Article
Neurosciences
Gwen van der Wijk, Jacqueline K. Harris, Stefanie Hassel, Andrew D. Davis, Mojdeh Zamyadi, Stephen R. Arnott, Roumen Milev, Raymond W. Lam, Benicio N. Frey, Geoffrey B. Hall, Daniel J. Muller, Susan Rotzinger, Sidney H. Kennedy, Stephen C. Strother, Glenda M. MacQueen, Andrea B. Protzner
Summary: By studying a large group of MDD patients and controls using fMRI data, differences in functional connectivity in patients were identified. Baseline connectivity of the anterior/posterior cingulate and insula seeds could differentiate patients with different treatment outcomes, highlighting features that might predict remission prior to pharmacotherapy.
Article
Neurosciences
Xiaodi Zhang, Eric A. Maltbie, Shella D. Keilholz
Summary: Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity using various methods. However, the results from these methods are just a simplification of the continuous process of brain activity.
Article
Psychology, Developmental
Jolinda Smith, Eric Wilkey, Ben Clarke, Lina Shanley, Virany Men, Damien Fair, Fred W. Sabb
Summary: This study aims to address the motion issue in fMRI studies of young children and develops a simple and effective method for a high-motion pediatric cohort. The method combines real-time monitoring of head motion with a preprocessing pipeline using volume censoring and ICA-based denoising. Results show that usable resting-state data can be obtained despite extreme motion.
DEVELOPMENTAL COGNITIVE NEUROSCIENCE
(2022)
Article
Clinical Neurology
William C. Palmer, Brenna A. Cholerton, Cyrus P. Zabetian, Thomas J. Montine, Thomas J. Grabowski, Swati Rane
Summary: The study found significant differences in cerebello-cortical functional connectivity between PD patients and normal controls, particularly in the somatomotor network. Cognitive function was found to be associated with connectivity of the cerebellar SMN and dorsal attention network. Altered cerebellar connectivity with frontoparietal and default mode networks was also correlated with the severity of motor function in PD.
FRONTIERS IN NEUROLOGY
(2021)
Article
Clinical Neurology
Hussam Metwali, Tamer Ibrahim, Mathijs Raemaekers
Summary: Resting-state networks (RSNs) under anesthesia can be used for intraoperative brain mapping and remapping during tumor resection, but there is a significant decrease in network connectivity with the continuation of anesthesia.
WORLD NEUROSURGERY
(2021)
Article
Engineering, Biomedical
Maliheh Ahmadi, Kamran Kazemi, Katarzyna Kuc, Anita Cybulska-Klosowicz, Mohammad Sadegh Helfroush, Ardalan Aarabi
Summary: The study found that children with ADHD exhibited various alterations in intra and inter-network connectivity in different dynamic connectivity states compared to typically developing children. They showed weaker intra-hemispheric connectivity with functional asymmetries in certain states and characteristic abnormalities in corticosubcortical and corticocerebellar connectivity in others. Additionally, significant alterations were observed in several strategic brain regions in both static and dynamic functional connectivity in the ADHD groups compared to typically developing children.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Neuroimaging
Cees J. Weeland, Odile A. van den Heuvel, T. White, H. Tiemeier, C. Vriend
Summary: This study investigated the functional brain characteristics of obsessive-compulsive symptoms (OCS) in children from the general population using a multiscale approach. The results suggest that network characteristics of OCS in children are partly symptom-specific and severity-dependent.
BRAIN IMAGING AND BEHAVIOR
(2022)
Article
Neurosciences
Serafeim Loukas, Lara Lordier, Djalel-Eddine Meskaldji, Manuela Filippa, Joana Sa de Almeida, Dimitri Van de Ville, Petra S. Hueppi
Summary: Research indicates that even during the newborn period, familiar music and unfamiliar music are processed differently by the brain. After music listening, functional connectivity between brain regions in all newborns is modulated. Premature infants exposed to music experience enhanced functional connectivity between brain regions after listening to music.
HUMAN BRAIN MAPPING
(2022)
Article
Neurosciences
Marilena Wilding, Anja Ischebeck, Natalia Zaretskaya
Summary: Subjective perceptual experience is influenced by both sensory information and brain state. This study examines the connection between brain areas related to illusory perception and an individual's general tendency to perceive it. The findings suggest that the connectivity of specific brain areas with intrinsic networks plays an important role in forming a perceptual tendency toward illusory perception.
Article
Clinical Neurology
Tingting Ji, Xiaodan Li, Jun Chen, Xuemin Ren, Lin Mei, Yue Qiu, Jie Zhang, Shengcai Wang, Zhifei Xu, Hongbin Li, Zheng Li, Yun Peng, Yue Liu, Xin Ni, Jun Tai, Jiangang Liu
Summary: Children with obstructive sleep apnea (OSA) exhibit abnormal neural activities in specific brain regions and impaired cognitive functions, with the former possibly being the neural mechanism of the latter.
Article
Anatomy & Morphology
Claire Deshayes, Veronique Paban, Marie-Helene Ferrer, Beatrice Alescio-Lautier, Caroline Chambon
Summary: Through studying cognitive function and brain resting functional connectivity, this research explored the definition of creative potential. The analysis showed distinct differences in cognitive function and brain connectivity between groups with high and low creative potential.
BRAIN STRUCTURE & FUNCTION
(2021)
Article
Neurosciences
Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam
Summary: Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive modality used to investigate functional connectomes in the brain. Effective noise removal is crucial in preprocessing rs-fMRI data. This study proposes an automatic deep learning framework for noise-related component identification, achieving remarkable performance and increasing noise detection speed.
Article
Neurosciences
Elijah Agoalikum, Benjamin Klugah-Brown, Hang Yang, Pan Wang, Shruti Varshney, Bochao Niu, Bharat Biswal
Summary: In this study, dynamic functional network connectivity differences in adult, adolescent, and child ADHD were investigated using resting-state functional magnetic resonance imaging data. The findings suggest that there are connectivity differences among the three age groups, providing new insights for future case-control studies and treatment strategies.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
HongKun Liu, HongYi Zheng, GengBiao Zhang, JiaYan Zhuang, WeiJia Li, BiXia Wu, WenBin Zheng
Summary: This study investigated the topological alterations of the whole-brain functional connectome in children with CO poisoning and found that their brain functional network topology was impaired, characterized by reduced global efficiency and small-worldness, and increased characteristic path length. The study also identified significant correlations between patients' coma time, node characteristics, and CO hemoglobin content concentration.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Automation & Control Systems
Armin W. Thomas, Ulman Lindenberger, Wojciech Samek, Klaus-Robert Mueller
Summary: Research has shown that transfer learning improves the performance of deep learning models in datasets with small sample sizes. In this study, the application of transfer learning to cognitive decoding analysis using functional neuroimaging data is systematically evaluated. Pre-trained deep learning models consistently achieve higher decoding accuracies and require less training time and data compared to models trained from scratch. The benefits of pre-training come from the ability to reuse learned features when training with new data. However, challenges arise when interpreting the decoding decisions of pre-trained models, as they may utilize fMRI data in unforeseen and counterintuitive ways.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Neurosciences
Seok Woo Moon, Lu Zhao, William Matloff, Sam Hobel, Ryan Berger, Daehong Kwon, Jaebum Kim, Arthur W. W. Toga, Ivo D. D. Dinov
Summary: This study examined the association between genetic and neuroimaging biomarkers in late-onset dementia-related cognitive impairment. The results showed significant correlations between specific genomic markers and neuroimaging markers, and identified key markers for distinguishing Alzheimer's disease and mild cognitive impairment.
CNS NEUROSCIENCE & THERAPEUTICS
(2023)
Article
Neurosciences
Elizabeth Haddad, Fabrizio Pizzagalli, Alyssa H. Zhu, Ravi R. Bhatt, Tasfiya Islam, Iyad Ba Gari, Daniel Dixon, Sophia I. Thomopoulos, Paul M. Thompson, Neda Jahanshad
Summary: Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. In this study, the reliability and compatibility of regional morphometric metrics derived from different versions of FreeSurfer were empirically assessed using test-retest data. The results showed lower compatibility between the latest version and older versions in terms of cortical thickness, surface area, and subcortical volumes. Replication studies in an independent sample confirmed these findings. The study highlights the importance of considering version-related inconsistencies in published findings.
HUMAN BRAIN MAPPING
(2023)
Article
Green & Sustainable Science & Technology
Manu Sharma, Sunil Luthra, Sudhanshu Joshi, Anil Kumar, Akshat Jain
Summary: The study aims to investigate the impact of green logistics on the adoption of Circular Economy (CE) in the era of Industry 4.0 (I4.0). By implementing green initiatives, the logistics sector shows its strong concern for environmental issues and promotes sustainable logistics and circular practices to address critical issues such as waste generation, resource scarcity, renewable resource use, and climate change. The study examines the relationships between green logistics practices, I4.0 technologies, and CE adoption while also considering the moderating effects of institutional pressure and supply chain flexibility. The findings suggest that firms can enhance CE adoption driven by I4.0 technologies with green logistics practices as a mediator, and the moderating effect of supply chain flexibility is significant while the moderating effect of institutional pressure is insignificant. The study provides valuable insights for managers and contributes to the theoretical understanding of green logistics and CE adoption.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Haoteng Tang, Lei Guo, Xiyao Fu, Yalin Wang, Scott Mackin, Olusola Ajilore, Alex D. Leow, Paul M. Thompson, Heng Huang, Liang Zhan
Summary: MRI-derived brain networks are widely used to understand interactions among brain regions and their relationships with brain development and diseases. Graph mining on these networks can help discover biomarkers for clinical phenotypes and neurodegenerative diseases. Most current studies focus on projecting structural networks onto functional networks to extract a fused representation.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Multidisciplinary Sciences
Chenzhong Yin, Phoebe Imms, Mingxi Cheng, Anar Amgalan, Nahian F. Chowdhury, Roy J. Massett, Nikhil N. Chaudhari, Xinghe Chen, Paul M. Thompson, Paul Bogdan, Andrei Irimia
Summary: This study introduces a convolutional neural network (CNN) to estimate brain age (BA) from magnetic resonance images (MRIs) and achieves lower estimation errors compared to previous studies. The CNN provides detailed brain aging maps that reveal sex differences and neurocognitive trajectories in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). BA is shown to capture dementia symptom severity, functional disability, and executive function better than chronological age (CA) in individuals with MCI. The proposed framework can systematically map the relationship between aging-related neuroanatomy changes and neurocognitive measures in both cognitively normal individuals and those with MCI or AD, aiding in early identification of AD risk.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Oncology
Hanif Abdul Rahman, Mohammad Ashraf Ottom, Ivo D. Dinov
Summary: This study aimed to evaluate machine learning algorithms in large-scale datasets, taking into account both younger and older adults from various regions and sociodemographics. The study found that a prediction model based on an artificial neural network performed well in predicting CRC and non-CRC phenotypes.
Article
Green & Sustainable Science & Technology
Manu Sharma, Sudhanshu Joshi, Mukesh Prasad, Shalini Bartwal
Summary: This study proposes a model to examine the critical barriers and suggest strategies for implementing Circular Economy (CE) in the Oil & gas (O&G) industry. The results show that 'knowledge barriers' are the most critical, and the strategies 'Developing collaborative model' and 'Internal research and development, innovation' are the most significant for reducing the barriers.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Multidisciplinary Sciences
Dipesh Niraula, Wenbo Sun, Jionghua Jin, Ivo D. Dinov, Kyle Cuneo, Jamalina Jamaluddin, Martha M. Matuszak, Yi Luo, Theodore S. Lawrence, Shruti Jolly, Randall K. Ten Haken, Issam El Naqa
Summary: This study developed an artificial intelligence-based decision-making framework to assist in dynamic treatment regimes (DTR) for oncology. The framework utilizes advanced machine learning analytics and information-rich multi-omics data to overcome the challenges posed by various variables, treatment response uncertainty, and patient heterogeneity. The framework, demonstrated in Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications, consists of two main components and has shown promising results in improving clinical decision-making and treatment outcomes.
SCIENTIFIC REPORTS
(2023)
Article
Green & Sustainable Science & Technology
Sudhanshu Joshi, Manu Sharma, Banu Y. Ekren, Yigit Kazancoglu, Sunil Luthra, Mukesh Prasad
Summary: Food waste reduction and security are major concerns in agri-food supply chains, as more than a third of global food production is wasted or lost due to mismanagement. Resilient food supply chains need to address challenges such as resource scarcity, climate change, and waste generation. This study explores supply chain innovations, using SWARA analysis, to identify the most significant innovations for developing resilient food supply chains. The results highlight the importance of business strategy and technological innovations in bringing resilience to food supply chains.
Article
Psychiatry
Alexander Weigard, Katherine L. McCurry, Zvi Shapiro, Meghan E. Martz, Mike Angstadt, Mary M. Heitzeg, Ivo D. Dinov, Chandra Sripada
Summary: This study developed and tested machine learning models to predict ADHD symptoms in children using neurocognitive abilities, demographics, and child and family characteristics. The models explained 15-20% of the variance in 1-year ADHD symptoms and 12-13% of the variance in 2-year ADHD symptoms. The models showed high generalizability and minimal predictive power loss when applied to new data.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Pharmacology & Pharmacy
Simeone Marino, Hassan Jassar, Dajung J. J. Kim, Manyoel Lim, Thiago D. D. Nascimento, Ivo D. D. Dinov, Robert A. A. Koeppe, Alexandre F. F. DaSilva
Summary: This study utilized a novel machine learning method to accurately identify migraine patients based on the analysis of central mu-opioid and dopamine D2/D3 receptors. The results showed that dysfunction in the μ-opioid and D2/D3 receptors in the neurotransmission of migraine patients may partly explain the severe impact of migraine and associated neuropsychiatric comorbidities.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Business
Manu Sharma, Deepak Kaushal, Sudhanshu Joshi
Summary: Social media has become a major source of information for young users, but it has a severe impact on mental state due to information overload, which is still a concern for researchers. The addiction of Generation Z users to mobile phones/gadgets is increasing with the rise of social media, leading to a complete transformation in their behavioral outcomes. Behavioral issues such as stress, fatigue, fear of missing out, phubbing, and anxiety are on the rise, but the relationship between these issues and information overload has not been sufficiently examined. The study finds that social media platforms need to understand users' compulsive usage, which results in fatigue and anxiety, and government information support plays a positive role in reducing fatigue and anxiety.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2023)
Review
Multidisciplinary Sciences
Sunil Luthra, Shruti Agrawal, Anil Kumar, Manu Sharma, Sudhanshu Joshi, Jatin Kumar
Summary: Psychological and mental well-being of young adults has been greatly influenced by the COVID-19 pandemic, leading to a global concern. This research study provides an overview of the international scientific studies on the effect of COVID-19 on young adults' psychological well-being. It reveals the top-cited authors, documents, journals, productive countries, keywords, and themes in this area. The United States has contributed the most publications, followed by the United Kingdom and Italy. The study highlights the importance of addressing the psychological health of young adults worldwide.
Article
Biotechnology & Applied Microbiology
Mohammad Ashraf Ottom, Hanif Abdul Rahman, Iyad M. Alazzam, Ivo D. Dinov
Summary: This study proposes an enhanced deep neural network approach, the 3D-Znet model, for segmenting brain tumors based on 3D neuroimaging data. It provides automated tumor diagnostics and can help in early tumor diagnosis, potentially saving lives.
BIOENGINEERING-BASEL
(2023)
Article
Biotechnology & Applied Microbiology
Hanif Abdul Rahman, Madeline Kwicklis, Mohammad Ottom, Areekul Amornsriwatanakul, Khadizah H. Abdul-Mumin, Michael Rosenberg, Ivo D. Dinov
Summary: This study utilized machine learning algorithms and artificial intelligence techniques to assess mental well-being and identified the most significant features associated with it. The findings are of great importance for providing cost-effective support and modernizing mental well-being assessment at both individual and university levels.
BIOENGINEERING-BASEL
(2023)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.
