Physiological fluctuations in white matter are increased in Alzheimer's disease and correlate with neuroimaging and cognitive biomarkers

Makedonov, Ilia; Chen, Jean; Masellis, Mario; MacIntosh, Bradley J.

doi:10.1016/j.neurobiolaging.2015.09.010

Cited by 49 publications

(74 citation statements)

References 47 publications

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“…As CV has been successfully used in various blood flow‐related signal measurement techniques in different scales from mouse microscopy to macroscopic human data (Jahanian et al., 2014; Kalchenko, Kuznetsov, Meglinski, & Harmelin, 2012; Makedonov et al., 2013, 2016), it would be interesting if one could use it in multimodal imaging as a way to produce scale invariant measures that could combine animal model data to clinical human brain data.…”

Section: Discussionmentioning

confidence: 99%

“…This assumption of identical signal distributions may not be true, since recently BOLD signal noise characteristics have been shown to be altered by disease (Khalil et al., 2017; Makedonov, Black, & MacIntosh, 2013; Makedonov, Chen, Masellis, & MacIntosh, 2016; Tuovinen et al., 2017). A recently emerged metric to assess BOLD signal properties is the coefficient of variation (CV), which has previously been used to reflect stability of a measured process.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, CV has also been shown to reflect changes in physiological fluctuations in BOLD signal in white matter. Moreover, altered values of CV have been detected in acute ischemic stroke (Khalil et al., 2017), Alzheimer's disease (Makedonov et al., 2016; Tuovinen et al., 2017), and small vessel disease (Makedonov et al., 2013). …”

Section: Introductionmentioning

confidence: 99%

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Altered physiological brain variation in drug‐resistant epilepsy

et al. 2018

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IntroductionFunctional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG‐fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level‐dependent signal, suggesting intrinsic alterations in the underlying brain physiology.MethodsIn this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra‐fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug‐resistant epilepsy (DRE).ResultsWe showed highly significant voxel‐level (p < 0.01, TFCE‐corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CVMREG values solely in DRE patients, enabling patient‐specific mapping of elevated physiological variance. The most apparent differences in group‐level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12–0.4 Hz) and very‐low‐frequency (VLF = 0.009–0.1 Hz) signal variances were most affected.ConclusionsThe CVMREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Altered physiological brain variation in drug‐resistant epilepsy

et al. 2018

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“…It is problematic to simply extrapolate such findings either for or against the validity of WM fMRI. Furthermore, artifacts of motion (Johnstone et al 2006), partial-volume (Jo et al 2010), and physiological origin (Makedonov et al 2015) are also of concern in WM fMRI. Hence, the fMRI signal in WM has an unclear basis and an inherently low signal to noise ratio (SNR); as such, it has been dismissed from analysis or interpretation in the vast majority of fMRI studies.…”

Section: Introductionmentioning

confidence: 99%

Mapping white-matter functional organization at rest and during naturalistic visual perception

Marussich

Wen

et al. 2017

NeuroImage

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Despite wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans in the resting state or watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were denoised and decomposed into spatially independent components, further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to the resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts.

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“…MCI patients had MMSE scores between 26 and 30, and a CDR score of 0.5. Participants in the AD cohort had MMSE scores between 17 and 26, and fulfilled the NINCDS‐ADRA (National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association) criteria for probable AD (Makedonov, Chen, Masellis, & MacIntosh, ).…”

Section: Resultsmentioning

confidence: 99%

A concise and persistent feature to study brain resting‐state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Kuang

Chen

Caselli

et al. 2018

Human Brain Mapping

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Alzheimer’s disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of non-invasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature – IPF) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1024 region-of-interests (ROIs). The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI’s, and much lower than normal control’s, providing empirical evidence for decreased functional integration in AD dementia and MCI.

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Physiological fluctuations in white matter are increased in Alzheimer's disease and correlate with neuroimaging and cognitive biomarkers

Cited by 49 publications

References 47 publications

Altered physiological brain variation in drug‐resistant epilepsy

Altered physiological brain variation in drug‐resistant epilepsy

Mapping white-matter functional organization at rest and during naturalistic visual perception

A concise and persistent feature to study brain resting‐state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

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