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Discipline
Biological, Medical
Keywords
Ageing
Gliosis
Amyloid Β
Tau
Lothian Birth Cohort 1936
Observation Type
Follow-up
Nature
Continuing the storyline
Submitted
Aug 4th, 2017
Published
Oct 11th, 2017
  • Abstract

    The decline in cognitive function is one of the most feared aspects of ageing. We are yet to fully understand why some people age with relatively intact cognition, while others experience a subtle cognitive decline or even dementia. The Lothian Birth Cohort 1936 (LBC1936) was established to investigate lifetime cognitive changes, with data collected at 11 years of age and again at 70 years old, onwards. The individuals have been extensively characterised in terms of genetics, cognitive function, and biomedical, psychological, and lifestyle factors. This pilot study characterises and quantifies morphological and pathological features of the first 9 donated brains from this cohort. Specifically, we have analysed amyloid-beta (Aβ), phosphorylated tau, microglia and astrocyte levels in 5 brain regions from 9 non-demented LBC1936 participants’ post-mortem brain tissue to determine how these factors vary between brain regions. Amyloid-β (Aβ) and phosphorylated tau tangles are hallmarks of Alzheimer's disease, the most prevalent form of dementia, although these have also been described in the brains of some non-demented aged individuals. In both ageing and dementia, immune-related changes are common, including microglia and astrocyte dysfunction. We found that tau tangles and glial cell coverage were highest in the hippocampus, in contrast to Aβ which was more abundant in the neocortex. We anticipate that this cohort will provide invaluable information about brain changes during normal ageing, and act as an age-matched control group for studies investigating neurodegenerative disorders with significant cognitive impairment, such as Alzheimer’s disease.

  • Figure
  • Introduction

    A common and devastating aspect of growing older is age-related cognitive loss. Ageing is also the biggest risk factor for developing dementia, an umbrella term encompassing disorders characterised by severe cognitive impairments in the elderly, such as Alzheimer’s disease (AD). The unmet need for ameliorating age-related cognitive loss, as well as the lack of understanding as to what constitutes normal cognitive ageing can be addressed through well characterised cohorts, such as the Lothian Birth Cohort 1936 (LBC1936).

    Participants of the LBC1936 originate from the Lothian region of Scotland, UK, and are part of a longitudinal study aiming to understand the aetiologies and mechanisms of people's differences in cognitive ageing. These individuals were first tested at 11 years of age in 1947 using a general intelligence test and since the age of 70 have been cognitively re-evaluated every 3 years. In addition to this longitudinal data on cognition, the LBC1936 study has accumulated an extensive database on genetics, biomedical, social and lifestyle factors, and longitudinal brain imaging, resulting in a highly characterised cohort. Post-mortem brains have been donated by 9 non-demented individuals, and to date, there is pre-mortem authorisation for brain donation from 173 individuals. A pilot characterisation of the first brain donor demonstrated remarkable preservation of synaptic integrity in the LBC1936 participant compared to an Alzheimer's patient.

    Neurodegenerative diseases are often characterised by the accumulation of misfolded proteins. In Alzheimer's disease, amyloid-β (Aβ) forms extracellular protein aggregates, or Aβ plaques, and the synaptotoxic oligomeric form is now shown to be a key driver of dementia-associated cognitive impairments. In addition, the microtubule stabilising protein tau can form neurofibrillary tangles (NFTs) when hyperphosphorylated. The combination of Aβ plaques and extensive NFTs are the hallmark of AD and are strongly implicated in cognitive decline and synapse degeneration. These pathologies also often accumulate in healthy, non-demented individuals in an age-related fashion, but it is not yet clear whether they contribute to a mild cognitive decline in the absence of frank dementia. Cognitive ageing and post-mortem pathology have been previously correlated in the Rush Religious Orders Study and the Rush Memory and Aging Project, where Aβ and tau were negatively associated with cognitive function. The LBC1936 neuropathology assessment aims to extend the characterisation from pre-mortem cognitive performance to post-mortem neuropathology in an attempt to discover underlying pathological changes that may explain the clinical phenotype. We have broadened the extent of post-mortem pathology investigated from well-established amyloid and tau analysis to the quantification of glial cell numbers. Specifically, microglia and astrocytes are immune cells essential for maintaining neuronal health through a range mechanisms, including synaptic pruning, phagocytosis, and myelin regeneration. During ageing, however, microglia and astrocytes become over-activated resulting in neuroinflammation and neurodegeneration. The combination of these protein accumulations and cellular changes in the ageing brain likely contribute to the cortical thinning observed in the elderly, and the devastating atrophy observed in individuals with dementia.

    For this study, we have used immunohistochemistry to study 5 brain regions from the 9 LBC1936 brain donors. 4 of the regions chosen are implicated in cognitive change during ageing and neurodegenerative diseases: Brodmann area (BA) 41/42 - superior temporal gyrus, BA44/45 - inferior frontal gyrus, BA46 - dorsolateral prefrontal cortex, and hippocampus. BA17, the primary visual cortex, was chosen as it is one of the cortical areas that is relatively spared during Alzheimer's disease.

    We found a regional variability in both pathological protein accumulation and gliosis in our 9 brains. On average, the hippocampus appeared to have the highest level of NFTs and glial coverage, yet contained the lowest burden of amyloid. This study shows that regional variability in brain changes is likely a common feature in aged brains and that the LBC1936 cohort is an excellent group to study the changes associated with cognitive changes during ageing. Ultimately this will lead to a greater understanding of the cellular and molecular changes both in healthy ageing and in the early stages of neurodegeneration leading to dementia, such as mild cognitive impairment (MCI).

  • Objective

    To quantify Aβ plaques, NFTs, gliosis, and cortical thickness in the 9 post-mortem brains of 9 LBC1936 non-demented aged individuals.

  • Results & Discussion

    Amyloid-β and Tau

    Aβ burdens were significantly variable between the 5 brain regions from the 9 individuals (Friedman test, p=0.0018). Specifically, we found that the hippocampus had the lowest amount of Aβ plaques compared to BA41/42 (p=0.0061), BA44/45 (p=0.0175), and BA46 (p=0.0365) but not BA17 (Dunn’s post-hoc test) (Fig. 1A and H). The deposition patterns between individuals varied considerably, with some showing very low Aβ burdens (SD017/16) and some showing extensive Aβ deposits throughout the grey matter (SD031/16). Strikingly, the participant with the highest Aβ burden (SD031/16) had an apolipoprotein (APOE) ε4 allele, a strong late-onset AD risk factor, and a Thal score 5, resembling AD-like pathology (Suppl. Tables 1 and 2). Moreover, cerebral amyloid angiopathy (CAA) with Aβ deposits around blood vessels was observed in some cases but was not always associated with Aβ plaques in the cortex (data not shown). Therefore, Aβ depositions are heterogeneous both between and within individuals.

    As expected from the early Braak stages of the individuals, BA17 and BA46 showed almost no phosphorylated tau species, whereas BA41/42 and BA44/45 showed low NFT densities (Fig. 1B and I). In contrast to our Aβ data, the hippocampus most commonly exhibited tau pathology, and significantly higher levels of tau pathology than BA17 (p=0.0026) and BA46 (p=0.0224) (Dunn’s post-hoc test). The only individual (SD031/16) with tau spread in all 5 brain regions also had the highest NFT density across all regions. This was also the case with the highest amyloid burdens and an Apo ε4 allele. Again, these preliminary data show evidence of p-tau heterogeneity not only between brain areas but also between individuals.

    Microglia and astrocytes

    Microglia and astrocytes, similar to tau, were found at highest levels in the hippocampus. Iba1-positive microglia, representing total microglia numbers, showed a significant difference in burdens between brain areas (p=0.0017), with the hippocampus having a higher burden than BA17 (p=0.0006) (Dunn’s post-hoc test) (Fig. 1C and J). CD68-positive microglia, a marker of phagocytic activity, showed no significant differences between the 5 brain areas (p=0.135) (Fig. 1D and K). By exploiting the fact that Iba1 stains most microglia and CD68 only stains phagocytic microglia, we generated a ratio of phagocytic versus total microglia in all 5 regions. No statistical differences of activation status were observed between brains areas (p=0.216) (Fig. 1E).

    Astrocyte burdens were significantly elevated in the hippocampus compared to both BA17 (p=0.0061) and BA41/42 (p=0.0287), but no further significant differences in burdens were found between other brain areas (Dunn’s post-hoc test) (Fig. 1F and L). Altogether, the hippocampus has statistically higher levels of glial cells than other brain areas, particularly BA17.

    Cortical thickness

    A significant difference in cortical thickness between cortical regions was observed (p=0.0133), with BA17 showing a significantly thinner cortex than BA41/42 (p=0.037) and BA46 (p=0.0209), but not BA44/45 (Dunn's post-hoc test) (Fig. 1G). These data show BA17 has a thinner cortex compared to more anterior areas. This observation is most likely explained by natural rostro-caudal differences in cortical thickness, rather than ageing-induced neuron loss.

    In this study, we have measured Aβ, phosphorylated tau (NFT), microglia, and astrocyte levels in 5 brain regions and have demonstrated both regional and individual variability in 9 non-demented LBC1936 participants.

    Our observed heterogeneity in tau and Aβ burdens heterogeneity reflects previous findings in the literature. Specifically, AD brains have higher levels of tau pathology in the hippocampus compared to cortical regions. Conversely, Aβ deposits are highest in the neocortex and moderately distributed in the hippocampus, until later stages of the disease. The greatest genetic risk factor for developing AD in non-familial cases is the possession of an APOE ε4 allele, whereas the ε2 allele appears to be protective against AD. Of note, the highest amyloid and tau pathology was observed in the only individual with an ε4 allele and the lowest amyloid pathology in the only individual with the ε2 allele (Suppl. Table 1). Nevertheless, these results have to be interpreted with caution due to the extremely low numbers involved and that the levels of Aβ and NFTs in this cohort are significantly lower than those found in AD cases. Overall, despite the small sample size, the data from this study have surprisingly, yet closely, depicted previously described features of the ageing brain.

    It is currently unclear if or how Aβ and tau interact in the ageing and AD brain. It is more evident that the quantity and spread of NFTs correlates strongly with, not only, cognitive decline but also synapse loss and gliosis. Indeed, in our study, glial cells were predominantly found in the hippocampus in the LBC1936 individuals (similar to NFTs), confirming preceding findings. The increased glial coverage and p-tau in the hippocampus may indicate a causative role in the early hippocampal neurodegeneration during ageing and AD, and by extension it may explain the early memory impairments observed in the elderly and demented. Interestingly, other studies have shown that Iba1+ve microglia burdens are correlated with normal cognitive function, whereas activated (CD68) microglia are correlated with poorer cognitive function in dementia. While this study is currently too small to assess these kinds of associations, as more brains become available this cohort will provide an invaluable opportunity to discover associations between post-mortem brain changes and detailed longitudinal cognitive performance.

    The hippocampus undergoes extensive spine remodelling and as a result, may require greater glial surveillance to ensure efficient temporal and spatial synaptic pruning. This could explain the higher numbers of glial cells we observe in the hippocampus compared to cortical regions (Fig. 1C). Synaptic health is critical for normal brain function, evidenced by the fact that synapse loss is the best correlate with cognitive impairment in AD. Synapse loss is also thought to correlate with poorer cognitive performance in normal ageing. During ageing, synaptic degeneration has been shown to impair electrophysiological properties of neurons by increasing the long-term depression (LTD) and reducing the long-term potentiation (LTP). Notably, Aβ and p-tau co-localize in synapses in AD brains, while microglia and astrocytes interact with synapses both in health and disease, marking synapses as critical points in normal and pathological ageing. Altogether, these age-related synaptic changes are hypothesised to occur before the onset of cognitive loss and to be key drivers of MCI and dementia. It would, therefore, be important to quantify synaptic puncta in the LBC1936 participants using high-resolution techniques such as array tomography to visualize how microglia, astrocytes, Aβ, and NFT’s interact with synapses. Furthermore, the data presented here must be compared to a younger cohort to establish if these brain area differences truly are age-associated phenomena, or region-specific throughout life.

    In the future, a greater sample size will allow us to correlate longitudinal cognitive function scores to synapse integrity and tau pathology or gliosis, in order to understand how these factors may mediate age-related cognitive impairments. Furthermore, by following-up on these individuals' cognitive function, we expect to detect MCI in some individuals and through our detailed post-mortem analyses, begin to get an understanding of its neuropathological origin. By doing so, prodromal phases of AD can be detected early and described post-mortem to the single synapse level, which will greatly improve our understanding of AD progression and thus provide better avenues for treatments.

  • Conclusions

    To summarise, Aβ plaques, NFTs, microglia, and astrocytes were differentially distributed in the brains of the 9 LBC1936 post-mortem cases, with NFTs and glial cells, but not Aβ, being elevated in the hippocampus. These data extend the phenotyping of this well-characterised cohort and form a building block for future studies of the neurobiological substrates of cognitive ageing.

  • Limitations

    The greatest limitation of this study is the small sample size (n=9). At the moment, the sample size is not large enough to make meaningful conclusions about the cellular and protein differences between brain areas, nor provide a causative relationship between the variables, and age-related impairments. However, as the post-mortem tissue availability increases, the study will be powered enough for making more robust conclusions.

  • Methods

    LBC1936 cohort

    All 1091 participants in the LBC1936, were born in 1936 and, when recruited to the study at about age 70 years, most lived in the Lothian region of Scotland. Most participants had taken part in the Scottish Mental Health survey in 1947. They were tested on the widely-used Moray House Test No12 at age 11 in 1947, in a school setting, to assess general intelligence. At 70 years of age, they were all contacted and asked to participate in a longitudinal study aiming to understand people's differences in cognitive ageing and health. Cognitive tests and other examinations are being repeated every 3 years.

    Post-mortem tissue preparation and Immunohistochemistry

    All post-mortem tissue of LBC1936 to date has been donated by non-demented individuals (n=9). Fixed tissue sections of 4 μm thickness from 5 brain areas were processed for immunohistochemistry. The areas analysed here are the primary visual cortex (BA17), the auditory cortex/temporal lobe (BA41/42), the inferior frontal gyrus (BA44/45), the dorsolateral prefrontal cortex (BA46), and the hippocampus. Amyloid-β was stained with 6F/3D (mouse monoclonal, Dako, M087201-2, 1:100, 98% formic acid, 5 min) and phosphorylated tau was stained with AT8 (mouse monoclonal, ThermoFischer MN1020, 1:2500). Microglia were stained using two antibodies, CD68 (mouse anti-human monoclonal primary antibody, Dako M0876, 1:100, citric acid in pressure cooker pre-treatment) and Iba1 (rabbit anti-human polyclonal primary antibody, Wako, 019-19741, 1:750, citric acid in pressure cooker pre-treatment). Astrocytes were stained with GFAP (rabbit anti-human polyclonal primary antibody Dako Z0334, 1:800). Each stain was performed on consecutive sections. Novolink Polymer detection system was used for enhancement of immunohistochemistry. The chromogen used for visualization is 3,3’-diaminobenzidine (DAB) with 0.05% hydrogen peroxide as substrate. Tissue was counterstained with haematoxylin for 30 s to visualise cell nuclei.

    Thresholding and burden quantification

    Amyloid-β (6F/3D), phosphorylated tau (AT8), microglia (Iba1 and CD68), and astrocytes (GFAP) were analysed within the grey matter. Stains were visualised using a ZEISS Imager.Z2 stereology microscope. All 6 layers of the grey matter were included in the analysis using the MBF Biosciences Stereo Investigator software. Cortical grey matter was outlined at 1.5X objective magnification and tile scans were obtained at 5X for quantification. Amyloid-β, and glia were quantified using an in-built software algorithm that identifies immune-labelled objects based on a colour and size threshold. Objects smaller than 100 μm2 and 10 μm2 were excluded in the astrocyte, and Aβ/microglia burden analysis, respectively, as they did not represent true immunostaining. The threshold and exposure remained constant throughout the analysis. The outlined objects were then transferred to Neurolucida Explorer, which calculated the total area of the region of interest and the summed area of the outlined objects. The stained area was divided by the total area to provide a percentage of microglia/astrocyte burden. Evaluation of tau pathology involved counting the number of neurofibrillary tangles and regions affected, to represent an estimation of the level and spread of pathology present. Densities of NFTs per mm2 were calculated from each brain region.

    Cortical thickness

    Slides were stained with haematoxylin and eosin (H&E) which distinctly marks the borders between the grey and white matter. Grey matter is stained pink, and white matter purple. For cortical thickness, MBF Biosciences Stereo Investigator was used for measuring the distance from the pial surface to the white matter. 10 measurements in random regions of the cortex were made to calculate a mean cortical thickness for each region from each case.

    Statistics

    All graphs were generated on GraphPad Prism 7. D’Agostino-Pearson normality tests were performed prior to any statistical test. Non-parametric Friedman tests with Dunn’s post-hoc were generated for all statistical analysis, where α=0.05 and p<0.05 for significance. All data are represented as means ± standard error of mean (SEM).

  • Funding statement

    We gratefully acknowledge funding from the UK Dementia Research Institute, European Research Council (ALZSYN), Alzheimer’s Research UK, Wellcome Trust-University of Edinburgh Institutional Strategic Support, Alzheimer’s Society, MND Scotland, the Euan MacDonald Centre for Motorneurone Disease Research, the Biotechnology and Biological Sciences Research Council UK The LBC1936 is supported by Age UK (Disconnected Mind project). The LBC1936 work was undertaken in The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, funded by the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) ((MR/K026992/1).

  • Acknowledgements

    We would like to thank the LBC1936 participants and their families for their generous donations. We also thank the LBC1936 team members for administration and data collection. TS-J is a member of the FENS Kavli Network of Excellence.

  • Ethics statement

    Use of human tissue for post-mortem studies has been reviewed and approved by the Edinburgh Brain Bank ethics committee and the ACCORD medical research ethics committee, AMREC (approval number 15-HV-016; ACCORD is the Academic and Clinical Central Office for Research and Development, a joint office of the University of Edinburgh and NHS Lothian). The Edinburgh Brain Bank is a Medical Research Council funded facility with research ethics committee (REC) approval (11/ES/0022). Tissue from 9 donors was used for this study and their details are found in supplementary information.

  • References
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    Matters12/20

    Assessing amyloid-β, tau, and glial features in Lothian Birth Cohort 1936 participants post-mortem

    Affiliation listing not available.
    Continuing the storyline of

  • Henstridge Christopher M., Jackson Rosemary J., Kim Jeesoo M.,more_horiz, Spires-Jones Tara L.
    Post-mortem brain analyses of the Lothian Birth Cohort 1936: extending lifetime cognitive and brain phenotyping to the level of the synapse
    Acta Neuropathologica Communications, 3/2015 DOI: 10.1186/s40478-015-0232-0chrome_reader_mode
  • Abstractlink

    The decline in cognitive function is one of the most feared aspects of ageing. We are yet to fully understand why some people age with relatively intact cognition, while others experience a subtle cognitive decline or even dementia. The Lothian Birth Cohort 1936 (LBC1936) was established to investigate lifetime cognitive changes, with data collected at 11 years of age and again at 70 years old, onwards. The individuals have been extensively characterised in terms of genetics, cognitive function, and biomedical, psychological, and lifestyle factors. This pilot study characterises and quantifies morphological and pathological features of the first 9 donated brains from this cohort. Specifically, we have analysed amyloid-beta (Aβ), phosphorylated tau, microglia and astrocyte levels in 5 brain regions from 9 non-demented LBC1936 participants’ post-mortem brain tissue to determine how these factors vary between brain regions. Amyloid-β (Aβ) and phosphorylated tau tangles are hallmarks of Alzheimer's disease, the most prevalent form of dementia, although these have also been described in the brains of some non-demented aged individuals. In both ageing and dementia, immune-related changes are common, including microglia and astrocyte dysfunction. We found that tau tangles and glial cell coverage were highest in the hippocampus, in contrast to Aβ which was more abundant in the neocortex. We anticipate that this cohort will provide invaluable information about brain changes during normal ageing, and act as an age-matched control group for studies investigating neurodegenerative disorders with significant cognitive impairment, such as Alzheimer’s disease.

    Figurelink

    Figure 1. Quantifying Aβ, NFTs, gliosis, and cortical thickness in LBC1936 participants' post-mortem brain tissue.

    Measurements in each of the 5 brain regions and representative images of each stain: A, H amyloid-β, B, I phosphorylated tau/ neurofibrillary tau tangles (NFTs), C, J Iba1, D, K CD68, E ratio of CD68 to Iba1 microglia, F, L GFAP, and G cortical thickness.

    Each data point represents one individual (n=9). Data are mean ± standard error of the mean (SEM). For statistical analysis, Friedman test with Dunn’s post-hoc, where *p=0.05, **p=0.01, and ***p=0.001. Scale bar: 150 μm.

    Introductionlink

    A common and devastating aspect of growing older is age-related cognitive loss[1]. Ageing is also the biggest risk factor for developing dementia, an umbrella term encompassing disorders characterised by severe cognitive impairments in the elderly, such as Alzheimer’s disease (AD)[2][3][4]. The unmet need for ameliorating age-related cognitive loss[5], as well as the lack of understanding as to what constitutes normal cognitive ageing can be addressed through well characterised cohorts, such as the Lothian Birth Cohort 1936 (LBC1936).

    Participants of the LBC1936 originate from the Lothian region of Scotland, UK, and are part of a longitudinal study aiming to understand the aetiologies and mechanisms of people's differences in cognitive ageing[6]. These individuals were first tested at 11 years of age in 1947 using a general intelligence test and since the age of 70 have been cognitively re-evaluated every 3 years. In addition to this longitudinal data on cognition, the LBC1936 study has accumulated an extensive database on genetics, biomedical, social and lifestyle factors, and longitudinal brain imaging, resulting in a highly characterised cohort[6][7][8][9]. Post-mortem brains have been donated by 9 non-demented individuals, and to date, there is pre-mortem authorisation for brain donation from 173 individuals. A pilot characterisation of the first brain donor demonstrated remarkable preservation of synaptic integrity in the LBC1936 participant compared to an Alzheimer's patient[10].

    Neurodegenerative diseases are often characterised by the accumulation of misfolded proteins. In Alzheimer's disease, amyloid-β (Aβ) forms extracellular protein aggregates, or Aβ plaques, and the synaptotoxic oligomeric form is now shown to be a key driver of dementia-associated cognitive impairments[11][12][13]. In addition, the microtubule stabilising protein tau can form neurofibrillary tangles (NFTs) when hyperphosphorylated. The combination of Aβ plaques and extensive NFTs are the hallmark of AD and are strongly implicated in cognitive decline and synapse degeneration[12]. These pathologies also often accumulate in healthy, non-demented individuals in an age-related fashion, but it is not yet clear whether they contribute to a mild cognitive decline in the absence of frank dementia[14]. Cognitive ageing and post-mortem pathology have been previously correlated in the Rush Religious Orders Study and the Rush Memory and Aging Project, where Aβ and tau were negatively associated with cognitive function[15][16]. The LBC1936 neuropathology assessment aims to extend the characterisation from pre-mortem cognitive performance to post-mortem neuropathology in an attempt to discover underlying pathological changes that may explain the clinical phenotype. We have broadened the extent of post-mortem pathology investigated from well-established amyloid and tau analysis to the quantification of glial cell numbers. Specifically, microglia and astrocytes are immune cells essential for maintaining neuronal health through a range mechanisms, including synaptic pruning[17][18], phagocytosis[19], and myelin regeneration[20]. During ageing, however, microglia and astrocytes become over-activated[21][22] resulting in neuroinflammation and neurodegeneration[23][24][25]. The combination of these protein accumulations and cellular changes in the ageing brain likely contribute to the cortical thinning observed in the elderly, and the devastating atrophy observed in individuals with dementia[26].

    For this study, we have used immunohistochemistry to study 5 brain regions from the 9 LBC1936 brain donors. 4 of the regions chosen are implicated in cognitive change during ageing and neurodegenerative diseases: Brodmann area (BA) 41/42 - superior temporal gyrus, BA44/45 - inferior frontal gyrus, BA46 - dorsolateral prefrontal cortex, and hippocampus. BA17, the primary visual cortex, was chosen as it is one of the cortical areas that is relatively spared during Alzheimer's disease[3][27].

    We found a regional variability in both pathological protein accumulation and gliosis in our 9 brains. On average, the hippocampus appeared to have the highest level of NFTs and glial coverage, yet contained the lowest burden of amyloid. This study shows that regional variability in brain changes is likely a common feature in aged brains and that the LBC1936 cohort is an excellent group to study the changes associated with cognitive changes during ageing. Ultimately this will lead to a greater understanding of the cellular and molecular changes both in healthy ageing and in the early stages of neurodegeneration leading to dementia, such as mild cognitive impairment (MCI).

    Objectivelink

    To quantify Aβ plaques, NFTs, gliosis, and cortical thickness in the 9 post-mortem brains of 9 LBC1936 non-demented aged individuals.

    Results & Discussionlink

    Amyloid-β and Tau

    Aβ burdens were significantly variable between the 5 brain regions from the 9 individuals (Friedman test, p=0.0018). Specifically, we found that the hippocampus had the lowest amount of Aβ plaques compared to BA41/42 (p=0.0061), BA44/45 (p=0.0175), and BA46 (p=0.0365) but not BA17 (Dunn’s post-hoc test) (Fig. 1A and H). The deposition patterns between individuals varied considerably, with some showing very low Aβ burdens (SD017/16) and some showing extensive Aβ deposits throughout the grey matter (SD031/16). Strikingly, the participant with the highest Aβ burden (SD031/16) had an apolipoprotein (APOE) ε4 allele, a strong late-onset AD risk factor, and a Thal score 5, resembling AD-like pathology (Suppl. Tables 1 and 2). Moreover, cerebral amyloid angiopathy (CAA) with Aβ deposits around blood vessels was observed in some cases but was not always associated with Aβ plaques in the cortex (data not shown). Therefore, Aβ depositions are heterogeneous both between and within individuals.

    As expected from the early Braak stages of the individuals, BA17 and BA46 showed almost no phosphorylated tau species, whereas BA41/42 and BA44/45 showed low NFT densities (Fig. 1B and I). In contrast to our Aβ data, the hippocampus most commonly exhibited tau pathology, and significantly higher levels of tau pathology than BA17 (p=0.0026) and BA46 (p=0.0224) (Dunn’s post-hoc test). The only individual (SD031/16) with tau spread in all 5 brain regions also had the highest NFT density across all regions. This was also the case with the highest amyloid burdens and an Apo ε4 allele. Again, these preliminary data show evidence of p-tau heterogeneity not only between brain areas but also between individuals.

    Microglia and astrocytes

    Microglia and astrocytes, similar to tau, were found at highest levels in the hippocampus. Iba1-positive microglia, representing total microglia numbers, showed a significant difference in burdens between brain areas (p=0.0017), with the hippocampus having a higher burden than BA17 (p=0.0006) (Dunn’s post-hoc test) (Fig. 1C and J). CD68-positive microglia, a marker of phagocytic activity, showed no significant differences between the 5 brain areas (p=0.135) (Fig. 1D and K). By exploiting the fact that Iba1 stains most microglia and CD68 only stains phagocytic microglia, we generated a ratio of phagocytic versus total microglia in all 5 regions. No statistical differences of activation status were observed between brains areas (p=0.216) (Fig. 1E).

    Astrocyte burdens were significantly elevated in the hippocampus compared to both BA17 (p=0.0061) and BA41/42 (p=0.0287), but no further significant differences in burdens were found between other brain areas (Dunn’s post-hoc test) (Fig. 1F and L). Altogether, the hippocampus has statistically higher levels of glial cells than other brain areas, particularly BA17.

    Cortical thickness

    A significant difference in cortical thickness between cortical regions was observed (p=0.0133), with BA17 showing a significantly thinner cortex than BA41/42 (p=0.037) and BA46 (p=0.0209), but not BA44/45 (Dunn's post-hoc test) (Fig. 1G). These data show BA17 has a thinner cortex compared to more anterior areas. This observation is most likely explained by natural rostro-caudal differences in cortical thickness, rather than ageing-induced neuron loss[28].

    In this study, we have measured Aβ, phosphorylated tau (NFT), microglia, and astrocyte levels in 5 brain regions and have demonstrated both regional and individual variability in 9 non-demented LBC1936 participants.

    Our observed heterogeneity in tau and Aβ burdens heterogeneity reflects previous findings in the literature. Specifically, AD brains have higher levels of tau pathology in the hippocampus compared to cortical regions[11][29]. Conversely, Aβ deposits are highest in the neocortex and moderately distributed in the hippocampus, until later stages of the disease[11][29]. The greatest genetic risk factor for developing AD in non-familial cases is the possession of an APOE ε4 allele, whereas the ε2 allele appears to be protective against AD[30]. Of note, the highest amyloid and tau pathology was observed in the only individual with an ε4 allele and the lowest amyloid pathology in the only individual with the ε2 allele (Suppl. Table 1). Nevertheless, these results have to be interpreted with caution due to the extremely low numbers involved and that the levels of Aβ and NFTs in this cohort are significantly lower than those found in AD cases. Overall, despite the small sample size, the data from this study have surprisingly, yet closely, depicted previously described features of the ageing brain.

    It is currently unclear if or how Aβ and tau interact in the ageing and AD brain. It is more evident that the quantity and spread of NFTs correlates strongly with, not only, cognitive decline but also synapse loss and gliosis[31][32]. Indeed, in our study, glial cells were predominantly found in the hippocampus in the LBC1936 individuals (similar to NFTs), confirming preceding findings[33][34]. The increased glial coverage and p-tau in the hippocampus may indicate a causative role in the early hippocampal neurodegeneration during ageing and AD, and by extension it may explain the early memory impairments observed in the elderly and demented. Interestingly, other studies have shown that Iba1+ve microglia burdens are correlated with normal cognitive function, whereas activated (CD68) microglia are correlated with poorer cognitive function in dementia[35]. While this study is currently too small to assess these kinds of associations, as more brains become available this cohort will provide an invaluable opportunity to discover associations between post-mortem brain changes and detailed longitudinal cognitive performance.

    The hippocampus undergoes extensive spine remodelling[36][37] and as a result, may require greater glial surveillance to ensure efficient temporal and spatial synaptic pruning. This could explain the higher numbers of glial cells we observe in the hippocampus compared to cortical regions (Fig. 1C). Synaptic health is critical for normal brain function, evidenced by the fact that synapse loss is the best correlate with cognitive impairment in AD[38][39]. Synapse loss is also thought to correlate with poorer cognitive performance in normal ageing[40]. During ageing, synaptic degeneration has been shown to impair electrophysiological properties of neurons by increasing the long-term depression (LTD) and reducing the long-term potentiation (LTP)[41][42]. Notably, Aβ and p-tau co-localize in synapses in AD brains[29], while microglia and astrocytes interact with synapses both in health and disease, marking synapses as critical points in normal and pathological ageing[43]. Altogether, these age-related synaptic changes are hypothesised to occur before the onset of cognitive loss and to be key drivers of MCI and dementia[44]. It would, therefore, be important to quantify synaptic puncta in the LBC1936 participants using high-resolution techniques such as array tomography to visualize how microglia, astrocytes, Aβ, and NFT’s interact with synapses. Furthermore, the data presented here must be compared to a younger cohort to establish if these brain area differences truly are age-associated phenomena, or region-specific throughout life.

    In the future, a greater sample size will allow us to correlate longitudinal cognitive function scores to synapse integrity and tau pathology or gliosis, in order to understand how these factors may mediate age-related cognitive impairments. Furthermore, by following-up on these individuals' cognitive function, we expect to detect MCI in some individuals and through our detailed post-mortem analyses, begin to get an understanding of its neuropathological origin. By doing so, prodromal phases of AD can be detected early and described post-mortem to the single synapse level, which will greatly improve our understanding of AD progression and thus provide better avenues for treatments.

    Conclusionslink

    To summarise, Aβ plaques, NFTs, microglia, and astrocytes were differentially distributed in the brains of the 9 LBC1936 post-mortem cases, with NFTs and glial cells, but not Aβ, being elevated in the hippocampus. These data extend the phenotyping of this well-characterised cohort and form a building block for future studies of the neurobiological substrates of cognitive ageing.

    Limitationslink

    The greatest limitation of this study is the small sample size (n=9). At the moment, the sample size is not large enough to make meaningful conclusions about the cellular and protein differences between brain areas, nor provide a causative relationship between the variables, and age-related impairments. However, as the post-mortem tissue availability increases, the study will be powered enough for making more robust conclusions.

    Methodslink

    LBC1936 cohort

    All 1091 participants in the LBC1936, were born in 1936 and, when recruited to the study at about age 70 years, most lived in the Lothian region of Scotland. Most participants had taken part in the Scottish Mental Health survey in 1947. They were tested on the widely-used Moray House Test No12 at age 11 in 1947, in a school setting, to assess general intelligence. At 70 years of age, they were all contacted and asked to participate in a longitudinal study aiming to understand people's differences in cognitive ageing and health. Cognitive tests and other examinations are being repeated every 3 years.

    Post-mortem tissue preparation and Immunohistochemistry

    All post-mortem tissue of LBC1936 to date has been donated by non-demented individuals (n=9). Fixed tissue sections of 4 μm thickness from 5 brain areas were processed for immunohistochemistry. The areas analysed here are the primary visual cortex (BA17), the auditory cortex/temporal lobe (BA41/42), the inferior frontal gyrus (BA44/45), the dorsolateral prefrontal cortex (BA46), and the hippocampus. Amyloid-β was stained with 6F/3D (mouse monoclonal, Dako, M087201-2, 1:100, 98% formic acid, 5 min) and phosphorylated tau was stained with AT8 (mouse monoclonal, ThermoFischer MN1020, 1:2500). Microglia were stained using two antibodies, CD68 (mouse anti-human monoclonal primary antibody, Dako M0876, 1:100, citric acid in pressure cooker pre-treatment) and Iba1 (rabbit anti-human polyclonal primary antibody, Wako, 019-19741, 1:750, citric acid in pressure cooker pre-treatment). Astrocytes were stained with GFAP (rabbit anti-human polyclonal primary antibody Dako Z0334, 1:800). Each stain was performed on consecutive sections. Novolink Polymer detection system was used for enhancement of immunohistochemistry. The chromogen used for visualization is 3,3’-diaminobenzidine (DAB) with 0.05% hydrogen peroxide as substrate. Tissue was counterstained with haematoxylin for 30 s to visualise cell nuclei.

    Thresholding and burden quantification

    Amyloid-β (6F/3D), phosphorylated tau (AT8), microglia (Iba1 and CD68), and astrocytes (GFAP) were analysed within the grey matter. Stains were visualised using a ZEISS Imager.Z2 stereology microscope. All 6 layers of the grey matter were included in the analysis using the MBF Biosciences Stereo Investigator software. Cortical grey matter was outlined at 1.5X objective magnification and tile scans were obtained at 5X for quantification. Amyloid-β, and glia were quantified using an in-built software algorithm that identifies immune-labelled objects based on a colour and size threshold. Objects smaller than 100 μm2 and 10 μm2 were excluded in the astrocyte, and Aβ/microglia burden analysis, respectively, as they did not represent true immunostaining. The threshold and exposure remained constant throughout the analysis. The outlined objects were then transferred to Neurolucida Explorer, which calculated the total area of the region of interest and the summed area of the outlined objects. The stained area was divided by the total area to provide a percentage of microglia/astrocyte burden. Evaluation of tau pathology involved counting the number of neurofibrillary tangles and regions affected, to represent an estimation of the level and spread of pathology present. Densities of NFTs per mm2 were calculated from each brain region.

    Cortical thickness

    Slides were stained with haematoxylin and eosin (H&E) which distinctly marks the borders between the grey and white matter. Grey matter is stained pink, and white matter purple. For cortical thickness, MBF Biosciences Stereo Investigator was used for measuring the distance from the pial surface to the white matter. 10 measurements in random regions of the cortex were made to calculate a mean cortical thickness for each region from each case.

    Statistics

    All graphs were generated on GraphPad Prism 7. D’Agostino-Pearson normality tests were performed prior to any statistical test. Non-parametric Friedman tests with Dunn’s post-hoc were generated for all statistical analysis, where α=0.05 and p<0.05 for significance. All data are represented as means ± standard error of mean (SEM).

    Funding Statementlink

    We gratefully acknowledge funding from the UK Dementia Research Institute, European Research Council (ALZSYN), Alzheimer’s Research UK, Wellcome Trust-University of Edinburgh Institutional Strategic Support, Alzheimer’s Society, MND Scotland, the Euan MacDonald Centre for Motorneurone Disease Research, the Biotechnology and Biological Sciences Research Council UK The LBC1936 is supported by Age UK (Disconnected Mind project). The LBC1936 work was undertaken in The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, funded by the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) ((MR/K026992/1).

    Acknowledgementslink

    We would like to thank the LBC1936 participants and their families for their generous donations. We also thank the LBC1936 team members for administration and data collection. TS-J is a member of the FENS Kavli Network of Excellence.

    Conflict of interestlink

    The authors declare no conflicts of interest.

    Ethics Statementlink

    Use of human tissue for post-mortem studies has been reviewed and approved by the Edinburgh Brain Bank ethics committee and the ACCORD medical research ethics committee, AMREC (approval number 15-HV-016; ACCORD is the Academic and Clinical Central Office for Research and Development, a joint office of the University of Edinburgh and NHS Lothian). The Edinburgh Brain Bank is a Medical Research Council funded facility with research ethics committee (REC) approval (11/ES/0022). Tissue from 9 donors was used for this study and their details are found in supplementary information.

    No fraudulence is committed in performing these experiments or during processing of the data. We understand that in the case of fraudulence, the study can be retracted by ScienceMatters.

    Referenceslink
    1. Martin George M.
      Defeating dementia
      Nature, 431/2004, pages 247-248 DOI: 10.1038/431247bchrome_reader_mode
    2. Hardy J., Higgins G.
      Alzheimer's disease: the amyloid cascade hypothesis
      Science, 256/1992, pages 184-185 DOI: 10.1126/science.1566067chrome_reader_mode
    3. Jeffrey N. Keller
      Age-related neuropathology, cognitive decline, and Alzheimer's disease
      Ageing Research Reviews, 5/2006, pages 1-13 DOI: 10.1016/j.arr.2005.06.002chrome_reader_mode
    4. Spires-Jones Tara L., Hyman Bradley T.
      The Intersection of Amyloid Beta and Tau at Synapses in Alzheimer’s Disease
    5. Cummings Jeffrey, Morstorf Travis, Lee Garam
      Alzheimer's drug-development pipeline: 2016
      Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2/2016, pages 222-232 DOI: 10.1016/j.trci.2016.07.001chrome_reader_mode
    6. Deary Ian J, Gow Alan J, Taylor Michelle D,more_horiz, Starr John M
      The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond
      BMC Geriatrics, 7/2007, page 28 DOI: 10.1186/1471-2318-7-28chrome_reader_mode
    7. Wardlaw Joanna M., Bastin Mark E., Valdés Hernández Maria C.,more_horiz, Deary Ian J.
      Brain Aging, Cognition in Youth and Old Age and Vascular Disease in the Lothian Birth Cohort 1936: Rationale, Design and Methodology of the Imaging Protocol
      International Journal of Stroke, 6/2011, pages 547-559 DOI: 10.1111/j.1747-4949.2011.00683.xchrome_reader_mode
    8. Ian J. Deary, Alan J. Gow, Alison Pattie, John M. Starr
      Cohort Profile: The Lothian Birth Cohorts of 1921 and 1936
      International Journal of Epidemiology, 41/2011, pages 1576-1584 DOI: 10.1093/ije/dyr197chrome_reader_mode
    9. Corley J., Cox S. R., Deary I. J.
      Healthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet
      Psychological Medicine, 2017, pages 1-21 DOI: 10.1017/s0033291717001489chrome_reader_mode
    10. Henstridge Christopher M., Jackson Rosemary J., Kim Jeesoo M.,more_horiz, Spires-Jones Tara L.
      Post-mortem brain analyses of the Lothian Birth Cohort 1936: extending lifetime cognitive and brain phenotyping to the level of the synapse
      Acta Neuropathologica Communications, 3/2015, page 53 DOI: 10.1186/s40478-015-0232-0chrome_reader_mode
    11. Braak H., Braak E.
      Neuropathological stageing of Alzheimer-related changes
      Acta Neuropathologica, 82/1991, pages 239-259 DOI: 10.1007/bf00308809chrome_reader_mode
    12. Cohen R. M., Rezai-Zadeh K., Weitz T. M.,more_horiz, Town T.
      A Transgenic Alzheimer Rat with Plaques, Tau Pathology, Behavioral Impairment, Oligomeric A , and Frank Neuronal Loss
      Journal of Neuroscience, 33/2013, pages 6245-6256 DOI: 10.1523/jneurosci.3672-12.2013chrome_reader_mode
    13. Ferreira Sergio T., Lourenco Mychael V., Oliveira Mauricio M., de Felice Fernanda G.
      Soluble amyloid-β oligomers as synaptotoxins leading to cognitive impairment in Alzheimer’s disease
      Frontiers in Cellular Neuroscience, 9/2015, page 191 DOI: 10.3389/fncel.2015.00191chrome_reader_mode
    14. Spires-Jones Tara L., Attems Johannes, Thal Dietmar Rudolf
      Interactions of pathological proteins in neurodegenerative diseases
      Acta Neuropathologica, 134/2017, pages 187-205 DOI: 10.1007/s00401-017-1709-7chrome_reader_mode
    15. Negash Selamawit, A. Bennett David, S. Wilson Robert,more_horiz, E. Arnold Steven
      Cognition and Neuropathology in Aging: Multidimensional Perspectives from the Rush Religious Orders Study and Rush Memory and Aging Project
      Current Alzheimer Research, 999/2011, pages 1-5 DOI: 10.2174/1567211212225922050chrome_reader_mode
    16. Bennett David A., Wilson Robert S., Boyle Patricia A.,more_horiz, Schneider Julie A.
      Relation of neuropathology to cognition in persons without cognitive impairment
      Annals of Neurology, 72/2012, pages 599-609 DOI: 10.1002/ana.23654chrome_reader_mode
    17. Hong Soyon, Dissing-Olesen Lasse, Stevens Beth
      New insights on the role of microglia in synaptic pruning in health and disease
      Current Opinion in Neurobiology, 36/2016, pages 128-134 DOI: 10.1016/j.conb.2015.12.004chrome_reader_mode
    18. Chung Won-Suk, Clarke Laura E., Wang Gordon X.,more_horiz, Barres Ben A.
      Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways
      Nature, 504/2013, pages 394-400 DOI: 10.1038/nature12776chrome_reader_mode
    19. Mosher Kira I, Andres Robert H, Fukuhara Takeshi,more_horiz, Wyss-Coray Tony
      Neural progenitor cells regulate microglia functions and activity
      Nature Neuroscience, 15/2012, pages 1485-1487 DOI: 10.1038/nn.3233chrome_reader_mode
    20. Miron Veronique E, Boyd Amanda, Zhao Jing-Wei,more_horiz, Ffrench-Constant Charles
      M2 microglia and macrophages drive oligodendrocyte differentiation during CNS remyelination
      Nature Neuroscience, 16/2013, pages 1211-1218 DOI: 10.1038/nn.3469chrome_reader_mode
    21. Perry V. Hugh, Holmes Clive
      Microglial priming in neurodegenerative disease
      Nature Reviews Neurology, 10/2014, pages 217-224 DOI: 10.1038/nrneurol.2014.38chrome_reader_mode
    22. Edel Hennessy, Eadaoin Griffin, Colm Cunningham
      Astrocytes are primed by chronic neurodegeneration to produce exaggerated chemokine and cell infiltration responses to acute stimulation with the cytokines IL-1β and TNFα.
    23. Heppner Frank L., Ransohoff Richard M., Becher Burkhard
      Immune attack: the role of inflammation in Alzheimer disease
      Nature Reviews Neuroscience, 16/2015, pages 358-372 DOI: 10.1038/nrn3880chrome_reader_mode
    24. Ransohoff R. M.
      How neuroinflammation contributes to neurodegeneration
      Science, 353/2016, pages 777-783 DOI: 10.1126/science.aag2590chrome_reader_mode
    25. Olmos-Alonso Adrian, Schetters Sjoerd T. T., Sri Sarmi,more_horiz, Gomez-Nicola Diego
      Pharmacological targeting of CSF1R inhibits microglial proliferation and prevents the progression of Alzheimer’s-like pathology
      Brain, 139/2016, pages 891-907 DOI: 10.1093/brain/awv379chrome_reader_mode
    26. Fox Nick C, Schott Jonathan M
      Imaging cerebral atrophy: normal ageing to Alzheimer's disease
      The Lancet, 363/2004, pages 392-394 DOI: 10.1016/s0140-6736(04)15441-xchrome_reader_mode
    27. Cui Jian-Guo, Hill James M., Zhao Yuhai, Lukiw Walter J.
      Expression of inflammatory genes in the primary visual cortex of late-stage Alzheimer??s disease
      NeuroReport, 18/2007, pages 115-119 DOI: 10.1097/wnr.0b013e32801198bcchrome_reader_mode
    28. Sun Tao, Hevner Robert F.
      Growth and folding of the mammalian cerebral cortex: from molecules to malformations
      Nature Reviews Neuroscience, 15/2014, pages 217-232 DOI: 10.1038/nrn3707chrome_reader_mode
    29. Fein Jeffrey A., Sokolow Sophie, Miller Carol A.,more_horiz, Gylys Karen Hoppens
      Co-Localization of Amyloid Beta and Tau Pathology in Alzheimer's Disease Synaptosomes
      The American Journal of Pathology, 172/2008, pages 1683-1692 DOI: 10.2353/ajpath.2008.070829chrome_reader_mode
    30. Liu Chia-Chen, Kanekiyo Takahisa, Xu Huaxi, Bu Guojun
      Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
      Nature Reviews Neurology, 9/2013, pages 184-184 DOI: 10.1038/nrneurol.2013.32chrome_reader_mode
    31. Giannakopoulos P., Herrmann F. R., Bussiere T.,more_horiz, Hof P. R.
      Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease
    32. Ingelsson M., Fukumoto H., Newell K. L.,more_horiz, Irizarry M. C.
      Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain
    33. Beach T.G., Walker R., McGeer E.G.
      Patterns of gliosis in alzheimer's disease and aging cerebrum
    34. David Jean-Philippe, Ghozali Farida, Fallet-Bianco Catherine,more_horiz, Delacourte André
      Glial reaction in the hippocampal formation is highly correlated with aging in human brain
      Neuroscience Letters, 235/1997, pages 53-56 DOI: 10.1016/s0304-3940(97)00708-8chrome_reader_mode
    35. Minett Thais, Mrc Cfas, Classey John,more_horiz, Boche Delphine
      Microglial immunophenotype in dementia with Alzheimer’s pathology
      Journal of Neuroinflammation, 13/2016, page 135 DOI: 10.1186/s12974-016-0601-zchrome_reader_mode
    36. Restivo L., Vetere G., Bontempi B., Ammassari-Teule M.
      The Formation of Recent and Remote Memory Is Associated with Time-Dependent Formation of Dendritic Spines in the Hippocampus and Anterior Cingulate Cortex
      Journal of Neuroscience, 29/2009, pages 8206-8214 DOI: 10.1523/jneurosci.0966-09.2009chrome_reader_mode
    37. Giachero Marcelo, Calfa Gaston D., Molina Victor A.
      Hippocampal dendritic spines remodeling and fear memory are modulated by GABAergic signaling within the basolateral amygdala complex
      Hippocampus, 25/2015, pages 545-555 DOI: 10.1002/hipo.22409chrome_reader_mode
    38. Terry Robert D., Masliah Eliezer, Salmon David P.,more_horiz, Katzman Robert
      Physical basis of cognitive alterations in alzheimer's disease: Synapse loss is the major correlate of cognitive impairment
      Annals of Neurology, 30/1991, pages 572-580 DOI: 10.1002/ana.410300410chrome_reader_mode
    39. Morrison John H., Baxter Mark G.
      The ageing cortical synapse: hallmarks and implications for cognitive decline
      Nature Reviews Neuroscience, 13/2012, pages 240-250 DOI: 10.1038/nrn3200chrome_reader_mode
    40. Peter R. Huttenlocher
      Synaptic density in human frontal cortex — Developmental changes and effects of aging
      Brain Research, 163/1979, pages 195-205 DOI: 10.1016/0006-8993(79)90349-4chrome_reader_mode
    41. Norris, Christopher M., Korol, Donna L., Foster, Thomas C.
      Increased Susceptibility to Induction of Long-Term Depression and Long-Term Potentiation Reversal during Aging
      Journal of Neuroscience, 16/1996, pages 5382-5392 chrome_reader_mode
    42. Burke Sara N., Barnes Carol A.
      Neural plasticity in the ageing brain
      Nature Reviews Neuroscience, 7/2006, pages 30-40 DOI: 10.1038/nrn1809chrome_reader_mode
    43. Liddelow Shane A., Guttenplan Kevin A., Clarke Laura E.,more_horiz, Barres Ben A.
      Neurotoxic reactive astrocytes are induced by activated microglia
      Nature, 541/2017, pages 481-487 DOI: 10.1038/nature21029chrome_reader_mode
    44. Henstridge Christopher M., Pickett Eleanor, Spires-Jones Tara L.
      Synaptic pathology: A shared mechanism in neurological disease
      Ageing Research Reviews, 28/2016, pages 72-84 DOI: 10.1016/j.arr.2016.04.005chrome_reader_mode
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