All the results went beyond the state of the art and provided incremental contributions to the field. Summary of the 3 main contributions: the work in VWFA published in PNAS set a framework for other works that used our results to build upon them (White et al. 2019 PNAS, or Yablonski et al., 2021, presented at the Annual Conference of the Society for the Neurobiology of Language). The NeuroImage paper proposed a framework for replication and generalization, and in top of that proposed a novel method to increase the precision of the metrics we use. Similar works have been published recently, as in Chamberland et al., (2021; Nature Computational Science). The Plos Comp. Biol. paper on software validation provided a novel and reproducible framework to check the validity of neuroimaging algorithm results, and in top of it, extended what we knew about the pRFs by discovering a dependence of the estimated size of the pRF on the HRF assumed by the model. We provided recommendations and improvements in how to minimize these dependencies. These findings will allow to revisit the literature with new eyes and will help design new experiment and new generation analysis tools. We already used the technology to show how 1 published result was wrong, most probably because of an error on the software they used. This work was published in Journal of Neuroscience. In my previous report I was hopeful to overcome the COVID related delays and that the BCBL would be able to scan the participants with dyslexia that were part of my third-year analyses. I was hoping to apply a novel computational method to try to rank individual dyslexic participants versus the baseline of the healthy readers in different metrics. We were not able to scan the dyslexic participants on time, so I continued working with the detailed baseline model and existing subjects in BCBL’s and Stanford’s databases. Using one database from Stanford, I was able to provide a new insight in how we use sensory and cognitive signals in the visual and reading ventro-occipital reading regions (this work is still under review in Nature Communications). I think that the impact of the previous results will be building over time. My plan is to continue focusing on using behavioural, functional and structural Magnetic Resonance Imaging techniques to investigate the neural basis of vision and reading and developing functional and structural MRI computational methods to further examine cognitive functions and enhance neuroimaging reproducibility, validity and generalizability. My long-term career objective is to develop a clinical magnetic resonance imaging diagnostic tool to help those struggling to read. Developmental dyslexia is the most prevalent reading disability in the population (3–7% depending on definitional criteria and language orthography), with its manifestations ranging from specific inabilities to decode words to higher level language limitations. The educational, social and economic impact on the individual can be life-altering. The diagnostic tool needs to be at the individual level and applicable in any standard clinic with a MRI machine. The impact of such a tool can be enormous in those suffering the disabilities in particular and the society in general.
