Vol 8-1 Case Report

Contrast Induced Encephalopathy: Case Report and Review of The Literature

Sarah Kawtharani1, Elias Horanieh2, Bader Ali3, Mohammad Housheimy1, Houssein Darwish1*

1Department of Neurosurgery, American University of Beirut Medical Center, Beirut, Lebanon

2Department of Surgery, University of Balamand, Beirut, Lebanon

3Medical Student, University of Balamand, Beirut, Lebanon

Background: Contrast Induced Encephalopathy is a known but rare complication of endovascular procedures. Patients show neurologic symptoms mimicking a stroke and include visual disturbances, motor or sensory deficits, headache, seizures, memory loss, confusion, aphasia, and coma. Irreversible neurological symptoms are rare and fatal encephalopathy is even more so.

Case Report: In this article we present a case of a 75-year-old female patient who showed neurological symptoms mimicking a stroke post cerebral Digital Subtraction Angiography that was done with Iohexol as a contrast agent, as a diagnostic work up to rule out a ruptured aneurysm. Further investigations showed no arterial spasms nor dissection. Symptoms reappeared after the second contrast administration but completely resolved after the administration of steroids and fluids.

Conclusion: Contrast Induced Encephalopathy should be further investigated with imaging to rule out other thromboembolic or hemorrhagic causes. Treatment via the administration of steroids and fluids have shown to be effective with complete remission of symptoms.

DOI: 10.29245/2572.942X/2024/1.1294 View / Download Pdf
Vol 8-1 Commentary

Commentary: Generalization of procedural motor sequence learning after a single practice trial

Fumiaki Iwane1*, Brian Johnson2, Leonardo G Cohen1

1Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA

2Department of Occupational Therapy, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA

DOI: 10.29245/2572.942X/2024/1.1295 View / Download Pdf
Vol 7-3 Mini Review

The Importance of Data Sources for Machine Learning Applications in Autism: A Mini Review

Itzhak Kurek*, Jean-Christophe Quillet, Michael Siani-Rose

Cannformatics, Inc., San Francisco, CA, USA

Autism spectrum disorder (ASD) is a group of lifelong heterogeneous neurodevelopmental conditions with a wide range of severity levels that affect social communication and social interaction. Diagnosis of ASD relies on subjective observation of these clinical phenotypes. The growing body of big data generated by subjective methods and more recently by objective high-throughput technologies such as omics for the detection of biomolecules, is being successfully applied to a rapidly-growing number of machine learning (ML) algorithms to inform research for diagnostics and interventions for patients with ASD. While most reviews in this area are focused on the ML approaches, we highlight the impact of the database on the expected outcomes in ML-based ASD research studies.

DOI: 10.29245/2572.942X/2023/3.1293 View / Download Pdf