Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease

Klaassens, Bernadet L.; Gerven, Joop M. A. van; Grond, Jeroen van der; Vos, Frank de; Möller, Christiane; Rombouts, Serge A.R.B.

doi:10.3389/fnagi.2017.00097

Cited by 66 publications

(46 citation statements)

References 92 publications

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“…The precuneus has been shown to be a critical component of the default mode network (DMN) (Cunningham, Tomasi, & Volkow, 2017;Fransson & Marrelec, 2008;Klaassens et al, 2017;Utevsky, Smith, & Huettel, 2014), the network of brain regions implicated in selfreferential thought and activated in the absence of a specific task.…”

Section: Model Resultsmentioning

confidence: 99%

“…White matter tracts through the cuneus connect the precuneus to the parietal lobe (Parker et al, 2014). The precuneus has been shown to be a critical component of the default mode network (DMN) (Cunningham, Tomasi, & Volkow, 2017; Fransson & Marrelec, 2008; Klaassens et al, 2017; Utevsky, Smith, & Huettel, 2014), the network of brain regions implicated in self-referential thought and activated in the absence of a specific task. MDD has been associated with an inability to downregulate the DMN (Sheline et al, 2009), which might be associated with maladaptive rumination and difficulties disengaging from negative cues.…”

Section: Discussionmentioning

confidence: 99%

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Development and evaluation of a multimodal marker of major depressive disorder

Yang

Zhang

Ahn

et al. 2018

Human Brain Mapping

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This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

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Section: Model Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development and evaluation of a multimodal marker of major depressive disorder

Yang

Zhang

Ahn

et al. 2018

Human Brain Mapping

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“…Functional MRI studies allow for the assessment of functional connectivity patterns associated with the generation and modulation of neural networks associated with decreased self-awareness in MCI and AD (Friston, 2011 ). While aging affects functional brain interactions, AD additionally specifically affects coherence between posterior default mode network and precuneus (Klaassens et al, 2017 ). Longitudinal functional connectivity data in AD patients compared to healthy subjects suggest disease-specific affected regions, namely, the frontoparietal network and precuneus (Hafkemeijer et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Functional Neural Correlates of Anosognosia in Mild Cognitive Impairment and Alzheimer’s Disease: a Systematic Review

2019

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Functional neuroimaging techniques (i.e. single photon emission computed tomography, positron emission tomography, and functional magnetic resonance imaging) have been used to assess the neural correlates of anosognosia in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). A systematic review of this literature was performed, following the Preferred Reporting Items for Systematic Reviews and Meta Analyses statement, on PubMed, EMBASE, and PsycINFO databases. Twenty-five articles met all inclusion criteria. Specifically, four brain connectivity and 21 brain perfusion, metabolism, and activation articles. Anosognosia is associated in MCI with frontal lobe and cortical midline regional dysfunction (reduced perfusion and activation), and with reduced parietotemporal metabolism. Reduced within and between network connectivity is observed in the default mode network regions of AD patients with anosognosia compared to AD patients without anosognosia and controls. During initial stages of cognitive decline in anosognosia, reduced indirect neural activity (i.e. perfusion, metabolism, and activation) is associated with the cortical midline regions, followed by the parietotemporal structures in later stages and culminating in frontotemporal dysfunction. Although the current evidence suggests differences in activation between AD or MCI patients with anosognosia and healthy controls, more evidence is needed exploring the differences between MCI and AD patients with and without anosognosia using resting state and task related paradigms. Electronic supplementary material The online version of this article (10.1007/s11065-019-09410-x) contains supplementary material, which is available to authorized users.

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“…We also cite next to each region's name previous literature that studied those regions in the context of Alzheimer's disease or dementia and found significant. These regions are insula (Bonthius, Solodkin and Van Hoesen, 2005), precuneus (Karas et al, 2007;Klaassens et al, 2017), pallidum (Lehéricy et al, 1991), inferior temporal (Scheff et al, 2011), parsorbital (McLimans andWillette, 2016), precentral (Canu et al, 2011), posteriorcingulate (Leech and Sharp, 2013), superior temporal (Gao et al, 2018), superior parietal (Migliaccio et al, 2015), middle temporal (Jack et al, 1998), paracentral (Karavasilis et al, 2017) and caudal middle frontal (Bakkour et al, 2013).…”

Section: Bayes Graphonmentioning

confidence: 99%

Bayesian modeling of the structural connectome for studying Alzheimer’s disease

Roy¹,

Ghosal²,

Prescott³

et al. 2019

Ann. Appl. Stat.

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We study possible relations between Alzheimer's disease progression and the structure of the connectome which is white matter connecting different regions of the brain. Regression models in covariates including age, gender, and disease status for the extent of white matter connecting each pair of regions of the brain are proposed. Subject inhomogeneity is also incorporated in the model through random effects with an unknown distribution. As there is a large number of pairs of regions, we also adopt a dimension reduction technique through graphon (Lovász and Szegedy (2006)) functions, which reduces the functions of pairs of regions to functions of regions. The connecting graphon functions are considered unknown but the assumed smoothness allows putting priors of low complexity on these functions. We pursue a nonparametric Bayesian approach by assigning a Dirichlet process scale mixture of zero to mean normal prior on the distributions of the random effects and finite random series of tensor products of B-splines priors on the underlying graphon functions. We develop efficient Markov chain Monte Carlo techniques for drawing samples for the posterior distributions using Hamiltonian Monte Carlo (HMC). The proposed Bayesian method overwhelmingly outperforms a competing method based on ANCOVA models in the simulation setup. The proposed Bayesian approach is applied on a dataset of 100 subjects and 83 brain regions and key regions implicated in the changing connectome are identified.

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Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease

Cited by 66 publications

References 92 publications

Development and evaluation of a multimodal marker of major depressive disorder

Development and evaluation of a multimodal marker of major depressive disorder

Functional Neural Correlates of Anosognosia in Mild Cognitive Impairment and Alzheimer’s Disease: a Systematic Review

Bayesian modeling of the structural connectome for studying Alzheimer’s disease

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