What Can Fear and Reward Learning Teach Us About Depression?
Current Topics in Behavioral Neurosciences, ISSN: 1866-3370, Vol: 14, Page: 223-242
2013
- 4Citations
- 32Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- CrossRef3
- Captures32
- Readers32
- 32
Book Chapter Description
The precise neural substrates of major depressive disorder (MDD) remain elusive, and FDA-approved antidepressants fail at least one-third of treatment-seeking patients. It is imperative, therefore, to identify novel research strategies to tackle the factors impeding progress. In this chapter we propose that the knowledge derived from computational investigations of associative learning might offer new insights into the neurobiology of MDD. © Springer-Verlag Berlin Heidelberg 2013.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84880077234&origin=inward; http://dx.doi.org/10.1007/7854_2012_236; http://www.ncbi.nlm.nih.gov/pubmed/23299804; https://link.springer.com/10.1007/7854_2012_236; https://dx.doi.org/10.1007/7854_2012_236; https://link.springer.com/chapter/10.1007/7854_2012_236
Springer Science and Business Media LLC
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