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Full-Text Articles in Medicine and Health Sciences
Cerebral Blood Flow And Brain Functional Connectivity Changes In Older Adults Participating In A Mindfulness-Based Stress Reduction Program, Aleeze Sattar Moss, Diane K. Reibel, Nancy Wintering, Faezeh Vedaei, Hannah Porter, Mohsen Khosravi, Justin Heholt, M. Alizadeh, Feroze Mohamed, Andrew B. Newberg
Cerebral Blood Flow And Brain Functional Connectivity Changes In Older Adults Participating In A Mindfulness-Based Stress Reduction Program, Aleeze Sattar Moss, Diane K. Reibel, Nancy Wintering, Faezeh Vedaei, Hannah Porter, Mohsen Khosravi, Justin Heholt, M. Alizadeh, Feroze Mohamed, Andrew B. Newberg
Department of Radiology Faculty Papers
There is a growing interest in the potential beneficial effects of mindfulness meditation training in protecting against age-related physical, emotional, and cognitive decline. The current prospective, single-center, single-arm study investigated if functional magnetic resonance imaging-based changes in cerebral blood flow and brain functional connectivity could be observed in 11 elderly adults (mean age 79) after participation in a Mindfulness-Based Stress Reduction (MBSR) program. The results showed significantly (p < 0.05) altered cerebral blood flow and functional connectivity in the cingulate gyrus, limbic structures, and subregions of the temporal and frontal lobes, similar to findings of other meditation-related studies in younger populations. Furthermore, these changes were also associated with significant improvements in depression symptoms. This study suggests that the MBSR program can potentially modify cerebral blood flow and connectivity in this population.
Identification Of Chronic Mild Traumatic Brain Injury Using Resting State Functional Mri And Machine Learning Techniques, Faezeh Vedaei, Najmeh Mashhadi, George Zabrecky, Daniel A. Monti, Emily Navarreto, Chloe Hriso, Nancy Wintering, Andrew B. Newberg, Feroze Mohamed
Identification Of Chronic Mild Traumatic Brain Injury Using Resting State Functional Mri And Machine Learning Techniques, Faezeh Vedaei, Najmeh Mashhadi, George Zabrecky, Daniel A. Monti, Emily Navarreto, Chloe Hriso, Nancy Wintering, Andrew B. Newberg, Feroze Mohamed
Department of Radiology Faculty Papers
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency …