scholarly journals Large-Scale Functional Brain Network Abnormalities in Alzheimer’s Disease: Insights from Functional Neuroimaging

2009 ◽  
Vol 21 (1-2) ◽  
pp. 63-75 ◽  
Author(s):  
Bradford C. Dickerson ◽  
Reisa A. Sperling
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medial Temporal Lobe ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Memory System ◽  
Fmri Data ◽  
Therapeutic Monitoring ◽  
Functional Brain Network

Functional MRI (fMRI) studies of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have begun to reveal abnormalities in large-scale memory and cognitive brain networks. Since the medial temporal lobe (MTL) memory system is a site of very early pathology in AD, a number of studies have focused on this region of the brain. Yet it is clear that other regions of the large-scale episodic memory network are affected early in the disease as well, and fMRI has begun to illuminate functional abnormalities in frontal, temporal, and parietal cortices as well in MCI and AD. Besides predictable hypoactivation of brain regions as they accrue pathology and undergo atrophy, there are also areas of hyperactivation in brain memory and cognitive circuits, possibly representing attempted compensatory activity. Recent fMRI data in MCI and AD are beginning to reveal relationships between abnormalities of functional activity in the MTL memory system and in functionally connected brain regions, such as the precuneus. Additional work with “resting state” fMRI data is illuminating functional-anatomic brain circuits and their disruption by disease. As this work continues to mature, it will likely contribute to our understanding of fundamental memory processes in the human brain and how these are perturbed in memory disorders. We hope these insights will translate into the incorporation of measures of task-related brain function into diagnostic assessment or therapeutic monitoring, which will hopefully one day be useful for demonstrating beneficial effects of treatments being tested in clinical trials.

scholarly journals Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

scholarly journals Spatial inhibition of return is impaired in mild cognitive impairment and mild Alzheimer’s disease

2020 ◽  
Author(s):  
Xiong Jiang ◽  
James H. Howard ◽  
G. Wiliam Rebeck ◽  
R. Scott Turner
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Medial Temporal Lobe ◽  
Inhibition Of Return ◽  
Memory Performance ◽  
Brain Regions ◽  
List Type ◽  
Older Individuals

ABSTRACTSpatial inhibition of return (IOR) refers to the phenomenon by which individuals are slower to respond to stimuli appearing at a previously cued location compared to un-cued locations. Here we provide evidence supporting that spatial IOR is mildly impaired in individuals with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD), and the impairment is readily detectable using a novel double cue paradigm. Furthermore, reduced spatial IOR in high-risk healthy older individuals is associated with reduced memory and other neurocognitive task performance, suggesting that the novel double cue spatial IOR paradigm may be useful in detecting MCI and early AD.SIGNIFICANCE STATEMENTNovel double cue spatial inhibition of return (IOR) paradigm revealed a robust effect IOR deficits in individuals with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD)Spatial IOR effect correlates with memory performance in healthy older adults at a elevated risk of Alzheimer’s disease (with a family history or APOE e4 allele)The data suggests that double cue spatial IOR may be sensitive to detect early AD pathological changes, which may be linked to disease progress at the posterior brain regions (rather than the medial temporal lobe)

Comparison of [11C]UCB-J and [18F]FDG PET in Alzheimer’s disease: A tracer kinetic modeling study

2021 ◽  
pp. 0271678X2110043
Author(s):  
Ming-Kai Chen ◽  
Adam P Mecca ◽  
Mika Naganawa ◽  
Jean-Dominique Gallezot ◽  
Takuya Toyonaga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Volume Of Distribution ◽  
Synaptic Density ◽  
Cognitively Normal ◽  
Tracer Kinetic ◽  
Suitable Marker

[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer’s disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or Ki R to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD –compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.

scholarly journals COMBINING MACHINE LEARNING WITH AUTOMATED NEMATODE LIFESPAN ANALYSIS TO IDENTIFY MODIFIERS OF ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S96-S96
Author(s):  
Joshua Russell ◽  
Matt Kaeberlein
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Data Sets ◽  
Data Types ◽  
C Elegans

Abstract Here we present new computational and experimental methods to leverage the gene expression and neuropathology data collected from several large-scale studies of Alzheimer’s disease . These data sets include diverse data types, including transcriptomics, neuropathology phenotypes such as quantification of amyloid beta plaques and tau tangles in different brain regions, as well as assessments of dementia prior to death. This meta-analysis is a complex undertaking because the available data are from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. We have therefore taken neural network and probabilistic computational approaches that reduce the data dimensionality, allowing statistical comparison across all brain samples. These approaches identify gene expression changes that are significantly associated with clinical and neuropathological assessment of Alzheimer’s disease. We then conduct in vivo validation of the genes through genetic screening of C. elegans models of Alzheimer's disease utilizing our automated robotic lifespan analysis platform. This approach allows for the greater leverage of existing Alzheimer’s disease biobank data to identify deep genetic signatures that could help identify new clinical gene-expression markers and pharmacological targets for Alzheimer’s disease.

scholarly journals Decoding brain functional connectivity implicated in AD and MCI

2019 ◽  
Author(s):  
Sukrit Gupta ◽  
Yi Hao Chan ◽  
Jagath C. Rajapakse ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Diagnostic Systems ◽  
Neuroimaging Data ◽  
Brain States ◽  
High Level ◽  
Brain Functional Connectivity

AbstractDeep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient features characterizing brain states further refines the focus of clinicians and allows design of better diagnostic systems. We demonstrate this while performing classification of resting-state functional magnetic resonance imaging (fMRI) scans of patients suffering from Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We use a 5-layer feed-forward deep neural network (DNN) to derive relevance scores of input features and show that an empirically selected subset of features improves accuracy scores for patient classification. The common distinctive salient brain regions were in the uncus and medial temporal lobe which closely correspond with previous studies. The proposed methods have cross-modal applications with several neuropsychiatric disorders.

scholarly journals Deciphering cellular transcriptional alterations in Alzheimer’s disease brains

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Shaoyu Li ◽  
Zachary S. Quicksall ◽  
Tulsi A. Patel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Complex Disease ◽  
Individual Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Bioinformatics Pipeline ◽  
Dorsolateral Prefrontal ◽  
Transcriptional Alterations

Abstract Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

scholarly journals Large-Scale Deep Multi-Layer Analysis of Alzheimer’s Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level

2021 ◽  
Author(s):  
Erik C.B. Johnson ◽  
E. Kathleen Carter ◽  
Eric B. Dammer ◽  
Duc M. Duong ◽  
Ekaterina S. Gerasimov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Large Scale ◽  
Brain Regions ◽  
Mapk Signaling ◽  
Proteomic Data ◽  
Tandem Mass Tag ◽  
Within Subjects ◽  
Decline Disease

AbstractThe biological processes that are disrupted in the Alzheimer’s disease (AD) brain remain incompletely understood. We recently performed a proteomic analysis of >2000 brains to better understand these changes, which highlighted alterations in astrocytes and microglia as likely key drivers of disease. Here, we extend this analysis by analyzing >1000 brain tissues using a tandem mass tag mass spectrometry (TMT-MS) pipeline, which allowed us to nearly triple the number of quantified proteins across cases. A consensus protein co-expression network analysis of this deeper dataset revealed new co-expression modules that were highly preserved across cohorts and brain regions, and strongly altered in AD. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism, and a module related to the matrisome. Analysis of paired genomic and proteomic data within subjects showed that expression level of the matrisome module was influenced by the APOE ε4 genotype, but was not related to the rate of cognitive decline after adjustment for neuropathology. In contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

scholarly journals A Whole-Brain Computational Modeling Approach to Explain the Alterations in Resting-State Functional Connectivity during Progression of Alzheimer’s Disease

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Juan Domingo Gispert ◽  
José Luis Molinuevo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Modeling Approach

AbstractUnderstanding the mechanisms behind Alzheimer’s disease (AD) is one of the most challenging problems in neuroscience. Recent efforts provided valuable insights on the genetic, biochemical and neuronal correlates of AD. The advances in structural and functional neuroimaging provided massive evidence for the AD related alterations in brain connectivity. In this study, we investigated the whole-brain resting state functional connectivity (FC) and variability in dynamic functional connectivity (v-FC) of the subjects with preclinical condition (PC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The synchronization in the whole-brain was monotonously decreasing during the course of the progression. However, only in the AD group the reduced synchronization produced significant widespread effects in FC. Furthermore, we found elevated variability of FC in PC group, which was reversed in AD group. We proposed a whole-brain computational modeling approach to study the mechanisms behind these alterations. We estimated the effective connectivity (EC) between brain regions in the model to reproduce observed FC of each subject. First, we compared ECs between groups to identify the changes in underlying connectivity structure. We found that the significant EC changes were restricted to temporal lobe. Then, based on healthy control subjects we systematically manipulated the dynamics in the model to investigate its effect on FC. The model showed FC alterations similar to those observed in clinical groups providing a mechanistic explanation to AD progression.

scholarly journals Deciphering cellular transcriptional alterations in Alzheimer’s disease brains

2020 ◽  
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Shaoyu Li ◽  
Zachary S. Quicksall ◽  
Tulsi A. Patel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Complex Disease ◽  
Individual Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Bioinformatics Pipeline ◽  
Dorsolateral Prefrontal ◽  
Transcriptional Alterations

AbstractLarge-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

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