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

(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.

Purpose: The purpose of this study is to evaluate the brain alterations in epileptic patients and normal adults in order to help differential diagnosis using volumetric Magnetic Resonance Imaging (MRI). Materials and Methods: The study case group included 11 subjects, 6 patients of whom with focal and secondary generalized seizures and 5 of whom were healthy people as a control group. Measurements and evaluations of the brain important regions were performed with volBrain software within 4 different pipelines. Results: Statistical results showed that the significant quantitative assessments were observed in the areas as follows: right Hippocampus (P-value<0.05), right cerebellar (P-value<0.1), thalamus Asymmetry (Pvalue<0.1), right CA1 (P-value<0.1), left SR-SL-SM (P-value<0.1), right subiculum (P-value<0.1), left cerebellum cortical thickness (P-value<0.05) and some cerebellar lobules. Conclusion: Structural MRI demonstrated significant brain alterations in epileptic subjects comparing normal adults. Assessment of brain lesions did not show any defect in Brain which implies that patients have disappearing lesions caused by seizures. Significant quantitative assessments were shown in the right lobule III, lobule IX mean cortical thickness, right cerebellum grey matter, right hippocampus and right cerebellar areas

Purpose: A powerful imaging method for evaluating brain patches is resting-state functional Magnetic Resonance (rs-fMRI) Imaging, in which the subject is at rest. Artificial Neural Networks (ANN) are one of the several Alzheimer's Disease (AD) analysis and diagnosis methods used in this study. We investigate ANNs' ability to diagnose AD using rs-fMRI data. Materials and Methods: The acquisition of functional and structural magnetic resonance imaging was applied for 15 AD, 17 mild cognitive impairment, and ten normal healthy participants. Time series of blood oxygen level-dependent were extracted from the multi-subject dictionary learning brain atlas after pre-processing. This study develops a one-dimensional Convolutional Neural Network (CNN) using extracted signals of the functional atlas for differential diagnosis of AD. Results: Applying the proposed method to rs-fMRI signals for classifying three classes of Alzheimer’s patients resulted in overall accuracy, F1-score, and precision of 0.685, 0.663, and 0.681, respectively. Using 39 regions in the brain and proposing a quite simple network than most of the available deep learning-based methods are the main advantages of this model. Conclusion: rs-fMRI signal recognition based on a functional atlas with the application of a deep neural network has a pattern recognition capability that can make a differential diagnosis with an acceptable level of accuracy and precision. Therefore, deep neural networks can be considered as a tool for the early diagnosis of AD.

The core mission of clinical MRI in Human Brain Mapping (HBM) is formed in a cycle of research, education and practice. Learning the effective diagnostic and treatment planning procedures occurs not in the classrooms but through engagement in active research. Clinical MRI research for HBM initiates with strategic and necessary demands of clinicians, e.g. neurologists, neurosurgeons, psychologists, psychiatrists, etc. who need specific clinical MRI acquisition and quantification techniques for better, faster and more accurate diagnostic and followup procedures. Neuro-radiologists are responsible for all aspects of a research MRI examination, including assessment of patient’s clinical symptoms, assigning the imaging protocol, reviewing the acquired images for their quality and interpretations, and finally, preparing the reports. MR physicists with their unique scientific qualifications and perception of clinical requirements play a critical role in optimization of the existing protocols, establishment of research investigations and development of effective techniques (including pulse sequences, analysis and quantification software, etc.) for clinical application of MRI in HBM, when responsibility of a clinical scientist is minimal when the research methodology development starts while the physicist starts with the maximum responsibility to develop the methodology, and vice versa when the methodology development progresses from early to the end stages closer to the clinical practice.