Background Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting‐state functional MRI (rs‐fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. Purpose To quantitatively assess FC patterns of resting‐state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late‐onset AD from normal. Study Type Prospective. Subjects A total of 14 normal, 16 aMCI, and 13 late‐onset AD. Field Strength/Sequence A 3.0 T; rs‐fMRI: single‐shot 2D‐EPI and T1‐weighted structure: MPRAGE. Assessment By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI‐to‐ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. Statistical Tests Region of interest (ROI)‐based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)‐corrected P < 0.05 cluster‐level threshold together with posthoc uncorrected P < 0.05 connection‐level threshold. Graph‐theory analysis (GTA): P‐FDR‐corrected < 0.05. One‐way ANOVA and Chi‐square tests were used to compare clinical characteristics. Results PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global‐efficiency (28.05 < 45), local‐efficiency (22.98 < 24.05), and betweenness‐centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local‐efficiency (33.46 > 24.05) and clustering‐coefficient (25 > 20.18) were found in aMCI compared to normal. Data Conclusion This study demonstrated resting‐state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. Evidence Level 1 Technical Efficacy Stage 2

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Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

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