Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction

Brand, Lodewijk; Nichols, Kai; Wang, Hua; Shen, Li; Huang, Heng

doi:10.1109/tmi.2019.2958943

Cited by 56 publications

(19 citation statements)

References 26 publications

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“…To date, there is no treatment for AD, but some interventions, including pharmacological (Massoud and Léger, 2011) and non-pharmacological (Shigihara et al, 2020;Zucchella et al, 2018) can decelerate its progress in particular when it is detected at an early stage (Yiannopoulou and Papageorgiou, 2020). Predicting AD progression and differentiating different stages of this disease are thus essential steps in early medical intervention for this mental disorder (Badhwar et al, 2017;Brand et al, 2019;Gupta et al, 2019;Kruthika et al, 2019;Lee, Garam, 2019;. Also, knowing that the initial stages of dementia show heterogeneous symptoms across patients, identifying individuals at risk for progression from mild cognitive impairment (MCI) to early or late dementia is challenging (Komarova and Thalhauser, 2011).…”

Section: Introductionmentioning

confidence: 99%

Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

Sendi

Zendehrouh

et al. 2021

Preprint

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BackgroundAlzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.MethodsGroup independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression.ResultsProgression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.ConclusionOur results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.Impact StatementBy assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.

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Section: Introductionmentioning

confidence: 99%

Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

Sendi

Zendehrouh

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Specifically, we first transformed the generated 3D information map of a subject into the same size as the input sMRI image using Eq. [6]. We then selected the 40th, 50th, 60th, and 70th 2D maps from the coronal, sagittal, and transverse views of the transformed 3D information map, respectively.…”

Section: Evaluation Of the Ismentioning

confidence: 99%

“…This is because brain changes induced by AD have been proven to occur 10-15 years before symptom onset (4), and these brain changes can be noninvasively captured by sMRI images (5). Currently, sMRI images are extensively employed for computer-aided MCI/AD diagnosis based on machine learning methods (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Among these methods, the convolutional neural network (CNN)-based methods (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23) have demonstrated outstanding performance due to their excellent ability to extract task-driven features.…”

Section: Introductionmentioning

confidence: 99%

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

Zhang

Gao

Jin

et al. 2021

Quant Imaging Med Surg

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Background: To assist doctors to diagnose mild cognitive impairment (MCI) and Alzheimer's disease (AD) early and accurately, convolutional neural networks based on structural magnetic resonance imaging (sMRI) images have been developed and shown excellent performance. However, they are still limited in their capacity in extracting discriminative features because of large sMRI image volumes yet small lesion regions and the small number of sMRI images.Methods: We proposed a task-driven hierarchical attention network (THAN) taking advantage of the merits of patch-based and attention-based convolutional neural networks for MCI and AD diagnosis. THAN consists of an information sub-network and a hierarchical attention sub-network. In the information subnetwork, an information map extractor, a patch-assistant module, and a mutual-boosting loss function are designed to generate a task-driven information map, which automatically highlights disease-related regions and their importance for final classification. In the hierarchical attention sub-network, a visual attention module and a semantic attention module are devised based on the information map to extract discriminative features for disease diagnosis.Results: Extensive experiments were conducted for four classification tasks: MCI versus (vs.) normal controls (NC), AD vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. Results demonstrated that THAN attained the accuracy of 81.6% for MCI vs. NC, 93.5% for AD vs. NC, 80.8% for AD vs. MCI, and 62.9% for AD vs. MCI vs. NC. It outperformed advanced attention-based and patch-based methods. Moreover, information maps generated by the information sub-network could highlight the potential biomarkers of MCI and AD, such as the hippocampus and ventricles. Furthermore, when the visual and semantic attention modules were combined, the performance of the four tasks was highly improved. Conclusions:The information sub-network can automatically highlight the disease-related regions.The hierarchical attention sub-network can extract discriminative visual and semantic features. Through the two sub-networks, THAN fully exploits the visual and semantic features of disease-related regions and meanwhile considers global features of sMRI images, which finally facilitate the diagnosis of MCI and AD.

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“…For example, those based on hierarchical, partitioning and model-based clustering algorithms/methods ( Dong et al, 2016 ; Racine et al, 2016 ; Dong et al, 2017 ; ten Kate et al, 2018 ; Young et al, 2018 ). Moreover, various machine learning and other statistical approaches have been proposed for both disease progression, prediction and subgroup identification in Alzheimer’s disease ( Fiot et al, 2014 ; Schmidt-Richberg et al, 2016 ; Cheng et al, 2017 ; Bhagwat et al, 2018 ; Khanna et al, 2018 ; de Jong et al, 2019 ; Martí-Juan et al, 2019 ; Brand et al, 2020 ; Golriz Khatami et al, 2020 ; Lei et al, 2020 ; Martí-Juan et al, 2020 ; Lin et al, 2021 ; Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

et al. 2021

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A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.

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Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction

Cited by 56 publications

References 26 publications

Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

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