Biology subjects

Wu, O.

Publications and source records attributed to Wu, O..

8 recordsLinked to original sources

bioRxiv2026

A Brain Circuit for Status Epilepticus

Status epilepticus (SE) is a life-threatening persistent epileptic seizure that can arise from various brain structures, leaving its brain circuit unknown. In this study, we utilize brain imaging changes during SE to reveal the brain architecture and circuit of persistent seizures. Multimodal lesion mapping identified that brain imaging changes during SE localize to a specific predisposed brain architecture characterized by increased metabolic rate, high synaptic and mitochondrial density, glutamate (mGLUR5 and NMDA) and GABA receptors. Gene expression patterns within lesion locations revealed a transcriptomic profile enriched for epilepsy pathologies (including SE), neuronal and synaptic processes, and glutamate signaling. Lesion network mapping demonstrated these same lesions map to a common brain circuit, unifying a traditionally heterogeneous patient population. Findings were validated in an independent cohort and the identified SE circuit distinguished brain imaging changes during SE from other lesion etiologies with excellent accuracy (91%), significantly outperforming all other tested maps. With this SE circuit, we identify therapeutic targets for precision therapy that could modulate this circuit. This study demonstrates brain imaging changes in SE converge on a unified brain circuit that could help diagnostic workup of patients in critical care and guide clinical trials of precision therapy for persistent seizures.

neuroscience↗

bioRxiv2024

Lesions that Cause Psychosis Map to a Common Brain Circuit in the Hippocampus

ImportanceIdentifying anatomy causally involved in psychosis could inform therapeutic neuromodulation targets for schizophrenia. ObjectiveTo assess whether lesions that cause secondary psychosis have functional connections to a common brain circuit. DesignThis case-control study mapped functional connections of published cases of lesions causing secondary psychosis compared with control lesions unassociated with psychosis. SettingThis study was conducted in a computational laboratory. ParticipantsPublished cases of lesion-induced psychosis were analyzed. Included subjects had documented brain lesions associated with new-onset psychotic symptoms without prior history of psychosis. Control cases included 1156 patients with lesions not associated with psychosis. Generalizability across lesional datasets was assessed using an independent cohort of 181 patients with brain lesions who subsequently underwent neurobehavioral testing. ExposuresLesions causing secondary psychosis. Main Outcomes and MeasuresPsychosis or no psychosis. Results153 lesions from published cases were determined to be causal of psychosis (65 [42%] male; mean [SD] age, 50.0 [20.8] years), 42 of which were described as "schizophrenia" or "schizophrenia-like". Lesions that caused secondary psychosis mapped to a common brain circuit defined by functional connectivity to the posterior subiculum of the hippocampus (84% functional overlap, pFWE<5 x 10-5). At a lower statistical threshold (75%> overlap, pFWE<5 x 10-4), this circuit included the ventral tegmental area, retrosplenial cortex, lobule IX and dentate nucleus of the cerebellum, and the mediodorsal and midline nuclei of the thalamus. This circuit was consistent when derived from "schizophrenia-like" cases (spatial r=0.98). We repeated these analyses after excluding lesions intersecting the hippocampus (n=47) and found a consistent functional connectivity profile (spatial r=0.98) with the posterior subiculum remaining the center of connectivity (>75% overlap, pFWE<5x10-5), demonstrating a circuit-level effect. In an independent observational cohort of patients with penetrating head trauma (n=181), lesions associated with symptoms of psychosis exhibited significantly similar connectivity profiles to the lesion-derived psychosis circuit (suspiciousness, p=0.025; unusual thought content, p=0.046). Voxels in the rostromedial prefrontal cortex (rmPFC) are highly correlated with this psychosis circuit (spatial r=0.82), suggesting the rmPFC as a promising TMS target for psychosis. Conclusions and RelevanceLesions that cause secondary psychosis affect a common brain circuit in the hippocampus. These results can help inform therapeutic neuromodulation targeting. Key PointsO_ST_ABSQuestionC_ST_ABSDo lesions that cause psychosis affect a common brain circuit? FindingsThis case-control study found that lesions causing psychosis specifically affected a common functional circuit aligning with the posterior subiculum of the hippocampus. This functional circuit was consistent across different psychotic symptoms, suggesting a shared neural substrate for psychosis. A similar circuit was derived when excluding lesions directly intersecting the hippocampus, indicating a circuit-level effect. MeaningIdentifying a common brain circuit causally involved in psychotic symptoms suggests that the hippocampus may be pivotal in the pathophysiology and treatment targeting of psychotic disorders.

neuroscience↗

bioRxiv2019

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

BackgroundThe ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brains connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function.\n\nMethodsIn a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brains pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length (L) was calculated for each connectome. The number of RC regions (NRC) affected were manually determined using each patients diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days; poor: mRS>2) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including NRC and L in outcome models, using linear regression and assessing the explained variance (R2).\n\nResultsOf 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences in L between patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS.\n\nConclusionIn this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

neuroscience↗

bioRxiv2019

White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95) and Pearson correlation of total brain volume (r=0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N=2783) and identify a decrease in total brain volume of -2.4cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2,533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

neuroscience↗

bioRxiv2019

Rich-Club organization: an important determinant of functional outcome after acute ischemic stroke

ObjectiveTo determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS). MethodsConsecutive AIS patients (N=344) with acute brain magnetic resonance imaging (MRI) (<48 hours) were eligible for this study. Each patient underwent a clinical MRI protocol, which included diffusion weighted imaging (DWI). All DWIs were registered to a template on which rich-club regions have been defined. Using manual outlines of stroke lesions, we automatically counted the number of affected rich-club regions and assessed its effect on the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS; obtained at 90 days post-stroke) scores through ordinal regression. ResultsOf 344 patients (median age 65, inter-quartile range 54-76 years) with a median DWI lesion volume (DWIv) of 3cc, 64% were male. We established that an increase in number of rich-club regions affected by a stroke increases the odds of poor stroke outcome, measured by NIHSS (OR: 1.77, 95%CI 1.41-2.21) and mRS (OR: 1.38, 95%CI 1.11-1.73). Additionally, we demonstrated that the OR exceeds traditional markers, such as DWIv (ORNIHSS 1.08, 95%CI 1.06-1.11; ORmRs 1.05, 95%CI 1.03-1.07) and age (ORNIHSS 1.03, 95%CI 1.01-1.05; ORmRs 1.05, 95%CI 1.03-1.07). ConclusionIn this proof-of-concept study, the number of rich-club nodes affected by a stroke lesion presents a translational biomarker of stroke outcome, which can be readily assessed using standard clinical AIS imaging protocols and considered in functional outcome prediction models beyond traditional factors.

neuroscience↗

bioRxiv2019

Identifying Severe Stroke Patients Likely to Benefit From Intervention Despite a Delay of a Day

Background and PurposeSevere ischemic strokes caused by large vessel occlusion (LVO) are treatable up to 24 hours after onset. But not all patients will benefit either because the infarct enlarges too rapidly or because the patient is not at center that is capable of treatment. The purpose was to find biomarkers that reliably identify patients who are likely to benefit from intervention despite long delays. MethodsThirty-eight acute ischemic stroke patients with LVO who did not undergo thrombolysis or thrombectomy had serial imaging with MRI at presentation and then at ~12 hours, ~1 day, & ~2 days after onset. Presenting clinical and neuroimaging predictors of a therapeutically auspicious ischemic core size (<50 ml) one day after stroke onset were identified. ResultsIschemic core initial growth rate (IGR) was the only independent predictor of functional outcome at 3 months (P=0.002), and a receiver operator characteristics (ROC) analysis revealed that an IGR of <5.4 ml/hr had a 95% sensitivity with a low (5.6%) false positive rate for identifying patients who would have an ischemic core of <50 ml at one day post ictus. Selection using an initial DWI lesion volume <37 ml was similarly powerful with a 90% sensitivity and a low false positive rate. Other imaging markers derived from CT scans were less effective, but may be more practical. Published reports of over 600 LVO ischemic stroke patients disclosed that about half met the highly favorable IGR criteria of <5.4 ml/hr at presentation. ConclusionsIschemic core initial growth rates of <5.4ml/hr and initial ischemic cores of <37 ml are excellent biomarkers for selecting LVO patients that are likely to benefit from intervention despite long delays due to transfer to thrombectomy-capable stroke centers. Published accounts suggest that about half of all LVO stroke patients meet these criteria.

clinical trials↗

bioRxiv2018

Spatial signature of white matter hyperintensities in stroke patients

PurposeWhite matter hyperintensity (WMH) is a common phenotype across a variety of neurological diseases, particularly prevalent in stroke patients; however, vascular territory dependent variation in WMH burden has not yet been identified. Here, we sought to investigate the spatial specificity of WMH burden in patients with acute ischemic stroke (AIS). Materials and MethodsWe created a novel age-appropriate high-resolution brain template and anatomically delineated the cerebral vascular territories. We used WMH masks derived from the clinical T2 FLAIR MRI scans and spatial normalization of the template to discriminate between WMH volume within each subjects anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA) territories. Linear regression modeling including age, sex, common vascular risk factors, and TOAST stroke subtypes was used to assess for spatial specificity of WMH volume (WMHv) in a cohort of 882 AIS patients. ResultsMean age of this cohort was 65.23{+/-}14.79 years, 61.7% were male, 63.6% were hypertensive, 35.8% never smoked. Mean WMHv was 11.58c +/-13.49 cc. There were significant differences in territory-specific, relative to global, WMH burden. In contrast to PCA territory, age (0.018{+/-}0.002, p<0.001) and small-vessel stroke subtype (0.212{+/-}0.098, p<0.001) were associated with relative increase of WMH burden within the anterior (ACA and MCA) territories, whereas male sex (-0.275{+/-}0.067, p<0.001) was associated with a relative decrease in WMHv. ConclusionsOur data establish the spatial specificity of WMH distribution in relation to vascular territory and risk factor exposure in AIS patients and offer new insights into the underlying pathology.

neuroscience↗

bioRxiv2017

Effective reserve: a latent variable to improve outcome prediction in stroke

BackgroundPrediction of functional outcome after stroke based on initial presentation remains an open challenge, suggesting that an important aspect is missing from these prediction models. There exists the notion of a protective mechanism called brain reserve, which may be utilized to understand variations in disease outcome. In this work we expand the concept of brain reserve (effective reserve) to improve prediction models of functional outcome after acute ischemic stroke (AIS).\n\nMethodsConsecutive AIS patients with acute brain MRI (<48 hours) were eligible for this study. White matter hyperintensity and acute infarct volume were determined on T2 fluid attenuated inversion recovery and diffusion weighted images, respectively. Modified Rankin Scale scores (mRS) were obtained at 90 days post-stroke. Effective reserve (eR) was defined as a latent variable using structural equation modeling by including age, systolic blood pressure, and intracranial volume measurements.\n\nResultsOf 453 AIS patients (mean age 66.6{+/-}14.7 years), 36% were male and 311 hypertensive. There was inverse association between eR and 90-day mRS (path coefficient -0.18{+/-}0.01, p<0.01). As compared to a model without eR, correlation between predicted mRS and observed mRS improved in the eR-based model (Spearmans {rho} 0.29{+/-}0.18 versus 0.15{+/-}0.17, p<0.001). Furthermore, hypertensive patients exhibited lower eR (p<10-6).\n\nConclusionUsing eR in prediction models of stroke outcome is feasible and leads to better model performance. Furthermore, higher eR is associated with more favorable functional post-stoke outcome and might correspond to an overall better vascular health.

neuroscience↗