Physiological signal variability in hMT+ reflects performance on a direction discrimination task

Abstract

Our ability to perceive visual motion is critically dependent on the human motion complex (hMT+) in the dorsal visual stream. Extensive electrophysiological research in the monkey equivalent of this region has demonstrated how neuronal populations code for properties such as speed and direction, and that neurometric functions relate to psychometric functions within the individual monkey. In humans, the physiological correlates of inter-individual perceptual differences are still largely unknown. To address this question, we used functional magnetic resonance imaging (fMRI) while participants viewed translational motion in different directions, and we measured thresholds for direction discrimination of moving stimuli in a separate psychophysics experiment. After determining hMT+ in each participant with a functional localizer, we were able to decode the different directions of visual motion from it using pattern classification (PC). We also characterized the variability of fMRI signal in hMT+ during stimulus and rest periods with a generative model. Relating perceptual performance to physiology, individual direction discrimination thresholds were significantly correlated with the variability measure in hMT+, but not with PC accuracies. Individual differences in PC accuracy were driven by non-physiological sources of noise, such as head-movement, which makes this method a poor tool to investigate inter-individual differences. In contrast, variability analysis of the fMRI signal was robust to non-physiological noise, and variability characteristics in hMT+ correlated with psychophysical thresholds in the individual participants. Higher levels of fMRI signal variability compared to rest correlated with lower discrimination thresholds. This result is in line with theories on stochastic resonance in the context of neuronal populations, which suggest that endogenous or exogenous noise can increase the sensitivity of neuronal populations to incoming signals.

Moment-to-moment brain signal variability: A next frontier in human brain mapping?

“…Ongoing young-only work from our group (Garrett et al, under review) suggests that the directional shift (up/down) in signal variability from fixation to task depends on the exact parametric level of task difficulty for a given participant. This new work may help address divergent findings between He (2011) and other rest versus task studies reviewed above (Garrett et al, 2013; Takahashi et al, 2009; Wutte et al, 2011). …”

“…The simplest forms of such measures include variance (He, 2011), standard deviation (Garrett et al, 2010, 2013; Wutte et al, 2011), and mean square successive differences (Leo et al, 2012; Samanez-Larkin et al, 2010). Each of these measures taps related forms of overall distributional width.…”

“…One study (Wutte et al, 2011) investigated fMRI signal variability in the human motion complex (hMT+) during rest and during a visual discrimination task. Higher levels of fMRI signal variability on task compared to rest correlated with lower discrimination thresholds ( R 2 = .36; see Fig.…”

“…For example, in any imaging paradigm, depending on researcher interests and available temporal resolution, one may wish to characterize single-trial variability within a given condition (McIntosh et al, 2008), measure the variability across trials within condition (Garrett et al, 2010; Garrett et al, 2011b; Samanez-Larkin et al, 2010), compare variability levels between conditions (Garrett et al, 2013; He, 2011; Mišić et al, 2010; Wutte et al, 2011), or compute variability across a number of conditions or states. It is not yet known how these different levels of variability analysis relate, which may be a potential source of between-study differences in directionality or power of research findings.…”

“…(A) Wutte et al (2011): Lower psychophysical thresholds covaried with greater increases (i.e., SDdiff) in BOLD signal variability from rest (SDrest) to task (SDstim). (B) He et al (2011): BOLD signal variance positively associated with power-law exponents, indicating that higher levels of signal variance closely approximated 1/f signal properties (i.e., when the power-law exponent is ~1).…”