MAO-A and the EEG Recognition Memory Signal in Left Parietal Cortex

A key part of episodic memory, or memory for the events of our lives, is recognition memory. Recognition memory is the ability to remember previously encountered stimuli. Studies have linked recognition memory to the old/new effect, an EEG indicator of stimulus familiarity. Monoamine oxidase A (MAO-A) is an enzyme that catalyzes monoamines, leading to the depletion of norepinephrine, epinephrine, serotonin, and dopamine. MAO-A is more efficiently transcribed in individuals with a 4 repeating sequence variation (4R) of the MAO-A gene leading to less monoamine availability. As many of these monoamines have been linked to episodic memory, we hypothesized that individuals homozygous for the 4R MAO-A polymorphism would show differences in mean EEG signal amplitudes during recognition memory. EEG data was recorded as participants viewed both new words and words that had been previously presented. Our results show that mean peak amplitudes over the left parietal cortex 500-800 ms post-stimulus presentation for hits were greater than those for correct rejections, indicating the old/new effect. Critically, our results revealed an interaction between mean hit and correct rejection amplitude over the left parietal cortex and MAO-A group. Individuals homozygous for the 4R variation (the High MAO-A group) do not show an old/new effect due to increased correct rejection amplitudes. These results suggest that less monoamine availability leads to new stimuli being identified as old by the left parietal cortex

Letter to the editor

In the current issue of NeuroImage (Vol 36, 2007), two Event-Related Potential (ERP) studies of recognition memory for faces are published back-to-back (Curran and Hancock, and MacKenzie and Donaldson). Both studies suggest that qualitatively distinct retrieval processes support recognition, consistent with “dual-process” models of recognition memory. However, the studies do so on the basis of apparently different results, a discrepancy that is surprising given the similarity of their designs. Here we place the studies in context, and highlight potential reasons for the discrepancy

Midazolam, hippocampal function, and transitive inference: Reply to Greene

The transitive inference (TI) task assesses the ability to generalize learned knowledge to new contexts, and is thought to depend on the hippocampus (Dusek & Eichenbaum, 1997). Animals or humans learn in separate trials to choose stimulus A over B, B over C, C over D and D over E, via reinforcement feedback. Transitive responding based on the hierarchical structure A > B > C > D > E is then tested with the novel BD pair. We and others have argued that successful BD performance by animals – and even humans in some implicit studies – can be explained by simple reinforcement learning processes which do not depend critically on the hippocampus, but rather on the striatal dopamine system. We recently showed that the benzodiazepene midazolam, which is thought to disrupt hippocampal function, profoundly impaired human memory recall performance but actually enhanced implicit TI performance (Frank, O'Reilly & Curran, 2006). We posited that midazolam biased participants to recruit striatum during learning due to dysfunctional hippocampal processing, and that this change actually supported generalization of reinforcement values. Greene (2007) questions the validity of our pharmacological assumptions and argues that our conclusions are unfounded. Here we stand by our original hypothesis, which remains the most parsimonious account of the data, and is grounded by multiple lines of evidence

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic

Comments arising from WJ Thompson "Uncertainty in probabilistic genotyping of low template DNA A case study comparing STRmix and TrueAllele"

Thompson reports a comparison of data from STRmix and TrueAllele. The data he has arises from different inputs to the two software. If the input data are made more similar the outputs become more similar. Thompson argues that the Analytical Threshold, AT, should be varied in casework. This produced different LRs but the analyst would be left deciding what to do with these options. This cannot be based on the LRs but should be based on whether any movement in the AT adds reliable or unreliable data. This is how most laboratories set their AT in the first place. Hence it is pointless, and potentially dangerous, to experimentally vary the AT in casework. The profile is low level and shows at most three peaks. Thompson argues that LR results assuming that the number of contributors (NoC) is 2 or 3 should be reported. Uncertainty in NoC should be treated as a nuisance variable and summed out.Comment: 9 pages 1 figur

Brain Potentials Reflect Behavioral Differences in True and False Recognition

People often falsely recognize nonstudied lures that are semantically similar to previously studied words. Behavioral research suggests that such false recognition is based on high semantic overlap between studied items and lures that yield a feeling of familiarity, whereas true recognition is more often associated with the recollection of details. Despite this behavioral evidence for differences between true and false recognition, research measuring brain activity (PET, fMRI, ERP) has not clearly differentiated corresponding differences in brain activity. A median split was used to separate subjects into Good and Poor performers based on their discrimination of studied targets from similar lures. Only Good performers showed late (1000-1500 msec), right frontal event-related brain potentials (ERPs) that were more positive for targets and lures compared with new items. The right frontal differences are interpreted as reflecting postretrieval evaluation processes that were more likely to be engaged by Good than Poor performers. Both Good and Poor performers showed a parietal ERP old/new effect (400-800 msec), but only Poor performers showed a parietal old/lure difference. These results are consistent with the view that the parietal and frontal ERP old/new effects reflect dissociable processes related to recollection.Psycholog

Predicting Temporal Patterns In The Environment: Toward Primitive Mechanisms Of Learning, Memory, And Generalization

Across a wide range of cognitive tasks, recent experience influences subsequent behavior. For example, when individuals repeatedly perform a speeded two-alternative choice task, response latencies vary dramatically based on the immediately preceding sequence. These sequential dependencies (SDs) have been interpreted as adaptation to the statistical structure of an uncertain, changing environment (e.g., Jones & Sieck, 2003; Mozer, Kinoshita, & Shettel, 2007; Yu & Cohen, 2009), and can shed light on how individuals learn and represent structure in binary stimulus sequences. Heretofore, theories have posited that SDs arise from rapidly (exponentially) decaying memory traces of various environmental statistics (e.g., Cho et al., 2002; Yu & Cohen, 2009).

We present a series of experiments and a model that place SDs on a fundamentally different foundation. We show that: (1) decay of recent experience can follow a power function curve, not an exponential, linking the SD literature
to a rich literature on human declarative memory; (2) the simple trace-based mechanism underlying existing accounts is inadequate, but incremental memory adjustments may be explained via error correction, linking the SD literature to the rich literature on human associative learning; and (3) distinct but interacting subsystems are found in the brain that jointly predict upcoming environmental events. 

We conducted three behavioral studies with EEG recordings of individuals performing discrimination of spatial location and motion coherence. Identifying the onset of the lateralized readiness potential (LRP) in an event-related EEG analysis, we are able to decompose the total response latency into two intervals—pre and post LRP onset—and to examine SDs in stimulus and response processing separately. We find evidence for two distinct mechanisms, one reflecting incremental learning of stimulus repetition rate (i.e., the probability that successive
stimuli will match), and the other reflecting incremental learning of response baserates. The data cannot be explained by a model that assumes these rates are based on independent traces, and calls for an account in which the two rates jointly predict future stimuli via error-correction learning. 

By manipulating the autocorrelation structure of the sequences (from a positive to a negative autocorrelation, indicated on the graphs by blue and red lines, respectively), we obtained evidence for incremental learning occurring over hundreds of trials, which is parsimoniously explained by a memory with power function decay. Together, the results highlight a tension between the two broad and well established classes of trace-based memory models and learning models based on error correction. Two attempts at reconciling these approaches via modeling are discussed

Individual differences in EEG correlates of recognition memory due to DAT polymorphisms

Introduction Although previous research suggests that genetic variation in dopaminergic genes may affect recognition memory, the role dopamine transporter expression may have on the behavioral and EEG correlates of recognition memory has not been well established. Objectives The study aims to reveal how individual differences in dopaminergic functioning due to genetic variations in the dopamine transporter gene influences behavioral and EEG correlates of recognition memory. Methods Fifty‐eight participants performed an item recognition task. Participants were asked to retrieve 200 previously presented words while brain activity was recorded with EEG. Regions of interest were established in scalp locations associated with recognition memory. Mean ERP amplitudes and event‐related spectral perturbations when correctly remembering old items (hits) and recognizing new items (correct rejections) were compared as a function of dopamine transporter group. Results Participants in the dopamine transporter group that codes for increased dopamine transporter expression (10/10 homozygotes) display slower reaction times compared to participants in the dopamine transporter group associated with the expression of fewer dopamine transporters (9R‐carriers). 10/10 homozygotes further displayed differences in ERP and oscillatory activity compared to 9R‐carriers. 10/10 homozygotes fail to display the left parietal old/new effect, an ERP signature of recognition memory associated with the amount of information retrieved. 10/10 homozygotes also displayed greater decreases of alpha and beta oscillatory activity during item memory retrieval compared to 9R‐carriers. Conclusion Compared to 9R‐carriers, 10/10 homozygotes display slower hit and correct rejection reaction times, an absence of the left parietal old/new effect, and greater decreases in alpha and beta oscillatory activity during recognition memory. These results suggest that dopamine transporter polymorphisms influence recognition memory

The N250 Brain Potential to Personally Familiar and Newly Learned Faces and Objects

Studies employing event-related potentials have shown that when participants are monitoring for a novel target face, the presentation of their own face elicits an enhanced negative brain potential in posterior channels approximately 250 ms after stimulus onset. Here, we investigate whether the own face N250 effect generalizes to other highly familiar objects, specifically, images of the participant’s own dog and own car. In our experiments, participants were asked to monitor for a pre-experimentally unfamiliar target face (Joe), a target dog (Experiment 1: Joe’s Dog) or a target car (Experiment 2: Joe’s Car). The target face and object stimuli were presented with non-target foils that included novel face and object stimuli, the participant’s own face, their own dog (Experiment 1), and their own car (Experiment 2). The consistent findings across the two experiments were the following: (1) the N250 potential differentiated the target faces and objects from the non-target face and object foils and (2) despite being non-targets, the own face and own objects produced an N250 response that was equal in magnitude to the target faces and objects by the end of the experiment. Thus, as indicated by its response to personally familiar and recently familiarized faces and objects, the N250 component is a sensitive index of individuated representations in visual memory