Towards precision well-being in medical education.

Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the 'Healthy Flourishers' well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the 'Getting By' cluster reported mild anxiety and depression and diminished flourishing. Membership in the 'At-Risk' cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers', whereas 36% were grouped into the 'Getting-By' cluster and 15% were identified as 'At-Risk'. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.

Examining the role of the uncinate fasciculus in proper noun naming: awake brain tumor resections and stereo EEG targeted electrical stimulation multiple case study.

While there is strong evidence from lesion and functional imaging studies implicating the left anterior temporal pole (LTP) in naming unique entities, less is known about white matter tracts in category-specific naming. We present evidence that implicates the uncinate fasciculus (UF) in proper noun naming. First, we describe two patients with left LTP gliomas who developed category specific worsening in proper noun naming in real time during awake surgery when the UF was surgically involved . We then describe a third case involving targeted electrical stimulation of the UF using stereo-electroencephalography (sEEG) that resulted in category specific naming disturbance for proper nouns..

Cortical feature analysis and machine learning improves detection of "MRI-negative" focal cortical dysplasia.

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies ('MRI-negative'), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as "lesional" when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions.

Domain-specific impairment in metacognitive accuracy following anterior prefrontal lesions.

Humans have the capacity to evaluate the success of cognitive processes, known as metacognition. Convergent evidence supports a role for anterior prefrontal cortex in metacognitive judgements of perceptual processes. However, it is unknown whether metacognition is a global phenomenon, with anterior prefrontal cortex supporting metacognition across domains, or whether it relies on domain-specific neural substrates. To address this question, we measured metacognitive accuracy in patients with lesions to anterior prefrontal cortex (n = 7) in two distinct domains, perception and memory, by assessing the correspondence between objective performance and subjective ratings of performance. Despite performing equivalently to a comparison group with temporal lobe lesions (n = 11) and healthy controls (n = 19), patients with lesions to the anterior prefrontal cortex showed a selective deficit in perceptual metacognitive accuracy (meta-d'/d', 95% confidence interval 0.28-0.64). Crucially, however, the anterior prefrontal cortex lesion group's metacognitive accuracy on an equivalent memory task remained unimpaired (meta-d'/d', 95% confidence interval 0.78-1.29). Metacognitive accuracy in the temporal lobe group was intact in both domains. Our results support a causal role for anterior prefrontal cortex in perceptual metacognition, and indicate that the neural architecture of metacognition, while often considered global and domain-general, comprises domain-specific components that may be differentially affected by neurological insult.