Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease

Balthazar, Márcio Luiz Figueredo; Campos, Brunno M.; Franco, Alexandre R.; Damasceno, Benito Pereira; Cendes, Fernando

doi:10.1016/j.pscychresns.2013.10.010

Cited by 62 publications

(56 citation statements)

References 27 publications

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“…A previous study has applied this approach to rs-fMRI data of people with mild to moderate AD dementia from one scanner (Schouten et al, 2016), reaching 77% accuracy in the mild AD subgroup. In our multicenter study, cross-validated accuracy of 80% discrimination between AD cases and controls from elastic net regression was higher than the accuracy in this previous study (Schouten et al, 2016), but still lower than results from previous monocenter studies lacking cross-validation (Koch et al, 2012; Balthazar et al, 2014). Our findings level of accurcy agrees with estimates from previous cross-validated monocenter studies using non-linear machine learning techniques for classification (Challis et al, 2015; Dyrba et al, 2015).…”

Section: Discussioncontrasting

confidence: 76%

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Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

Teipel

Grothe

Metzger

et al. 2017

Front. Aging Neurosci.

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The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD.

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Section: Discussioncontrasting

confidence: 76%

“…Previous research on rs-fMRI in AD dementia has often focused on the DMN regions (Greicius et al, 2004; Koch et al, 2012; Balthazar et al, 2014). This approach reduces potential problems from collinearity through a priori feature selection.…”

Section: Discussionmentioning

confidence: 99%

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

Teipel

Grothe

Metzger

et al. 2017

Front. Aging Neurosci.

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“…Our findings demonstrate that variables known to influence pain perception are highly important to consider in the development of functional neuroimaging biomarkers. Specifically, we found that mood altered fcMRI of the DMN, which has been named as a candidate biomarker for a wide variety of clinical conditions [3,8,28,37,61,79,92], including at least five chronic pain conditions [13,48,55,73,91]. …”

Section: Discussionmentioning

confidence: 99%

Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers

Letzen

2016

PAIN

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The default mode network (DMN) has been proposed as a biomarker for several chronic pain conditions. DMN functional connectivity (fcMRI) is typically examined during resting-state fMRI, in which participants are instructed to let thoughts wander. However, factors at the time of data collection (e.g., negative mood) that might systematically impact pain perception and its brain activity, influencing the application of the DMN as a pain biomarker, are rarely reported. The present study measured whether positive and negative moods altered DMN fcMRI patterns in chronic low back pain (CLBP) patients, specifically focusing on negative mood due to its clinical-relevance. Thirty-three participants (CLBP = 17) underwent resting-state fMRI scanning before and after sad and happy mood inductions, and rated levels of mood and pain intensity at the time of scanning. Two-way repeated measures ANOVAs were conducted on resting-state functional connectivity data. Significant group (CLBP > HC) X condition (sadness > baseline) interaction effects were identified in clusters spanning parietal operculum/postcentral gyrus, insular cortices, anterior cingulate cortex, frontal pole, and a portion of the cerebellum (pFDR < .05). However, only one significant cluster covering a portion of the cerebellum was identified examining a two-way repeated measures ANOVA for happiness > baseline (pFDR < .05). Overall, these findings suggest that DMN fcMRI is affected by negative mood in individuals with and without CLBP. It is possible that DMN fcMRI seen in chronic pain patients is related to an affective dimension of pain, which is important to consider in future neuroimaging biomarker development and implementation.

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“…Imaging of these functional networks offers the opportunity to study brain function and dysfunction in AD and bvFTD [8,12]. AD patients show abnormalities in functional network connectivity in the posterior hippocampal-cingulo-temporal-parietal network known as the default mode network [13][14][15][16]. Patients with bvFTD show functional connectivity abnormalities in the anterior frontoinsular-cingulo-orbitofrontal network often called the salience network [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer’s Disease

Hafkemeijer

Möller

Dopper

et al. 2016

JAD

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Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease

Cited by 62 publications

References 27 publications

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer’s Disease

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