Notes

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Although it is widely accepted that the orbitofrontal cortex (OFC) is important for decision making, its precise contribution to behavior remains a topic of debate. While many loss of function experiments have been conducted in animals, causal studies of human OFC function are relatively scarce. This review discusses recent causal investigations into the human OFC, with an emphasis on advances in network-based brain stimulation approaches to indirectly perturb OFC function. Findings show that disruption of human OFC impairs decisions that require mental simulation of outcomes. Taken together, these results support the idea that human OFC contributes to decision making by representing a cognitive map of the task environment, facilitating inference of outcomes not yet experienced. Future work may utilize similar non-invasive approaches in clinical settings to mitigate decision making deficits in neuropsychiatric disorders.Gambling involves placing something of value at risk in exchange for the opportunity to potentially gain something of greater value in return. A variety of gambling paradigms have been designed to study the maladaptive decision-making that underlies problematic gambling. Central to these gambling models are the definitions of "risk" and "loss", especially when translating the results from rodent studies to clinical applications. Risk and loss are not mutually exclusive but rather share some overlap. With careful interpretation and consideration of the limitations of these behavioral paradigms, results from rodent models may provide insights into the neurobiology of risky decision-making that leads to problematic gambling in humans.In the past several years, there has been an explosion of interest in animal models of risk-based decision-making, a fundamental process associated with gambling disorder. While early work focused on establishing various tasks for assaying decision-making, current studies are determining the (subtle and not-so-subtle) influence of cues in driving risky decisions to better understand problem gambling. In addition, these decision-making paradigms are now being used to investigate comorbid conditions such as substance dependence or brain injury and replicating observations from human patients. These animal models have now developed to a point where therapeutic interventions may be assessed for not just gambling disorder, but also a number of other conditions which engender risky decision-making.
Cervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.

Two cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network
As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implemwith DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.

NCT, NCT03385005. Registered September 26, 2017.
NCT, NCT03385005. Registered September 26, 2017.Purpose This study aims to develop and compare human-engineered radiomics methodologies that use multiparametric magnetic resonance imaging (mpMRI) to diagnose breast cancer. Approach The dataset comprises clinical multiparametric MR images of 852 unique lesions from 612 patients. Each MR study included a dynamic contrast-enhanced (DCE)-MRI sequence and a T2-weighted (T2w) MRI sequence, and a subset of 389 lesions were also imaged with a diffusion-weighted imaging (DWI) sequence. Lesions were automatically segmented using the fuzzy C-means algorithm. Radiomic features were extracted from each MRI sequence. Two approaches, feature fusion and classifier fusion, to utilizing multiparametric information were investigated. A support vector machine classifier was trained for each method to differentiate between benign and malignant lesions. Area under the receiver operating characteristic curve (AUC) was used to evaluate and compare diagnostic performance. Analyses were first performed on the entire dataset and then on the subset that was imaged using the three-sequence protocol. Results When using the full dataset, the single-parametric classifiers yielded the following AUCs and 95% confidence intervals AUC DCE = 0.84 [0.82, 0.87], AUC T 2 w = 0.83 [0.80, 0.86], and AUC DWI = 0.69 [0.62, 0.75]. The two multiparametric classifiers both yielded AUCs of 0.87 [0.84, 0.89] and significantly outperformed all single-parametric methods classifiers. When using the three-sequence subset, the mpMRI classifiers' performances significantly decreased. OXPHOS inhibitor Conclusions The proposed mpMRI radiomics methods can improve the performance of computer-aided diagnostics for breast cancer and handle missing sequences in the imaging protocol.
The spread of SARS-CoV-2 and the COVID-19 pandemic have caused significant morbidity and mortality worldwide. The clinical characteristics and outcomes of hospitalized patients with SARS-CoV-2 and HIV co-infection remain uncertain.

We conducted a matched retrospective cohort study of adults hospitalized with a COVID-19 illness in New York City between March 3, 2020, and May 15, 2020. We matched 30 people with HIV (PWH) with 90 control group patients without HIV based on age, sex, and race/ethnicity. Using electronic health record data, we compared demographic characteristics, clinical characteristics, and clinical outcomes between PWH and control patients.

In our study, the median age (interquartile range) was 60.5 (56.6-70.0) years, 20% were female, 30% were black, 27% were white, and 24% were of Hispanic/Latino/ethnicity. There were no significant differences between PWH and control patients in presenting symptoms, duration of symptoms before hospitalization, laboratory markers, or radiographic findings on chest x-ray.
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