1
|
Hammers DB, Bothra S, Polsinelli A, Apostolova LG, Duff K. Evaluating practice effects across learning trials - ceiling effects or something more? J Clin Exp Neuropsychol 2024; 46:630-643. [PMID: 39258597 DOI: 10.1080/13803395.2024.2400107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 08/28/2024] [Indexed: 09/12/2024]
Abstract
BACKGROUND Practice effects (PE) are traditionally considered improvements in performance observed resulting from repeated exposure to test materials across multiple testing sessions. While PE are commonly observed for memory tests, this effect has only been considered in summary total scores. The current objective was to consider PE in summary total scores, individual learning trials, and learning slopes. METHOD One-week PE for individual trial and learning slope performance was examined on the BVMT-R and HVLT-R in 151 cognitively intact participants and 131 participants with Mild Cognitive Impairment (MCI) aged 65 years and older. RESULTS One-week PE were observed across all trials and summary total scores for both memory measures and diagnostic classifications, despite the potential for ceiling effects to limit improvement on retesting. PE were largest on the first trial relative to subsequent learning trials. This effect was diminished - but not eliminated - in participants with MCI. Conversely, no PE were observed for learning slope scores, which was counter to expectations and likely confounded by ceiling effects. CONCLUSIONS PE were present across learning trials but not learning slopes, and the initial learning trial at follow-up tended to benefit most from PE relative to subsequent learning trials. Ceiling effects appeared to influence PE for learning slopes more than learning trials. These results highlight the potential diagnostic utility of PE across individual learning trials and inform how they are distributed at follow-up, while also suggesting that learning slopes may be generally stable during longitudinal assessment.
Collapse
Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shreya Bothra
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Angelina Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin Duff
- Department of Neurology, Oregon Health and Science University, Portland, OR, USA
| |
Collapse
|
2
|
Schäfer S, Tröger J, Kray J. Modern scores for traditional tests - Review of the diagnostic potential of scores derived from word list learning tests in mild cognitive impairment and early Alzheimer's Disease. Neuropsychologia 2024; 201:108908. [PMID: 38744410 DOI: 10.1016/j.neuropsychologia.2024.108908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 05/11/2024] [Accepted: 05/11/2024] [Indexed: 05/16/2024]
Abstract
Episodic memory impairment is one of the early hallmarks in Alzheimer's Disease. In the clinical diagnosis and research, episodic memory impairment is typically assessed using word lists that are repeatedly presented to and recalled by the participant across several trials. Until recently, total learning scores, which consist of the total number of words that are recalled by participants, were almost exclusively used for diagnostic purposes. The present review aims at summarizing evidence on additional scores derived from the learning trials which have recently been investigated more frequently regarding their diagnostic potential. These scores reflect item acquisition, error frequencies, strategy use, intertrial fluctuations, and recall consistency. Evidence was summarized regarding the effects of clinical status on these scores. Preclinical, mild cognitive impairment and mild Alzheimer's Disease stages were associated with a pattern of reduced item acquisition, more errors, less strategy use, and reduced access of items, indicating slowed and erroneous encoding. Practical implications and limitations of the present research will be discussed.
Collapse
Affiliation(s)
| | | | - Jutta Kray
- Saarland University, Saarbrücken, Germany
| |
Collapse
|
3
|
Hardcastle C, Kraft JN, Hausman HK, O'Shea A, Albizu A, Evangelista ND, Boutzoukas EM, Van Etten EJ, Bharadwaj PK, Song H, Smith SG, Porges E, DeKosky ST, Hishaw GA, Wu SS, Marsiske M, Cohen R, Alexander GE, Woods AJ. Learning ratio performance on a brief visual learning and memory test moderates cognitive training gains in Double Decision task in healthy older adults. GeroScience 2024; 46:3929-3943. [PMID: 38457007 PMCID: PMC11226577 DOI: 10.1007/s11357-024-01115-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/28/2024] [Indexed: 03/09/2024] Open
Abstract
Cognitive training using a visual speed-of-processing task, called the Useful Field of View (UFOV) task, reduced dementia risk and reduced decline in activities of daily living at a 10-year follow-up in older adults. However, there was variability in the achievement of cognitive gains after cognitive training across studies, suggesting moderating factors. Learning trials of visual and verbal learning tasks recruit similar cognitive abilities and have overlapping neural correlates with speed-of-processing/working memory tasks and therefore could serve as potential moderators of cognitive training gains. This study explored the association between the Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-Revised (BVMT-R) learning with a commercial UFOV task called Double Decision. Through a secondary analysis of a clinical trial, we assessed the moderation of HVLT-R and BVMT-R learning on Double Decision improvement after a 3-month speed-of-processing/attention and working memory cognitive training intervention in a sample of 75 cognitively healthy older adults. Multiple linear regressions showed that better baseline Double Decision performance was significantly associated with better BVMT-R learning (β = - .303). This association was not significant for HVLT-R learning (β = - .142). Moderation analysis showed that those with poorer BVMT-R learning improved the most on the Double Decision task after cognitive training. This suggests that healthy older adults who perform below expectations on cognitive tasks related to the training task may show the greatest training gains. Future cognitive training research studying visual speed-of-processing interventions should account for differing levels of visuospatial learning at baseline, as this could impact the magnitude of training outcomes and efficacy of the intervention.
Collapse
Affiliation(s)
- Cheshire Hardcastle
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Jessica N Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Hanna K Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Andrew O'Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Nicole D Evangelista
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emanuel M Boutzoukas
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emily J Van Etten
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Pradyumna K Bharadwaj
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Hyun Song
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Samantha G Smith
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Eric Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Steven T DeKosky
- Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Georg A Hishaw
- Department Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
- Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Samuel S Wu
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Michael Marsiske
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Ronald Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Gene E Alexander
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA.
| |
Collapse
|
4
|
Ikanga J, Patrick SD, Schwinne M, Patel SS, Epenge E, Gikelekele G, Tshengele N, Kavugho I, Mampunza S, Yarasheski KE, Teunissen CE, Stringer A, Levey A, Rojas JC, Chan B, Lario Lago A, Kramer JH, Boxer AL, Jeromin A, Alonso A, Spencer RJ. Sensitivity of the African neuropsychology battery memory subtests and learning slopes in discriminating APOE 4 and amyloid pathology in adult individuals in the Democratic Republic of Congo. Front Neurol 2024; 15:1320727. [PMID: 38601333 PMCID: PMC11004441 DOI: 10.3389/fneur.2024.1320727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 03/14/2024] [Indexed: 04/12/2024] Open
Abstract
Background The current study examined the sensitivity of two memory subtests and their corresponding learning slope metrics derived from the African Neuropsychology Battery (ANB) to detect amyloid pathology and APOEε4 status in adults from Kinshasa, the Democratic Republic of the Congo. Methods 85 participants were classified for the presence of β-amyloid pathology and based on allelic presence of APOEε4 using Simoa. All participants were screened using CSID and AQ, underwent verbal and visuospatial memory testing from ANB, and provided blood samples for plasma Aβ42, Aβ40, and APOE proteotype. Pearson correlation, linear and logistic regression were conducted to compare amyloid pathology and APOEε4 status with derived learning scores, including initial learning, raw learning score, learning over trials, and learning ratio. Results Our sample included 35 amyloid positive and 44 amyloid negative individuals as well as 42 without and 39 with APOEε4. All ROC AUC ranges for the prediction of amyloid pathology based on learning scores were low, ranging between 0.56-0.70 (95% CI ranging from 0.44-0.82). The sensitivity of all the scores ranged between 54.3-88.6, with some learning metrics demonstrating good sensitivity. Regarding APOEε4 prediction, all AUC values ranged between 0.60-0.69, with all sensitivity measures ranging between 53.8-89.7. There were minimal differences in the AUC values across learning slope metrics, largely due to the lack of ceiling effects in this sample. Discussion This study demonstrates that some ANB memory subtests and learning slope metrics can discriminate those that are normal from those with amyloid pathology and those with and without APOEε4, consistent with findings reported in Western populations.
Collapse
Affiliation(s)
- Jean Ikanga
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
- Department of Psychiatry, School of Medicine, University of Kinshasa and Catholic University of Congo, Kinshasa, Democratic Republic of Congo
| | - Sarah D. Patrick
- Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Megan Schwinne
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States
| | - Saranya Sundaram Patel
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Emmanuel Epenge
- Department of Neurology, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Guy Gikelekele
- Department of Psychiatry, School of Medicine, University of Kinshasa and Catholic University of Congo, Kinshasa, Democratic Republic of Congo
| | - Nathan Tshengele
- Department of Psychiatry, School of Medicine, University of Kinshasa and Catholic University of Congo, Kinshasa, Democratic Republic of Congo
| | | | - Samuel Mampunza
- Department of Psychiatry, School of Medicine, University of Kinshasa and Catholic University of Congo, Kinshasa, Democratic Republic of Congo
| | | | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, Netherlands
| | - Anthony Stringer
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Allan Levey
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - Julio C. Rojas
- Department of Neurology, University of San Francisco, Memory and Aging Center, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Brandon Chan
- Department of Neurology, University of San Francisco, Memory and Aging Center, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Argentina Lario Lago
- Department of Neurology, University of San Francisco, Memory and Aging Center, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Joel H. Kramer
- Department of Neurology, University of San Francisco, Memory and Aging Center, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Adam L. Boxer
- Department of Neurology, University of San Francisco, Memory and Aging Center, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | | | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Robert J. Spencer
- Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI, United States
| |
Collapse
|
5
|
Hall MG, Wollman SC, Haines ME, Katschke JL, Boyle MA, Richardson HK, Hammers DB. Clinical validation of an aggregate learning ratio from the neuropsychological assessment battery. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-10. [PMID: 38527375 DOI: 10.1080/23279095.2024.2329974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Quantifying learning deficits provides valuable information in identifying and diagnosing mild cognitive impairment and dementia. Previous research has found that a learning ratio (LR) metric, derived from the list learning test from the Neuropsychological Assessment Battery (NAB), was able to distinguish between those with normal cognition versus memory impairment. The current study furthers the NAB LR research by validating a NAB story LR, as well as an aggregate LR. The aggregate LR was created by combining the individual list and story LRs. Participants were classified as those with normal cognition (n = 51), those with MCI (n = 39) and those with dementia (n = 35). Results revealed the story LR was able to accurately distinguish normal controls from those with mild cognitive impairment and those with dementia and offers enhanced discriminability beyond the story immediate recall score (sum of trial 1 and trial 2). Further, the aggregate LR provided superior discriminability beyond the individual list and story LRs and accounted for additional variance in diagnostic group classification. The NAB aggregate LR provides improved sensitivity in detecting declines in impaired learning, which may assist clinicians in making diagnoses earlier in a disease process, benefiting the individual through earlier interventions.
Collapse
Affiliation(s)
- Matthew G Hall
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Mary E Haines
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Mellisa A Boyle
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Dustin B Hammers
- Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| |
Collapse
|
6
|
Boscarino JJ, Weitzner DS, Bailey EK, Kamper JE, Vanderbleek EN. Utility of learning ratio scores from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word List Memory Test in distinguishing patterns of cognitive decline in veterans referred for neuropsychological evaluation. Clin Neuropsychol 2024:1-13. [PMID: 38494420 DOI: 10.1080/13854046.2024.2330144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
Background: The Learning Ratio (LR) is a novel learning score that has shown improved utility over other learning metrics in detecting Alzheimer's disease (AD) across multiple memory tasks. However, its utility on the Consortium to Establish a Registry for Alzheimer's Disease Word List Memory Test (CERAD WLMT), a widely used list learning measure sensitive to decline in neurodegenerative disease, is unknown. The goal of the current study was to determine the utility of LR on the CERAD WLMT in differentiating between diagnostic (MiNCD vs MaNCD) and etiologic groups (VaD vs AD) in a veteran sample. Methods: Raw learning slope (RLS) and LR scores were examined in 168 veterans diagnosed with major neurocognitive disorder (MaNCD), mild neurocognitive disorder (MiNCD), or normal aging following neuropsychological evaluation. Patients with MaNCD were further classified by suspected etiology (i.e. microvascular disease vs AD). Results: Whereas RLS scores were not significantly different between MiNCD and MaNCD, LR scores were significantly different between all diagnostic groups (p's < .05). Those with AD had lower LR scores and RLS scores compared to those with VaD (p's < .05). LR classification accuracy was acceptable for MiNCD (AUC = .76), excellent for MaNCD (AUC = .86) and VaD (AUC = .81), and outstanding for AD (AUC = .91). Optimal cutoff scores for WLMT LR were derived from Youden's index. Conclusion: Results support the use of LR scores over RLS when interpreting the CERAD WLMT and highlight the clinical utility of LR in differentiating between diagnostic groups and identifying suspected etiology.
Collapse
Affiliation(s)
- Joseph J Boscarino
- Mental Health and Behavioral Service, James A. Haley Veterans' Hospital, Tampa, Florida, USA
| | - Daniel S Weitzner
- Mental Health and Behavioral Service, James A. Haley Veterans' Hospital, Tampa, Florida, USA
| | - Erin K Bailey
- Mental Health and Behavioral Service, James A. Haley Veterans' Hospital, Tampa, Florida, USA
- Department of Psychiatry, University of South Florida Morsani College of Medicine, Tampa, Florida, USA
| | - Joel E Kamper
- Mental Health and Behavioral Service, James A. Haley Veterans' Hospital, Tampa, Florida, USA
| | - Emily N Vanderbleek
- Mental Health and Behavioral Service, James A. Haley Veterans' Hospital, Tampa, Florida, USA
| |
Collapse
|
7
|
Hammers DB, Nemes S, Diedrich T, Eloyan A, Kirby K, Aisen P, Kramer J, Nudelman K, Foroud T, Rumbaugh M, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Weintraub S, Wingo TS, Wolk DA, Wong B, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Learning slopes in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S19-S28. [PMID: 37243937 PMCID: PMC10806757 DOI: 10.1002/alz.13159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Investigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity. A particular metric-the learning ratio (LR)-outperformed other learning slope calculations across analyses CONCLUSIONS: Learning slopes appear to be sensitive to early-onset dementias, even when controlling for the effect of total learning and cognitive severity. The LR may be the learning measure of choice for such analyses. HIGHLIGHTS Learning is impaired in amyloid-positive EOAD, beyond cognitive severity scores alone. Amyloid-positive EOAD participants perform worse on learning slopes than amyloid-negative participants. Learning ratio appears to be the learning metric of choice for EOAD participants.
Collapse
Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Taylor Diedrich
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steve Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Sandra Weintraub
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bonnie Wong
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | | |
Collapse
|
8
|
Waner JL, Hausman HK, Kraft JN, Hardcastle C, Evangelista ND, O'Shea A, Albizu A, Boutzoukas EM, Van Etten EJ, Bharadwaj PK, Song H, Smith SG, DeKosky ST, Hishaw GA, Wu SS, Marsiske M, Cohen R, Alexander GE, Porges EC, Woods AJ. Connecting memory and functional brain networks in older adults: a resting-state fMRI study. GeroScience 2023; 45:3079-3093. [PMID: 37814198 PMCID: PMC10643735 DOI: 10.1007/s11357-023-00967-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023] Open
Abstract
Limited research exists on the association between resting-state functional network connectivity in the brain and learning and memory processes in advanced age. This study examined within-network connectivity of cingulo-opercular (CON), frontoparietal control (FPCN), and default mode (DMN) networks, and verbal and visuospatial learning and memory in older adults. Across domains, we hypothesized that greater CON and FPCN connectivity would associate with better learning, and greater DMN connectivity would associate with better memory. A total of 330 healthy older adults (age range = 65-89) underwent resting-state fMRI and completed the Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-Revised (BVMT-R) in a randomized clinical trial. Total and delayed recall scores were assessed from baseline data, and a learning ratio calculation was applied to participants' scores. Average CON, FPCN, and DMN connectivity values were obtained with CONN Toolbox. Hierarchical regressions controlled for sex, race, ethnicity, years of education, and scanner site, as this was a multi-site study. Greater within-network CON connectivity was associated with better verbal learning (HVLT-R Total Recall, Learning Ratio), visuospatial learning (BVMT-R Total Recall), and visuospatial memory (BVMT-R Delayed Recall). Greater FPCN connectivity was associated with better visuospatial learning (BVMT-R Learning Ratio) but did not survive multiple comparison correction. DMN connectivity was not associated with these measures of learning and memory. CON may make small but unique contributions to learning and memory across domains, making it a valuable target in future longitudinal studies and interventions to attenuate memory decline. Further research is necessary to understand the role of FPCN in learning and memory.
Collapse
Affiliation(s)
- Jori L Waner
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Hanna K Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Jessica N Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Cheshire Hardcastle
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Nicole D Evangelista
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Andrew O'Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Emanuel M Boutzoukas
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emily J Van Etten
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Pradyumna K Bharadwaj
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Hyun Song
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Samantha G Smith
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Steven T DeKosky
- Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Georg A Hishaw
- Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
- Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Samuel S Wu
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Michael Marsiske
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Ronald Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Gene E Alexander
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA
| | - Eric C Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA.
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA.
| |
Collapse
|
9
|
Spencer RJ, Williams TF, Kordovski VM, Patrick SD, Lengu K, Gradwohl BD, Hammers DB. A quantitative review of competing learning slope metrics: effects of age, sex, and clinical diagnosis. J Clin Exp Neuropsychol 2023; 45:744-757. [PMID: 38357915 DOI: 10.1080/13803395.2024.2314741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 12/27/2023] [Indexed: 02/16/2024]
Abstract
INTRODUCTION In learning and memory tests that involve multiple presentations of the same material, learning slope refers to the degree to which examinees improve performances over successive learning trials. We aimed to quantitatively review the traditional raw learning slope (RLS), and the newly created learning ratio (LR) to understand the effects of demographic variables and clinical diagnoses on learning slope (e.g., limited improvement over multiple trials), and to develop demographically sensitive norms. METHOD A systematic literature search was conducted to evaluate the potential for these aims to be examined across the most popular contemporary multi-trial learning tests. Two databases were searched. Following this, hierarchical linear modeling was used to examine how demographic variables predict learning slope indices. These results were in turn used to contrast the performance of clinical groups with the predicted performance of demographically similar healthy controls. Finally, preliminary normative estimates for learning slope indices were presented. RESULTS A total of 82 studies met criteria for inclusion in this study. However, the Rey Auditory Verbal Learning Test (RAVLT) was the only test to have sufficient trial-level learning and demographic data. Fifty-eight samples from 19 studies were quantitatively examined. Hierarchical linear models provided evidence of sex differences and a curvilinear decline in learning slope with age, with strongest and most consistent effects for LR relative to RLS. Regression-based norms for demographically corrected RLS and LR scores for the RAVLT are presented. The effect of clinical diagnoses was consistently stronger for LR, and Alzheimer's disease had the strongest effect, followed by invalid performances, severe traumatic brain injury, and seizures/epilepsy. CONCLUSION Overall, LR enjoys both conceptual and demonstrated psychometric advantages over RLS. Replication of these findings can be completed by reanalyzing existing datasets. Further work may focus on the utility of using LR in diagnosis and prediction of clinical prognosis.
Collapse
Affiliation(s)
- Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USA
| | - Trevor F Williams
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Victoria M Kordovski
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Sarah D Patrick
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - Ketrin Lengu
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Physical Medicine and Rehabilitation, The MetroHealth System, Cleveland, OH, USA
| | - Brian D Gradwohl
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Trinity Health Hauenstein Neurosciences, Trinity Health, Muskegon, MI, USA
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| |
Collapse
|
10
|
Hammers DB, Pentchev JV, Kim HJ, Spencer RJ, Apostolova LG. The relationship between learning slopes and Alzheimer's Disease biomarkers in cognitively unimpaired participants with and without subjective memory concerns. J Clin Exp Neuropsychol 2023; 45:727-743. [PMID: 37676258 PMCID: PMC10916703 DOI: 10.1080/13803395.2023.2254444] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVE Learning slopes represent serial acquisition of information during list-learning tasks. Although several calculations for learning slopes exist, the Learning Ratio (LR) has recently demonstrated the highest sensitivity toward changes in cognition and Alzheimer's disease (AD) biomarkers. However, investigation of learning slopes in cognitively unimpaired individuals with subjective memory concerns (SMC) has been limited. The current study examines the association of learning slopes to SMC, and the role of SMC in the relationship between learning slopes and AD biomarkers in cognitively unimpaired individuals. METHOD Data from 950 cognitively unimpaired participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 89) were used to calculate learning slope metrics. Learning slopes among those with and without SMC were compared with demographic correction, and the relationships of learning slopes with AD biomarkers of bilateral hippocampal volume and β-amyloid pathology were determined. RESULTS Learning slopes were consistently predictive of hippocampal atrophy and β-amyloid deposition. Results were heightened for LR relative to the other learning slopes. Additionally, interaction analyses revealed different associations between learning slopes and hippocampal volume as a function of SMC status. CONCLUSIONS Learning slopes appear to be sensitive to SMC and AD biomarkers, with SMC status influencing the relationship in cognitively unimpaired participants. These findings advance our knowledge of SMC, and suggest that LR - in particular - can be an important tool for the detection of AD pathology in both SMC and in AD clinical trials.
Collapse
Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Julian V. Pentchev
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA, 48105
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA, 48105
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA, 46202
| | | |
Collapse
|
11
|
Hall MG, Wollman SC, Haines ME, Boyle MA, Richardson HK, Hammers DB. Novel learning ratio from the NAB list learning test distinguishes between clinical groups: clinical validation and sex-related differences. J Clin Exp Neuropsychol 2023; 45:715-726. [PMID: 37477412 DOI: 10.1080/13803395.2023.2236772] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/09/2023] [Indexed: 07/22/2023]
Abstract
List-learning tasks provide a wealth of information about an individual's cognitive abilities: attention, encoding, storage, retrieval, recognition. A more recently developed metric, the Learning Ratio (LR), supplements information about cognitive ability and can assist the clinician in determining whether an individual has cognitive impairment. The LR is calculated by taking the difference between the individuals' raw score on the first learning trial and their raw score on the last learning trial, which is then divided by the number of words left to be learned after the first learning trial. A LR derived from the list-learning task from the Neuropsychological Assessment Battery (NAB) was evaluated to determine ability to distinguish those with normal cognition from those with mild cognitive impairment (MCI) and dementia. Results from the present study indicate the NAB LR is able to distinguish between clinical groups; recommended cutoffs for the NAB LR scores are provided. We also found a significant female sex-advantage for the NAB LR in those with normal memory ability and demonstrated the female sex advantage decreased with increasing memory impairment. Taken together, the NAB LR may assist clinicians in making an accurate and early diagnosis and may be helpful for tracking learning and functioning across multiple assessments. .
Collapse
Affiliation(s)
- Matthew G Hall
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | | | - Mary E Haines
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | - Mellisa A Boyle
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | | | - Dustin B Hammers
- Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| |
Collapse
|
12
|
Jiang L, Xu M, Xia S, Zhu J, Zhou Q, Xu L, Shi C, Wu D. Reliability and validity of the electronic version of the Hopkins verbal learning test-revised in middle-aged and elderly Chinese people. Front Aging Neurosci 2023; 15:1124731. [PMID: 37377673 PMCID: PMC10292015 DOI: 10.3389/fnagi.2023.1124731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Background The aging population is increasing, making it essential to have a standardized, convenient, and valid electronic memory test that can be accessed online for older people and caregivers. The electronic version of the Hopkins Verbal Learning Test-Revised (HVLT-R) as a test with these advantages and its reliability and validity has not yet been tested. Thus, this study examined the reliability and validity of the electronic version of the HVLT-R in middle-aged and elderly Chinese people to provide a scientific basis for its future dissemination and use. Methods We included 1,925 healthy participants aged over 40, among whom 38 were retested after 3-6 months. In addition, 65 participants completed both the pad and paper-and-pencil versions of the HVLT-R (PAP-HVLT-R). We also recruited 42 Alzheimer's disease (AD) patients, and 45 amnestic mild cognitive impairment (aMCI) patients. All participants completed the Pad-HVLT-R, the Hong Kong Brief Cognitive Test (HKBC), the Brief Visual Memory Test-Revised (BVMT-R), and the Logical Memory Test (LM). Results (1) Reliability: the Cronbach's α value was 0.94, the split-half reliability was 0.96. The test-retest correlation coefficients were moderate, ranging from 0.38 to 0.65 for direct variables and 0.16 to 0.52 for derived variables; (2) Concurrent validity: the Pad-HVLT-R showed a moderate correlation with the HKBC and BVMT-R, with correlation coefficients between total recall of 0.41 and 0.54, and between long-delayed recall of 0.42 and 0.59, respectively. It also showed a high correlation with the LM, with correlation coefficients of 0.72 for total recall and 0.62 for long-delayed recall; (3) Convergent validity: the Pad-HVLT-R was moderately correlated with the PAP version, with correlation coefficients ranging from 0.29 to 0.53 for direct variables and 0.15 to 0.43 for derived variables; (4) Discriminant capacity: the Pad-HVLT-R was effective in differentiating AD patients, as demonstrated by the ROC analysis with AUC values of 0.834 and 0.934 for total recall and long-delayed recall, respectively. Conclusion (1) The electronic version of HVLT-R has good reliability and validity in middle-aged and elderly Chinese people; (2) The electronic version of HVLT-R can be used as an effective tool to distinguish AD patients from healthy people.
Collapse
Affiliation(s)
- Lichen Jiang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ming Xu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shunyao Xia
- Institute of Mental Health, Peking University Sixth Hospital, Peking University, Beijing, China
- NHC Key Laboratory for Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing, China
| | - Jiahui Zhu
- Institute of Mental Health, Peking University Sixth Hospital, Peking University, Beijing, China
- NHC Key Laboratory for Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing, China
| | - Qi Zhou
- Institute of Mental Health, Peking University Sixth Hospital, Peking University, Beijing, China
- NHC Key Laboratory for Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing, China
| | - Luoyi Xu
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chuan Shi
- Institute of Mental Health, Peking University Sixth Hospital, Peking University, Beijing, China
- NHC Key Laboratory for Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing, China
| | - Daxing Wu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Medical Psychological Institute, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, Changsha, Hunan, China
| |
Collapse
|
13
|
Hammers DB, Duff K, Spencer RJ. Demographically-corrected normative data for the RBANS learning ratio in a sample of older adults. Clin Neuropsychol 2022; 36:2221-2236. [PMID: 34313182 PMCID: PMC8792095 DOI: 10.1080/13854046.2021.1952308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/01/2021] [Indexed: 01/27/2023]
Abstract
BACKGROUND A novel learning slope score - the Learning Ratio (LR) - has recently been developed that appears to be sensitive to memory performance and AD pathology more optimally than traditional learning slope calculations. While promising, this research to date has been both experimental and based on group differences, and therefore does not aid in the interpretation of individual LR performance for either clinical or research settings. The objective of the current study was to develop demographically-corrected normative data on these LR learning slopes on verbal learning measures from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHOD The current study examined the influence of age and education on LR metrics for the List Learning, Story Memory, and an Aggregated RBANS score in 200 cognitively intact adults aged 65 or older using linear regression. RESULTS Age and education correlated with most LR metrics, but no sex differences were observed. Linear regression permitted the prediction of LR values from age and education, which are then compared to observed LR values. The result is demographically-corrected T scores for these LR metrics. CONCLUSIONS By comparing observed and predicted LR scores calculated from regression-based prediction equations, this represents the first step towards interpretation of individual performances on this metric for clinical decision making and treatment planning purposes. With future replication in diverse and heterogenous samples, we hope to offer a new clinical tool for the examination of learning slopes in older adults.
Collapse
Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
| |
Collapse
|
14
|
Hammers DB, Spencer RJ, Apostolova LG. Validation of and Demographically Adjusted Normative Data for the Learning Ratio Derived from the RAVLT in Robustly Intact Older Adults. Arch Clin Neuropsychol 2022; 37:981-993. [PMID: 35175287 PMCID: PMC9618160 DOI: 10.1093/arclin/acac002] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The learning ratio (LR) is a novel learning slope score that was developed to identify learning more accurately by considering the proportion of information learned after the first trial of a multi-trial learning task. Specifically, LR is the number of items learned after trial one divided by the number of items yet to be learned. Although research on LR has been promising, convergent validation, clinical characterization, and demographic norming of this LR metric are warranted to understand its clinical utility when derived from the Rey Auditory Verbal Learning Test (RAVLT). METHOD Data from 674 robustly cognitively intact older participants from the Alzheimer's Disease Neuroimaging Initiative (aged 54- 89) were used to calculate the LR metric. Comparison of LR's relationship with standard memory measures was undertaken relative to other traditional learning slope metrics. In addition, retest reliability at 6, 12, and 24 months was examined, and demographically adjusted normative comparisons were developed. RESULTS Lower LR scores were associated with poorer performances on memory measures, and LR scores outperformed traditional learning slope calculations across all analyses. Retest reliability exceeded acceptability thresholds across time, and demographically adjusted normative equations suggested better performance for cognitively intact participants than those with mild cognitive impairment. CONCLUSIONS These results suggest that this LR score possesses sound retest reliability and can better reflect learning capacity than traditional learning slope calculations. With the added development and validation of regression-based normative comparisons, these findings support the use of the RAVLT LR as a clinical tool to inform clinical decision-making and treatment.
Collapse
Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Department of Psychiatry, Michigan Medicine, Neuropsychology Section, Ann Arbor MI, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | |
Collapse
|
15
|
Hammers DB, Duff K, Spencer RJ. Demographically-corrected normative data for the HVLT-R, BVMT-R, and Aggregated Learning Ratio values in a sample of older adults. J Clin Exp Neuropsychol 2021; 43:290-300. [PMID: 33899697 PMCID: PMC8259561 DOI: 10.1080/13803395.2021.1917523] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/10/2021] [Indexed: 10/21/2022]
Abstract
Background: The Learning Ratio (LR) is a novel learning slope score that has been developed to reduce the inherent competition between the first trial and subsequent trials in traditional learning slopes. Recent findings suggest that LR is sensitive to AD pathology along the AD continuum - more so than the traditional learning calculations that employ raw changes across trials. However, research is still experimental and not yet directly applicable to clinical settings. Consequently, the objective of the current study was to develop demographically-corrected normative data on these LR learning slopes.Method: The current study examined the influence of age and education on LR scores for the HVLT-R, BVMT-R, and an Aggregated HVLT-R/BVMT-R in 200 cognitively intact adults aged 65 years and older using linear regression.Results: Age negatively correlated with all LR metrics, and education positively correlated with most. No sex differences were identified. LR values were predicted from age and education, which can be compared to observed LR values and converted into demographically-corrected T scores.Conclusions: By comparing observed and predicted LR scores calculated from regression-based prediction equations, interpretations are permitted that aid in clinical decision making and treatment planning. Co-norming of the HVLT-R and BVMT-R also allows for comparisons between verbal and visual learning slope scores in individual patients. We hope normative data for LR enhances its utility as a clinical tool for examining learning slopes in older adults administered the HVLT-R and/or BVMT-R.
Collapse
Affiliation(s)
- Dustin B. Hammers
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
| |
Collapse
|