Can a Resting-State Functional Connectivity Index Identify Patients with Alzheimer's Disease and Mild Cognitive Impairment Across Multiple Sites?

Onoda, Keiichi; Yada, Nobuhiro; Ozasa, Kentaro; Hara, Shinji; Yamamoto, Yasuhiko; Kitagaki, Hajime; Yamaguchi, Shûhei

doi:10.1089/brain.2017.0507

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…In contrast, the activity flow model assumes that the whole-brain activation state translates a connectivity profile into a task activation, consistent with the proposed mechanism of neural activity (and associated cognitive information) propagating across paths weighted by restFC. Another limitation to a reliance on data-driven optimization is the evidenced risk of overfitting to noise in the training set (Onoda et al, 2017;Teipel et al, 2017;Fountain-Zaragoza et al, 2019). Whilst the need to test the generalizability of connectivity-based predictive models across out-of-set samples and scanner sites also applies to activity flow, it is possible that an increased emphasis on neuroscientific theory (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Recent reports have extended towards using restFC to quantitatively predict or classify age-related conditions (Dosenbach et al, 2010;Woo et al, 2017;Du et al, 2018). However, failures of predictive models generalizing out-of-sample (Onoda et al, 2017;Teipel et al, 2017;Fountain-Zaragoza et al, 2019) highlight limitations of entirely data-driven approaches to predicting Alzheimer's-related pathologies, especially as artifactual contaminants of restFC can drive clinical group differences (Siegel et al, 2016;Hodgson et al, 2017). These findings again call for increased efforts to clarify the cognitive relevance of restFC to identify clear mechanisms by which restFC alterations impact on AD and other pathologies.…”

Section: Introductionmentioning

confidence: 99%

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Predicting dysfunctional age-related task activations from resting-state network alterations

Mill

Gordon

Balota

et al. 2019

Preprint

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Number of pages=32 Number of figures=7 Number of words for Abstract=237, Introduction=636, and Discussion=1485Abstract Alzheimer's disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimer's disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimer's-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks. Significance StatementDeveloping analytic approaches that can reliably predict features of Alzheimer's disease is a major goal for cognitive and clinical neuroscience, with particular emphasis on identifying such diagnostic features early in the timeline of disease. We demonstrate the utility of an activity flow modeling approach, which predicts fMRI cognitive task activations in subjects identified as at-risk for Alzheimer's disease. The approach makes activation predictions by transforming a healthy aged activation template via the at-risk subjects' individual pattern of fMRI resting-state functional connectivity (restFC). The observed prediction accuracy supports activity flow as a mechanism linking age-related alterations in restFC and task activations, thereby providing a theoretical basis for incorporating restFC into imaging biomarker and personalized medicine interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting dysfunctional age-related task activations from resting-state network alterations

Mill

Gordon

Balota

et al. 2019

Preprint

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“…With BFN features, machine learning can be of great help for detecting disease-related abnormal patterns and increasing accuracy for individualized MCI diagnosis. Vast majority of these studies have used simple but very effective classifiers, such as support vector machine (SVM) [12], [13]. However, these classifiers might face problem when dealing with the highly-complex spatiotemporal connectomics information and lead to unsatisfactory eMCI diagnostic accuracy, partially due to more subtle changes in the BFNs compared to the late MCI.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection

Kam

Zhang

Jiao

et al. 2020

IEEE Trans. Med. Imaging

119

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While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to restingstate functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, dynamic BFN features can complement static BFN features and, at meantime, different BFNs can help each other towards a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in earlystage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and highdimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for better individualized diagnosis of various neurological diseases.

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“…These included two healthy participants, five MCI patients, and two AD dementia patients. This is a procedure that is widely used to minimise the impact of excessive motion [ 46 – 50 ]. One additional patient with dementia and one additional control were excluded because of signal artefacts.…”

Section: Methodsmentioning

confidence: 99%

A study of within-subject reliability of the brain’s default-mode network

Postema

Marco

Colato

et al. 2019

Magn Reson Mater Phy

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Objective Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer’s disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain. Materials and methods Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability. Results No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients. Discussion Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.

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Can a Resting-State Functional Connectivity Index Identify Patients with Alzheimer's Disease and Mild Cognitive Impairment Across Multiple Sites?

Cited by 12 publications

References 37 publications

Predicting dysfunctional age-related task activations from resting-state network alterations

Predicting dysfunctional age-related task activations from resting-state network alterations

Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection

A study of within-subject reliability of the brain’s default-mode network

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