Altered default mode network connectivity in alzheimer's disease—A resting functional MRI and bayesian network study

Abstract

A number of functional magnetic resonance imaging (fMRI) studies reported the existence of default mode network (DMN) and its disruption due to the presence of a disease such as Alzheimer’s disease (AD). In this current investigation, firstly, we used the independent component analysis (ICA) technique to confirm the DMN difference between patients with AD and normal control (NC) reported in previous studies. Consistent with previous studies, the decreased resting-state functional connectivity of DMN in AD was identified in posterior cingulated cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal cortex (IPC), inferior temporal cortex (ITC) and hippocampus (HC). Moreover, we introduced Bayesian Network (BN) to study the effective connectivity of DMN and the difference between AD and NC. Compared the DMN effective connectivity in AD to the one in NC using a non-parametric random permutation test, we found that connections from left HC to left IPC, left ITC to right HC, right HC to left IPC, to MPFC and to PCC were all lost. In addition, in AD group, the connection directions between right HC and left HC, between left HC and left ITC, and between right IPC and right ITC were opposite to those in NC group. The connections of right HC to other regions, except left HC, within the BN were all statistically in-distinguishable from 0, suggesting an increased right hippocampal pathological and functional burden in AD. The altered effective connectivity in patients with AD may reveal more characteristics of the disease and may serve as a potential biomarker.

Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI

“…As for the effective connectivity method, there are a variety of methods used to evaluate effective connectivity such as structural equation modeling (SEM) (Mclntosh and GonzalezϋLima, 1994;Schlösser et al, 2003), dynamic causal modeling (DCM) (Friston et al, 2003;Friston et al, 2014), granger causality mapping (GCM) (Goebel et al, 2003;Liao et al, 2010) and Bayesian network (BN) (Wu et al, 2011;Zheng and Rajapakse, 2006). Among these methods, both SEM and DCM are model-driven algorithms which need assumption of priori model, hence, may not suited for resting state fMRI (Heckerman, 2008).…”

“…It can reflect how one brain region influences another and can quantitatively depict the strength of such influence (Friston, 1994). Studies have shown that the altered effective connectivity may serve as a potential biomarker to reveal characteristics of neurological disease such as Alzheimer's disease (Wu et al, 2011) and primary progressive aphasia (Sonty et al, 2007). Similarly, the application of effective connectivity in the depression studies may reflect more pathophysiological characteristics of depression patients from a new perspective and potential use as a biomarker.…”

Electroencephalographic Upper/Low Alpha Frequency Power Ratio Relates to Cortex Thinning in Mild Cognitive Impairment

“…Our results are in line with these previous influential studies. A possible integrative view of all the results could be as follows: (1) the higher neuronal activity in the hub regions starts from a dysfunction of cellular inhibition; (2) the consequent disinhibition drives the neural network to an oversynchronization; (3) this oversynchronization is peculiar to the hub regions with a higher amyloid burden; (4) these overactivated regions are prone to degeneration and atrophy; (5) a possible neurophysiological sign of this oversynchronization is the increase in the α 3 /α 2 power ratio we found in typical hub regions [74,75,76,77]. It is of great interest that there is an overlap between the brain regions associated with an increase in the EEG α 3 /α 2 power ratio (hypersynchronization of upper α) in our study and the regions associated with a higher amyloid burden related to memory processes [70,71].…”

Structural correlation in the default mode network in Alzheimer's disease

“…It has been suggested that these brain regions may connect directly, and share a correlative cortical atrophy [13] caused in AD. In addition, Wu et al suggested that the increased correlation may be due to the connection compensation 239 among brain regions [26], that is some brain regions would enhance their own activity to compensate the decline of synchronism in other regions. Moreover, there were some decreased correlations in AD group, such as MPFC and bilateral hippocampus, left hippocampus and left lateral parietal cortex, left and right inferior temporal cortex as well, which could result from lacking mutually trophic influences of anatomical connectivity between these brain regions [13].…”