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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] [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.
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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
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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.
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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
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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.
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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
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Wright LM, De Marco M, Venneri A. A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks. Front Aging Neurosci 2021; 13:676618. [PMID: 34322008 PMCID: PMC8311855 DOI: 10.3389/fnagi.2021.676618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 06/04/2021] [Indexed: 01/12/2023] Open
Abstract
In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18-39 (n = 75), 40-64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer's type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer's dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer's disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.
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Affiliation(s)
- Laura M Wright
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Matteo De Marco
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.,Department of Life Sciences, Brunel University London, London, United Kingdom
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