Researchers at the Center for Cognitive Neuroscience (CCN) take advantage of all major methods in cognitive neuroscience to investigate the neural mechanisms of cognition: (1) behavioral measures, (2) electrophysiology, (3) neuroimaging, (4) brain stimulation, (5) study of brain dysfunction, and (6) mathematical and statistical modeling.
1. Behavioral Measures
The ultimate goal of cognitive neuroscience is to explain the neural mechanism of cognitive and affective behavior, and hence, behavior is the most critical dependent variable. Thus, all CCN core faculty investigate behavior using a variety of methods, including choice, accuracy, and reaction time measures derived from manual responses, eye movement, virtual and real navigation, and overt verbal responses.
Electrophysiology provides a relatively direct measure of the activity of neurons and groups of neurons, and can be measured more directly using intracranial electrodes in non-human animals or epileptic patients awaiting surgery, or less directly using electrodes placed on the scalp. Both methods are used by CCN researchers. The Groh and Sommer labs use intracranial electrodes in monkeys to make single-cell recordings in monkeys, while several labs (Woldorff and Overath) use scalp recordings in humans to obtain electro-encephalographic (EEG) and event-related potential (ERP) measures.
Neuroimaging measures provide one of the most powerful tools for investigating the neural mechanisms of cognition. Neuroimaging provides better spatial resolution than EEG and ERPs, but the latter have better temporal resolution, so some labs at CCN (Woldorff) are combining these two neuroimaging methods. Most CCN labs investigate MRI-based measures of brain structure (structural MRI), white-matter integrity (diffusion tensor imaging—DTI), and brain activity (functional MRI—fMRI). They take advantage of the world-class MRI facilities at the nearby Brain Imaging and Analysis Center, which has multiple top-of-the line GE scanners dedicated to brain research. All CCN labs use the most sophisticated analytic techniques available, including quantitative DTI tractography, multivariate pattern analysis (MVPA), spatio-temporal partial least square (PLS) analysis, representational similarity analysis (RSA), and graph theory analyses.
4. Brain Stimulation
Although fMRI can identify the complex network of regions associated with cognitive performance, this link is correlational and should be complemented with approaches than can assess the necessity of these regions for successful performance and hence establish a causal relationship between brain and behavior. These approaches include study of brain dysfunction and use of brain stimulation. Brain stimulation can be performed intra-cranially in non-human animals (Groh and Sommer labs) or non-invasively in humans using transcranial magnetic stimulation (TMS) and other techniques. The Sommer lab investigates the neural mechanisms of TMS in non-human primates. Several CCN labs (Cabeza, De Brigard, and Egner) investigate TMS in humans at nearby cutting-edge CCN-affiliated TMS facilities housed in the psychiatry department.
5. Study of Brain Dysfunction
Like brain stimulation, brain dysfunction can establish a causal relationship between brain and behavior. Brain dysfunction may reflect accidental or surgical brain damage, psychiatric or neurological disorders, or developmental changes (aging). Most CCN researchers investigate one or more of these types of dysfunction. For example, the Adcock lab investigates psychosis and vulnerability, Adcock and LaBar labs investigate affective disorders; Cabeza and Madden labs investigate healthy and pathological aging; the Sinnott-Armstrong lab investigates psychopathy and scrupulosity; and the Woldorff lab investigates attentional disorders.
6. Mathematical and Statistical Modeling
As cognitive neuroscience theories develop, they seek to develop mathematical models that can translate vague hypotheses into unambiguous equations, which can yield exact quantitative predictions that are easier to falsify. Statistical modeling is important to simplify complex patterns of data, which is critical for ‘big data’ such as the findings of neuroimaging studies. Several CCN faculty are using both types of models with the help of Katherine Heller, an expert in machine learning and Bayesian statistics. Models used by CCN labs to analyze neuroimaging data include graph theory (Adcock and Cabeza labs), Bayesian models (Adcock, Egner, and Huettel labs), game theory (Huettel lab), diffusion models (Madden lab), and biologically plausible neural circuit models (Adcock and Groh labs).