Carol Mathews
University of Florida, FL·Psychology
Psychiatric diagnoses such as obsessive-compulsive disorder (OCD) and anxiety disorders still rest mostly on interviews and self-report. There are few objective, reliable brain measures that can tell disorders apart or track how severe they are.
- Fields
Psychology
Neuroscience
Medicine & Clinical Research
- Topics
obsessive-compulsive disorder
anxiety disorders
electroencephalography
- Grants
- 2 known grants, through Oct 2028
What they work on
- Test whether a dimension from hypervigilance to perceptual avoidance, measured with visual evoked potentials and frequency-tagged steady-state EEG potentials, distinguishes obsessive-compulsive and anxiety diagnoses and predicts severity.
- Use a mechanistic computational model of perception and attention to reduce EEG data and improve the reliability of visuocortical markers in a large clinical sample.
- Extract resting-EEG measures of spectral shape and alpha-rhythm activity and establish their reliability and internal consistency, using a Bayesian hierarchical model.
- Relate resting-EEG indices to clinical data to test whether they separate diagnostic categories and predict transdiagnostic variables such as severity and comorbidity.
More about the research
Both projects use electroencephalography (EEG), which records the brain's electrical activity through scalp sensors. The goal is to build continuous, objective markers of brain function across the obsessive-compulsive and anxiety spectrum. Each project relates these markers to clinical data from a large sample of people with symptoms.
Why it matters
People with OCD and anxiety disorders could benefit from objective brain-based measures that complement interviews. Such measures might identify patients with hypervigilance versus avoidance, or with attention problems versus broader cortical slowing. In the future, these differences could help assign patients to individualized treatments, such as exposure-based therapy, and predict how well treatment will work.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
John O'Doherty
California Institute of Technology, CA·Neuroscience
Separate occasion-related noise, meaningful state changes and stable trait differences in computational parameters by administering a computational assessment battery repeatedly over one year online and fitting hierarchical Bayesian models.
Edmund Lalor
University of Rochester, NY·Neuroscience
Measure "perceptual echoes", reverberating responses in early visual cortex, with high-density EEG to isolate top-down predictive feedback in healthy volunteers and in patients with schizophrenia or bipolar disorder.
Joni D Wallis
University of California-Berkeley, CA·Neuroscience
Test whether the orbitofrontal cortex suppresses habitual responses in the caudate nucleus when deliberate choice is needed, using a task with conflicting reward rules that changes decision time and eye movements.
The school
University of Florida: 65 labs on PI Pages, $1.3B a year on research, 795 research doctorates a year.
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