The Disconnection Hypothesis in Alzheimer’s Disease Studied Through Multimodal Magnetic Resonance Imaging: Structural, Perfusion, and Diffusion Tensor Imaging

Lacalle‐Aurioles, María; Navas‐Sánchez, Francisco J.; Alemán‐Gómez, Yasser; Olazarán, Javier; Guzmán-De-Villoria, Juan A; Cruz-Orduña, Isabel; Mateos-Pérez, Jose María; Desco, Manuel

doi:10.3233/jad-150288

Cited by 17 publications

(17 citation statements)

References 70 publications

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“…The top 5 brain connections listed in Table 3 have much higher selected frequency times than others, which may serve as the more promising connectivity-based biomarker for AD diagnosis. These results are totally consistent with previous findings, and added new findings to the disconnection hypothesis of the AD (Delbeuck et al, 2003 ; Lacalle-Aurioles et al, 2016 ). The brain regions identified in the top 5 connections are frequently reported as highly associated with the AD pathology.…”

Section: Discussionsupporting

confidence: 92%

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

Liu

Huang

et al. 2018

Front. Neuroinform.

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Background/Aims: Brain functional connectivity networks constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for classifying Alzheimer's disease (AD) from normal controls (NC). However, conventional correlation analysis methods only capture the pairwise information, which may not be capable of revealing an adequate and accurate functional connectivity relationship among brain regions in the whole brain. Additionally, the non-sparse connectivity networks commonly contain a large number of spurious or insignificant connections, which are inconsistent with the sparse connectivity of actual brain networks in nature and may deteriorate the classification performance of Alzheimer's disease.Methods: To address these problems, in this paper, a new classification framework is proposed by combining the Group-constrained topology structure detection with sparse inverse covariance estimation (SICE) method to build the functional brain sub-network for each brain region. Particularly, to tune the sensitive analysis of the regularized parameters in the SICE method, a nested leave-one-out cross-validation (LOOCV) method is adopted. Sparse functional connectivity networks are thus effectively constructed by using the optimal regularized parameters. Finally, a decision classification tree (DCT) classifier is trained for classifying AD from NC based on these optimal functional brain sub-networks. The convergence performance of our proposed method is furthermore evaluated by the trend of coefficient variation.Results: Experiment results indicate that a LOOCV classification accuracy of 81.82% with a sensitivity of 80.00%, and a specificity of 83.33% can be obtained by using the proposed method for the classification AD from NC, and outperforms the most state-of-the-art methods in terms of the classification accuracy. Additionally, the experiment results of the convergence performance further suggest that our proposed scheme has a high rate of convergence. Particularly, the abnormal brain regions and functional connections identified by our proposed framework are highly associated with the underpinning pathological mechanism of the AD, which are consistent with previous studies.Conclusion: These results have demonstrated the effectiveness of the proposed Group- constrained SICE method, and are capable of clinical value to the diagnosis of Alzheimer's disease.

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Section: Discussionsupporting

confidence: 92%

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

Liu

Huang

et al. 2018

Front. Neuroinform.

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“…As shown in Figure S3 of the Supplementary Material, we observed decreasing DC values in the right hippocampus, dependent on AD disease state. These results demonstrate potential isolation of the hippocampus from the rest of the brain in AD subjects [51].…”

Section: Discussionmentioning

confidence: 58%

Functional Connectivity between the Resting-State Olfactory Network and the Hippocampus in Alzheimer’s Disease

Testa

Jordan

et al. 2019

Brain Sciences

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Olfactory impairment is associated with prodromal Alzheimer’s disease (AD) and is a risk factor for the development of dementia. AD pathology is known to disrupt brain regions instrumental in olfactory information processing, such as the primary olfactory cortex (POC), the hippocampus, and other temporal lobe structures. This selective vulnerability suggests that the functional connectivity (FC) between the olfactory network (ON), consisting of the POC, insula and orbital frontal cortex (OFC) (Tobia et al., 2016), and the hippocampus may be impaired in early stage AD. Yet, the development trajectory of this potential FC impairment remains unclear. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to investigate FC changes between the ON and hippocampus in four groups: aged-matched cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. FC was calculated using low frequency fMRI signal fluctuations in the ON and hippocampus (Tobia et al., 2016). We found that the FC between the ON and the right hippocampus became progressively disrupted across disease states, with significant differences between EMCI and LMCI groups. Additionally, there were no significant differences in gray matter hippocampal volumes between EMCI and LMCI groups. Lastly, the FC between the ON and hippocampus was significantly correlated with neuropsychological test scores, suggesting that it is related to cognition in a meaningful way. These findings provide the first in vivo evidence for the involvement of FC between the ON and hippocampus in AD pathology. Results suggest that functional connectivity (FC) between the olfactory network (ON) and hippocampus may be a sensitive marker for Alzheimer’s disease (AD) progression, preceding gray matter volume loss.

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“…These results direct future research toward an integrative approach in studying ON dysfunction in AD. This approach may be critical to chisel out the disconnection pathophysiology potentially leading to AD dementia (Delbeuck, Van der Linden, & Collette, ; Lacalle‐Aurioles et al., ). In sum, olfactory fMRI and behavioral testing when coupled with the ON‐DMN network model will provide a unique opportunity to directly and noninvasively address the functional consequences of neuropathological changes in the AD brain.…”

Section: Discussionmentioning

confidence: 99%

Disruptions of the olfactory and default mode networks in Alzheimer's disease

Yang

Zhang

et al. 2019

Brain and Behavior

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Introduction Olfactory deficits are prevalent in early Alzheimer's disease (AD) and are predictive of progressive memory loss and dementia. However, direct neural evidence to relate AD neurodegeneration to deficits in olfaction and memory is limited. Methods We combined the University of Pennsylvania Smell Identification Test (UPSIT) with olfactory functional magnetic resonance imaging (fMRI) to investigate links between neurodegeneration, the olfactory network (ON) and the default mode network (DMN) in AD. Results Behaviorally, olfactory and memory scores showed a strong positive correlation in the study cohorts. During olfactory fMRI, the ON showed reduced task‐related activation and the DMN showed reduced task‐related suppression in mild cognitive impairment (MCI) and AD subjects compared to age‐matched cognitively normal subjects. Conclusions The results provide in vivo evidence for selective vulnerability of ON and DMN in AD and significantly improves the viable clinical applications of olfactory testing. A network‐based approach, focusing on network integrity rather than focal pathology, seems beneficial to olfactory prediction of dementia in AD.

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The Disconnection Hypothesis in Alzheimer’s Disease Studied Through Multimodal Magnetic Resonance Imaging: Structural, Perfusion, and Diffusion Tensor Imaging

Cited by 17 publications

References 70 publications

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

Functional Connectivity between the Resting-State Olfactory Network and the Hippocampus in Alzheimer’s Disease

Disruptions of the olfactory and default mode networks in Alzheimer's disease

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