(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.

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

Introduction: Long bone segmental deficiencies are challenging complications to treat. Hereby, the effects of the scaffold derived from the human demineralized bone matrix (hDBMS) plus human adipose stem cells (hADSs) plus photobiomodulation (PBM) (in vitro and or in vivo) on the catabolic step of femoral bone repair in rats with critical size femoral defects (CDFDs) were evaluated with stereology and high stress load (HSL) assessment methods. Methods: hADSs were exposed to PBM in vitro; then, the mixed influences of hDBMS+hADS+PBM on CSFDs were evaluated. CSFDs were made on both femurs; then hDBMSs were engrafted into both CSFDs of all rats. There were 6 groups (G)s: G1 was the control; in G2 (hADS), hADSs only were engrafted into hDBMS of CSFD; in G3 (PBM) only PBM therapy for CSFD was provided; in G4 (hADS+PBM in vivo), seeded hADSs on hDBMS of CSFDs were radiated with a laser in vivo; in G5 (hADSs+PBM under in vitro condition), hADSs in a culture system were radiated with a laser, then transferred on hDBMS of CSFDs; and in G6 (hADS+PBM in conditions of in vivo and in vitro), laser-exposed hADSs were transplanted on hDBMS of CSFDs, and then CSFDs were exposed to a laser in vivo. Results: Groups 4, 5, and 6 meaningfully improved HSLs of CSFD in comparison with groups 3, 1, and 2 (all, P=0.001). HSL of G5 was significantly more than G4 and G6 (both, P=0.000). Gs 6 and 4 significantly increased new bone volumes of CSFD compared to Gs 2 (all, P=0.000) and 1 (P=0.001 & P=0.003 respectively). HSL of G 1 was significantly lower than G5 (P=0.026). Conclusion: HSLs of CSFD in rats that received treatments of hDBMS plus hADS plus PBM were significantly higher than treatments with hADS and PBM alone and control groups.

Alzheimer's disease (AD) is a network connection dysfunction syndrome. An approximate picture of functional integration and statistical dependence on responses between different regions of the brain can be defined by functional connectivity (FC). Explanation of the statistical dependencies and estimating how the dynamics of neurons affect each other remotely is done by effective connectivity (EC). Studying directional interactions between different regions of the brain plays a key role in our understanding of the functional integration of brain networks.