Functional connectivity among brain regions affected in Alzheimer's disease is associated with CSF TNF-α in APOE4 carriers

Contreras, Joey A.; Aslanyan, Vahan; Sweeney, Melanie D.; Sanders, Lianne; Sagare, Abhay P.; Zloković, Berislav V.; Toga, Arthur W.; Han, S. Duke; Morris, John C.; Massoumzadeh, Parinaz; Benzinger, Tammie L.S.; Pa, Judy

doi:10.1016/j.neurobiolaging.2019.10.013

Cited by 26 publications

(25 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding functional connectivity in the EEG/MEG signals in patients with AD, many previous studies have reported a reduction in functional connectivity at the alpha, beta, and gamma bands (38,45,46,64). Further, recent studies of AD pathology have revealed that the reduction in functional connectivity is caused by neuroinflammation and deposition of amyloid-b and tau proteins (65)(66)(67). Similar reductions in functional connectivity were also observed in this study.…”

Section: Resultssupporting

confidence: 88%

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

et al. 2020

View full text Add to dashboard Cite

Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among widespread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-andregional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods

show abstract

Section: Resultssupporting

confidence: 88%

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Serum concentrations of TNF-α are also found to be upregulated in AD and in MCI 34 , and increased TNF-α serum levels have an associated four-fold greater rate of cognitive decline than AD patients with low baseline TNF-α 35 . Elevated cerebrospinal fluid (CSF) levels of TNF-α have positively correlated with APOE4 status and decline in functional connectivity between regions of the brain involving memory, language, and higher decision making 36 . Collectively, these studies suggest an enduring link between high serum and CSF TNF-α levels, inflammation, and progressive cognitive decline in AD.…”

Section: Resultsmentioning

confidence: 99%

Cromolyn inhibits the secretion of inflammatory cytokines by human microglia (HMC3)

Wang

Monteagudo

Downey

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Cromolyn is a known mast cell stabilizer and is approved for treatment of asthma and for other allergic indications. Cromolyn, in a new redesigned dry powder formulation, is being tested in a pivotal clinical trial in combination with low dose ibuprofen to treat early Alzheimer’s Disease (AD) subjects. To better understand the mechanistic effect cromolyn has in slowing down or halting the neuroinflammatory response associated with AD progression, we tested the effect of cromolyn to dampen the inflammatory response in the human HMC3 microglia cell line. The direct effect of cromolyn on HMC3 microglia is on cytokines and chemokines production following their activation by the inflammatory cytokine TNF-α. Cromolyn and a new fluorinated analog dramatically reduced the secretion of a wide spectrum of inflammatory mediators, which included cytokines such as IL-1β, IL-6, IL-8 and IFN-γ, and chemokines such as CXCL10, CCL2, CCL3 and CCL4. These results bolster our understanding of how our cromolyn platform modulates toxic microglia behavior as a dynamic future treatment option for neurodegenerative disorders.

show abstract

“…In corroboration of a role for TNF-α in AD, a strong association between elevated TNF-α levels and decreased functional connectivity in an AD susceptible cohort has been observed at a whole-brain level (34). Aside from TNF-α as a consistently elevated proin ammatory marker in AD, its regulation of the NF-κB in ammatory cascade is well characterized (35,36).…”

Section: Discussionmentioning

confidence: 92%

Higher baseline levels of CSF inflammation increase risk of incident mild cognitive impairment and Alzheimer’s disease dementia

Contreras

Aslanyan

Albrecht

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background Few studies have investigated how inflammation early in the disease course may affect AD progression over time despite converging evidence that elevated levels of inflammation are associated with AD in cross-sectional studies. Methods Two-hundred ninety-two research participants with CSF biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were included in this study. Cox proportional hazards models were used to investigate whether baseline levels of inflammatory CSF markers were associated with incident mild cognitive impairment or AD and time to conversion. Potential moderating effects of sex and APOE4 were also examined. Results Elevated levels of pro-inflammatory markers TNF-α, IL-9, and IL-12p40 at baseline were associated with higher rates of conversion to MCI/AD. Interactions with sex and APOE4 were observed, such that women with elevated TNF- α and all APOE4 carriers with elevated IL-9 levels had shorter times to conversion. Additionally, TNF-α mediated the relationship between elevated IL-12p40 and IL-9. Conclusion Elevated inflammation markers are associated with incident MCI or AD, and the factors of sex and APOE4 status modify the time to conversion.

show abstract

Functional connectivity among brain regions affected in Alzheimer's disease is associated with CSF TNF-α in APOE4 carriers

Cited by 26 publications

References 92 publications

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

Cromolyn inhibits the secretion of inflammatory cytokines by human microglia (HMC3)

Higher baseline levels of CSF inflammation increase risk of incident mild cognitive impairment and Alzheimer’s disease dementia

Contact Info

Product

Resources

About