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Research experience

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Electroencephalography (EEG)

  • Acquisition of EEG data (64 and 128 channel EEG). 

  • Ability to assist with movement of participants and testing equipment. 

  • Ability to recognize specific abnormal EEG patterns related to aging and dementia.  

  • Preprocessing/processing of EEG data using MATLAB. 

  • Analysis of evoked Potentials (endogenous and exogenous) in young/old adults and dementia patients. 

  • Spectral analysis in young and old adults. 

  • Analysis of EEG connectivity in young and old adults. 

  • Analysis of alpha wave during both eyes closed and eyes opened condition in young/old adults and dementia patients. Alpha reactivity analysis. 

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Magnetic Resonance Imaging 
(MRI)

  • Acquisition of MRI data. Safety: Level 1 user.

  • Ability to assist with participant screening prior to exams (metal objects, MRI safety)

  • Interview participants to explain MRI protocols and procedures.

  • Preprocessing/processing of MRI data using MRIcron, SPM12, CAT12, FSL, FreeSurfer

  • Analysis of Voxel-based morphometry (VBM). 

  • Analysis of cortical thickness, gyrification. 

  • Diffusion tensor imaging (DTI). 

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Neuropsychology

  • Interview participants to obtain comprehensive medical histories.

  • Identify and communicate risks associated with specific procedures related to the experimental protocol.

  • Conduct neuropsychological evaluations for research purposes. This includes assessments of attention, concentration, language, learning, and memory. 

  • Data analysis of neuropsychological variables using varied statistics and machine learning. 

​Language of evaluations, analysis, and data report: English and Spanish. 

Biological samples

  • Collection, labelling and storage of urine, blood, and saliva samples for research purposes.

  • blood processing.

  • urine aliquoting.

  • Data analysis of sex hormones and genotypes

Statistics, machine learning and programming

  • Extraction and analysis of raw data.

  • Data cleaning, and quality evaluations.

  • Advanced level of Biostatistics with R.  

  • Strong analytical problem-solving skills.

  • Ability to perform basic and complex statistical analysis using R and MATLAB: descriptive stats, univariate models, multivariate models, factorial analyses, machine learning, AI.

  • Creation of graphs, charts, and tables to report on results of statistical analysis.

  • Deep learning. 

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