Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study.

(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. 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). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (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. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.

Frequency Domain Electroretinography in Retinitis Pigmentosa versus Normal Eyes.

PURPOSE: To compare electroretinogram (ERG) characteristics in patients with retinitis pigmentosa (RP) and normal subjects using frequency domain analysis. METHODS: Five basic ERG recordings were performed in normal subjects and patients with a clinical diagnosis of RP according to the ISCEV (International Society of Clinical Electrophysiology of Vision) protocol. Frequency domain analysis was performed by MATLAB software. Different frequency domain parameters were compared between the study groups. RESULTS: Peak frequency (Fmod) of flicker and oscillatory responses in RP patients showed significant (P<0.0001) high pass response as compared to normal controls. Peak frequency (Fmod) of the other responses was not significantly different between the two groups. CONCLUSION: In addition to conventional ERG using time domain methods, frequency domain analysis may be useful for diagnosis of RP. Oscillatory and flicker responses may be analyzed in frequency domain. Fast Fourier transform may reveal two distinct high pass responses (shift to higher frequencies) in Fmod. Time and frequency domain analyses may be performed simultaneously with many modern ERG machines and may therefore be recommended in RP patients.