scholarly journals A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease

2021 ◽  
Vol 17 (8) ◽  
pp. e1009252
Author(s):  
Luke Tait ◽  
Marinho A. Lopes ◽  
George Stothart ◽  
John Baker ◽  
Nina Kazanina ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Healthy Aging ◽  
Computational Modelling ◽  
Brain Network ◽  
Healthy Controls ◽  
Network Organization ◽  
Cortical Tissue

People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

scholarly journals A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer’s Disease

2021 ◽  
Author(s):  
Luke Tait ◽  
Marinho A Lopes ◽  
George Stothart ◽  
John Baker ◽  
Nina Kazanina ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Structure ◽  
Large Scale ◽  
Network Models ◽  
Brain Network ◽  
Healthy Controls ◽  
Cortical Tissue ◽  
Whole Brain ◽  
Network Mechanism

AbstractPeople with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider increased excitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, both local dynamics and large-scale brain network structure are believed to be crucial for determining seizure likelihood and phenotype. In this study, we combine computational modelling with electrophysiological data to demonstrate a potential large-scale brain network mechanism for increased seizure propensity in people with AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions using an approach previously validated on participants with epilepsy vs controls. As cortical tissue excitability was increased in the simulations, network models of AD simulations were more likely to transition into seizures than simulations from healthy controls. Our results suggest an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. These included cingulate, medial temporal, and orbital regions. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with amyloid/tau pathology and cortical atrophy.Author summaryPeople with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.

scholarly journals Comparison of Amyloid β and Tau Spread Models in Alzheimer’s Disease

2018 ◽  
Vol 29 (10) ◽  
pp. 4291-4302 ◽  
Author(s):  
Hang-Rai Kim ◽  
Peter Lee ◽  
Sang Won Seo ◽  
Jee Hoon Roh ◽  
Minyoung Oh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Amyloid Β ◽  
Brain Network ◽  
Amyloid Positron Emission Tomography ◽  
Local Spread ◽  
Positron Emission ◽  
Spread Model ◽  
Graph Theoretical Approach

Abstract Tau and amyloid β (Aβ), 2 key pathogenic proteins in Alzheimer’s disease (AD), reportedly spread throughout the brain as the disease progresses. Models of how these pathogenic proteins spread from affected to unaffected areas had been proposed based on the observation that these proteins could transmit to other regions either through neural fibers (transneuronal spread model) or through extracellular space (local spread model). In this study, we modeled the spread of tau and Aβ using a graph theoretical approach based on resting-state functional magnetic resonance imaging. We tested whether these models predict the distribution of tau and Aβ in the brains of AD spectrum patients. To assess the models’ performance, we calculated spatial correlation between the model-predicted map and the actual map from tau and amyloid positron emission tomography. The transneuronal spread model predicted the distribution of tau and Aβ deposition with significantly higher accuracy than the local spread model. Compared with tau, the local spread model also predicted a comparable portion of Aβ deposition. These findings provide evidence of transneuronal spread of AD pathogenic proteins in a large-scale brain network and furthermore suggest different contributions of spread models for tau and Aβ in AD.

scholarly journals Modeling the β secretase cleavage site and humanizing Amyloid-Beta Precursor Protein in rat and mouse to study Alzheimer Disease.

2020 ◽  
Author(s):  
Lutgarde Serneels ◽  
Dries T'Syen ◽  
Laura Perez-Benito ◽  
Tom Theys ◽  
Bart De Strooper
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amino Acid ◽  
Alzheimer Disease ◽  
Early Onset ◽  
Computational Modelling ◽  
Original Strain ◽  
Rodent Models ◽  
App Processing

Abstract Background Three amino acid differences between rodent and human APP affect medically important features including β-secretase cleavage of APP and aggregation of the Aβ peptide(1–3). Most rodent models for Alzheimer’s disease (AD) are therefore based on the human APP sequence expressed from artificial mini-genes randomly inserted in the rodent genome. While these models mimic rather well biochemical aspects of the disease such as Aβ-aggregation, they are also prone to overexpression artifacts and to complex phenotypical alterations due to genes affected in or close to the insertion sites of the mini-genes(4,5). Knock-in strategies introducing clinical mutants in a humanized endogenous rodent APP sequence(6) represent useful improvements, but need to be compared with appropriate humanized wild type (WT) mice.Methods Computational modelling of the human β-CTF bound to BACE1 was used to study the differential processing of rodent and human APP. We humanized the three pivotal residues G676R, F681Y and R684H (labeled according to the human APP770 isoform) in the mouse as well as in the rat by a CRISPR-Cas9 approach. These new models, termed mouse and rat App hu/hu , express APP from the endogenous promotor. We also introduced the early-onset familial Alzheimer’s disease (FAD) mutation M139T into the endogenous Rat Psen 1 gene.Results We show that the three amino acid substitutions in the rodent sequence lower the affinity of APP substrate for BACE1 cleavage. The effect on β-secretase processing was confirmed as both humanized rodent models produce three times more (human) Aβ compared to their WT rodent original strain. These models represent suitable controls or starting points for studying the effect of transgenes or knock-in mutations on APP processing(6). We introduced the early-onset familial Alzheimer disease (FAD) mutation M139T into the endogenous Rat Psen 1 gene and provide an initial characterization of Aβ processing in this novel rat AD model.Conclusion The different humanized APP models (rat and mouse) expressing human Aβ and PSEN1 M139T are valuable controls to study APP processing in vivo and allow to implement the use of human Aβ Elisa which is more sensitive than their rodent counterpart. These animals will be made available to the research community.

P3-308: Functional brain network organization in Alzheimer's disease

2013 ◽  
Vol 9 ◽  
pp. P670-P670 ◽  
Author(s):  
Hanneke de Waal ◽  
Cornelis Stam ◽  
Marieke Lansbergen ◽  
F. Maestú ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Network ◽  
Network Organization ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Resting-State Connectivity of Auditory and Reward Systems in Alzheimer’s Disease and Mild Cognitive Impairment

2020 ◽  
Author(s):  
Diana Wang ◽  
Alexander Belden ◽  
Suzanne Hanser ◽  
Maiya R. Geddes ◽  
Psyche Loui
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
Healthy Aging ◽  
Superior Temporal Gyrus ◽  
Healthy Controls ◽  
Reward Systems

AbstractMusic-based interventions have become increasingly widely adopted for dementia and related disorders. Previous research shows that music engages reward-related regions through functional connectivity with the auditory system. Here we characterize intrinsic connectivity of the auditory and reward systems in healthy aging, mild cognitive impairment (MCI) - a predementia phase of cognitive dysfunction, and Alzheimer’s disease (AD). Using resting-state fMRI data from the Alzheimer’s Database Neuroimaging Initiative, we tested functional connectivity within and between auditory and reward systems in older adults with MCI, AD, and age-matched healthy controls (N=105). Seed-based correlations were assessed from regions of interest (ROIs) in the auditory network, i.e. anterior superior temporal gyrus (aSTG), posterior superior temporal gyrus (pSTG), Heschl’s Gyrus, and reward network (i.e., nucleus accumbens, caudate, putamen, and orbitofrontal cortex [OFC]). AD individuals were lower in both within-network and between-network functional connectivity in the auditory network and reward networks compared to MCI and healthy controls. Furthermore, graph theory analyses showed that MCI individuals had higher clustering, local efficiency, degrees, and strengths than both AD individuals and healthy controls. Together, the auditory and reward systems show preserved within- and between-network connectivity in MCI relative to AD. These results suggest that music-based interventions have the potential to make an early difference in individuals with MCI, due to the preservation of functional connectivity in reward-related regions and between auditory and reward networks at that initial stage of neurodegeneration.

scholarly journals Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer’s disease

2020 ◽  
Vol 6 (40) ◽  
pp. eabc5802
Author(s):  
Qi Zhang ◽  
Cheng Ma ◽  
Lih-Shen Chin ◽  
Lian Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Large Scale ◽  
Site Occupancy ◽  
Glycosylation Site ◽  
System Level ◽  
Disease Signatures ◽  
And Control

Protein N-glycosylation plays critical roles in controlling brain function, but little is known about human brain N-glycoproteome and its alterations in Alzheimer’s disease (AD). Here, we report the first, large-scale, site-specific N-glycoproteome profiling study of human AD and control brains using mass spectrometry–based quantitative N-glycoproteomics. The study provided a system-level view of human brain N-glycoproteins and in vivo N-glycosylation sites and identified disease signatures of altered N-glycopeptides, N-glycoproteins, and N-glycosylation site occupancy in AD. Glycoproteomics-driven network analysis showed 13 modules of co-regulated N-glycopeptides/glycoproteins, 6 of which are associated with AD phenotypes. Our analyses revealed multiple dysregulated N-glycosylation–affected processes and pathways in AD brain, including extracellular matrix dysfunction, neuroinflammation, synaptic dysfunction, cell adhesion alteration, lysosomal dysfunction, endocytic trafficking dysregulation, endoplasmic reticulum dysfunction, and cell signaling dysregulation. Our findings highlight the involvement of N-glycosylation aberrations in AD pathogenesis and provide new molecular and system-level insights for understanding and treating AD.

P4-164: Mapping neuroinflammation in vivo in healthy aging and Alzheimer's disease: A PET study using a novel translocator protein 18kDA (TSPO) radioligand, [18F]-FEPPA

2012 ◽  
Vol 8 (4S_Part_19) ◽  
pp. P693-P693 ◽  
Author(s):  
Ivonne Suridjan ◽  
Rusjan Pablo ◽  
Bruce Pollock ◽  
Aristotle Voineskos ◽  
Alan Wilson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Translocator Protein

scholarly journals Imaging beta-amyloid (Aβ) burden in the brains of middle-aged individuals with alcohol-use disorders: a [11C]PIB PET study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margaret R. Flanigan ◽  
Sarah K. Royse ◽  
David P. Cenkner ◽  
Katelyn M. Kozinski ◽  
Clara J. Stoughton ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Alcohol Use ◽  
Cortical Thickness ◽  
Pet Imaging ◽  
Healthy Controls ◽  
Middle Aged ◽  
Neurocognitive Dysfunction ◽  
Amyloid Aβ

AbstractNo in vivo human studies have examined the prevalence of Alzheimer’s disease (AD) neuropathology in individuals with alcohol-use disorder (AUD), although recent research suggests that a relationship between the two exists. Therefore, this study used Pittsburgh Compound-B ([11C]PiB) PET imaging to test the hypothesis that AUD is associated with greater brain amyloid (Aβ) burden in middle-aged adults compared to healthy controls. Twenty healthy participants (14M and 6F) and 19 individuals with AUD (15M and 4F), all aged 40–65 years, underwent clinical assessment, MRI, neurocognitive testing, and positron emission tomography (PET) imaging. Global [11C]PiB standard uptake value ratios (SUVRs), cortical thickness, gray matter volumes (GMVs), and neurocognitive function in subjects with AUD were compared to healthy controls. These measures were selected because they are considered markers of risk for future AD and other types of neurocognitive dysfunction. The results of this study showed no significant differences in % global Aβ positivity or subthreshold Aβ loads between AUD and controls. However, relative to controls, we observed a significant 6.1% lower cortical thickness in both AD-signature regions and in regions not typically associated with AD, lower GMV in the hippocampus, and lower performance on tests of attention as well as immediate and delayed memory in individuals with AUD. This suggest that Aβ accumulation is not greater in middle-aged individuals with AUD. However, other markers of neurodegeneration, such as impaired memory, cortical thinning, and reduced hippocampal GMV, are present. Further studies are needed to elucidate the patterns and temporal staging of AUD-related pathophysiology and cognitive impairment. Imaging β-amyloid in middle age alcoholics as a mechanism that increases their risk for Alzheimer’s disease; Registration Number: NCT03746366.

scholarly journals Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability

2021 ◽  
Vol 1 (3) ◽  
pp. 201-210
Author(s):  
Michael Keegan ◽  
Hava T. Siegelmann ◽  
Edward A. Rietman ◽  
Giannoula Lakka Klement ◽  
Jack A. Tuszynski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Free Energy ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Neurological Diseases ◽  
Pearson Correlation ◽  
Brain Network ◽  
Network Organization ◽  
Protein Protein Interactions

Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, have begun to change the paradigms of neurodegenerative disorders. In this paper, we explore the use of thermodynamics for protein–protein network interactions in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. To assess the validity of using network interactions in neurological diseases, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. We found a linear correlation (Pearson correlation of −0.828) between disease disability (a clinically validated measurement of a person’s functional status) and Gibbs free energy (a thermodynamic measure of protein–protein interactions). In other words, we found an inverse relationship between disease disability and thermodynamic energy. Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer’s disease could potentially be prevented by therapeutically correcting the changes in Gibbs free energy.

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