Literature DB >> 34630918

Age-related episodic memory decline and the role of amyloid-β: a systematic review.

Jandirlly Julianna Souto1,2, Gabriella Medeiros Silva1,2, Natalia Leandro Almeida1,2, Irina Ivanovna Shoshina3, Natanael Antonio Santos1,2, Thiago Paiva Fernandes1,2.   

Abstract

Aging has been associated with the functional decline of episodic memory (EM). Unanswered questions are whether the decline of EM occurs even during healthy aging and whether this decline is related to amyloid-β (Aβ) deposition in the hippocampus.
OBJECTIVE: The main purpose of this study was to investigate data on the relationship between the age-related EM decline and Aβ deposition.
METHODS: We searched the Cochrane, MEDLINE, Scopus, and Web of Science databases and reference lists of retrieved articles that were published in the past 10 years. The initial literature search identified 517 studies. After screening the title, abstract, key words, and reference lists, 56 studies met the inclusion criteria.
RESULTS: The overall results revealed that increases in Aβ are related to lower hippocampal volume and worse performance on EM tests. The results of this systematic review revealed that high levels of Aβ may be related to EM deficits and the progression to Alzheimer's disease.
CONCLUSIONS: We discussed the strengths and pitfalls of various tests and techniques used for investigating EM and Aβ deposition, methodological issues, and potential directions for future research.

Entities:  

Keywords:  aging; beta amyloid; episodic; healthy aging; memory; systematic review

Year:  2021        PMID: 34630918      PMCID: PMC8485646          DOI: 10.1590/1980-57642021dn15-030002

Source DB:  PubMed          Journal:  Dement Neuropsychol        ISSN: 1980-5764


INTRODUCTION

Aging has been associated with a functional decline in episodic memory (EM). An assessment of the memory function in healthy aging can represent a meaningful alternative since Alzheimer’s disease (AD) tends to be diagnosed at more advanced stages, especially when memory impairments appear. Approximately, 30% of elderly individuals with normal aging, above 60 years old, can have high levels of amyloid-β (Aβ) deposition in the hippocampus.[1] Hence, they tend to be more likely to develop AD.[2] Age-related memory decline, especially in those related to EM, is common in aging, and Aβ deposition has been associated with a progressive conversion to AD.[3] EM can be understood as the integration of the “what,” “when,” and “where” (or “which”) components, which is a substantial factor in the social life of an individual.[4] However, it is unclear whether hippocampal volume atrophy and age-related EM decline in healthy aging are entirely related to the deposition of Aβ.[5] One of the reasons is because there are links between the neocortex and the hippocampus. In this way, it is essential to understand the relationship of the hippocampus with Aβ deposition to further unravel the other areas related to EM.[3] The hypothetical model of biomarkers in AD indicated that the presence of Aβ is related to the functioning of the vascular systems and growth.[6,7] This model describes the integration of AD biomarkers, which may reflect an underlying pathophysiological sequence of the following events: (1) the presence of amyloid-β42 in the cerebrospinal fluid (CSF) is first detected (the same is true for tau blood biomarkers);[8] (2) levels of tau protein in CSF are significantly increased; (3) hypometabolism of fluorodeoxyglucose occurs; (4) brain atrophy occurs; and (5) cognitive decline is noted. The model of biomarkers in AD suggests that the relationship between Aβ and cognitive impairment is not immediately successive, and therefore it should be less evident than the relationship between neurodegenerative biomarkers and cognitive impairment.[8] Notwithstanding, the deposition of Aβ before the clinical diagnosis of AD remains a challenge, since less, if any, relationship between a decline in cognition in healthy aging and the deposition of Aβ in the hippocampus can be observed. In this manner, it is comprehensible that cortical thickness exerts a substantial influence on Aβ deposition and the consequent decline in EM.[9] Age-related dementia typically begins slowly and gradually with brain atrophy before the onset of clinical symptoms.[7,8,10] Some authors have suggested that the cognitive decline in aging is related to undetected diseases and, hence, is not a characteristic of normal aging.[11] An important implication of detectable brain atrophy and dementia is that some cases with undetected diseases can help create inferences about normal brain aging.[12] This is of particular concern, since the proportion of elderly people with undetected neurodegenerative disease is expected to increase, potentially leading to baseless conclusions of accelerated age-related decline in the cortical areas vulnerable to disease pathology, especially in the entorhinal cortex and the hippocampus.[13] Therefore, the contribution of latent pathology to age-related decline in healthy aging remains an open question. However, differences in AD biomarkers, such as brain atrophy and Aβ deposition, may clarify this issue. This review is based on the assumption that the presence of Aβ in people with healthy aging can serve as a prodromal state of AD. Thus, brain atrophy and Aβ deposition are biomarkers of the disorder. Here, our main purpose was to investigate the data on the relationship between EM and Aβ deposition in healthy aging.

METHODS

Search strategy

The PRISMA guidelines were used.[14,15] Exhaustive electronic searches were conducted on the studies that were published from 2010 to 2021 in the following databases: COCHRANE, Medline, Scopus, and Web of Science (PROSPERO: CRD42020190981). The following search strategy was used: Aging OR Senescence OR Aged OR Elderly OR “Healthy Aging” OR “Aging Well” OR “Healthy Ageing” AND “Amyloid beta-Peptides” OR “Amyloid beta Peptides” OR “Alzheimer beta-Protein” OR “Alzheimer’s ABP” OR “beta-Amyloid Protein” OR “Amyloid beta Protein” OR “Amyloid Protein A4” OR “beta Amyloid” OR “Amyloid AD-AP” OR “Amyloid” OR “Amyloid Substance” OR “Amyloid Fibril” OR β-amyloid OR amyloid-β OR “amyloid β-peptide” OR “amyloid-beta” AND “Histology” OR “Histocytochemistry” OR “Immunohistochemistry” OR “Immunolabeling Technique” OR “Immunolabeling Technic” OR “Immunogold Technique” OR “Immunohistocytochemistry” OR Positron-Emission Tomography” OR “Positron Emission Tomography” OR “Positron Emission Tomography Imaging” OR “PET Scan” OR “PET Imaging” AND “Episodic Memory” OR “Autobiographical Memory” OR “Prospective Memory.” The key words were chosen even in the absence of a specific term (according to the MeSH) to prioritize sensitivity over the specified theme. In addition, we examined the reference lists in the retrieved studies.

Selection criteria

We included studies that investigated Aβ deposition in healthy aging and its relationship with decline in EM.[16,17,18,19,20] We adopted the following inclusion criteria: (a) we investigated Aβ in the hippocampus in individuals with healthy aging and (b) we used the tasks to assess EM. We excluded studies that (a) used animal models, (b) did not assess the hippocampus, (c) evaluated another type of memory instead of episodic, and (d) were literature reviews.

Data extraction

For each study, data were extracted independently by two authors (GS and JS) using a structured form. The discrepancies were resolved by consulting a third author (NA) if needed. If there was insufficient information in the studies, the respective author was contacted. The following variables were extracted: (1) demographic and clinical characteristics (e.g., number of patients); (2) study design; (3) characteristics of the techniques; (4) task for assessing EM; and (5) main findings.

Quality assessment

We performed individual and comprehensive quality assessments for each study. The studies were also evaluated based on internal validity (i.e., selection bias or attrition bias) and construct validity (i.e., adequacy of the operational criteria used). In general, the quality and evidence of the studies were assessed based on three main measures, namely, (a) limitations (e.g., poorly designed strategies), (b) consistency of the results, and (c) accuracy (i.e., ability to generalize findings and provide sufficient data). A quality assessment was conducted using the PEDro scale and the Appraisal Tool for Cross-Sectional Studies (AXIS).

RESULTS

The initial search of the databases identified 517 studies. After screening the title, abstracts, key words, and article references, a total of 56 studies were in compliance with the inclusion criteria. Figure 1 presents the diagram flow and the details used to identify studies in our review.
Figure 1.

Flowchart of the present study.

General characteristics of the studies

Table 1 presents the selected studies, which were published between 2010 and 2021. Thirty studies (53.6%) were published between 2010 and 2016, and the other 26 studies (46.4%) were published between 2017 and 2021. Regarding the sample size, the studies showed a variance between 45 and 2,908 participants, the majority (76.8%) being more than 100 participants.
Table 1.

Characteristics of the selected studies.

AuthorsSampleAβ techniqueOthers techniquesRegions of interestEM taskMain findingsPEDroAXIS
Chételat et al.[58] 93PiB-PETN/AGray matter, white matter, and CSFCVLT-II And RCFTEM involvement is related to Aβ deposition, especially in the temporal neocortex, and regardless of hippocampal atrophy415
Lim et al.[59] 141PiB-PETBlood examCerebellumCVLT-II and PALHigh Aβ showed significantly greater decline in verbal and visual EM at 18 months515
Marchant et al.[60] 54PiB-PETMRI and FLAIRCSF, WMH, frontal, parietal, and temporal cortex, posterior cingulate, and precuneusCVLT and MASAβ did not explain changes in EM516
Perrotin et al.[61] 48PiB-PETMRIMedial PFC/ACC, precuneus/PCC/ICC, and temporal lobeCVLT-II and WMS-RHigh Aβ performed worse than low Aβ just on the EM measure test CVLT515
Rodrigueet al.[62] 137F-florbetapir PETBlood examHVLT and CANTABAβ subgroup has no significant association with EM516
Adamczuk et al.[63] 64F-flutemetamol PETMRIFrontal, parietal, anterior cingulate, precuneus/posterior cingulate, and lateral temporalBNT, AVF, and RPMAβ and EM were negatively correlated only in the BDNF group of met +ve/APOE ε 4 +ve514
Doré et al.[64] 133PiB-PETMRIGray matter, white matter, CSF, and cerebellumCVLT-II and LMT-IINo significant differences in EM between the Aβ- and Aβ+ groups. There was a significant reduction in the precuneus and hippocampus513
Ellis et al. [65] 178PiB-PETBlood examFrontal, superior parietal, lateral temporal, lateral occipital, and anterior and posterior cingulateCVLT-II, LMR, and RCFTHigh Aβ showed greater decline in EM514
Hedden et al.[66] 168PiB-PETMRI and FLAIRFrontal, lateral, parietal, temporal, and retrosplenial corticesFNAME, STSRT, and MCTAβ burden and WMH had distinct cognitive profiles. Aβ was associated with a decline in EM514
Lim et al.[67] 234PiB-PETBlood examCerebellar cortex, frontal, superior parietal, lateral temporal, lateral occipital, and anterior and posterior cingulateCVLT-II and RCFTNo differences were observed between HC high and low Aβ groups415
Sperling et al.[68] 78Florbetapir F 18 PETMRIGrey matter, frontal, temporal, parietal cortices, anterior cingulate, and posterior cingulateWLM-I, WLM-D, and DSSThe highest SUVr correlated with the lowest immediate memory816
Wirth et al.[69] 38PiB-PETFDG-PET and MRIFrontal, temporal, parietal, and anterior/posterior cingulateLMR and VRTPiB positivity was associated with nonmemory decline314
Lim et al.[70] 225PiB-PETBlood examCerebellar cortexCVLT-IIHigh Aβ HC and MCI groups showed moderate decline in EM716
Lim et al.[71] 413PiB-PETBlood examCerebellar cortexLMR, CVLT-II, RCFT, and COCLTAβ+ showed greater decline on the verbal EM and visual EM716
Ossenkoppele et al. [72] 81PiB-PETFDG-PET and MRIGray matter, posterior cingulate cortex, bilateral angular gyri, and bilateral inferior temporal gyriCVLT and VRTAβ was associated with higher metabolic activity and lower visual episodic memory scores515
Villeneuve et al.[9] 67PiB-PETMRIRight frontal, parietal, temporal, occipital, and precuneusMASHigh Aβ was associated with cortical thinning and lower performance of EM516
Amariglio et al.[73] 257PiB-PETFDG-PET and MRIHippocampal volumeFNAME, STSRT, and MCTAβ and ND biomarkers predict biggest changes in EM816
Aschenbrenner et al.[74] 238PiB-PETCerebrospinal fluid assessmentOrbitofrontal, parietal, frontal, frontal, and temporalSRTEM was not correlated with levels of Aβ516
Lim et al. [75] 227PiB-PETMRIGray matter, white matter, cerebrospinal fluid, and hippocampusOCLAβ+ CN and MCI groups showed decline on the EM measures615
Lim et al. [76] 333PiB-PETBlood examCerebellar cortexLMR, CVLT-II, RCFT, and COCLTAβ+ ɛ4+ individuals showed cognitive decline across all domains. Aβ+ ɛ4− individuals showed faster decline only in verbal EM817
Mander et al.[77] 26PiB-PETfMRI and EEGHippocampus and mPFCTWPTAβ impairs sleep and has indirect implications for EM decline818
Mattsson et al.[52] 743Florbetapir PETFDG-PET and MRIGray matterLMR and AVLTAβ+ was associated with lower EM scores416
Pietrzak et al.[78] 333PiB-PETCVLT and LMRHigh Aβ was associated with a subtle decrease in EM816
Racine et al.[79] 175ElectrochemiluminescenceMRI and FLAIRRAVLT and WMS-RLMHigh levels of Aβ and higher rates of decline in EM tests418
Wang et al.[80] 188CSF collectionMRIEntorhinal cortex, fusiform gyrus, inferior, middle, and superior temporal gyri, superior and inferior parietal lobules, posterior cingulate gyrus, and precuneusN/AReduced CSF Aβ42 was related to poorer performance on EM316
Wang et al.[81] 263PiB-PETBlood examLMIRD and WLIRDFor asymptomatic carriers, Aβ burden was predictive of longitudinal decline in EM815
Bischof et al.[82] 147Florbetapir PETHVLT and CANTABHigher Aβ may have an influence on EM, especially between 30 and 55 years of age716
Lim et al.[83] 423PiB-PETBlood examCerebellar cortexCVLT, LMR, and RCFTSignificantly increased decline in EM in Aβ+ APOE ε4 carriers717
Mielke et al.[84] 465PiB-PETAVLT, WMS-RLM, WMS-RVR, and CogStateHigh Aβ was not associated with changes in EM516
Song et al.[85] 82F18-AV-45-florbetapir PETMRI and fMRIHVLT and CANTABAβ had no effects or interaction in EM414
Ayton et al.[86] 117PiB-PETHippocampusCVLT and RCFTAβ pathology and higher levels of quantitative susceptibility of the hippocampus predicted accelerated deterioration in EM617
Boots et al.[87] 140PiB-PETRAVLTBDNF was associated with a decline in EM and was exacerbated by a greater Aβ load617
Farrell et al.[88] 174F-florbetapir PETAβ+ and the increase in baseline SUVR predicted an increasing decline in EM716
Lim et al.[89] 989PiB-PETBlood exam and MRIHippocampus volumeCVLT-II, LMR, and RCFTAPOEɛ4 homozygotes (ɛ4/ɛ4) showed significantly worse EM and higher Aβ levels than heterozygous ɛ4514
Lim et al.[90] 446PiB-PETBlood examHippocampus volumeLMR, CVLT-II, and RCFTAβ- Val and Aβ+ Val homozygotes showed a decline in EM417
Pietrzak et al.[91] 416PiB-PETMRIPlasmatic cortisolCVLT-II, LMR, and RCFTOlder Aβ+ adults experienced a faster decline in EM316
Vogel et al.[92] 136PiB-PETN/ADecline in EM was observed only when cognitive decline and Aβ were present717
van Bergen et al.[93] 116Flutemetamol PETMRIMMSE and VLMTLocal correlation between iron and β-amyloid is related to levels of cognitive performance518
Bilgel et al.[94] 171PiB-PETMRIHippocampusCVLT and BVRTAmyloidosis or hippocampal atrophy had longitudinal declines in EM verbal and learning317
Farrell et al.[95] 126Florbetapir PETMRIHippocampal and cortical volumeHVLIR and CANTABDecline in EM and increase in Aβ accumulation718
Jansen et al.[96] 2,908PiB-PETVWLTAβ positivity was associated with low memory617
Leal et al.[97] 71PiB-PETWMS - II and CVLTHigher levels of Aβ are associated with the decline of EM516
Lim et al.[98] 447PiB-PETMRIRAVLTWorsens in EM of Aβ+ than Aβ− and may exacerbate with age518
Mecca et al.[99] 45PiB-PETMRIGray matterN/AIn middle-aged individuals, Aβ load does not affect EM performance618
Ko et al.[100] 762PiB-PETRAVLT and ADAS-CogEM was a predictor for Aβ positivity617
Pothier et al.[101] 65F-florbetapir PETFCSRTSignificant difference for EM over time, with better performance in Aβ- compared with Aβ+.819
Rabin et al.[102] 253PiB-PETMRIFornixWMS-RLM, FCSRT, and STSRTElevated Aβ load has been associated with a faster decline in EM over time417
Rahayel et al.[103] 104PiB-PETMRIWMS - III and RAVLTHigher Aβ had worse episodic memory517
Yu et al.[104] 148SRMAβ is unrelated with decline in EM819
Dupont et al.[105] 104PiB-PETMRIWMHsLMT -IIDeposition Aβ predicts weaker EM performance818
Joannette et al.[106] 104PiB-PETMRIRAVLTEM performance is associated with Aβ load618
Lim et al.[107] 213PiB-PETBlood examCVLT and RAVLTAβ is related to the decline of EM716
Lindbergh et al.[23] 149Florbetapir PETGray matterRAVLT and RCFTWomen with high Aβ are more vulnerable to declining EM than men618
Squarzoni et al.[108] 108PiB-PETRAVLTAβ load and poorer memory performance were detected only in stages +616
Busatto Filho et al.[109] 124PiB-PETMRI and FDG-PETHippocampal subregionsAVLT and SCPTSubicular volumes were inversely correlated with the degree of Aβ deposition. Verbal EM scores were significantly lower in both (N)+ groups515
Han et al.[110] 154PiB-PETMRIVL and WSSENASAβ influence retention in EM change619

6-TSRT: 6-Trial Selective Reminding Test; Aβ: amyloid-β; AD: Alzheimer’s disease; ADAS-Cog: Alzheimer’s Disease Assessment Scale–Cognitive Subscale; APOE: apolipoprotein E; AVF: Animal Verbal Fluency Test; AVLT: Auditory Verbal Learning Test; BDNF: brain-derived neurotrophic factor; BNT: Boston Naming Test; BVRT: Benton Visual Retention Test; CAA: cerebral amyloid angiopathy; CANTAB: Cambridge Neuropsychological Test Automated Battery; CSF: cerebrospinal fluid; COBT: Cogstate One Back Task; COCLT: Cogstate One Card Learning task; CVLT-II: California Verbal Learning Test, second edition; DMST: Delayed Matching-to-Sample Task; DTI: diffusion tensor imaging; EEG: electroencephalogram; ELISA: enzyme-linked immunosorbent assay; EM: episodic memory; FCSRT: Free and Cued Selective Reminding Test; FDG: fluorodeoxyglucose; FLAIR: fluid attenuation inversion recovery; fMRI: functional magnetic resonance imaging; FNAME: Face–Name Associative Memory Exam; FNET: Face–Name Experimental Task; FSRT: Free and Selective Reminding Test; HVLIR: Hopkins Verbal Learning Immediate Recall; HVLT: Hopkins Verbal Leaning Task; PiB-PET: Pittsburgh compound B positron emission tomography; IA: immunosorbent assay; LMT-II: Logical Memory Test, second edition; LMR: Logical Memory Recall; LMIR: Logical Memory Immediate Recall and Delayed Recall; MAC-Q: Memory Complaint Questionnaire; MAS: Memory Assessment Scale; MBT: Memory Binding Test; MMSE: Mini Mental State Examination; MPRAGE: magnetization-prepared rapid gradient echo; MRI: magnetic resonance imaging; MCT: Memory Capacity Test; mPFC: medial prefrontal cortex; MR: magnetic resonance; MTL: medial temporal lobe; N/A: no data available; OCL: One Card Learning; PAL: paired-associate learning; P-tau: tau pathology; RAVLT: Rey Auditory Verbal Learning Test; RCFT: Rey Complex Figure Test; ROCF: Rey-Osterrieth complex figure; RPM: Raven’s Progressive Matrices; SCC: subjective cognitive concerns; SCPT: Short Cognitive Performance Test; SENAS: Spanish and English Neuropsychological Assessment Scale; SMD: subjective memory decline; SRM: selected reaction monitoring; SRT: Selective Reminding Test; STSRT: Six-Trial Selective Reminding Test; SUVR: neocortical standardized uptake value ratio; TMADA: TaqMan allelic discrimination assay; TWPT: the word-pairs task; VPATS: Visual Paired Associates Total Score; VL: vascular load; VRM: verbal recognition memory; VRT: Visual Reproduction test; VWLT: Verbal Word Learning Test; WAIS: Wechsler Adult Intelligence Scale; WLM-V: Wechsler Logical Memory–Delayed Recall; WM: working memory; WMS-RLM: Wechsler Memory Scale-Revised Logical Memory; WMS-RVR II: Wechsler Memory Scale-Revised Visual Reproductions II; WLIRD: Word List Immediate Recall and Delayed Recall; WLM-I: Wechsler Logical Memory I and II Story A Immediate; WMH: white matter hyperintensities.

6-TSRT: 6-Trial Selective Reminding Test; Aβ: amyloid-β; AD: Alzheimer’s disease; ADAS-Cog: Alzheimer’s Disease Assessment Scale–Cognitive Subscale; APOE: apolipoprotein E; AVF: Animal Verbal Fluency Test; AVLT: Auditory Verbal Learning Test; BDNF: brain-derived neurotrophic factor; BNT: Boston Naming Test; BVRT: Benton Visual Retention Test; CAA: cerebral amyloid angiopathy; CANTAB: Cambridge Neuropsychological Test Automated Battery; CSF: cerebrospinal fluid; COBT: Cogstate One Back Task; COCLT: Cogstate One Card Learning task; CVLT-II: California Verbal Learning Test, second edition; DMST: Delayed Matching-to-Sample Task; DTI: diffusion tensor imaging; EEG: electroencephalogram; ELISA: enzyme-linked immunosorbent assay; EM: episodic memory; FCSRT: Free and Cued Selective Reminding Test; FDG: fluorodeoxyglucose; FLAIR: fluid attenuation inversion recovery; fMRI: functional magnetic resonance imaging; FNAME: Face–Name Associative Memory Exam; FNET: Face–Name Experimental Task; FSRT: Free and Selective Reminding Test; HVLIR: Hopkins Verbal Learning Immediate Recall; HVLT: Hopkins Verbal Leaning Task; PiB-PET: Pittsburgh compound B positron emission tomography; IA: immunosorbent assay; LMT-II: Logical Memory Test, second edition; LMR: Logical Memory Recall; LMIR: Logical Memory Immediate Recall and Delayed Recall; MAC-Q: Memory Complaint Questionnaire; MAS: Memory Assessment Scale; MBT: Memory Binding Test; MMSE: Mini Mental State Examination; MPRAGE: magnetization-prepared rapid gradient echo; MRI: magnetic resonance imaging; MCT: Memory Capacity Test; mPFC: medial prefrontal cortex; MR: magnetic resonance; MTL: medial temporal lobe; N/A: no data available; OCL: One Card Learning; PAL: paired-associate learning; P-tau: tau pathology; RAVLT: Rey Auditory Verbal Learning Test; RCFT: Rey Complex Figure Test; ROCF: Rey-Osterrieth complex figure; RPM: Raven’s Progressive Matrices; SCC: subjective cognitive concerns; SCPT: Short Cognitive Performance Test; SENAS: Spanish and English Neuropsychological Assessment Scale; SMD: subjective memory decline; SRM: selected reaction monitoring; SRT: Selective Reminding Test; STSRT: Six-Trial Selective Reminding Test; SUVR: neocortical standardized uptake value ratio; TMADA: TaqMan allelic discrimination assay; TWPT: the word-pairs task; VPATS: Visual Paired Associates Total Score; VL: vascular load; VRM: verbal recognition memory; VRT: Visual Reproduction test; VWLT: Verbal Word Learning Test; WAIS: Wechsler Adult Intelligence Scale; WLM-V: Wechsler Logical Memory–Delayed Recall; WM: working memory; WMS-RLM: Wechsler Memory Scale-Revised Logical Memory; WMS-RVR II: Wechsler Memory Scale-Revised Visual Reproductions II; WLIRD: Word List Immediate Recall and Delayed Recall; WLM-I: Wechsler Logical Memory I and II Story A Immediate; WMH: white matter hyperintensities. A longitudinal study design was used in almost all of the selected studies (71.4%), whereas eight studies (28.6%) employed a cross-sectional design. Follow-up in these studies ranged between 6 months and 23 years. Diagnostic criteria also varied between studies, that is, 24 studies used the Mini-Mental State Examination (85.7%), 16 studies used the Clinical Dementia Rating (57.1%), 12 studies used the Geriatric Depression Scale (42.8%), and 5 studies used the Hospital Anxiety and Depression Scale (17.8%).

Techniques for investigating Aβ

We observed four types of techniques for investigating Aβ. The main technique used was positron emission tomography (PET), performed in 53 studies (94.6%). Three types of radioligands were used for the PET technique. Pittsburgh compound B was used in most of the studies (75%), while florbetapir F 18 and flutemetamol F 18 were used by a limited number of studies, i.e., nine (16%) and two (3.6%) studies, respectively. In addition, electrochemiluminescence techniques (in one study, 1.8%), CSF collection (in one study, 1.8%), and selected reaction monitoring (in one study, 1.8%) were also used.

Tasks used for the assessment of episodic memory

Most studies (67.9%) utilized between two and four different neuropsychological tests to assess EM. Approximately, 25 of these studies (44.6%) concentrated their assessments based on two types of tests. Predominantly, most of them used neuropsychological measures, such as California Verbal Learning Test (CVLT; 35.7%), Rey Complex Figure Test (RCFT; 19.6%), Logical Memory Recall (17.8%), Rey Auditory Verbal Learning Test (16%), and the Wechsler Memory Scale-Revised (12.5%). Five studies did not specify the tests used in the neuropsychological assessment.

Main findings

The main findings are presented in Table 1. The scope of the selected studies included heterogeneous objectives.[21,22] Nevertheless, all the studies evaluated common aspects of Aβ deposition in cortical structures, especially in the hippocampus, and its relationship with EM. Hence, the results have specificities. In general, most studies (83.9%) indicated that the increase in Aβ was related to worse performance in EM tests. One of these studies even indicated that Aβ deposition in women makes them more vulnerable to declines in EM.[23] In contrast, some studies (16.1%) indicated that the increase in Aβ did not correspond to differences in EM. Based on the PEDro scale, which comprised 11 items, 56 studies obtained an overall average of 5.7 points, approaching the score considered by the moderate- to high-quality instrument (≥6.0 points — moderate to high quality). Overall, 53.6% were considered of moderate quality (4.0–5.0 points) and the other 46.4% were of moderate to high quality (6.0–9.0 points). Based on the AXIS, the overall average was 16.2 points on a scale ranging from 0 to 20 points. Overall, 28.6% (16 studies) scored from 0 to 15 points and the other 71.4% (40 studies) reached ≥15.0 points, showing a lower risk of bias and higher quality of the studies. Both the assessment tools showed good results with regard to the quality of the selected studies. The discrepancies between the evaluations within these two scales may be related to the objective for which each one was designed. While AXIS provides an evaluation of more general items,[24] PEDro provides an assessment for more specific aspects in clinical trials,[25] so the lowest PEDro score, especially in items related to blinding, can signal possible biases, like exaggerating or reporting fewer symptoms. These biases can induce different rates of, for example, co-intervention, friction, and placebo effect.[26,27] Furthermore, our results suggest that future studies should exercise caution in the definition and methodological execution, so that the studies are in accordance with the guidelines of the designs to which they are proposed.

DISCUSSION

There are significant increases in research aimed at identifying diagnostic and prognostic biomarkers of AD. The updated AD research structure proposed by the National Institute on Aging and Alzheimer’s Association working group defines AD as a biological construct, and the research focused on the diagnosis of AD in those who are alive using biomarkers that cover the presymptomatic and symptomatic stages of the disease. Biomarkers are grouped into those of Aβ deposition, pathological tau, and neurodegeneration. Although it is possible that Aβ plaques and neurofibrillary tau deposits are not the cause of AD pathogenesis, these abnormal protein deposits define AD as a unique neurodegenerative disease among various diseases that can lead to dementia. The preclinical imaging methods used by diagnostic of amyloid accumulation and neurodegeneration (i.e., imaging PET) and biofluids (CSF and blood plasma) are very expensive and difficult to use in research. Therefore, the attention of researchers is directed to the search for predictors of the disease, which indirectly reflect the functional activity of the structures involved in the pathological process. With regard to the prognostic value, the most relevant studies sought to identify biomarkers at early stages of AD pathogenesis, particularly the studies involving groups of healthy elderly individuals, characterized by the accumulation of cerebral amyloid plaques in the absence of clinical symptoms of mild cognitive impairment (MCI) or dementia. The hippocampus is a key brain region that processes EM and is a primary structure that is susceptible to the accumulation of amyloid plaques. However, some structures such as the entorhinal cortex and the cingulate gyrus are also relevant to the matter. Currently, some studies aimed at studying the relationship of minimal cognitive impairment, including the decline of EM, in different groups of subjects such as those with AD symptoms and/or MCI and the elderly with preservation of cognitive functions.[28,29,30] The drawback is that the methods and procedures used to investigate EM vary and do not always accurately reflect EM. It is also important to mention that visual processing[31,32] is one of the main biomarkers for cognitive decline.[33] An episode can be classified as “what” happens “where,” with contextual information (temporal “when,” or circumstantial “which”) that fosters contextual and behavioral criteria.[34] As follows, the hippocampal formation is necessary for learning and memory,[35,36,37] particularly for spatial components.[38] The trigger between pyramidal neurons in the CA1 and CA3 regions is clearly correlated with the location of an individual.[38,39] The ablation of the hippocampal formation, in turn, impairs spatial navigation ability. The tests such as CVLT and RCFT have considerable clinical predictive value and reliability and are widely used for the clinical assessment of EM.[16,17] However, classic neuropsychological tests are limited to evaluating the recall of focal elements, such as lists of images or words, freely or with the use of cues.[18,19] Thus, they are more associated with semantic or verbal processes than episodic processes, and they do not consider contextual elements.[19] Most studies that were discussed in this review attempted to circumvent this hindrance by employing at least two types of classic neuropsychological tests to assess EM. However, the set of tests that are used by these studies may not provide an assessment that fully integrates “what,” “where,” and “when” components that are essential for predicting real memory performance in everyday life.[19,20]. Additionally, using a battery of tests can be tiring for respondents, especially for the elderly people. The Treasure Hunt task[20] is proposed to investigate three components (i.e., what, where, and when) and be useful for further studies. This task was developed by Cheke[20] and is proposed to assess the memory of object information (“what”), location information (“where”), and temporal order information (“when”) within the same testing paradigm. This is important because it integrates all three features into a classic what–where–when framework. This task can also identify the extent and pattern of EM deficits that might be present in several diseases. Although no normative data have been published for the Treasure Hunt task, its application can still be useful. It is difficult to identify whether there is a specific deficit of EM, and the Treasure Hunt task can be an important alternative or complement to the existing assessment tools. The divergence between age and cognitive functioning may be grounded in the medial temporal lobe (MTL). The MTL, comprising interconnected structures of the hippocampus, dentate gyrus, peri- and entorhinal cortices, and parahippocampal gyrus, undergoes a prolonged period of postnatal development in humans, nonhuman primates, and rodents, with different maturation timelines. The MTL also impacts learning and memory functions differently in time.[40] These areas have functions in general mnemonic processes, specifically EM in humans and rats. It has been suggested that the function of the hippocampus is to integrate details of events that have been experienced, including spatial locations, together to gather episodic memories.[41] EM is a multisensory neurocognitive process of linking many elements. The unification of many elements in an episode occurs because of the long-term potentiation in the MTL and the activity of hippocampal neurons in the theta phase.[42,43]Studies have been demonstrated that theta cycles determined the process packaging of principal cell spiking into functional ensembles via the provision of discrete windows in which incoming streams of information from different systems are processed.[44,45] In addition, there is ample evidence that the N-methyl-d-aspartate glutamatergic receptor has a fundamental function in inducing synaptic plasticity and memory formation for various tasks involving aversive conditioning,[47] training of spatial memory,[48] nonspatial, and nonaversive tasks[49] in rodents[46]. These studies provided evidence that the deposition of neocortical and hippocampal Aβ in elderly people with normal cognitive aging is associated with functional changes in EM.[50]. In general, aging is associated with a reduced ability to modulate MTL activity, that is, with aging, the hippocampus shows a decrease in activation, and the entorhinal cortex decreases inhibition during an EM task. In addition, among elderly individuals, Aβ deposition was associated with a reduction in the entorhinal cortex regions associated with standard network functioning.[1,5,9] It is strongly suggested that in elderly individuals with a high concentration of Aβ, the preclinical processes of AD have begun despite normal cognitive functioning, even in the absence of changes in clinical findings. It is important to mention that this can be detected over a short period of time through the use of neuropsychological measures.[51]Age and Aβ deposition contribute to a collapse of the network between the hippocampus and regions of the neocortex, suggesting the declines in EM. Aging-related dementia usually begins gradually, with hippocampal atrophy manifesting several years after the onset of clinical symptoms. Aβ deposition is a part of the pathophysiology of AD, and therefore Aβ is more focused as a biomarker of AD. It is known that the role of Aβ in neurodegeneration culminates in a cascade of harmful events such as dementia and AD. However, this review noted that even in the presence of cognitive aging, Aβ can be detected in some individuals. The factors or issues that make individuals to be at risk of developing AD are still unclear.[51,52] One key to improving our understanding of the relationship between normal aging and initial stages of AD is related to neuroplasticity and cognitive decline that results from a lack of a compensatory response to the accumulation of Aβ. The leading genetic risk factor for AD, namely, apolipoprotein ε4, is related to neural plasticity. The high levels of cognitive reserves are associated with the level of education and socioeconomic status of an individual and delay in the diagnosis of AD[53]. Individuals with greater cognitive reserves can maintain cognitive function in the face of the higher levels of cerebral Aβ deposition[54]. Weak correlations between the levels of Aβ deposition and cognitive function suggest that other mechanisms, such as functional compensation, influence cognitive ability. The views that cognitive function can be maintained during aging by compensatory cognitive processes and that this decline is seen when a person is no longer able to compensate for a decrease in the function of primary brain structures and circuits are well supported in the contemporary research.[55,56] High-performing elderly adults are quite interesting in this regard. High-performing elderly individuals exhibit global preservation of the cerebral cortex, especially the anterior cingulate gyrus, and the volume of the hippocampus is higher than in people of normal age. The histological analyses of this group also revealed lower amyloid burden and lower neurofibrillary tangles compared with cognitively normal elderly controls. Thus, further studies of high-performing older adults are likely to provide additional insights in the role of amyloid deposition in the hippocampus during the aging process.[57] Despite the strengths of this review, it has some limitations. First, this review included studies that involved age ranges, which created challenges in interpreting these results (e.g., age is directly associated with amyloid deposition). Second, several studies used tests that assessed only specific parts of EM (e.g., delayed recall), thus raising the need to use extensive neuropsychological assessments or tasks, such as the Treasure Hunt task, that encompass all three main aspects of EM. Third, the analysis of various brain regions raises the need to define possible neural networks that are responsible for EM processing. Finally, we noted the need for further research in this area. It is necessary to investigate this concept in order to understand the prodromal symptoms of AD and the emergence of new practices and techniques that can identify and map Aβ deposition during healthy aging.
  107 in total

1.  Decline in cognitive function over 18 months in healthy older adults with high amyloid-β.

Authors:  Kathryn A Ellis; Yen Ying Lim; Karra Harrington; David Ames; Ashley I Bush; David Darby; Ralph N Martins; Colin L Masters; Christopher C Rowe; Greg Savage; Cassandra Szoeke; Victor L Villemagne; Paul Maruff
Journal:  J Alzheimers Dis       Date:  2013       Impact factor: 4.472

2.  Effect of APOE Genotype on Amyloid Deposition, Brain Volume, and Memory in Cognitively Normal Older Individuals.

Authors:  Yen Ying Lim; Robert Williamson; Simon M Laws; Victor L Villemagne; Pierrick Bourgeat; Christopher Fowler; Stephanie Rainey-Smith; Olivier Salvado; Ralph N Martins; Christopher C Rowe; Colin L Masters; Paul Maruff
Journal:  J Alzheimers Dis       Date:  2017       Impact factor: 4.472

3.  Relationships between performance on the Cogstate Brief Battery, neurodegeneration, and Aβ accumulation in cognitively normal older adults and adults with MCI.

Authors:  Yen Ying Lim; Robert H Pietrzak; Pierrick Bourgeat; David Ames; Kathryn A Ellis; Alan Rembach; Karra Harrington; Olivier Salvado; Ralph N Martins; Peter J Snyder; Colin L Masters; Christopher C Rowe; Victor L Villemagne; Paul Maruff
Journal:  Arch Clin Neuropsychol       Date:  2014-12-02       Impact factor: 2.813

4.  Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease.

Authors:  Vincent Doré; Victor L Villemagne; Pierrick Bourgeat; Jurgen Fripp; Oscar Acosta; Gael Chetélat; Luping Zhou; Ralph Martins; Kathryn A Ellis; Colin L Masters; David Ames; Oliver Salvado; Christopher C Rowe
Journal:  JAMA Neurol       Date:  2013-07       Impact factor: 18.302

Review 5.  Cognitive reserve in ageing and Alzheimer's disease.

Authors:  Yaakov Stern
Journal:  Lancet Neurol       Date:  2012-11       Impact factor: 44.182

6.  Cortical β-amyloid burden, gray matter, and memory in adults at varying APOE ε4 risk for Alzheimer's disease.

Authors:  Adam P Mecca; Nicole M Barcelos; Shuo Wang; Anna Brück; Nabeel Nabulsi; Beata Planeta-Wilson; Jennifer Nadelmann; Amanda L Benincasa; Jim Ropchan; Yiyun Huang; Joel Gelernter; Peter H Van Ness; Richard E Carson; Christopher H van Dyck
Journal:  Neurobiol Aging       Date:  2017-10-06       Impact factor: 4.673

7.  Colour discrimination thresholds in type 1 Bipolar Disorder: a pilot study.

Authors:  Thiago Monteiro Paiva Fernandes; Suellen Marinho Andrade; Michael Jackson Oliveira de Andrade; Renata Maria Toscano Barreto Lyra Nogueira; Natanael Antonio Santos
Journal:  Sci Rep       Date:  2017-11-27       Impact factor: 4.379

8.  Association of Longitudinal Cognitive Decline With Amyloid Burden in Middle-aged and Older Adults: Evidence for a Dose-Response Relationship.

Authors:  Michelle E Farrell; Kristen M Kennedy; Karen M Rodrigue; Gagan Wig; Gérard N Bischof; Jennifer R Rieck; Xi Chen; Sara B Festini; Michael D Devous; Denise C Park
Journal:  JAMA Neurol       Date:  2017-07-01       Impact factor: 18.302

9.  Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches.

Authors:  Hyunwoong Ko; Jung-Joon Ihm; Hong-Gee Kim
Journal:  Front Aging Neurosci       Date:  2019-04-26       Impact factor: 5.750

10.  Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior.

Authors:  Vítor Lopes-Dos-Santos; Gido M van de Ven; Alexander Morley; Stéphanie Trouche; Natalia Campo-Urriza; David Dupret
Journal:  Neuron       Date:  2018-10-18       Impact factor: 17.173

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