A meta-analysis of event-related potential correlates of recognition memory.

A longstanding question in memory research is whether recognition is supported by more than one mnemonic process. Dual-process models distinguish recollection of episodic detail from familiarity, while single-process models explain recognition in terms of one process that varies in strength. Dual process models have drawn support from findings that recollection and familiarity elicit distinct electroencephalographic event-related potentials (ERPs): a mid-frontal ERP effect that occurs at around 300-500 ms post-stimulus onset and is often larger for familiarity than recollection contrasts, and a parietal ERP effect that occurs at around 500-800 ms and is larger for recollection than familiarity contrasts. We sought to adjudicate between dual- and single-process models by investigating whether the dissociation between these two ERP effects is reliable over studies. We extracted effect sizes from 41 experiments that had used Remember-Know, source memory, and associative memory paradigms (1,000 participants). Meta-analysis revealed a strong interaction between ERP effect and mnemonic process of the form predicted by dual-process models. Although neither ERP effect was significantly process-selective taken alone, a moderator analysis revealed a larger mid-frontal effect for familiarity than recollection contrasts in studies using the Remember-Know paradigm. Mega-analysis of raw data from six studies further showed significant process-selectivity for both mid-frontal and parietal ERPs in the predicted time windows. On balance, the findings favor dual- over single-process theories of recognition memory, but point to a need to promote sharing of raw data.

More elaborate processing of own-race faces and less elaborate processing of other-race faces contribute to the other-race effect in face memory.

Research suggests that own-race faces are naturally memorized in a more elaborate (e.g., many features of a face or the whole face) way, whereas other-race faces are memorized in a less elaborate (e.g., only selected features of the face) manner. Here, we tested if instructions for judgements about the whole face and a single facial feature modulated the other-race effect in face memory. White participants performed whole-face and single-feature tasks while memorizing White and Black faces for later recognition. Encoding instructions had a stronger impact on own-race than other-race faces. Whole-face instructions increased the other-race effect, whereas single-feature instructions decreased it. Own-race faces in the whole-face task demonstrated event-related potential (ERP) patterns of memory encoding comparable to previously observed natural memory encoding, suggesting naturally more elaborate encoding of own-race faces. ERPs of memory encoding for other-race faces were similar between task conditions and comparable to previously observed natural encoding patterns, suggesting naturally less elaborate encoding. No impact of the encoding tasks was found on ERPs related to memory retrieval, which may be an artefact of a perceptual task that does not enhance semantic details in memory. The current results indicate that some contributions to the other-race effect are more elaborate (more detailed) memory encoding for own-race faces and less elaborate (less detailed) memory encoding for other-race faces. This study also provides evidence for more malleability of own-race than other-race faces through task instructions, consistent with assumptions of perceptual learning theories of the other-race effect.

An Event-Related Potential Investigation of Early Visual Processing Deficits During Face Perception in Youth at Clinical High Risk for Psychosis.

Impairments in early visual face perception are well documented in patients with schizophrenia. Specifically, event-related potential (ERP) research in patients with schizophrenia has demonstrated deficits in early sensory processing of stimulus properties (P1 component) and the structural encoding of faces (N170 component). However, it is not well understood if similar impairments are present in individuals at clinical high risk (CHR) for psychosis (ie, those in the putative prodromal stage of the illness). Thus, it is unknown if face perception deficits are the result of illness onset or are present in the high-risk period for the illness. The present study used the ERP technique to examine neural activation when viewing facial emotion expressions and objects in 44 CHR and 47 control adolescents and young adults (N = 91). P1 amplitude was similar across groups, indicating that early sensory processing impairments did not substantially contribute to face perception deficits in CHR youth. CHR youth exhibited reduced N170 amplitude compared to controls when viewing faces but not objects, implicating a specific deficit in the structural encoding of faces rather than a general perceptual deficit. Further, whereas controls demonstrated the expected face-selective N170 effect (ie, larger amplitude for faces than objects), CHR youth did not, which suggests that facial emotion expressions do not elicit the expected preferential perceptual processing for critical social information in individuals at CHR for psychosis. Together, these findings provide valuable information regarding the specific impairments contributing to face perception deficits in the high-risk period where treatment stands to aid in preventing illness progression.

Dissociations between performance and visual fixations after subordinate- and basic-level training with novel objects.

Previous work suggests that subordinate-level object training improves exemplar-level perceptual discrimination over basic-level training. However, the extent to which visual fixation strategies and the use of visual features, such as color and spatial frequency (SF), change with improved discrimination was not previously known. In the current study, adults (n = 24) completed 6 days of training with 2 families of computer-generated novel objects. Participants were trained to identify one object family at the subordinate level and the other object family at the basic level. Before and after training, discrimination accuracy and visual fixations were measured for trained and untrained exemplars. To examine the impact of training on visual feature use, image color and SF were manipulated and tested before and after training. Discrimination accuracy increased for the object family trained at the subordinate-level, but not for the family trained at the basic level. This increase was seen for all image manipulations (color, SF) and generalized to untrained exemplars within the trained family. Both subordinate- and basic-level training increased average fixation duration and saccadic amplitude and decreased the number of total fixations. Collectively, these results suggest a dissociation between discrimination accuracy, indicative of recognition, and the associated pattern of changes present for visual fixations.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Bird expertise does not increase motion sensitivity to bird flight motion.

While motion information is important for the early stages of vision, it also contributes to later stages of object recognition. For example, human observers can detect the presence of a human, judge its actions, and judge its gender and identity simply based on motion cues conveyed in a point-light display. Here we examined whether object expertise enhances the observer's sensitivity to its characteristic movement. Bird experts and novices were shown point-light displays of upright and inverted birds in flight, or upright and inverted human walkers, and asked to discriminate them from spatially scrambled point-light displays of the same stimuli. While the spatially scrambled stimuli retained the local motion of each dot of the moving objects, it disrupted the global percept of the object in motion. To estimate a detection threshold in each object domain, we systematically varied the number of noise dots in which the stimuli were embedded using an adaptive staircase approach. Contrary to our predictions, the experts did not show disproportionately higher sensitivity to bird motion, and both groups showed no inversion cost. However, consistent with previous work showing a robust inversion effect for human motion, both groups were more sensitive to upright human walkers than their inverted counterparts. Thus, the result suggests that real-world experience in the bird domain has little to no influence on the sensitivity to bird motion and that birds do not show the typical inversion effect seen with humans and other terrestrial movement.

Acute effects of naturalistic THC vs. CBD use on recognition memory: a preliminary study.

The ratio of ∆9-tetrahydrocannabinol (THC) to cannabidiol (CBD) varies widely across cannabis strains. CBD has opposite effects to THC on a variety of cognitive functions, including acute THC-induced memory impairments. However, additional data are needed, especially under naturalistic conditions with higher potency forms of cannabis, commonly available in legal markets. The goal of this study was to collect preliminary data on the acute effects of different THC:CBD ratios on memory testing in a brief verbal recognition task under naturalistic conditions, using legal-market Colorado dispensary products. Thirty-two regular cannabis users consumed cannabis of differing THC and CBD levels purchased from a dispensary and were assessed via blood draw and a verbal recognition memory test both before (pretest) and after (posttest) ad libitum home administration in a mobile laboratory. Memory accuracy decreased as post-use THC blood levels increased (n = 29), whereas performance showed no relationship to CBD blood levels. When controlling for post-use THC blood levels as a covariate, participants using primarily THC-based strains showed significantly worse memory accuracy post-use, whereas subjects using strains containing both THC and CBD showed no differences between pre- and post-use memory performance. Using a brief and sensitive verbal recognition task, our study demonstrated that naturalistic, acute THC use impairs memory in a dose dependent manner, whereas the combination of CBD and THC was not associated with impairment.

Neural and behavioral effects of subordinate-level training of novel objects across manipulations of color and spatial frequency.

Perceptual expertise is marked by subordinate-level recognition of objects in the expert domain. In this study, participants learned one family of full-color, artificial objects at the subordinate (species) level and another family at the basic (family) level. Discrimination of trained and untrained exemplars was tested before and after training across several image manipulations [full-color, grayscale, low spatial frequency (LSF) and high spatial frequency (HSF)] while event-related potentials (ERPs) were recorded. Regardless of image manipulation, discrimination (indexed by d') of trained and of untrained exemplars was enhanced after subordinate-level training, but not after basic-level training. Enhanced discrimination after subordinate-level training generalized to untrained exemplars and to grayscale images and images in which LSF or HSF information was removed. After training, the N170 and N250, recorded over occipital and occipitotemporal brain regions, were both more enhanced after subordinate-level training than after basic-level training. However, the topographic distribution of enhanced responses differed across components. The N170 latency predicted reaction time after both basic-level training and subordinate-level training, highlighting an association between behavioral and neural responses. These findings further elucidate the role of the N170 and N250 as ERP indices of subordinate-level expert object processing and demonstrate how low-level manipulations of color and spatial frequency impact behavior and the N170 and N250 components independent of training or expertise.

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.

Multivariate group-level analysis for task fMRI data with canonical correlation analysis.

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Color and spatial frequency differentially impact early stages of perceptual expertise training.

The current study examined the role of color and spatial frequency on the early acquisition of perceptual expertise after one week of laboratory training with bird stimuli. Participants learned to categorize finches (or warblers) at the subordinate species level (e.g., purple finch) and categorize warblers (or finches) at the more general family level. Training images were presented in their natural colors across 6 sessions. Participants completed a subordinate level species matching task prior to training, one day after training and one week after training while event-related potentials (ERPs) were recorded. Bird images were presented in either their natural congruent color, incongruent color, grayscale, low spatial frequency (LSF < 8 cycles per image) or high spatial frequency (HSF > 8 cycles per image). Replicating previous training studies, performance benefited more from subordinate- than basic-level training. Before training, any color helped performance, but color congruence effects (congruent > incongruent) only emerged after subordinate-level training. Spatial frequency manipulations did not interact with training. The N170 ERP component was sensitive to spatial frequency manipulations, but not color. N170 spatial frequency effects did not interact with training, and training effects generalized to all manipulations except the LSF images. Like performance, color congruence effects on the N250 were only observed after subordinate level training. These results are consistent with previous reports suggesting that effects of perceptual expertise training on performance are more clearly indexed by N250 than N170 effects. Taken together, our behavioral and ERP results show that color plays an important role in both low- and high- level visual processing, supporting surface-plus-edge-based theories for object processing and recognition.

Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences.

We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier's performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects' familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400-500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects' behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.

Neural evidence for the contribution of holistic processing but not attention allocation to the other-race effect on face memory.

Multiple mechanisms have been suggested to contribute to the other-race effect on face memory, the phenomenon of better memory performance for own-race than other-race faces. Here, two of these mechanisms, increased attention allocation and greater holistic processing during memory encoding for own-race than other-race faces, were tested in two separate experiments. In these experiments event-related potentials were measured during study (the difference due to memory, Dm) and test phase (old/new effects) to examine brain activation related to memory encoding and retrieval, allowing for selective investigations of these memory sub-processes. In Experiment 1, participants studied own-race (Caucasian) and other-race (Chinese) faces under focused or divided attention. In Experiment 2, participants studied own-race (Caucasian) and other-race (African American) faces presented upright or upside down (i.e., inverted). Both experiments showed decreases in memory performance when attention allocation or holistic processing was reduced, but these effects were similar for own-race and other-race faces. Manipulations of holistic processing, but not attention allocation, influenced the neural other-race effects during memory encoding. Inverted own-race faces showed similar neural patterns as upright other-race faces, indicating that when holistic processing of own-race faces was reduced, these faces were encoded similarly as upright other-race faces. No influences of the experimental manipulations on other-race effects during memory retrieval were found. The present study provides the first neural evidence that increased holistic processing during memory encoding contributes to the other-race effect on face memory.

MAO-A Phenotype Effects Response Sensitivity and the Parietal Old/New Effect during Recognition Memory.

A critical problem for developing personalized treatment plans for cognitive disruptions is the lack of understanding how individual differences influence cognition. Recognition memory is one cognitive ability that varies from person to person and that variation may be related to different genetic phenotypes. One gene that may impact recognition memory is the monoamine oxidase A gene (MAO-A), which influences the transcription rate of MAO-A. Examination of how MAO-A phenotypes impact behavioral and event-related potentials (ERPs) correlates of recognition memory may help explain individual differences in recognition memory performance. Therefore, the current study uses electroencephalography (EEG) in combination with genetic phenotyping of the MAO-A gene to determine how well-characterized ERP components of recognition memory, the early frontal old/new effect, left parietal old/new effect, late frontal old/new effect, and the late posterior negativity (LPN) are impacted by MAO-A phenotype during item and source memory. Our results show that individuals with the MAO-A phenotype leading to increased transcription have lower response sensitivity during both item and source memory. Additionally, during item memory the left parietal old/new effect is not present due to increased ERP amplitude for correct rejections. The results suggest that MAO-A phenotype changes EEG correlates of recognition memory and influences how well individuals differentiate between old and new items.

3D spatially-adaptive canonical correlation analysis: Local and global methods.

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.

A family of locally constrained CCA models for detecting activation patterns in fMRI.

Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

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.

FN400 and LPC memory effects for concrete and abstract words.

According to dual-process models, recognition memory depends on two neurocognitive mechanisms: familiarity, which has been linked to the frontal N400 (FN400) effect in studies using ERPs, and recollection, which is reflected by changes in the late positive complex (LPC). Recently, there has been some debate over the relationship between FN400 familiarity effects and N400 semantic effects. According to one view, these effects are one and the same. Proponents of this view have suggested that the frontal distribution of the FN400 could be due to stimulus concreteness: recognition memory experiments commonly use highly imageable or concrete words (or pictures), which elicit semantic ERPs with a frontal distribution. In the present study, we tested this claim using a recognition memory paradigm in which subjects memorized concrete and abstract nouns; half of the words changed font color between study and test. FN400 and LPC old/new effects were observed for abstract as well as concrete words, and were stronger over right hemisphere electrodes for concrete words. However, there was no difference in anteriority of the FN400 effect for the two word types. These findings challenge the notion that the frontal distribution of the FN400 old/new effect is fully explained by stimulus concreteness.

A Meta-analytic Review of Auditory Event-Related Potential Components as Endophenotypes for Schizophrenia: Perspectives From First-Degree Relatives.

INTRODUCTION: As endophenotypes bridge the gap between genetics and phenotypic disease expression, identifying reliable markers is important for fostering understanding of pathophysiology. The present aim was to conduct current meta-analyses of 3 key auditory event-related potential (ERP) components that have been held as potential endophenotypes for schizophrenia: P50, P300 amplitude and latency, and mismatch negativity (MMN), reflective of sensory gating, attention and classification speed, and perceptual discrimination ability, respectively. In order to assess endophenotype viability, these components were examined in unaffected relatives of patients with schizophrenia and healthy controls. METHODS: Effect sizes (ES) were examined between relatives and controls for P50 suppression (10 studies, n = 360 relatives, 473 controls), P300 amplitude (20 studies, n = 868 relatives, 961 controls), P300 latency (17 studies, n = 674 relatives, 792 controls), and MMN (11 studies, n = 377 relatives, 552 controls). RESULTS: Reliable differences in P50 suppression (ES = 0.86, P < .001), P300 amplitude (ES = -0.52, P < .001), and P300 latency (ES = 0.44, P < .05) were found between unaffected relatives and controls. A trend was found between relatives and controls for MMN (ES = 0.21, P = 0.06), and the use of extraneous channels was found to be a significant moderator (P = 0.01). When MMN was analyzed using frontocentral channel Fz, a significant difference was found (ES = 0.26, P < 0.01). DISCUSSION: The results indicate that P50 suppression, P300 amplitude and P300 latency, and MMN may serve as viable endophenotypes for schizophrenia.

Separating the FN400 and N400 potentials across recognition memory experiments.

There is a growing debate as to whether frontally distributed FN400 potentials reflect familiarity-based recognition or are functionally identical to centro-parietal N400 reflecting semantic processing. We conducted two experiments in which event-related potentials (ERPs) associated with semantic priming and recognition were recorded, either when priming was embedded within a recognition test (Experiment 1), or when these two phases were separated (Experiment 2). In Experiment 1, we observed 300-500 ms differences between primed and unprimed old words as well as differences between old and new primed words, but these two effects did not differ topographically and both showed midline central maxima. In Experiment 2, the N400 for priming was recorded exclusively during encoding and again showed a midline central distribution. The ERP component of recognition was only found for unrelated words (not primed previously during encoding), and also showed a midline central maximum, but, in addition, was present in the left frontal area of the scalp. Conversely, the priming effect was absent in the left frontal cluster. This pattern of results indicate that FN400 and N400 potentials share similar neural generators; but when priming and recognition are not confounded, these potentials do not entirely overlap in terms of topographical distribution and presumably reflect functionally distinct processes.