Implementation of Institutional Triaging Algorithms Decreases Head and Neck MDCT Use in Blunt Trauma.

Background Multidetector CT (MDCT) enables rapid and accurate diagnosis of head and neck (HN) injuries in patients with blunt trauma (BT). However, MDCT is overused, and appropriate selection of patients for imaging could improve workflow. Purpose To investigate the effect of implementing clinical triaging algorithms on use of MDCT in the HN in patients who have sustained BT. Materials and Methods In this retrospective study, patients aged 15 years or older with BT admitted between October 28, 2007, and December 31, 2013, were included. Patients were divided into pre- and postalgorithm groups. The institutional trauma registry and picture archiving and communication system reports were reviewed to determine which patients underwent MDCT of the head, MDCT of the cervical spine (CS), and MDCT angiography of the HN at admission and whether these examinations yielded positive results. Injury Severity Score, Acute Physiology and Chronic Health Evaluation II score (only those patients in the intensive care unit), length of hospital stay (LOS), length of intensive care unit stay (ICULOS), and mortality were obtained from the trauma registry. Results A total of 8999 patients (mean age, 45 years ± 20 [standard deviation]; age range, 15-101 years; 6027 male) were included in this study. A lower percentage of the postalgorithm group versus the prealgorithm group underwent MDCT of the head (55.8% [2774 of 4969 patients]; 95% CI: 54.4, 57.2 vs 64.2% [2589 of 4030 patients]; 95% CI: 62.8, 65.7; P < .001) and CS (49.4% [2452 of 4969 patients]; 95% CI: 48.0, 50.7 vs 60.5% [2438 of 4030 patients]; 95% CI: 59.0, 62.0; P < .001) but not MDCT angiography of the HN (9.7% [480 of 4969 patients]; 95% CI: 8.9, 10.5 vs 9.8% [393 of 4030 patients]; 95% CI: 8.9, 10.7; P > .99). Pre- versus postalgorithm groups did not differ in LOS (mean, 4.8 days ± 7.1 vs 4.5 days ± 7.1, respectively; P = .42), ICULOS (mean, 4.6 days ± 6.6 vs 4.8 days ± 6.7, respectively; P > .99), or mortality (2.9% [118 of 4030 patients]; 95% CI: 2.5, 3.5; vs 2.8% [141 of 4969 patients]; 95% CI: 2.4, 3.3; respectively; P > .99). Conclusion Implementation of a clinical triaging algorithm resulted in decreased use of multidetector CT of the head and cervical spine in patients who experienced blunt trauma, without increased adverse outcomes. © RSNA, 2021 See also the editorial by Munera and Martin in this issue.

Nonhomogeneous Gadolinium Retention in the Cerebral Cortex after Intravenous Administration of Gadolinium-based Contrast Agent in Rats and Humans.

Background Gadolinium retention after repeated gadolinium-based contrast agent (GBCA) exposure has been reported in subcortical gray matter. However, gadolinium retention in the cerebral cortex has not been systematically investigated. Purpose To determine whether and where gadolinium is retained in rat and human cerebral cortex. Materials and Methods The cerebral cortex in Sprague-Dawley rats treated with gadopentetate dimeglumine (three doses over 4 weeks; cumulative gadolinium dose, 7.2 mmol per kilogram of body weight; n = 6) or saline (n = 6) was examined with antemortem MRI. Two human donors with repeated GBCA exposure (three and 15 doses; 1 and 5 months after exposure), including gadopentetate dimeglumine, and two GBCA-naive donors were also evaluated. Elemental brain maps (gadolinium, phosphorus, zinc, copper, iron) for rat and human brains were constructed by using laser ablation inductively coupled plasma mass spectrometry. Results Gadopentetate dimeglumine-treated rats showed region-, subregion-, and layer-specific gadolinium retention in the neocortex (anterior cingulate cortex: mean gadolinium concentration, 0.28 µg ∙ g-1 ± 0.04 [standard error of the mean]) that was comparable (P > .05) to retention in the allocortex (mean gadolinium concentration, 0.33 µg ∙ g-1 ± 0.04 in piriform cortex, 0.24 µg ∙ g-1 ± 0.04 in dentate gyrus, 0.17 µg ∙ g-1 ± 0.04 in hippocampus) and subcortical structures (0.47 µg ∙ g-1 ± 0.10 in facial nucleus, 0.39 µg ∙ g-1 ± 0.10 in choroid plexus, 0.29 µg ∙ g-1 ± 0.05 in caudate-putamen, 0.26 µg ∙ g-1 ± 0.05 in reticular nucleus of the thalamus, 0.24 µg ∙ g-1 ± 0.04 in vestibular nucleus) and significantly greater than that in the cerebellum (0.17 µg ∙ g-1 ± 0.03, P = .01) and white matter tracts (anterior commissure: 0.05 µg ∙ g-1 ± 0.01, P = .002; corpus callosum: 0.05 µg ∙ g-1 ± 0.02, P = .001; cranial nerve: 0.02 µg ∙ g-1 ± 0.01, P = .004). Retained gadolinium colocalized with parenchymal iron. T1-weighted MRI signal intensification was not observed. Gadolinium retention was detected in the cerebral cortex, pia mater, and pia-ensheathed leptomeningeal vessels in two GBCA-exposed human brains but not in two GBCA-naive human brains. Conclusion Repeated gadopentetate dimeglumine exposure is associated with gadolinium retention in specific regions, subregions, and layers of cerebral cortex that are critical for higher cognition, affect, and behavior regulation, sensorimotor coordination, and executive function. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kanal in this issue.

MRI-detected spinal disc degenerative changes in athletes participating in the Rio de Janeiro 2016 Summer Olympics games.

OBJECTIVE: To describe the frequency and the distribution of degenerative disc disease (DDD) detected in athletes who underwent spine MRI in the 2016 Summer Olympic Games in Rio de Janeiro. METHODS: Data on spine MRI examinations from the 2016 Summer Olympics were retrospectively analyzed. We assessed the frequency of DDD of the cervical (Cs), thoracic (Ts), and lumbar (Ls) spine using Pfirrmann's classification. Grade II and III were considered as mild, grade IV as moderate, and grade V as severe disc degeneration. Data were analyzed according to the location of the degenerative disc, type of sport, age-groups, and gender of the athletes. RESULTS: One hundred out of 11,274 athletes underwent 108 spine MRI's (21 C, 6 T, and 81 L) (53% Females (F), 47% Males (M)). The frequency of DDD was 40% (42% F, 58% M) over the entire spine (28% mild, 9% moderate and 3% severe). There were 58% (12%F, 88%M) of the cervical spine discs that showed some degree of degeneration (44% mild, 13.5% moderate and 1% severe). Athletics, Boxing, and Swimming were the sports most affected by DDD in the Cs. There were 12.5% of the thoracic discs that showed some degree of degeneration, all were mild DDD and were exclusively seen in female athletes. There were 39% (53% F, 47% M) of the lumbar discs with DDD (26% mild, 9% moderate, and 4% severe). CONCLUSION: Athletes who underwent spine MRI during the 2016 Summer Olympic Games show a high frequency of DDD of cervical and lumbar spines. Recognition of these conditions is important to develop training techniques that may minimize the development of degenerative pathology of the spine.

Posterior Reversible Encephalopathy Syndrome in Patients with Coronavirus Disease 2019: Two Cases and A Review of The Literature.

INTRODUCTION: Encephalopathy is a common complication of coronavirus disease 2019. Although the encephalopathy is idiopathic in many cases, there are several published reports of patients with posterior reversible encephalopathy syndrome in the setting of coronavirus disease 2019. OBJECTIVE: To describe the diverse presentations, risk factors, and outcomes of posterior reversible encephalopathy syndrome in patients with coronavirus disease 2019. METHODS: We assessed patients with coronavirus disease 2019 and a diagnosis of posterior reversible encephalopathy syndrome at our institution from April 1 to June 24, 2020. We performed a literature search to capture all known published cases of posterior reversible encephalopathy syndrome in patients with coronavirus disease 2019. RESULTS: There were 2 cases of posterior reversible encephalopathy syndrome in the setting of coronavirus 2019 at our institution during a 3-month period. One patient was treated with anakinra, an interleukin-1 inhibitor that may disrupt endothelial function. The second patient had an underlying human immunodeficiency virus infection. We found 13 total cases in our literature search, which reported modest blood pressure fluctuations and a range of risk factors for posterior reversible encephalopathy syndrome. One patient was treated with tocilizumab, an interleukin-6 inhibitor that may have effects on endothelial function. All patients had an improvement in their neurological symptoms. Interval imaging, when available, showed radiographic improvement of brain lesions. CONCLUSIONS: Risk factors for posterior reversible encephalopathy syndrome in patients with coronavirus disease 2019 may include underlying infection or immunomodulatory agents with endothelial effects in conjunction with modest blood pressure fluctuations. We found that the neurological prognosis for posterior reversible encephalopathy syndrome in the setting of coronavirus disease 2019 infection is favorable. Recognition of posterior reversible encephalopathy syndrome in this patient population is critical for prognostication and initiation of treatment, which may include cessation of potential offending agents and tight blood pressure control.

Imaging of Intracranial Infections.

Central nervous system (CNS) infections are a major source of morbidity and mortality despite the remarkable progress in prevention and treatment of infectious disease. Because of the difficulty of direct tissue sampling, imaging plays a crucial role in detecting, diagnosing, and monitoring the therapeutic response of CNS infections. An accurate diagnosis in CNS infections is especially rewarding, given the availability of potential effective antimicrobials.Bacterial, viral, fungal, and parasitic infections may affect the brain and/or its meningeal coverings, and can be characterized by a variety of radiological patterns that help narrow the differential diagnoses and eventually tailor the optimal management.This review addresses the typical imaging findings of intracranial infectious diseases in both immunocompetent and immunocompromised patients.

Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity.

PURPOSE: To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI). MATERIALS AND METHODS: This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer's Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts. RESULTS: The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi. CONCLUSIONS: The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer's disease dementia. KEY POINTS: • The MCI high executive ability group yielded a larger network. • The MCI high executive ability group had greater FA in numerous tracts. • White matter reserve may confer greater protection of executive abilities. • Loss of executive reserve may lead to greater impairment in AD dementia.

Functional brain networks involved in decision-making under certain and uncertain conditions.

PURPOSE: The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states. METHODS: In this cross-sectional study, 19 healthy subjects ages 18-35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB. RESULTS: The uncertain > certain comparison yielded three clusters-a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition. CONCLUSION: The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control.

Radiological imaging features of the basal ganglia that may predict progression to hemicraniectomy in large territory middle cerebral artery infarct.

PURPOSE: Predicting which patients are at risk for hemicraniectomy can be helpful for triage and can help preserve neurologic function if detected early. We evaluated basal ganglia imaging predictors for early hemicraniectomy in patients with large territory anterior circulation infarct. METHODS: This retrospective study evaluated patients with ischemic infarct admitted from January 2005 to July 2011. Patients with malignant cerebral edema refractory to medical therapy or with herniating signs such as depressed level of consciousness, anisocoria, and contralateral leg weakness were triaged to hemicraniectomy. Admission images were reviewed for presence of caudate, lentiform nucleus (putamen and globus pallidus), or basal ganglia (caudate + lentiform nucleus) infarction. RESULTS: Thirty-one patients with large territory MCA infarct, 10 (32%), underwent hemicraniectomy. Infarction of the caudate nucleus (9/10 vs 6/21, p = 0.002) or basal ganglia (5/10 vs 2/21, p = 0.02) predicted progression to hemicraniectomy. Infarction of the lentiform nucleus only did not predict progression to hemicraniectomy. Sensitivity for patients who did and did not have hemicraniectomy were 50% (5/10) and 90.5% (19/21). For caudate nucleus and caudate plus lentiform nucleus infarcts, the crude- and age-adjusted odds of progression to hemicraniectomy were 9.5 (1.4-64.3) and 6.6 (0.78-55.4), respectively. CONCLUSION: Infarction of the caudate nucleus or basal ganglia correlated with patients progressing to hemicraniectomy. Infarction of the lentiform nucleus alone did not.

Differential Assessment of Internal Jugular Vein Stenosis in Patients Undergoing CT and MRI with Contrast.

OBJECTIVE: Internal Jugular Vein Stenosis (IJVS) is hypothesized to play a role in the pathogenesis of diverse neurological diseases. We sought to evaluate differences in IJVS assessment between CT and MRI in a retrospective patient cohort. METHODS: We included consecutive patients who had both MRI of the brain and CT of the head and neck with contrast from 1 June 2021 to 30 June 2022 within the same admission. The degree of IJVS was categorized into five grades (0-IV). RESULTS: A total of 35 patients with a total of 70 internal jugular (IJ) veins were included in our analysis. There was fair intermodality agreement in stenosis grades (κ = 0.220, 95% C.I. = [0.029, 0.410]), though categorical stenosis grades were significantly discordant between imaging modalities, with higher grades more frequent in MRI (χ2 = 27.378, p = 0.002). On CT-based imaging, Grade III or IV stenoses were noted in 17/70 (24.2%) IJs, whereas on MRI-based imaging, Grade III or IV stenoses were found in 40/70 (57.1%) IJs. Among veins with Grade I-IV IJVS, MRI stenosis estimates were significantly higher than CT stenosis estimates (77.0%, 95% C.I. [35.9-55.2%] vs. 45.6%, 95% C.I. [35.9-55.2%], p < 0.001). CONCLUSION: MRI with contrast overestimates the degree of IJVS compared to CT with contrast. Consideration of this discrepancy should be considered in diagnosis and treatment planning in patients with potential IJVS-related symptoms.

Contrast Staining in Noninfarcted Tissue after Endovascular Treatment of Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Contrast staining is a common finding after endovascular treatment of acute ischemic stroke. It typically occurs in infarcted tissue and is considered an indicator of irreversible brain damage. Contrast staining in noninfarcted tissue has not been systematically investigated. We sought to assess the incidence, risk factors, and clinical significance of contrast staining in noninfarcted tissue after endovascular treatment. MATERIALS AND METHODS: We conducted a retrospective review of consecutive patients who underwent endovascular treatment for anterior circulation large-vessel occlusion acute ischemic stroke. Contrast staining, defined as new hyperdensity on CT after endovascular treatment, was categorized as either contrast staining in infarcted tissue if the stained region demonstrated restricted diffusion on follow-up MR imaging or contrast staining in noninfarcted tissue if the stained region demonstrated no restricted diffusion. Baseline differences between patients with and without contrast staining in noninfarcted tissue were compared. Logistic regression was used to identify independent associations for contrast staining in noninfarcted tissue after endovascular treatment. RESULTS: Among 194 patients who underwent endovascular treatment for large-vessel occlusion acute ischemic stroke and met the inclusion criteria, contrast staining in infarcted tissue was noted in 52/194 (26.8%) patients; contrast staining in noninfarcted tissue, in 26 (13.4%) patients. Both contrast staining in infarcted tissue and contrast staining in noninfarcted tissue were noted in 5.6% (11/194). Patients with contrast staining in noninfarcted tissue were found to have a higher likelihood of having an ASPECTS of 8-10, to be associated with contrast staining in infarcted tissue, and to achieve successful reperfusion compared with those without contrast staining in noninfarcted tissue. In contrast staining in noninfarcted tissue regions, the average attenuation was 40 HU, significantly lower than the contrast staining in infarcted tissue regions (53 HU). None of the patients with contrast staining in noninfarcted tissue had clinical worsening during their hospital stay. The median discharge mRS was significantly lower in patients with contrast staining in noninfarcted tissue than in those without (3 versus 4; P = .018). No independent predictors of contrast staining in noninfarcted tissue were found. CONCLUSIONS: Contrast staining can be seen outside the infarcted tissue after endovascular treatment of acute ischemic stroke, likely attributable to the reversible disruption of the BBB in ischemic but not infarcted tissue. While generally benign, understanding its characteristics is important because it may mimic pathologic conditions such as infarcted tissue and cerebral edema.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

Crossover from Femoral to Radial Access During Mechanical Thrombectomy of Large Vessel Occlusion Stroke.

BACKGROUND: Challenging arterial anatomy may prevent timely endovascular treatment (EVT) of acute ischemic stroke (AIS) through a transfemoral approach prompting the use of alternative access routes. We determined the crossover rate from femoral to radial access during EVT of AIS due to large vessel occlusion and identified its radiological predictors and clinical outcomes. MATERIALS AND METHODS: Retrospective review of all AIS patients who underwent EVT at a single institution from January 2016 to March 2021 was performed. A primary and a secondary radial group depending on whether the radial approach was used primarily or secondary to failure of transfemoral approach were compared. RESULTS: A total of 358 consecutive AIS patients with large vessel occlusion underwent EVT. Radial approach was used primarily in 6 patients (primary radial [PR]) and secondarily in 16 patients (secondary radial [SR]). The rate of femoral to radial crossover was 4.7%. Type III arch and bovine arch configurations were the most common characteristic in the crossover group. Radial access was successful to secure intracranial access in all cases of PR and in 87% of crossover cases. There was no significant difference between the rates of successful reperfusion (53.3% SR, 83% PR, P = 0.20), National Institutes of Health Stroke Scale score on discharge (19 SR, 18 PR group, P = 0.90), or good outcome defined as modified Rankin Scale score 0-2 (13.3% SR, 33.3% PR, P = 0.29). CONCLUSIONS: A radial approach can be considered during EVT of AIS due to large vessel occlusion either primarily or secondarily with a lower threshold to switch from the femoral approach in cases of challenging anatomy.

Deep learning for risk-based stratification of cognitively impaired individuals.

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-ß levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Imaging findings of "anesthesia mumps" (acute postoperative sialadenitis) after general anesthesia.

We present a rare case of acute postoperative sialadenitis or "anesthesia mumps" necessitating emergent intubation in a 16-year-old girl after biopsy of a brainstem mass under general anesthesia. Postoperative computed tomography of the brain demonstrated extensive right-sided facial swelling, parotid gland enlargement, and pharyngeal edema extending to the parapharyngeal space, soft palate, and uvula with significant narrowing of the airway. To our knowledge, this entity has not been previously described in the radiologic literature.

Association Between Antemortem FLAIR White Matter Hyperintensities and Neuropathology in Brain Donors Exposed to Repetitive Head Impacts.

BACKGROUND AND OBJECTIVES: Late neuropathologies of repetitive head impacts from contact sports can include chronic traumatic encephalopathy (CTE) and white matter degeneration. White matter hyperintensities (WMH) on fluid-attenuated inversion recovery (FLAIR) MRI scans are often viewed as microvascular disease from vascular risk, but might have unique underlying pathologies and risk factors in the setting of repetitive head impacts. We investigated the neuropathologic correlates of antemortem WMH in brain donors exposed to repetitive head impacts. The association between WMH and repetitive head impact exposure and informant-reported cognitive and daily function were tested. METHODS: This imaging-pathologic correlation study included symptomatic male decedents exposed to repetitive head impacts. Donors had antemortem FLAIR scans from medical records and were without evidence of CNS neoplasm, large vessel infarcts, hemorrhage, or encephalomalacia. WMH were quantified using log-transformed values for total lesion volume (TLV), calculated using the lesion prediction algorithm from the Lesion Segmentation Toolbox. Neuropathologic assessments included semiquantitative ratings of white matter rarefaction, cerebrovascular disease, hyperphosphorylated tau (p-tau) severity (CTE stage, dorsolateral frontal cortex), and ß-amyloid (Aß). Among football players, years of play was a proxy for repetitive head impact exposure. Retrospective informant-reported cognitive and daily function were assessed using the Cognitive Difficulties Scale (CDS) and Functional Activities Questionnaire (FAQ). Regression models controlled for demographics, diabetes, hypertension, and MRI resolution. Statistical significance was defined as p ≤ 0.05. RESULTS: The sample included 75 donors: 67 football players and 8 nonfootball contact sport athletes or military veterans. Dementia was the most common MRI indication (64%). Fifty-three (70.7%) had CTE at autopsy. Log TLV was associated with white matter rarefaction (odds ratio [OR] 2.32, 95% confidence interval [CI] 1.03, 5.24; p = 0.04), arteriolosclerosis (OR 2.38, 95% CI 1.02, 5.52; p = 0.04), CTE stage (OR 2.58, 95% CI 1.17, 5.71; p = 0.02), and dorsolateral frontal p-tau severity (OR 3.03, 95% CI 1.32, 6.97; p = 0.01). There was no association with Aß. More years of football play was associated with log TLV (unstandardized ß 0.04, 95% CI 0.01, 0.06; p = 0.01). Greater log TLV correlated with higher FAQ (unstandardized ß 4.94, 95% CI 0.42, 8.57; p = 0.03) and CDS scores (unstandardized ß 15.35, 95% CI -0.27, 30.97; p = 0.05). DISCUSSION: WMH might capture long-term white matter pathologies from repetitive head impacts, including those from white matter rarefaction and p-tau, in addition to microvascular disease. Prospective imaging-pathologic correlation studies are needed. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence of associations between FLAIR white matter hyperintensities and neuropathologic changes (white matter rarefaction, arteriolosclerosis, p-tau accumulation), years of American football play, and reported cognitive symptoms in symptomatic brain donors exposed to repetitive head impacts.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Ruptured Spinal Aneurysms: Diagnosis and Management Paradigms.

BACKGROUND: Spinal aneurysms (SA) are rare neurovascular pathologies with an unclear natural history and management strategy. We review the clinical and radiologic manifestations, management, and outcome of patients who presented with spinal subarachnoid hemorrhage (SAH) secondary to ruptured spinal aneurysms over a 10-year period. We provide a literature review about this condition and its management. METHODS: All patients diagnosed with nontraumatic spinal SAH were collected from a single-center prospectively maintained database of patients with SAH between January 2010 and January 2020. Patients diagnosed with spinal aneurysms were reviewed. For each patient, demographic data, clinical presentation, imaging findings, management strategies, and outcomes are reviewed and discussed. RESULTS: Between January 2010 and January 2020, ten patients were diagnosed with nontraumatic spinal SAH (3 patients presented with isolated spinal SAH and 7 patients with concomitant spinal and posterior fossa SAH). Among those, 4 patients were found to have a spinal aneurysm as the cause of SAH. The aneurysms were located in the cervical regions in 3 patients (75%) and at the thoracic level in 1 patient. Two aneurysms (50%) involved the anterior spinal artery, and 2 aneurysms (50%) involved a radiculomedullary artery. One aneurysm was a flow-related aneurysm of the anterior spinal artery in the setting of bilateral vertebral artery occlusion and was treated by surgical clipping with good outcome. Three aneurysms were idiopathic pseudoaneurysms with a negative diagnostic evaluation for secondary causes. These pseudoaneurysms were treated conservatively; 2 patients did well and 1 patient passed away from severe intracranial vasospasm. Two aneurysms resolved on diagnostic angiogram, and 1 aneurysm was absent on initial angiogram and appeared on follow-up diagnostic imaging 3 months later. CONCLUSIONS: Spinal aneurysms are rare neurovascular pathologies that should be considered in the setting of spinal and/or posterior fossa subarachnoid hemorrhage. Conservative treatment may be a potential safe alternative to interventional treatment. Before the initiation of surgical or endovascular treatment, spinal angiography should be repeated because of the potential for spontaneous resolution.

Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning.

BACKGROUND: Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer's disease (AD) classification performance. METHODS: T1-weighted brain MRI scans from 151 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer's Coordinating Center (NACC, n = 565) were used for model validation. RESULTS: The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. CONCLUSION: This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.