Digital Commons Network^™

Editorial: Surface Emg And Other Measurement Techniques In Rehabilitation Research And Practice: Are New Educational Programs Needed?, Marco Tramontano, Sheng Li, Roberto Merletti

Faculty, Staff and Student Publications

No abstract provided.

Safety Of Indocyanine Green Microdosing For Clinical Imaging Of Csf Ventricular Dynamics And Extracranial Outflow, Miriam Zamorano, Banghe Zhu, Ahmed T Massoud, Jonathan Hendricks, H Alex Choi, Pedram Peesh, Brandon A Miller, Xinhai Robert Zhang, Manish N Shah, Eva M Sevick-Muraca

Faculty, Staff and Student Publications

Background and purpose: Intravenous administration of indocyanine green (ICG) has been approved in brain surgeries for decades, yet concerns about neurotoxicity prevent its direct administration into the cerebrospinal fluid (CSF). Armed with prior animal studies demonstrating the feasibility of using ICG microdosing into the CSF, we sought to evaluate its nonclinical safety profile and obtain surrogate measures in adults prior to its use in human neonates.

Methods: Evaluation of ICG toxicity was conducted in mixed primary CNS cell cultures and in an extended safety study of juvenile rat pups deploying intraventricular injections of saline (as control) or ICG. Analysis of …

Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor

Masters

The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.

Unmasking The Nexus Of Race, Ethnicity, And Health: An Intersectional Analysis Of The Epistemology Of Race In Medicine, Medical Curricula, And Health Disparities, Antoinette Leonard-Jean Charles

Antioch University Dissertations & Theses

The persistent health disparities faced by Black, Indigenous, and People of Color (BIPOC) in the United States are deeply rooted in systemic racism embedded within medical education and clinical practice. Historical acceptance of racial supremacy in the United States has shaped both the foundations and continued practices of medical education, leading to entrenched biases that affect healthcare delivery and patient outcomes. This dissertation critically examines the intersection of race, education, and health by analyzing how historical and contemporary understandings of race and ethnicity shape medical curricula, healthcare practices, and patient care. Through the integration of Critical Race Theory (CRT), Social …

The Role Of Floodplain Complexity In Wood Turtle Habitat Use, Laura S. Deming

Antioch University Dissertations & Theses

Wood turtles occupy cold, low-gradient riverine habitat throughout the northeastern United State and southeastern Canada. Stream alterations, such as dams, channel straightening, and floodplain development have diminished the natural hydrologic and sedimentation regimes that result in complex channel and floodplain geomorphology. Studies throughout the species’ range have quantified many aspects of their habitat, but none have quantified components of geomorphology of wood turtle sites. The goal of this study was to explore the role of stream channel and floodplain geomorphology in wood turtle habitat use, with specific objectives to (1) quantify components of stream channel and floodplain geomorphic complexity, (2) …

Experiences Of Dissertating Students: What Works?, Laura Durkin

Antioch University Dissertations & Theses

This study explores the experiences of doctoral graduates from Council for Accreditation of Counseling and Related Educational Programs (CACREP)-accredited Counselor Education and Supervision (CES) programs. Guided by theoretical frameworks on student attrition and institutional racism, the research investigates three primary questions: (1) What dissertation- specific programmatic factors influence time to completion for CES doctoral students? (2) What interventions most strongly promote a sense of social integration and belonging? and (3) What interventions most strongly promote academic integration and confidence in students’ ability to succeed? Data were collected anonymously through an online survey, which included demographic and open-ended questions. Twelve participants …

The Effect Of Temperature And Salinity On Margalefidinium Polykrikoides Group Iii Va, Usa Strain Growth, Eduardo Pérez-Vega, Margaret R. Mulholland, Katherine E. Crider, Kimberly E. Powell, P. Dreux Chappell, Alexander Bochdansky

OES Faculty Publications

Margalefidinium polykrikoides is a cosmopolitan dinoflagellate that blooms in coastal waters. Despite genomic evidence that it belongs to Group III and so closely related to isolates from Puerto Rico, Malaysia, North America, and Central America, M. polykrikoides blooms in the Chesapeake Bay at warmer temperatures and lower salinities than in coastal ecosystems occupied by its closest relatives. In this study, the effect of temperature and salinity on the growth rate and total cell yield of an M. polykrikoides VA culture isolate were examined and compared with environmental observations made during M. polykrikoides blooms in the Chesapeake Bay. M. polykrikoides group …

Group Antenatal Care Positively Transforms The Care Experience: Results Of An Effectiveness Trial In Malawi, Crystal L. Patil, Kathleen F. Noor, Esnath Kapito, Li C. Liu, Xiaohan Mei, Elizabeth Chodzaza, Genesis Chorwe-Sungani, Ursula Kafuulafula, Elizabeth T. Abrams, Allissa Desloge, Ashley Gresh, Rohan D. Jeremiah, Dhruvi R. Patel, Anne Batchelder, Heidy Wang, Jocelyn Faydenko, Sharon S. Rising, Ellen Chirwa

Department of Medicine Faculty Publications

Background

We developed and tested a Centering-based group antenatal (ANC) model in Malawi, integrating health promotion for HIV prevention and mental health. We present effectiveness data and examine congruence with only the Group ANC theory of change model, which identifies key processes as supportive relationships, empowered partners in learning and care, and meaningful services, leading to better ANC experiences and outcomes.

Methods

We conducted a hybrid effectiveness-implementation trial at seven clinics in Blantyre District, Malawi, comparing outcomes for 1887 pregnant women randomly assigned to Group ANC or Individual ANC. Group effects on outcomes were summarized and evaluated using t-tests, Mann-Whitney, …

Pediatric Cancer Incidence, Temporal Trends, And Mortality In The United States By Health Disparities Indicators, Seer (1973-2014), Prachi P. Chavan, Laurens Holmes Jr.

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pediatric cancer incidence has been increasing in the United States, despite improvement in pediatric cancer survival. This steady increase in incidence trends is not completely understood but maybe associated with social and environmental factors. In this study we aimed to assess the cumulative incidence, temporal trends, and mortality rates in pediatric cancer. Additionally, we examined sub-group variability in both incidence and mortality rates. Methods: Data from Surveillance, Epidemiology, and End Results (SEER) −18 from 1973–2014 were used for the purpose of analysis in this study. Age-adjusted incidence rates were used to assess temporal trends in cancer among children aged < 1–19 years. Univariable and multivariable binomial regression models were used to examine the association between race and cancer mortality while adjusting for potential confounders. Results: There were 92,594 cancer diagnoses during this period. White children comprised 74,758, (80.7%), black children 10,030, (10.8%), and other races 6648, (7.2%). Overall the age-adjusted cumulative incidence was slightly higher among white children (16.4%) than black children (12.4%) and other (13.0%). Children aged 15–19 years and those in metropolitan regions were more likely to be diagnosed with pediatric cancer. Relative to females, males were 16% more likely to die from the disease [adjusted Risk Ratio (aRR): 1.16, 95% Confidence Interval (CI): 1.09–1.22]. Additionally, compared to white children, black children had higher mortality rates [(aRR): 1.37, 99% CI: 1.23–1.52]. Conclusions: There is an increasing trend in pediatric cancer incidence; while white children have the highest incidence, black children and males indicated a survival disadvantage, indicative of racial and sex variability in overall pediatric cancer in the United States.

Symbol-Temporal Consistency Self-Supervised Learning For Robust Time Series Classification, Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao

Computer Science Faculty Publications

The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity …

Food Rescue, Food Waste, And Policy In New York: A Mixed-Methods Study Of Food System Impacts, Mariana Torres Arroyo

Electronic Theses & Dissertations (2024 - present)

This dissertation investigates how food donation policies in New York State influence the recovery, redistribution, and waste of surplus fresh produce, with a focus on fruits and vegetables. Using an interdisciplinary, systems-based approach that integrates system dynamics modeling with qualitative, community-engaged research, the study analyzes the effectiveness and unintended consequences of three major policies: Nourish New York, the Farm to Food Bank tax credit, and the Food Donation and Food Scraps Recycling Law.

Chapter 2 uses simulation modeling to examine how the tax credit and waste ban affect produce recovery and redistribution. Results show that while both policies can boost …

How Strong Is Strong Enough? Assessing When Physical Performance Tests Cease To Be Predictive Of Sprint Performance In Trained Football Players, Shayne Vial, Mark Scanlan, Philipp Beranek, Daniel Kadlec, Oliver R. Barley, Jodie Cochrane Wilkie

Research outputs 2022 to 2026

Vial, S, Scanlan, M, Beranek, P, Kadlec, D, Barley, OR, and Cochrane Wilkie, J. How strong is strong enough? Assessing when physical performance tests cease to be predictive of sprint performance in trained football players. J Strength Cond Res XX(X): 000-000, 2025 - Quantifying underlying capacities of sprint performance are useful for monitoring training and guiding interventions. Traditional statistical techniques, such as correlation and regression analyses, have been widely used to model relationships between physical capacity and sprint performance. However, these methods often assume linearity, potentially oversimplifying complex, nonlinear interactions, and generalizing group-level data that may misrepresent individual trends. For …

Demographic Factors As Predictors Of Academic Performance In First- And Second-Year Occupational Therapy Students, Eileen Du Plooy, Daleen Casteleijn, Denise Franzsen, Gopika Ramkilawon

Journal of Occupational Therapy Education

Significant disparities in academic performance that may be associated with specific demographics can be utilized for supporting diverse student cohorts. Strategies can be developed for students to enhance equity and inclusivity in undergraduate occupational therapy education to ensure student success. This study aimed to determine the predictive demographic factors influencing academic performance among first- and second-year undergraduate occupational therapy students at a university in South Africa. A quantitative, cross-sectional design was utilized, and the sample included 129 first- and second-year students. Data were collected retrospectively from university records and student dashboards. Key demographic and academic variables were analyzed, including personal …

Understanding And The Perceived Value Of Contemporary Pain Neuroscience Education Among Occupational Therapy Students, Sue E. Curfman, Elicia Dunn Cruz, Diana A. Harrison, Will J. Best, Kathryn Schmidt

Journal of Occupational Therapy Education

Chronic pain is a leading cause of global disability, yet occupational therapy education often lacks sufficient coverage of multidisciplinary pain management approaches. The purposes of this study were to 1) determine the level of understanding of contemporary pain neuroscience in occupational therapy doctoral (OTD) students; 2) evaluate the effectiveness of instructional techniques to improve their knowledge of contemporary pain neuroscience; and 3) explore their perceptions of pain neuroscience and its implications for their future roles. This pre-post test within-subject experimental design study, involving 39 OTD students, assessed the impact and perceptions of a two-hour pain education module. The revised Neurophysiology …

Dough Rheology Properties As Affected By The Inclusion Of Oat Drink Residue Flour: A Multivariate Analysis Approach, Mahmoud Said Rashed, Eimear Gallagher, Jesus M. Frias, Milica Pojić, Shivani Pathania

Articles

The global shift towards plant-based food consumption presents significant environmental benefits, yet the consequential rise in related by-products still imposes a challenge to be addressed. The present work was carried out to evaluate the rheological properties of bread dough as affected by the incorporation of dry fractionated oat drink residue flours as a functional ingredient to identify the optimum condition for incorporating fibre-rich ingredients in bakery products. Mechanically dry fractionated, dried oat drink residue flour of particle size F1: > 150, F2: 150-224, and F3: 224-300 μm was blended with strong wheat flour at 10% and 20% substitution levels of the …

A Case Study Using The Transparency Framework And Artificial Intelligence To Promote Effective Writing And Student Success In A Writing-Intensive Course, Elizabeth A. Brown, Maria Kronenburg, Ashlee Steeley, Diana Tagbor

Health Behavior, Policy & Management Faculty Publications

Program evaluation data suggest that undergraduate students struggle with writing in a clear and concise manner and appropriately citing. Faculty implemented the plan-do study-act cycle to pilot the Transparency in Learning and Teaching (TILT) project framework and to explore the use of artificial intelligence (AI) and discuss approaches to using AI, along with the TILT framework, in a writing-intensive course to identify the pros and cons of using ChatGPT in an online classroom. The TILT framework reinforces adult learning by helping students clearly understand the assignment's purpose and establish a clear relationship between assignment and students' professional lives. Faculty encouraged …

A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn

Health Behavior, Policy & Management Faculty Publications

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challenges in academic settings, students increasingly seek digital tools that provide holistic support for physical, mental, and financial health. Through focus groups, this qualitative study examines students' preferences for personalized health tracking, educational content, and flexible reminders within a private, supportive community. Key findings emphasize students' desire for a balanced, all-in-one app that integrates health and wellness tools without overwhelming them with notifications. Students also highlighted the importance of social media integration for outreach, though concerns were raised about potential stress …

Forecasting Covid-19 Cases, Hospital Admissions, And Deaths Based On Wastewater Sars-Cov-2 Surveillance Using Gaussian Copula Time Series Marginal Regression Model, Hueiwang Anna Jeng, Norou Diawara, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with …

Copula-Based Bayesian Model For Detecting Differential Gene Expression, Prasansha Liyanaarachchi, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Changes In Functional Connectivity Are Associated With Functional Independence In The Early Postoperative Period Following Awake Surgical Resection Of Language-Eloquent Glioma, Kyle R Noll, Evan D Bander, Henry S Chen, Mariana Bradshaw, Jeffrey S Wefel, Vinodh A Kumar, Sujit S Prabhu, Ho-Ling Liu

Faculty, Staff and Student Publications

Background: Neurocognitive decline in patients with primary brain tumors is associated with alterations in the functional connectome and reduced independence in daily living. This study explores postoperative connectomic changes associated with functional independence outcomes in patients with eloquent glioma, and how these associations differ from neurocognitive-connetcomic relationships.

Methods: Fifteen patients with left perisylvian glioma underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological evaluation within 2 weeks before and on average 1 month after resection. Functional independence was measured with the Physical Self-Maintenance Scale (PSMS) and the Instrumental Activities of Daily Living scale (IADL). Graph theoretical analysis quantified functional brain …