop diagnostic, prognostic and therapeutic biomarkers for critically ill patients with severe brain injuries. In studies of healthy volunteers and non-critically ill patients, prospective cardiorespiratory data are routinely collected to remove non-neuronal fluctuations in the resting-state functional MRI signal during analysis. However, the feasibility and utility of collecting cardiorespiratory data in critically ill patients on a clinical MRI scanner are unknown. We concurrently acquired resting-state functional MRI (repetition time = 1250 ms) and cardiac and respiratory data in 23 critically ill patients with acute severe traumatic brain injury and in 12 healthy control subjects. We compared the functional connectivity results from two approaches that are commonly used to correct cardiorespiratory noise: (i) denoising with cardiorespiratory data (i.e. image-based method for retrospective correction of physiological motion effects in functional MRI) and (ii) standard bandpass filtering. Resting-state functional MRI data in 7 patients could not be analysed due to imaging artefacts. In 6 of the remaining 16 patients (37.5%), cardiorespiratory data were either incomplete or corrupted. In patients (n = 10) and control subjects (n = 10), the functional connectivity results corrected with the image-based method for retrospective correction of physiological motion effects in functional MRI did not significantly differ from those corrected with bandpass filtering of 0.008-0.125 Hz. Collectively, these findings suggest that, in critically ill patients with severe traumatic brain injury, there is limited feasibility and utility to denoising the resting-state functional MRI signal with prospectively acquired cardiorespiratory data.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Introduction: Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods: Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results: The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion: This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

In situ mass spectrometry imaging reveals heterogeneous glycogen stores in human normal and cancerous tissues.

Glycogen dysregulation is a hallmark of aging, and aberrant glycogen drives metabolic reprogramming and pathogenesis in multiple diseases. However, glycogen heterogeneity in healthy and diseased tissues remains largely unknown. Herein, we describe a method to define spatial glycogen architecture in mouse and human tissues using matrix-assisted laser desorption/ionization mass spectrometry imaging. This assay provides robust and sensitive spatial glycogen quantification and architecture characterization in the brain, liver, kidney, testis, lung, bladder, and even the bone. Armed with this tool, we interrogated glycogen spatial distribution and architecture in different types of human cancers. We demonstrate that glycogen stores and architecture are heterogeneous among diseases. Additionally, we observe unique hyperphosphorylated glycogen accumulation in Ewing sarcoma, a pediatric bone cancer. Using preclinical models, we correct glycogen hyperphosphorylation in Ewing sarcoma through genetic and pharmacological interventions that ablate in vivo tumor growth, demonstrating the clinical therapeutic potential of targeting glycogen in Ewing sarcoma.

Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease.

Introduction: Multiple trials have demonstrated broad performance ranges for tests attempting to detect coronary artery disease. The most common test, SPECT, requires capital-intensive equipment, the use of radionuclides, induction of stress, and time off work and/or travel. Presented here are the development and clinical validation of an office-based machine learned algorithm to identify functionally significant coronary artery disease without radiation, expensive equipment or induced patient stress. Materials and methods: The IDENTIFY trial (NCT03864081) is a prospective, multicenter, non-randomized, selectively blinded, repository study to collect acquired signals paired with subject meta-data, including outcomes, from subjects with symptoms of coronary artery disease. Time synchronized orthogonal voltage gradient and photoplethysmographic signals were collected for 230 seconds from recumbent subjects at rest within seven days of either left heart catheterization or coronary computed tomography angiography. Following machine learning on a proportion of these data (N = 2,522), a final algorithm was selected, along with a pre-specified cut point on the receiver operating characteristic curve for clinical validation. An unseen set of subject signals (N = 965) was used to validate the algorithm. Results: At the pre-specified cut point, the sensitivity for detecting functionally significant coronary artery disease was 0.73 (95% CI: 0.68-0.78), and the specificity was 0.68 (0.62-0.74). There exists a point on the receiver operating characteristic curve at which the negative predictive value is the same as coronary computed tomographic angiography, 0.99, assuming a disease incidence of 0.04, yielding sensitivity of 0.89 and specificity of 0.42. Selecting a point at which the positive predictive value is maximized, 0.12, yields sensitivity of 0.39 and specificity of 0.88. Conclusion: The performance of the machine learned algorithm presented here is comparable to common tertiary center testing for coronary artery disease. Employing multiple cut points on the receiver operating characteristic curve can yield the negative predictive value of coronary computed tomographic angiography and a positive predictive value approaching that of myocardial perfusion imaging. As such, a system employing this algorithm may address the need for a non-invasive, no radiation, no stress, front line test, and hence offer significant advantages to the patient, their physician, and healthcare system.

The Lichen Photobiont Genus Rhizonema (cyanobacteria) Exhibits Diverse Modes of Branching, Both False and True.

The recently described genus Rhizonema is among the most important cyanobacterial partners in lichen symbioses, but its morphological characterization in the genus diagnosis-true branching of the T-type-appears at odds with several published figures showing false branching. We investigated cyanobiont branching and cell division with light microscopy in two basidiolichens from Florida and one from Japan, including aposymbiotically cultured material of the latter. Mycobiont species identities (Cyphellostereum jamesianum, Dictyonema darwinianum, and D. moorei) and photobiont genus identity (Rhizonema) were corroborated with ITS and rbcLX sequences, respectively. Single and paired false branching occurred commonly in all three strains examined. False branches developed adjacent to necridic cells or heterocytes, or by separation of vegetative cells at compression folds in the trichome. Non-transverse cell divisions, usually oblique, were observed in two of the three Rhizonema strains examined. T-type true branches sometimes arose from such divisions, although oblique growth from the branch cell often resulted in ambiguous branch junctions. Additionally, Y-type true branches appeared to grow from contorted filaments. In cultured material, a kind of pseudo-branch sometimes arose from single- or several-celled segments liberated from trichome apices. The segments attached secondarily to filaments and grew there as apparent branches. We conclude that Rhizonema is a genus of considerable morphological flexibility, with multiple modes of branching possible in a single strain. While true branching or non-transverse divisions, when observable, may help distinguish Rhizonema from the phenotypically similar Scytonema, false branching occurs commonly in both genera, and therefore cannot be used to distinguish them.

Return to Work Within Four Months of Grade 3 Diffuse Axonal Injury.

Neuroprognostication following diffuse axonal injury (DAI) has historically relied on neuroimaging techniques with lower spatial resolution and contrast than techniques currently available in clinical practice. Since the initial studies of DAI classification and prognosis in the 1980s and 1990s, advances in neuroimaging have improved detection of brainstem microbleeds, a hallmark feature of Grade 3 DAI that has traditionally been associated with poor neurologic outcome. Here, we report clinical and radiologic data from two patients with severe traumatic brain injury and grade 3 DAI who recovered functional independence and returned to work within 4 months of injury. Importantly, both patients were scanned using 3 Tesla MRI protocols that included susceptibility-weighted imaging (SWI), a technique that provides enhanced sensitivity for detecting brainstem microbleeds. These observations highlight the importance of developing approaches to DAI classification and prognosis that better align with contemporary neuroimaging capabilities.