Ilke Öztekin

Separable Prefrontal Cortex Contributions to Free Recall

In everyday life, we often must remember the past in the absence of helpful cues in the environment. In these cases, the brain directs retrieval by relying on internally maintained cues and strategies. Free recall is a widely used behavioral paradigm for studying retrieval with minimal cue support. During free recall, individuals often recall semantically related items consecutively—an effect termed semantic clustering—and previous studies have sought to understand clustering to gain leverage on the basic mechanisms supporting strategic recall. Successful recall and semantic clustering depend on the prefrontal cortex (PFC). However, as a result of methodological limitations, few functional magnetic resonance imaging (fMRI) studies have assessed the neural mechanisms at encoding that support subsequent recall, and none have tested the event-related correlates of recall itself. Thus, it remains open whether one or several frontal control mechanisms operate during encoding and recall. Here, we applied a recently developed method (Öztekin et al., 2010) to assess event-related fMRI signal changes during free recall. During encoding, dorsolateral prefrontal cortex (DLPFC) activation was predictive of subsequent semantic clustering. In contrast, subregions of ventrolateral prefrontal cortex (VLPFC) were predictive of subsequent recall, whether clustered or nonclustered, and were inversely associated with clustering during recall. These results suggest that DLPFC supports relational processes at encoding that are sufficient to produce category clustering effects during recall. Conversely, controlled retrieval mechanisms supported by VLPFC support item-specific search during recall.

Ventral Fronto-Temporal Pathway Supporting Cognitive Control of Episodic Memory Retrieval

Achieving our goals often requires guiding access to relevant information from memory. Such goal-directed retrieval requires interactions between systems supporting cognitive control, including ventrolateral prefrontal cortex (VLPFC), and those supporting declarative memory, such as the medial temporal lobes (MTL). However, the pathways by which VLPFC interacts with MTL during retrieval are underspecified. Prior neuroanatomical evidence suggests that a polysynaptic ventral fronto-temporal pathway may support VLPFC-MTL interactions. To test this hypothesis, human participants were scanned using fMRI during performance of a source-monitoring task. The strength of source information was varied via repetition during encoding. Single encoding events should produce a weaker memory trace, thus recovering source information about these items should demand greater cognitive control. Results demonstrated that cortical targets along the ventral path--anterior VLPFC, temporal pole, anterior parahippocampus, and hippocampus--exhibited increases in univariate BOLD response correlated with increases in controlled retrieval demand, independent of factors related to response selection. Further, a functional connectivity analysis indicated that these regions functionally couple and are distinguishable from a dorsal pathway related to response selection demands. These data support a ventral retrieval pathway linking PFC and MTL.

Distributed Patterns of Brain Activity that Lead to Forgetting

Proactive interference (PI), in which irrelevant information from prior learning disrupts memory performance, is widely viewed as a major cause of forgetting. However, the hypothesized spontaneous recovery (i.e., automatic retrieval) of interfering information presumed to be at the base of PI remains to be demonstrated directly. Moreover, it remains unclear at what point during learning and/or retrieval interference impacts memory performance. In order to resolve these open questions, we employed a machine-learning algorithm to identify distributed patterns of brain activity associated with retrieval of interfering information that engenders PI and causes forgetting. Participants were scanned using functional magnetic resonance imaging during an item recognition task. We induced PI by constructing sets of three consecutive study lists from the same semantic category. The classifier quantified the magnitude of category-related activity at encoding and retrieval. Category-specific activity during retrieval increased across lists, consistent with the category information becoming increasingly available and producing interference. Critically, this increase was correlated with individual differences in forgetting and the deployment of frontal lobe mechanisms that resolve interference. Collectively, these findings suggest that distributed patterns of brain activity pertaining to the interfering information during retrieval contribute to forgetting. The prefrontal cortex mediates the relationship between the spontaneous recovery of interfering information at retrieval and individual differences in memory performance.

Relationship between measures of working memory capacity and the time course of short-term memory retrieval and interference resolution.

The response-signal speed-accuracy trade-off (SAT) procedure was used to investigate the relationship between measures of working memory capacity and the time course of short-term item recognition. High- and low-span participants studied sequentially presented 6-item lists, immediately followed by a recognition probe. Analyses of composite list and serial position SAT functions found no differences in retrieval speed between the 2 span groups. Overall accuracy was higher for high spans than low spans, with more pronounced differences for earlier serial positions. Analysis of false alarms to recent negatives (lures from the previous study list) revealed no differences in the timing or magnitude of early false alarms, thought to reflect familiarity-based judgments. However, analyses of false alarms later in retrieval indicated that recollective information accrues more slowly for low spans, which suggests that recollective information may also contribute less to judgments concerning studied items for low-span participants. These findings can provide an explanation for the greater susceptibility of low spans to interference.

Analyzing the information distribution in the fMRI measurements by estimating the degree of locality

In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each seed voxel and its surroundings by minimizing Akaikes Final Prediction Error (FPE) with respect to the mesh size. The degree of locality is represented by the optimum mesh size. Our results indicate that the local mesh size with the highest discriminative power varies across individual participants. The proposed method was tested on an fMRI study consisting of item recognition (IR) and judgment of recency (JOR) tasks. For each participant, the estimated arc weights of each local mesh with different mesh size are used to classify the type of memory judgment (i.e.IR or JOR). Classification accuracy for each participant was derived using k-Nearest Neighbor (k-NN) method. The results indicate that the proposed local mesh model with optimal mesh size can successfully represent discriminative information for neuroimaging data.

Relationship between emotion and forgetting.

A major determinant of forgetting in memory is the presence of interference in the retrieval context. Previous research has shown that proactive interference has less impact for emotional than neutral study material (Levens & Phelps, 2008). However, it is unclear how emotional content affects the impact of interference in memory. Emotional content could directly affect the buildup of interference, leading to reduced levels of interference. Alternatively, emotional content could affect the controlled processes that resolve interference. The present study employed the response deadline speed-accuracy trade-off procedure to independently test these hypotheses. Participants studied 3-item lists consisting of emotional or neutral images, immediately followed by a recognition probe. Results indicated a slower rate of accrual for interfering material (lures from previous study list) and lower levels of interference for emotional than neutral stimuli, suggesting a direct impact of emotion on the buildup of interference. In contrast to this beneficiary effect, resolution of interference for emotional material was less effective than neutral material. These findings can provide insight into the interactions of emotion and memory processes.

Optimizing design efficiency of free recall events for fmri

Free recall is a fundamental paradigm for studying memory retrieval in the context of minimal cue support. Accordingly, free recall has been extensively studied using behavioral methods. However, the neural mechanisms that support free recall have not been fully investigated due to technical challenges associated with probing individual recall events with neuroimaging methods. Of particular concern is the extent to which the uncontrolled latencies associated with recall events can confer sufficient design efficiency to permit neural activation for individual conditions to be distinguished. The present study sought to rigorously assess the feasibility of testing individual free recall events with fMRI. We used both theoretically and empirically derived free recall latency distributions to generate simulated fMRI data sets and assessed design efficiency across a range of parameters that describe free recall performance and fMRI designs. In addition, two fMRI experiments empirically assessed whether differential neural activation in visual cortex at onsets determined by true free recall performance across different conditions can be resolved. Collectively, these results specify the design and performance parameters that can provide comparable efficiency between free recall designs and more traditional jittered event-related designs. These findings suggest that assessing BOLD response during free recall using fMRI is feasible, under certain conditions, and can serve as a powerful tool in understanding the neural bases of memory search and overt retrieval.

Mesh learning approach for brain data modeling

The major goal of this study is to model the memory process using neural activation patterns in the brain. To achieve this goal, neural activation was acquired using functional Magnetic Resonance Imaging (fMRI) during memory encoding and retrieval. fMRI are known are trained for each class using a learning system. The most important component of this learning system is feature space. In this project, an original feature space for the fMRI data is proposed. This feature space is defined by a mesh network which models the relationship between voxels. In the suggested mesh network, the distance between voxels is determined by using physical and functional neighborhood concepts. For the functional neighborhood, the similarities between the time series, gained from voxels, are measured. With the proposed method, a data set with 10 classes is used for the encoding and retrieval processes, and the classifier is trained with the learning algorithms in order to predict the class the data belongs.

A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

How neurons influence each others firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cognitive state by the edge weights of these meshes each of which is estimated assuming a regularized linear relationship among voxel time series in a predefined locality. The estimated mesh edge weights provide a better representation of information in the brain for cognitive state or task classification. We examine the representative power of our mesh edge weights on visual recognition and emotional memory retrieval experiments by training a support vector machine classifier. Also, we use mesh edge weights as feature vectors of inter-subject classification on Human Connectome Project task fMRI dataset, and test their performance. We observe that mesh edge weights perform better than the popular fMRI features, such as, raw voxel intensity values, pairwise correlations, features extracted using PCA and ICA, for classifying the cognitive states.

Learning Deep Temporal Representations for fMRI Brain Decoding

Functional magnetic resonance imaging fMRI produces low number of samples in high dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study, we propose a combination of autoencoding and temporal convolutional neural network architecture which aims to reduce the feature dimensionality along with improved classification performance. The proposed network learns temporal representations of voxel intensities at each layer of the network by leveraging unlabeled fMRI data with regularized autoencoders. Learned temporal representations capture the temporal regularities of the fMRI data and are observed to be an expressive bank of activation patterns. Then a temporal convolutional neural network with spatial pooling layers reduces the dimensionality of the learned representations. By employing the proposed method, raw input fMRI data is mapped to a low-dimensional feature space where the final classification is conducted. In addition, a simple decorrelated representation approach is proposed for tuning the model hyper-parameters. The proposed method is tested on a ten class recognition memory experiment with nine subjects. Results support the efficiency and potential of the proposed model, compared to the baseline multi-voxel pattern analysis techniques.