The Neuropsychological Battery table contains data for multiple assessments, including: Logical Memory, Rey Auditory Verbal Learning Test, Clock Drawing, Clock Copying, Category Fluency, Trail Making Test, Boston Naming Test, ANART, and Digit Span. At any given visit, only a subset of those tests are administered. The Digit Span was dropped after ADNI1. The Category Fluency - Animals was retained, but Vegetables were dropped after ADNI1. A Spanish language option for the Boston Naming Test became available for participants testing in Spanish beginning with ADNIGO. The data dictionary for this table can be difficult to parse to identify which variables belong to which assessment. For clarity, the field/variable names associated with each test are identified here:

Logical Memory - Immediate Recall: LMSTORY, LIMMTOTAL, LIMMEND

Logical Memory - Delayed Recall: LDELBEGIN, LDELTOTAL, LDELCUE.

Rey Auditory Verbal Learning Test: AVTOT1, AVERR1, AVTOT2, AVERR2, AVTOT3, AVERR3, AVTOT4, AVERR4, AVTOT5, AVERR5, AVTOT6, AVERR6, AVTOTB, AVERRB, AVENDED

Rey Auditory Verbal Learning Test - Delayed: AVDELBEGAN, AVDEL30MIN, AVDELERR1, AVDELTOT, AVDELERR2

Clock Drawing: CLOCKCIRC, CLOCKSYM, CLOCKNUM, CLOCKHAND, CLOCKTIME, CLOCKSCOR

Clock Copying: COPYCIRC, COPYSYM, COPYNUM, COPYHAND, COPYTIME, COPYSCOR

Category Fluency (Vegetables ADNI1 only): CATANIMSC, CATANPERS, CATANINTR, CATVEGESC, CATVGPERS, CATVGINTR

Trail Making Test: TRAASCOR, TRAAERRCOM, TRAAERROM, TRABSCOR, TRABERRCOM, TRABERROM

Boston Naming Test (Spanish version provided in ADNIGO/2): BNTND, BNTSPONT, BNTSTIM, BNTCSTIM, BNTPHON, BNTCPHON, BNTTOTAL

American National Adult Reading Test (ANART): ANARTND, ANARTERR

Digit Span (ADNI1 only): DSPANFOR, DSPANFLTH, DSPANBAC, DSPANBLTH

A dataset with variables as follows:

  • MINTUNCUED N NA MINT Total Uncued Correct

  • MINTSEMCUE N NA MINT Total Correct - with Semantic Cue

  • MINTTOTAL N NA MINT Total Correct (Uncued + Correct with Semantic cue)

  • ANART N NA Was ANART conducted?

  • RAVLT.immediate N RAVLT Immediate (sum of 5 trials)

  • RAVLT.learning N RAVLT Learning (trial 5 - trial 1)

  • RAVLT.forgetting N RAVLT Forgetting (trial 5 - delayed)

  • RAVLT.perc.forgetting N RAVLT Percent Forgetting

  • LIMMEND T HHMM Time Ended (24-hour clock)

  • LDELBEGIN T HHMM Time Began (24-hour clock)

  • AVENDED T HHMM AVLT Time Trial B Ended

  • AVDELBEGAN T HHMM Time Began (24-hour clock)

  • LMSTORY N NA Which Logical Memory story was used?

  • RID N Participant roster ID

  • SITEID N Site ID

  • VISCODE T Visit code

  • USERDATE S Date record created

  • EXAMDATE D Examination Date

  • CLOCKCIRC N 1. Approximately circular face

  • CLOCKSYM N 2. Symmetry of number placement

  • CLOCKNUM N 3. Correctness of numbers

  • CLOCKHAND N 4. Presence of the two hands

  • CLOCKTIME N 5. Presence of the two hands, set to ten after eleven

  • CLOCKSCOR N Total Score

  • COPYCIRC N 1. Approximately circular face

  • COPYSYM N 2. Symmetry of number placement

  • COPYNUM N 3. Correctness of numbers

  • COPYHAND N 4. Presence of the two hands

  • COPYTIME N 5. Presence of the two hands, set to ten after eleven

  • COPYSCOR N Total Score

  • LIMMTOTAL N Logical Memory - Immediate Recall <br />Total Number of Story Units Recalled:

  • AVTOT1 N Trial 1 Total

  • AVERR1 N Total Intrusions

  • AVTOT2 N Trial 2 Total

  • AVERR2 N Total Intrusions

  • AVTOT3 N Trial 3 Total

  • AVERR3 N Total Intrusions

  • AVTOT4 N Trial 4 Total

  • AVERR4 N Total Intrusions

  • AVTOT5 N Trial 5 Total

  • AVERR5 N Total Intrusions

  • AVTOT6 N Trial 6 Total

  • AVERR6 N Total Intrusions

  • AVTOTB N List B Total

  • AVERRB N Total Intrusions

  • DSPANFOR N Forward: Total Correct

  • DSPANFLTH N Forward: Length

  • DSPANBAC N Backward: Total Correct

  • DSPANBLTH N Backward: Length

  • CATANIMSC N Category Fluency (Animals) - Total Correct

  • CATANPERS N Category Fluency (Animals) - Perseverations

  • CATANINTR N Category Fluency (Animals) - Intrusions

  • CATVEGESC N Category Fluency (Vegetables) - Total Correct

  • CATVGPERS N Category Fluency (Vegetables) - Perseverations

  • CATVGINTR N Category Fluency (Vegetables) - Intrusions

  • TRAASCOR N Part A - Time to Complete

  • TRAAERRCOM N Errors of Commission

  • TRAAERROM N Errors of Omission

  • TRABSCOR N Part B - Time to complete

  • TRABERRCOM N Errors of Commission

  • TRABERROM N Errors of Omission

  • DIGITSCOR N Total Correct

  • LDELTOTAL N Logical Memory - Delayed Recall <br />Total Number of Story Units Recalled:

  • LDELCUE N Cue used?

  • BNTND T Check here if:

  • BNTSPONT N 1. Number of spontaneously given correct responses

  • BNTSTIM N 2. Number of semantic cues given

  • BNTCSTIM N 3. Number of correct responses following a semantic cue

  • BNTPHON N 4. Number of phonemic cues given

  • BNTCPHON N 5. Number of correct responses following a phonemic cue

  • BNTTOTAL N Total Number Correct (1+3)

  • AVDEL30MIN N 30 Minute Delay Total

  • AVDELERR1 N Total Intrusions

  • AVDELTOT N Recognition Score

  • AVDELERR2 N Recognition Errors

  • ANARTND T Check here if:

  • ANARTERR N ANART Total Score (Total # of errors)

  • USERDATE2 S Date record last updated

data(neurobat)

Format

A data frame with 13712 rows and 80 variables

References

Estevez-Gonzalez, A., Kulisevsky, J., Boltes, A., Otermin, P., & Garcia-Sanchez, C. (2003). Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase of Alzheimer's disease: comparison with mild cognitive impairment and normal aging. International Journal of Geriatric Psychiatry. 18 (11), 1021.

Examples

# NOT RUN {
neurobat$RAVLT.immediate <- apply(neurobat[, paste('AVTOT', 1:5, sep = '')],
  1, sum, na.rm = FALSE)
neurobat$RAVLT.learning <- neurobat$AVTOT5-neurobat$AVTOT1
neurobat$RAVLT.forgetting <- neurobat$AVTOT5-neurobat$AVDEL30MIN
neurobat$RAVLT.perc.forgetting <- 100*neurobat$RAVLT.forgetting / neurobat$AVTOT5
neurobat$RAVLT.perc.forgetting <- ifelse(neurobat$AVTOT5 == 0, NA, neurobat$RAVLT.perc.forgetting)
neurobat$RAVLT <- neurobat$RAVLT.learning
label(neurobat$RAVLT) <- 'RAVLT Learning'

# }