1
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Schuler N, Shepard L, Saxton A, Russo J, Johnston D, Saba P, Holler T, Smith A, Kulason S, Yee A, Ghazi A. Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics. Urol Pract 2023; 10:447-455. [PMID: 37347812 DOI: 10.1097/upj.0000000000000426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 06/24/2023]
Abstract
INTRODUCTION Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise. METHODS Video, robotic kinematics, and force sensor data were collected from 35 board-certified urologists at the 2022 AUA conference. Video was annotated for surgical gestures. Objective performance indicators were derived from robotic system kinematic data. Force metrics were calculated from hydrogel model integrated sensors. Data were fitted to 3 supervised machine learning models-logistic regression, support vector machine, and k-nearest neighbors-which were used to predict procedure-specific learning curve proficiency. Recursive feature elimination was used to optimize the best performing model. RESULTS Logistic regression predicted caseload with the highest AUC score for 5/7 possible data combinations (force, 64%; objective performance indicators + gestures, 94%; objective performance indicators + force, 90%; gestures + force, 93%; objective performance indicators + gestures + force, 94%). Support vector machine predicted the highest AUC score for objective performance indicators (82%) and gestures (94%). Logistic regression with recursive feature elimination was the most effective model reaching 96% AUC in predicting case-specific experience. Most contributory features were identified across all model types. CONCLUSIONS We have created a machine learning-based algorithm utilizing a novel combination of objective performance indicators, gesture analysis, and integrated force metrics to predict surgical experience, capable of discriminating between surgeons with low or high robot-assisted radical prostatectomy caseload with 96% AUC in a standardized, simulation-based environment.
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Affiliation(s)
- Nathan Schuler
- Simulation Innovation Lab, Carnegie Center for Surgical Innovation, Johns Hopkins University, Baltimore, Maryland
| | - Lauren Shepard
- Simulation Innovation Lab, Carnegie Center for Surgical Innovation, Johns Hopkins University, Baltimore, Maryland
| | - Aaron Saxton
- Department of Urology, University of Rochester Medical Center, Rochester, New York
| | - Jillian Russo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Daniel Johnston
- University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Patrick Saba
- SUNY Upstate Medical University Norton College of Medicine, Syracuse, New York
| | - Tyler Holler
- Department of Urology, University of Rochester Medical Center, Rochester, New York
| | - Andrea Smith
- Data and Analytics, Intuitive Surgical, Inc, Peachtree Corners, Georgia
| | - Sue Kulason
- Data and Analytics, Intuitive Surgical, Inc, Peachtree Corners, Georgia
| | - Andrew Yee
- Data and Analytics, Intuitive Surgical, Inc, Peachtree Corners, Georgia
| | - Ahmed Ghazi
- Brady Urological Institute, Johns Hopkins University, Baltimore, Maryland
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2
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Stouffer KM, Chen C, Kulason S, Xu E, Witter MP, Ceritoglu C, Albert MS, Mori S, Troncoso J, Tward DJ, Miller MI. Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease. Neuroimage Clin 2023; 38:103374. [PMID: 36934675 PMCID: PMC10034129 DOI: 10.1016/j.nicl.2023.103374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/17/2023]
Abstract
Previous research has emphasized the unique impact of Alzheimer's Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in cognitively unimpaired (CU) controls, CU individuals who progressed to mild cognitive impairment (MCI), and individuals with MCI who progressed to dementia of the AD type (DAT), using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both groups of 'converters' (CU→MCI, MCI→DAT) compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. We localize atrophy within the amygdala within each of these groups using fixed effects modeling. Controlling for the familywise error rate highlights the medial regions of the amygdala as those with significantly higher atrophy in both groups of converters than in controls. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the spatial distribution of MR measured atrophy rates, revealing high densities (and atrophy) in the amygdala (particularly medial), ERC, and rostral third of the MTL. The similarity of the location of NFTs in AD and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA.
| | - Claire Chen
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Sue Kulason
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Eileen Xu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Can Ceritoglu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Marilyn S Albert
- Departments of Neurology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Juan Troncoso
- Department of Pathology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Daniel J Tward
- Departments of Computational Medicine and Neurology, University of California, Los Angeles, UCLA Brain Mapping Center, 660 Charles E. Young Drive South, Los Angeles 90095, CA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
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3
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Kulason S, Ratnanather JT, Miller MI, Kamath V, Hua J, Yang K, Ma M, Ishizuka K, Sawa A. A comparative neuroimaging perspective of olfaction and higher-order olfactory processing: on health and disease. Semin Cell Dev Biol 2022; 129:22-30. [PMID: 34462249 PMCID: PMC9900497 DOI: 10.1016/j.semcdb.2021.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023]
Abstract
Olfactory dysfunction is often the earliest indicator of disease in a range of neurological and psychiatric disorders. One tempting working hypothesis is that pathological changes in the peripheral olfactory system where the body is exposed to many adverse environmental stressors may have a causal role for the brain alteration. Whether and how the peripheral pathology spreads to more central brain regions may be effectively studied in rodent models, and there is successful precedence in experimental models for Parkinson's disease. It is of interest to study whether a similar mechanism may underlie the pathology of psychiatric illnesses, such as schizophrenia. However, direct comparison between rodent models and humans includes challenges under light of comparative neuroanatomy and experimental methodologies used in these two distinct species. We believe that neuroimaging modality that has been the main methodology of human brain studies may be a useful viewpoint to address and fill the knowledge gap between rodents and humans in this scientific question. Accordingly, in the present review article, we focus on brain imaging studies associated with olfaction in healthy humans and patients with neurological and psychiatric disorders, and if available those in rodents. We organize this review article at three levels: 1) olfactory bulb (OB) and peripheral structures of the olfactory system, 2) primary olfactory cortical and subcortical regions, and 3) associated higher-order cortical regions. This research area is still underdeveloped, and we acknowledge that further validation with independent cohorts may be needed for many studies presented here, in particular those with human subjects. Nevertheless, whether and how peripheral olfactory disturbance impacts brain function is becoming even a hotter topic in the ongoing COVID-19 pandemic, given the risk of long-term changes of mental status associated with olfactory infection of SARS-CoV-2. Together, in this review article, we introduce this underdeveloped but important research area focusing on its implications in neurological and psychiatric disorders, with several pioneered publications.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Vidyulata Kamath
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jun Hua
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA; Johns Hopkins Schizophrenia Center, Baltimore, MD, USA
| | - Minghong Ma
- Department of Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Koko Ishizuka
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA; Johns Hopkins Schizophrenia Center, Baltimore, MD, USA
| | - Akira Sawa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA; Johns Hopkins Schizophrenia Center, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Takayanagi Y, Kulason S, Sasabayashi D, Takahashi T, Katagiri N, Sakuma A, Ohmuro N, Katsura M, Nishiyama S, Kido M, Furuichi A, Noguchi K, Matsumoto K, Mizuno M, Ratnanather JT, Suzuki M. Volume Reduction of the Dorsal Lateral Prefrontal Cortex Prior to the Onset of Frank Psychosis in Individuals with an At-Risk Mental State. Cereb Cortex 2021; 32:2245-2253. [PMID: 34649274 DOI: 10.1093/cercor/bhab353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 08/11/2021] [Accepted: 08/30/2021] [Indexed: 11/14/2022] Open
Abstract
Although some individuals with at-risk mental states (ARMS) develop overt psychosis, surrogate markers which can reliably predict a future onset of psychosis are not well established. The dorsal lateral prefrontal cortex (DLPFC) is thought to be involved in psychotic disorders such as schizophrenia. In this study, 73 ARMS patients and 74 healthy controls underwent 1.5-T 3D magnetic resonance imaging scans at three sites. Using labeled cortical distance mapping, cortical thickness, gray matter (GM) volume, and surface area of DLPFC were estimated. These measures were compared across the diagnostic groups. We also evaluated cognitive function among 36 ARMS subjects to clarify the relationships between the DLPFC morphology and cognitive performance. The GM volume of the right DLPFC was significantly reduced in ARMS subjects who later developed frank psychosis (ARMS-P) relative to those who did not (P = 0.042). There was a positive relationship between the right DLPFC volume and the duration prior to the onset of frank psychosis in ARMS-P subjects (r = 0.58, P = 0.018). Our data may suggest that GM reduction of the DLPFC might be a potential marker of future onset of psychosis in individuals with ARMS.
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Affiliation(s)
- Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Arisawabashi Hospital, Toyama 9392704, Japan
| | - Sue Kulason
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama 9300194, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama 9300194, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo 1438541, Japan
| | - Atsushi Sakuma
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi 9808574, Japan
| | - Noriyuki Ohmuro
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi 9808574, Japan.,Osaki Citizen Hospital, Sendai, Miyagi 9896183, Japan
| | - Masahiro Katsura
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi 9808574, Japan
| | - Shimako Nishiyama
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Health Administration Center, University of Toyama, Toyama 9308555, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama 9300194, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama 9300194, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan
| | - Kazunori Matsumoto
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi 9808574, Japan.,Kokoro no Clinic OASIS, Sendai, Miyagi 9800802, Japan
| | - Masafumi Mizuno
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo 1438541, Japan
| | - J Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama 9300194, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama 9300194, Japan
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5
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Brown KC, Bhattacharyya KD, Kulason S, Zia A, Jarc A. How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery. Visc Med 2020; 36:463-470. [PMID: 33447602 DOI: 10.1159/000512437] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/20/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction A surgeon's technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. Methods We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. Results We determine that for the majority of skills, only a small subset of OPIs (2-10) are required to achieve the highest model accuracies (80-95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80-98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. Conclusion We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.
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Affiliation(s)
- Kristen C Brown
- Advanced Product Development, Intuitive Surgical, Inc., Norcross, Georgia, USA
| | | | - Sue Kulason
- Advanced Product Development, Intuitive Surgical, Inc., Norcross, Georgia, USA
| | - Aneeq Zia
- Advanced Product Development, Intuitive Surgical, Inc., Norcross, Georgia, USA
| | - Anthony Jarc
- Advanced Product Development, Intuitive Surgical, Inc., Norcross, Georgia, USA
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Kulason S, Xu E, Tward DJ, Bakker A, Albert M, Younes L, Miller MI. Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease. Front Neurosci 2020; 14:804. [PMID: 32973425 PMCID: PMC7472871 DOI: 10.3389/fnins.2020.00804] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/09/2020] [Indexed: 12/20/2022] Open
Abstract
This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Eileen Xu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Daniel J Tward
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, United States
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7
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Takayanagi Y, Kulason S, Sasabayashi D, Takahashi T, Katagiri N, Sakuma A, Ohmuro N, Katsura M, Nishiyama S, Nakamura M, Kido M, Furuichi A, Noguchi K, Matsumoto K, Mizuno M, Ratnanather JT, Suzuki M. Structural MRI Study of the Planum Temporale in Individuals With an At-Risk Mental State Using Labeled Cortical Distance Mapping. Front Psychiatry 2020; 11:593952. [PMID: 33329144 PMCID: PMC7732500 DOI: 10.3389/fpsyt.2020.593952] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Background: Recent studies have demonstrated brain structural changes that predate or accompany the onset of frank psychosis, such as schizophrenia, among individuals with an at-risk mental state (ARMS). The planum temporale (PT) is a brain region involved in language processing. In schizophrenia patients, gray matter volume reduction and lack of normal asymmetry (left > right) of PT have repeatedly been reported. Some studies showed progressive gray matter reduction of PT in first-episode schizophrenia patients, and in ARMS subjects during their development of psychosis. Methods: MRI scans (1.5 T field strength) were obtained from 73 ARMS subjects and 74 gender- and age-matched healthy controls at three sites (University of Toyama, Toho University and Tohoku University). Participants with ARMS were clinically monitored for at least 2 years to confirm whether they subsequently developed frank psychosis. Cortical thickness, gray matter volume, and surface area of PT were estimated using FreeSurfer-initiated labeled cortical distance mapping (FSLCDM). PT measures were compared among healthy controls, ARMS subjects who later developed overt psychosis (ARMS-P), and those who did not (ARMS-NP). In each statistical model, age, sex, intracranial volume, and scanning sites were treated as nuisance covariates. Results: Of 73 ARMS subjects, 18 developed overt psychosis (12 schizophrenia and 6 other psychoses) within the follow-up period. There were no significant group differences of PT measures. In addition, significant asymmetries of PT volume and surface area (left > right) were found in all diagnostic groups. PT measures did not correlate with the neurocognitive performance of ARMS subjects. Discussion: Our results suggest that the previously-reported gray matter reduction and lack of normal anatomical asymmetry of PT in schizophrenia patients may not emerge during the prodromal stage of psychosis; taken together with previous longitudinal findings, such PT structural changes may occur just before or during the onset of psychosis.
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Affiliation(s)
- Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Arisawabashi Hospital, Toyama, Japan
| | - Sue Kulason
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - Atsushi Sakuma
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Noriyuki Ohmuro
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan.,Osaki Citizen Hospital, Sendai, Japan
| | - Masahiro Katsura
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Shimako Nishiyama
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Health Administration Center, University of Toyama, Toyama, Japan
| | - Mihoko Nakamura
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Kazunori Matsumoto
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan.,Kokoro no Clinic OASIS, Sendai, Japan
| | - Masafumi Mizuno
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - J Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
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8
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Kulason S, Tward DJ, Brown T, Sicat CS, Liu CF, Ratnanather JT, Younes L, Bakker A, Gallagher M, Albert M, Miller MI. Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment. Neuroimage Clin 2018; 21:101617. [PMID: 30552075 PMCID: PMC6412863 DOI: 10.1016/j.nicl.2018.101617] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/19/2018] [Accepted: 11/24/2018] [Indexed: 11/24/2022]
Abstract
This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease. The transentorhinal cortex is significantly thinner in subjects with mild cognitive impairment than controls. Mildly cognitively impaired subjects show a significantly greater atrophy rate in the transentorhinal cortex than controls.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
| | - Daniel J Tward
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chelsea S Sicat
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chin-Fu Liu
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Laurent Younes
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michela Gallagher
- Department of Psychological and Brain Sciences, Johns Hopkins School of Arts and Sciences, Baltimore, MD 21218, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
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Takayanagi Y, Kulason S, Sasabayashi D, Takahashi T, Katagiri N, Sakuma A, Obara C, Nakamura M, Kido M, Furuichi A, Nishikawa Y, Noguchi K, Matsumoto K, Mizuno M, Ratnanather JT, Suzuki M. Reduced Thickness of the Anterior Cingulate Cortex in Individuals With an At-Risk Mental State Who Later Develop Psychosis. Schizophr Bull 2017; 43:907-913. [PMID: 28338751 PMCID: PMC5472106 DOI: 10.1093/schbul/sbw167] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Despite the fact that only a part of the individuals with at-risk mental state (ARMS) for psychosis do develop psychosis, biological markers of future transition to psychosis have not been well documented. Structural abnormality of the anterior cingulate gyrus (ACG), which probably exists prior to the onset of psychosis, could be such a risk marker. METHODS We conducted a multicenter magnetic resonance imaging (MRI) study of 3 scanning sites in Japan. 1.5-T 3D MRI scans were obtained from 73 ARMS subjects and 74 age- and gender-matched healthy controls. We measured thickness, volume, and surface area of the ACG using labeled cortical distance mapping and compared these measures among healthy controls, ARMS subjects who later converted to overt psychosis (ARMS-C), and those who did not (ARMS-NC). RESULTS Seventeen of 73 (23%) ARMS subjects developed overt psychosis within the follow-up period. The thickness of the left ACG was significantly reduced in ARMS-C relative to healthy subjects (P = .026) while both ARMS-C (P = .001) and ARMS-NC (P = .01) had larger surface areas of the left ACG compared with healthy controls. CONCLUSION Further studies will be needed to identify potential markers of future transition to psychosis though cortical thinning of the ACG might be one of the candidates.
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Affiliation(s)
- Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Sue Kulason
- Center for Imaging Science and Institute for Computational Medicine, The Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - Atsushi Sakuma
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Chika Obara
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Mihoko Nakamura
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Yumiko Nishikawa
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Kazunori Matsumoto
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Masafumi Mizuno
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyo, Japan
| | - J. Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, The Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 9300194, Japan
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Powers RJ, Kulason S, Atilgan E, Brownell WE, Sun SX, Barr-Gillespie PG, Spector AA. The local forces acting on the mechanotransduction channel in hair cell stereocilia. Biophys J 2015; 106:2519-28. [PMID: 24896132 DOI: 10.1016/j.bpj.2014.03.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Revised: 03/12/2014] [Accepted: 03/13/2014] [Indexed: 01/18/2023] Open
Abstract
In hair cells, mechanotransduction channels are located in the membrane of stereocilia tips, where the base of the tip link is attached. The tip-link force determines the system of other forces in the immediate channel environment, which change the channel open probability. This system of forces includes components that are out of plane and in plane relative to the membrane; the magnitude and direction of these components depend on the channel environment and arrangement. Using a computational model, we obtained the major forces involved as functions of the force applied via the tip link at the center of the membrane. We simulated factors related to channels and the membrane, including finite-sized channels located centrally or acentrally, stiffness of the hypothesized channel-cytoskeleton tether, and bending modulus of the membrane. Membrane forces are perpendicular to the directions of the principal curvatures of the deformed membrane. Our approach allows for a fine vectorial picture of the local forces gating the channel; membrane forces change with the membrane curvature and are themselves sufficient to affect the open probability of the channel.
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Affiliation(s)
- Richard J Powers
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Sue Kulason
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Erdinc Atilgan
- Department of Microbiology, Columbia University, New York, New York
| | - William E Brownell
- Bobby R. Alford Department of Otolaryngology-Head & Neck Surgery, Baylor College of Medicine, Houston, Texas
| | - Sean X Sun
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Peter G Barr-Gillespie
- Oregon Hearing Research Center and Vollum Institute, Oregon Health & Science University, Portland, Oregon
| | - Alexander A Spector
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland.
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