» Articles » PMID: 35198768

MRI Biomarkers for Alzheimer's Disease: the Impact of Functional Connectivity in the Default Mode Network and Structural Connectivity Between Lobes on Diagnostic Accuracy

Overview
Journal Heliyon
Specialty Social Sciences
Date 2022 Feb 24
PMID 35198768
Authors
Affiliations
Soon will be listed here.
Abstract

Background: At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency.

Methods: 20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi.Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age.

Results: Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05).Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age.

Conclusions: Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.

Citing Articles

Advances in the fMRI analysis of the default mode network: a review.

Sanz-Morales E, Melero H Brain Struct Funct. 2024; 230(1):22.

PMID: 39738718 DOI: 10.1007/s00429-024-02888-z.


Which neuroimaging and fluid biomarkers method is better in theranostic of Alzheimer's disease? An umbrella review.

Mohammadi H, Ariaei A, Ghobadi Z, Charkhat Gorgich E, Rustamzadeh A IBRO Neurosci Rep. 2024; 16:403-417.

PMID: 38497046 PMC: 10940808. DOI: 10.1016/j.ibneur.2024.02.007.


Beyond CSF and Neuroimaging Assessment: Evaluating Plasma miR-145-5p as a Potential Biomarker for Mild Cognitive Impairment and Alzheimer's Disease.

Wen Q, Wittens M, Engelborghs S, van Herwijnen M, Tsamou M, Roggen E ACS Chem Neurosci. 2024; 15(5):1042-1054.

PMID: 38407050 PMC: 10921410. DOI: 10.1021/acschemneuro.3c00740.


Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer's disease.

Mao X, Han D, Guo W, Zhang W, Wang H, Zhang G Geroscience. 2023; 46(2):2295-2315.

PMID: 37940789 PMC: 10828179. DOI: 10.1007/s11357-023-01000-3.


Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments.

Ghaderi S, Mohammadi S, Mohammadi M Brain Behav. 2023; 13(11):e3262.

PMID: 37743582 PMC: 10636416. DOI: 10.1002/brb3.3262.


References
1.
Morrison J, Hof P . Selective vulnerability of corticocortical and hippocampal circuits in aging and Alzheimer's disease. Prog Brain Res. 2002; 136:467-86. DOI: 10.1016/s0079-6123(02)36039-4. View

2.
Cardoso M, Leung K, Modat M, Keihaninejad S, Cash D, Barnes J . STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation. Med Image Anal. 2013; 17(6):671-84. DOI: 10.1016/j.media.2013.02.006. View

3.
Caso F, Agosta F, Filippi M . Insights into White Matter Damage in Alzheimer's Disease: From Postmortem to in vivo Diffusion Tensor MRI Studies. Neurodegener Dis. 2015; 16(1-2):26-33. DOI: 10.1159/000441422. View

4.
Basser P, Mattiello J, Lebihan D . MR diffusion tensor spectroscopy and imaging. Biophys J. 1994; 66(1):259-67. PMC: 1275686. DOI: 10.1016/S0006-3495(94)80775-1. View

5.
Raffelt D, Tournier J, Fripp J, Crozier S, Connelly A, Salvado O . Symmetric diffeomorphic registration of fibre orientation distributions. Neuroimage. 2011; 56(3):1171-80. DOI: 10.1016/j.neuroimage.2011.02.014. View