Author: Roeland Hancock

OVPR REP Awarded to BIRC Faculty!

Congratulations to Robert Astur (PI) and Fumiko Hoeft (co-PI) on their Research Excellence Program (REP) award for Using Transcranial Magnetic Stimulation to Reduce Problematic Cannabis Use in Undergraduates! Their project will test whether cravings and real-life use of cannabis can be reduced using TMS in UConn undergraduates who are at risk for cannabis use disorder.


Congratulations to BIRC Seed Grant Recipients

In Spring 2020, BIRC awarded four seed grants, including two student/trainee grants. Congratulations to:

  • Postdoc Airey Lau (Psychological Sciences) and faculty supervisors Devin Kearns (Education) and Fumiko Hoeft (Psychological Sciences) for Intervention For Students With Reading And Math Disabilities: The Unique Case Of Comorbidity
  • Graduate student Sahil Luthra (Psychological Sciences) and faculty supervisors Emily Myers (Speech, Language & Hearing Sciences) and James Magnuson (Psychological Sciences) for Hemispheric Organization Underlying Models of Speech Sounds and Talkers
  • Natalie Shook (PI, Nursing) and Fumiko Hoeft (Co-I, Psychological Sciences) for Identifying Neural Pathways Implicated in Older Adults’ Emotional Well-being
  • William Snyder (PIm Linguistics) for Adult processing of late-to-develop syntactic structures: An fMRI study

BIRC provides seed grants to facilitate the future development of external grant applications. Seed grants are provided in the form of a limited number of allocated hours on MRI, EEG and/or TMS equipment at BIRC. These hours are intended to enable investigators to demonstrate feasibility, develop scientific and technical expertise, establish collaborations, and, secondarily, publish in peer-reviewed journals. Seed grants are intended for investigators with experience in the proposed methods, as well as those with little or no experience who have developed a collaborative plan to acquire such experience. New investigators are encouraged to consult with BIRC leadership early in the development of their project. For more information about the program, please visit our seed grant page.

IBRAiN students publish in Nature

Congratulations to two teams of IBRAiN (IBACS-BIRC Research Assistantships in Neuroimaging) graduate students (Yanina Prystauka, Emily Yearling, and Xu Zhang; Charles Davis and Monica Li) on their contribution to a recent study that examined variability in the analysis of neuroimaging data.

The scientific process involves many steps, such as developing a theory, creating hypotheses, collecting data and analyzing the data. Each of these steps can potentially affect the final conclusions, but to what extent? For example, will different researchers reach different conclusions based on the same data and hypotheses? In the Neuroimaging Analysis, Replication and Prediction Study (NARPS) almost 200 researchers (from fields including neuroscience, psychology, statistics, and economics) teamed up to estimate how variable the findings of brain imaging research are as a result of researchers’ choices about how to analyze the data. The project was spearheaded by Dr. Rotem Botvinik-Nezer (formerly a PhD student at Tel Aviv University and now a postdoctoral researcher at Dartmouth College) and her mentor Dr. Tom Schonberg from Tel Aviv University, along with Dr. Russell Poldrack from Stanford University.

First, a brain imaging dataset was collected from 108 participants performing a monetary decision-making task at the Strauss imaging center at Tel Aviv University. The data were then distributed to 70 analysis teams from across the world. Each team independently analyzed the same data, using their standard analysis methods to test the same 9 pre-defined hypotheses. Each of these hypotheses asked whether activity in a particular part of the brain would change in relation to some aspect of the decisions that the participants made, such as how much money they could win or lose on each decision.

The analysis teams were given up to 3 months to analyze the data, after which they reported both final outcomes for the hypotheses as well as detailed information on the way they analyzed the data and intermediate statistical results. The fraction of analysis teams reporting a statistically significant outcome for each hypothesis varied substantially; for 5 of the hypotheses there was substantial disagreement, with 20-40% of the analysis teams reporting a statistically significant result. The other 4 hypotheses showed more consistency across analysis teams. Interestingly, the underlying statistical brain maps were more similar across analysis teams than expected based on the diverse results from the final hypothesis tests. Hence, even very similar intermediate results led to different outcomes across analysis teams. In addition, a meta-analysis (combining data across experiments, or in this case across analysis teams, in order to analyze them together) that was performed on the analysis teams’ intermediate results showed convergence across teams for most hypotheses. The data did not allow testing of all of the factors related to variability, but some aspects of the analysis procedures were found to lead to more or fewer positive results.

A group of leading economists and behavioral finance experts provided the initial impetus for the project and led the prediction market part of the project: Dr. Juergen Huber, Dr. Michael Kirchler and Dr. Felix Holzmeister from the University of Innsbruck, Dr. Anna Dreber and Dr. Magnus Johannesson from the Stockholm School of Economics and Dr. Colin Camerer from the California Institute of Technology. Prediction markets are a tool that provides participants with real money they can then invest in a market – in this case, a market for the outcomes on each of the nine tested scientific hypotheses. Here, they were used to test whether researchers in the field could predict the results. The prediction markets revealed that researchers in the field were over-optimistic regarding the likelihood of significant results, even if they had analyzed the data themselves.

The researchers emphasize the importance of transparency and data and code sharing, and indeed all analyses in this paper are fully reproducible with openly available computer code and data.

The results of NARPS show for the first time that there is considerable variance when the same complex neuroimaging dataset is analyzed with different analysis pipelines to test the same hypotheses. This should certainly raise awareness for members of the neuroimaging research community, as well as for every other field with complex analysis procedures where researchers have to make many choices about how to analyze the data. However, at the same time, the findings that the underlying statistical maps are relatively consistent across groups, and meta-analyses led to more convergent results, suggest ways to improve research.

Importantly, the fact that almost 200 individuals were each willing to put tens or hundreds of hours into such a critical self-assessment demonstrates the strong dedication of scientists in this field to assessing and improving the quality of data analyses.

The scientific community aims constantly to gain knowledge about human behavior and the physical world. Studying such complex processes frequently requires complex methods, big data and complex analyses. The variability in outcomes demonstrated in this study is an inherent part of the complex process of obtaining scientific results, and we must understand it in order to know how to tackle it. As the recent COVID-19 pandemic made clear, even when taking into account the uncertainty inherent to the scientific process, there is no substitute for the self-correcting scientific method to allow the global human society to address the challenges we are facing.


The CT Institute for the Brain and Cognitive Sciences (IBaCS) offers IBACS-BIRC Research Assistantships in Neuroimaging (IBRAiN). After formal training, IBRAiN fellows provide a teaching resource to help BIRC users design and implement experimental procedures for fMRI, EEG, TMS and other methodologies, provide resources for data analysis, and oversee use of equipment by others. Click here for more information about applying to this program.

Call for InCHIP-BIRC Seed Grants

UConn’s Institute for Collaboration on Health, Intervention, and Policy (InCHIP) and the UConn Brain Imaging Research Center (BIRC) are co-sponsoring a seed grant funding opportunity for faculty at UConn Storrs, UConn Health, and the regional campuses. This grant is designed to fund an innovative pilot project that will directly support an external grant application in health behavior or health policy with a substantial neuroimaging component. Health is broadly defined and includes physical and mental health and outcomes with critical implications for health. The funded pilot project must involve neuroimaging- related research that includes MRI, TMS, tDCS/tACS, and/or EEG usage at BIRC.

One seed grant of $30,000 is available through this seed grant competition. Funding in the amount of $15,000 (half of the seed grant) will be provided in the form of a limited number of allocated hours on MRI, EEG, and/or TMS equipment at BIRC. The remaining $15,000 may be used to fund other research costs associated with the pilot project.

This funding is intended to enable investigators to demonstrate feasibility, develop scientific and technical expertise, establish collaborations, and, secondarily, publish in peer-reviewed journals.

One of the goals of this funding mechanism is to encourage incorporation of BIRC’s neuroimaging services into the pilot project. Therefore, at least one of the Principle Investigators on the seed grant application must be new to BIRC and not have previously utilized BIRC’s neuroimaging services.

Investigators with experience in neuroimaging methods, as well as those with little or no experience may apply for this grant, but those with limited experience must include a collaborative plan for how they will acquire such experience.

An external grant application should be submitted through InCHIP within one year of completing the pilot project.

Key Dates

  • Friday, February 15, 2019 FOA posted
  • Monday, April 8, 2019 12-12:45pm optional webinar (email to reserve)
  • Friday, April 26, 2019 Letters of Intent (required) due by 11:59 PM EST
  • Friday, May 3, 2019 Applicants notified of LOI approval decision
  • Friday, May 17, 2019 InCHIP Affiliate Application due by 11:59 PM EST
  • Friday, May 31, 2019 Full Proposals due by 11:59 PM EST
  • June 2019 Applicants notified of award decision
  • July 1, 2019 – June 30, 2021 Award period

To Apply

Click here for the full FOA and application form.

Tips You Won’t Get at Grant Writing Courses, 10/3 1:30-2:30pm

The second BIRC Speaker Series talk of the term will be this Wednesday 10/3 1:30-2:30 in Arjona 307. Please join us for this talk and the complete series!

Tips You Won’t Get at Grant Writing Courses: Presentation and discussions on NIH, NSF, foundation grants and philanthropy
with speakers Emily Myers PhD, James Magnuson PhD, Fumiko Hoeft MD PhD, and Rachel Marshall and Louis Bach from UConn Foundation.

Vistors from UCHC are encourage to use the UCHC-Storrs shuttle service. Talks can also be joined remotely.

Talk: Neuroimaging Markers of Cognitive Reserve and Brain Aging

Lihong Wang PhD
UCONN Health, Dept of Psychiatry
Wednesday September 5, 2018 1:30-2:30pm in Arjona 307


Our brain can reorganize its function and neural resources to counteract neural damages. The ability of reorganization of brain function depends on cognitive reserve capacity. To examine dynamic changes of cognitive reserve over time, we developed a new measure for evaluating neural compensatory capacity, a core factor of cognitive reserve, using independent component analysis and a cognitively very challenging task in older adults. Interestingly, we find higher neural compensatory capacity to be related to working memory function. In another study, we show a one-month physical exercise training to improve working memory as well as neural compensatory capacity through activating addition neural networks, i.e., the cerebellar and motor cortex. We believe the new measure on neural compensatory capacity can be applied to broad lines of research on neuroplasticity. Other imaging markers related to brain aging and cognitive decline will also be discussed.


Dr. Wang obtained her Ph.D. degree in neurology from Japan and has six years of experience as a neurologist in China. She has performed neuroimaging-related research in depression at Duke University for over 12 years, primarily focused on geriatric depression and cognitive neuroscience. Her recent research centers on neural signatures of depression vulnerability and neural plasticity in patients with late-life depression and mild cognitive decline.

Vistors from UCHC are encourage to use the UCHC-Storrs shuttle service. Talks can also be joined remotely. Please contact us if you are interested in meeting with the speaker.

Download and post a flyer in your area.

Talk: Stephen Wilson 4/4 3:30

Stephen Wilson, PhD

Wednesday, April 4, 2018

Imaging the language network: functional neuroanatomy, acquired aphasia, and recovery

What is the functional architecture of the language network? How is it impacted by damage to its various nodes and connections? And when it is damaged, how can it reorganize to support recovery of language function? To address these questions, we have carried out a series of multimodal neuroimaging studies in individuals with acquired language deficits of diverse etiologies–stroke, neurodegenerative disease, and resective surgery–as well as neurologically normal volunteers. Our findings, along with those of others, reveal a complex, variegated language network in which numerous distinct regions and tracts in the temporal, frontal and parietal lobes play distinct functional roles. Yet the network is strikingly resilient to most patterns of damage, indicating that in many cases, functional specialization is graded rather than absolute. Our findings suggest that recovery from aphasia depends primarily on reconfiguration of spared language regions, rather than macroscopic reorganization of the whole system.

Talk: Evelina Fedorenko 3/28 3:30pm

Evelina Fedorenko, PhD
Assistant Professor
Harvard Medical School and Massachusetts General Hospital

Wednesday March 28, 2018
3:30-5:00pm, BOUS A106

The cognitive and neural architecture of the human language system

Brain regions that support high-level language processing are strikingly selective. This selectivity rules out a few prominent hypotheses — e.g., that left frontal lobe structures support language via domain-general executive processes, or that language relies on an abstract syntactic processing mechanism shared by other domains — but leaves open the nature of the exact computations that the language system supports. I will discuss three lines of work that, in tandem, suggest that the language network is fundamentally concerned with meaning, including both the processing of individual word meanings and semantic composition.
First, both lexical and combinatorial processing elicit robust responses throughout the fronto-temporal language network (e.g., Fedorenko et al., 2010; Blank et al., 2016). Further, some language regions show stronger responses to lexico-semantic processing and represent lexico-semantic information more robustly than structural information (Fedorenko et al., 2012), but no regions show the opposite pattern. In recent work (Mollica et al., in prep.), we further found that stimuli that are not well-formed but interpretable elicit as strong a response as intact sentences, in line with current sentence comprehension models whereby our interpretation mechanisms are robust to signal corruption (e.g., Levy et al., 2009; Gibson et al., 2013).
Second, intracranial recordings from the surface of the human brain show that neural activity, indexed by gamma power, increases monotonically over the course of a sentence across the language system (Fedorenko et al., 2016). Having ruled out a number of alternative explanations of this effect in terms of generic attention, working memory, and cognitive load, we argue that the most likely explanation is that this response increase reflects the increasing complexity of the evolving representation of the sentence meaning and is thus a candidate neural marker of complex meaning construction.
And third, we have recently developed a new approach for decoding linguistic meanings from the brain (Pereira et al., in press), based on a procedure for broadly sampling a semantic space constructed from massive text corpora. After the system was trained on imaging data of individual concepts, it could decode sentences from a wide variety of topics. These decoded representations were sufficiently detailed to distinguish even semantically similar sentences. Thus, we established the viability of using distributed semantic representations to probe meaning representations in the brain, laying a foundation for future development and evaluation of precise hypotheses about how concepts are represented and combined.
We encourage members of our MRI community, including students, to schedule a meeting; please email with your availability.