Jensen, Michael A.

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.

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.

Abstract The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy). We analyzed electrocorticography (ECoG) and local field potentials (LFP) in the visual cortex of human and non-human primates (macaque) to investigate the extent to which broadband signals (BB) and narrowband gamma (NBG) oscillations conveyed synergistic or redundant information about images. In both species, the information conveyed by BB signals was highly synergistic within and between visual areas. By contrast, the information carried by NBG was primarily redundant within and between the same visual areas. Finally, the information conveyed by BB signals emerged early after stimulus onset, while NBG sustained information at later time points. These results suggest a potential dual role of BB and NBG cortical dynamics in visual processing, with broadband dynamics supporting nonlinear pattern recognition and oscillations facilitating information maintenance across the cortex.

Abstract Deep brain stimulation (DBS) is a targeted manipulation of brain circuitry to treat neurological and neuropsychiatric conditions. Optimal DBS lead placement is essential for treatment efficacy. Current targeting practice is based on preoperative and intraoperative brain imaging, intraoperative electrophysiology, and stimulation mapping. Electrophysiological mapping using extracellular microelectrode recordings aids in identifying functional subdomains, anatomical boundaries, and disease-correlated physiology. The shape of single-unit action potentials may differ due to different biophysical properties between cell-types and brain regions. Here, we describe a technique to parameterize the structure and duration of sorted spike units using a novel algorithmic approach based on canonical response parameterization, and illustrate how it may be used on DBS microelectrode recordings. Isolated spike shapes are parameterized then compared using a spike similarity metric and grouped by hierarchical clustering. When spike morphology is associated with anatomy, we find regional clustering in the human globus pallidus. This method is widely applicable for spike removal and single-unit characterization and could be integrated into intraoperative array-based technologies to enhance targeting and clinical outcomes in DBS lead placement. Author summary We developed a new algorithmic approach to capture the structure of single neuron electrophysiology from microelectrode recordings during deep brain stimulation surgery. This method has broad applications for spike characterization, clustering, removal from the background field potential, and comparison across recording sites. Using this technique, we illustrate regional clustering of spike morphologies in the human globus pallidus.

BACKGROUND AND OBJECTIVES: Stereotactic procedures are used to manage a diverse set of patients across a variety of clinical contexts. The stereotactic devices and software used in these procedures vary between surgeons, but the fundamental principles that constitute safe and accurate execution do not. The aim of this work is to describe these principles to equip readers with a generalizable knowledge base to execute and understand stereotactic procedures. METHODS: A combination of a review of the literature and empirical experience from several experienced surgeons led to the creation of this work. Thus, this work is descriptive and qualitative by nature, and the literature is used to support instead of generate the ideas of this framework. RESULTS: The principles detailed in this work are categorized based on 5 clinical domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. Illustrations and tables are used throughout to convey the concepts in an efficient manner. CONCLUSION: Stereotactic procedures are complex, requiring a thorough understanding of each step of the workflow. The concepts described in this work enable functional neurosurgeons with the fundamental knowledge necessary to provide optimal patient care.

BACKGROUND AND OBJECTIVES: Identifying and characterizing sources of targeting error in stereotactic procedures is essential to maximizing accuracy, potentially improving surgical outcomes. We aim to describe a generic framework which characterizes sources of stereotactic inaccuracy. METHODS: We assembled a list of stereotactic systems: ROSA, Neuromate, Mazor Renaissance, ExcelsiusGPS, Cirq, STarFix (FHC), Nexframe, ClearPoint, CRW, and Leksell. We searched the literature for qualitative and quantitative work identifying and quantifying potential sources of inaccuracy and describing each system's implementation using Standards for Reporting Qualitative Research guidelines. Our literature search spanned 1969 to 2024, and various studies were included, with formats ranging from phantom studies to systematic reviews. Keyword searches were conducted, and the details about each system were used to create a framework for identifying and describing the unique targeting error profile of each system. RESULTS: We describe and illustrate the details of various sources of stereotactic inaccuracies and generate a framework to unify these sources into a single framework. This framework entails 5 domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. This framework was applied to 10 stereotactic systems. CONCLUSION: This framework provides a rubric to analyze the sources of error for any stereotactic system. Illustrations allow the reader to understand sources of error conceptually so that they may apply them to their practice.