Cortical Neuropathology in Aging and Dementing Disorders

Hof, Patrick R.; Bouras, Constantin; Morrison, John H.

doi:10.1007/978-1-4615-4885-0_8

Cited by 25 publications

(4 citation statements)

References 673 publications

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“…With only one notable exception (Durany et al, 2000), decreased BDNF levels were reported (Phillips et al, 1991;Murray et al, 1994;Connor et al, 1997;Ferrer et al, 1999;Hock et al, 2000;Holsinger et al, 2000;Michalski and Fahnestock, 2003). In all studies reporting decreased BDNF levels, however, late stages of AD were studied in which considerable neuron loss has already occurred (Braak and Braak, 1991;Hof et al, 1999). Because this cell loss affects BDNF mRNA-expressing cells and non-BDNF mRNA-expressing cells indiscriminately (Murer et al, 1999), decreased BDNF levels are a logical consequence.…”

Section: Changes Of Bdnf Expression In Admentioning

confidence: 99%

Induction of Brain-Derived Neurotrophic Factor in Plaque-Associated Glial Cells of Aged APP23 Transgenic Mice

Burbach¹,

Hellweg²,

Haas³

et al. 2004

J. Neurosci.

116

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Brain-derived neurotrophic factor (BDNF) is a versatile neurotrophic factor that has been implicated in cell survival, cell differentiation, axonal growth, and activity-dependent synaptic plasticity. Changes in BDNF expression have also been reported during the course of several neurological disorders, including Alzheimer's disease (AD). The role of BDNF in AD, however, has remained elusive. To learn more about this neurotrophic factor, we investigated BDNF expression in brain of amyloid precursor protein overexpressing mice (APP23 transgenic mice). In situ hybridization revealed BDNF mRNA signals associated with amyloid plaques. Laser microdissection in combination with quantitative RT-PCR demonstrated a sixfold increase of BDNF mRNA in the immediate plaque vicinity, a threefold increase in a tissue ring surrounding the plaque, and control levels in interplaque areas comparable with those measured in age-matched nontransgenic mice. Double immunofluorescence localized BDNF to microglial cells and astrocytes surrounding the plaque. Cortical BDNF protein levels were quantified by ELISA demonstrating a Ͼ10-fold increase compared with age-matched controls. This upregulation of BDNF protein significantly correlated with the ␤-amyloid load in the transgenic animals. Taken together, our data demonstrate a plaque-associated upregulation of BDNF in APP23 transgenic mice and implicate this neurotrophin in the regulation of inflammatory and axonal growth processes in the plaque vicinity.

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Section: Changes Of Bdnf Expression In Admentioning

confidence: 99%

Induction of Brain-Derived Neurotrophic Factor in Plaque-Associated Glial Cells of Aged APP23 Transgenic Mice

Burbach¹,

Hellweg²,

Haas³

et al. 2004

J. Neurosci.

116

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“…1,2 In addition to neurodegenerative tauopathies, cognitively normal individuals develop a small number of neurofibrillary tangles (NFTs) in hippocampus and limbic structures as they age. 3 Thus, aging is a risk factor for the formation of NFTs in specific brain regions. Six highly soluble tau isoforms are generated from a single gene by alternative splicing and expressed predominantly in axons of the adult human brain.…”

mentioning

confidence: 99%

Age-Dependent Induction of Congophilic Neurofibrillary Tau Inclusions in Tau Transgenic Mice

Ishihara¹,

Zhang²,

Higuchi³

et al. 2001

The American Journal of Pathology

157

108

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Intraneuronal filamentous tau inclusions such as neurofibrillary tangles (NFTs) are neuropathological hallmarks of Alzheimer's disease (AD) and related sporadic and familial tauopathies. NFTs identical to those found in AD brains have also been detected in the hippocampus and entorhinal cortex of cognitively normal individuals as they age. To recapitulate ageinduced NFT formation in a mouse model, we examined 12-to 24-month-old transgenic (Tg) mice overexpressing the smallest human brain tau isoform. These Tg mice develop congophilic tau inclusions in several brain regions including the hippocampus, amygdala, and entorhinal cortex. NFT-like inclusions were first detected in Tg mice at 18 to 20 months of age and they were detected by histochemical dyes that bind specifically to crossed ␤-pleated sheet structures (eg, Congo red, Thioflavin S). Moreover, ultrastructurally these lesions contained straight tau filaments comprised of both mouse and human tau proteins but not other cytoskeletal proteins (eg, neurofilaments, microtubules). Isolated tau filaments were also recovered from detergent-insoluble tau fractions and insoluble tau proteins accumulated in brain in an agedependent manner. Thus, overexpression of the smallest human brain tau isoform resulted in late onset and age-dependent formation of congophilic tau inclusions with properties similar to those in the tangles of human tauopathies, thereby implicating aging in the pathogenesis of fibrous tau inclusions. Abnormal tau proteins are implicated in mechanisms of brain degeneration in Alzheimer's disease (AD), frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), Pick's disease, and related neurodegenerative tauopathies. Indeed, the neuropathological hallmarks of tauopathies are abundant aggregates of paired helical filaments (PHFs) and/or straight filaments composed of aberrantly phosphorylated tau proteins in central nervous system (CNS) neurons and/or glia.1,2 In addition to neurodegenerative tauopathies, cognitively normal individuals develop a small number of neurofibrillary tangles (NFTs) in hippocampus and limbic structures as they age. 3 Thus, aging is a risk factor for the formation of NFTs in specific brain regions. Six highly soluble tau isoforms are generated from a single gene by alternative splicing and expressed predominantly in axons of the adult human brain. 4 -6 The functions of these proteins include binding to and stabilizing microtubules (MTs) in the polymerized state.7,8 However, it has been shown that abnormally phosphorylated filamentous tau proteins cannot perform these important functions and aggregate in neurons to form insoluble NFTs in AD 9 -11 or similar fibrillary neuronal and/or glial inclusions in other tauopathies. 1,2To study the pathogenesis of neurodegenerative tauopathies, we previously generated transgenic (Tg) mice that overexpressed the shortest human brain tau isoform in CNS neurons, and showed that 3-to 12-monthold Tg mice accumulate insoluble intraneuronal filamentous hyperphosphorylated ta...

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“…AD is the most prevalent form of dementia and has been characterized as a “disconnection syndrome” [ 16 , 17 ]. Neurofibrillary tangles preferentially accumulate in pyramidal neurons in association cortices in layers 2/3 and 5, the neurons of which make long cortico-cortical connections [ 18 , 19 , 20 ]. Furthermore, amyloid beta accumulates within the highly-connected hubs of the default mode network, which are also comprised of association cortex [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer’s Disease

et al. 2017

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Whole-brain networks derived from diffusion tensor imaging (DTI) data require the identification of seed and target regions of interest (ROIs) to assess connectivity patterns. This study investigated how initiating tracts from gray matter (GM) or white matter (WM) seed ROIs impacts (1) structural networks constructed from DTI data from healthy elderly (control) and individuals with Alzheimer’s disease (AD) and (2) between-group comparisons using these networks. DTI datasets were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Deterministic tractography was used to build two whole-brain networks for each subject; one in which tracts were initiated from WM ROIs and another in which they were initiated from GM ROIs. With respect to the first goal, in both groups, WM-seeded networks had approximately 400 more connections and stronger connections (as measured by number of streamlines per connection) than GM-seeded networks, but shared 94% of the connections found in the GM-seed networks. With respect to the second goal, between-group comparisons revealed a stronger subnetwork (as measured by number of streamlines per connection) in controls compared to AD using both WM-seeded and GM-seeded networks. The comparison using WM-seeded networks produced a larger (i.e., a greater number of connections) and more significant subnetwork in controls versus AD. Global, local, and nodal efficiency were greater in controls compared to AD, and between-group comparisons of these measures using WM-seeded networks had larger effect sizes than those using GM-seeded networks. These findings affirm that seed location significantly affects the ability to detect between-group differences in structural networks.

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Cortical Neuropathology in Aging and Dementing Disorders

Cited by 25 publications

References 673 publications

Induction of Brain-Derived Neurotrophic Factor in Plaque-Associated Glial Cells of Aged APP23 Transgenic Mice

Induction of Brain-Derived Neurotrophic Factor in Plaque-Associated Glial Cells of Aged APP23 Transgenic Mice

Age-Dependent Induction of Congophilic Neurofibrillary Tau Inclusions in Tau Transgenic Mice

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer’s Disease

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