College of Liberal Arts
The overarching goal of this research is to investigate how psychological constructs and constellations of symptoms may be represented in the brain and reproducible across groups of individuals, which is a keystone element for furthering understanding on the mechanisms underlying mental illness. To serve this goal the researchers make use of publicly available large-scale fMRI datasets as well as neuroimaging datasets collected by their own group. There are three specific projects underway in this group:
- Decoding individual differences with resting-state fMRI data. Work on the Human Connectome Project dataset, which consists of neuroimaging data from more than 1,000 subjects, has continued through the past year. The researchers completed a study on the measurement properties of resting-state fMRI data. The next step is a collaboration with Dr. Nathaniel Helwig from the statistics department to validate a novel algorithm for predicting psychotic-like experiences in healthy adults with the HCP dataset.
- In a close collaboration with Dr. Scott Sponheim, the researchers are working to identify abnormal brain connectivity in probands with psychosis and their first-degree relatives. They are preprocessing this data with the advance Human Connectome Project preprocessing pipelines.
- A new project investigates people with delusions in three datasets, including one publically available large-scale fMRI datasets known as INDI-1000 and two task fMRI datasets.
In 2020, the researchers continue to investigate the datasets described above and download new data from these publicly available datasets as they are released. They will continue to advance their knowledge of ways to understand how brain connectivity in psychiatric samples differ from healthy subjects, and to examine how intrinsic connectivity may be important to individual differences in behavior. Collective insights from these two perspectives will advance a growing understanding of the underlying structure of personality and psychopathology and how this may be represented in the neural signals of the brain. Ultimately, this work could lead to the derivation of biomarkers that could be further examined for their utility in providing earlier detection rates of psychiatric illness, and thus earlier treatment, for these disability conditions.