Article
Psychiatry
Kyle Woisard, Joel L. Steinberg, Liangsuo Ma, Edward Zuniga, Michael Lennon, F. Gerard Moeller
Summary: Resting state functional magnetic resonance imaging (fMRI) has been used to study the functional connectivity of brain networks in addictions. This study assessed the functional and effective connectivity of the executive control network (ECN), default mode network (DMN), and salience network (SN) in cocaine dependent subjects compared to healthy control subjects. The results showed that cocaine dependent subjects had greater effective connectivity in the right ECN to left ECN pathway and negative associations between delay discounting and effective connectivity in DMN to ECN and SN to DMN pathways.
FRONTIERS IN PSYCHIATRY
(2023)
Article
Nutrition & Dietetics
Nicolas Guerithault, Samuel M. McClure, Chinedum O. Ojinnaka, B. Blair Braden, Meg Bruening
Summary: In this study, fMRI was used to investigate the differences in functional connectivity across cognitive networks at rest among college students with different levels of food security. The results suggest that food insecurity is associated with poorer executive function and altered functional connectivity in specific brain regions, which may contribute to executive function difficulties in college students.
Article
Neurosciences
Xin Hao, Taicheng Huang, Yiying Song, Xiangzhen Kong, Jia Liu
Summary: The study reveals age-related changes in the navigation network organization, with increasing modularity under resting-state and increasing flexibility under task-state. Task-modulated FC changes were found to be greater in adults than in children, suggesting differences in network organization between age groups during tasks.
Article
Neuroimaging
Suraya Meghji, Alicia J. Hilderley, Kara Murias, Brian L. Brooks, John Andersen, Darcy Fehlings, Nomazulu Dlamini, Adam Kirton, Helen L. Carlson
Summary: Perinatal stroke is an early brain vascular injury that often leads to lifelong disability. Children with perinatal stroke commonly have comorbidities such as attention-deficit hyperactivity disorder (ADHD) and deficits in executive function. This study found differences in functional connectivity within and between networks in children with perinatal stroke, and these differences were associated with ADHD symptoms and executive function.
BRAIN IMAGING AND BEHAVIOR
(2023)
Article
Neurosciences
Sara M. Motlaghian, Aysenil Belger, Juan R. Bustillo, Judith M. Ford, Armin Iraji, Kelvin Lim, Daniel H. Mathalon, Bryon A. Mueller, Daniel O'Leary, Godfrey Pearlson, Steven G. Potkin, Adrian Preda, Theo G. M. van Erp, Vince D. Calhoun
Summary: In this work, the researchers focused on explicitly nonlinear relationships in functional networks by introducing a technique using normalized mutual information (NMI). They demonstrated their proposed approach using simulated data and applied it to a dataset of schizophrenia patients and healthy controls. The analysis showed a modularized nonlinear relationship among brain functional networks, particularly in the sensory and visual cortex. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between the two groups, with controls showing more nonlinearity in most cases. The results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function.
HUMAN BRAIN MAPPING
(2022)
Article
Audiology & Speech-Language Pathology
Tanya Dash, Yves Joanette, Ana Ines Ansaldo
Summary: This study explores the effects of bilingualism on different subcomponents of attention using resting state functional connectivity analysis. The results show a positive correlation between behavioral performance and functional connectivity in the alerting and orienting networks, but not in the executive control network. Moreover, the levels of bilingualism modulate the functional connectivity of attention networks, with objective measures affecting all three networks and subjective measures only affecting specific networks. Therefore, language performance is a more effective measure in understanding the impact of bilingualism on attention processes.
BRAIN AND LANGUAGE
(2022)
Article
Psychology, Multidisciplinary
Victor M. Vergara, Flor A. Espinoza, Vince D. Calhoun
Summary: Alcohol use disorder (AUD) is a significant burden on society, but its detection and assessment are challenging. Recent studies have shown that machine learning algorithms can be effective tools in studying and detecting AUD. This study used control samples without comorbid substance use to evaluate the performance of commonly used machine learning classifiers in detecting AUD using resting state functional network connectivity data derived from independent component analysis.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Medicine, General & Internal
Benedikt Sundermann, Reinhold Feldmann, Christian Mathys, Johanna M. H. Rau, Stefan Garde, Anna Braje, Josef Weglage, Bettina Pfleiderer
Summary: This study used rs-fMRI to assess the functional connectivity (FC) in cognition-related networks of young adults with Fetal Alcohol Syndrome (FAS). The results showed altered FC globally, within 7 out of 10 networks, and between networks in FAS participants compared to controls, with the most significant changes seen in attention-related network components.
Article
Neurosciences
Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi
Summary: This study used rs-fMRI data to characterize connectivity patterns in drug-resistant epilepsy, revealing significant connectivity changes in the default mode network (DMN) and the dorsal attention network (DAN). The combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Endocrinology & Metabolism
Annelies Van't Westeinde, Nelly Padilla, Sara Fletcher-Sandersjoo, Olle Kaempe, Sophie Bensing, Svetlana Lajic
Summary: Our study found that individuals with autoimmune Addison disease (AAD) have stronger resting-state functional connectivity (rs-fc) in the bilateral orbitofrontal cortex (OFC), left medial visual, and left posterior default mode network compared to healthy controls. Higher replacement dosage of glucocorticoid (GC) is associated with stronger rs-fc in a small part of the left OFC. There is no clear association between rs-fc and executive functions or mental fatigue.
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
(2023)
Article
Multidisciplinary Sciences
Won Beom Jung, Haiyan Jiang, Soohyun Lee, Seong-Gi Kim
Summary: In order to advance fMRI-based brain science, it is important to analyze fMRI activity at the circuit level. This study combines whole-brain fMRI with neuronal silencing to dissect the responses and circuits of the somatosensory network.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Medicine, General & Internal
Stephen J. Suss, Anna Manelis, Joao Paulo Lima Santos, Cynthia L. Holland, Richelle S. Stiffler, Hannah B. Bitzer, Sarrah Mailliard, Madelyn Shaffer, Kaitlin Caviston, Michael W. Collins, Mary L. Phillips, Anthony P. Kontos, Amelia Versace
Summary: Concussion among adolescents is a public health concern, and the differences in brain function between adolescents with and without a history of concussion are not well understood. This study used resting state functional magnetic resonance imaging (fMRI) to investigate these differences and found disrupted functional connectivity between the hippocampal network and the right inferior frontal gyrus in adolescents with concussion.
JOURNAL OF CLINICAL MEDICINE
(2022)
Article
Medicine, General & Internal
Ji-Won Hur, Taekwan Kim, Kang Ik K. Cho, Jun Soo Kwon
Summary: Exploring disruptions in intrinsic resting-state networks in schizophrenia-spectrum disorders provides insight into disease-specific pathophysiology. This study investigated abnormalities in RSNs in schizotypal personality disorder (SPD) and found impaired large-scale intrinsic brain networks in individuals with SPD, correlating with cognitive-perceptual deficits and social functioning impairments.
JOURNAL OF CLINICAL MEDICINE
(2021)
Article
Oncology
Lu Jin, Chuzhong Li, Yazhuo Zhang, Taoyang Yuan, Jianyou Ying, Zhentao Zuo, Songbai Gui
Summary: This study investigated the dynamic alterations of functional connectivity within the language network in glioma patients, revealing a distinct pattern of connectivity changes modulated by tumor position. Left hemisphere gliomas had a broader impact on functional connectivity compared to right hemisphere gliomas. The findings highlight the modulatory effects of core-periphery mechanisms on language processing.
FRONTIERS IN ONCOLOGY
(2021)
Article
Neuroimaging
Weiliang Yang, Xuexin Xu, Chunxiang Wang, Yongying Cheng, Yan Li, Shuli Xu, Jie Li
Summary: In this study, we computed dynamic functional network connectivity using the sliding window method in patients with schizophrenia and healthy controls. Our results showed that patients with schizophrenia had higher occurrences in the weakly connected state, positively correlated with negative symptoms. They also had fewer occurrences in the strongly connected state compared to healthy controls. Additionally, the dynamic functional network connectivity between certain brain networks was decreased in schizophrenia patients.
BRAIN IMAGING AND BEHAVIOR
(2022)
Article
Neurosciences
Pujie Feng, Rongtao Jiang, Lijiang Wei, Vince D. Calhoun, Bin Jing, Haiyun Li, Jing Sui
Summary: This study investigates the impact of four confounding factors on individual trait prediction using resting-state functional connectivity (RSFC) data. The results suggest that the appropriate time series length and brain parcellation choice can improve prediction performance. Functional connectivity calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost. Moreover, cognitive traits with larger variance among subjects can be better predicted.
Article
Cardiac & Cardiovascular Systems
Rongtao Jiang, Vince D. Calhoun, Stephanie Noble, Jing Sui, Qinghao Liang, Shile Qi, Dustin Scheinost
Summary: This study utilizes machine learning and functional connectivity to investigate the neurobiological correlates of blood pressure at an individual level. The results identify specific brain regions that are associated with blood pressure and provide evidence for meaningful neural representations of blood pressure in connectivity profiles.
CARDIOVASCULAR RESEARCH
(2023)
Article
Psychology, Developmental
Na Luo, Xiangsheng Luo, Suli Zheng, Dongren Yao, Min Zhao, Yue Cui, Yu Zhu, Vince D. Calhoun, Li Sun, Jing Sui
Summary: This study investigates the temporal and frequency abnormalities in ADHD and its subtypes using high-density EEG. The results show differences in the salience network and frequency power between ADHD patients and healthy controls. Subtype differences primarily exist in the visual network, with ADHD-C patients showing a more activated visual network. Furthermore, the support vector machine model achieves high accuracy in classifying ADHD and its subtypes.
EUROPEAN CHILD & ADOLESCENT PSYCHIATRY
(2023)
Article
Neurosciences
Md Abdur Rahaman, Jiayu Chen, Zening Fu, Noah Lewis, Armin Iraji, Theo G. M. van Erp, Vince D. Calhoun
Summary: Characterizing neuropsychiatric disorders is challenging, but combining structural and functional neuroimaging with genomic data in a multimodal classification framework can improve the classification of disorders and explore underlying neural and biological mechanisms. By developing neural networks for feature learning and implementing an adaptive control unit for fusion, we achieved high accuracy in schizophrenia prediction and identified critical neural features and genes/biological pathways associated with the disorder.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Kaicheng Li, Qingze Zeng, Xiao Luo, Shile Qi, Xiaopei Xu, Zening Fu, Luwei Hong, Xiaocao Liu, Zheyu Li, Yanv Fu, Yanxing Chen, Zhirong Liu, Vince D. Calhoun, Peiyu Huang, Minming Zhang
Summary: The study found that concomitant neuropsychiatric symptoms are associated with accelerated Alzheimer's disease progression. Using multimodal brain imaging, a pattern associated with these symptoms was identified and found to be correlated with the development of Alzheimer's disease. The pattern was also found to be associated with multiple cognitive domains and could predict cognitive decline.
HUMAN BRAIN MAPPING
(2023)
Article
Computer Science, Interdisciplinary Applications
Irina Belyaeva, Ben Gabrielson, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, Tulay Adali
Summary: Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns. This study proposes a tensor-based approach for extracting developmental signatures of multi-subject MEG data. The results demonstrate that this approach can produce descriptive features of the multidimensional MEG data and be used to study group differences in brain patterns and cognitive function of healthy children.
Article
Biochemistry & Molecular Biology
Constantinos Constantinides, Laura K. M. Han, Clara Alloza, Linda Antonella Antonucci, Celso Arango, Rosa Ayesa-Arriola, Nerisa Banaj, Alessandro Bertolino, Stefan Borgwardt, Jason Bruggemann, Juan Bustillo, Oleg Bykhovski, Vince Calhoun, Vaughan Carr, Stanley Catts, Young-Chul Chung, Benedicto Crespo-Facorro, Covadonga M. Diaz-Caneja, Gary Donohoe, Stefan Du Plessis, Jesse Edmond, Stefan Ehrlich, Robin Emsley, Lisa T. Eyler, Paola Fuentes-Claramonte, Foivos Georgiadis, Melissa Green, Amalia Guerrero-Pedraza, Minji Ha, Tim Hahn, Frans A. Henskens, Laurena Holleran, Stephanie Homan, Philipp Homan, Neda Jahanshad, Joost Janssen, Ellen Ji, Stefan Kaiser, Vasily Kaleda, Minah Kim, Woo-Sung Kim, Matthias Kirschner, Peter Kochunov, Yoo Bin Kwak, Jun Soo Kwon, Irina Lebedeva, Jingyu Liu, Patricia Mitchie, Stijn Michielse, David Mothersill, Bryan Mowry, Victor Ortiz-Garcia de la Foz, Christos Pantelis, Giulio Pergola, Fabrizio Piras, Edith Pomarol-Clotet, Adrian Preda, Yann Quide, Paul E. Rasser, Kelly Rootes-Murdy, Raymond Salvador, Marina Sangiuliano, Salvador Sarro, Ulrich Schall, Andre Schmidt, Rodney J. Scott, Pierluigi Selvaggi, Kang Sim, Antonin Skoch, Gianfranco Spalletta, Filip Spaniel, Sophia Thomopoulos, David Tomecek, Alexander S. Tomyshev, Diana Tordesillas-Gutierrez, Therese van Amelsvoort, Javier Vazquez-Bourgon, Daniela Vecchio, Aristotle Voineskos, Cynthia S. Weickert, Thomas Weickert, Paul M. Thompson, Lianne Schmaal, Theo G. M. van Erp, Jessica Turner, James H. Cole, Danai Dima, Esther Walton
Summary: Schizophrenia patients show evidence of advanced brain ageing, which is not associated with clinical characteristics.
MOLECULAR PSYCHIATRY
(2023)
Article
Computer Science, Interdisciplinary Applications
Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J. Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun L. W. Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rudiger Bruhl, Jean-Luc Martinot, Marie-Laure Paillere Martinot, Eric Artiges, Frauke Nees, Tomas Paus, Luise Poustka, Juliane H. Frohner, Lauren Robinson, Michael N. Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A. Turner, Anand D. Sarwate, Sergey M. Plis, Vivek Benegal, Gunter Schumann, Vince D. Calhoun
Summary: With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks.
Article
Computer Science, Information Systems
Xiang Li, Sheri L. Towe, Ryan P. Bell, Rongtao Jiang, Shana A. Hall, Vince D. Calhoun, Christina S. Meade, Jing Sui
Summary: Neurocognitive impairment is common in people living with HIV, and identifying reliable biomarkers is crucial for understanding neural foundations and clinical care. This study used connectome-based predictive modeling to predict cognitive functioning in PLWH, achieving high prediction accuracy by combining multiple modalities and incorporating clinical measures.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Information Systems
Ying Xing, Peter Kochunov, Theo G. M. van Erp, Tianzhou Ma, Vince D. Calhoun, Yuhui Du
Summary: Feature selection is important in identifying biomarkers of mental disorders. In this study, a new method based on neighborhood rough set (NRS) was proposed to select biomarkers of schizophrenia using fMRI data. The method combined NRS with information entropy and multi-granularity fusion to obtain the most discriminative features. The method achieved higher classification accuracies compared to other methods, revealing meaningful substrates of schizophrenia. This study highlights the potential of exploring neuroimaging-based biomarkers using the NRS-based feature selection method.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Neurosciences
Noah Lewis, Robyn Miller, Harshvardhan Gazula, Vince Calhoun
Summary: Deep learning is effective for classifying biological sex based on fMRI, but research on the most relevant brain features for this classification is lacking. Model interpretability is important for understanding deep learning models, but little work has been done on the relationship between temporal dimension of fMRI signals and sex classification. In this study, a methodology is provided to address underspecification and instability in feature explanation models, and sex differences in functional brain networks are explored using intrinsic connectivity networks.
Article
Neurosciences
Marlena Duda, Armin Iraji, Judith M. Ford, Kelvin O. Lim, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin, Adrian Preda, Theo G. M. Van Erp, Vince D. Calhoun
Summary: By using spatially constrained independent component analysis (scICA), this study found that rsfMRI scans of just 2-5 minutes can provide good clinical utility without significant loss of individual functional network connectivity (FNC) information from longer scan lengths.
HUMAN BRAIN MAPPING
(2023)
Article
Psychiatry
Zening Fu, Christopher C. Abbott, Jeremy Miller, Zhi-De Deng, Shawn M. McClintock, Mohammad S. E. Sendi, Jing Sui, Vince D. Calhoun
Summary: Electroconvulsive therapy (ECT) is effective for depression treatment, and its mechanism involves changing brain's functional organization through electrical current stimulation. This study investigated the relationship between whole-brain electric field (E-field), cerebro-cerebellar functional network connectivity (FNC), and clinical outcomes of ECT. The results showed that E-field influenced cognitive performance through cerebellum to middle occipital gyrus (MOG)/posterior cingulate cortex (PCC) FNC mediation, and had an effect on antidepressant outcomes through cerebellum to parietal lobule FNC mediation. Furthermore, larger E-field was associated with increased FNC between cerebellum and MOG and decreased FNC between cerebellum and PCC, which were linked with decreased cognitive performance.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Psychiatry
Kuaikuai Duan, Jiayu Chen, Vince D. D. Calhoun, Wenhao Jiang, Kelly Rootes-Murdy, Gido Schoenmacker, Rogers F. F. Silva, Barbara Franke, Jan K. K. Buitelaar, Martine Hoogman, Jaap Oosterlaan, Pieter J. J. Hoekstra, Dirk Heslenfeld, Catharina A. A. Hartman, Emma Sprooten, Alejandro Arias-Vasquez, Jessica A. A. Turner, Jingyu Liu
Summary: In this study, a genomic pattern underlying the gray matter variation in the frontal cortex related to working memory deficit in ADHD was revealed through a multivariate analysis. The identified genes are involved in modulating neuronal substrates underlying high-level cognition in ADHD, providing insights into the pathology of ADHD persistence.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Engineering, Biomedical
Lan Yang, Chen Qiao, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang
Summary: This study proposes an explainable multimodal deep dictionary learning method to uncover the commonality and specificity of different modalities in brain developmental differences. The results show that the proposed model can achieve better reconstruction and identify age-related differences in reoccurring patterns.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)