RESEARCH
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RESEARCH
Research
Executing successful movements requires the brain to predict the consequences of actions. It is believed that the brain builds internal models of our body and the environment in order to simulate the sensory and motor outcomes of movements.
Due to the constant changes in our body and environment (for instance, those due to fatigue, tool-use, or disease) these models require constant re-calibration, called motor adaptation, to keep us moving in predictable ways.
Where in the brain these models reside, how they are formed, and how they are updated following bodily or environmental changes remains unclear.
The goal of the laboratory is to reverse engineer the neural circuits that drive adaptive motor behavior. We hope that by understanding the neural basis of adaptive motor control we can open new avenues in therapeutic research for neurological disease and provide fundamental insights into brain function.
Novel mouse paradigms for studying adaptive motor control
We believe behavior is an essential component to understanding neural function.
We have developed a set of skilled motor tasks where mice can learn from a dynamically changing sensory landscape. By combining concepts from optimal motor control with the power of the mouse's genetics and accessibility, our lab aims to uncover fundamental principles that guide motor adaptation, learning, and control.
Technology
We are using and building on the latest techniques in 2-photon and deep brain imaging (including utilizing multi-area imaging with a 2-photon mesoscope), to uncover the neural correlates of adaptive behavior. We use optogenetics and chemogenetics to test what roles diverse areas have during behavior. Furthermore, we develop computational models and tools to generate testable hypotheses and analyze our data. Lastly, we aim to develop new robotic tools for the mouse community.
Team
Team
People
Mackenzie Mathis, PhD
Principal Investigator
mathis@rowland.harvard.edu
Office Location: 3rd Floor, 308
Google Scholar
I received my BSc from the University of Oregon, then moved to NYC to work in the labs of Hynek Wichterle and Christopher Henderson at Columbia University to build in vitro models of ALS. I then attended Harvard University for my PhD, where I worked in the laboratory of Nao Uchida investigating the role of reward and sensory prediction errors in guiding motor learning. Before starting at the Rowland, I was a postdoctoral fellow in the group of Matthias Bethge (University of Tübingen).
In the News: RJF position, Peralta Prize, NSF Fellowship
Tanmay completed his PhD focused on developing machine learning tools for biological systems. He joined the Mathis Lab in January of 2018.
Tanmay Nath, PhD
Postdoctoral Fellow - Data Scientist
nath@rowland.harvard.edu
Office location: 3rd Floor, 309
Alexander is our "in house" collaborator. He collaborates closely with us on computational models of motor adaptation and learning. Alexander studied Mathematics at the Ludwig Maximilians University in Munich. For his dissertation he derived properties of grid cells from optimal coding principles. As a Marie-Curie Fellow, he works on trail tracking in mice with Venki Murthy (MCB, Harvard) and Matthias Bethge (University of Tübingen). He strives to understand how internal representations guide behavior and how internal models are implemented in the brain.
Alexander Mathis, PhD
Lab Mathematician
Formally: a postdoc in Murthy and Bethge labs
amathis@fas.harvard.edu
Office Location: 3rd Floor, 310
Publications & website
Adrian is completing his master's thesis in the lab through the University of Tübingen. He received a BS in Physics from Heidelberg University.
Adrian Hoffmann
Masters student -University of Tübingen, Neural Information Processing
hoffmann@rowland.harvard.edu
Eric joined the Rowland in the fall of 2017, and provides technical support to several Fellows labs.
Eric Hepler
Animal Technician
Collaborators:
Matthias Bethge, PhD
Bethgelab.org
Travis DeWolf, PhD
Applied Brain Research - Publications & Blog
Nicolas Renier, PhD
Group leader at Institut du Cerveau et de la Moelle épinière- Publications & lab website
Alumni:
Melody Tong - Undergraduate Researcher|Harvard College Class of '18
- Melody was co-mentored by Mackenzie and Nao Uchida. Her thesis work was focused on characterizing a rapidly learned freely-moving reaching & pulling task in mice. She will be attending medical school in the fall of 2018!
News
News
News
April 2018
Our first preprint from the lab, in collaboration with Matthias Bethge, is up on arxiv (check out our abstract page)
March 2018
Congratulations to Melody Tong on completing her senior thesis!
Adrian Hoffmann (masters student) joins the lab!
Jan 2018
Tanmay Nath, PhD (Postdoctoral fellow) joins the lab!
Dec 2017
NVIDIA GPU Grant awarded to the lab! We thank NVIDIA Corporation for supporting our research.
September 1st, 2017
The lab doors are open!
August 2017
Our very talented friend, Taiga Abe, who completed his Harvard College thesis (Analysis and modeling of movement kinematics in a mouse model of motor adaptation) with Mackenzie, Alexander, and Nao, started his PhD graduate studies at Columbia University today! Congratulations!
May 2017
We will be presenting new work at NCMDub this week! Stop by our poster cluster to learn more about our past and future work.
Our paper is out today in Neuron!
Somatosensory Cortex Plays an Essential Role in Forelimb Motor Adaptation in Mice
March 22, 2017
March 2017
L to R: Prof. Nao Uchida, Dr. Mackenzie Mathis, Prof. Venki Murthy
Ph.D. successfully defended!
Here is a story from MCB Harvard about the new lab:
MCO GRADUATE TO OPEN LAB THROUGH THE ROWLAND INSTITUTE AT HARVARD
Feb 2017
First motor behavior paper accepted - stay tuned!
January 2017
Please consider joining! http://www2.rowland.harvard.edu/employment
come see our poster at COSYNE 2017!
Somatosensory cortex plays an essential role in forelimb motor adaptation in mice
Mathis, M.W., Mathis, A., Uchida, N. (2017). Cosyne Abstracts 2017, Salt Lake City USA
It's official! The lab will open at the Rowland Sept 1st!
December 2016
Funding
Funding
Funding:
We gratefully acknowledge the funding sources that make our research possible:
Current:
2017 - '22 | Rowland Fellowship to M. Mathis
2018 |NVIDIA GPU Grant Award
Past:
2017 | WATB/Project ALS - Postdoctoral Fellowship to M. Mathis
