A meta-analysis of fMRI decoding: Quantifying influences on human visual population codes

Abstract

Information in the human visual system is encoded in the activity of distributed populations of neurons, which in turn is reflected in functional magnetic resonance imaging (fMRI) data. Over the last fifteen years, activity patterns underlying a variety of perceptual features and objects have been decoded from the brains of participants in fMRI scans. Through a novel multi-study meta-analysis, we have analyzed and modeled relations between decoding strength in the visual ventral stream, and stimulus and methodological variables that differ across studies. We report findings that suggest: i) several organizational principles of the ventral stream, including a gradient of pattern granulation and an increasing abstraction of neural representations as one proceeds anteriorly; ii) how methodological choices affect decoding strength. The data also show that studies with stronger decoding performance tend to be reported in higher-impact journals, by authors with a higher h-index. As well as revealing principles of regional processing, our results and approach can help investigators select from the thousands of design and analysis options in an empirical manner, to optimize future studies of fMRI decoding.

Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations

“…These results suggest that highly salient task-irrelevant information is difficult to ignore, especially because its processing poses no measurable cost to that of the task-relevant feature. This is consistent with monkey neurophysiology studies showing the prominent role of saliency in driving parietal responses (e.g., Gottlieb et al, 1998; see also Constantinidis and Steinmetz, 2005;Bisley and Goldberg, 2006). Nevertheless, the present study showed that dorsal regions exhibited a stronger filtering of the task-irrelevant information than ventral regions, consistent with prior findings using fMRI response amplitude measures (e.g., Xu, 2010;Jeong and Xu, 2013).…”

“…Attention to specific visual features can increase the gain of the neuronal responses to these features (electrophysiology studies: e.g., Motter, 1994;McAdams and Maunsell, 1999;Reynolds et al, 2000; and human imaging studies: e.g., Wojciulik et al, 1998;Serences et al, 2004;Baldauf and Desimone, 2014), and produce a tuning shift of the neuronal responses (Connor et al, 1997;David et al, 2008) or fMRI voxels (Ç ukur et al, 2013). Both of these changes could result in increased selectivity to the attended feature and thus more distinctive fMRI response patterns (Peelen et al, 2009;Reddy et al, 2009).…”

“…However, both monkey and human studies over the last two decades have reported robust representations of a variety of "what" information in the dorsal pathway (Sereno and Maunsell, 1998;Sawamura et al, 2005;Janssen et al, 2008;Konen and Kastner, 2008;Liu et al, 2011;Christophel et al, 2012;Hou and Liu, 2012;Ester et al, 2015;Xu and Jeong, 2015;Bettencourt and Xu, 2016;Bracci et al, 2016;Freud et al, 2016;Jeong and Xu, 2016). These findings challenge the two-pathway view and argue for a convergence between the two pathways.…”

Representational Connectivity Analysis: Identifying Networks of Shared Changes in Representational Strength through Jackknife Resampling

“…VT was selected as a seed region. VT is implicated in the representation of higher-level visual and conceptual features within multi-voxel patterns (Coutanche, Solomon, & Thompson-Schill, 2016;Haxby et al, 2001), including taxonomy-and species-relevant information for animal stimuli (Connoly et al, 2012;Coutanche and Koch, 2018). We defined VT within individuals based on a procedure outlined in Coutanche and Koch (2018) in which the region is defined as extending from 20 to 70 mm posterior to the anterior commissure, incorporating the lingual, fusiform, parahippocampal and inferior temporal gyri.…”

Just above chance: is it harder to decode information from human prefrontal cortex BOLD signals?

“…For comparison, we also derived base rates for decoding visual information from occipital and temporal cortex BOLD patterns. These were computed from meta-analytic data previously compiled by Coutanche and colleagues 47 . Compared to prefrontal cortex base rates, both the occipital and temporal cortex (median) base rates were significantly higher at 66.6% (95-CI: 61.5-72%) and 71.0% (95-CI: 68.0-75.0%) respectively.…”