JOAN GUÀRDIA-OLMOS

Grups de Tècniques Estadistiques Avançades Aplicades a la Psicologia

ORCID Research Profile

JOAN GUÀRDIA-OLMOS

Principal Investigator

jguardia (at) ub.edu

Research team

 

Maribel Pero Cebollero

Full Professor

 

Nuria Mancho Fora

Early stage researcher

 

Laia Farras Permanyer

Associate Professor

 

Maria Carbó Carreté

Others

 

Sonia Benitez Borrego

Associate Professor

 

Marc Montalà

Early stage researcher

 

Cristina Cañete Massé

Early stage researcher

 

Vicenç Quera

 

Antonio Solanas Pérez

 

Francesc Salvador Beltran

 

David Leiva Ureña

 

Rumen Rumenov Manolov

 

Ruth Dolado Guivernau

 

Elisabet Gimeno Rosell

Contact details

 

Prof. Joan Guàrdia-Olmos

Department of Social Psychology and Quantitative Psychology

Faculty of Psychology, Passeig Vall d’Hebron 171

08035 Barcelona (Spain)

+34 933125090

jguardia (at) ub.edu

www.ub.edu/gteaap

Research Interests

 

The study of brain connectivity is one of the main challenges when studying the active brain. Brain connectivity is operationally defined as the estimation of the relation between brain areas (Regions of interest, ROIs, or Volumes of interest, VOIs); these relations are established when a specific cognitive task is being solved or when resting. In order to estimate the connectivity different brain signals can be used, each of which has different neurofucntional properties. One of signals showing great ability to represent the active brain is the BOLD signal, obtained when using functional magnetic resonance imaging (fMRI). What is registered is the modification of the magnetic field that takes place due to the increment of the presence of oxygen is certain brain areas when these are activated during a cognitive task. When the brain is resting, the signal shows the basal state used as a reference.

In order to estimate the connectivity networks, the value of the BOLD signal is isolated, throughout the recording period, in these voxels that anatomically or statistically present significant activation as compared to resting. In each grouping of voxels, a value for this ROI is estimated via dimensionality reduction techniques and the connectivity networks are tentatively established. Currently, there are two main models for estimation of connectivity: one based on structural equations models (SEM) one estimating the effective connectivity via dynamic causal models (DCM). Both models reflect different perspectives on the neurobiological bases of the network, but there are also some similarities in their mathematical and statistical properties.

Current Research Lines

 

  • Multivariate Statistical Analysis
  • Quantitative Neuroscience
  • Functional and Effective Connectivity
  • Structural Equation Models
  • Advanced Statistical Data Analysis and Visualization

Technologies / methods

 

  • Generation of statistical advanced models applied to the big data structures and brain signal.
  • Psychometric technology.
  • Advanced Multivariate Analysis.

Highlighted publications

 

· Gallardo, G., González, A., Gudayol, E. & Guardia, J. (2015). Type 1 Diabetes modifies brain activation in young patients while performing visuospatial working memory tasks. Journal of Diabetes Research, Article ID 703512.

 

· Gudayol, E.; Peró, M.; González, A. & Guàrdia, J. (2015). Changes in brain connectivity related to the treatment of depression measured through fMRI: A systematic review. Frontiers in Human Neuroscience, 9; 582.

 

· Carbo, M.; Guàrdia, J. & Giné, C. (2015). A structural equation model to study the relationship among physical activity-related factors and quality of life. International Journal of Clinical and Health Psychology.

 

· Guàrdia, J., Pero, M., Zarabozo, D., González, A. & Gudayol, E. (2015). Effective connectivity of visual word recognition and homophone orthographic errors. Frontiers in Psychology, 6, Article 640.

 

· Farràs, L., Guàrdia, J. & Peró, M. (2015). Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art. Frontiers in Psychology, 6.

 

· Farras-Permanyer, L., Mancho-Fora, N., Montala-Flaquer, M., Gudayol-Ferre, E., Bearitz Gallardo-Moreno, G., ZarabozoHurtado, D., Villuendas-Gonzalez, E., Pero-Cebollero, M., & Guardia-Olmos, J. (2019). Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment. Brain Sciences, 9(12). https://doi.org/10.3390/brainsci9120350

 

· Betancourt, A., Busquets, S., Ponce, M., Toledo, M., GuàrdiaOlmos, J., Peró-Cebollero, M., López-Soriano, F. J., & Argilés, J. M. (2019). The animal cachexia score (ACASCO). Animal Models and Experimental Medicine, 2(3), 201–209. https://doi.org/10.1002/ame2.12082

 

· Facal, D., Guardia-Olmos, J., Pereiro, A. X., Lojo-Seoane, C., Pero, M., & Juncos-Rabadan, O. (2019). Using an Overlapping Time Interval Strategy to Study Diagnostic Instability in Mild Cognitive Impairment Subtypes. Brain sciences, 9(9). https://doi.org/10.3390/brainsci9090242

 

· Farras-Permanyer, L., Mancho-Fora, N., Montala-Flaquer, M., Bartres-Faz, D., Vaque-Alcazar, L., Pero-Cebollero, M., & Guardia-Olmos, J. (2019). Age-related changes in resting-state functional connectivity in older adults. Neural Regeneration Research, 14(9), 1544-1555. https://doi.org/10.4103/1673-5374.255976

 

· Krieger, V., Antonio Amador-Campos, J., & Pero-Cebollero, M. (2019). Interrater agreement on behavioral executive function measures in adolescents with Attention Deficit Hyperactivity Disorder. International Journal of Clinical and Health Psychology, 19(2), 141–149. https://doi.org/10.1016/j.ijchp.2019.02.007