Faculty Bibliography

Current models of eye movement control propose that motor neurons responsible for moving the eyes contribute to all eye movements, regardless of context. A recent study in larval zebrafish has instead identified specialized neural circuits, including subtypes of motor neurons, for two different types of fast eye movement, one for exploration and the other for hunting.

Triple-negative breast cancer (TNBC) is highly glycolytic and lacks effective biomarkers for therapy response. The BREAKFAST trial showed that a fasting-mimicking diet (FMD) improved pathological complete response (pCR) rates to 56.6% compared to historical chemotherapy averages (30%-40%), with minimal severe adverse events. FMD's metabolic and immune-modulating effects warrant further study with immunotherapy.

The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field strengths of 3 and 0.55 T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively sixfold undersampled data obtained at 3 T. We then evaluated the DL reconstructions against standard techniques, parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. We also tested DL reconstructions with prospectively accelerated acquisitions and evaluated their robustness when changing fields strengths from 3 to 0.55 T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 s for MRCP at 3 T/0.55 T. In both retrospective and prospective undersampling, PSNR and SSIM of DL reconstructions were higher than those of PI and CS. At the same time, DL reconstructions preserved the image quality of undersampled data, including sharpness and the visibility of hepatobiliary ducts. In addition, both DL approaches produced high-quality reconstructions at 0.55 T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3 T/0.55 T while maintaining the image quality of conventional acquisitions.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Goal-concordant transition to hospice is an important facet of end-of-life care for people living with dementia. The objective of this integrative review was to appraise existing evidence and gaps focused on interventions and predictors of transition to hospice and end-of-life care for persons living with dementia across healthcare to inform future research. RESEARCH DESIGN AND METHODS/METHODS:Using integrative review methodology by Whittemore and Knafl, five databases were searched (PubMed, CINAHL, Web of Science, Google Scholar, Cochrane Database for Systematic Reviews) for articles between 2000 and 2023. The search focused on dementia, hospice care, transitions, care management and/or coordination, and intervention studies. RESULTS:Fourteen articles met inclusion criteria after critical appraisal. Most were cross-sectional in design and conducted in nursing homes and hospitals in the U.S. persons living with dementia had multiple chronic conditions including cancer, diabetes, heart disease, and stroke. Interventions included components of hospice decision-making delivered through advance care planning, checklist-based care management for hospice transition, and palliative care for those with severe dementia. Predictors included increasing severity of illness including functional decline, organ failure, intensive care use, and the receipt of palliative care. Other predictors were related to insurance status, race and ethnicity, and caregiver burden. Overall, despite moderate to high-quality evidence, the studies were limited in scope and sample and lacked racial and ethnic diversity. DISCUSSION AND IMPLICATIONS/CONCLUSIONS:Prospective, multisite randomized trials and population-based analyses including larger and diverse samples are needed for improved end-of-life dementia illness counseling and hospice care transitions for persons living with dementia and their caregivers.

BACKGROUND:Early childhood obesity prevention interventions that aim to change parent/caregiver practices related to infant (milk) feeding, food provision and parent feeding, movement (including activity, sedentary behaviour) and/or sleep health (i.e. target parental behaviour domains) are diverse and heterogeneously reported. We aimed to 1) systematically characterise the target behaviours, delivery features, and Behaviour Change Techniques (BCTs) used in interventions in the international Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration, and 2) explore similarities and differences in BCTs used in interventions by target behaviour domains. METHODS:Annual systematic searches were performed in MEDLINE, Embase, Cochrane (CENTRAL), CINAHL, PsycINFO, and two clinical trial registries, from inception to February 2023. Trialists from eligible randomised controlled trials of parent-focused, behavioural early obesity prevention interventions shared unpublished intervention materials. Standardised approaches were used to code target behaviours, delivery features and BCTs in both published and unpublished intervention materials. Validation meetings confirmed coding with trialists. Narrative syntheses were performed. RESULTS:Thirty-two trials reporting 37 active intervention arms were included. Interventions targeted a range of behaviours. The most frequent combination was targeting all parental behaviour domains (infant [milk] feeding, food provision and parent feeding, movement, sleep health; n[intervention arms] = 15/37). Delivery features varied considerably. Most interventions were delivered by a health professional (n = 26/36), included facilitator training (n = 31/36), and were interactive (n = 28/36). Overall, 49 of 93 unique BCTs were coded to at least one target behaviour domain. The most frequently coded BCTs were: Instruction on how to perform a behaviour (n[intervention arms, separated by domain] = 102), Behavioural practice and rehearsal (n = 85), Information about health consequences (n = 85), Social support (unspecified) (n = 84), and Credible source (n = 77). Similar BCTs were often used for each target behaviour domain. CONCLUSIONS:Our study provides the most comprehensive description of the behaviour change content of complex interventions targeting early childhood obesity prevention available to date. Our analysis revealed that interventions targeted multiple behaviour domains, with significant variation in delivery features. Despite the diverse range of BCTs coded, five BCTs were consistently identified across domains, though certain BCTs were more prevalent in specific domains. These findings can be used to examine effectiveness of components and inform intervention development and evaluation in future trials. TRIAL REGISTRATION/BACKGROUND:PROSPERO registration no. CRD42020177408.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

OBJECTIVE:Post-prandial glucose response (PPGR) is a risk factor for cardiovascular disease. Meal carbohydrate content is an important predictor of PPGR, but dietary interventions to mitigate PPGR are not always successful. A personalized approach, considering behaviour and habitual pattern of glucose excursions assessed by continuous glucose monitor (CGM), may be more effective. RESEARCH DESIGN AND METHODS/METHODS:), with prediabetes (n = 35) or early onset type 2 diabetes (n = 3), together with sleep and physical activity by actigraphy. We assessed the predictive value of habitual CGM glucose excursions and fasting glucose on PPGR after a research meal (hereafter MEAL-PPGR) and during an oral glucose tolerance test (hereafter OGTT-PPGR). RESULTS:increase from 0.723 to 0.761). Neither anthropometrics, age nor habitual sleep and physical activity added to the prediction models significantly. CONCLUSION/CONCLUSIONS:These data support a CGM-guided personalized nutrition and medicine approach to control PPGR in older individuals with prediabetes and diet and/or metformin-treated type 2 diabetes.