scholarly journals A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Min Wang ◽  
Zhuangzhi Yan ◽  
Shu-yun Xiao ◽  
Chuantao Zuo ◽  
Jiehui Jiang
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Inferior Frontal Gyrus ◽  
Predictive Performance ◽  
Brain Regions ◽  
Precentral Gyrus ◽  
Metabolic Dysfunction ◽  
Feature Extraction Method ◽  
Positron Emission ◽  
Metabolic Connectivity

Objective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. Methods. In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. Results. As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. Conclusion. Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.

scholarly journals β-Amyloid is Associated with Aberrant Metabolic Connectivity in Subjects with Mild Cognitive Impairment

2014 ◽  
Vol 34 (7) ◽  
pp. 1169-1179 ◽  
Author(s):  
Felix Carbonell ◽  
Arnaud Charil ◽  
Alex P Zijdenbos ◽  
Alan C Evans ◽  
Barry J Bedell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Process ◽  
Brain Regions ◽  
Apoe Ε4 ◽  
Positron Emission ◽  
Β Amyloid ◽  
Glucose Hypometabolism

Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein ε4 (APOE ε4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ε4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ε4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns.

scholarly journals Precuneus Structure Changes in Amnestic Mild Cognitive Impairment

2016 ◽  
Vol 32 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Robert Haussmann ◽  
Annett Werner ◽  
Antonia Gruschwitz ◽  
Antje Osterrath ◽  
Jan Lange ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Structural Changes ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Network Characteristics ◽  
Structure Changes ◽  
Positron Emission ◽  
Higher Cognitive Functions

Patients with amnestic mild cognitive impairment (aMCI) are at risk for developing Alzheimer’s disease. Due to their prominent memory impairment, structural magnetic resonance imaging (MRI) often focuses on the hippocampal region. However, recent positron-emission tomography data suggest that within a network of frontal and temporal changes, patients with aMCI show metabolic alterations in the precuneus, a key region for higher cognitive functions. Using high-resolution MRI and whole-brain cortical thickness analyses in 28 patients with aMCI and 25 healthy individuals, we wanted to investigate whether structural changes in the precuneus would be associated with cortical thickness reductions in frontal and temporal brain regions in patients with aMCI. In contrast to healthy people, patients with aMCI showed an association of cortical thinning in the precuneus with predominantly left-hemispheric thickness reductions in medial temporal and frontal cortices. Our data highlight structural neuronal network characteristics among patients with aMCI.

scholarly journals EEG-fMRI Signal Coupling Is Modulated in Subjects With Mild Cognitive Impairment and Amyloid Deposition

2021 ◽  
Vol 13 ◽  
Author(s):  
Lars Michels ◽  
Florian Riese ◽  
Rafael Meyer ◽  
Andrea M. Kälin ◽  
Sandra E. Leh ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Inferior Frontal Gyrus ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Amyloid Deposition ◽  
Brain Physiology ◽  
Amyloid Pathology ◽  
Fmri Signal ◽  
Cerebral Amyloid

Cognitive impairment indicates disturbed brain physiology which can be due to various mechanisms including Alzheimer's pathology. Combined functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings (EEG-fMRI) can assess the interplay between complementary measures of brain activity and EEG changes to be localized to specific brain regions. We used a two-step approach, where we first examined changes related to a syndrome of mild cognitive impairment irrespective of pathology and then studied the specific impact of amyloid pathology. After detailed clinical and neuropsychological characterization as well as a positron emission tomography (PET) scans with the tracer 11-[C]-Pittsburgh Compound B to estimate cerebral amyloid deposition, 14 subjects with mild cognitive impairment (MCI) (mean age 75.6 SD: 8.9) according to standard criteria and 21 cognitively healthy controls (HCS) (mean age 71.8 SD: 4.2) were assessed with EEG-fMRI. Thalamo-cortical alpha-fMRI signal coupling was only observed in HCS. Additional EEG-fMRI signal coupling differences between HCS and MCI were observed in parts of the default mode network, salience network, fronto-parietal network, and thalamus. Individuals with significant cerebral amyloid deposition (amyloid-positive MCI and HCS combined compared to amyloid-negative HCS) displayed abnormal EEG-fMRI signal coupling in visual, fronto-parietal regions but also in the parahippocampus, brain stem, and cerebellum. This finding was paralleled by stronger absolute fMRI signal in the parahippocampus and weaker absolute fMRI signal in the inferior frontal gyrus in amyloid-positive subjects. We conclude that the thalamocortical coupling in the alpha band in HCS more closely reflects previous findings observed in younger adults, while in MCI there is a clearly aberrant coupling in several networks dominated by an anticorrelation in the posterior cingulate cortex. While these findings may broadly indicate physiological changes in MCI, amyloid pathology was specifically associated with abnormal fMRI signal responses and disrupted coupling between brain oscillations and fMRI signal responses, which especially involve core regions of memory: the hippocampus, para-hippocampus, and lateral prefrontal cortex.

scholarly journals Distinctive Alterations of Functional Connectivity Strength between Vascular and Amnestic Mild Cognitive Impairment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hui Li ◽  
Shuai Gao ◽  
Xiuqin Jia ◽  
Tao Jiang ◽  
Kuncheng Li
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Resting State Functional Connectivity ◽  
Functional Connectivity Strength ◽  
Connectivity Strength

Widespread structural and functional alterations have been reported in the two highly prevalent mild cognitive impairment (MCI) subtypes, amnestic MCI (aMCI) and vascular MCI (VaMCI). However, the changing pattern in functional connectivity strength (FCS) remains largely unclear. The aim of the present study is to detect the differences of FCS and to further explore the detailed resting-state functional connectivity (FC) alterations among VaMCI subjects, aMCI subjects, and healthy controls (HC). Twenty-six aMCI subjects, 31 VaMCI participants, and 36 HC participants underwent cognitive assessments and resting-state functional MRI scans. At first, one-way ANCOVA and post hoc analysis indicated significant decreased FCS in the left middle temporal gyrus (MTG) in aMCI and VaMCI groups compared to HC, especially in the VaMCI group. Then, we selected the left MTG as a seed to further explore the detailed resting-state FC alterations among the three groups, and the results indicated that FC between the left MTG and some frontal brain regions were significantly decreased mainly in VaMCI. Finally, partial correlation analysis revealed that the FC values between the left MTG and left inferior frontal gyrus were positively correlated with the cognitive performance episodic memory and negatively related to the living status. The present study demonstrated that different FCS alterations existed in aMCI and VaMCI. These findings may provide a novel insight into the understanding of pathophysiological mechanisms underlying different MCI subtypes.

scholarly journals Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tun-Wei Hsu ◽  
Jong-Ling Fuh ◽  
Da-Wei Wang ◽  
Li-Fen Chen ◽  
Chia-Jung Chang ◽  
...  
Keyword(s):  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Network Connectivity ◽  
Brain Regions ◽  
Imaging Data ◽  
Cholinergic Pathways ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
pp. 1-14
Author(s):  
Fangmei He ◽  
Yuchen Zhang ◽  
Xiaofeng Wu ◽  
Youjun Li ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Microstructure Changes ◽  
Disease Trajectory ◽  
Development Trajectory ◽  
The Right

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer’s disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient’s Mini-Mental State Examination scores. Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

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)

scholarly journals Predicting the future development of mild cognitive impairment in the cognitively healthy elderly

2020 ◽  
Author(s):  
Bryan Strange ◽  
Linda Zhang ◽  
Alba Sierra-Marcos ◽  
Eva Alfayate ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Memory ◽  
Statistical Tests ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Healthy Individuals ◽  
Healthy Elderly ◽  
Out Of Sample

Identifying measures that predict future cognitive impairment in healthy individuals is necessary to inform treatment strategies for candidate dementia-preventative and modifying interventions. Here, we derive such measures by studying converters who transitioned from cognitively normal at baseline to mild-cognitive impairment (MCI) in a longitudinal study of 1213 elderly participants. We first establish reduced grey matter density (GMD) in left entorhinal cortex (EC) as a biomarker for impending cognitive decline in healthy individuals, employing a matched sampling control for several dementia risk-factors, thereby mitigating the potential effects of bias on our statistical tests. Next, we determine the predictive performance of baseline demographic, genetic, neuropsychological and MRI measures by entering these variables into an elastic net-regularized classifier. Our trained statistical model classified converters and controls with validation Area-Under-the-Curve>0.9, identifying only delayed verbal memory and left EC GMD as relevant predictors for classification. This performance was maintained on test classification of out-of-sample converters and controls. Our results suggest a parsimonious but powerful predictive model for MCI development in the cognitively healthy elderly.

The Effects of Mild Cognitive Impairment (MCI) on Longitudinal Volume Change of Default Mode Brain Regions

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S90
Author(s):  
LL Beason-Held ◽  
I Driscoll ◽  
Y An ◽  
C Davatzikos ◽  
SM Resnick
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Volume Change ◽  
Brain Regions ◽  
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