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Magnetic resonance imaging of hippocampal internal architecture (HIA) at 3T is challenging. HIA is defined by layers of gray and white matter that are less than 1 mm thick in the coronal plane. To visualize HIA, conventional MRI approaches have relied on sequences with high in-plane resolution (≤0.5 mm) but comparatively thick slices (2-5 mm). However, thicker slices are prone to volume averaging effects that result in loss of HIA clarity and blurring of the borders of the hippocampal subfields in up to 61% of slices as has been reported. In this work we describe an approach to hippocampal imaging that provides consistently high HIA clarity using a commonly available sequence and post-processing techniques that is flexible and may be applicable to any MRI platform. We refer to this approach as High Resolution Multiple Image Co-registration and Averaging (HR-MICRA). This approach uses a variable flip angle turbo spin echo sequence to repeatedly acquire a whole brain T2w image volume with high resolution in three dimensions in a relatively short amount of time, and then co-register the volumes to correct for movement and average the repeated scans to improve SNR. We compared the averages of 4, 9, and 16 individual scans in 20 healthy controls using a published HIA clarity rating scale. In the body of the hippocampus, the proportion of slices with good or excellent HIA clarity was 90%, 83%, and 67% for the 16x, 9x, and 4x HR-MICRA images, respectively. Using the 4x HR-MICRA images as a baseline, the 9x HR-MICRA images were 2.6 times and 16x HR-MICRA images were 3.2 times more likely to have high HIA ratings (p < 0.001) across all hippocampal segments (head, body, and tail). The thin slices of the HR-MICRA images allow reformatting in any plane with clear visualization of hippocampal dentation in the sagittal plane. Clear and consistent visualization of HIA will allow application of this technique to future hippocampal structure research, as well as more precise manual or automated segmentation.

Magnetic resonance imaging of hippocampal internal architecture (HIA) at 3T is challenging. HIA is defined by layers of gray and white matter that are less than 1 mm thick in the coronal plane. To visualize HIA, conventional MRI approaches have relied on sequences with high in-plane resolution (≤0.5 mm) but comparatively thick slices (2-5 mm). However, thicker slices are prone to volume averaging effects that result in loss of HIA clarity and blurring of the borders of the hippocampal subfields in up to 61% of slices as has been reported. In this work we describe an approach to hippocampal imaging that provides consistently high HIA clarity using a commonly available sequence and post-processing techniques that is flexible and may be applicable to any MRI platform. We refer to this approach as High Resolution Multiple Image Co-registration and Averaging (HR-MICRA). This approach uses a variable flip angle turbo spin echo sequence to repeatedly acquire a whole brain T2w image volume with high resolution in three dimensions in a relatively short amount of time, and then co-register the volumes to correct for movement and average the repeated scans to improve SNR. We compared the averages of 4, 9, and 16 individual scans in 20 healthy controls using a published HIA clarity rating scale. In the body of the hippocampus, the proportion of slices with good or excellent HIA clarity was 90%, 83%, and 67% for the 16x, 9x, and 4x HR-MICRA images, respectively. Using the 4x HR-MICRA images as a baseline, the 9x HR-MICRA images were 2.6 times and 16x HR-MICRA images were 3.2 times more likely to have high HIA ratings (p < 0.001) across all hippocampal segments (head, body, and tail). The thin slices of the HR-MICRA images allow reformatting in any plane with clear visualization of hippocampal dentation in the sagittal plane. Clear and consistent visualization of HIA will allow application of this technique to future hippocampal structure research, as well as more precise manual or automated segmentation.

Knee osteoarthritis (OA) is a leading cause of late life pain and disability, and non-Hispanic black (NHB) adults experience greater OA-related pain and disability than non-Hispanic whites (NHWs). Recent evidence implicates psychosocial stress, cognitive-attentional processes, and altered central pain processing as contributors to greater OA-related pain and disability among NHBs. To address these ethnic/race disparities, this clinical trial will test whether a mindfulness intervention (Breathing and Attention Training, BAT) combined with transcranial direct current stimulation (tDCS) will enhance pain modulatory balance and pain-related brain function, reduce clinical pain, and attenuate ethnic differences therein, among NHBs and NHWs with knee OA. Participants will complete assessments of clinical pain, function, psychosocial measures, and quantitative sensory testing (QST), including mechanical temporal summation and conditioned pain modulation. Neuroimaging will be performed to examine pain-related brain structure and function. Then, participants will be randomized to one of four groups created by crossing two BAT conditions (Real vs. Sham) with two tDCS conditions (Real vs. Sham). Participants will then undergo five treatment sessions during which the assigned BAT and tDCS interventions will be delivered concurrently for 20 min over one week. After the fifth intervention session, participants will undergo assessments of clinical pain and function, QST and neuroimaging identical to the pretreatment measures, and monthly follow-up assessments of pain will be conducted for three months. This will be the first study to determine whether mindfulness and tDCS treatments will show additive or synergistic effects when combined, and whether treatment effects differ across ethnic/race groups.

The healthcare industry is undergoing a digital revolution driven by the advancements in artificial intelligence (AI). AI is redefining traditional approaches, from disease diagnosis to drug discovery. The success of these initiatives depends heavily on the expertise of data scientists, AI researchers and engineers. These individuals play a pivotal role in collecting, cleaning, training and analyzing vast amounts of healthcare data to extract valuable insights. By leveraging advanced machine learning and data analytics techniques, they enable the development of AI solutions that can improve patient outcomes and optimize healthcare delivery. This article examines the critical roles these professionals play in developing and delivering healthcare AI applications. Drawing on insights from 11 experts, it also highlights the intersection of data science, AI and project management in this domain. [ABSTRACT FROM AUTHOR]

The healthcare industry is transforming, with Artificial Intelligence (AI) emerging as a powerful tool for revolutionizing patient care, medical research, and administrative efficiency. However, successfully implementing AI projects in this highly regulated and complex environment requires a unique blend of expertise. Enter the project manager (PM) and the Project Management Office (PMO) -- two crucial roles that are the backbone for ensuring the successful integration of AI in healthcare. This article, the first of two parts, delves into the critical roles PMs and PMOs play in navigating the complexities of AI-enabled healthcare projects. With input from 22 experts, we have investigated specific challenges PMs and PMOs face, the skillsets they need, and the strategies they could employ to ensure the successful development, deployment, and continuous improvement of AI solutions within the healthcare landscape. While part 1 of this series focused on crucial project manager concepts, this concluding article focuses on the role of PMOs in AI-driven healthcare. [ABSTRACT FROM AUTHOR]

The healthcare industry is transforming, with Artificial Intelligence (AI) emerging as a powerful tool for revolutionizing patient care, medical research, and administrative efficiency. However, successfully implementing AI projects in this highly regulated and complex environment requires a unique blend of expertise. Enter the project manager (PM) and the Project Management Office (PMO) - two crucial roles that are the backbone for ensuring the successful integration of AI in healthcare. This article, the first of two parts, delves into the critical roles PMs and PMOs play in navigating the complexities of AI-enabled healthcare projects. With input from 22 experts, we have investigated specific challenges PMs and PMOs face, the skillsets they need, and the strategies they could employ to ensure the successful development, deployment, and continuous improvement of AI solutions within the healthcare landscape. [ABSTRACT FROM AUTHOR]

Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs. Index Terms--Activity, automated, behavior, classification, instrumentation, mice, noninvasive, respiration, wake.

Compelling evidence exists that non-Hispanic blacks (NHB) engage in pain catastrophizing (negatively evaluate one's ability to cope with pain) more often than non-Hispanic whites (NHW). Functional neuroimaging studies revealed that individuals with high levels of trait pain catastrophizing show increased cerebral responses to pain in several pain-related brain regions (e.g., insula, primary somatosensory cortex [S1]), but associations between brain structure and catastrophizing remain largely unexplored. The current investigation was conducted at the University of Florida and the University of Alabama at Birmingham. Participants were 129 community-dwelling adults with or at risk of knee osteoarthritis (OA). Participants completed the pain catastrophizing subscale of the Coping Strategies Questionnaire-Revised and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain intensity subscale. Magnetic Resonance Imaging data were obtained. MANOVA and Chi-Square analyses assessed sociodemographic/clinical differences stratified by ethnicity/race. Multivariate regression analyses with insula and somatosensory cortical thickness entered as dependent variables with catastrophizing and the interaction between catastrophizing and ethnicity/race as the independent variables. Covariates include education, body mass index, study site, and WOMAC pain (ethnicity/race was an additional covariate in non-stratified analyses). There were significant interactions between ethnicity/race, pain catastrophizing, and brain structure. Higher pain catastrophizing was associated with thinner S1 bilaterally (ps <.05) in NHW, but not NHB participants with or at risk for knee OA. These results suggest that pain catastrophizing might have differing effects on pain-related central pathways and may contribute to ethnic/race group differences in individuals with or at risk for knee OA. [ABSTRACT FROM AUTHOR]