High Accuracy Research Articles

Crack Evolvement of Ancient Brick Masonry Under Uniaxial Compression Fatigue Loading

ABSTRACT Cracking is an essential intuitive indicator of damage in masonry structures. Evaluating the progression of fatigue cracks in ancient brick masonry structures is vital for the preservation and safety of ancient buildings. By conducting amplitude cyclic uniaxial compression tests on ancient brick masonry specimens, using the video image correlate-3d (VIC-3D) technology to collect images of the entire process from loading to failure, the development of fatigue cracks is analyzed and concluded. A reversed S-shaped function for fatigue crack evolution was established based on the fatigue damage mechanism. The model parameters’ physical significance, value range, and their impact on the model curve were discussed. The fatigue damage evolution equation was derived from the fatigue crack evolution model. The research results show that a reversed S-shaped function for fatigue crack evolution covers various types of fatigue crack evolution laws, with strong adaptability and high accuracy. The damage evolution curve established, based on the fatigue crack evolution model, has a reversed S-shaped change rule, and was divided into three stages: the first stage (0 N f ~ 0.1 N f ) fatigue damage grows faster; the second stage (0.1 N f ~ 0.9 N f ) fatigue damage growth is stable, close to linear change; when the cycle ratio exceeds 0.9, it enters the third stage, and fatigue damage increases rapidly.

Skull base “intrinsic” bony mass lesions: conventional, diffusion and perfusion imaging with a proposed imaging approach

Background and purposeImaging with conventional MRI plays a pivotal role in characterization of skull base bone-intrinsic lesions, yet some lesions are very challenging. The purpose of this study is to evaluate the role of diffusion and perfusion by T2* dynamic susceptibility contrast (DSC) in characterization of such lesions.ResultsLesions showed mostly correlated with approach: Chordomas had low perfusion and intermediate to high perfusion, while chondrosarcoma had ADC value > 1.6 × 10–3/cm2 and more perfused. Metastases had variable ADC values usually intermediate with high perfusion. Plasmacytomas had similar features yet with characteristic conventional morphology and single number. Lymphoma (primary bony) had high perfusion and lowest diffusion ADC (= 04–0.7 × 10–3/cm2). Giant cell tumors and hemangiopericytomas had lowest perfusion.ConclusionThe proposed imaging approach showed very good results and high accuracy in differentiation of skull base bony lesions.

Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study’s major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter-defibrillator interventions: a prospective SafeHeart substudy

Abstract Introduction Physical activity has shown association with ventricular arrhythmia, however, the role of specific behavioral patterns over a 24-hour cycle remains unknown. Therefore, we aimed to explore associations between physical behavior and appropriate implantable cardioverter-defibrillator (ICD) therapy. Methods We included patients with an ICD at two European sites, who wore wrist-based accelerometers capturing 24-hour movement and sleep behaviors for 28 days. Behavioral measures included activity volume, duration and intensity, sleep duration and efficiency. Patients were followed for 12 months for the outcome of appropriate ICD therapy. Cox proportional hazard models with restricted cubic splines were used for the analysis. Lastly, the predictive capacity was tested. Results : A total of 253 ICD patients were included (mean age 63.8 (±10.2), 50 (19.8%) female). During follow-up, 40 patients (15.8%) received appropriate ICD therapy; 32 ATP only (12.6%), 5 shock only (2.0%) and 3 combined ATP and shock (1.2%). In the adjusted model, high inactive duration (HR 1.40 (95% 1.10-1.78), peak walking cadence (HR 1.07 (95% 1.03-1.12) and total sleep duration (HR 1.50 (1.02-2.22) were associated with the outcome. The dose-response relationship was U-shaped for inactive duration with a cutoff at 16 hours, and linear for peak cadence and sleep. The prediction model reached an AUROC of 0.70 ±0.03, with highest accuracy in the first months. Conclusion Wearable-derived 24-hour movement and sleep behaviors collected over 28 days were associated with later appropriate ICD therapy risk. Testing of the predictive value of digital biomarkers for enhanced risk stratification of ventricular arrhythmia warrants larger prospective studies. Trial registration National Trial Registration (NL9218, http://onderzoekmetmensen.nl/)

A Study of Library Mobile Robot Book Classification and Transportation by Integrating DA and RMM

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value.

A Novel Approach for Predicting the Survival of Colorectal Cancer Patients Using Machine Learning Techniques and Advanced Parameter Optimization Methods

Background: Colorectal cancer is one of the most prevalent forms of cancer and is associated with a high mortality rate. Additionally, an increasing number of adults under 50 are being diagnosed with the disease. This underscores the importance of leveraging modern technologies, such as artificial intelligence, for early diagnosis and treatment support. Methods: Eight classifiers were utilized in this research: Random Forest, XGBoost, CatBoost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset from Brazil, containing information on tens of thousands of patients. Results: The models developed in this study demonstrated high classification accuracy in predicting one-, three-, and five-year survival, as well as overall mortality and cancer-specific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest classifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks. Conclusions: This research enabled the development of effective classification models that can be applied in clinical practice.

Analytical and Clinical Validation of the Oncomine Dx Target Test to Assess HER2 Mutation Status in Tumor Tissue Samples From Patients With Non–Small Cell Lung Cancer Treated With Trastuzumab Deruxtecan in the DESTINY-Lung01 and DESTINY-Lung02 Studies

Context.— Trastuzumab deruxtecan (T-DXd), a human epidermal growth factor receptor 2 (HER2)–targeted therapy, has demonstrated durable anticancer activity in patients with advanced, metastatic HER2 (also known as ERBB2)–mutant (HER2m) non–small cell lung cancer (NSCLC) who have limited treatment options and poor prognosis. Objective.— To analytically validate and assess the clinical utility of the Oncomine Dx Target (ODxT) Test as a companion diagnostic to identify patients with HER2m NSCLC. Design.— Tumor samples from patients in DESTINY-Lung01 and DESTINY-Lung02 were retrospectively analyzed alongside commercially procured samples using the ODxT test and compared to the assays used for screening in these clinical trials. Results.— Positive percent agreement (PPA) and negative percent agreement (NPA) between the ODxT Test and TruSight Tumor 170 assay when testing DESTINY-Lung01 and commercially procured samples met prespecified thresholds (PPA and NPA ≥90%) for analytical accuracy (100% and 99.1%). The ODxT Test results were highly concordant with clinical trial assays (CTAs) used in DESTINY-Lung01 (PPA and NPA, 98.0% and 100%) and DESTINY-Lung02 (PPA and NPA, 96.7% and 100%) to identify activating HER2 mutations in tumor samples. Confirmed objective response rates were similar between patients with HER2m tumors identified by the ODxT Test and by CTAs in DESTINY-Lung01 (58.3% and 54.9%) and DESTINY-Lung02 (53.6% and 53.8%). Response duration was 12.0 and 9.3 months for patients identified by the ODxT Test and CTAs, respectively, in DESTINY-Lung01. Conclusions.— The ODxT Test detected HER2 mutations in NSCLC with high analytical and clinical accuracy and identified HER2m populations with response rates similar to populations identified by CTAs, supporting clinical utility of the ODxT Test to inform treatment decisions for HER2m NSCLC.

High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity

AbstractGlioblastoma, the most aggressive primary brain cancer, has a dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we systematically screened repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling more than 2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug–target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of more than 1 million compounds with high patient validation accuracy. Deep multimodal profiling confirmed Ca2+-driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the anti-depressant vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology.

Privacy-preserving large language models for structured medical information retrieval

Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) “Llama 2” to extract quantitative information from clinical text and evaluates its performance in identifying features of decompensated liver cirrhosis. The LLM identified five key clinical features in a zero- and one-shot manner from 500 patient medical histories in the MIMIC IV dataset. We compared LLMs of three sizes and various prompt engineering approaches, with predictions compared against ground truth from three blinded medical experts. Our pipeline achieved high accuracy, detecting liver cirrhosis with 100% sensitivity and 96% specificity. High sensitivities and specificities were also yielded for detecting ascites (95%, 95%), confusion (76%, 94%), abdominal pain (84%, 97%), and shortness of breath (87%, 97%) using the 70 billion parameter model, which outperformed smaller versions. Our study successfully demonstrates the capability of locally deployed LLMs to extract clinical information from free text with low hardware requirements.

A Multi-Criteria Decision Making for Employee Selection Using SAW and Profile Matching

Multi-criteria decision making process has been one of the fastest growing areas during the last decades depending on the change in the business sector. Multi-criteria decision making is the most important branch of operation research by which people make complex decision daily life. The major steps in the decision- making approach are selecting the most preferred alternative for the decision-maker, ranking alternatives in order of importance for selection problems, and screening alternatives for the final decision. Employees are an important element of a company that determines its progress. Conventional (manual) recruitment methods are vulnerable to non-technical factors, such as frequent duplication or invalid data. In this study, simple additive weighting and profile matching are proposed to solve the employee selection problem. This study was conducted at the (UPT) Career Development and Entrepreneurship Universitas Brawijaya Malang using data collected from written test selection in 2019. The effectiveness of both methods was analyzed using confusion matrix. The SAW method provides an accuracy rate of 94.7%, a precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, profile matching method obtained the Accuracy rate of 90.4%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. Thus, it can be concluded that both methods have high accuracy values accompanied by high precision values when used for the selection process. This system can also effectively reduce the bias rate of the same data very well, as can be seen from the high recall and F-measure rates.

A kind regularization method with Poisson kernel to solve Cauchy problem of Schrödinger equation

In this work, a precise regularization technique based on the mollification method is proposed to solve the ill-posed Schr o ¨ dinger Cauchy problem with potential-free field. In the sense of Hadamard, this problem is severely ill-posed. Using the mollification regularization method based on Poisson kernel, some new error estimates are obtained under the priori and posteriori parameter selection rules. The regular approximate solutions are created, and the convergence evidences are provided. The good performance and high accuracy of this technique are demonstrated through various examples. The solution works well and is resistant to data disruption noise.

Antimicrobial susceptibility testing of bone and joint pathogens using isothermal microcalorimetry

The rise in osteomyelitis and periprosthetic joint infections, in combination with increasing life expectancy and the prevalence of diabetes, underscores the urgent need for rapid and accurate diagnostic tools. Conventional culture‐based methods are often time‐consuming and prone to false‐negatives, leading to prolonged and inappropriate antibiotic treatments. This study aims to improve osteomyelitis diagnostics by decreasing the time to detection and the time to an antibiotic susceptibility result to enable a targeted treatment using isothermal microcalorimetry (IMC). IMC measures heat flow in real‐time, providing insights into bacterial metabolism without the need for labeling. Using clinical isolates from bone infections, assessing their response to antibiotics through IMC, we demonstrated that IMC could detect bacteria within 4 h and determine antimicrobial susceptibility profiles within 2–22 h (median 4.85, range 1.28–21.78). This is significantly faster than traditional methods. A decision tree, based on antibiotic susceptibility, accurately categorized pathogens, achieving high accuracy (74–100%), sensitivity (100%), and specificity (65–100%). These findings suggest that IMC could redefine diagnostics of bone and joint infections and potentially infections in general, offering timely and precise treatment guidance, thereby improving patient outcomes and reducing health care burdens. Further optimization and clinical validation are needed to fully integrate IMC into routine diagnostics.

Diabetic retinopathy detection and categorizing using a lightweight deep learning approach

Diabetic retinopathy is an ocular disorder that has the potential to result in visual impairment and complete loss of vision in those diagnosed with diabetes. This illness affects the retinal blood vessels inside the light-sensitive tissue layer at the posterior of the eye, known as the retina. This paper presents a complete approach to diagnosing and categorizing diabetic retinopathy using deep learning models. A lightweight Convolutional Neural Network (CNN) is used to detect diabetic retinopathy in fundus images. This CNN has been developed to have fewer parameters and calculations, making it suited for resource-constrained environments while retaining decent performance. The categorization of diabetic retinopathy is carried out with the help of EfficientNet. This model uses an innovative compound scaling approach to strike a balance between the model's depth, width, and resolution. As a result, it maximizes computing efficiency while preserving high accuracy. The proposed detection model obtained an accuracy of 95%, and the classification model produced an accuracy of 84%.

Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram

Background This study aims to examine, through artificial intelligence, specifically machine learning, the emotional impact generated by disclosures about mental health on social media. In contrast to previous research, which primarily focused on identifying psychopathologies, our study investigates the emotional response to mental health-related content on Instagram, particularly content created by influencers/celebrities. This platform, especially favored by the youth, is the stage where these influencers exert significant social impact, and where their analysis holds strong relevance. Analyzing mental health with machine learning techniques on Instagram is unprecedented, as all existing research has primarily focused on Twitter. Methods This research involves creating a new corpus labelled with responses to mental health posts made by influencers/celebrities on Instagram, categorized by emotions such as love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness. The study is complemented by modelling a set of machine learning algorithms to efficiently detect the emotions arising when faced with these mental health disclosures on Instagram, using the previous corpus. Results Results have shown that machine learning algorithms can effectively detect such emotional responses. Traditional techniques, such as Random Forest, showed decent performance with low computational loads (around 50%), while deep learning and Bidirectional Encoder Representation from Transformers (BERT) algorithms achieved very good results. In particular, the BERT models reached accuracy levels between 86–90%, and the deep learning model achieved 72% accuracy. These results are satisfactory, considering that predicting emotions, especially in social networks, is challenging due to factors such as the subjectivity of emotion interpretation, the variability of emotions between individuals, and the interpretation of emotions in different cultures and communities. Discussion This cross-cutting research between mental health and artificial intelligence allows us to understand the emotional impact generated by mental health content on social networks, especially content generated by influential celebrities among young people. The application of machine learning allows us to understand the emotional reactions of society to messages related to mental health, which is highly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms’ high accuracy (86–90%) in social contexts like mental health, where detecting negative emotions is crucial, presents a promising research avenue. Achieving such levels of accuracy is highly valuable due to the significant implications of false positives or false negatives in this social context.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range.

Advancing rheumatology with natural language processing: insights and prospects from a systematic review

Abstract Objectives Natural Language Processing (NLP) and Large Language Models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analyzing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis, and patient management. Methods We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following PRISMA guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias. Results Out of 1,491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like BERT and GPT. High accuracy was observed in detecting conditions such as rheumatoid arthritis, spondyloarthropathies, and gout. The use of NLP also showed promise in managing diseases and predicting flares. Conclusion NLP show significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like rheumatoid arthritis and gout are well-developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice.

A machine learning‐based exploration of resilience and food security

AbstractLeveraging advancements in remote data collection and using the Food Insecurity Experience Scale (FIES) as a proxy measure of resilience, we show that machine learning models (such as Gradient Boosting Classifier, eXtreme Gradient Boosting, and Artificial Neural Networks), can predict resilience with relatively high accuracy (up to 81%). Key household‐level predictors include access to financial institutions, asset ownership, the adoption of agricultural mechanization as evidenced by the use of tractors, the number of crops cultivated, and ownership of nonfarm enterprises. Our analysis offers insights to researchers and policymakers interested in the development of targeted interventions to bolster household resilience.

Nomogram based on lymphocyte-associated inflammatory indexes predicts portal vein thrombosis after splenectomy with esophagogastric devascularization

ObjectiveThe relationship between lymphocyte-associated inflammatory indices and portal vein thrombosis (PVT) following splenectomy combined with esophagogastric devascularization (SED) is currently unclear. This study aims to investigate the association between these inflammatory indices and PVT, and to develop a nomogram based on these indices to predict the risk of PVT after SED, providing an early warning tool for clinical practice.MethodsWe conducted a retrospective analysis of clinical data from 131 cirrhotic patients who underwent SED at Lanzhou University’s Second Hospital between January 2014 and January 2024. Independent risk factors for PVT were identified through univariate and multivariate logistic regression analyses, and the best variables were selected using the Akaike Information Criterion (AIC) to construct the nomogram. The model’s predictive performance was assessed through receiver operating characteristic (ROC), calibration, decision, and clinical impact curves, with bootstrap resampling used for internal validation.ResultsThe final model incorporated five variables: splenic vein diameter (SVD), D-Dimer, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and red cell distribution width-to-lymphocyte ratio (RLR), achieving an area under the curve (AUC) of 0.807, demonstrating high predictive accuracy. Calibration and decision curves demonstrated good calibration and significant clinical benefits. The model exhibited good stability through internal validation.ConclusionThe nomogram model based on lymphocyte-associated inflammatory indices effectively predicts the risk of portal vein thrombosis after SED, demonstrating high accuracy and clinical utility. Further validation in larger, multicenter studies is needed.

A Novel Directed Seed-Based Connectivity Analysis Toolbox Applied to Human and Marmoset Resting-State fMRI

Estimating the direction of functional connectivity (FC) can help to further elucidate complex brain function. However, the estimation of directed FC at the voxel level in fMRI data, and evaluating its performance, has yet to be done. We therefore developed a novel directed seed-based connectivity analysis (SCA) method based on normalized pairwise Granger causality that provides greater detail and accuracy over ROI-based methods. We evaluated its performance against 145 cortical retrograde tracer injections in male and female marmosets that were used as ground truth cellular connectivity on a voxel-by-voxel basis. The ROC curve was calculated for each injection, and we achieved AUC (Area Under the ROC Curve) of 0.95 for undirected and 0.942 for directed SCA in the case of high cell count threshold. This indicates that SCA can reliably estimate the strong cellular connections between voxels in fMRI data. We then used our directed SCA method to analyze the human default mode network (DMN) and found that dlPFC (dorsolateral prefrontal cortex) and temporal lobe were separated from other DMN regions, forming part of the language-network that works together with the core DMN regions. We also found that the cerebellum (Crus I & II) was strongly targeted by the posterior parietal cortices and dlPFC, but reciprocal connections were not observed. Thus, the cerebellum may not be a part of, but instead a target of, the DMN and language-network. Summarily, our novel directed SCA method, visualized with a new functional flat mapping technique, opens a new paradigm for whole-brain functional analysis.Significant StatementWe developed a novel directed seed-based connectivity analysis (SCA) method. To evaluate its performance, we used 145 retrograde tracer injections into the common marmoset left cortex as ground truth cellular connectivity. These were compared with directed and undirected SCA on a voxel-by-voxel basis. We achieved AUC=0.95 for undirected and AUC=0.942 for directed SCA, thus SCA can estimate strong cellular connections with high accuracy. Then, we analysed the human default mode network (DMN) with directed SCA and found that the dorsolateral prefrontal cortex and temporal lobe were not part of the DMN, but part of the language network. Secondly, our analysis showed that the cerebellum may not be part of, but instead a target of, the DMN and language network.