In the past decade there has been increasing interest in using functional neuroimaging (especially fMRI) to uncover the intrinsic, functional organization of the human brain. This is often accomplished by collecting data while subjects lie quietly in MRI machines (commonly referred to as resting-state brain scanning). As with all new techniques and procedures, there is considerable debate on how best to analyze these data in order to provide the most valid view of the brain's functional architecture. During this past year, we have focused considerable effort on these methodological issues by completing a series of investigations aimed at identifying and ridding fMRI of sources of noise known to distort and corrupt the data (Power et al.PNAS,2018;Power et al.,PloS One, 2017; Power et al.,NeuroImage, 2017). These investigations have yielded important findings on the most promising procedures for collecting and analyzing resting-state data to provide the best possible view of the human brain's intrinsic functional organization. A major limitation of using fMRI, however, is that although this technique has excellent spatial resolution (on the order of millimeters), its temporal resolution is quite poor (on the order of seconds). To circumvent this limitation, we have turned to MEG to record on-going brain activity with millisecond precision. Using this technology, we have been able to uncovered a temporal frequency-based map of the entire human cortex based on distinct temporal profiles (Mellem et al., Journal of Neurophysiology, 2017). This study lays the ground work for future investigations aimed at identifying large-scale brain networks defined by both their spatial and temporal characteristics. Using these techniques, progress has also been made on understanding the functional organization of ventral temporal cortex. One of the most robust and oft-replicated findings in cognitive neuroscience is that different regions of ventral temporal cortex respond preferentially to different categories of concrete objects. However, the determinants of this category-related organization remain to be fully established. We, and others, have recently proposed that a major contributing factor to this organization is privileged connectivity from each of these ventral temporal regions to other brain regions that store property information associated with that category. To test this hypothesis, we used fMRI to define category-related brain regions of interest (ROIs) in a large group of subjects (Stevens et al., 2017). We then used these ROIs in resting-state functional connectivity MRI analyses to explore functional connectivity among these regions. Our results demonstrate that distinct category-preferring regions of ventral temporal cortex show differentially stronger functional connectivity with other regions that have congruent category preferences. Moreover, the strength of these connections varied with behavior. The better subjects performed on a specific cognitive task like word reading, the stronger the connectivity between regions responsible for visual word identification and language comprehension (Stevens et al., 2017). These findings support the claim that privileged connectivity with other cortical regions provides a powerful constraint on the category-related organization of this region of the brain.
| Power, Jonathan D; Plitt, Mark; Gotts, Stephen J et al. (2018) Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data. Proc Natl Acad Sci U S A 115:E2105-E2114 |
| Stevens, W Dale; Kravitz, Dwight J; Peng, Cynthia S et al. (2017) Privileged Functional Connectivity between the Visual Word Form Area and the Language System. J Neurosci 37:5288-5297 |
| Power, Jonathan D; Laumann, Timothy O; Plitt, Mark et al. (2017) On Global fMRI Signals and Simulations. Trends Cogn Sci 21:911-913 |
| Avery, Jason A; Gotts, Stephen J; Kerr, Kara L et al. (2017) Convergent gustatory and viscerosensory processing in the human dorsal mid-insula. Hum Brain Mapp 38:2150-2164 |
| Power, Jonathan D; Plitt, Mark; Laumann, Timothy O et al. (2017) Sources and implications of whole-brain fMRI signals in humans. Neuroimage 146:609-625 |
| Mellem, Monika S; Wohltjen, Sophie; Gotts, Stephen J et al. (2017) Intrinsic frequency biases and profiles across human cortex. J Neurophysiol 118:2853-2864 |
| Vattikuti, Shashaank; Thangaraj, Phyllis; Xie, Hua W et al. (2016) Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory. PLoS Comput Biol 12:e1004903 |
| Martin, Alex (2016) GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Psychon Bull Rev 23:979-90 |
| Mellem, Monika S; Jasmin, Kyle M; Peng, Cynthia et al. (2016) Sentence processing in anterior superior temporal cortex shows a social-emotional bias. Neuropsychologia 89:217-224 |
| Meoded, Avner; Morrissette, Arthur E; Katipally, Rohan et al. (2015) Cerebro-cerebellar connectivity is increased in primary lateral sclerosis. Neuroimage Clin 7:288-96 |
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