Two centuries of biodiversity discovery and loss in Singapore.

There is an urgent need for reliable data on the impacts of deforestation on tropical biodiversity. The city-state of Singapore has one of the most detailed biodiversity records in the tropics, dating back to the turn of the 19th century. In 1819, Singapore was almost entirely covered in primary forest, but this has since been largely cleared. We compiled more than 200 y of records for 10 major taxonomic groups in Singapore (>50,000 individual records; >3,000 species), and we estimated extinction rates using recently developed and novel statistical models that account for "dark extinctions," i.e., extinctions of undiscovered species. The estimated overall extinction rate was 37% (95% CI [31 to 42%]). Extrapolating our Singapore observations to a future business-as-usual deforestation scenario for Southeast Asia suggests that 18% (95% CI [16 to 22%]) of species will be lost regionally by 2100. Our extinction estimates for Singapore and Southeast Asia are a factor of two lower than previous estimates that also attempted to account for dark extinctions. However, we caution that particular groups such as large mammals, forest-dependent birds, orchids, and butterflies are disproportionately vulnerable.

Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.

BACKGROUND: Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification. OBJECTIVE: The current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS. METHODS: Videos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions. RESULTS: Area under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample. CONCLUSIONS: The algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder.

Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.

Are we really targeting and stimulating DLPFC by placing transcranial electrical stimulation (tES) electrodes over F3/F4?

In many clinical trials involving transcranial electrical stimulation (tES), target electrodes are typically placed over DLPFC with the assumption that this will primarily stimulate the underlying brain region. However, our study aimed to evaluate the electric fields (EF) that are actually delivered and identify prefrontal regions that may be inadvertently targeted in DLPFC tES. Head models were generated from the Human Connectome Project database's T1 + T2-weighted MRIs of 80 healthy adults. Two common DLPFC montages were simulated; symmetric-F4/F3, and asymmetric-F4/Fp1. Averaged EF was extracted from (1) the center of the target electrode (F4), and (2) the top 1% of voxels showing the strongest EF in individualized EF maps. Interindividual variabilities were quantified with the standard deviation of EF peak location/value. Similar steps were repeated with 66 participants with methamphetamine use disorder (MUDs) as an independent clinical population. In healthy adults, the group-level location of EF peaks was situated in the medial-frontopolar, and the individualized EF peaks were positioned in a cube with a volume of 29 cm3 /46 cm3 (symmetric/asymmetric montages). EFs in the frontopolar area were significantly higher than EF "under" the target electrode in both symmetric (peak: 0.41 ± 0.06, F4:0.22 ± 0.04) and asymmetric (peak: 0.38 ± 0.04, F4:0.2 ± 0.04) montages (Heges'g > 0.7). Similar results with slight between-group differences were found in MUDs. We highlighted that in common DLPFC tES montages, in addition to interindividual/intergroup variability, the frontopolar received the highest EFs rather than DLPFC as the main target. We specifically recommended considering the potential involvement of the frontopolar area as a mechanism underlying the effectiveness of DLPFC tES protocols.

Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

Penalized model-based clustering of fMRI data.

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.

Validation of ketamine as a pharmacological model of thalamic dysconnectivity across the illness course of schizophrenia.

N-methyl-D-aspartate receptor (NMDAR) hypofunction is a leading pathophysiological model of schizophrenia. Resting-state functional magnetic resonance imaging (rsfMRI) studies demonstrate a thalamic dysconnectivity pattern in schizophrenia involving excessive connectivity with sensory regions and deficient connectivity with frontal, cerebellar, and thalamic regions. The NMDAR antagonist ketamine, when administered at sub-anesthetic doses to healthy volunteers, induces transient schizophrenia-like symptoms and alters rsfMRI thalamic connectivity. However, the extent to which ketamine-induced thalamic dysconnectivity resembles schizophrenia thalamic dysconnectivity has not been directly tested. The current double-blind, placebo-controlled study derived an NMDAR hypofunction model of thalamic dysconnectivity from healthy volunteers undergoing ketamine infusions during rsfMRI. To assess whether ketamine-induced thalamic dysconnectivity was mediated by excess glutamate release, we tested whether pre-treatment with lamotrigine, a glutamate release inhibitor, attenuated ketamine's effects. Ketamine produced robust thalamo-cortical hyper-connectivity with sensory and motor regions that was not reduced by lamotrigine pre-treatment. To test whether the ketamine thalamic dysconnectivity pattern resembled the schizophrenia pattern, a whole-brain template representing ketamine's thalamic dysconnectivity effect was correlated with individual participant rsfMRI thalamic dysconnectivity maps, generating "ketamine similarity coefficients" for people with chronic (SZ) and early illness (ESZ) schizophrenia, individuals at clinical high-risk for psychosis (CHR-P), and healthy controls (HC). Similarity coefficients were higher in SZ and ESZ than in HC, with CHR-P showing an intermediate trend. Higher ketamine similarity coefficients correlated with greater hallucination severity in SZ. Thus, NMDAR hypofunction, modeled with ketamine, reproduces the thalamic hyper-connectivity observed in schizophrenia across its illness course, including the CHR-P period preceding psychosis onset, and may contribute to hallucination severity.

Preoperative Immuno-Nutrition and Complications After Colorectal Surgery: Results of a 2-Year Prospective Study.

INTRODUCTION: Preoperative immuno-nutrition has been associated with reductions in infectious complications and length of stay, but remains unstudied in the setting of an enhanced recovery protocol. The objective was to evaluate outcomes after elective colorectal surgery with the addition of a preoperative immuno-nutrition supplement. METHODS: In October 2017, all major colorectal surgeries were given an arginine-based supplement prior to surgery. The control group consisted of cases within the same enhanced recovery protocol from three years prior. The primary outcome was a composite of overall morbidity. Secondary outcomes were infectious complications and length of stay with subgroup analysis based on degrees of malnutrition. RESULTS: Of 826 patients, 514 were given immuno-nutrition prospectively and no differences in complication rates (21.5% versus 23.9%, P = 0.416) or surgical site infections (SSIs) (6.4% versus 6.9%, P = 0.801) were observed. Hospitalization was slightly shorter in the immuno-nutrition cohort (5.0 [3.0, 7.0], versus 5.5 days [3.6, 7.9], P = 0.002). There was a clinically insignificant difference in prognostic nutrition index scores between cohorts (35.2 ± 5.6 versus 36.1 ± 5.0, P = 0.021); however, subgroup analysis (< 33, 34-38 and > 38) failed to demonstrate an association with complications (P = 0.275) or SSIs (P = 0.640) and immuno-nutrition use. CONCLUSIONS: Complication rates and SSIs were unchanged with the addition of immuno-nutrition before elective colorectal surgery. The association with length of stay is small and without clinical significance; therefore, the routine use of immuno-nutrition in this setting is of questionable benefit.

Microcalcification and Thoracic Aortopathy: A Window Into Disease Severity.

BACKGROUND: Patients with thoracic aortopathy are at increased risk of catastrophic aortic dissection, carrying with it substantial mortality and morbidity. Although granular medial calcinosis (medial microcalcification) has been associated with thoracic aortopathy, its relationship to disease severity has yet to be established. METHODS: One hundred one thoracic aortic specimens were collected from 57 patients with thoracic aortopathy and 18 control subjects. Standardized histopathologic scores, immunohistochemistry, and nanoindentation (tissue elastic modulus) were compared with the extent of microcalcification on von Kossa histology and 18F-sodium fluoride autoradiography. RESULTS: Microcalcification content was higher in thoracic aortopathy samples with mild (n=28; 6.17 [2.71-10.39]; P≤0.00010) or moderate histopathologic degeneration (n=30; 3.74 [0.87-11.80]; P<0.042) compared with control samples (n=18; 0.79 [0.36-1.90]). Alkaline phosphatase (n=26; P=0.0019) and OPN (osteopontin; n=26; P=0.0045) staining were increased in tissue with early aortopathy. Increasingly severe histopathologic degeneration was related to reduced microcalcification (n=82; Spearman ρ, -0.51; P<0.0001)-a process closely linked with elastin loss (n=82; Spearman ρ, -0.43; P<0.0001) and lower tissue elastic modulus (n=28; Spearman ρ, 0.43; P=0.026).18F-sodium fluoride autoradiography demonstrated good correlation with histologically quantified microcalcification (n=66; r=0.76; P<0.001) and identified areas of focal weakness in vivo. CONCLUSIONS: Medial microcalcification is a marker of aortopathy, although progression to severe aortopathy is associated with loss of both elastin fibers and microcalcification.18F-sodium fluoride positron emission tomography quantifies medial microcalcification and is a feasible noninvasive imaging modality for identifying aortic wall disruption with major translational promise.

Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders.

OBJECTIVE: Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts have been limited in their ability to identify individual-level models of eating disorder psychopathology and to develop and apply an individually tailored treatment for a given individual's personalized model of psychopathology. In addition, research is still needed to identify causal relationships within a given individual's model of eating disorder psychopathology. Addressing this limitation of the current state of precision medicine-related research in the field will allow us to progress toward advancing research and practice for eating disorders treatment. METHOD: We present a novel set of analytic tools, causal discovery analysis (CDA) methods, which can facilitate increasingly fine-grained, person-specific models of causal relations among cognitive, behavioral, and affective symptoms. RESULTS: CDA can advance the identification of an individual's causal model that maintains that individuals' eating disorder psychopathology. DISCUSSION: In the current article, we (1) introduce CDA methods as a set of promising analytic tools for developing precision medicine methods for eating disorders including the potential strengths and weaknesses of CDA, (2) provide recommendations for future studies utilizing this approach, and (3) outline the potential clinical implications of using CDA to generate personalized models of eating disorder psychopathology. PUBLIC SIGNIFICANCE STATEMENT: CDA provides a novel statistical approach for identifying causal relationships among variables of interest for a given individual. Person-specific causal models may offer a promising approach to individualized treatment planning and inform future personalized treatment development efforts for eating disorders.

Rapid, reliable mobile assessment of affect-related motor processing.

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Evaluation of functional MRI-based human brain parcellation: a review.

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

Association Between Surgeon and Anesthesiologist Sex Discordance and Postoperative Outcomes: A Population-based Cohort Study.

OBJECTIVE: The aim of this study was to examine the effect of surgeon-anesthesiologist sex discordance on postoperative outcomes. SUMMARY BACKGROUND DATA: Optimal surgical outcomes depend on teamwork, with surgeons and anesthesiologists forming two key components. There are sex and sex-based differences in interpersonal communication and medical practice which may contribute to patients' perioperative outcomes. METHODS: We performed a population-based, retrospective cohort study among adult patients undergoing 1 of 25 common elective or emergent surgical procedures from 2007 to 2019 in Ontario, Canada. We assessed the association between differences in sex between surgeon and anesthesiologists (sex discordance) on the primary endpoint of adverse postoperative outcome, defined as death, readmission, or complication within 30 days following surgery using generalized estimating equations. RESULTS: Among 1,165,711 patients treated by 3006 surgeons and 1477 anesthesiologists, 791,819 patients were treated by sex concordant teams (male surgeon/male anesthesiologist: 747,327 and female surgeon/female anesthesiologist: 44,492), whereas 373,892 were sex discordant (male surgeon/female anesthesiologist: 267,330 and female surgeon/male anesthesiologist: 106,562). Overall, 12.3% of patients experienced >1 adverse postoperative outcomes of whom 1.3% died. Sex discordance between surgeon and anesthesiologist was not associated with a significant increased likelihood of composite adverse postoperative outcomes (adjusted odds ratio 1.00, 95% confidence interval 0.97-1.03). CONCLUSIONS: We did not demonstrate an association between intraoperative surgeon and anesthesiologist sex discordance on adverse postoperative outcomes in a large patient cohort. Patients, clinicians, and administrators may be reassured that physician sex discordance in operating room teams is unlikely to clinically meaningfully affect patient outcomes after surgery.

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

Quantitative brain MRI morphology in severe and attenuated forms of mucopolysaccharidosis type I.

OBJECTIVE: To assess our hypothesis that brain macrostructure is different in individuals with mucopolysaccharidosis type I (MPS I) and healthy controls (HC), we conducted a comprehensive multicenter study using a uniform quantitative magnetic resonance imaging (qMRI) protocol, with analyses that account for the effects of disease phenotype, age, and cognition. METHODS: Brain MRIs in 23 individuals with attenuated (MPS IA) and 38 with severe MPS I (MPS IH), aged 4-25 years, enrolled under the study protocol NCT01870375, were compared to 98 healthy controls. RESULTS: Cortical and subcortical gray matter, white matter, corpus callosum, ventricular and choroid plexus volumes in MPS I significantly differed from HC. Thicker cortex, lower white matter and corpus callosum volumes were already present at the youngest MPS I participants aged 4-5 years. Age-related differences were observed in both MPS I groups, but most markedly in MPS IH, particularly in cortical gray matter metrics. IQ scores were inversely associated with ventricular volume in both MPS I groups and were positively associated with cortical thickness only in MPS IA. CONCLUSIONS: Quantitatively-derived MRI measures distinguished MPS I participants from HC as well as severe from attenuated forms. Age-related neurodevelopmental trajectories in both MPS I forms differed from HC. The extent to which brain structure is altered by disease, potentially spared by treatment, and how it relates to neurocognitive dysfunction needs further exploration.

Association between isolated abnormal coagulation profile on transfusion following major surgery: A NSQIP analysis of individuals without bleeding disorders.

INTRODUCTION: Preoperative coagulation screening for patients without bleeding disorders remains controversial. The combinatorial risk of INR, aPTT, and platelet count (PLT) abnormalities leading to bleeding requiring transfusion is not known in these patients. We examined the association between abnormal coagulation profile and the risk of transfusion following common elective surgery in patients without bleeding disorders. STUDY DESIGN AND METHODS: We utilized the National Surgical Quality Improvement Program (NSQIP) database from 2004 to 2018 to identify patients without a history of bleeding disorders undergoing common 23 major elective procedures across 10 specialties. Multivariable logistic regression was used to assess the association between coagulation profile and bleeding requiring packed red blood cell transfusion intra-/post-operatively. RESULTS: Of the 672,075 patients meeting inclusion criteria, 53.7% presented with normal coagulation profile preoperatively. Overall, 12.2% (n = 82,368) received transfusion. In the setting of normal aPTT/PLT, both Equivocal INR of 1.1-1.5 (aOR 1.41, 95% CI 1.38-1.44) and Abnormal INR of >1.5 (aOR 1.81, 95% CI 1.71-1.93) were significantly associated with an increased risk of transfusion. Equivocal (60-70) and Abnormal (>70) aPTT with normal INR/PLT did not demonstrate a comparable risk of transfusion. We observed a synergistic effect of combinatorial lab abnormalities on the risk of transfusion when both Abnormal INR/aPTT and Low PLT of <100,000 were present (aOR 5.18, 95% CI 3.04-8.84), compared to the effect of Abnormal INR/aPTT and normal/elevated PLT (aOR 1.90, 95% CI 1.48-2.45). DISCUSSION: The preoperative presence of abnormal findings in INR or PLT was significantly associated with the risk of bleeding requiring transfusion during intraoperative and postoperative periods.

Quantifying heterogeneity in mood-alcohol relationships with idiographic causal models.

BACKGROUND: Ecological momentary assessment (EMA) studies have provided conflicting evidence for the mood regulation tenet that people drink in response to positive and negative moods. The current study examined mood-to-alcohol relationships idiographically to quantify the prevalence and intensity of relationships between positive and negative moods and drinking across individuals. METHOD: We used two EMA samples: 96 heavy drinking college students (sample 1) and 19 young adults completing an ecological momentary intervention (EMI) for drinking to cope (sample 2). Mood and alcohol use were measured multiple times per day for 4-6 weeks. Mood-alcohol relationships were examined using three different analytic approaches: standard multilevel modeling, group causal modeling, and idiographic causal modeling. RESULTS: Both multilevel modeling and group causal modeling showed that participants in both samples drank in response to positive moods only. However, idiographic causal analyses revealed that only 63% and 21% of subjects (in samples 1 and 2, respectively) drank following any positive mood. Many subjects (24% and 58%) did not drink in response to either positive or negative mood in their daily lives, and very few (5% and 16%) drank in response to negative moods throughout the EMA protocol, despite sample 2 being selected specifically because they endorse drinking to cope with negative mood. CONCLUSION: Traditional group-level analyses and corresponding population-wide theories assume relative homogeneity within populations in mood-alcohol relationships, but this nomothetic approach failed to characterize accurately the relationship between mood and alcohol use in approximately half of the subjects in two samples that were demographically and clinically homogeneous. Given inconsistent findings in the mood-alcohol relationships to date, we conclude that idiographic causal analyses can provide a foundation for more accurate theories of mood and alcohol use. In addition, idiographic causal models may also help improve psychosocial treatments through direct use in clinical settings.

A phase II clinical trial of neoadjuvant sasanlimab and stereotactic body radiation therapy as an in situ vaccine for cisplatin-ineligible MIBC: the RAD VACCINE MIBC trial.

The utilization of neoadjuvant immune checkpoint inhibitor therapy, specifically anti-PD-1/L1 agents, prior to radical cystectomy is an emerging paradigm in muscle-invasive bladder cancer (MIBC). In situ vaccination represents a strategy to manipulate the tumor in order to augment the immune response toward improved local and distant cancer control. The authors describe the study rationale, design and objectives for RAD VACCINE MIBC, a single-arm, single-institution, phase II trial evaluating the efficacy and safety of combination neoadjuvant sasanlimab (humanized IgG monoclonal antibody that targets PD-1) with stereotactic body radiotherapy as an in situ vaccine in cisplatin-ineligible patients with MIBC. The results from this trial will establish the safety profile of this combination strategy and evaluate pathologic complete response rates.

RAD VACCINE MIBC is a phase II clinical trial that aims to determine the safety and effectiveness of a study drug called sasanlimab (an immune checkpoint inhibitor), combined with radiation therapy (stereotactic body radiation therapy) prior to surgery to remove the bladder (known as radical cystectomy [RC]) in muscle-invasive bladder cancer patients. For this type of cancer, patients typically receive chemotherapy followed by RC as the standard of care. However, many patients who have pre-existing medical conditions such as poor kidney function are unable to receive chemotherapy. These patients undergo RC alone at the risk of less optimal cancer control. Bladder cancer is known to inhibit the immune cells (T cells) from attacking it, which is an important way in which the body controls cancer cells. Sasanlimab allows T cells that are specific to the cancer to potentially reactivate. Ongoing studies have shown that drugs similar to sasanlimab can be used to achieve improvement in cancer control in the bladder (as measured by shrinking the cancer or eradicating it) before surgery. The authors are studying the use of the study drug with the addition of stereotactic body radiotherapy (SBRT) as a combined therapy. The role of SBRT as a combined therapy to immune checkpoint inhibition has been well studied to help improve the process of how immune cells recognize cancer cells. By giving both the study drug and SBRT together before RC, the authors aim to demonstrate the safety of this technique and its effectiveness in eradicating all cancer in the bladder. Clinical Trial Registration: NCT05241340 (ClinicalTrials.gov).

Resting State Hypoconnectivity of Reward Networks in Binge Eating Disorder.

The clinical presentation of binge eating disorder (BED) and data emerging from task-based functional neuroimaging research suggests that this disorder may be associated with alterations in reward processing. However, there is a dearth of research investigating the functional organization of brain networks that mediate reward in BED. To address this gap, 27 adults with BED and 21 weight-matched healthy controls (WMC) completed a multimodel assessment consisting of a resting functional magnetic resonance imaging scan, behavioral tasks measuring reward-based decision-making (i.e., delay discounting and reversal learning), and self-report assessing clinical symptoms. A seed-based approach was employed to examine the resting state functional connectivity (rsFC) of the striatum (nucleus accumbens [NAcc] and ventral and dorsal caudate), a collection of regions implicated in reward processing. Compared with WMC, the BED group exhibited lower rsFC of striatal seeds, with frontal regions mediating executive functioning (e.g., superior frontal gyrus [SFG]) and posterior, parietal, and temporal regions implicated in emotional processing. Lower NAcc-SFG rsFC was associated with more difficulties with reversal learning and binge eating frequency in the BED group. Results suggest that hypoconnectivity of striatal networks that integrate self-regulation and reward processing may promote the clinical phenomenology of BED. Interventions for BED may benefit from targeting these circuit-based disturbances.

The psychosis human connectome project: An overview.

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.