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.

P2-256: Differential Regional Cerebral Hypometabolism Patterns According to the Level of Cerebral Amyloid Deposition in Mild Cognitive Impairment

2016 ◽  
Vol 12 ◽  
pp. P724-P725
Author(s):  
So Yeon Jeon ◽  
Dahyun Yi ◽  
Min Soo Byun ◽  
Hyo Jung Choi ◽  
Hyun Jung Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Deposition ◽  
Cerebral Amyloid

scholarly journals In vivo staging of regional amyloid deposition

Neurology ◽  
2017 ◽  
Vol 89 (20) ◽  
pp. 2031-2038 ◽  
Author(s):  
Michel J. Grothe ◽  
Henryk Barthel ◽  
Jorge Sepulcre ◽  
Martin Dyrba ◽  
Osama Sabri ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Amyloid Deposition ◽  
Stage Model ◽  
Older Individuals ◽  
Full Cohort ◽  
Cross Sectional ◽  
Amyloid Pathology

Objectives:To estimate a regional progression pattern of amyloid deposition from cross-sectional amyloid-sensitive PET data and evaluate its potential for in vivo staging of an individual's amyloid pathology.Methods:Multiregional analysis of florbetapir (18F-AV45)–PET data was used to determine individual amyloid distribution profiles in a sample of 667 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including cognitively normal older individuals (CN) as well as patients with mild cognitive impairment and Alzheimer disease (AD) dementia. The frequency of regional amyloid positivity across CN individuals was used to construct a 4-stage model of progressing amyloid pathology, and individual distribution profiles were used to evaluate the consistency of this hierarchical stage model across the full cohort.Results:According to a 4-stage model, amyloid deposition begins in temporobasal and frontomedial areas, and successively affects the remaining associative neocortex, primary sensory-motor areas and the medial temporal lobe, and finally the striatum. Amyloid deposition in these brain regions showed a highly consistent hierarchical nesting across participants, where only 2% exhibited distribution profiles that deviated from the staging scheme. The earliest in vivo amyloid stages were mostly missed by conventional dichotomous classification approaches based on global florbetapir-PET signal, but were associated with significantly reduced CSF Aβ42 levels. Advanced in vivo amyloid stages were most frequent in patients with AD and correlated with cognitive impairment in individuals without dementia.Conclusions:The highly consistent regional hierarchy of PET-evidenced amyloid deposition across participants resembles neuropathologic observations and suggests a predictable regional sequence that may be used to stage an individual's progress of amyloid pathology in vivo.

AMPLITUDE OF LOW FREQUENCY FLUCTUATION AT DIFFERENT FREQUENCY BANDS IN EARLY AMNESTIC MILD COGNITIVE IMPAIRMENT: RESULTS FROM ADNI

2012 ◽  
Vol 05 (01) ◽  
pp. 1150003 ◽  
Author(s):  
LONG-LONG JING ◽  
LI-YU HUANG ◽  
DENG-FENG HUANG ◽  
JIE NIU ◽  
ZHENG ZHONG
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Frequency Band ◽  
Inferior Frontal Gyrus ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Amnestic Mild Cognitive Impairment ◽  
Posterior Lobe ◽  
Frequency Bands ◽  
Normal Controls

We used resting-state functional magnetic resonance imaging (fMRI) to determine whether there are any abnormalities in different frequency bands between amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) and between 10 early amnestic mild cognitive impairment (EMCI) patients and eight normal controls participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We showed widespread difference in ALFF/fALFF between two frequency bands (slow-4: 0.027–0.073 Hz, slow-5: 0.01–0.027 Hz) in many brain areas including posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), suprasellar cistern (SC) and ambient cistern (AC). Compared to the normal controls, the EMCI patients showed increased ALFF values in PCu , cerebellum, occipital lobe and cerebellum posterior lobe in frequency band slow-4. While in frequency band slow-5, the EMCI patients showed decreased ALFF values in temporal lobe, left cerebrum and middle temporal gyrus5. Moreover, the EMCI patients showed increased fALFF values in frontal lobe and inferior frontal gyrus in band slow-5. While in frequency band slow-4, the EMCI patients showed decreased fALFF values in limbic lobe, cingulate gyrus and corpus callosum. These results demonstrated that EMCI patients had widespread abnormalities of amplitude of LFF in different frequency bands.

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 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.

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