All Publications


  • Dopamine-Related Alterations of Frontostriatal Habit Circuitry Underlie Stimulus-Response Binge Eating Wang, A., Kuijper, F. M., Barbosa, D., Hagan, K., Lee, E., Tong, E., Bohon, C., Halpern, C. H. ELSEVIER SCIENCE INC. 2021: S233-S234
  • Anticipatory human subthalamic area beta-band power responses to dissociable tastes correlate with weight gain. Neurobiology of disease Kakusa, B., Huang, Y., Barbosa, D. A., Feng, A., Gattas, S., Shivacharan, R., Lee, E. B., Kuijper, F. M., Saluja, S., Parker, J. J., Miller, K. J., Keller, C., Bohon, C., Halpern, C. H. 2021: 105348

    Abstract

    The availability of enticing sweet, fatty tastes is prevalent in the modern diet and contribute to overeating and obesity. In animal models, the subthalamic area plays a role in mediating appetitive and consummatory feeding behaviors, however, its role in human feeding is unknown. We used intraoperative, subthalamic field potential recordings while participants (n = 5) engaged in a task designed to provoke responses of taste anticipation and receipt. Decreased subthalamic beta-band (15-30 Hz) power responses were observed for both sweet-fat and neutral tastes. Anticipatory responses to taste-neutral cues started with an immediate decrease in beta-band power from baseline followed by an early beta-band rebound above baseline. On the contrary, anticipatory responses to sweet-fat were characterized by a greater and sustained decrease in beta-band power. These activity patterns were topographically specific to the subthalamic nucleus and substantia nigra. Further, a neural network trained on this beta-band power signal accurately predicted (AUC ≥ 74%) single trials corresponding to either taste. Finally, the magnitude of the beta-band rebound for a neutral taste was associated with increased body mass index after starting deep brain stimulation therapy. We provide preliminary evidence of discriminatory taste encoding within the subthalamic area associated with control mechanisms that mediate appetitive and consummatory behaviors.

    View details for DOI 10.1016/j.nbd.2021.105348

    View details for PubMedID 33781923

  • The insulo-opercular cortex encodes food-specific content under controlled and naturalistic conditions. Nature communications Huang, Y., Kakusa, B. W., Feng, A., Gattas, S., Shivacharan, R. S., Lee, E. B., Parker, J. J., Kuijper, F. M., Barbosa, D. A., Keller, C. J., Bohon, C., Mikhail, A., Halpern, C. H. 2021; 12 (1): 3609

    Abstract

    The insulo-opercular network functions critically not onlyin encoding taste, but also inguiding behavior based on anticipated food availability. However, there remains no direct measurement of insulo-opercular activity when humans anticipate taste. Here, we collect direct, intracranial recordings during a food task that elicits anticipatory and consummatory taste responses, and during ad libitum consumption of meals. While cue-specific high-frequency broadband (70-170Hz) activity predominant in the left posterior insula is selective for taste-neutral cues, sparse cue-specific regions in the anterior insulaare selective for palatable cues. Latency analysis reveals this insular activity is preceded by non-discriminatory activity in the frontal operculum. During ad libitum meal consumption, time-locked high-frequency broadband activity at the time of food intake discriminates food types and is associated with cue-specific activity during the task. These findings reveal spatiotemporally-specificactivity in the human insulo-opercular cortex that underlies anticipatory evaluation of food across both controlled and naturalistic settings.

    View details for DOI 10.1038/s41467-021-23885-4

    View details for PubMedID 34127675

  • Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges. Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) Lee, E. B., Wang, S. Y., Chang, R. T. 2021; 10 (3): 261-267

    Abstract

    Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, including ophthalmology. However, only a few algorithms tailored to specific health conditions have been able to achieve regulatory approval for autonomous diagnosis. There is now an international effort to establish optimized thresholds for algorithm performance benchmarking in a rapidly evolving artificial intelligence field. This review examines the largest deep learning studies in glaucoma, with special focus on identifying recurrent challenges and limitations within these studies which preclude widespread clinical deployment. We focus on the 3 most common input modalities when diagnosing glaucoma, namely, fundus photographs, spectral domain optical coherence tomography scans, and standard automated perimetry data. We then analyze 3 major challenges present in all studies: defining the algorithm output of glaucoma, determining reliable ground truth datasets, and compiling representative training datasets.

    View details for DOI 10.1097/APO.0000000000000395

    View details for PubMedID 34383718