Mariana Zurita

ORCID: 0000-0002-4847-311X
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About
Contact & Profiles
Research Areas
  • Multiple Sclerosis Research Studies
  • Functional Brain Connectivity Studies
  • EEG and Brain-Computer Interfaces
  • Neurogenesis and neuroplasticity mechanisms
  • Neural dynamics and brain function
  • Engineering Education and Curriculum Development
  • Experimental Learning in Engineering
  • Systemic Lupus Erythematosus Research
  • Advanced Neuroimaging Techniques and Applications
  • Neurological Disease Mechanisms and Treatments
  • Mathematics Education and Programs
  • Neural and Behavioral Psychology Studies

University College London
2021-2022

Pontificia Universidad Católica de Chile
2018-2021

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination this imaging analysis complemented examination. Therefore, other biomarkers necessary to better understand disease. In paper, we capitalize machine learning techniques classify relapsing-remitting multiple sclerosis patients healthy volunteers techniques,...

10.1016/j.nicl.2018.09.002 article EN cc-by-nc-nd NeuroImage Clinical 2018-01-01

The activity of the human prefrontal cortex automatically encodes usefulness an item according to current goal.

10.1126/sciadv.abd5363 article EN cc-by Science Advances 2021-04-07

ABSTRACT Background: Multiple sclerosis exhibits specific neuropathological phenomena driving to both global and regional brain atrophy. At the clinical level, disease is related functional decline in cognitive domains as working memory, processing speed, verbal fluency. However, compromise of social-cognitive abilities has concentrated some interest recent years despite available evidence suggesting risk disorganization social life. Recent studies have used MiniSEA test assess cognition...

10.1590/0004-282x-anp-2020-0162 article EN cc-by Arquivos de Neuro-Psiquiatria 2021-08-01

This paper discusses the design of engineering mathematics assessment that encourages learning beyond algorithmic recall. Our approach is based on MATH (mathematical task hierarchy) taxonomy. We propose using as a tool to analyse relatable problems and produce clear deliverables ensure academic knowledge translated real-life situations. Creating scenarios was instrumental in fostering active engagement, enquiry, creativity reducing opportunities for misconduct our first-year assessments. An...

10.1109/educon52537.2022.9766484 article EN 2022 IEEE Global Engineering Education Conference (EDUCON) 2022-03-28
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