BACKGROUND AND PURPOSE:Artificial intelligence (AI) models have shown promise in neuroradiology, yet their real-world generalizability remains uncertain, partly due to variability in imaging acquisition and protocols. We aimed to evaluate the impact of data source, scanner manufacturer, scan mode, slice thickness, and the AI models, developed by participating teams, on AI performance in this secondary analysis of the 2019 American Society of Functional Neuroradiology (ASFNR) AI Competition. MATERIALS AND METHODS:We included 1177 anonymized noncontrast head CT scans from 5 institutions. Four teams participated, developing models to detect acute ischemic stroke, intracranial hemorrhage, and mass effect and to assess age-appropriate normality. Generalized estimating equations were used to evaluate the effects of the variables on model performance, and collinearity diagnostics were applied to exclude redundant variables. RESULTS:Due to collinearity with the scanner manufacturer, data source was excluded from the model. Across all tasks, the AI model significantly influenced the performance. The scanner manufacturer was significantly associated with accuracy in detecting intracranial hemorrhage and acute ischemic stroke but not mass effect or age-based normality. Slice thickness was significantly associated with detection of intracranial hemorrhage and mass effect, with thinner slices yielding higher accuracy, but it showed no effect on ischemic stroke or normality assessments. The scan mode did not significantly influence performance for any task. CONCLUSIONS:This secondary analysis demonstrates that imaging acquisition and protocol variability may significantly affect AI model performance. Scanner manufacturer, slice thickness, and the developed AI model were significantly associated with model accuracy, whereas scan mode had no significant impact. Among these, the developed AI model consistently proved the most influential, reflecting the importance of training data, model architecture, and preprocessing methods.

BACKGROUND: Molecular imaging, particularly PET, has advanced the diagnosis and management of disease by visualizing biologic processes at a cellular and molecular level. PET imaging of the brain, spine, and head/neck, summarized under the umbrella term neuro-PET, enables noninvasive diagnosis and monitoring of diseases such as dementia, epilepsy, cancer, movement, or autoimmune disorders. The increasing prevalence of these conditions, as well as new treatment options necessitating response assessment, are expected to escalate neuro-PET imaging volumes, with projections for an increase in the need for specialized imaging services. This increasing clinical need highlights existing workforce shortages and underscores the need for neuroradiologists to acquire proficiency in molecular imaging. This expanded role seeks to address the growing demand. To this end, we propose a rigorous, structured, patient-centered, and collaborative framework for expanding neuroradiologists? training and practice to include neuro-PET interpretation. METHODS: This American Society of Neuroradiology consensus statement outlines competency recommendations, training pathways, and implementation strategies to incorporate neuro-PET into neuroradiology practice. This approach is based on existing guidelines and was informed by survey data from neuroradiologists and molecular imaging subspecialists revealing current practice patterns and training needs. For neuroradiology fellows, structured training encompasses hands-on neuro-PET imaging experience, understanding the biologic and molecular basis of radiopharmaceuticals used in neuro-PET, and integrating molecular insights with anatomic data. Neuroradiologists beyond fellowship can undertake practice-based curriculum involving supervised case interpretation, standardized reader training courses, continuing medical education (CME), and peer review. KEY MESSAGE: Neuroradiologists, with their in-depth expertise of central nervous system structure and function, are well positioned to meld molecular imaging data with traditional anatomic findings. They can achieve competency and should be granted practice privileges in interpreting neuro-PET studies through a comprehensive combination of structured training, hands-on clinical experience, and documented CME hours.

Mild traumatic brain injury (mTBI) is associated with persistent physical, cognitive, and emotional symptoms, yet the functional brain changes that accompany recovery remain incompletely understood. This longitudinal study investigated brain entropy (BEN), a resting-state measure of irregularity or complexity of spontaneous BOLD activity derived from resting-state functional magnetic resonance imaging (rs-fMRI), during the first year following mTBI. Data of rs-fMRI were acquired from 48 patients with mTBI at the acute (< 14 days), 6-month, and 12-month post-injury visits, and 34 healthy controls (HCs). BEN was quantified using sample entropy. Patients with mTBI also underwent behavioral assessments evaluating quality of life and cognitive function. Behavioral assessments demonstrated progressive improvements in patient-reported quality of life and objective cognitive performance. Cross-sectional analyses revealed dynamic, time-dependent alterations in BEN. Compared with HCs, patients with mTBI exhibited higher BEN in precuneus, temporal, occipital, and sensorimotor regions during the acute and 6-month stages, whereas lower BEN was observed in sensorimotor, occipital, and frontoparietal regions at the 12-month follow-up. Longitudinal analyses demonstrated progressive regional reductions in BEN over time. Several associations between longitudinal changes in regional BEN and cognitive performance were nominally significant, particularly in the precuneus, but none survived multiple comparison corrections. These findings indicate that mTBI recovery is accompanied by dynamic, regionally specific changes in resting state BOLD irregularity. BEN may therefore provide a promising imaging biomarker for tracking the evolving neural dynamics underlying recovery following mTBI.

BACKGROUND AND PURPOSE:Tranexamic acid (TXA) may reduce the progression of vasogenic edema in traumatic brain injury (TBI), potentially providing a survival benefit in specific patients. This study evaluates a CT-based quantitative imaging tool for identifying patients with acute TBI who may benefit from prehospital TXA. MATERIALS AND METHODS:This is a post hoc analysis of the Prehospital Tranexamic Acid Use for Traumatic Brain Injury trial, a multicenter, placebo-controlled trial that randomized patients with moderate or severe TBI to either a 1-g TXA bolus followed by 1-g infusion, a 2-g TXA bolus, or a placebo. CT images were analyzed using a novel Matlab-based algorithm to calculate the percentage of voxels within various density ranges. The region of interest was defined as the entire brain parenchyma after skull removal. The 10-20 HU range, identified as most representative of vasogenic edema based on correlation with mean ADC values in a subset of 102 patients, was used for further analysis. Logistic regression was performed to evaluate the relationship between voxel percentages in the 10-20 HU range and in-hospital mortality. Threshold analysis identified the minimum voxel percentage within the 10-20 HU range associated with significant TXA survival benefit. Relative risk reduction in mortality was calculated for patients above and below this threshold. RESULTS:In a cohort of 550 patients, logistic regression showed that the association between TXA use and in-hospital survival varied with the percentage of brain voxels in the 10-20 HU range (interaction P = .04). Threshold analysis identified a cutoff of 3% of brain parenchymal voxels in the 10-20 HU range, corresponding to approximately 40 mL of vasogenic edema, beyond which TXA administration was associated with a significant survival benefit (P = .04). In this subgroup, TXA was associated with a relative risk of mortality of 0.41 (95% CI, 0.17-0.99) compared with a placebo. CONCLUSIONS:A voxel percentage of ≥3% within the 10-20 HU CT density range serves as a promising imaging biomarker associated with a survival benefit from TXA in patients with acute TBI in the prehospital setting. Prospective validation is required to confirm these findings before integrating this biomarker into clinical decision-making for personalized TBI management.

Hydrocephalus is defined by abnormal accumulation of cerebrospinal fluid (CSF) within the ventricles, resulting in ventriculomegaly with variable effects on intracranial pressure. Historically classified as obstructive or communicating, contemporary frameworks further categorize hydrocephalus by chronicity, age of onset, and etiology, particularly distinguishing idiopathic from secondary causes in adults. Hydrocephalus most commonly arises from impaired CSF circulation or absorption, with less frequent contribution from altered CSF production. Increasing attention has been directed toward alternative CSF clearance mechanisms, including the glymphatic system, which remain incompletely defined. Radiologic evaluation is central to the diagnosis and management of hydrocephalus, enabling accurate assessment of ventricular morphology, associated parenchymal changes, and potential underlying etiologies. Normal pressure hydrocephalus, a chronic communicating hydrocephalus of older adults, is characterized by a clinical triad of gait disturbance, cognitive decline, and urinary dysfunction. Conventional structural and advanced imaging markers may assist in diagnosis, prognostication, and selection of patients for CSF diversion, in conjunction with clinical assessment. This review summarizes fundamental physiologic concepts of CSF dynamics and imaging features of hydrocephalus, with particular emphasis on imaging in normal pressure hydrocephalus.

PET imaging plays a vital role in the initial staging and post-treatment surveillance of patients with head and neck cancer. Although fluoro-d-glucose-PET remains the workhorse of PET imaging, novel tracers promise in being able to improve staging accuracy, refine radiation planning, and also provide tissue-specific diagnoses. Response assessment on PET may be accomplished using qualitative methods and by a variety of quantitative methods that have been validated in clinical trials. Simultaneous PET-MR is a promising technique, the implementation of which faces obstacles primarily related to cost, operation, availability, and lack of standardization of imaging techniques.

OBJECTIVE:This study examines the most common causes of pulsatile tinnitus (PT) found with a modified CTA (mCTA) protocol and analyzes the protocol's diagnostic success rate. STUDY DESIGN:Retrospective cohort. SETTING:A single academic institution. PATIENTS:Adult patients presenting for evaluation of PT from 2011 to 2021 who underwent mCTA were included. INTERVENTION:mCTA is a bone-windowed CT angiogram with delayed postcontrast image acquisition permitting both arterial and venous imaging. MAIN OUTCOME MEASURE:Demographics, audiometric data, imaging, and final diagnoses were analyzed. The patients were divided into 2 groups: no prior imaging (NPI) and prior imaging (PI). Top diagnoses were determined and rates of efficacy and failure of the mCTA protocol were compared. RESULTS:One hundred nine patients were recommended to obtain mCTA, of which 22 were lost to follow-up before obtaining imaging. The remaining 87 patients were included in data analysis, 42 in the NPI group and 45 in the PI group. The most common etiology of PT was transverse sinus stenosis with sigmoid sinus wall anomalies, affecting 22 patients (25.3%). The mCTA protocol efficacy rate was 96.5% and failed to capture an important imaging diagnosis at a rate of 3.5%. There was no significant difference in efficacy between the NPI and PI groups. CONCLUSIONS:mCTA was found to be an effective initial diagnostic tool for all patients, regardless of prior imaging status. The most common etiology of PT was transverse sinus stenosis with sigmoid sinus wall anomalies.

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.

BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient?s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

Letter to the Editor re: Sathya A, Nguyen TN, Klein P, et al. Endovascular vs surgical treatment of sigmoid sinus diverticulum causing pulsatile tinnitus: A systematic review. Interv Neuroradiol. 2024 Mar 22:15910199241231325

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

ObjectiveComatose survivors of cardiac arrest (CA) pose a complex challenge for physicians reliant on imperfect studies to determine the extent of neurologic injury. Clinically available imaging is frequently relied upon despite limited sensitivity. We conducted a prospective pilot study comparing diffusion kurtosis imaging (DKI)-MRI and somatosensory evoked potentials (SSEPs) in comatose survivors of CA to investigate the benefit of utilizing higher diffusion b-values to enhance prediction of arousal recovery.MethodsSurvivors of CA admitted from June 2015 through November 2019 with DKI-MRI and SSEPs were evaluated. Advanced diffusion metrics differentiating present or absent SSEPs were analyzed using whole-brain voxelwise nonparametric permutation inference with threshold-free cluster enhancement.ResultsTwenty survivors of CA were included, mean age 52, 45% female and out-of-hospital arrests accounting for 75% of cases. Baseline characteristics and examination findings were not statistically different between groups at admission or 48 h after achieving normothermia. A decrease in mean diffusivity (MD) and increase in mean kurtosis (MK) was demonstrated in subjects without arousal recovery potential, most prominently in the posterior mesial temporal, parietal and occipital lobes.ConclusionDKI-MRI may improve early arousal recovery prediction during the immediate phase of post-CA care, especially where SSEPs are unavailable or unreliable.

CT, MRI, and FDG PET/CT play major roles in the diagnosis, staging, treatment planning, and surveillance of head and neck cancers. Nonetheless, an evolving understanding of head and neck cancer pathogenesis, advances in imaging techniques, changing treatment regimens, and a lack of standardized guidelines have led to areas of uncertainty in the imaging of head and neck cancer. This narrative review aims to address four issues in the contemporary imaging of head and neck cancer. The first issue relates to the standard and advanced sequences that should be included in MRI protocols for head and neck cancer imaging. The second issue relates to approaches to surveillance imaging after treatment of head and neck cancer, including the choice of imaging modality, the frequency of surveillance imaging, and the role of standardized reporting through the Neck Imaging Reporting and Data System. The third issue relates to the role of imaging in the setting of neck carcinoma of unknown primary. The fourth issue relates to the role of simultaneous PET/MRI in head and neck cancer evaluation. The authors of this review provide consensus opinions for each issue.

Rationale: Nervous system toxicity is a rare complication of metronidazole. Prompt identification of metronidazole toxicity combined with a comprehensive physical rehabilitation program is essential to maximizing the patient’s functional outcome. Patient concerns: A 58-year-old female was treated with metronidazole for embolic versus hematogenous spread of bacteria resulting in multifocal brain abscesses. Two weeks after discharge, the patient returned to the emergency department with slurred speech, muscle aches, generalized weakness, inability to ambulate, and poor oral intake. Diagnosis: Head magnetic resonance imaging revealed symmetric enhanced T2/FLAIR signaling in the dentate nuclei were also present bilaterally, a finding pathognomonic for metronidazole toxicity. Intervention: Metronidazole was discontinued, and the patient was enrolled in a comprehensive rehabilitation program. Outcomes: She began inpatient rehabilitation dependent for all activities of daily living and requiring moderate assistance for transfers. She could only walk 10 feet with a front-wheeled walker with a 2-person assist. The patient rapidly improved with a comprehensive rehabilitation program, and due to these improvements, she was discharged after 5 days of inpatient rehabilitation. At the time of discharge, she was independent with all activities of daily living and could walk 160 feet independently with a front-wheeled walker. Lessons: Prompt recognition and discontinuation of metronidazole remains the only known effective treatment. A comprehensive approach to treatment and rehabilitation is achieved with an early referral to rehabilitation services. This is crucial to minimize morbidity and optimize functional outcomes in this patient population.

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.

Background The etiology of idiopathic intracranial hypertension (IIH) is uncertain. Studies suggest the fundamental cause of the Chiari 1 malformation, a congenitally hypoplastic posterior fossa, may explain the genesis of IIH in some patients. Purpose To assess the hypothesis that linear and volumetric measurements of the posterior fossa (PF) can be used as predictors of IIH. Material and Methods A retrospective analysis of magnetic resonance imaging (MRI) studies on 27 patients with IIH and 14 matched controls was performed. A volumetric sagittal magnetization prepared rapid acquisition gradient echo sequence was used to derive 10 linear cephalometric measurements. Total intracranial and bony posterior fossa volumes (PFVs) were derived by manual segmentation. The ratio of PFV to total intracranial volume was calculated. Results In total, 41 participants were included, all women. Participants with IIH had higher median body mass index (BMI). No significant differences in linear cephalometric measurements, total intracranial volumes, and PFVs between the groups were identified. Linear measurements were not predictive of volumetric measurements. However, on multivariate logistic regression analysis, the likelihood of IIH decreased significantly per unit increase in relative PFV (odds ratio [OR]=3.66 x 10(-50); 95% confidence interval [CI]=1.39 x 10(-108) to 1.22 x 10(-5); P = 0.04). Conversely, the likelihood of IIH increased per unit BMI increase (OR=1.19; 95% CI=1.04-1.47; P = 0.02). Conclusion MRI-based volumetric measurements imply that PF alterations may be partly responsible for the development of IIH and Chiari 1 malformations. Symptoms of IIH may arise due to an interplay between these and metabolic, hormonal, or other factors.

Abstract Cerebral metabolic energy crisis (CMEC), often defined as a cerebrospinal fluid (CSF) lactate: pyruvate ratio (LPR) >40, occurs in various diseases and is associated with poor neurologic outcomes. Cerebral malaria (CM) causes significant mortality and neurodisability in children worldwide. Multiple factors that could lead to CMEC are plausible in these patients, but its frequency has not been explored. Fifty-three children with CM were enrolled and underwent analysis of CSF lactate and pyruvate levels. All 53 patients met criteria for a CMEC (median CSF LPR of 72.9 [interquartile range [IQR]: 58.5–93.3]). Half of children met criteria for an ischemic CMEC (median LPR of 85 [IQR: 73–184]) and half met criteria for a nonischemic CMEC (median LPR of 60 [IQR: 54–79]. Children also underwent transcranial doppler ultrasound investigation. Cerebral blood flow velocities were more likely to meet diagnostic criteria for low flow (<2 standard deviation from normal) or vasospasm in children with an ischemic CMEC (73%) than in children with a nonischemic CMEC (20%, p  = 0.04). Children with an ischemic CMEC had poorer outcomes (pediatric cerebral performance category of 3–6) than those with a nonischemic CMEC (46 vs. 22%, p  = 0.03). CMEC was ubiquitous in this patient population and the processes underlying the two subtypes (ischemic and nonischemic) may represent targets for future adjunctive therapies.