START UP RESEARCH 2017: Studies in Neural Data Science pp 131-156 | Cite as

Challenges in the Analysis of Neuroscience Data

  • Michele GuindaniEmail author
  • Marina Vannucci
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 257)


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.


Brain imaging data fMRI data Clustering Dynamic functional connectivity Multimodal analysis Imaging genetics 


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Authors and Affiliations

  1. 1.Department of StatisticsUniversity of CaliforniaIrvineUSA
  2. 2.Department of StatisticsRice UniversityHoustonUSA

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