Alzheimer’s

Subterms

    Latest story

    63 Shares129 Views

    Machine learning and the microscope

    by

    With recent advances in imaging, genomics and other technologies, the life sciences are awash in data. If a biologist is studying cells taken from the brain tissue of Alzheimer’s patients, for example, there could be any number of characteristics they want to investigate — a cell’s type, the genes it’s expressing, its location within the tissue, or more. However, while cells can now be probed experimentally using different kinds of measurements simultaneously, when it comes to analyzing the data, scientists usually can only work with one type of measurement at a time.Working with “multimodal” data, as it’s called, requires new computational tools, which is where Xinyi Zhang comes in.The fourth-year MIT PhD student is bridging machine learning and biology to understand fundamental biological principles, especially in areas where conventional methods have hit limitations. Working in the lab of MIT Professor Caroline Uhler in the Department of Electrical Engineering and Computer Science, the Laboratory for Information and Decision Systems, and the Institute for Data, Systems, and Society, and collaborating with researchers at the Eric and Wendy Schmidt Center at the Broad Institute and elsewhere, Zhang has led multiple efforts to build computational frameworks and principles for understanding the regulatory mechanisms of cells.“All of these are small steps toward the end goal of trying to answer how cells work, how tissues and organs work, why they have disease, and why they can sometimes be cured and sometimes not,” Zhang says.The activities Zhang pursues in her down time are no less ambitious. The list of hobbies she has taken up at the Institute include sailing, skiing, ice skating, rock climbing, performing with MIT’s Concert Choir, and flying single-engine planes. (She earned her pilot’s license in November 2022.)“I guess I like to go to places I’ve never been and do things I haven’t done before,” she says with signature understatement.Uhler, her advisor, says that Zhang’s quiet humility leads to a surprise “in every conversation.”“Every time, you learn something like, ‘Okay, so now she’s learning to fly,’” Uhler says. “It’s just amazing. Anything she does, she does for the right reasons. She wants to be good at the things she cares about, which I think is really exciting.”Zhang first became interested in biology as a high school student in Hangzhou, China. She liked that her teachers couldn’t answer her questions in biology class, which led her to see it as the “most interesting” topic to study.Her interest in biology eventually turned into an interest in bioengineering. After her parents, who were middle school teachers, suggested studying in the United States, she majored in the latter alongside electrical engineering and computer science as an undergraduate at the University of California at Berkeley.Zhang was ready to dive straight into MIT’s EECS PhD program after graduating in 2020, but the Covid-19 pandemic delayed her first year. Despite that, in December 2022, she, Uhler, and two other co-authors published a paper in Nature Communications.The groundwork for the paper was laid by Xiao Wang, one of the co-authors. She had previously done work with the Broad Institute in developing a form of spatial cell analysis that combined multiple forms of cell imaging and gene expression for the same cell while also mapping out the cell’s place in the tissue sample it came from — something that had never been done before.This innovation had many potential applications, including enabling new ways of tracking the progression of various diseases, but there was no way to analyze all the multimodal data the method produced. In came Zhang, who became interested in designing a computational method that could.The team focused on chromatin staining as their imaging method of choice, which is relatively cheap but still reveals a great deal of information about cells. The next step was integrating the spatial analysis techniques developed by Wang, and to do that, Zhang began designing an autoencoder.Autoencoders are a type of neural network that typically encodes and shrinks large amounts of high-dimensional data, then expand the transformed data back to its original size. In this case, Zhang’s autoencoder did the reverse, taking the input data and making it higher-dimensional. This allowed them to combine data from different animals and remove technical variations that were not due to meaningful biological differences.In the paper, they used this technology, abbreviated as STACI, to identify how cells and tissues reveal the progression of Alzheimer’s disease when observed under a number of spatial and imaging techniques. The model can also be used to analyze any number of diseases, Zhang says.Given unlimited time and resources, her dream would be to build a fully complete model of human life. Unfortunately, both time and resources are limited. Her ambition isn’t, however, and she says she wants to keep applying her skills to solve the “most challenging questions that we don’t have the tools to answer.”She’s currently working on wrapping up a couple of projects, one focused on studying neurodegeneration by analyzing frontal cortex imaging and another on predicting protein images from protein sequences and chromatin imaging.“There are still many unanswered questions,” she says. “I want to pick questions that are biologically meaningful, that help us understand things we didn’t know before.” More

    More stories

    • 150 Shares109 Views

      in Data Management & Statistics

      2023-24 Takeda Fellows: Advancing research at the intersection of AI and health

      by

      The School of Engineering has selected 13 new Takeda Fellows for the 2023-24 academic year. With support from Takeda, the graduate students will conduct pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

      Now in its fourth year, the MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

      The 2023-24 Takeda Fellows are:

      Adam Gierlach

      Adam Gierlach is a PhD candidate in the Department of Electrical Engineering and Computer Science. Gierlach’s work combines innovative biotechnology with machine learning to create ingestible devices for advanced diagnostics and delivery of therapeutics. In his previous work, Gierlach developed a non-invasive, ingestible device for long-term gastric recordings in free-moving patients. With the support of a Takeda Fellowship, he will build on this pathbreaking work by developing smart, energy-efficient, ingestible devices powered by application-specific integrated circuits for at-home, long-term diagnostics. These revolutionary devices — capable of identifying, characterizing, and even correcting gastrointestinal diseases — represent the leading edge of biotechnology. Gierlach’s innovative contributions will help to advance fundamental research on the enteric nervous system and help develop a better understanding of gut-brain axis dysfunctions in Parkinson’s disease, autism spectrum disorder, and other prevalent disorders and conditions.

      Vivek Gopalakrishnan

      Vivek Gopalakrishnan is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnan’s goal is to develop biomedical machine-learning methods to improve the study and treatment of human disease. Specifically, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, offering a safe alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that deliver high-quality, 3D intraoperative image guidance by extracting and fusing information from multimodal neuroimaging data. These algorithms could allow surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

      Hao He

      Hao He is a PhD candidate in the Department of Electrical Engineering and Computer Science. His research interests lie at the intersection of generative AI, machine learning, and their applications in medicine and human health, with a particular emphasis on passive, continuous, remote health monitoring to support virtual clinical trials and health-care management. More specifically, He aims to develop trustworthy AI models that promote equitable access and deliver fair performance independent of race, gender, and age. In his past work, He has developed monitoring systems applied in clinical studies of Parkinson’s disease, Alzheimer’s disease, and epilepsy. Supported by a Takeda Fellowship, He will develop a novel technology for the passive monitoring of sleep stages (using radio signaling) that seeks to address existing gaps in performance across different demographic groups. His project will tackle the problem of imbalance in available datasets and account for intrinsic differences across subpopulations, using generative AI and multi-modality/multi-domain learning, with the goal of learning robust features that are invariant to different subpopulations. He’s work holds great promise for delivering advanced, equitable health-care services to all people and could significantly impact health care and AI.

      Chengyi Long

      Chengyi Long is a PhD candidate in the Department of Civil and Environmental Engineering. Long’s interdisciplinary research integrates the methodology of physics, mathematics, and computer science to investigate questions in ecology. Specifically, Long is developing a series of potentially groundbreaking techniques to explain and predict the temporal dynamics of ecological systems, including human microbiota, which are essential subjects in health and medical research. His current work, supported by a Takeda Fellowship, is focused on developing a conceptual, mathematical, and practical framework to understand the interplay between external perturbations and internal community dynamics in microbial systems, which may serve as a key step toward finding bio solutions to health management. A broader perspective of his research is to develop AI-assisted platforms to anticipate the changing behavior of microbial systems, which may help to differentiate between healthy and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel methods to address these issues, Long’s research has the potential to offer powerful contributions to medicine and global health.

      Omar Mohd

      Omar Mohd is a PhD candidate in the Department of Electrical Engineering and Computer Science. Mohd’s research is focused on developing new technologies for the spatial profiling of microRNAs, with potentially important applications in cancer research. Through innovative combinations of micro-technologies and AI-enabled image analysis to measure the spatial variations of microRNAs within tissue samples, Mohd hopes to gain new insights into drug resistance in cancer. This work, supported by a Takeda Fellowship, falls within the emerging field of spatial transcriptomics, which seeks to understand cancer and other diseases by examining the relative locations of cells and their contents within tissues. The ultimate goal of Mohd’s current project is to find multidimensional patterns in tissues that may have prognostic value for cancer patients. One valuable component of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Center and Harvard Medical School to auto-detect cancer epithelial cells from other cell types in a tissue sample and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making innovative contributions at the interface of microsystem technology, AI-based image analysis, and cancer treatment, which could significantly impact medicine and human health.

      Sanghyun Park

      Sanghyun Park is a PhD candidate in the Department of Mechanical Engineering. Park specializes in the integration of AI and biomedical engineering to address complex challenges in human health. Drawing on his expertise in polymer physics, drug delivery, and rheology, his research focuses on the pioneering field of in-situ forming implants (ISFIs) for drug delivery. Supported by a Takeda Fellowship, Park is currently developing an injectable formulation designed for long-term drug delivery. The primary goal of his research is to unravel the compaction mechanism of drug particles in ISFI formulations through comprehensive modeling and in-vitro characterization studies utilizing advanced AI tools. He aims to gain a thorough understanding of this unique compaction mechanism and apply it to drug microcrystals to achieve properties optimal for long-term drug delivery. Beyond these fundamental studies, Park’s research also focuses on translating this knowledge into practical applications in a clinical setting through animal studies specifically aimed at extending drug release duration and improving mechanical properties. The innovative use of AI in developing advanced drug delivery systems, coupled with Park’s valuable insights into the compaction mechanism, could contribute to improving long-term drug delivery. This work has the potential to pave the way for effective management of chronic diseases, benefiting patients, clinicians, and the pharmaceutical industry.

      Huaiyao Peng

      Huaiyao Peng is a PhD candidate in the Department of Biological Engineering. Peng’s research interests are focused on engineered tissue, microfabrication platforms, cancer metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI techniques for the development of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC), an especially lethal and difficult-to-treat cancer, with the goal of gaining new insights into progression and effective treatments. Peng’s project, supported by a Takeda Fellowship, will be one of the first to use cells from serous tubal intraepithelial carcinoma lesions found in the fallopian tubes of many HGSOC patients. By examining the cellular and molecular changes that occur in response to treatment with small molecule inhibitors, she hopes to identify potential biomarkers and promising therapeutic targets for HGSOC, including personalized treatment options for HGSOC patients, ultimately improving their clinical outcomes. Peng’s work has the potential to bring about important advances in cancer treatment and spur innovative new applications of AI in health care. 

      Priyanka Raghavan

      Priyanka Raghavan is a PhD candidate in the Department of Chemical Engineering. Raghavan’s research interests lie at the frontier of predictive chemistry, integrating computational and experimental approaches to build powerful new predictive tools for societally important applications, including drug discovery. Specifically, Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility in regimes where little data is available (the most realistic regimes). A Takeda Fellowship will enable Raghavan to push the boundaries of her research, making innovative use of low-data and multi-task machine learning approaches, synthetic chemistry, and robotic laboratory automation, with the goal of creating an autonomous, closed-loop system for the discovery of high-yielding organic small molecules in the context of underexplored reactions. Raghavan’s work aims to identify new, versatile reactions to broaden a chemist’s synthetic toolbox with novel scaffolds and substrates that could form the basis of essential drugs. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and could help make the lengthy drug-discovery process significantly faster and cheaper.

      Zhiye Song

      Zhiye “Zoey” Song is a PhD candidate in the Department of Electrical Engineering and Computer Science. Song’s research integrates cutting-edge approaches in machine learning (ML) and hardware optimization to create next-generation, wearable medical devices. Specifically, Song is developing novel approaches for the energy-efficient implementation of ML computation in low-power medical devices, including a wearable ultrasound “patch” that captures and processes images for real-time decision-making capabilities. Her recent work, conducted in collaboration with clinicians, has centered on bladder volume monitoring; other potential applications include blood pressure monitoring, muscle diagnosis, and neuromodulation. With the support of a Takeda Fellowship, Song will build on that promising work and pursue key improvements to existing wearable device technologies, including developing low-compute and low-memory ML algorithms and low-power chips to enable ML on smart wearable devices. The technologies emerging from Song’s research could offer exciting new capabilities in health care, enabling powerful and cost-effective point-of-care diagnostics and expanding individual access to autonomous and continuous medical monitoring.

      Peiqi Wang

      Peiqi Wang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Wang’s research aims to develop machine learning methods for learning and interpretation from medical images and associated clinical data to support clinical decision-making. He is developing a multimodal representation learning approach that aligns knowledge captured in large amounts of medical image and text data to transfer this knowledge to new tasks and applications. Supported by a Takeda Fellowship, Wang will advance this promising line of work to build robust tools that interpret images, learn from sparse human feedback, and reason like doctors, with potentially major benefits to important stakeholders in health care.

      Oscar Wu

      Haoyang “Oscar” Wu is a PhD candidate in the Department of Chemical Engineering. Wu’s research integrates quantum chemistry and deep learning methods to accelerate the process of small-molecule screening in the development of new drugs. By identifying and automating reliable methods for finding transition state geometries and calculating barrier heights for new reactions, Wu’s work could make it possible to conduct the high-throughput ab initio calculations of reaction rates needed to screen the reactivity of large numbers of active pharmaceutical ingredients (APIs). A Takeda Fellowship will support his current project to: (1) develop open-source software for high-throughput quantum chemistry calculations, focusing on the reactivity of drug-like molecules, and (2) develop deep learning models that can quantitatively predict the oxidative stability of APIs. The tools and insights resulting from Wu’s research could help to transform and accelerate the drug-discovery process, offering significant benefits to the pharmaceutical and medical fields and to patients.

      Soojung Yang

      Soojung Yang is a PhD candidate in the Department of Materials Science and Engineering. Yang’s research applies cutting-edge methods in geometric deep learning and generative modeling, along with atomistic simulations, to better understand and model protein dynamics. Specifically, Yang is developing novel tools in generative AI to explore protein conformational landscapes that offer greater speed and detail than physics-based simulations at a substantially lower cost. With the support of a Takeda Fellowship, she will build upon her successful work on the reverse transformation of coarse-grained proteins to the all-atom resolution, aiming to build machine-learning models that bridge multiple size scales of protein conformation diversity (all-atom, residue-level, and domain-level). Yang’s research holds the potential to provide a powerful and widely applicable new tool for researchers who seek to understand the complex protein functions at work in human diseases and to design drugs to treat and cure those diseases.

      Yuzhe Yang

      Yuzhe Yang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Yang’s research interests lie at the intersection of machine learning and health care. In his past and current work, Yang has developed and applied innovative machine-learning models that address key challenges in disease diagnosis and tracking. His many notable achievements include the creation of one of the first machine learning-based solutions using nocturnal breathing signals to detect Parkinson’s disease (PD), estimate disease severity, and track PD progression. With the support of a Takeda Fellowship, Yang will expand this promising work to develop an AI-based diagnosis model for Alzheimer’s disease (AD) using sleep-breathing data that is significantly more reliable, flexible, and economical than current diagnostic tools. This passive, in-home, contactless monitoring system — resembling a simple home Wi-Fi router — will also enable remote disease assessment and continuous progression tracking. Yang’s groundbreaking work has the potential to advance the diagnosis and treatment of prevalent diseases like PD and AD, and it offers exciting possibilities for addressing many health challenges with reliable, affordable machine-learning tools.  More

    • 100 Shares119 Views

      in Data Management & Statistics

      Neurodegenerative disease can progress in newly identified patterns

      by

      Neurodegenerative diseases — like amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease), Alzheimer’s, and Parkinson’s — are complicated, chronic ailments that can present with a variety of symptoms, worsen at different rates, and have many underlying genetic and environmental causes, some of which are unknown. ALS, in particular, affects voluntary muscle movement and is always fatal, but while most people survive for only a few years after diagnosis, others live with the disease for decades. Manifestations of ALS can also vary significantly; often slower disease development correlates with onset in the limbs and affecting fine motor skills, while the more serious, bulbar ALS impacts swallowing, speaking, breathing, and mobility. Therefore, understanding the progression of diseases like ALS is critical to enrollment in clinical trials, analysis of potential interventions, and discovery of root causes.

      However, assessing disease evolution is far from straightforward. Current clinical studies typically assume that health declines on a downward linear trajectory on a symptom rating scale, and use these linear models to evaluate whether drugs are slowing disease progression. However, data indicate that ALS often follows nonlinear trajectories, with periods where symptoms are stable alternating with periods when they are rapidly changing. Since data can be sparse, and health assessments often rely on subjective rating metrics measured at uneven time intervals, comparisons across patient populations are difficult. These heterogenous data and progression, in turn, complicate analyses of invention effectiveness and potentially mask disease origin.

      Now, a new machine-learning method developed by researchers from MIT, IBM Research, and elsewhere aims to better characterize ALS disease progression patterns to inform clinical trial design.

      “There are groups of individuals that share progression patterns. For example, some seem to have really fast-progressing ALS and others that have slow-progressing ALS that varies over time,” says Divya Ramamoorthy PhD ’22, a research specialist at MIT and lead author of a new paper on the work that was published this month in Nature Computational Science. “The question we were asking is: can we use machine learning to identify if, and to what extent, those types of consistent patterns across individuals exist?”

      Their technique, indeed, identified discrete and robust clinical patterns in ALS progression, many of which are non-linear. Further, these disease progression subtypes were consistent across patient populations and disease metrics. The team additionally found that their method can be applied to Alzheimer’s and Parkinson’s diseases as well.

      Joining Ramamoorthy on the paper are MIT-IBM Watson AI Lab members Ernest Fraenkel, a professor in the MIT Department of Biological Engineering; Research Scientist Soumya Ghosh of IBM Research; and Principal Research Scientist Kenney Ng, also of IBM Research. Additional authors include Kristen Severson PhD ’18, a senior researcher at Microsoft Research and former member of the Watson Lab and of IBM Research; Karen Sachs PhD ’06 of Next Generation Analytics; a team of researchers with Answer ALS; Jonathan D. Glass and Christina N. Fournier of the Emory University School of Medicine; the Pooled Resource Open-Access ALS Clinical Trials Consortium; ALS/MND Natural History Consortium; Todd M. Herrington of Massachusetts General Hospital (MGH) and Harvard Medical School; and James D. Berry of MGH.

      Play video

      MIT Professor Ernest Fraenkel describes early stages of his research looking at root causes of amyotrophic lateral sclerosis (ALS).

      Reshaping health decline

      After consulting with clinicians, the team of machine learning researchers and neurologists let the data speak for itself. They designed an unsupervised machine-learning model that employed two methods: Gaussian process regression and Dirichlet process clustering. These inferred the health trajectories directly from patient data and automatically grouped similar trajectories together without prescribing the number of clusters or the shape of the curves, forming ALS progression “subtypes.” Their method incorporated prior clinical knowledge in the way of a bias for negative trajectories — consistent with expectations for neurodegenerative disease progressions — but did not assume any linearity. “We know that linearity is not reflective of what’s actually observed,” says Ng. “The methods and models that we use here were more flexible, in the sense that, they capture what was seen in the data,” without the need for expensive labeled data and prescription of parameters.

      Primarily, they applied the model to five longitudinal datasets from ALS clinical trials and observational studies. These used the gold standard to measure symptom development: the ALS functional rating scale revised (ALSFRS-R), which captures a global picture of patient neurological impairment but can be a bit of a “messy metric.” Additionally, performance on survivability probabilities, forced vital capacity (a measurement of respiratory function), and subscores of ALSFRS-R, which looks at individual bodily functions, were incorporated.

      New regimes of progression and utility

      When their population-level model was trained and tested on these metrics, four dominant patterns of disease popped out of the many trajectories — sigmoidal fast progression, stable slow progression, unstable slow progression, and unstable moderate progression — many with strong nonlinear characteristics. Notably, it captured trajectories where patients experienced a sudden loss of ability, called a functional cliff, which would significantly impact treatments, enrollment in clinical trials, and quality of life.

      The researchers compared their method against other commonly used linear and nonlinear approaches in the field to separate the contribution of clustering and linearity to the model’s accuracy. The new work outperformed them, even patient-specific models, and found that subtype patterns were consistent across measures. Impressively, when data were withheld, the model was able to interpolate missing values, and, critically, could forecast future health measures. The model could also be trained on one ALSFRS-R dataset and predict cluster membership in others, making it robust, generalizable, and accurate with scarce data. So long as 6-12 months of data were available, health trajectories could be inferred with higher confidence than conventional methods.

      The researchers’ approach also provided insights into Alzheimer’s and Parkinson’s diseases, both of which can have a range of symptom presentations and progression. For Alzheimer’s, the new technique could identify distinct disease patterns, in particular variations in the rates of conversion of mild to severe disease. The Parkinson’s analysis demonstrated a relationship between progression trajectories for off-medication scores and disease phenotypes, such as the tremor-dominant or postural instability/gait difficulty forms of Parkinson’s disease.

      The work makes significant strides to find the signal amongst the noise in the time-series of complex neurodegenerative disease. “The patterns that we see are reproducible across studies, which I don’t believe had been shown before, and that may have implications for how we subtype the [ALS] disease,” says Fraenkel. As the FDA has been considering the impact of non-linearity in clinical trial designs, the team notes that their work is particularly pertinent.

      As new ways to understand disease mechanisms come online, this model provides another tool to pick apart illnesses like ALS, Alzheimer’s, and Parkinson’s from a systems biology perspective.

      “We have a lot of molecular data from the same patients, and so our long-term goal is to see whether there are subtypes of the disease,” says Fraenkel, whose lab looks at cellular changes to understand the etiology of diseases and possible targets for cures. “One approach is to start with the symptoms … and see if people with different patterns of disease progression are also different at the molecular level. That might lead you to a therapy. Then there’s the bottom-up approach, where you start with the molecules” and try to reconstruct biological pathways that might be affected. “We’re going [to be tackling this] from both ends … and finding if something meets in the middle.”

      This research was supported, in part, by the MIT-IBM Watson AI Lab, the Muscular Dystrophy Association, Department of Veterans Affairs of Research and Development, the Department of Defense, NSF Gradate Research Fellowship Program, Siebel Scholars Fellowship, Answer ALS, the United States Army Medical Research Acquisition Activity, National Institutes of Health, and the NIH/NINDS. More