Variance as a function of analysis window and frequency bin.

<p>The log₁₀ raw amplitude variance (normalized by the N-1 samples) was computed for each subject within two windows—the whole 17 sec trial window (red line) and the 6 sec delay period (blue line)–and plotted as a function of frequency (a). Subject differences in log₁₀ raw amplitude variance (whole trial window minus delay period window) are plotted as a function of frequency (b) and show that whole trial window variances are larger than delay period variances in the range between 6 and 40 Hz, with local peak differences occurring at 8 Hz and 22 Hz. Error bars are subject standard errors of the mean.</p

Temporal spectral evolution at the sensor level.

<p>At each sensor, the average TSE for a single subject with 94 trials surviving artifact detection is shown. Positive TSE change is visible after the onset of the delay period (vertical red line), while negative TSE change is evident before and after the onset and offset of the delay period. At each sensor, the x-axis of the TSE matrix shows time relative to the event, and the y-axis displays linearly scales with frequency. TSE amplitude intensities are color coded where blue is negative and red is positive. Note positive red change dominates during the delay period, whereas blue is most prominent during the encoding and probe phases.</p

Average delay window TSE and significant clusters of correlation with performance across all subjects.

<p>Average delay period TSE plot (computed using whole delay window as baseline) with no statistical significance overlay (a) shows initial negative change from 6 to 28 Hz at the start of the delay period 0 to 600 ms. Weak positive change (red) is then present from around 500ms to 2925 ms at 6–26 Hz. Stronger positive change is present in the 4–8 Hz from around 275 ms to 1650 ms. A switch to negative change (blue) is present from approximately 4125 ms to the end of the delay period in the 8–26 Hz range and from 2500 ms to 5475 ms in the 4–8 Hz range. Correlation of TSE with subject d-prime revealed three significant clusters (b) in descending order from most significant. Significant clusters are represented by darker shades of red or blue overlaid on TSE activity. Cluster 1 (c, latency 4775–6000 ms and frequency range 4–36 Hz) and Cluster 3 (d, latency 3850–4800 ms and frequency range 4–24 Hz) were negatively correlated with d-prime (Cluster 1: <i>Cluster Value = -993</i>.<i>061</i>, p = .017; Cluster 3: <i>Cluster Value = -640</i>.<i>877</i>, <i>p</i> = .032) and Cluster 2 (e, latency 1400–2450 ms and frequency range 4–24 Hz) was positively correlated with d-prime (<i>Cluster Value = 665</i>.<i>685</i>, <i>p</i> = .022). Dark black lines on the graphs (c-e) are linear regression fits and dotted lines are 95% confidence intervals.</p

Temporal spectral evolution at the source level.

<p>A brain region source montage shows the average TSE for a single subject with 94 trials surviving artifact detection. Positive TSE change (red) is evident after the onset of the delay period (vertical red line), while negative TSE change (blue) is evident before and after the onset and offset of the delay period. These changes are particularly noticeable in medial frontal pole (FpM_BR), left posterior temporal lobe (TPL_BR), left parietal lobe (PL_BR), medial parietal lobe (PM_BR), right parietal lobe (PR_BR), right posterior temporal lobe (TPR_BR) and medial occipital pole (OpM_BR).</p

A significant cluster of TSE-performance correlation in the scrambled period.

<p>During the 5 second scrambled scene period after probe presentation there was a cluster of positive correlation found between TSE amplitude and d-prime (red, <i>Cluster Value</i> = 660.673, p = 0.017) between 900 ms and 1600 ms after the onset of the scrambled scene (a). Individual subject amplitudes expressed as change relative to the mean across the 5 sec scrambled period are plotted as red filled dots with dark line indicating linear regression fits and boundary lines indicating 95% confidence intervals (b).</p

Effect of baseline window on TSE-performance correlations.

<p>When the whole 17 seconds window from encoding to scrambled scene presentation was used as baseline in computation of TSE (a) a single cluster (latency 1400–3150 ms and frequency range 4–28 Hz) was found that positively correlated with d-prime (<i>Cluster Value = 1109</i>.<i>9</i>, <i>p</i> = .041). When the 4 seconds period of encoding was used as baseline in computation of TSE (b) a single cluster (latency 900–3700 ms, frequency range 4–12 Hz) was found that correlated positively with d-prime (<i>Cluster Value = 830</i>.<i>1</i>, <i>p</i> = .024). When the 5 seconds period of scrambled scene presentation after the probe stimulus was used as the baseline in computation of TSE (c), there were no significant clusters found that correlated with d-prime.</p

Example scene working memory trials.

<p>In a load 2 working memory trial, two encoding images are each presented for 2 seconds followed by a 6 seconds delay (maintenance) period. After the delay, a probe image is shown for 2 seconds followed by a 5 seconds scrambled scene baseline. Some trials contain a negative probe (a) where the probe stimulus is not one of the previously presented encoding images. Other trials contain a positive probe (b) where the probe stimulus is one of the previously presented encoding images. An example average EEG trace from one subject’s 94 artifact free trials is shown (c) with trial phases labeled. Onset of the delay period is marked by a short vertical red line, while offset of the delay period is marked by a longer vertical red line.</p

Scalp electrode montage used for EEG recordings.

<p>Scalp electrode positions used in the recordings are shown displayed on a head model. Contour lines represent 3D voltage amplitude mapping in a single subject 2000 ms after delay period onset. Note right posterior focus.</p

Data_Sheet_1_Visual continuous recognition reveals behavioral and neural differences for short- and long-term scene memory.pdf

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory at timescales relevant to working and longer-term memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 s) or longer-term intervals of between 30 s and 3 min or 4 and 10 min. Overall recognition was far above chance, with better performance at shorter- than longer-term intervals. Sensor-level ANOVA and post hoc pairwise comparisons of event related potentials (ERPs) revealed three main findings: (1) occipital and parietal amplitudes distinguishing new and old from scrambled scenes; (2) frontal amplitudes distinguishing old from new scenes with a central positivity highest for hits compared to misses, false alarms and correct rejections; and (3) frontal and parietal changes from ∼300 to ∼600 ms distinguishing among old scenes previously encountered at short- and longer-term retention intervals. These findings reveal how distributed spatiotemporal neural changes evolve to support short- and longer-term recognition of complex scenes.</p

Nucleolin phosphorylation regulates PARN deadenylase activity during cellular stress response

<p>Nucleolin (NCL) is an abundant stress-responsive, RNA-binding phosphoprotein that controls gene expression by regulating either mRNA stability and/or translation. NCL binds to the AU-rich element (ARE) in the 3′UTR of target mRNAs, mediates miRNA functions in the nearby target sequences, and regulates mRNA deadenylation. However, the mechanism by which NCL phosphorylation affects these functions and the identity of the deadenylase involved, remain largely unexplored. Earlier we demonstrated that NCL phosphorylation is vital for cell cycle progression and proliferation, whereas phosphorylation-deficient NCL at six consensus CK2 sites confers dominant-negative effect on proliferation by increasing p53 expression, possibly mimicking cellular DNA damage conditions. In this study, we show that NCL phosphorylation at those CK2 consensus sites in the N-terminus is necessary to induce deadenylation upon oncogenic stimuli and UV stress. NCL-WT, but not hypophosphorylated NCL-6/S*A, activates poly (A)-specific ribonuclease (PARN) deadenylase activity. We further demonstrate that NCL interacts directly with PARN, and under non-stress conditions also forms (a) complex (es) with factors that regulate deadenylation, such as p53 and the ARE-binding protein HuR. Upon UV stress, the interaction of hypophosphorylated NCL-6/S*A with these proteins is favored. As an RNA-binding protein, NCL interacts with PARN deadenylase substrates such as <i>TP53</i> and <i>BCL2</i> mRNAs, playing a role in their downregulation under non-stress conditions. For the first time, we show that NCL phosphorylation offers specificity to its protein-protein, protein-RNA interactions, resulting in the PARN deadenylase regulation, and hence gene expression, during cellular stress responses.</p