Publications by Year: 2024

INTRODUCTION: High blood pressure (HBP) is an independent, modifiable driver of cardiovascular (CV) morbidity and mortality. Nocturnal hypertension and non-dipping of blood pressure (NdBP) may be early markers of HBP. Similar to patients with NdBP, individuals with non-dipping of heart rate (NdHR) during sleep have an increased risk of CV disease, CV events, and CV-related mortality. The aim of this analysis was to evaluate if cardiopulmonary coupling (CPC) analysis-derived sleep states [stable/unstable non-rapid eye movement (NREM) sleep] and concomitant heart rate (HR) changes can provide information about nocturnal blood pressure (BP).

METHOD: Plethysmogram (pleth) signals from the HeartBEAT study (NCT01086800) were analyzed for CPC sleep states. Included in the analysis are sleep recordings from participants with acceptable pleth-signal quality at baseline (n = 302) and follow-up (n = 267), all having confirmed CV disease or CV-disease risk factors. The participants had a high prevalence of obstructive sleep apnea (OSA), 98.4% with moderate-OSA [apnea-hypopnea index (AHI) ≥ 15) and 29.6% severe OSA (AHI ≥ 30). A "heart-rate module" was created to evaluate the utility of identifying patients more likely to have BP dipping during sleep. Patients who did not have a decrease of ≥10% in their BP from wake to sleep were defined as NdBP and NdHR if their heart rate during stable-NREM sleep was higher than during unstable-NREM sleep.

RESULTS: The most significant difference in minimum HR (HRmin) was observed when comparing BP dippers [56 ± 4 beats per minute (BPM)] and non-BP dippers (59 ± 4 BPM; p < 0.0001) during diastolic blood pressure in stable-NREM sleep. Higher HRmin were associated with an increased likelihood of being a non-dipper, with the strongest relationship with diastolic BP and stable-NREM sleep. Every increase of 1 BPM during stable-NREM sleep was associated with an  4.4% increase in the probability of NdBP (p = 0.001). Subjects with NdHR have higher mean BP during sleep and wake periods than HR dippers. When continuous positive airway pressure therapy is efficacious, and a dipping pattern is achieved-physical and mental health is improved.

CONCLUSION: HR analytics in relation to the sleep period and the CPC spectrogram-estimated sleep states can provide novel and potentially clinically useful information on autonomic health. HR dipping (or not) may be a useful screener of BP dipping or non-dipping to identify individuals who may benefit from a formal assessment of 24-h ambulatory BP. Such a stepped approach may enable a more practical and applicable approach to diagnosing HBP.

CLINICAL TRIAL REGISTRATION: The Heart Biomarker Evaluation in Apnea Treatment (HeartBEAT) study is registered at clinicaltrials.gov/ct2/show/NCT01086800.

3D reconstruction of cerebral vasculature from 2D biplanar projections could significantly improve diagnosis and treatment planning. We introduce a novel approach to tackle this challenging task by initially backprojecting the two projections, a process that traditionally results in unsatisfactory outcomes due to inherent ambiguities. To overcome this, we employ a U-Net approach trained to resolve these ambiguities, leading to significant improvement in reconstruction quality. The process is further refined using a Maximum A Posteriori strategy with a prior that favors continuity, leading to enhanced 3D reconstructions. We evaluated our approach using a comprehensive dataset comprising segmentations from approximately 700 MR angiography scans, from which we generated paired realistic biplanar DRRs. Upon testing with held-out data, our method achieved an 80% Dice similarity w.r.t the ground truth, superior to existing methods. Our code and dataset are available at https://github.com/Wapity/3DBrainXVascular.

GRECC Connect, a national program with interprofessional teams at urban-based VA medical facilities, partner with VA community-based outpatient clinics (CBOCs) to provide geriatric specialty care via telemedicine to rural, older Veterans. Our QI project explored factors affecting program uptake. February-May 2020 we conducted 50 interviews with CBOC staff across the US; 60-80% of patients were rural/highly rural older Veterans. CBOC staff described social determinants of health negatively impacting telemedicine access. Patients on the edge of the digital divide were at risk of diminished access due to changes in physical, cognitive or emotional health and/or socio-economic status. CBOC staff also described highly rural Veterans caught in the digital divide, without access to reliable internet, devices or computer knowledge/skills; included in this subgroup were Veterans staff described as 'off the grid' due to histories of trauma resulting in mental/physical health challenges, distrust of institutions and technology, and desire for geographic/social isolation. This work differentiated rural, older Veterans GRECC Connect served through telemedicine, from those CBOC partners struggled to reach, even by phone. This digital divide may grow given the aging population. Unique contextual factors influencing telemedicine use among older adult populations are important to elucidate to inform structural supports for enhanced access.

Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: https://github.com/ReubenDo/MHVAE-Seg.

We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/.

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.

Psychological researchers are increasingly striving to enhance methodological integrity, including in qualitative methods. Although computerized text analysis tools originally emerged as a potential replacement for manual coding approaches, recent studies have underscored the unique yet complementary value of employing several methods. The current study applies two text analysis methods across one qualitative dataset to explore whether each method yields information not clearly evidenced by the other, nor through traditional thematic analysis. Interviews exploring the experiences of paraprofessionals delivering Brief Psychological Interventions (BPIs) were analyzed through Linguistic Inquiry and Word Count (LIWC) and the Meaning Extraction Method (MEM). Results revealed LIWC, MEM, and thematic analysis to be complementary in nature, each providing unique insights that could be missed by implementing any one method alone. Moreover, text analyses can serve as a form of validation for more traditional qualitative approaches while also revealing otherwise indiscernible relationships and patterns within texts.

Social isolation and social impairment are hallmarks of progression as well as predictors of relapse in psychiatric disorders. We conducted a pilot study to assess the feasibility of sensing the social activity phenotype and loneliness using active and passive markers collected using a smartphone application. The study included 9 schizophrenia and bipolar disorder patients followed in the Bipolar Longitudinal study for at least 1 month and for whom mobile communication data was collected using the Beiwe smartphone application. Subjects completed daily surveys on digital and in-person social activity, and feelings of being outgoing or lonely. We described the level and variability of social activity features. We employed k-means clustering to identify "important contacts". Further, we investigated whether social network-derived features of mobile communication are independent predictors of weekly counts of outgoing calls and text, weekly average self-reported digital social activity, and loneliness using mixed effect models and clustering with dynamic time warping distance. Subjects were followed between 5 and 208 weeks (number of days of observation = 2538). The k-means cluster analysis approach identified the number of "important contacts" among close friends and family members as reported in clinical interviews. The cluster analysis and longitudinal regression analysis indicate that the number of individuals a person communicates with on their phone is an independent predictor of perceived loneliness, with stronger evidence when "important contacts" only are included. This study provides preliminary evidence that the number of "important contacts" a person communicates with on their phone is a promising marker to capture subjects' engagement in mobile communication activity and perceived loneliness.

Technology is playing an increasing role in healthcare, especially in mental health. Traditional mental healthcare, whether in-person or via telehealth, cannot by itself address the massive need for services. Standalone technology such as smartphone apps, while easily accessible, have seen limited engagement and efficacy on their own. Hybrid care - the combination of synchronous in-person or telehealth appointments with the use of asynchronous digital tools such as smartphone applications, wearable devices, or digital therapeutics - has the potential to offer the best of both worlds, providing both increased access and higher engagement and efficacy. In this paper, we present a framework highlighting the key components of hybrid care models: digital intervention, human support, and target population. This framework can be used to evaluate existing models in the literature and in practice, identify areas of need and opportunity, and serve as a blueprint for key elements to consider when designing new hybrid care models.

Psychiatric trials have some of the lowest success rates across therapeutic areas, resulting in decreased investment in psychopharmacological drug development even as the need for more effective treatments grows. Digital measures and digital biomarkers (DBMs) provide one potential avenue for ameliorating three of the largest problems impeding clinical trial success in psychiatry: diagnostic heterogeneity, endpoint subjectivity, and high placebo response rates. First, DBMs may address heterogeneity and comorbidity in psychiatric nosology by identifying predictive DBMs of treatment response via the targeting of drugs to psychiatric subtypes. Second, DBMs can provide objective measures of physiology and behavior that when grounded in meaningful aspects of health (MAH) could support use for regulatory decision-making. By objectively and continuously measuring aspects of a patient's disease that the patient wants to improve or prevent from getting worse, DBMs might provide clinical trial endpoints that are more sensitive to treatment effects as compared to traditional clinician-reported outcomes. Lastly, DBMs could help address challenges surrounding high placebo response rates. Development of predictive DBMs of placebo response may allow for improved enrichment study designs to reduce placebo response. Objective digital measures may also be more robust against the placebo effect and offer an improved study endpoint alternative. Successful deployment of DBMs to address the historical challenges facing psychiatric drug trials will require close collaboration between industry, academic, and regulatory partners.