Supplementary Scores for the Uniform Data Set 3.0: Normative data and
exploration in clinical populations 
   Lead Investigator:    Jared Benge
   Institution      :    Baylor Scott and White Health
   E-Mail           :    jared.benge@bswhealth.org
   Proposal ID      :    1039


   Publications:


1  Benge J
   Supplementary and Discrepancy Scores for the UDS 3.0 Neuropsychological Normative Data
   2018   Abstract  2019 International Neuropsychological Meeting , 

   SUBMITTED



2  Benge J
   Quantitative and Qualitative Features of Executive Dysfunction in Frontotemporal and Alzheimer's Dementia
   2018   Abstract  2019 International Neuropsychological Meeting , 

   SUBMITTED



3  Chavda R, Cao JS, Benge JF
   Neuropsychological Impact of White Matter Hyperintensities in Older Adults without Dementia
   2019   Journal Article  Clin Neuropsychol , 

   SUBMITTED



4  Devora PV, Beevers S, Kiselica A, Benge JF
   Normative Data for Derived Measures and Discrepancy Scores for the Uniform Data Set 3.0 Neuropsychological Battery
   Journal Article  Arch Clin Neuropsychol,35:75-89,2019,PMC7014972

   PUBLISHED



5  Kiselica AM, Benge J
   Quantitative and Qualitative Features of Executive Dysfunction in Frontotemporal and Alzheimer's Dementia
   2019   Journal Article  Clin Neuropsychol , 

   SUBMITTED





   Proposal Description:
   Recently published normative data for the UDS 3.0 included information for core measures.  However, there are a number of derived indices, such as phonemic versus semantic fluency ratios, benefit from cueing on the MINT,  retention, and TMT A/B discrepancies that may provide clinical value in differential diagnosis and characterization of participants.  As such, the specific aims are to:                                                                                1) Describe common derived scores (Retention for memory tests, Visual/Verbal memory splits, TMT AB discrepancies, Phonemic versus semantic fluency discrepancies, etc.) in normative control populations.                             2) Explore the frequency of these discrepancies in clinical samples, including evaluating for any increase in sensitivity/specificity for differential diagnosis.