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Benjamin M. Hampstead

Researcher at University of Michigan

Publications -  80
Citations -  2310

Benjamin M. Hampstead is an academic researcher from University of Michigan. The author has contributed to research in topics: Medicine & Transcranial direct-current stimulation. The author has an hindex of 18, co-authored 53 publications receiving 1794 citations. Previous affiliations of Benjamin M. Hampstead include Emory University & Veterans Health Administration.

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Incomplete evidence that increasing current intensity of tDCS boosts outcomes.

TL;DR: Understanding dose- response in human applications of tDCS is needed for protocol optimization including individualized dose to reduce outcome variability, which requires intelligent design of dose-response studies.
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Activation and Effective Connectivity Changes Following Explicit-Memory Training for Face–Name Pairs in Patients With Mild Cognitive Impairment: A Pilot Study

TL;DR: The authors’ findings suggest that the effectiveness of explicit-memory training in patients with MCI is associated with training-specific increases in activation and connectivity in a distributed neural system that includes areas involved in explicit memory.
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Mnemonic strategy training partially restores hippocampal activity in patients with mild cognitive impairment.

TL;DR: Cognitive rehabilitation techniques may help mitigate hippocampal dysfunction in MCI patients by facilitating hippocampal functioning in a partially restorative manner, as defined anatomically.
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Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

TL;DR: Model-free Big Data machine learning-based classification methods can outperform model-based techniques in terms of predictive precision and reliability, and it is observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data.