The purpose of this proposal is to investigate the relationship between neural dynamics and attention in normal human subjects.
We aim to combine insights from human macroscopic experimental measures and computational neural network modeling to test the hypothesis that selective attention affects cortical activity measured in both the time and frequency domain, and that this activity is mediated by specific cellular level neuronal events. This will be accomplished using a two-fold approach. First, we will experimentally probe the effects of attention of cortical rhythms using a sensory task. Specifically, we will use techniques recently developed at the Arthinoula A. Martinos center to simultaneously measure magnetoencephalography (MEG) and electroencephalography (EEG) signals during median nerve (MN) stimulation. We will analyze the signals generated in the primary (SI) and secondary (SII) somatosensory system in both the time and frequency domain. In the time domain, we will measure amplitudes and latencies of evoked responses, and in the frequency domain will measure spectral power and phase-locking. We will compare these measures within and between SI and SII when the subject is attending or not attending to the MN stimulation. Second, we will use neural network modeling to test if changes in the level of acetylcholine that accompany attention create a biophysical link between changes in time and frequency domain activity. This approach will entail the development of a model of a laminated cortical column(s) that reproduces the oscillatory current dipoles that are measured extracranially with MEG/EEG. Simulations with the model can also lead to new experimentally testable predictions of the effects of attention on cortical activity. This two-fold approach may lead to a better understanding of the macroscopic and cellular mechanisms of attention. This proposed five-year training program will combine the candidate's background in mathematics and computational neural network modeling with the mentor's expertise in MEG/EEG and neuroscience to investigate the influence of attention on brain neurodynamics. The broad long term objective is to create a biophysically realistic neural network model that can be used in conjunction with non-invasive clinical imaging techniques as a tool capable of diagnosing and treating neurological attention disorders.
|Sacchet, Matthew D; LaPlante, Roan A; Wan, Qian et al. (2015) Attention drives synchronization of alpha and beta rhythms between right inferior frontal and primary sensory neocortex. J Neurosci 35:2074-82|
|Kerr, Catherine E; Sacchet, Matthew D; Lazar, Sara W et al. (2013) Mindfulness starts with the body: somatosensory attention and top-down modulation of cortical alpha rhythms in mindfulness meditation. Front Hum Neurosci 7:12|
|Wan, Qian; Kerr, Catherine; Pritchett, Dominique et al. (2011) Dynamics of dynamics within a single data acquisition session: variation in neocortical alpha oscillations in human MEG. PLoS One 6:e24941|
|Kerr, Catherine E; Jones, Stephanie R; Wan, Qian et al. (2011) Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex. Brain Res Bull 85:96-103|
|Ziegler, David A; Pritchett, Dominique L; Hosseini-Varnamkhasti, Paymon et al. (2010) Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study. Neuroimage 52:897-912|
|Jones, Stephanie R; Kerr, Catherine E; Wan, Qian et al. (2010) Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex. J Neurosci 30:13760-5|
|Jones, Stephanie R; Pritchett, Dominique L; Sikora, Michael A et al. (2009) Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses. J Neurophysiol 102:3554-72|
|Jones, Stephanie R; Pritchett, Dominique L; Stufflebeam, Steven M et al. (2007) Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J Neurosci 27:10751-64|