The mental health crisis among youth and young adults has prompted growing concern over the psychological impacts of social media use (SMU), particularly on image-based platforms like Instagram. However, there is still relatively little known about how specific psychological factors come into play and interact on specific platforms to affect young adults’ mental health and wellbeing. In the present study, we tested how individual differences in contingent self-worth (CSW), social comparison tendencies, and motives for Instagram use relate to health and wellbeing outcomes, including measures of perceived stress, fatigue, anxiety and depression symptoms, as well as indicators of overall wellbeing such as life satisfaction. Across two independent samples—college students (N = 250) and a general young adult population (N = 458)—we conducted preregistered analyses in which we examined associations between Instagram-specific CSW, upward social comparison behaviors on Instagram, and health and wellbeing indicators. We also tested whether various motives for Instagram use might moderate these relationships. Findings generally supported a priori hypotheses, indicating that, across both samples, higher Instagram-based CSW and greater upward social comparison tendencies on Instagram were associated with poorer mental health, including elevated stress, anxiety, and depressive symptoms. Additionally, some of these relationships were moderated by motives for Instagram use, such that multiple motives interacted with upward social comparison tendencies to predict worse mental health, as indicated by more depression symptoms. Taken together, these results highlight the importance of CSW, upward social comparison, and usage motives when considering associations between SMU and wellbeing. They also highlight the need for personalized interventions that target risk factors such as maladaptive self-worth contingencies and social comparison behaviors in digital environments.

In addition to core knowledge, the prenatal environment, parental care, and early life experience play substantial roles in the establishment of social cognition. Given the evolutionary importance of early life care in humans and mammals in general, there are innate reward and stress based neuroendocrine mechanisms that are critical to maternal infant bonding as well as later social behavior.

BackgroundMillions of people each year suffer from chronic low back pain (cLBP), which adversely affects their physical and mental health. While non-pharmacological interventions such as mindfulness are known to be effective in treating cLBP, not all patients experience the same benefit. Determining who these treatments might work best for is difficult, as there are no reliable predictors of the response to mindfulness for cLBP. The objective of the current study was to apply predictive machine learning to data collected from a completed clinical trial of mindfulness for cLBP to identify phenotypes characterizing those who did and did not respond to the intervention.MethodsThe analyses here focused on 132 participants in the intervention arm of the clinical trial of mindfulness for cLBP. The Random Forest machine learning technique was used to identify key characteristics of responders (49) and non-responders (83).ResultsThe top three responder phenotypes were able to identify 26 out of the 49 responders with 92%–100% precision. The top three non-responder phenotypes were able to identify 36 out of 83 non-responders, all with 100% precision.ConclusionsResults from this machine learning based phenotyping can guide clinician and patient decision-making to maximize clinical efficiency, patient outcomes, and resource use as well as inform research and development of mindfulness-based treatments for pain.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. In 2024 it affected approximately 1 in 9 people aged 65 and older in the U.S., 6.9 million individuals. Early detection and accurate AD diagnosis are crucial for improving patient outcomes. Magnetic resonance imaging (MRI) has emerged as a valuable tool for examining brain structure and identifying potential AD biomarkers. This study performs predictive analyses by employing machine learning techniques to identify key brain regions associated with AD using numerical data derived from anatomical MRI scans, going beyond standard statistical methods. Using the Random Forest Algorithm, we achieved 92.87 % accuracy in detecting AD from Mild Cognitive Impairment and Cognitive Normals. Subgroup analyses across nine sex- and age-based cohorts (69-76 years, 77-84 years, and unified 69-84 years) revealed the hippocampus, amygdala, and entorhinal cortex as con- sistent top-rank predictors. These regions showed distinct volume reductions across age and sex groups, reflecting distinct age- and sex-related neuroanatomical patterns. Younger males and females (aged 69-76) exhibited volume decreases in the right hippocampus, suggesting its importance in the early stages of AD. Older males (77-84) showed substantial volume decreases in the left inferior temporal cortex. The left middle temporal cortex showed decreased volume in females, suggesting a potential female-specific influence, while the right entorhinal cortex may have a male-specific impact. These age-specific sex differences could inform clinical research and treatment strategies, aiding in identifying neuroanatomical markers and therapeutic targets for future clinical interventions.

Background Chronic pain is one of the most common drivers of healthcare utilization and a marked domain for health disparities, as African American/Black populations experience high rates of chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence-based integrative medicine, and medical group visits. In a parent randomized controlled trial, this approach was tested as an adjunct treatment in a diverse, medically underserved population with chronic pain and depression. Objective To determine race-based heterogeneity in the effects of a mindfulness based treatment for chronic pain. Methods This secondary analysis of the parent trial assessed heterogeneity of treatment effects along racialized identity in terms of primary patient-reported pain outcomes in a racially diverse sample suffering from chronic pain and depression. The analytic approach examined comorbidities and sociodemographics between racialized groups. RMANOVAs examined trajectories in pain outcomes (average pain, pain severity, and pain interference) over three timepoints (baseline, 9, and 21 weeks) between participants identifying as African American/Black (n = 90) vs White (n = 29) across both intervention and control conditions. Results At baseline, African American/Black participants had higher pain severity and had significantly different age, work status, and comorbidity profiles. RMANOVA models also identified significant race-based differences in the response to the parent IMGV intervention. There was reduced pain severity in African American/Black subjects in the IMGV condition from baseline to 9 weeks. This change was not observed in White participants over this time period. However, there was a reduction in pain severity in White participants over the subsequent interval from 9 to 21 week where IMGV had no significant effect in African American/Black subjects during this latter time period. Conclusion Interactions between pain and racialization require further investigation to understand how race-based heterogeneity in the response to integrative medicine treatments for chronic pain contribute to the broader landscape of health inequity.

Genetic studies in the social sciences could be augmented through the additional consideration of functional (transcriptome, methylome, metabolome) and/or multimodal genetic data when attempting to understand the genetics of social phenomena. Understanding the biological pathways linking genetics and the environment will allow scientists to better evaluate the functional importance of polygenic scores.

This paper presents the design, fabrication, and experimental validation of a photoacoustic (PA) imaging probe for robotic surgery. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. When equipped with a PA probe, a surgical robot can provide intraoperative guidance to the operating physician, alerting them of the presence of vital substrate anatomy (e.g., nerves or blood vessels) invisible to the naked eye. Our probe is designed to work with the da Vinci surgical system to produce three-dimensional PA images: We propose an approach wherein the robot provides Remote Center-of-Motion (RCM) scanning across a region of interest, and successive PA tomographic images are acquired and interpolated to produce a three-dimensional PA image. To demonstrate the accuracy of the PA guidance in scanning 3D tomography actuated by the robot, we conducted an experimental study that involved the imaging of a multi-layer wire phantom. The computed Target Registration Error (TRE) between the acquired PA image and the phantom was 1.5567±1.3605 mm. The ex vivo study demonstrated the function of the proposed laparoscopic device in 3D vascular detection. These results indicate the potential of our PA system to be incorporated into clinical robotic surgery for functional anatomical guidance.

Chronic pain is one of the most common reasons adults seek medical care in the US, with estimates of prevalence ranging from 11% to 40% and relatively higher rates in diverse populations. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. The objective of the present study was to assess the effects of race on the primary pain outcomes and evaluate potential relationships between race and additional patient characteristics in data from a randomized clinical trial of IMGV in socially diverse, marginalized patients suffering from chronic pain and depression. It was hypothesized that there would be racial differences in the effects of IMGV on pain outcomes. Our analyses identified significant racial differences in the response to IMGV. Black subjects had increased pain severity throughout the duration of the 21-week study but were less likely to respond to the pain intervention compared to White subjects. These results may be related to differential comorbidity rates, catastrophizing, and digital health literacy among these participant groups. To improve patient outcomes in similar studies, interactions between pain outcomes and these factors require further investigation to affect levels and trajectory of pain severity and enhance the response to complimentary interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT02262377 ### Funding Statement NCT from clinicaltrials.gov: 02262377 This research was funded through a Patient-Centered Outcomes Research Institute (PCORI) Award AD- 1304-6218. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Boston University Medical Campus Institutional Review Board (IRB) and the community health centers (CHC) research committees (IRB Approval Number: H33096). We registered this randomized controlled trial (RCT) in the international trial register [ClinicalTrials.gov: Identifier [NCT02262377][1]]. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Correspondence concerning this article should be directed to Paula Gardiner. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02262377&atom=%2Fmedrxiv%2Fearly%2F2023%2F03%2F22%2F2023.03.03.23286767.atom

This paper describes a framework allowing intraoperative photoacoustic (PA) imaging integrated into minimally invasive surgical systems. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. With PA imaging, a surgical robot can provide intraoperative neurovascular guidance to the operating physician, alerting them of the presence of vital substrate anatomy invisible to the naked eye, preventing complications such as hemorrhage and paralysis. Our proposed framework is designed to work with the da Vinci surgical system: real-time PA images produced by the framework are superimposed on the endoscopic video feed with an augmented reality overlay, thus enabling intuitive three-dimensional localization of critical anatomy. To evaluate the accuracy of the proposed framework, we first conducted experimental studies in a phantom with known geometry, which revealed a volumetric reconstruction error of 1.20 ± 0.71 mm. We also conducted an ex vivo study by embedding blood-filled tubes into chicken breast, demonstrating the successful real-time PA-augmented vessel visualization onto the endoscopic view. These results suggest that the proposed framework could provide anatomical and functional feedback to surgeons and it has the potential to be incorporated into robot-assisted minimally invasive surgical procedures.

In the domain of brain imaging of small animals including rats, ultrasound (US) imaging is an appealing tool because it offers a high frame rate, easy access, and involves no radiation. However, the rat skull causes artifacts that influence brain image quality in terms of contrast and resolution. Therefore, minimizing the skull-induced artifacts in US imaging is a significant challenge. Unfortunately, the amount of literature on rat skull-induced artifacts is limited, and there is a particular lack of studies exploring reducing skull-induced artifacts. Due to the difficulty of experimentally imaging the same rat brain with and without a skull, numerical simulation becomes a reasonable approach to studying skull-induced artifacts. In this work, we investigated the effects of skull-induced artifacts by simulating a grid of point targets inside the skull cavity and quantifying the pattern of skull-induced artifacts. With the capacity to automatically capture the artifact pattern given a large amount of paired training data, deep learning (DL) models can effectively reduce image artifacts in multiple modalities. This work explored the feasibility of using DL-based methods to reduce skull-induced artifacts in US imaging. Simulated data were used to train a U-Net-derived, image-to-image regression network. US channel data with artifact signals served as inputs to the network, and channel data with reduced artifact signals were the regression outcomes. Results suggest the proposed method can reduce skull-induced artifacts and enhance target signals in B-mode images.

Laparoscopic surgery is commonly used in the abdominal creating minimal trauma to the patient. With the current standard videoscope, visualizing vessels under the tissue is challenging. Photoacoustic (PA) imaging is laser-generated ultrasound imaging that offers vascular mapping. However, most in vivo PA imaging is limited by the lack of depth penetration due to the insufficient illumination in deep tissue caused by the light scattering and the risk of over-irradiation near laser contact tissue. Having a method to bring the signal sensing device closer to the target area is critical for the PA-guided intervention. A miniaturized PA laparoscopic should increase the maneuverability inside the abdominal for better visualization of the operational region. A PA imaging probe mainly consists of optical fibers and an ultrasound (US) transducer. While the US device miniaturization has been investigated, previous works used customized angled-tips fiber for side-illumination to miniaturize the light delivery, requiring multiple fibers or having limited illumination region. Diffusing fiber can illuminate a wider region with fewer fibers than angled-tip side-illumination fibers. This could further minimize the diameter of the entire PA laparoscopic by several millimeters. We propose a PA laparoscopic system imaging with only two diffusing side-illumination fibers while maintaining high image contrast. The phantom study shows the system can image 0.30 mm resolution with a 30 dB signal-to-noise ratio in an optical scattering environment. The result demonstrated the feasibility of delivering sufficient energy with diffusing fiber to miniaturize the dimensions of the PA laparoscopic device.

One novel example and/or perspective in support of "Why the learning account fails" is the impressive ability of humans to recognize and memorize facial features and accurately and reliably connect those to related identities. Furthermore, neuroimaging analysis presents an example in support of the crucial role of standardization in the lack of adoption of ideography.

BACKGROUND:Latinas in the United States suffer disproportionately high levels of pre- and postnatal depression. However, little is understood regarding the biopsychosocial mechanisms linking socio-environmental factors to this increase in mental health risk. The oxytocinergic system, with its roles in the stress response, social behaviour and mood regulation, may be an important modulator of this sensitivity. We have previously reported prenatal discrimination to be a significant predictor of postnatal depression in Latinas; here we tested whether sensitivity to discrimination stress might depend on oxytocinergic system activity. METHODS:A sample of 148 Latina women residing in the US were assessed prenatally at 24-32 weeks' gestation and 46 weeks postnatally for perceived discrimination levels, acculturation, and depression and anxiety symptoms. Plasma oxytocin (OXT) levels and DNA methylation of the oxytocin receptor (OXTR) were measured prenatally together with genotyping for the OXTR SNP, rs53576. RESULTS:In mothers with low OXT levels and low OXTR methylation, acculturation level was associated with postnatal depression and anxiety symptoms. No such associations were found in those with higher OXT levels and higher OXTR methylation. We also found a significant relationship between prenatal psychosocial factors (discrimination and acculturation) and postnatal depression and anxiety in carriers of the G-allele at rs53576, but not AA genotypes. Finally, OXTR methylation positively correlated with mothers reports of experiencing affiliative social touch. Moreover, social touch mediated the relationship between discrimination and postnatal depression in those with low OXTR methylation. CONCLUSION:These results support the hypothesis that the oxytocinergic system modulates sensitivity to prenatal stress in the development of postnatal mood and anxiety disorders in Latina mothers.

ABSTRACT Background Chronic pain is one of the most common reasons adults seek medical care in the US, with estimates of prevalence ranging from 11% to 40%. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. Objective and Design The objective of the present study was to use a blended analytical approach of machine learning and regression analyses to evaluate the potential relationship between depression and chronic pain in data from a randomized clinical trial of IMGV in socially diverse, low income patients suffering from chronic pain and depression. Methods This approach used machine learning to assess the predictive relationship between depression and pain and identify and select key mediators, which were then assessed with regression analyses. It was hypothesized that depression would predict the pain outcomes of average pain, pain severity, and pain interference. Results Our analyses identified and characterized a predictive relationship between depression and chronic pain interference. This prediction was mediated by high perceived stress, low pain self-efficacy, and poor sleep quality, potential targets for attenuating the adverse effects of depression on functional outcomes. Conclusions In the context of the associated clinical trial and similar interventions, these insights may inform future treatment optimization, targeting, and application efforts in racially diverse, low income populations, demographics often neglected in studies of chronic pain.

Background: Breastfeeding has many positive effects on the health of infants and mothers, however, the effect of breastfeeding on maternal mental health is largely unknown. The goal of this systematic review was to (1) synthesize the existing literature on the effects of breastfeeding on maternal mental health, and (2) inform breastfeeding recommendations.Materials and Methods: A literature search was conducted in electronic databases using search terms related to breastfeeding (e.g., breastfeeding, infant feeding practices) and mental health conditions (e.g., mental illness, anxiety, depression), resulting in 1,110 records. After reviewing article titles and abstracts, 339 articles were advanced to full-text review. Fifty-five articles were included in the final analysis.Results: Thirty-six studies reported significant relationships between breastfeeding and maternal mental health outcomes, namely symptoms of postpartum depression and anxiety: 29 found that breastfeeding is associated with fewer mental health symptoms, one found it was associated with more, and six reported a mixed association between breastfeeding and mental health. Five studies found that breastfeeding challenges were associated with a higher risk of negative mental health symptoms.Conclusions: Overall, breastfeeding was associated with improved maternal mental health outcomes. However, with challenges or a discordance between breastfeeding expectations and actual experience, breastfeeding was associated with negative mental health outcomes. Breastfeeding recommendations should be individualized to take this into account. Further research, specifically examining the breastfeeding experiences of women who experienced mental health conditions, is warranted to help clinicians better personalize breastfeeding and mental health counseling.

Hypertension-related illnesses are a leading cause of disability and death in the United States, where 46% of adults have hypertension and only half have it controlled. It is critical to reduce hypertension, and either new classes of interventions are required, or we need to develop enhanced approaches to improve medical regimen adherence. The Mindfulness-Based Blood Pressure Reduction program (MB-BP) is showing novel mechanisms and early evidence of efficacy, but the neural correlates are unknown. The objectives of this study were to identify structural neural correlates of MB-BP using diffusion tensor magnetic resonance imaging (DTI) and assess potential correlations with key clinical outcomes. In a subset of participants from a larger randomized controlled trial, MB-BP participants exhibited increased interoception and decreased depressive symptoms compared to controls. Analyses of DTI data revealed significant group differences in several white matter neural tracts associated with the limbic system and/or hypertension. Specific changes in neural structural connectivity were significantly associated with measures of blood pressure, depression anxiety and symptoms, mindfulness, and emotional regulation. It is concluded that MB-BP has extensive and substantial effects on brain structural connectivity which could mediate beneficial changes in depression, interoceptive awareness, blood pressure, and related measures in individuals with hypertension.

Chronic pain is currently diagnosed using verbal self-reports, which present a challenge for patients with cognitive or physiological disorders. Prior work has explored machine learning prediction of pain from clinical data, which requires active user involvement and does not capture their behavior in natural settings. Passive objective assessment is desirable. Circadian Rhythms, including sleep–wake cycles, are biological processes that reoccur every 24 h and can be derived from physiological data such as heart rate, activity, and sleep, gathered using widely-owned smart wearables. This study investigated the feasibility of using machine learning and rest-activity circadian rhythm features to predict patients’ pain, including pain intensity, its interference with the patient’s life (dysregulation), and their difficulty in performing physical functions using passively gathered actigraphy data. To predict pain on day N, actigraphy data collected over that day were analyzed. Three sets of feature were extracted: (1) Activity (total sedentary bouts/time/breaks, % in sedentary/light/ moderate activity), (2) Sleep (sleep efficiency/latency, wake after sleep onset), and (3) Rest Activity Rhythm (mesor, acrophase, Intradaily Variability (IV)). These features were then classified using various machine learning algorithms. Our proposed PainRhythms approach achieved an average AUC-ROC of 0.97 with a stacking machine learning classifier for predicting pain, 0.67 and 0.62 with logistic regression for pain intensity and interference, and 0.56 with gradient boosting for physical function. We found that chronic pain predictions were more accurate using rest-activity rhythm features than sleep or activity features. Of all the rhythmic features, Intradaily Variability (IV) was the most predictive feature, with elevated values in pain associated with disturbed sleep. PainRhythms provides preliminary evidence that rest-activity rhythms can effectively detect subjects with chronic pain. In future work, we aim to gather more data and confirm our preliminary findings on a large, class-balanced and diverse dataset.