Poster B80, Tuesday, August 20, 2019, 3:15 – 5:00 pm, Restaurant Hall
Characterizing the spatiotemporal pattern of neural activity and word representation during visual word recognition
Laura Long1,2, Minda Yang1, Michael Sperling3, Ashwini Sharan3, Bradley Lega5, Alexis Burks5, Greg Worrell6, Robert Gross7, Barbara Jobst8, Kathryn Davis9, Kareem A. Zaghloul10, Sameer Sheth1, Joel Stein9, Sandhitsu Das9, Richard Gorniak3, Paul Wanda4, Michael Kahana4, Joshua Jacobs1, Nima Mesgarani1,2;1Columbia University, 2Mortimer B. Zuckerman Mind, Brain, & Behavior Institute, 3Thomas Jefferson University Hospital, 4University of Pennsylvania, 5University of Texas Southwestern, 6Mayo Clinic, 7Emory School of Medicine, 8Dartmouth Medical Center, 9Hospital of the University of Pennsylvania, 10National Institute of Neurological Disorders & Stroke
Visual word recognition (VWR) is the process of mapping the written form of a word to its underlying linguistic item, and is crucial for successful written communication. Characterization of healthy VWR neural mechanisms holds promise for improving treatment of reading deficits and deepening our understanding of literacy. While noninvasive neuroimaging studies have identified putative brain regions (fMRI, PET) and event-related potentials (EEG, MEG) involved in VWR, the spatiotemporal flow of information through the brain remains unclear. In this study, we analyzed high gamma neural activity of more than 13000 electrodes from more than 140 intracranial neurophysiology patients as they read visually-presented words. We find that over 3000 electrodes show a task-sensitive response, with the most task-sensitive electrodes in occipital lobe, followed by frontal lobe, then parietal and temporal lobes. We observe a difference in the excitatory/inhibitory balance between task-sensitive electrodes in different lobes: occipital lobe has the highest proportion of excitatory responses, followed by temporal lobe, parietal lobe, and finally frontal lobe with an almost even excitatory/suppressive balance. Latency analyses reveal that on average, occipital lobe responds most quickly, followed by temporal lobe, with the slowest responses from frontal and parietal lobes. Middle occipital gyrus, cuneus, and fusiform gyrus (which contains the visual word form area) are among the fastest areas on average. By clustering the responses of all task-sensitive electrodes, we identified a variety of response types including excitatory and inhibitory responses, onset and offset responses, and responses sustained for the duration of the word presentation. Furthermore, we investigated the neural representation of the stimuli’s visual, phonemic, lexical, and semantic features. From each feature set, we predicted each electrode’s response and investigated the properties of the best-predicted electrodes. Visually-predicted and phoneme-predicted electrode groups had mostly low latencies and excitatory peaks. Visually-predicted electrodes were spread between occipital and frontal lobes, while more phoneme-predicted electrodes were in temporal lobe. Lexically-predicted and semantically-predicted electrode groups included more suppressive responses, with linguistically-predicted electrodes being distributed across frontal, temporal, and occipital lobes, and a plurality of semantically-predicted electrodes in frontal lobe. We further investigated the encoding of these electrode groups of the feature sets over time using representational similarity analysis, which revealed that visual features peak quickly after word onset, followed by phonemic, then linguistic, then semantic features, with lexical features having a later onset. Further investigation into individual word features shows that low-level features like word length and bitmap were represented early, with other features such as frequency represented later. Together, these results provide a high-resolution look at the spatiotemporal pattern and representation of neural activity during visual word recognition in the human brain.
Themes: Reading, Computational Approaches
Method: Electrophysiology (MEG/EEG/ECOG)