Citations

Hyperglycemia in prediabetes is accompanied by increased amino acid (AA) concentrations and impaired suppression of glucagon secretion by glucose. It is unknown whether an abnormal response to AA contributes or hepatic steatosis exacerbates α-cell dysfunction. Therefore, lean participants and participants with obesity (with and without hepatic steatosis) were studied on two occasions in random order using a graded glucose infusion. Saline or an AA mixture was infused. The glucagon secretion rate (GSR) was derived from peripheral glucagon concentrations, and the change in glucose required to reduce GSR by 50% (G50) was calculated for each participant. AAs were measured during the experiment. On both occasions, experimental conditions produced a progressive rise in insulin secretion and an inverse exponential suppression of glucagon. G50 did not differ between groups in response to glucose alone. AA infusion did not change G50 in lean participants. G50 increased during AA infusion in participants with obesity with and without hepatic steatosis, leading us to report these participants as a single group. The relative change in AA concentrations across study days was generally similar between groups. We conclude that despite a normal response to glucose, obesity increases α-cell responses to AA in humans. Article Highlights The aim of this study was to understand the pathogenesis of α-cell dysfunction in prediabetes. The primary hypothesis was that α-cell responsivity to glucose (reduction of the glucagon secretion rate by 50%) is impaired by amino acid (AA) infusion. A secondary hypothesis was that this abnormal response is exacerbated by hepatic steatosis. AA infusion impaired α-cell suppression in response to rising glucose in participants with obesity, but not in lean participants, despite a normal response to glucose alone. This abnormal response was unaffected by the presence or absence of hepatic steatosis. It seems likely that an abnormal α-cell response to AAs precedes an abnormal response to glucose alone.

ABSTRACT Introduction The insula is an increasingly important target for functional neurosurgery given its involvement in a range of neurological and neuropsychiatric disorders, including epilepsy and chronic pain. As this practice evolves, optimal targeting will require standardized outcome measures that relate electrode or laser trajectory to postprocedural outcome. Traditional whole- brain registration approaches fail to capture the substantial person-to-person variability in insular gyral configuration, including the relative internal rotation of the insular gyri with respect to standard stereotactic space. Objective We propose and validate a stereotactic coordinate system based on local anatomical landmarks to facilitate surgical planning and standardized outcome assessment within the insular cortex. Methods Our approach transforms brain MRI first into standard AC-PC space, and then into an insular-specific space defined by five anatomical landmarks: four points along the central sulcus of the insula and one point at the middle cerebral artery (MCA) bifurcation (at the limen insulae). The system calculates two angles - θ (axial) and φ (sagittal) - between the AC-PC line and the insular axis, and the brain volume undergoes sequential rotation through these angles followed by translation to place the coordinate system’s origin along the insular axis. Results In a sample of 32 patients, the angle between the AC-PC line and the insular axis ranged from -17° to 17° in the axial plane (θ) and 31° to 69° in the sagittal plane (φ). In the resulting coordinate system, the insular axis defines z = 0 and the MCA turning point defines y = 0. We developed a custom, open-access MATLAB graphical interface that allows intuitive implementation of this system for both surgical planning and postoperative analysis; implanted electrodes, laser fiber position, and ablation geometry can each be localized within this common space. As a demonstration of its utility for pooling data across subjects, we applied the transformation to a previously acquired intracranial electrophysiology dataset and found that anatomically consistent, effector-specific motor representations emerged across 18 subjects once electrode positions were expressed in insular-specific coordinates. Conclusion As stereotactic surgery for insular targets becomes more common with expanding scientific inquiry, an insular-specific coordinate system may facilitate operative planning and functional mapping, and may help standardize outcome assessment across patients and institutions. SIGNIFICANCE STATEMENT The insular cortex represents an increasingly important surgical target for therapeutic interventions, yet substantial person-to-person anatomical variability hampers standardized targeting and outcome comparison. The insula is simultaneously the subject of expanding scientific inquiry — into interoception, pain, autonomic regulation, salience processing, and sensorimotor representation — much of it now pursued through intracranial recording and stimulation in humans, where cohorts are small, electrode sampling is idiosyncratic, and progress therefore depends on pooling data across patients in a frame that respects insular gyral architecture. We present “ Insulotaxy ,” a stereotactic coordinate system built from consistent, easily identifiable local anatomical landmarks that accounts for the insula’s unique rotational relationship to standard brain coordinates. An open-source MATLAB tool transforms imaging into insular-specific coordinates, facilitating surgical planning for ablation and electrode placement while enabling standardized outcome reporting across institutions. By providing locally anchored, anatomically aligned coordinates rather than relying on whole-brain registration, this framework addresses a practical gap in functional neurosurgery and lays a foundation for pooling clinical and electrophysiological data as insular interventions become more prevalent.

Abstract Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

Abstract Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional brain regions. Yet, many neuroanatomical boundaries remain invisible to current imaging and electrophysiology methods, limiting precision and contributing to suboptimal patient outcomes. Here, we introduce a self-supervised artificial intelligence (AI) framework that learns to delineate functional subregions directly from the spectral content of intraoperative local field potential (LFP) recordings, without the need for predefined biomarkers or anatomical labels. The framework identifies physiologic structure across the full spectrum of the signal and, through explainable AI (XAI), reveals the specific frequency components underlying these distinctions. Validated in the subthalamic nucleus (STN), the model aligned with clinically defined borders and rediscovered known beta oscillations. Applied to the motor thalamus in tremor patients, it consistently identified functional transitions corresponding to the ventral oralis posterior (Vop) and ventral intermediate (Vim) nuclei—regions where conventional methods fail to provide reliable boundaries. To assess clinical relevance, physiologically defined clusters were functionally evaluated using monopolar review data at their first DBS clinic postoperative visit, demonstrating distinct stimulation-response profiles across clusters and linking electrophysiologic segmentation to clinically meaningful programming outcomes. These findings demonstrate that intraoperative LFP recordings can be transformed into both a real-time guidance resource and a data-driven platform for biomarker discovery, establishing a foundation for more precise, individualized neuromodulation therapies and advancing our understanding of functional brain organization.

Understanding motor representation in the human brain requires mapping beyond the primary motor cortex, into the distributed networks that coordinate complex movements. The insular cortex, a multifunctional hub buried within the Sylvian fissure, has been implicated in motor control through clinical observations and neuroimaging. Yet its precise relation to primary sensorimotor processing remains one of the least understood aspects of motor neurophysiology. To address this gap, we quantified electrophysiological changes from implanted depth electrodes in patients performing simple movement tasks combined with single-pulse electrical stimulation (SPES) to map effective connectivity. The movement data reveal somatotopically specific representation bilaterally, as well as intereffector regions that are active for different movement types. Hand representation is centered along the contralateral ventral aspect of the middle and posterior short gyri bilaterally, while tongue/mouth tuned sites cluster in the dorsal posterior short gyrus and the dorsal long gyri. Insular activity temporally follows the primary motor cortex (M1) and precedes movement onset. SPES revealed somatotopically specific connectivity between corresponding sites in M1 and insula (hand-to-hand, tongue-to-tongue) and between bilateral insulae. These observations establish that somatotopy is a conserved property of distributed motor control incorporating the insular representations and connectivity.

Adipose tissue (AT) lipolysis insulin resistance results in excess free fatty acid (FFA) release. We tested the hypothesis that the ability of insulin to suppress AT lipolysis is unrelated to the ability of niacin to suppress lipolysis, because niacin acts through a different proximal signaling pathway. Ten volunteers (5 women and 5 men) with upper-body obesity and/or type 2 diabetes mellitus (T2DM) underwent two study visits with overnight intravenous infusions of niacin (1.4 mg/min) or saline, followed by a hyperinsulinemic-euglycemic clamp. FFA-palmitate Ra was measured using [U-13C] and [2H9]palmitate infusions; abdominal AT biopsies were performed before and during the insulin clamp. The suppression of FFA-palmitate Ra by insulin on the saline control day and by niacin after an overnight infusion were highly correlated (r = −0.93, P < 0.001). Fasting AT Akt (pAktS473/474-to-panAkt ratio, P = 0.01) and perilipin 1 (PLN1) (pPLN1S552-to-panPLN1 ratio, P = 0.02) phosphorylation were less during niacin treatment than in the saline control study. Because the suppression of lipolysis by insulin and niacin are highly correlated within individuals and because niacin and insulin act through different proximal signaling pathways, we propose dysregulated AT lipolysis in obesity/T2DM is due to dysfunction(s) in distal lipolysis proteins rather than isolated “insulin resistance.” Article Highlights We undertook this study to compare adipose tissue lipolysis responses to insulin and niacin in humans. We tested the hypothesis that adipose tissue insulin resistance would be unrelated to adipose tissue niacin resistance. The suppression of lipolysis by insulin and niacin were highly correlated. Dysregulated adipose tissue lipolysis in obesity/type 2 diabetes is due to dysfunction(s) in distal lipolysis proteins rather than isolated “insulin resistance.”

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity [for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP] and with higher VPD [HR=1.24 per SD (95% CI: 1.05, 1.45)] (Table ). The AI-malignancy score alone was also associated with BC risk [HR=1.16 per 1 unit score (95%CI: 1.09,1.23)] and showed similar discriminatory accuracy [C-statistic=0.626 (95%CI: 0.586, 0.665)] as the model with BBD severity and VPD combined [C-statistic=0.627, (95%CI: 0.585, 0.669)] (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC [HR=1.14 (95%CI: 1.07, 1.21); PLRT<0.001] but only achieved a marginally significant improvement in discriminatory accuracy [C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047)]. Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-02.

Background: Whether there are sex and obesity differences in fat oxidation and free fatty acid (FFA) mobilization during low-intensity exercise is unclear. Methods: We studied 21 (nine females) participants without and 24 (16 females) with obesity during 5 h of treadmill walking (~2 mph). We measured energy expenditure (EE) and substrate oxidation (indirect calorimetry) and FFA-palmitate kinetics and oxidation with isotopic palmitate infusions ([U- 13 C]palmitate and [1- 14 C]palmitate or [9,10- 3 H]palmitate). Skeletal muscle samples were collected at 1.5 and 5 h of exercise and 24 h after exercise. We calculated fatty acid (FA) oxidation, palmitate rate of disappearance (Rd), and intramyocellular triglyceride-derived palmitate fractions during exercise. Results: At rest, EE was 1.24 ± 0.23 kcal/min, with FA oxidation contributing 49 ± 13% of total EE. During exercise, FA oxidation accounted for 73 ± 8% of EE, and was not different between males and females ( P = 0.34). Plasma FFA-palmitate concentrations were greater in females than males ( P = 0.003), mean palmitate Rd and plasma palmitate clearance were not different ( P = 0.32 and 0.14, respectively). Plasma FFA-palmitate Rd was correlated with EE ( r = 0.49, P = 0.001), with a proportionally greater Rd response in females ( P < 0.001). Although absolute plasma FFA-palmitate oxidation rates were comparable between sexes, the percent contribution of FFA-palmitate oxidation to EE was greater in females ( P = 0.001). During exercise, intramyocellular triglyceride-palmitate fraction amounts remained stable. Conclusions: Lipid oxidation during low-intensity exercise is similar in males and females, independent of obesity, although next-day values were greater in participants with normal weight. EE correlates with plasma palmitate disappearance rates, but is proportionally greater in females. The contribution of plasma palmitate oxidation is greater in females than in males during low-intensity exercise.