Correlation Between Cognitive Impairment and the Rey Auditory-Verbal Learning Test in a Population With Alzheimer Disease

Barzotti, T.; Gargiulo, Aldo; Marotta, Marco; Tedeschi, Gioacchino; Zannino, Gian Daniele; Guglielmi, S; Dell’Armi, A.; Ettorre, Evaristo; Marigliano, Vincenzo

doi:10.1016/j.archger.2004.04.010

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…Nonetheless, areas detected for RAVLT, although showing a global cortical effect, have also shown partial overlap with areas affected from the treatment as well as with areas affected from diabetes duration. Verbal learning tests have been considered a useful tool for the early diagnosis of cognitive decline and Alzheimer disease ( 39 , 40 ). As such, these overlapping regions might be particularly important for investigating the relationship between diabetes, its treatment, and dementia.…”

Section: Discussionmentioning

confidence: 99%

Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose

et al. 2014

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OBJECTIVEUnderstanding the effect of diabetes as well as of alternative treatment strategies on cerebral structure is critical for the development of targeted interventions against accelerated neurodegeneration in type 2 diabetes. We investigated whether diabetes characteristics were associated with spatially specific patterns of brain changes and whether those patterns were affected by intensive versus standard glycemic treatment.RESEARCH DESIGN AND METHODSUsing baseline MRIs of 488 participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study, we applied a new voxel-based analysis methodology to identify spatially specific patterns of gray matter and white matter volume loss related to diabetes duration and HbA1c. The longitudinal analysis used 40-month follow-up data to evaluate differences in progression of volume loss between intensive and standard glycemic treatment arms.RESULTSParticipants with longer diabetes duration had significantly lower gray matter volumes, primarily in certain regions in the frontal and temporal lobes. The longitudinal analysis of treatment effects revealed a heterogeneous pattern of decelerated loss of gray matter volume associated with intensive glycemic treatment. Intensive treatment decelerated volume loss, particularly in regions adjacent to those cross-sectionally associated with diabetes duration. No significant relationship between low versus high baseline HbA1c levels and brain changes was found. Finally, regions in which cognitive change was associated with longitudinal volume loss had only small overlap with regions related to diabetes duration and to treatment effects.CONCLUSIONSApplying advanced quantitative image pattern analysis methods on longitudinal MRI data of a large sample of patients with type 2 diabetes, we demonstrate that there are spatially specific patterns of brain changes that vary by diabetes characteristics and that the progression of gray matter volume loss is slowed by intensive glycemic treatment, particularly in regions adjacent to areas affected by diabetes.

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Section: Discussionmentioning

confidence: 99%

Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose

et al. 2014

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“…Last, the participant is asked to recognize the words in List A when presented a sheet containing the 15 List A words plus 15 distractor words, and examiner records the number of successes (recognition score). In the current study, the 30-mintute delayed recall score, which ranges from 0 to 15 [24], is the outcome of interest.…”

Section: Methodsmentioning

confidence: 99%

Evaluating trajectories of episodic memory in normal cognition and mild cognitive impairment: Results from ADNI

et al. 2019

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Background Memory assessment is a key factor for the diagnosis of cognitive impairment. However, memory performance over time may be quite heterogeneous within diagnostic groups. Method To identify latent trajectories in memory performance and their associated risk factors, we analyzed data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who were classified either as cognitively normal or as Mild Cognitive Impairment (MCI) at baseline and were administered the Rey Auditory Verbal Learning test (RAVLT) for up to 9 years. Group-based trajectory modeling on the 30-minute RAVLT delayed recall score was applied separately to the two baseline diagnostic groups. Results There were 219 normal subjects with mean age 75.9 (range from 59.9 to 89.6) and 52.5% male participants, and 372 MCI subjects with mean age 74.8 (range from 55.1 to 89.3) and 63.7% male participants included in the analysis. For normal subjects, six trajectories were identified. Trajectories were classified into three types, determined by the shape, each of which may comprise more than one trajectory: stable (~30% of subjects), curvilinear decline (~ 28%), and linear decline (~ 42%). Notably, none of the normal subjects assigned to the stable stratum progressed to dementia during the study period. In contrast, all trajectories identified for the MCI group tended to decline, although some participants were later re-diagnosed with normal cognition. Age, sex, and education were significantly associated with trajectory membership for both diagnostic groups, while APOE ɛ4 was only significantly associated with trajectories among MCI participants. Conclusion Memory trajectory is a strong indicator of dementia risk. If likely trajectory of memory performance can be identified early, such work may allow clinicians to monitor or predict progression of individual patient cognition. This work also shows the importance of longitudinal cognitive testing and monitoring.

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“…The ADNI neuropsychological assessment procedures have been previously described3940. We used a modified Alzheimer’s Disease Assessment Scale - cognitive subscale (ADAS-cog)4142, the most widely used standard cognitive measure in the AD population, and included the Digit Span Test, BNT43, Rey Auditory Verbal Learning Test (RAVLT)44, Clock Drawing Test45, Trail Making Test (TMT)4647, Digit Symbol Substitution Test48 and Category Fluency Test40. Composite scores including ADNI-EF and ADNI-Mem were also used4950.…”

Section: Methodsmentioning

confidence: 99%

Robust Identification of Alzheimer’s Disease subtypes based on cortical atrophy patterns

Park

Kim

et al. 2017

Sci Rep

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Accumulating evidence suggests that Alzheimer’s disease (AD) is heterogenous and can be classified into several subtypes. Here, we propose a robust subtyping method for AD based on cortical atrophy patterns and graph theory. We calculated similarities between subjects in their atrophy patterns throughout the whole brain, and clustered subjects with similar atrophy patterns using the Louvain method for modular organization extraction. We applied our method to AD patients recruited at Samsung Medical Center and externally validated our method by using the AD Neuroimaging Initiative (ADNI) dataset. Our method categorized very mild AD into three clinically distinct subtypes with high reproducibility (>90%); the parietal-predominant (P), medial temporal-predominant (MT), and diffuse (D) atrophy subtype. The P subtype showed the worst clinical presentation throughout the cognitive domains, while the MT and D subtypes exhibited relatively mild presentation. The MT subtype revealed more impaired language and executive function compared to the D subtype.

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Correlation Between Cognitive Impairment and the Rey Auditory-Verbal Learning Test in a Population With Alzheimer Disease

Cited by 13 publications

References 14 publications

Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose

Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose

Evaluating trajectories of episodic memory in normal cognition and mild cognitive impairment: Results from ADNI

Robust Identification of Alzheimer’s Disease subtypes based on cortical atrophy patterns

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