Mark D'Esposito, Professor

Closed (1) Network mechanisms of cognitive control

Applications for Fall 2017 are now closed for this project.

Cognitive control is a cognitive capacity that allows us to adaptively regulate sensory, perceptual, motor, and other mental processes for goal-directed behaviors. A prominent theory of cognitive control suggests that the frontal cortex is the source of top-down control signals that bias lower-order brain processes to generate voluntary behaviors. However, how control signals are communicated to distribute cortical regions to enhance task-relevant and inhibit task-irrelevant processes remains much debated. The goal of this project is to understand how signal enhancement and signal inhibition are mediated through cortico-cortical and cortico-thalamo-cortical networks. This will be achieved through a combination of fMRI, TMS, and behavioral testing.

The applicant will learn how to collect and process behavioral, TMS, and fMRI data. This can include all steps from pre-processing to advanced connectivity analyses. In particular, the applicant will responsible for subject testing, imaging data collection, quality control of MRI data, and execution of MRI processing scripts. If interested, the applicant will also have the opportunity to learn advanced neuroimaging analytical methods. The ideal candidate will learn to adapt existing processing scripts, apply them to the dataset, and independently run analyses. Progress and results will be supervised and discussed frequently with the student's post-doc supervisor.

The student will be encouraged to attend weekly lab meetings, where they will have the opportunity to interact with Dr. D'Esposito and other lab members.

Day-to-day supervisor for this project: Kai Hwang, PhD, Post-Doc

Qualifications: Preferred qualifications include the following: interest in cognitive neuroscience, neuropsychology; some background or coursework in cognitive neuroscience or related discipline; organization and attention to detail; experience with programming/scripting languages, such as python or Matlab will be a plus but not required.

Weekly Hours: to be negotiated

Related website: http://despolab.berkeley.edu/

Closed (2) Reorganization of functional brain networks following brain injury

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

The brain has a remarkable ability to respond to damage (e.g. stroke, traumatic brain injury) by reorganizing its connections. By examining the particular patterns of change following injury, we can not only learn about how the brain responds to insults, but also about how the brain is organized in healthy individuals. This project uses functional Magnetic Resonance Imaging (fMRI) and graph-theory network analyses to investigate connectivity changes in a large sample of brain injury patients recruited from across the Bay Area.

Working closely with a team of graduate students and postdoctoral fellows, the apprentice will initially be responsible for using MRI scans to identify and delineate areas of damaged brain tissue. As they build their knowledge and skills, the apprentice will have the opportunity to become more involved in data analysis.

The apprentice will learn:
- How to identify and characterize damaged brain tissue in MRI images.
- Technical skills required for working with MRI and fMRI data.
- Graph-theory approaches to analyzing neuroimaging data.

The apprentice will also be encouraged to attend weekly lab meetings, where they will have the opportunity to interact with Dr. D'Esposito and other members of the D'Esposito Lab.

Day-to-day supervisor for this project: Dan Lurie, Graduate Student

Qualifications: A strong interest in cognitive neuroscience, neurology, or neuropsychology is required, as is knowledge of basic brain anatomy/physiology. Applicants should be independent, reliable, and self-directed, with strong organizational skills and good attention to detail. Preference will be given to applicants who plan to pursue advanced training in a related area (e.g. MD or PhD). Programming skills (Python in particular) and previous experience working in a Linux/Unix environment is desirable but not essential. We particularly encourage applications from members of underrepresented groups (including but not limited to women, people of color, LGBTQ individuals, and non-traditional students).

Weekly Hours: 6-9 hrs

Related website: http://despolab.berkeley.edu
Related website: http://despolab.berkeley.edu/main/files/2012_gratton1.pdf

Closed (3) Neural mechanisms of working memory

Applications for Fall 2017 are now closed for this project.

Visual working memory (VWM) is essential for our ability to maintain information about stimuli that are no longer in direct view. In daily life, we rely on this skill to maintain relevant information about objects in our environment and to facilitate action planning. Importantly, the complex neural mechanisms that support working memory are not yet well characterized, and careful research in this area is critical for understanding how the healthy human brain actively maintains precise visual information in memory.

Previous research has shown, for instance, that the lateral prefrontal cortex is involved in maintaining working memory information during distraction, while the superior parietal cortex is sensitive to the amount of information that is maintained. We don't, however, know the precise causal contributions that these regions make to temporary maintenance of internal information. The goals of this project are to identify 1) how brain networks configure and interact for working memory maintenance and distractor resistance using functional magnetic resonance imaging (fMRI), and 2) how particular network nodes are causally involved in those processes using noninvasive magnetic brain stimulation (TMS).


The student will be trained in basic neuroimaging (fMRI) and neurostimulation (TMS) techniques. The student will be responsible for training participants on a memory task, assisting with fMRI and TMS data collection, and performing basic analyses and quality control assessment of the MRI data. In addition, the student will be encouraged to contribute to the development of Matlab analysis scripts to investigate participants’ patterns of behavioral responses in the memory task. The student will gain experience with fMRI and TMS research, and develop an understanding of how current neuroimaging methods can be used to investigate questions about human memory. In addition, the student will be encouraged to attend weekly lab meetings, as well as bi-weekly working memory journal club meetings, where they will have the opportunity to interact with Dr. D'Esposito and other lab members.

Day-to-day supervisor for this project: Anastasia Kiyonaga, Post-Doc

Qualifications: A strong interest in psychology and cognitive neuroscience is requested. There is a preference for students that have completed, or will be simultaneously, taking psychology, biopsychology and/or cognitive neuroscience courses. As the student will be programming in Matlab, basic programming skills are required. Organizational skills are essential. At least three blocks of 2-3 hours a week are required to apply for this project.

Weekly Hours: to be negotiated

Related website: http://despolab.berkeley.edu
Related website: http://psychology.berkeley.edu/participant-recruitment/rsvp-research-subject-volunteer-pool

Closed (4) Brain network predictors of cognitive performance and training-related gains

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

The project examines the relationship between specific cognitive abilities and brain organization. The brain operates via networked activity in separable groups of regions called modules. Modularity is quantified as the relative number of connections between modules to connections to within modules, and thus characterizes the balance of integration and segregation in a network. We examine how graph theory metrics such as modularity can predict cognitive performance and response to interventions such as cognitive training. The student will analyze structural and functional brain scans obtained from magnetic resonance imaging (MRI).

The student will learn how to process neuroimaging data. This can include all steps from pre-processing to advanced connectivity analyses. In particular, the student will be responsible for quality control of MRI data, and execution of MRI processing scripts. The ideal candidate will learn to adapt existing processing scripts, apply them to the dataset, and independently run analyses. The student will be encouraged to contribute to writing of analysis scripts (python-based).

The student will learn general concepts of graph representation of complex network data, with specific focus on applications in functional brain imaging.

The student is encouraged to attend weekly lab meetings with Prof. D'Esposito, as well as a bi-weeky graph theory methods journal club in the lab where we read relevant publications.

Progress and results will be supervised and discussed frequently with the student's post-doc supervisor (Pauline Baniqued) and graduate fellow supervisor (Courtney Gallen).

Day-to-day supervisor for this project: Pauline Baniqued, Post-Doc

Qualifications: An interest in cognitive neuroscience or neuropsychology, with some background or coursework in cognitive neuroscience is desired. The student must be reliable, independent, and have strong organizational skills and attention to detail. Experience with programming languages, such as python is preferred. Previous experience working in a Linux/Unix environment is desirable but not essential.

Weekly Hours: 6-9 hrs

Related website: http://despolab.berkeley.edu