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Challenges in the Analysis of Neuroscience Data

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Studies in Neural Data Science (START UP RESEARCH 2017)

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Abstract

In the last two decades, our understanding of the mechanisms underlying the functioning and disruption of the human brain has advanced considerably. The previous chapters of the book have provided a compelling argument for demonstrating the advantages of thoughtful, non-naive, statistical approaches for analyzing brain imaging data. Here, we provide a review of the main themes highlighted in those chapters, and we further discuss some of the challenges that statistical imaging is currently confronted with. In particular, we emphasize the importance of developing analytical frameworks that allow to characterize the heterogeneity typically observed in brain imaging both within- and between- subjects, by capturing the main sources of variability in the data. More specifically, we focus on clustering methods that identify groups of subjects characterized by similar patterns of brain responses to a task; on dynamic temporal models that characterize the heterogeneity in individual functional connectivity networks; and on multimodal imaging analysis and imaging genetics that combine information from multiple data sources in order to achieve a better understanding of brain processes.

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Authors and Affiliations

  1. Department of Statistics, University of California, Irvine, USA

    Michele Guindani

  2. Department of Statistics, Rice University, Houston, USA

    Marina Vannucci

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  1. Michele Guindani
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  2. Marina Vannucci
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Correspondence to Michele Guindani .

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Editors and Affiliations

  1. Department of Statistical Sciences, University of Padova, Padua, Italy

    Antonio Canale

  2. Department of Decision Sciences, Bocconi University, Milan, Italy

    Daniele Durante

  3. Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy

    Lucia Paci

  4. Department of Statistical Sciences, University of Padova, Padua, Italy

    Bruno Scarpa

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Guindani, M., Vannucci, M. (2018). Challenges in the Analysis of Neuroscience Data. In: Canale, A., Durante, D., Paci, L., Scarpa, B. (eds) Studies in Neural Data Science. START UP RESEARCH 2017. Springer Proceedings in Mathematics & Statistics, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-00039-4_8

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