In these patients, decreased connectivity within the DMN were associated with higher connectivity strength in the SN, possibly due to compensatory mechanisms. The SN, involved in autonomic and homeostatic processing, has shown increased connectivity in AD patients. al 2017, in a resting-state DCM study, reported aberrancies in the triple network’s (salience network (SN), executive control network, and DMN) interaction in MCI compared to the normal group, and they attributed these disorders to cognitive impairment. They found altered causal interactions in the resting-state network of AD patients (weaker interactions in the default mode network (DMN) and more robust causal connectivity in the memory network and executive control network), and they inferred that these findings may help in determining the neurophysiological mechanisms. 2012 performed GCA to evaluate the EC among the resting-state networks. This DCM estimates the directional connectivity in coupled populations of neurons and offers a way to understand interregional interactions between predefined brain areas in resting-state networks. spDCM is used to model the intrinsic dynamics of a resting-state network and to infer the interaction between resting-state latent neural states. Among them, spectral DCM (or spDCM) is an efficient biological model that explains and predicts BOLD signals caused by endogenous neural oscillations. Various DCM methods were developed to infer directional effects in brain regions. DCM, one of the most widely used modeling frameworks for EC inference from fMRI data, is a generative model of latent neural states. Different methods of signal pathway investigation in fMRI, such as Granger Causality Analysis (GCA) and dynamic causal modeling )DCM(, have shown altered EC in resting-state networks of AD patients compared to the normal participants. Deposition of pathologic markers of Alzheimer’s disease in the brain region can affect the direction of the signal path. This can provide a good understanding of the patterns of interaction between different areas of the brain. The study of effective connectivity (EC) or directional effects allows the evaluation of intensity flow and signaling pathways in functional regions, which aids in diagnosing and predicting therapeutic responses in neurological and mental disorders. ![]() Effective connectivity can serve as a potential marker of Alzheimer’s pathophysiology, even in the early stages of the disease. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. (1) Background: Alzheimer’s disease (AD) is a neurodegenerative disease with a high prevalence.
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