Sebastian Toro Arana

All Publications

• Genetic diversity loss in the Anthropocene. Science (New York, N.Y.) Exposito-Alonso, M., Booker, T. R., Czech, L., Gillespie, L., Hateley, S., Kyriazis, C. C., Lang, P. L., Leventhal, L., Nogues-Bravo, D., Pagowski, V., Ruffley, M., Spence, J. P., Toro Arana, S. E., WeiSS, C. L., Zess, E. 2022; 377 (6613): 1431-1435

  Abstract

  Anthropogenic habitat loss and climate change are reducing species' geographic ranges, increasing extinction risk and losses of species' genetic diversity. Although preserving genetic diversity is key to maintaining species' adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations' post-2020 targets for genetic preservation.

  View details for DOI 10.1126/science.abn5642

  View details for PubMedID 36137047

• Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model. Cell Friedman, A. n., Hueske, E. n., Drammis, S. M., Toro Arana, S. E., Nelson, E. D., Carter, C. W., Delcasso, S. n., Rodriguez, R. X., Lutwak, H. n., DiMarco, K. S., Zhang, Q. n., Rakocevic, L. I., Hu, D. n., Xiong, J. K., Zhao, J. n., Gibb, L. G., Yoshida, T. n., Siciliano, C. A., Diefenbach, T. J., Ramakrishnan, C. n., Deisseroth, K. n., Graybiel, A. M. 2020

  Abstract

  Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

  View details for DOI 10.1016/j.cell.2020.09.060

  View details for PubMedID 33113354

• Chronic Stress Alters Striosome-Circuit Dynamics, Leading to Aberrant Decision-Making CELL Friedman, A., Homma, D., Bloem, B., Gibb, L. G., Amemori, K., Hu, D., Delcasso, S., Truong, T. F., Yang, J., Hood, A. S., Mikofalvy, K. A., Beck, D. W., Nguyen, N., Nelson, E. D., Arana, S., Bruegge, R., Goosens, K. A., Graybiel, A. M. 2017; 171 (5): 1191-+

  Abstract

  Effective evaluation of costs and benefits is a core survival capacity that in humans is considered as optimal, "rational" decision-making. This capacity is vulnerable in neuropsychiatric disorders and in the aftermath of chronic stress, in which aberrant choices and high-risk behaviors occur. We report that chronic stress exposure in rodents produces abnormal evaluation of costs and benefits resembling non-optimal decision-making in which choices of high-cost/high-reward options are sharply increased. Concomitantly, alterations in the task-related spike activity of medial prefrontal neurons correspond with increased activity of their striosome-predominant striatal projection neuron targets and with decreased and delayed striatal fast-firing interneuron activity. These effects of chronic stress on prefronto-striatal circuit dynamics could be blocked or be mimicked by selective optogenetic manipulation of these circuits. We suggest that altered excitation-inhibition dynamics of striosome-based circuit function could be an underlying mechanism by which chronic stress contributes to disorders characterized by aberrant decision-making under conflict. VIDEO ABSTRACT.

  View details for DOI 10.1016/j.cell.2017.10.017

  View details for Web of Science ID 000415317000020

  View details for PubMedID 29149606

  View details for PubMedCentralID PMC5734095

• Analysis of complex neural circuits with nonlinear multidimensional hidden state models PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Friedman, A., Slocum, J. F., Tyulmankov, D., Gibb, L. G., Altshuler, A., Ruangwises, S., Shi, Q., Arana, S., Beck, D. W., Sholes, J. C., Graybiel, A. M. 2016; 113 (23): 6538–43

  Abstract

  A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.

  View details for DOI 10.1073/pnas.1606280113

  View details for Web of Science ID 000377155400055

  View details for PubMedID 27222584

  View details for PubMedCentralID PMC4988606