Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity.

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve.

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning.

BACKGROUND: Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians is whether to treat or postpone the revascularization of lesions that fall within the intermediate range given by an invasive fractional flow reserve (FFR) measurement. The paper presents a monocentric study for lesions significance assessment that can potentially cause ischemia on the large coronary arteries. METHODS: A new dataset is acquired, comprising the optical coherence tomography (OCT) images, clinical parameters, echocardiography and FFR measurements collected from 80 patients with 102 lesions, with stable multivessel coronary artery disease. Having the ground truth given by the invasive FFR measurement, the dataset is challenging because almost 40% of the lesions are in the gray zone, having an FFR value between 0.75 and 0.85. Twenty-six features are extracted from OCT images, clinical characteristics, and echocardiography and the most relevant are identified by examining the models' accuracy. An ensembled learning is performed for solving the binary classification problem of lesion significance considering the leave-one-out cross-validation approach. RESULTS: Ensemble models are designed from the multi-features voting from 5 features models by prediction aggregation with a maximum accuracy of 81.37% and a maximum area under the curve score (AUC) of 0.856. CONCLUSIONS: The proposed explainable supervised learning-based lesion classification is a new method that can be improved by training with a larger multicenter dataset for further designing a tool for guiding the decision making of the clinician for the cases outside the gray zone and for the other situation extra clinical information about the lesion is needed.

Unusual echocardiographic finding as cause of acute coronary syndrome with ST- segment elevation.

Biventricular metastatic heart tumors from gynecological malignancies presented as an acute coronary syndrome with ST segment elevation are an unusual finding. We present a case of stage-4 vulvar carcinoma that metastasized in both the left and right ventricle. The particularity of the case is the echocardiographic aspect in the emergency room: multiple, large, hyperechogenic masses disseminated in the myocardium, with pericardial extension, in context of acute coronary syndrome with ST segment elevation.

In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach.

Background: In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML). Objectives: The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI). Methods: Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables. Results: The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter. Conclusion: ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.

Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol.

Ischemic heart disease represent a heavy burden for the medical systems irrespective of the methods used for diagnosis and treatment of such patients in the daily medical routine. The present paper depicts the protocol of a study whose main aim is to develop, implement and test an artificial intelligence algorithm and cloud based platform for fully automated PCI guidance using coronary angiography images. We propose the utilisation of multiple artificial intelligence based models to produce three-dimensional coronary anatomy reconstruction and assess function- post-PCI FFR computation- for developing an extensive report describing and motivating the optimal PCI strategy selection. All the relevant artificial intelligence model outputs (anatomical and functional assessment-pre- and post-PCI) are presented to the clinician via a cloud platform, who can then take the utmost treatment decision. The physician will be provided with multiple scenarios and treatment possibilities for the same case allowing a real-time evaluation of the most appropriate PCI strategy planning and follow-up. The artificial intelligence algorithms and cloud based PCI selection workflow will be verified and validated in a pilot clinical study including subjects prospectively to compare the artificial intelligence services and results against annotations and invasive measurements.

A Bayesian Modeling Approach to Examine the Role of Testosterone Administration on the Endowment Effect and Risk-Taking.

Financial risk-taking and loss aversion are multifaceted phenomena that are the focus of neuroscience, psychology, and economics research. A growing number of studies highlighted the role of hormones (particularly of testosterone) on socio-economic decision-making. However, the effects of testosterone on risk-taking under framing and consumer-based choices and preferences are inconclusive. We investigated the effects of 100 mg testosterone administration on aspects of decision-making within the Prospect Theory framework which is the most used descriptive model of decision-making under risk. We assessed risk-taking under framing and the endowment effect (effect of possession) using Bayesian modeling. Forty men participated in this double-blind placebo-controlled fully-randomized cross-over experiment and performed two tasks. One was a risk-taking task with binary choices under positive and negative framing associated with different probabilities. In the second task participants had to bid money for hedonic and utilitarian items. We observed a significant increase in serum testosterone concentrations after transdermal application. Compared to placebo, testosterone administration increased risk-taking under the positive framing (very large effect size) and decreased under the negative framing (moderate to small). The sensitivity to gain was positive in each framing. Our model showed that decision-making is jointly influenced by testosterone and the trade-off between gains and losses. However, while the endowment effect was more pronounced for hedonic than for utilitarian items, the effect was independent of testosterone. The findings provide novel information on the complex modulatory role of testosterone on risk-taking within the framework of prospect theory and shed light on mechanisms of behavioral economic biases. The proposed models of effects of individual differences in testosterone on risk-taking could be used as predictive models for reference-depended behavior under positive and negative framing with low and high probabilities.

Pre- and postnatal antibiotic exposure and risk of developing attention deficit hyperactivity disorder-A systematic review and meta-analysis combining evidence from human and animal studies.

This study investigated the effects of early antibiotic exposure on ADHD risk by (1) integrating meta-analytical evidence from human observational studies examining the association between prenatal or early postnatal antibiotic exposure on the risk of developing ADHD; and (2) reviewing evidence from experimental animal studies on the effects of early antibiotic exposure on behavior. Sixteen human studies and five rodent studies were reviewed. A quantitative meta-analysis with 10 human studies indicated an increased risk for ADHD after prenatal antibiotic exposure (summary effect estimate Hazard Ratio (HR) 1.23, 95% CI 1.09-1.38; N = 2,398,475 subjects) but not after postnatal exposure within the first two years of life (summary effect estimate HR 1.12, 95% CI 0.95-1.32; N = 1,863,867 subjects). The rodent literature suggested that peri-natal antibiotic exposure has effects on social behavior, anxiety and aggression, alongside changes in gut microbial composition. Human and rodent findings thus suggest prenatal antibiotic exposure as a possible risk factor for ADHD, and suggest that an early disruption of the gut microbiome by antibiotics may interfere with neurodevelopment.

Testosterone administration does not alter the brain activity supporting cognitive and affective empathy.

Although there is evidence that testosterone has deteriorating effects on cognitive and affective empathy, whether testosterone administration influences both routes to understanding others has not yet been simultaneously investigated. We conducted a functional magnetic resonance imaging (fMRI) pharmacological study using a within-subjects, randomized, placebo-controlled, double-blind crossover design to examine the effects of 100 mg transdermal testosterone administration on brain activation during a task that examines affective and cognitive empathy simultaneously in a sample of 23 healthy right-handed adult men. Relative to placebo, testosterone did not alter affective or cognitive empathy functional brain networks. Instead, the task yielded activation in the canonical networks associated with both types of empathy. Affective empathy yielded activation in the inferior and middle frontal gyri, inferior temporal gyri, and the cingulate cortex. Cognitive empathy was associated with activation of the temporoparietal junction, medial prefrontal cortex, middle and inferior temporal gyri, and temporal pole. Behaviourally, testosterone administration decreased error rates and increased participants' confidence in their responses regardless of response accuracy. Independent of testosterone administration, participants reported higher affective responses during emotionally negative scenarios. Even though our results provide further evidence that testosterone administration in healthy men does not alter brain activity underlying cognitive and affective empathy, testosterone administration does influence the empathic concern and hence socio-cognitive processes. The reproducibility and variability of the current and previous findings should nevertheless be addressed in upcoming studies.

Lifetime Prevalence of Victimization and Perpetration as Related to Men's Health: Clinical Insights.

Violence is a known risk factor for health problems. In this epidemiological study across 5,385 male patients, we investigate the prevalence of perpetrated violence, exposure to violence, their overlap and the relationship between violence, mental, and psychosomatic health, as well as adverse health behaviors, such as self-harming behavior and the consumption of drugs. Participants completed an anonymous questionnaire addressing violence experience (i.e., both expose and perpetration), age of victimization/perpetration, frequency, and perceived severity of violence exposure. We considered physical, psychological as well as sexual violence. Information on health status and adverse health behaviors complemented the data. Results showed that 48.4% of the sample reported having experienced violence (perpetration, victimization, or both). The victim-perpetrator overlap formed the largest group, in which the incidence of having experienced multiple types of violence was significantly higher compared to victims and perpetrators. The age-crime curve flattened more slowly with increasing age in this group. Although the perceived severity of exposure to violence is lower in the overlap group, its health status and adverse health behaviors were worse. Interventions should focus on this group since they constitute a burden for the healthcare system.

Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography.

PURPOSE: Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data. METHODS: We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations. RESULTS: We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only [Formula: see text] minutes on a standard hardware configuration. CONCLUSION: The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

Perceptual Load and Sex-Specific Personality Traits.

The impact of sex-specific personality traits has often been investigated for visuospatial tasks such as mental rotation, but less is known about the influence of personality traits on visual search. We investigated whether the Big Five personality traits Extroversion (E), Openness (O), Agreeableness (A), Conscientiousness (C), and Neuroticism (N) and the Autism Quotient (AQ) influence visual search in a sample of N = 65 men and women. In three experiments, we varied stimulus complexity and predictability. As expected, latencies were longer when the target was absent. Pop-out search was faster than conjunction search. A large number of distracters slowed down reaction times (RTs). When stimulus complexity was not predictable in Experiment 3, this reduced search accuracy by about half. As could be predicted based on previous research on long RT tails, conjunction search in target absent trials revealed the impact of personality traits. The RT effect in visual search of the accelerating "less social" AQ score was specific to men, while the effects of the "more social" decelerating Big Five Inventory factors agreeableness and conscientiousness were specific to women. Thus, sex-specific personality traits could explain decision-making thresholds, while visual stimulus complexity yielded an impact of the classic personality traits neuroticism and extroversion.

A meta-analysis on shared and distinct neural correlates of the decision-making underlying altruistic and retaliatory punishment.

Individuals who violate social norms will most likely face social punishment sanctions. Those sanctions are based on different motivation aspects, depending on the context. Altruistic punishment occurs if punishment aims to re-establish the social norms even at cost for the punisher. Retaliatory punishment is driven by anger or spite and aims to harm the other. While neuroimaging research highlighted the neural networks supporting decision-making in both types of punishment in isolation, it remains unclear whether they rely on the same or distinct neural systems. We ran an activation likelihood estimation meta-analysis on functional magnetic resonance imaging data on 24 altruistic and 19 retaliatory punishment studies to investigate the neural correlates of decision-making underlying social punishment and whether altruistic and retaliatory punishments share similar brain networks. Social punishment reliably activated the bilateral insula, inferior frontal gyrus, midcingulate cortex (MCC), and superior and medial frontal gyri. This network largely overlapped with activation clusters found for altruistic punishment. However, retaliatory punishment revealed only one cluster in a posterior part of the MCC, which was not recruited in altruistic punishment. Our results support previous models on social punishment and highlight differential involvement of the MCC in altruistic and retaliatory punishments, reflecting the underlying different motivations.

What determines violent behavior in men? Predicting physical, psychological, and sexual violent offending based on classification and regression tree analysis.

Despite compelling evidence that victimization and offending co-occur, it remains unclear what types of victimization are linked to specific forms of perpetration. Here we examined the relationship between physical, psychological, and sexual violence with respect to influencing variables including mental health, risk-taking behaviors, and coping strategies. Data from 5385 men were collected as part of an epidemiological study on violence experience. A classification and regression tree analysis identified the main predictors of violence perpetration and classified violent offending into high- and low-risk groups. Results indicate that violence is best predicted by previous exposure to violence and polyvictimization. Physical violence is best predicted by prior exposure to physical violence and this is further influenced by the frequency of and the age at which violence was experienced. Drug use was a strong predictor of physical and psychological violence. The latter is best predicted by a history of polyvictimization, the severity and the originator of violence. Sexual violence is strongly predicted by one's sexual violence experience. Other factors such as demographic characteristics are less predictive. Our results may contribute to the development of early prevention and intervention approaches that account for different risk factors. The significance of violence exposure suggest that intervention measures must focus on victims of early and prolonged experience of violence. On the strength of the link between drug use and violence, exposure to violence should be considered in drug prevention and intervention and vice versa.

Replication of Previous Findings? Comparing Gray Matter Volumes in Transgender Individuals with Gender Incongruence and Cisgender Individuals.

The brain structural changes related to gender incongruence (GI) are still poorly understood. Previous studies comparing gray matter volumes (GMV) between cisgender and transgender individuals with GI revealed conflicting results. Leveraging a comprehensive sample of transmen (n = 33), transwomen (n = 33), cismen (n = 24), and ciswomen (n = 25), we employ a region-of-interest (ROI) approach to examine the most frequently reported brain regions showing GMV differences between trans- and cisgender individuals. The primary aim is to replicate previous findings and identify anatomical regions which differ between transgender individuals with GI and cisgender individuals. On the basis of a comprehensive literature search, we selected a set of ROIs (thalamus, putamen, cerebellum, angular gyrus, precentral gyrus) for which differences between cis- and transgender groups have been previously observed. The putamen was the only region showing significant GMV differences between cis- and transgender, across previous studies and the present study. We observed increased GMV in the putamen for transwomen compared to both transmen and ciswomen and for all transgender participants compared to all cisgender participants. Such a pattern of neuroanatomical differences corroborates the large majority of previous studies. This potential replication of previous findings and the known involvement of the putamen in cognitive processes related to body representations and the creation of the own body image indicate the relevance of this region for GI and its potential as a structural biomarker for GI.

Brain structure changes associated with sexual orientation.

Biological sex differences in brain function and structure are reliably associated with several cortico-subcortical brain regions. While sexual orientation (hetero- versus homosexuality) has been similarly linked to functional differences in several phylogenetically-old brain areas, the research on morphological brain phenotypes associated with sexual orientation is far from conclusive. We examined potential cerebral structural differences linked to sexual orientation in a group of 74 participants, including 37 men (21 homosexual) and 37 women (19 homosexual) using voxel-based morphometry (VBM). Gray matter volumes (GMV) were compared with respect to sexual orientation and biological sex across the entire sample using full factorial designs controlling for total intracranial volume, age, handedness, and education. We observed a significant effect of sexual orientation for the thalamus and precentral gyrus, with more GMV in heterosexual versus homosexual individuals, and for the putamen, with more GMV in homosexual + than heterosexual individuals. We found significant interactions between biological sex and sexual orientation, indicating that the significant effect for the putamen cluster was driven by homosexual women, whereas heterosexual women had increased precentral gyrus GMV. Heterosexual men exhibited more GMV in the thalamus than homosexual men. This study shows that sexual orientation is reflected in brain structure characteristics and that these differ between the sexes. The results emphasize the need to include or control for potential effects of participants' sexual orientation in neuroimaging studies. Furthermore, our findings provide important new insights into the brain morphology underlying sexual orientation and likely have important implications for understanding brain functions and behavior.

Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies.

Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. In this paper, we evaluate the performance of a purely image based workflow relying on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies. A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible. A second DNN predicts cardiac phase labels for each frame. Only in the training and evaluation phases for the second DNN, ECG signals are used to provide ground truth labels for each angiographic frame. The networks were trained on 56,655 coronary angiographies from 6820 patients and evaluated on 20,780 coronary angiographies from 6261 patients. No exclusion criteria related to patient state (stable or acute CAD), previous interventions (PCI or CABG), or pathology were formulated. Cardiac phase detection had an accuracy of 98.8 %, a sensitivity of 99.3 % and a specificity of 97.6 % on the evaluation set. EDF prediction had a precision of 98.4 % and a recall of 97.9 %. Several sub-group analyses were performed, indicating that the cardiac phase detection performance is largely independent from acquisition angles, the heart rate of the patient, and the angiographic view (LCA / RCA). The average execution time of cardiac phase detection for one angiographic series was on average less than five seconds on a standard workstation. We conclude that the proposed image based workflow potentially obviates the need for manual frame selection and ECG acquisition, representing a relevant step towards automated CAD assessment.

Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

Meta-analytic evidence for a joint neural mechanism underlying response inhibition and state anger.

Although anger may weaken response inhibition (RI) by allowing outbursts to bypass deliberate processing, it is equally likely that RI deficits precipitate a state of anger (SA). In adolescents, for instance, anger occurs more frequently and often leads to escalating aggressive behaviors. Even though RI is considered a key component in explaining individual differences in SA expression, the neural overlap between SA and RI remains elusive. Here, we aimed to meta-analytically revisit and update the neural correlates of motor RI, to determine a consistent neural architecture of SA, and to identify their joint neural network. Considering that inhibitory abilities follow a protracted maturation until early adulthood, we additionally computed RI meta-analyses in youths and adults. Using activation likelihood estimation, we calculated twelve meta-analyses across 157 RI and 39 SA experiments on healthy individuals. Consistent with previous findings, RI was associated with a broad frontoparietal network including the anterior insula/inferior frontal gyrus (aI/IFG), premotor and midcingulate cortices, extending into right temporoparietal areas. Youths showed convergent activity in right midcingulate and medial prefrontal areas, left aI/IFG, and the temporal poles. SA, on the other hand, reliably recruited the right aI/IFG and anterior cingulate cortex. Conjunction analyses between RI and SA yielded a single convergence cluster in the right aI/IFG. While frontoparietal networks and bilateral aI are ubiquitously recruited during RI, the right aI/IFG cluster likely represents a node in a dynamically-adjusting monitoring network that integrates salient information thereby facilitating the execution of goal-directed behaviors under highly unpredictable scenarios.

Serum Testosterone and Cortisol Concentrations After Single-Dose Administration of 100-Mg Transdermal Testosterone in Healthy Men.

The growing interest in testosterone's effects on men's social behaviors, in particular aggressive, risk-taking, or status maintenance behaviors, is accompanied by a paucity of dose-dependent pharmacokinetic data. Examining the neurophysiological effects of transdermal testosterone typically includes a 4h delay before further brain-behavior measurements. Nevertheless, high heterogeneity regarding the timing of follow-up measurements and dosage remains. In a double-blind placebo-controlled design, we examined the short-term pharmacokinetic profile of 100-mg transdermal testosterone (Testotop®) to determine the optimal time for detecting testosterone-mediated effects. Across two studies, 35 healthy men received a single dose of testosterone and placebo in two separate sessions. In study one (n = 16), serum testosterone and cortisol were assessed serially every 30 min up to 2 h posttreatment. In study two (n = 19), we assessed serum testosterone and cortisol at baseline, 2 h, and 4.15 h (255 min) posttreatment. Relative to baseline and placebo, transdermal testosterone significantly increased total serum testosterone concentrations 90 min posttreatment, reaching maximum concentration between 2 h and 3 h posttreatment. Albeit elevated, serum testosterone levels gradually decreased between 2 h and 4 h following treatment. Transdermal testosterone did not suppress cortisol release. Instead, cortisol concentrations decreased according to cortisol's known circadian rhythm. Unlike previous findings showing significant testosterone concentration increases as soon as 60 min and as late as 3 h post 150-mg testosterone treatment, our 100-mg testosterone manipulation significantly increased testosterone concentrations 90 min following treatment. These pharmacokinetic data are important in facilitating the optimization of timing parameters for future testosterone challenge studies.