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Multimodal characterization of the visual word form area: An integrative computational model

Periodic Reporting for period 1 - ReCiModel (Multimodal characterization of the visual word form area: An integrative computational model)

Reporting period: 2018-09-01 to 2020-08-31

The ventral occipito-temporal (vOT) association cortex contributes significantly to recognize different types of visual patterns. It is widely accepted that a subset of this circuitry, including the visual word form area (VWFA), becomes trained to identify word forms. Nevertheless, due to heterogeneous experimental procedures across studies and intrinsic limitations of functional and structural MRI tools, the exact cortical location of what it is referred as the VWFA typically differs between studies. Additionally, it should be expected that different adjacent brain tissue within the vOT to perform different specific computations.

I proposed to conduct the first systematic investigation combining functional and structural MRI data to further characterize spatially segregated VWFAs. Analogously, the functional and structural connectivity between these VWFAs and other relevant visual and language brain regions was examined as well. In the last year of the grant, out of the scope of this report, these data will be incorporated into a detailed statistical model intended to predict reading behavior, as the ultimate goal of the project is to create a highly detailed characterization of the early stages of reading and to establish a baseline model and parameter range that will serve to clarify differences between typical and atypical readers.
First, I was able to finish the main objectives set up for the period, and most importantly, to validate the hypotheses delineated in the project. Those results were published in a high impact journal (PNAS), and they are being highly cited (33 at the moment). Figure 1 summarizes the main functional, behavioral and structural findings obtained in the project until now (see figure 1 attached).

I am still working with this dataset, for example, I repeated the analyses in the right hemisphere, and I am in the writing the manuscript stage. I am performing a highly detailed work in individual space as well, trying to quantify how well these results hold at the individual subject level. This work led to some past and ongoing collaborations as well. One of those collaborations was already published in Nature Communications.

Second, I was able to work in state-of-the art neuroimaging tools and procedures to be able to accomplish the next objective of the project: creating a model and baseline of healthy readers. In order to create models that can be used across scanners and be useful outside the research environment, I started working on quantitative MRI methods and the translation of research findings to the clinic, trying to find ways of identifying differences at the individual subject level. I developed a conceptual and experimental project to understand the replication and generalization in applied neuroimaging, and the result of that work was published in the journal NeuroImage. Next, in order to have a platform to validate the results of our analyses, I worked in a software validation platform because I wanted to apply it to a quantitative fMRI models (population receptive fields -pRF-). This work was recently published in PLOS Computational Biology.

I have another paper in second revision and another two that will be submitted in the present month: one about pRFs and reading (related to the PNAS paper) and the other one about the shapes of pRFs in early visual cortex.
All the results detailed above went beyond the state of the art and provided incremental contributions to the field. Summary of the three main contributions: the work in VWFA published in PNAS set a framework for other works that used our results to build upon them. 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. 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 furthermore, they will help design new experiment and new generation analysis tools.

During this last year I think I will be able to publish another 4 first author papers and some middle author papers. I hope that we will be able to overcome the COVID related delays and the BCBL will be able to scan the participants with dyslexia that were part of my third year analyses. I hope to apply a novel computational method to try to rank individual dyslexic participants versus the baseline of the healthy readers in different metrics. The work I have been doing until now will be crucial to achieve this goal. If we are not able to scan the dyslexic participants on time, I will continue working with the detailed baseline model and existing subjects in BCBL’s and Stanford’s databases.

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 (MRI) techniques to investigate the neural basis of vision and reading and developing functional and structural MRI 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 (MRI) diagnostic tool to help those struggling to read. Developmental dyslexia is the most prevalent reading disability in the population (i.e. 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.