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Accessing the viscera: Technologies for interoception research

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Current Opinion in Neurobiology 2025年 93卷 103050-103050页

作者： Pang, Karen K.L. Mondal, Rajib Sahasrabudhe, Atharva Anikeeva, Polina Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States K. Lisa Yang Brain-Body Center Massachusetts Institute of Technology United States McGovern Institute for Brain Research Massachusetts Institute of Technology United States Research Laboratory of Electronics Massachusetts Institute of Technology United States MIT-Harvard Graduate Program in Health Sciences and Technology United States Department of Materials Science and Engineering Massachusetts Institute of Technology United States

Interoception, or the perception and regulation of body signals by the central nervous system, is critical for maintaining homeostasis and coordination of behaviors. Deciphering the mechanisms of interoception requires identifying pathways and decoding of diverse signals across the brain-body axis. These studies are enabled by tools to modulate and record physiological processes in the brain and visceral organs. While numerous advanced neurotechnologies are well-established in the brain, these techniques often offer limited utility for other organs, such as the gastrointestinal tract, heart, liver, or bladder. In this review, we highlight recent advances in technologies for recording and modulation of visceral organ physiology in small animals in vivo, with a focus on implantable bioelectronic organ interfaces that can be deployed in behaving animals. We discuss how such interfaces are made possible through innovations in materials and electronics and outline unmet technological challenges in interoception research. © 2025 Elsevier Ltd

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Family dementia caregivers with suicidal ideation improve with mentalizing imagery therapy: Results from a pilot study

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Journal of Affective Disorders Reports 2024年 16卷 100721-100721页

作者： Madarasmi, Saira Gutierrez-Ramirez, Paulina Barsoum, Nader Banerjee, Sreya Gomez, Liliana Ramirez Melero-Dominguez, Maria Gitlin, Laura N. Pederson, Aderonke Liu, Richard T. Jain, Felipe A. Department of Psychiatry Depression Clinical and Research Program Massachusetts General Hospital Boston MA United States Harvard Medical School Boston MA United States Department of Neurology Massachusetts General Hospital Boston MA United States College of Nursing and Health Professions Drexel University Philadelphia PA United States Harvard-Massachusetts Institute of Technology (MIT) Division of Health Sciences and Technology Boston MA United States

Background: Family caregivers of persons living with dementia often experience increased depression and suicidal ideation (SI). However, the feasibility and impact of therapies on caregiver SI has remained largely unexplored. Mentalizing imagery therapy (mit) helps reduce psychological symptoms through mindfulness and guided imagery. This pilot study examined the feasibility of participation by caregivers with SI in a randomized controlled trial (RCT) of mit versus a psychosocial support group (SG), and the respective impact of group on SI, depression, and secondary outcomes. Methods: A secondary analysis of data from an RCT of 4-week mit or SG for caregivers (n = 46) was performed, identifying SI (n = 23) and non-SI (n = 23) cohorts. Group attendance and home practice were compared between cohorts. In the SI cohort (total n = 23, mit n = 11, SG n = 12), group differences in SI, depression, and secondary outcomes were evaluated post-group and at 4-month follow-up. Results: Attendance in both groups and home practice in mit were similar between SI and non-SI cohorts. In the SI cohort, mit evinced greater improvements relative to SG in SI (p=.02) and depression (p=.02) post-group, and other secondary outcomes at follow-up. Limitations: Limitations include small sample size and single-item assessments of SI from validated depression rating scales. Conclusions: Participation in an RCT was feasible for caregivers with SI. mit resulted in important benefits for SI and depression, while SG showed no acute SI benefit. The role of mit in improving SI should be confirmed with adequately powered trials, as effective therapies to address caregiver SI are critical. © 2024 The Author(s)

关键词： Dementia Depression Family caregivers Guided imagery Mindfulness Suicidal ideation

Molecular-driven Foundation Model for Oncologic Pathology

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arXiv 2025年

作者： Vaidya, Anurag Zhang, Andrew Jaume, Guillaume Song, Andrew H. Ding, Tong Wagner, Sophia J. Lu, Ming Y. Doucet, Paul Robertson, Harry Almagro-Pérez, Cristina Chen, Richard J. ElHarouni, Dina Ayoub, Georges Bossi, Connor Ligon, Keith L. Gerber, Georg Le, Long Phi Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich – German Research Center for Environment and Health Munich Germany School of Computation Information and Technology TUM Munich Germany CambridgeMA United States Sydney Precision Data Science Center The University of Sydney CamperdownNSW Australia Department of Oncologic Pathology Dana-Farber Cancer Institute BostonMA02215 United States Department of Pathology Boston Children’s Hospital BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction and survival prediction THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community. © 2025, CC BY-NC-ND.

关键词： Transfer learning

A General-Purpose Self-Supervised Model for Computational Pathology

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arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

Detecting QT prolongation From a Single-lead ECG With Deep Learning

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arXiv 2023年

作者： Alam, Ridwan Aguirre, Aaron D. Stultz, Collin M. Research Laboratory of Electronics MIT CambridgeMA United States Computer Science & Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Wellman Center for Photomedicine Massachusetts General Hospital BostonMA United States Division of Cardiology Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Background and Aims For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Methods Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. Results QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Conclusions Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies. © 2023, CC BY-NC-ND.

关键词： Electrocardiograms

BPS2025 - Time-resolved high-pressure freezing for dynamic, nanoscale imaging

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Biophysical Journal 2025年第3期124卷 312a-312a页

作者： Ami U. Thakrar Daphne-Eleni Archonta Maxim B. Prigozhin Harvard-MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA Department of Molecular and Cellular Biology Harvard University Cambridge MA USA Bioengineering Program Harvard University Cambridge MA USA Applied Physics Program Harvard University Cambridge MA USA

Towards a Visual-Language Foundation Model for Computational Pathology

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

BPS2025 - VIS-À-VIS: Visualization and vitrification of sub-second cellular dynamics

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Biophysical Journal 2025年第3期124卷 172a-172a页

作者： Daphne-Eleni Archonta Ami U. Thakrar Maxim B. Prigozhin Harvard John A. Paulson School Of Engineering and Applied Sciences Harvard University Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA Department of Molecular and Cellular Biology Harvard University Cambridge MA USA

Multimodal Whole Slide Foundation Model for Pathology

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning