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Introduction: Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes., Methods: Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes., Results: Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes., Discussion: This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes., Highlights: Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images., (© 2024. The Author(s).)

Introduction: Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis., Methods: Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale., Results: A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach., Discussion: Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification., Highlights: Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but first outputs a fully commented processing script that the users can read, query, interpret and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting state FMRI example commands.

Breast cancer is the second major cause of cancer death in women globally. Avicennia marina is a medicinal plant that belongs to the family Acanthaceae and is known as grey or white mangrove. It has antioxidant, antiviral, anticancer, anti-inflammatory, and antibacterial activity in the treatment of various diseases including cancer. The goal of the study is to use a network pharmacology method to identify the potential phenomena of bioactive compounds of A. marina in the treatment of breast cancer and explore clinical biochemistry related aspects. A total of 74 active compounds of A. marina were retrieved from various databases as well as a literature review and collectively 429 targets of these compounds were identified by STITCH and Swiss Target Prediction databases. Breast cancer related 15606 potential targets were retrieved from the GeneCards database. A Venn diagram was drawn to find common key targets. To check the biological functions, the GO enrichment and KEGG pathways analysis of 171 key targets were performed through the DAVID database. To understand the interactions among key targets, Protein-protein interaction (PPI) studies were completed using the STRING database, and the Protein-Protein Interaction (PPI) network, as well as the compound-target-pathway network, was constructed using Cytoscape 3.9.0. Finally, molecular docking analysis of 5 hub genes named tumor protein 53 (TP53), catenin beta 1 (CTNNB1), interleukin 6 (IL6), tumor necrosis factor (TNF), and RAC-alpha serine/threonine protein kinases 1 (AKT1) with the active constituent of A. marina against breast cancer were performed. Additionally, a molecular docking study demonstrates that active drugs have a higher affinity for the target that may be used to decrease breast cancer. The molecular dynamic simulation analysis predicted the very stable behavior of docked complexes with no global structure deviations seen. The MMGBSA further supported strong intermolecular interactions with net energy values as; AKT1_Betulinic_acid (-20.97 kcal/mol), AKT1_Stigmasterol (-44.56 kcal/mol), TNF_Betulinic_acid (-28.68 kcal/mol) and TNF_Stigmastero (-29.47 kcal/mol).Communicated by Ramaswamy H. Sarma.

Background: Self-efficacy is a popular psychological concept that refers to an individual's perception or belief in his ability to perform specific actions. This study aimed to assess the predictive value of self-efficacy, measured using the Self-Efficacy for Managing Chronic Disease 6-Item Scale (SEM6S) questionnaire, for diabetes management and overall well-being in patients with diabetes., Subject and Methods: An anonymous online cross-sectional study was conducted to evaluate the self-efficacy of diabetic patients in the Asser region of Saudi Arabia. The participants were requested to upload their most recent glycated hemoglobin A1C (HbA1C) measurements taken in the last three months, which helped in the accurate categorization of their diabetes as either controlled or uncontrolled. We used the valid Arabic version of the SEM6S and WHO-5 well-being questionnaires to assess patient self-efficacy and well-being., Results: A cohort of 342 patients was enrolled in the study, 67.25% were married, their mean age was 43.17 ± 17.61 years, and 52.69% had university-level or higher education. Among the participants, 46.0% exhibited well-being, while 24.9% reported poor well-being, including 9.4% who were identified as experiencing depression. The mean scores of self-efficacy and well-being were significantly higher among those with controlled diabetes versus uncontrolled diabetes (40.86 ± 13.26 vs. 36.48 ± 13.26) and (67.35 ± 21.22 vs. 60.93 ± 25.05), respectively. The predictors of glycemic control were self-efficacy [Odds ratio (OR)=1.03 (95%CI, 1.01-1.06, P =0.002], having other chronic diseases [OR=3.25 (95%CI), P <0.001], having type 1 diabetes [OR=7.16, 95%CI, P =0.005], being Saudi [OR=7.67, (95%CI, P =0.027], working in a public sector [OR=0.15, (95%CI, 0.05-0.44), P =0.005], being unemployed [OR=0.19, (95%CI, 0.06-0.59), P =0.005], being a smoker [OR=0.44, 95%CI, 0.19-0.98, P =0.048], and duration of diabetes between 6-10 years [OR= 0.33, 95%CI, 0.11-0.95), P =0.043] or more than 10 years OR=0.32, 95%CI, 0.12-0.86), P =0.026]. The main determinants of well-being were having self-efficacy [OR=1.07 (95%CI, 1.04-1.09), P = 0.0001], having public health insurance [OR=4.36 (95%CI, P =0.015], and education level (read and write) [OR=0.13 (95%CI,.02-.70), P =0.021]., Conclusions: The study reveals that non-modifiable and modifiable factors, including self-efficacy, play a crucial role in diabetes control. The study recommends providing targeted educational interventions, using different social media platforms, psychosocial support programs, and inclusive healthcare policies to improve diabetes control and mental well-being among diabetic patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Alshaikh, Al-Qahtani, Alqahtani, AlFarhan, Al Nuwayhidh, Madkhali, AlQahtani, AlAsmari, Alserhani, Alqubaisi, Saeed Aldawh, Al Bin Ahmad and Ghazy.)

The mechanisms by which the apolipoprotein E ε4 (APOEε4) allele influences the pathophysiological progression of Alzheimer's disease (AD) are poorly understood. Here we tested the association of APOEε4 carriership and amyloid-β (Aβ) burden with longitudinal tau pathology. We longitudinally assessed 94 individuals across the aging and AD spectrum who underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aβ ([ 18 F]AZD4694) and tau ([ 18 F]MK-6240) at baseline, as well as a 2-year follow-up tau-PET scan. We found that APOEε4 carriership potentiates Aβ effects on longitudinal tau accumulation over 2 years. The APOEε4-potentiated Aβ effects on tau-PET burden were mediated by longitudinal plasma phosphorylated tau at threonine 217 (p-tau217 + ) increase. This longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline. Our results suggest that the APOEε4 allele plays a key role in Aβ downstream effects on the aggregation of phosphorylated tau in the living human brain., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Many risk factors have been listed that predispose to the occurrence of high blood pressure (BP). Although high body mass index (BMI) is a recognized risk factor for hypertension, the cutoff value for the high BMI was not taken into consideration as a predictor risk. There is no clear data on the occurrence of hypertension in Iraq in the highly educated population. Moreover, studies on hypertension in Iraq have been limited to a few studies. To assess and study the prevalence of BP in the academic population of Babylon city, a prospective study of 100 people was conducted. In the course of 3 months, during their work at the university in the morning, the questionnaire was filled out along with the body weight measurement. The receiver operating characteristic curve was used to measure the maximum area under the curve for the BMI score. Outcomes demonstrated that despite the high level of education, the prevalence of hypertension remains unacceptable. Awareness raising about the risk factors should be addressed through ongoing health education in health sectors and media. BMI of more than 25 could be considered a predictable risk value., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Journal of Advanced Pharmaceutical Technology & Research.)

Introduction: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aβ) peptides in plasma and the extent to which they improve the prediction of amyloid positivity., Methods: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aβ assays. Statistical tests were performed to determine whether the plasma Aβ measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype., Results: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons)., Discussion: Measurement of Aβ in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study., Highlights: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aβ) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aβ42/40 predicted amyloid positron emission tomography status better than Aβ42 or Aβ40 alone., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Plasma p217+tau has shown high concordance with cerebrospinal fluid (CSF) and positron emission tomography (PET) measures of amyloid-β (Aβ) and tau in Alzheimer's Disease (AD). However, its association with longitudinal cognition and comparative performance to PET Aβ and tau in predicting cognitive decline are unknown., Objectives: To evaluate whether p217+tau can predict the rate of cognitive decline observed over two-year average follow-up and compare this to prediction based on Aβ (18F-NAV4694) and tau (18F-MK6240) PET. We also explored the sample size required to detect a 30% slowing in cognitive decline in a 2-year trial and selection test cost using p217+tau (pT+) as compared to PET Aβ (A+) and tau (T+) with and without p217+tau pre-screening., Design: A prospective observational cohort study., Setting: Participants of the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) and Australian Dementia Network (ADNeT)., Participants: 153 cognitively unimpaired (CU) and 50 cognitively impaired (CI) individuals., Measurements: Baseline p217+tau Simoa® assay, 18F-MK6240 tau-PET and 18F-NAV4694 Aβ-PET with neuropsychological follow-up (MMSE, CDR-SB, AIBL-PACC) over 2.4 ± 0.8 years., Results: In CI, p217+tau was a significant predictor of change in MMSE (β = -0.55, p < 0.001) and CDR-SB (β =0.61, p < 0.001) with an effect size similar to Aβ Centiloid (MMSE β = -0.48, p = 0.002; CDR-SB β = 0.43, p = 0.004) and meta-temporal (MetaT) tau SUVR (MMSE: β = -0.62, p < 0.001; CDR-SB: β = 0.65, p < 0.001). In CU, only MetaT tau SUVR was significantly associated with change in AIBL-PACC (β = -0.22, p = 0.008). Screening pT+ CI participants into a trial could lead to 24% reduction in sample size compared to screening with PET for A+ and 6-13% compared to screening with PET for T+ (different regions). This would translate to an 81-83% biomarker test cost-saving assuming the p217+tau test cost one-fifth of a PET scan. In a trial requiring PET A+ or T+, p217+tau pre-screening followed by PET in those who were pT+ would cost more in the CI group, compared to 26-38% biomarker test cost-saving in the CU., Conclusions: Substantial cost reduction can be achieved using p217+tau alone to select participants with MCI or mild dementia for a clinical trial designed to slow cognitive decline over two years, compared to participant selection by PET. In pre-clinical AD trials, p217+tau provides significant cost-saving if used as a pre-screening measure for PET A+ or T+ but in MCI/mild dementia trials this may add to cost both in testing and in the increased number of participants needed for testing., Competing Interests: Christopher C. Rowe has received research grants from NHMRC, Enigma Australia, Biogen, Eisai and Abbvie. He is on the scientific advisory board for Cerveau Technologies and has consulted for Prothena, Eisai, Roche, and Biogen Australia. Victor L. Villemagne is and has been a consultant or paid speaker at sponsored conference sessions for Eli Lilly, Life Molecular Imaging, GE Healthcare, IXICO, Abbvie, Lundbeck, Shanghai Green Valley Pharmaceutical Co Ltd, and Hoffmann La Roche. Ashley I. Bush is a shareholder in Alterity Ltd, Cogstate Ltd and Mesoblast Ltd. He is a paid consultant for, and has a profit share interest in Collaborative Medicinal Development Pty Ltd. He has received lecture fees from Biogen P/L. Paul Maruff was a full-time employee of Cogstate. Ziad S. Saad, Gallen Triana-Baltzer, Randy Slemmon and Hartmuth C. Kolb are employees of Janssen Pharmaceuticals. The other authors did not report any conflict of interest.

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

This systematic review and meta-analysis aimed to synthesize the evidence on the adverse events (AEs) of coronavirus disease 2019 (COVID-19) vaccinations in Saudi Arabia. A computerized search in MEDLINE via PubMed and OVID, Scopus, CENTRAL, and Web of Science was conducted using relevant keywords. The NIH tools were used for the quality assessment. A total of 14 studies (16 reports) were included. The pooled analysis showed that the incidence of AEs post-COVID-19 vaccination was 40.4% (95% CI:6.4% to 87%). Compared to the AstraZeneca vaccine, the Pfizer-BioNTech vaccine was associated with a lower risk ratio (RR) of wheezing (RR = 0.04), fever (RR = 0.32), chills (RR = 0.41), headache (RR = 0.47), dizziness (RR = 0.49), and joint pain (RR = 0.51). The Pfizer-BioNTech vaccine was associated with significantly higher RR of general allergic reactions (RR = 1.62), dyspnea (RR = 1.68), upper respiratory tract symptoms (RR = 1.71), and lymphadenopathy (RR = 8.32). The current evidence suggests that the incidence of AEs following COVID-19 vaccines is 40%; however, most of these AEs were mild and for a short time. The overall number of participants with AEs was higher in the Pfizer group compared to the AstraZeneca group; however, the AstraZeneca vaccine was associated with a higher RR of several AEs.

Carbonic anhydrases (EC 4.2.1.1) catalyse the reversible hydration of CO 2 into bicarbonate and protons. As a hypoxia-sensitive and tumour-associated isoform, isoform CA IX, is significantly overexpressed in various malignancies, being a validated target for new anticancer/antimetastatic drugs. A multitude of studies has shown that CA IX inhibition decreases cancer cell proliferation and metastasis through pHe/pHi modulation and enhancement of ferroptosis among others. Numerous studies demonstrated increased efficacy of cytotoxic drugs combined with CA inhibitors (CAIs) in various cancer types. We tested the inhibitory effect of boric acid (BA), an inorganic Lewis acid, on CA IX as well as other isoforms (CA I, II, and XII). BA acted as a millimolar in vitro CAI, decreased proliferation of two cancer cell lines, although not strong correlations between the in vitro inhibition and in vivo effects were observed. The mechanism of antiproliferative action of BA should be investigated in more detail.

Background: Health-care workers have to use the N95 mask as a part of the protection kit during the COVID-19 pandemic. The adverse effects of such practice are not fully elucidated. The study aims to evaluate negative impacts of N95 face masks on health-care personnel at COVID-19 care units., Materials and Methods: One hundred and twenty-two health-care workers (aged 20-58 years) from various health-care settings in Sulaimani, Iraq, from January to August 2020, were enrolled in this prospective, cross-sectional study. The physiological variables (blood pressure, heart rate, and oxygen saturation) were recorded before putting on the N95 mask and postremoval of the mask. The incidence of adverse effects such as headache, difficulty breathing, redness, irritation, and dizziness were also reported as a number and percent at the end of the work shift., Results: There was a statistically significant difference in the physiological parameters after removal of the mask compared with baseline. Only diastolic pressure was significantly lower in those working >6 h when compared to those working 1-6 h. The changes in physiological markers were poorly and nonsignificantly associated with the duration of wearing the mask. Moreover, 67.2%-70.5% of the participants complain of headaches and breathing difficulties, while 45.9%-51.6% reported signs of itching, redness, and irritation. However, health-care workers who put on the face mask >6 h showed signs of headache, breathing difficulties, and itching at the exposed areas higher than those working for 1-6 h., Conclusion: N95 mask negatively impacts the physiological variables of health-care providers. The adverse effects may lead to excessive exhaustion after long shifts in the intensive care unit during treatment of COVID-19 patients., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Journal of Education and Health Promotion.)

Introduction: We evaluated a new Simoa plasma assay for phosphorylated tau (P-tau) at aa217 enhanced by additional p-tau sites (p217+tau)., Methods: Plasma p217+tau levels were compared to 18 F-NAV4694 amyloid beta (Aβ) positron emission tomography (PET) and 18 F-MK6240 tau PET in 174 cognitively impaired (CI) and 223 cognitively unimpaired (CU) participants., Results: Compared to Aβ- CU, the plasma levels of p217+tau increased 2-fold in Aβ+ CU and 3.5-fold in Aβ+ CI. In Aβ- the p217+tau levels did not differ significantly between CU and CI. P217+tau correlated with Aβ centiloids P = .67 (CI, P = .64; CU, P = .45) and tau SUVR MT P = .63 (CI, P = .69; CU, P = .34). Area under curve (AUC) for Alzheimer's disease (AD) dementia versus Aβ- CU was 0.94, for AD dementia versus other dementia was 0.93, for Aβ+ versus Aβ- PET was 0.89, and for tau+ versus tau- PET was 0.89., Discussion: Plasma p217+tau levels elevate early in the AD continuum and correlate well with Aβ and tau PET., Competing Interests: Christopher C. Rowe has received research grants from NHMRC, Enigma Australia, Biogen, Eisai, and Abbvie. He is on the scientific advisory board for Cerveau Technologies and consulted for Prothena, Eisai, Roche, and Biogen Australia. Victor Villemagne is and has been a consultant or paid speaker at sponsored conference sessions for Eli Lilly, Life Molecular Imaging, GE Healthcare, Abbvie, Lundbeck, Shanghai Green Valley Pharmaceutical Co Ltd, and Hoffmann La Roche. Ashley Bush is a shareholder in Alterity Ltd, Cogstate Ltd, and Mesoblast Ltd. He is a paid consultant for, and has a profit share interest in, Collaborative Medicinal Development Pty Ltd. He has received lecture fees from Biogen and Merck Sharp & Dohme P/L. Ziad Saad, Gallen Triana‐Baltzer, Randy Slemmon, and Hartmuth Kolb are employees of Janssen R&D. The other authors did not report any conflict of interest., (© 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Objective: To evaluate the cancer registry in Iraq over a 5 year period from 2013 to 2018 (except 2017 as software data was not accessable) in all of Iraqi's provinces. Further more to study the incidence rate and the pattern of distribution of breast cancer in various provinces of Iraq., Methods: All data was collected from the cancer registration centres of Iraq. Original data is set to highlight and update the cancer state of the country., Results: On data analysis it was observed that the incidence of cancer with the number of new cases each year, were higher in some cities compared to others. The major cause for this difference is the displacement of people from their home cities due to ISIS attack, which changed the demographic distribution of people in Iraq. Current results revealed an increase in the number of new cases registered annually during 1994-2018. Moreover, cancer incidence rate per 1005 population showed that Baghdad, Karbala and Al Najaf have a higher incidence in the period between 2013-2018. Breast cancer in females is the most frequent malignancy in Iraq, followed by colon cancer and leukaemia. This reveals the importance of cancer awareness programmes in drawing the attention to early diagnosis and treatment., Conclusions: This study provides the incidence of cancer, especially breast cancer in Iraq. This will assist the researchers to identify regional differences in the prevalence figures.

Use of resistant cultivars is considered the most effective tool in managing clubroot. Three clubroot-resistant commercial winter oilseed rape (OSR) cultivars and a susceptible 'Cultivar mix' were evaluated for disease severity index (DSI) and yield performance in field soils, selected for varying abundance of natural inoculum of Plasmodiophora brassicae . Seven field trials were carried out during 2017-2019 in winter OSR crops, and comparative bioassays were performed in a growth chamber. Substantial variation in clubroot infection between years was observed in the field trials. For Cultivar mix, a negative correlation (y = -252.3ln(x) + 58,897.6) was found between inoculum density and seed yield in five trials, whereas no correlation was found for the resistant cultivars. In bioassays, Cultivar mix exhibited a significantly high correlation between DSI b and number of gene copies g -1 soil (R 2 = 0.72). For resistant cvs., Mentor and Alister, correlation was R 2 = 0.45 and 0.58, respectively, indicating that resistance was under pressure. In field trials, DSI f of the resistant cultivars was lower (<27). The recommendation is thus to use clubroot-resistant cultivars of OSR as part of Integrated Pest Management in situations where abundance of P. brassicae DNA exceeds 1300 gene copies g -1 soil.

JNJ-42165279 is a selective inhibitor of fatty acid amide hydrolase (FAAH), the enzyme responsible for the degradation of fatty acid amides (FAA) including anandamide (AEA), palmitoylethanolamide (PEA), and N-oleoylethanolamide (OEA). We assessed the efficacy, safety, tolerability, pharmacokinetics, and pharmacodynamics of treatment with JNJ-42165279 in subjects with social anxiety disorder (SAD). This was a multicenter, double-blind, placebo-controlled study randomizing subjects to 12 weeks of treatment with either JNJ-42165279 (25 mg daily) or placebo (PBO). The primary endpoint was the change in the Liebowitz Social Anxiety Scale (LSAS) total score from baseline to end of study. Secondary endpoints included the Hamilton Anxiety Scale (HAM-A), Hamilton Depression Rating Scale (HDRS 17 ), and the Clinical Global Impression-Improvement (CGI-I). Samples were collected for plasma concentration of AEA, PEA, OEA, and JNJ-42165279. A total of 149 subjects were enrolled with a mean baseline LSAS total score of 102.6 (SD 16.84). The mean change from baseline (SD) in LSAS total score at week 12 was numerically greater for JNJ-42165279: -29.4 (27.47) compared to PBO: -22.4 (23.57) but not significant. The percentage of subjects with ≥30% improvement from baseline in the LSAS total score was significantly higher for JNJ-42165279 (42.4%) compared to PBO (23.6%) (p value = 0.04). The percentage of subjects with a CGI-I score of much or very much improved was also significantly higher for JNJ-42165279 (44.1%) than for PBO (23.6%) (p value = 0.02). The drug was well tolerated. JNJ-42165279 appears to elicit an anxiolytic effect in subjects with SAD although trough concentrations with 25 mg once daily appeared to be insufficient to completely inhibit FAAH activity which may have led to suboptimal efficacy. ClinicalTrials.gov Identifier: NCT02432703.

Importance: Atabecestat, a nonselective oral β-secretase inhibitor, was evaluated in the EARLY trial for slowing cognitive decline in participants with preclinical Alzheimer disease. Preliminary analyses suggested dose-related cognitive worsening and neuropsychiatric adverse events (AEs)., Objective: To report efficacy, safety, and biomarker findings in the EARLY trial, both on and off atabecestat treatment, with focus on potential recovery of effects on cognition and behavior., Design, Setting, and Participants: Randomized, double-blind, placebo-controlled, phase 2b/3 study conducted from November 2015 to December 2018 after being stopped prematurely. The study was conducted at 143 centers across 14 countries. Participants were permitted to be followed off-treatment by the original protocol, collecting safety and efficacy data. From 4464 screened participants, 557 amyloid-positive, cognitively normal (Clinical Dementia Rating of 0; aged 60-85 years) participants (approximately 34% of originally planned 1650) were randomized before the trial sponsor stopped enrollment., Interventions: Participants were randomized (1:1:1) to atabecestat, 5 mg (n = 189), 25 mg (n = 183), or placebo (n = 185)., Main Outcomes and Measures: Primary outcome: change from baseline in Preclinical Alzheimer Cognitive Composite score. Secondary outcomes: change from baseline in the Cognitive Function Index and the Repeatable Battery for the Assessment of Neuropsychological Status total scale score. Safety was monitored throughout the study., Results: Of 557 participants, 341 were women (61.2%); mean (SD) age was 70.4 (5.56) years. In May 2018, study medication was stopped early owing to hepatic-related AEs; participants were followed up off-treatment for 6 months. Atabecestat, 25 mg, showed significant cognitive worsening vs placebo for Preclinical Alzheimer Cognitive Composite at month 6 (least-square mean difference, -1.09; 95% CI, -1.66 to -0.53; P < .001) and month 12 (least-square mean, -1.62; 95% CI, -2.49 to -0.76; P < .001), and at month 3 for Repeatable Battery for the Assessment of Neuropsychological Status (least-square mean, -3.70; 95% CI, -5.76 to -1.63; P < .001). Cognitive Function Index participant report showed nonsignificant worsening at month 12. Systemic and neuropsychiatric-related treatment-emergent AEs were greater in atabecestat groups vs placebo. After stopping treatment, follow-up cognitive testing and AE assessment provided evidence of reversibility of drug-induced cognitive worsening and AEs in atabecestat groups., Conclusions and Relevance: Atabecestat treatment was associated with dose-related cognitive worsening as early as 3 months and presence of neuropsychiatric treatment-emergent AEs, with evidence of reversibility after 6 months off treatment., Trial Registration: ClinicalTrials.gov Identifier: NCT02569398.

This study was designed to examine the interaction of neratinib (NRB) with human serum albumin (HSA) in presence of flavonoids quercetin and rutin. Both quercetin and rutin can compete with NRB to bind to HSA and displace NRB from its binding site. The interaction mechanism was studied with several spectroscopic techniques and molecular docking. Static fluorescence quenching mechanism was observed on interaction of HSA with NRB. van der Waals force and hydrogen bond were involved in the HSA-NRB interaction as per the results of thermodynamic parameters. Further, the conformational changes were observed in the HSA on its interaction with NRB. Interaction of NRB with HSA in presence of quercetin and rutin resulted in changes in the binding constants of HSA-NRB suggesting some impact on the binding of NRB in the presence of flavonoids., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background Adverse drug reactions (ADRs) are undesirable effects to drugs at doses normally used in the clinical setting for diagnosis, treatment or prophylaxis of diseases. Spontaneous ADR reporting is currently considered as a critical part of ADRs detection. Objective The study aims to assess the knowledge, attitude, and practice of healthcare providers towards adverse drug reaction reporting in public and private settings within the Baghdad area. Method We follow a quantitative cross-sectional study design, using a self-administered questionnaire to collect data from the healthcare providers regarding their reporting practice. Main outcome The outcome measures include the experiences of reporting of adverse drug reactions and the available systems, in addition to the expected benefits to clinical practice. Results Among 485 respondents, 114 were able to differentiate ADRs from side effects. About half of them (47.6%) recognized the availability of reporting systems and 43.3% knew how to report adverse drug reactions. 43.7% of the respondents mentioned that ADRs should be reported only when they are life-threatening. Among the 188 respondents who encountered ADRs, 145 reported the events to the accessible local authority. Conclusion The healthcare providers working in Baghdad healthcare institutions have a good attitude toward ADR reporting. There is a relatively below-standard reporting culture of the encountered adverse reactions. The inadequate knowledge about adverse reactions and the unavailability of reporting forms played a major role in discouraging healthcare professionals to detect and report ADRs.

Fine needle aspiration biopsy (FNAB) is a standard procedure for the detection of thyroid nodules malignancy, yet 10-25% of the sample diagnosed may go undetermined or suspicious. The utility of cancer stem cell markers (CSCM) as a differential diagnosis molecular marker in nodules of suspicious decision in FNAB was hypothesized. Papillary thyroid carcinoma (PTC) and thyroid fibroadenoma (TFA) samples were selected to test the hypothesis. The samples employed in this study were from patients who had thyroid hyperplasia and a suspicious or undetermined diagnosis by FNAB. The patient underwent a successful thyroidectomy at Al-Yarmouk Teaching Hospital in Baghdad between January 2015 and December 2017. All nodule samples underwent a systematic histopathological examination after resection. Tumors diagnosed as PTC and those diagnosed as fibroadenoma (TFA) were selected for this study. Collectively 39 PTC and 11 TFA nodules were included. Quantitative reverse transcriptase real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were used to determine levels of mRNA and proteins of CSCM ALDH1A1, CD44, ABCG2, and Oct3/4 in both types of tumors were used. This study revealed that the expression levels of CSCM were significantly increased in PTC tissues when compared to benign tissues and the positive correlation was found between the CSCM expression levels and tumor stage, size, and gender. In conclusion, for a more precise diagnosis, we suggest these markers be included in what is currently available to characterize malignancy from what is not in thyroid cancer, as well as for the staging process of PTC.

Background: Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center., Methods: Proton-density, T 2 -weighted, T 1 -weighted, brain-free water, 3D FLAIR, 3D T 2 -weighted, and 3D T 2 *-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three)., Results: C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS., Conclusion: C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.

Tumor initiation cells or cancer stem cell markers ABCG2 and ALDH1 play pivotal roles in invasion, metastasis and resistance to cytotoxic agents. In this study, we evaluated the expression levels of ALDH1 and ABCG2 in Iraqi patients with colon cancer and/or benign colon tumors. We also investigated the association between the expression levels of these markers and patient clincopathological features. The expression levels of ALDH1 and ABCG2 in cancer tissues as well as in benign tumor samples were determined by immunohistochemistry using tumor tissues microarray of TNM (Tumor, Node, Metastasis) in 42 patients with colon cancer samples as well as in 18 corresponding benign tumors. Immunohistochemistry showed that ALDH1 and ABCG2 expression levels increased to 80% and 76%, respectively in colon cancer tissues as compared to 33% and 28% in benign tumor tissues. The expression levels of ALDH1 and ABCG2 were associated with tumor stages. No significant association was found between the expressions levels of these markers and tumor size, gender, patients' age, and lymph node involvement. These results indicate that the expression levels of ALDH1 and ABCG2 increased in colon cancer tissues compared to benign tumor tissues in Iraqi patients., Competing Interests: Authors state no conflict of interest

Background: Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT., New Method: ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab ® , thus providing an easy, automated and standard tool for ECoG electrode localization., Results: ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method., Comparison With Existing Methods: A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects., Conclusions: The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi-automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface-based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring-based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface-based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions., (© 2017 Wiley Periodicals, Inc.)

Introduction: Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia., Materials and Methods: Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR)., Results: Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, p<0.0003), however the effective CNR was greater for TSP compared to TTP (352.3 vs 283.5, t(19) = 2.6, p<0.03.) and MTT (228.3, t(19) = 2.8, p<0.03)., Discussion: TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making.

Before their disappearance from the fossil record approximately 40,000 years ago, Neanderthals, the ancient hominin lineage most closely related to modern humans, interbred with ancestors of present-day humans. The legacy of this gene flow persists through Neanderthal-derived variants that survive in modern human DNA; however, the neural implications of this inheritance are uncertain. Here, using MRI in a large cohort of healthy individuals of European-descent, we show that the amount of Neanderthal-originating polymorphism carried in living humans is related to cranial and brain morphology. First, as a validation of our approach, we demonstrate that a greater load of Neanderthal-derived genetic variants (higher "NeanderScore") is associated with skull shapes resembling those of known Neanderthal cranial remains, particularly in occipital and parietal bones. Next, we demonstrate convergent NeanderScore-related findings in the brain (measured by gray- and white-matter volume, sulcal depth, and gyrification index) that localize to the visual cortex and intraparietal sulcus. This work provides insights into ancestral human neurobiology and suggests that Neanderthal-derived genetic variation is neurologically functional in the contemporary population.

The present study was performed to examine the effects of chronic soft drink consumption (SDC) on oxidative stress, biochemical alterations, gene biomarkers and histopathology of bone, liver and kidney. Free drinking water of adult male Wistar rats was substituted with three different soft drinks: Coca‑Cola, Pepsi and 7‑Up, for three consecutive months. The serum and organs were collected for examining the biochemical parameters associated with bone, liver and kidney functions. Semi‑quantitative reverse transcription polymerase chain reaction was used to observe the changes in the expression of genes in the liver and kidney, which are associated with oxidative stress resistance. Histopathological investigations were performed to determine the changes in bone, liver and kidney tissues using hematoxylin and eosin stains. SDC affected liver, kidney and bone function biomarkers. Soft drinks increased oxidative stress, which is represented by an increase in malondialdehyde and a decrease in antioxidant levels. SDC affected serum mineral levels, particularly calcium and phosphorus. Soft drinks downregulated the expression levels of glutathione‑S‑transferase and super oxide dismutase in the liver compared with that of control rats. Rats administered Coca‑Cola exhibited a hepatic decrease in the mRNA expression of α2‑macroglobulin compared with rats administered Pepsi and 7‑Up. On the other hand, SDC increased the mRNA expression of α1‑acid glycoprotein. The present renal studies revealed that Coca‑Cola increased the mRNA expression levels of desmin, angiotensinogen and angiotensinogen receptor compared with the other groups, together with mild congestion in renal histopathology. Deleterious histopathological changes were reported predominantly in the bone and liver of the Coca‑Cola and Pepsi groups. In conclusion, a very strict caution must be considered with SDC due to the increase in oxidative stress biomarkers and disruption in the expression of certain genes associated with the bio‑vital function of both the liver and kidney.

Cadmium (Cd) is the most prevalent toxic metal present in livestock feed; therefore, the present study aimed to examine the ameliorative effects of grape seed extract (GSE) on cadmium chloride (CdCl2)‑induced testicular dysfunction of Wistar rats. Male adult Wistar rats (40 rats; n=10/group) were divided into four equal groups. Group one was used as a control, and was given ad libitum access to food and water. Groups 2‑4 were treated with CdCl2 [5 mg/kg body weight (BW)], GSE (400 mg/kg BW, orally), and GSE plus CdCl2, respectively. Blood and testicular tissues were collected and assayed for biochemical and histopathological changes, respectively. Testicular genes were expressed using semi‑quantitative RT‑PCR analysis. The results of the present study demonstrated that there was a decrease in serum testosterone levels following CdCl2 toxicity, which were normalized after GSE co-administration. Furthermore, CdCl2 significantly increased the serum levels of malondialdehyde, and decreased levels of antioxidants. At the histopathological level, the testes of the CdCl2 group exhibited congestion, edema in the interstitial blood vessels, irregular arrangement of the epithelial lining of the seminiferous tubules, and degeneration and sloughing of the spermatogenic cells, which accumulated in the center of the seminiferous tubules. Such pathological alterations were ameliorated following treatment with GSE in the CdCl2 plus GSE group. The immunohistochemical expression of B‑cell lymphoma 2‑associated X protein was high in the CdCl2 group, and low in the control and GSE groups. Co‑treatment with GSE and CdCl2 exhibited ameliorative effects on the immunoreactivity of B‑cell lymphoma 2‑associated X protein. CdCl2 toxicity induced a significant downregulation in the mRNA expression levels of cytochrome P450 cholesterol side‑chain cleavage enzyme, cytochrome P450 17A1, 3β‑hydroxysteroid dehydrogenase (3β‑HSD), 17β‑HSD, androgen receptor, steroidogenic acute regulatory protein, and follicle‑stimulating hormone receptor. GSE administration exhibited a stimulatory effect on steroidogenesis‑associated enzymes, and co‑treatment with GSE and CdCl2 normalized and upregulated the mRNA expression levels of these examined genes. This study concluded that GSE has beneficial protective effects against the deleterious effects of CdCl2 on the testis.

Brain connectivity investigations are becoming increasingly multimodal and they present challenges for quantitatively characterizing and interactively visualizing data. In this study, we present a new set of network-based software tools for combining functional and anatomical connectivity from magnetic resonance imaging (MRI) data. The computational tools are available as part of Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), a toolbox that interfaces with Analysis of Functional NeuroImages (AFNI) and SUrface MApping (SUMA) for interactive queries and visualization. This includes a novel, tractographic mini-probabilistic approach to improve streamline tracking in networks. We show how one obtains more robust tracking results for determining white matter connections by utilizing the uncertainty of the estimated diffusion tensor imaging (DTI) parameters and a few Monte Carlo iterations. This allows for thresholding based on the number of connections between target pairs to reduce the presence of tracts likely due to noise. To assist users in combining data, we describe an interface for navigating and performing queries in two-dimensional and three-dimensional data defined over voxel, surface, tract, and graph domains. These varied types of information can be visualized simultaneously and the queries performed interactively using SUMA and AFNI. The methods have been designed to increase the user's ability to visualize and combine functional MRI and DTI modalities, particularly in the context of single-subject inferences (e.g., in deep brain stimulation studies). Finally, we present a multivariate framework for statistically modeling network-based features in group analysis, which can be implemented for both functional and structural studies.

Inhibitory transcranial magnetic stimulation (TMS), of which continuous theta burst stimulation (cTBS) is a common form, has been used to inhibit cortical areas during investigations of their function. cTBS applied to the primary motor area (M1) depresses motor output excitability via a local effect and impairs procedural motor learning. This could be due to an effect on M1 itself and/or to changes in its connectivity with other nodes in the learning network. To investigate this issue, we used functional magnetic resonance imaging to measure changes in brain activation and connectivity during implicit procedural learning after real and sham cTBS of M1. Compared to sham, real cTBS impaired motor sequence learning, but caused no local or distant changes in brain activation. Rather, it reduced functional connectivity between motor (M1, dorsal premotor & supplementary motor areas) and visual (superior & inferior occipital gyri) areas. It also increased connectivity between frontal associative (superior & inferior frontal gyri), cingulate (dorsal & middle cingulate), and temporal areas. This potentially compensatory shift in coupling, from a motor-based learning network to an associative learning network accounts for the behavioral effects of cTBS of M1. The findings suggest that the inhibitory TMS affects behavior via relatively subtle and distributed effects on connectivity within networks, rather than by taking the stimulated area "offline"., (Published by Elsevier Ltd.)

Deep brain stimulation (DBS) is an effective surgical treatment for movement disorders. Although stimulation sites for movement disorders such as Parkinson's disease are established, the therapeutic mechanisms of DBS remain controversial. Recent research suggests that specific white-matter tract and circuit activation mediates symptom relief. To investigate these questions, we have developed a patient-specific open-source software pipeline called 'DBSproc' for (1) localizing DBS electrodes and contacts from postoperative CT images, (2) processing structural and diffusion MRI data, (3) registering all images to a common space, (4) estimating DBS activation volume from patient-specific voltage and impedance, and (5) understanding the DBS contact-brain connectivity through probabilistic tractography. In this paper, we explain our methodology and provide validation with anatomical and tractographic data. This method can be used to help investigate mechanisms of action of DBS, inform surgical and clinical assessments, and define new therapeutic targets., (Published 2015. This article is a U.S. Government work and is in the public domain in the USA.)

It was recently shown that when large amounts of task-based blood oxygen level-dependent (BOLD) data are combined to increase contrast- and temporal signal-to-noise ratios, the majority of the brain shows significant hemodynamic responses time-locked with the experimental paradigm. Here, we investigate the biological significance of such widespread activations. First, the relationship between activation extent and task demands was investigated by varying cognitive load across participants. Second, the tissue specificity of responses was probed using the better BOLD signal localization capabilities of a 7T scanner. Finally, the spatial distribution of 3 primary response types--namely positively sustained (pSUS), negatively sustained (nSUS), and transient--was evaluated using a newly defined voxel-wise waveshape index that permits separation of responses based on their temporal signature. About 86% of gray matter (GM) became significantly active when all data entered the analysis for the most complex task. Activation extent scaled with task load and largely followed the GM contour. The most common response type was nSUS BOLD, irrespective of the task. Our results suggest that widespread activations associated with extremely large single-subject functional magnetic resonance imaging datasets can provide valuable information about the functional organization of the brain that goes undetected in smaller sample sizes., (Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape (FSM) or adjusted-shape (ASM) methods may fail to detect some shape subtleties (e.g., speed of rise or recovery, or undershoot). In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve (AUC) or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. With the whole HDR shape integrity maintained as input at the group level, the approach allows the investigator to substantiate these more nuanced effects through the unique HDR shape features. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the modeling approach to an inclusive platform that is more adaptable than the conventional GLM. With multiple effect estimates from ESM for each condition, linear mixed-effects (LME) modeling should be used at the group level when there is only one group of subjects without any other explanatory variables. Under other situations, an approximate approach through dimension reduction within the MVM framework can be adopted to achieve a practical equipoise among representation, false positive control, statistical power, and modeling flexibility. The associated program 3dMVM is publicly available as part of the AFNI suite.

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.

Electrocorticography (ECoG) in humans yields data with unmatched spatio-temporal resolution that provides novel insights into cognitive operations. However, the broader application of ECoG has been confounded by difficulties in accurately depicting individual data and performing statistically valid population-level analyses. To overcome these limitations, we developed methods for accurately registering ECoG data to individual cortical topology. We integrated this technique with surface-based co-registration and a mixed-effects multilevel analysis (MEMA) to control for variable cortical surface anatomy and sparse coverage across patients, as well as intra- and inter-subject variability. We applied this surface-based MEMA (SB-MEMA) technique to a face-recognition task dataset (n=22). Compared against existing techniques, SB-MEMA yielded results much more consistent with individual data and with meta-analyses of face-specific activation studies. We anticipate that SB-MEMA will greatly expand the role of ECoG in studies of human cognition, and will enable the generation of population-level brain activity maps and accurate multimodal comparisons., (Copyright © 2014 Elsevier Inc. All rights reserved.)

All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in the field, and the following two issues were raised: a) the improper formulation of test statistics in some univariate GLM implementations when a within-subject factor is involved in a data structure with two or more factors, and b) the unjustified presumption of uniform sphericity violation and the practice of estimating the variance-covariance structure through pooling across brain regions., (Published by Elsevier Inc.)

Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5-10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.

Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.

The hemispheric lateralization of certain faculties in the human brain has long been held to be beneficial for functioning. However, quantitative relationships between the degree of lateralization in particular brain regions and the level of functioning have yet to be established. Here we demonstrate that two distinct forms of functional lateralization are present in the left vs. the right cerebral hemisphere, with the left hemisphere showing a preference to interact more exclusively with itself, particularly for cortical regions involved in language and fine motor coordination. In contrast, right-hemisphere cortical regions involved in visuospatial and attentional processing interact in a more integrative fashion with both hemispheres. The degree of lateralization present in these distinct systems selectively predicted behavioral measures of verbal and visuospatial ability, providing direct evidence that lateralization is associated with enhanced cognitive ability.

We have previously argued from a theoretical basis that the standard practice of regression of the Global Signal from the fMRI time series in functional connectivity studies is ill advised, particularly when comparing groups of participants. Here, we demonstrate in resting-state data from participants with an Autism Spectrum Disorder and matched controls that these concerns are also well founded in real data. Using the prior theoretical work to formulate predictions, we show: (1) rather than simply altering the mean or range of correlation values amongst pairs of brain regions, Global Signal Regression systematically alters the rank ordering of values in addition to introducing negative values, (2) it leads to a reversal in the direction of group correlation differences relative to other preprocessing approaches, with a higher incidence of both long-range and local correlation differences that favor the Autism Spectrum Disorder group, (3) the strongest group differences under other preprocessing approaches are the ones most altered by Global Signal Regression, and (4) locations showing group differences no longer agree with those showing correlations with behavioral symptoms within the Autism Spectrum Disorder group. The correlation matrices of both participant groups under Global Signal Regression were well predicted by our previous mathematical analyses, demonstrating that there is nothing mysterious about these results. Finally, when independent physiological nuisance measures are lacking, we provide a simple alternative approach for assessing and lessening the influence of global correlations on group comparisons that replicates our previous findings. While this alternative performs less well for symptom correlations than our favored preprocessing approach that includes removal of independent physiological measures, it is preferable to the use of Global Signal Regression, which prevents unequivocal conclusions about the direction or location of group differences.

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details., (Published by Elsevier Inc.)

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias-taken to be a manifestation of the motion effect on correlation-is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMe LOCAL , as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

Tobacco smoking represents major national and international health hazard that interferes with wide range of physiological functions and biomarkers. In the current study we have investigated the influence of tobacco smoking on some biological markers such as serum amyloid protein-A, rheumatoid factor, serum glucose level and lipid profile in Saudi population. The fore mentioned parameters were investigated in a total of 275 cases in 3 different age categories (less than 20 years old, 20-40 years old and older than 40 years old). Long term survey was adopted in all cases; yet, lightly smoking and heavily smoking groups were compared to never smoking healthy population. Results obtained showed significant increase in serum amyloid protein-A and rheumatoid factor in correlation to the degree of smoking nonetheless in the age category older than 40 years old. Serum glucose, triglyceride, and total cholesterol was not affected by smoking in all studied age categories; however serum LDL-cholesterol was elevated and serum HDL-cholesterol was depressed in correlation to the degree of smoking in all age categories. In conclusion, tobacco smoking represents major cardiovascular risk factor in Saudi population in all age categories and serum amyloid protein-A and rheumatoid factor might be used as a serological surrogate marker for such risk.

Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts., (Published by Elsevier Inc.)

We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.