Stimulus-specific regulation of visual oddball differentiation in posterior parietal cortex

Abstract

The frequency at which a stimulus is presented determines how it is interpreted. For example, a repeated image may be of less interest than an image that violates the prior sequence. This process involves integration of sensory information and internal representations of stimulus history, functions carried out in higher-order sensory areas such as the posterior parietal cortex (PPC). Thus far, there are few detailed reports investigating the single-neuron mechanisms for processing of stimulus presentation frequency in PPC. To address this gap in knowledge, we recorded PPC activity using 2-photon calcium imaging and electrophysiology during a visual oddball paradigm. Calcium imaging results reveal differentiation at the level of single neurons for frequent versus rare conditions which varied depending on whether the stimulus was preferred or non-preferred by the recorded neural population. Such differentiation of oddball conditions was mediated primarily by stimulus-independent adaptation in the frequent condition.

Figure 1

Posterior parietal cortex (PPC) multi-unit firing activity and event-related potentials (ERPs) exhibit tuning to drifting gratings in opposing directions and stimulus-specific mismatch negativity (MMN). (A) To assess population tuning, head-fixed ferrets were presented a series of drifting gratings during simultaneous electrophysiology in visual cortex (VC) and PPC. (B) Trial-averaged event-related multi-unit firing for each drifting grating direction (indicated by color) for a representative animal. Black bar represents the duration of stimulus presentation. Right: Polar histogram of normalized time-averaged (300–1,500 ms) responses as a function of drifting grating direction. Note the two opposing directions that elicit the largest responses. This indicates the presence of orientation tuning. In addition, one of the two directions exhibited a stronger response than the opposing direction, confirming the presence of direction tuning (differential response to two stimuli of identical orientation but opposing directions). The direction with the strongest response is referred to as the preferred stimulus, the opposite direction is referred to as the opponent stimulus. (C) Event-related firing responses in VC as a function of stimulus direction for the same animal as in (B). (D) Same format and data from same animal as in (B) but with PPC event-related local field potential (LFP) response. Polar histogram on the right indicates peak magnitude of the LFP during the 150–250 ms MMR window after stimulus presentation. (E) Same format and data from same animal as in (D) but ERP responses from VC. (F) Electrophysiology experiment in PPC during visual oddball paradigm. Each session consisted of three randomized blocks of 300 randomized trials. Drifting grating stimuli of opposing motion directions were presented for 400 ms with an inter-trial interval of 1,000–1,500 ms. The three blocks allowed for comparing the responses to the same stimulus across the standard (frequent presentation: 90% of trials), deviant (rare presentation: 10% of trials), and control (rare, but without a history of a repeated stimulus: 10% of trials) contexts. (G) Session-averaged (n = 15) ERPs as a function of stimulus (top and bottom panels) and context (colored traces) for PPC. PPC exhibited significant mismatch negativity (MMN) (*p < 0.05) during the classical 150–250 ms MMN epoch. Time zero represents stimulus onset for all following plots. (H) Time-averaged (150–250 ms) ERP responses as a function of context (bar color) and stimulus (top: preferred, bottom: opponent stimulus). Error bar represents SEM. *p < 0.05.

Figure 2

Stimulus-specific MMN is observed in the gamma frequency band. (A) Power spectrograms for event-related PPC activity as a function of condition (standard, deviant, control in left, middle, and right columns respectively) and stimuli (top row: preferred, bottom row: opponent stimulus). Color represents power normalized to a baseline pre-stimulus epoch (− 100 to 0 ms relative to stimulus onset) on a trial basis. Data are averaged across sessions (n = 15). (B,C) Frequency-averaged gamma frequency band (25–50 Hz) power fluctuations in PPC as a function of condition (traces) and stimulus (top row: preferred, bottom row: opponent stimulus). (C) Time-averaged quantification of the data in (B). For the preferred stimulus, PPC exhibited significantly higher gamma power for the deviant compared to the standard condition during the 150–250 ms MMN epoch (paired t test, *p < 0.05). Shaded error bars: SEM computed across sessions. (D,E) Frequency-averaged alpha frequency band (12–14 Hz, centered at the individual alpha peak frequency of the animal) power fluctuations in PPC as a function of condition (traces) and stimulus (top row: preferred, bottom row: opponent stimulus). (D) Time-averaged quantification of the data in (E). No differences were found between conditions in the MMN epoch for either stimulus. Shaded error bars: SEM computed across sessions.

Figure 3

2-photon calcium imaging reveals stimulus-specific MMR in PPC. (A) Schematic of cranial window implant and calcium imaging setup. (B) Sample coronal section of PPC expression of GCaMP6f. Bold labels represent anatomical region names. Italicized labels represent functional region names. Red scale bar represents 5 mm distance. Inset: close up of recording area with cells labeled with GCaMP6f. Abbreviations: lateral gyrus (LG), posterior parietal cortex (PPC), cingulate gyrus (CG), retrosplenial cortex (RSC), suprasylvian gyrus (SSG), middle ectosylvian gyrus (MEG), hippocampus (Hip). Labels based on published ferret brain atlas. (C) Left: Sample max fluorescence projection of a calcium imaging recording. Black contours represent regions of interest (ROIs) that were identified using a calcium imaging cell detection algorithm (Suite2p). Insets are close-ups of sample ROIs. Colored contours correspond to the sample fluorescence activity traces shown on the right. Signal extraction from ROIs was performed using a custom-designed algorithm with neuropil subtraction and dF/F calculation. (D) Calcium imaging preprocessing pipeline. Raw calcium imaging frames were first motion corrected then underwent ROI segmentation using the Suite2p toolbox. ROI activity time-series were calculated by averaging across ROI pixels and subtracting the corresponding background (BG) activity defined by pixels in a ring around the ROI. Normalized activity was calculated as change in fluorescence over mean fluorescence across the session. To ensure all ROIs defined plausible cells, we manually curated the data based on ROI contours and time-series. Finally visually-responsive ROIs were chosen for subsequent analysis based on a stringent threshold for stimulus-evoked responses. (E) To assess direction tuning at the level of single neurons, head-fixed ferrets were presented a series of drifting gratings during 2-photon calcium imaging sessions. (F) Trial- and ROI-averaged event-related calcium responses for each drifting grating direction (in color) for a representative animal. Black bar represents the duration of stimulus presentation. (G) Polar histogram of normalized time-averaged (300–1,500 ms) calcium responses as a function of drifting grating direction. Note the two opposing directions that elicit the largest responses: the preferred and opponent stimulus.

Figure 4

2-photon calcium imaging reveals stimulus-specific MMR is composed of stimulus-specific adaptation. (A) 2-photon calcium imaging experiment in PPC during visual oddball paradigm. Oddball paradigm is identical to the one utilized in the electrophysiology experiments (Fig. 1F). (B) Top: Heatmaps of each ROI’s trial-averaged activity as a function of time relative to stimulus onset for the preferred stimulus. ROIs are sorted by mean activity during the calcium response (300–1,500 ms) for the preferred stimulus in the deviant condition. The black bar on the x-axis represents stimulus presentation duration. Bottom: the same format but for the opponent stimulus. ROIs are resorted according to the opponent stimulus’ deviant condition activity. (C) ROI-averaged (n = 73) calcium responses for each condition (traces in color) and stimulus (left panel: preferred stimulus, right panel: opponent stimulus). The black bar on the x-axis represents stimulus presentation duration. (D) Time- and trial-averaged calcium responses as a function of condition and stimulus (n = 73 ROIs). *p < 0.01; **p < 0.001. Condition contrast definitions: mismatch response (MMR: deviant—standard), deviance detection (DD: deviant—control), stimulus-specific adaptation (SSA: control—standard). (E) Example ROIs (columns) mean fluorescence close-ups (top row), trial-averaged event-related calcium responses for the preferred stimulus (middle row), and responses for the opponent stimulus (bottom row). Note that ROI 1 shows both MMR (p < 0.001) and DD (p < 0.01) for the preferred stimulus. ROI 2 shows preferred stimulus MMR (p < 0.001), but no differences between conditions for the opponent stimulus (p > 0.05). ROI 3 shows MMR (p < 0.001) for the preferred stimulus. Error bars represent SEM. Statistical tests were unpaired t-tests with Holm-Bonferroni correction. (F) Left panel: Distribution of trial-averaged MMR across all ROIs for the preferred stimulus. Note the shift of the distribution towards higher values, indicating a significant MMR for the preferred stimulus. Middle panel: Distribution of trial-averaged MMR across all ROIs for the opponent stimulus. No MMR was found for the opponent stimulus. Right panel: Distribution of trial-averaged, stimulus-difference (preferred-opponent stimulus) MMR across all ROIs. Note the shift of the distribution towards higher values, indicating a difference in MMR between stimuli, specifically stronger MMR for the preferred stimulus. (G) Same format as in (F), but for DD. Note the significant shift of the opponent stimulus distribution towards negative values, indicating smaller responses in the deviant condition compared to the control. In the DD stimulus contrast, the distribution was shifted to higher values, indicating a significant difference between DD for the two stimuli. (H) Same format as in (F), but for SSA. Note that for both stimuli, the distributions were shifted to higher values, indicating significant SSA; however, SSA magnitude did not differ between stimuli indicated by the stimulus contrast.

Figure 5

MMR scales with preferred stimulus control condition magnitude and stimulus-preference congruency with the population. (A) Left: Time-averaged (300–1,500 ms) MMR plotted against time-averaged control condition activity for each ROI for the preferred stimulus. A significant positive correlation (p = 0.008) between the two measurements indicates that ROIs with strong preferred stimulus control activity also show strong MMR. Right: same format as in the left panel but for the opponent stimulus. The opponent stimulus showed no clear relationship between MMR and control condition activity (p = 0.919). (B) Distribution of control condition modulation index (MI) across ROIs. A positive value represents congruent stimulus preference with the population average, whereas a negative value represents preference to the opponent stimulus. While there was variability in stimulus preference, the majority of ROIs showed congruent stimulus preference with the population. The distribution was split at 0 into high and low control MI groups. (C) Preferred stimulus time-averaged MMR plotted against the control condition MI for each ROI. A significant positive correlation between the two measurements indicate that ROIs with congruent stimulus preference with the population exhibit strong MMRs. These ROIs contributed strongly to the correlation (p = 0.00007) whereas ROIs with opponent stimulus preference seemed to show the same level of preferred stimulus MMR invariant of MI magnitude. Further, the high MI group exhibited higher magnitude preferred stimulus MMR compared to the low MI group (p = 0.020).