Professional Education

  • Doctor of Philosophy, Universidad Del Pais Vasco (2017)
  • Master of Science, Universitat Ramon Llull (2009)
  • Master of Business Admin, Universitat Ramon Llull (2004)
  • Master of Engineering, Universidad De Navarra (1999)
  • Bachelor of Elec Engineering, Universidad De Navarra (1996)

All Publications

  • The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. NeuroImage Tozzi, L., Staveland, B., Holt-Gosselin, B., Chesnut, M., Chang, S. E., Choi, D., Shiner, M. L., Wu, H., Lerma-Usabiaga, G., Sporns, O., Barch, D., Gotlib, I. H., Hastie, T. J., Kerr, A. B., Poldrack, R. A., Wandell, B. A., Wintermark, M., Williams, L. M. 2020: 116715


    Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

    View details for DOI 10.1016/j.neuroimage.2020.116715

    View details for PubMedID 32147367

  • Replication and generalization in applied neuroimaging. NeuroImage Lerma-Usabiaga, G., Mukherjee, P., Ren, Z., Perry, M. L., Wandell, B. A. 2019: 116048


    There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

    View details for DOI 10.1016/j.neuroimage.2019.116048

    View details for PubMedID 31356879

  • Separate lanes for adding and reading in the white matter highways of the human brain. Nature communications Grotheer, M., Zhen, Z., Lerma-Usabiaga, G., Grill-Spector, K. 2019; 10 (1): 3675


    Math and reading involve distributed brain networks and have both shared (e.g. encoding of visual stimuli) and dissociated (e.g. quantity processing) cognitive components. Yet, to date, the shared vs. dissociated gray and white matter substrates of the math and reading networks are unknown. Here, we define these networks and evaluate the structural properties of their fascicles using functional MRI, diffusion MRI, and quantitative MRI. Our results reveal that there are distinct gray matter regions which are preferentially engaged in either math (adding) or reading, and that the superior longitudinal and arcuate fascicles are shared across the math and reading networks. Strikingly, within these fascicles, reading- and math-related tracts are segregated into parallel sub-bundles and show structural differences related to myelination. These findings open a new avenue of research that examines the contribution of sub-bundles within fascicles to specific behaviors.

    View details for DOI 10.1038/s41467-019-11424-1

    View details for PubMedID 31417075

  • Converging evidence for functional and structural segregation within the left ventral occipitotemporal cortex in reading PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Lerma-Usabiaga, G., Carreiras, M., Paz-Alonso, P. M. 2018; 115 (42): E9981–E9990


    The ventral occipitotemporal cortex (vOTC) is crucial for recognizing visual patterns, and previous evidence suggests that there may be different subregions within the vOTC involved in the rapid identification of word forms. Here, we characterize vOTC reading circuitry using a multimodal approach combining functional, structural, and quantitative MRI and behavioral data. Two main word-responsive vOTC areas emerged: a posterior area involved in visual feature extraction, structurally connected to the intraparietal sulcus via the vertical occipital fasciculus; and an anterior area involved in integrating information with other regions of the language network, structurally connected to the angular gyrus via the posterior arcuate fasciculus. Furthermore, functional activation in these vOTC regions predicted reading behavior outside of the scanner. Differences in the microarchitectonic properties of gray-matter cells in these segregated areas were also observed, in line with earlier cytoarchitectonic evidence. These findings advance our understanding of the vOTC circuitry by linking functional responses to anatomical structure, revealing the pathways of distinct reading-related processes.

    View details for PubMedID 30224475