P3-353: DIFFERENTIAL ASSOCIATIONS BETWEEN VOLUMES OF ATROPHIC CORTICAL BRAIN REGIONS AND MEMORY PERFORMANCES IN EARLY AND LATE MILD COGNITIVE IMPAIRMENT

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.

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

10.1101/2020.05.11.089383 ◽

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)

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

NeuroImage ◽

10.1016/s1053-8119(09)70698-6 ◽

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 ◽

Default Mode

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Differential Associations Between Volumes of Atrophic Cortical Brain Regions and Memory Performances in Early and Late Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2019.00245 ◽

2019 ◽

Vol 11 ◽

Cited By ~ 3

Author(s):

Dong Woo Kang ◽

Hyun Kook Lim ◽

Soo-hyun Joo ◽

Na Rae Lee ◽

Chang Uk Lee

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Regions

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Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer’s Disease Dementia

Cerebral Cortex ◽

10.1093/cercor/bhz286 ◽

2020 ◽

Vol 30 (5) ◽

pp. 2948-2960 ◽

Cited By ~ 6

Author(s):

Nicholas M Vogt ◽

Jack F Hunt ◽

Nagesh Adluru ◽

Douglas C Dean ◽

Sterling C Johnson ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cortical Thickness ◽

Gray Matter ◽

Brain Regions ◽

Cortical Atrophy ◽

Microstructural Alterations ◽

Additional Information

Abstract In Alzheimer’s disease (AD), neurodegenerative processes are ongoing for years prior to the time that cortical atrophy can be reliably detected using conventional neuroimaging techniques. Recent advances in diffusion-weighted imaging have provided new techniques to study neural microstructure, which may provide additional information regarding neurodegeneration. In this study, we used neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion model, in order to investigate cortical microstructure along the clinical continuum of mild cognitive impairment (MCI) and AD dementia. Using gray matter-based spatial statistics (GBSS), we demonstrated that neurite density index (NDI) was significantly lower throughout temporal and parietal cortical regions in MCI, while both NDI and orientation dispersion index (ODI) were lower throughout parietal, temporal, and frontal regions in AD dementia. In follow-up ROI analyses comparing microstructure and cortical thickness (derived from T1-weighted MRI) within the same brain regions, differences in NODDI metrics remained, even after controlling for cortical thickness. Moreover, for participants with MCI, gray matter NDI—but not cortical thickness—was lower in temporal, parietal, and posterior cingulate regions. Taken together, our results highlight the utility of NODDI metrics in detecting cortical microstructural degeneration that occurs prior to measurable macrostructural changes and overt clinical dementia.

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A Study on PHF-Tau Network Effected by Apolipoprotein E4

American Journal of Alzheimer s Disease & Other Dementias® ◽

10.1177/1533317520971414 ◽

2020 ◽

Vol 35 ◽

pp. 153331752097141

Author(s):

Yuan Li ◽

Zhijun Yao ◽

Yongqing Yang ◽

Feng Zhao ◽

Yu Fu ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Tau Protein ◽

Paired Helical Filaments ◽

Brain Regions ◽

Primary Role ◽

Node Failure ◽

Random Node ◽

Network Properties ◽

Tau Pet

Apolipoprotein E 4 Allele (APOE 4) is an important factors in Mild cognitive impairment (MCI) and Alzheimer’s disease(AD). It plays a primary role in abnormal modification of aggregated Tau protein-paired helical filaments Tau (PHF-Tau). In this study, 143 subjects with PHF-Tau PET were divided into 2 groups (APOE 4 carriers and noncarriers). The measurements of the PHF-Tau network properties and resilient were calculated for 2 group networks respectively. APOE 4 carriers group showed significant differences in all the network properties in the results. We also found significant differences of betweenness centrality in some brain regions for APOE 4 carriers. Moreover, the APOE 4 carriers showed less resilient to targeted or random node failure. Our results indicated that the effects of APOE 4 may lead to abnormalities of PHF-Tau protein network. These findings may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

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Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2013/127271 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Shangjie Chen ◽

Lijun Bai ◽

Maosheng Xu ◽

Fang Wang ◽

Liang Yin ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Granger Causality ◽

Effective Connectivity ◽

Brain Networks ◽

Brain Regions ◽

Causality Analysis ◽

Granger Causality Analysis ◽

Cognitive States ◽

Dorsolateral Prefrontal

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

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Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on 3D-Convolutional Neural Network

10.1101/2019.12.20.884932 ◽

2019 ◽

Author(s):

Jinhyeong Bae ◽

Jane Stocks ◽

Ashley Heywood ◽

Youngmoon Jung ◽

Lisanne Jenkins ◽

...

Keyword(s):

Neural Network ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Brain Regions ◽

Pharmacological Intervention ◽

Dementia Of Alzheimer’S Type ◽

3D Cnn

AbstractDementia of Alzheimer’s Type (DAT) is associated with a devastating and irreversible cognitive decline. As a pharmacological intervention has not yet been developed to reverse disease progression, preventive medicine will play a crucial role for patient care and treatment planning. However, predicting which patients will progress to DAT is difficult as patients with Mild Cognitive Impairment (MCI) could either convert to DAT (MCI-C) or not (MCI-NC). In this paper, we develop a deep learning model to address the heterogeneous nature of DAT development. Structural magnetic resonance imaging was utilized as a single biomarker, and a three-dimensional convolutional neural network (3D-CNN) was developed. The 3D-CNN was trained using transfer learning from the classification of Normal Control and DAT scans at the source task. This was applied to the target task of classifying MCI-C and MCI-NC scans. The model results in 82.4% classification accuracy, which outperforms current models in the field. Furthermore, by implementing an occlusion map approach, we visualize key brain regions that significantly contribute to the prediction of MCI-C and MCI-NC. Results show the hippocampus, amygdala, cerebellum, and pons regions as significant to prediction, which are consistent with current understanding of disease. Finally, the model’s prediction value is significantly correlated with rates of change in clinical assessment scores, indicating the model is able to predict an individual patient’s future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI. This model could also be useful for selection of participants for clinical trials.

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A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.774607 ◽

2021 ◽

Vol 13 ◽

Author(s):

Cuibai Wei ◽

Shuting Gong ◽

Qi Zou ◽

Wei Zhang ◽

Xuechun Kang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Path Length ◽

Brain Networks ◽

Brain Regions ◽

Clustering Coefficient ◽

Characteristic Path Length ◽

Glucose Intake ◽

Item Scores ◽

Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

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A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Sensors ◽

10.3390/s21165416 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5416

Author(s):

Fatma El-Zahraa A. El-Gamal ◽

Mohammed Elmogy ◽

Ali Mahmoud ◽

Ahmed Shalaby ◽

Andrew E. Switala ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Brain Regions ◽

Computer Aided Diagnosis ◽

Cortical Region ◽

Diagnosis System ◽

Computer Aided ◽

Aided Diagnosis

Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

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