Impact of load-related neural processes on feature binding in visuospatial working memory

Background

The capacity of visual working memory (WM) is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood.

Objective

To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval.

Methods and Findings

18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position) in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network.

Conclusions and Significance

The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be ‘automatic’ but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this network. These findings provide new insights into how feature binding operates within the capacity-limited WM system.     

Visual working memory capacity and proactive interference

Background

Visual working memory capacity is extremely limited and appears to be relatively immune to practice effects or the use of explicit strategies. The recent discovery that visual working memory tasks, like verbal working memory tasks, are subject to proactive interference, coupled with the fact that typical visual working memory tasks are particularly conducive to proactive interference, suggests that visual working memory capacity may be systematically under-estimated.

Methodology/Principal Findings

Working memory capacity was probed behaviorally in adult humans both in laboratory settings and via the Internet. Several experiments show that although the effect of proactive interference on visual working memory is significant and can last over several trials, it only changes the capacity estimate by about 15%.

Conclusions/Significance

This study further confirms the sharp limitations on visual working memory capacity, both in absolute terms and relative to verbal working memory. It is suggested that future research take these limitations into account in understanding differences across a variety of tasks between human adults, prelinguistic infants and nonlinguistic animals.     

Visual working memory capacity does not modulate the feature-based information filtering in visual working memory

Background

The limited capacity of visual working memory (VWM) requires us to select the task relevant information and filter out the irrelevant information efficiently. Previous studies showed that the individual differences in VWM capacity dramatically influenced the way we filtered out the distracters displayed in distinct spatial-locations: low-capacity individuals were poorer at filtering them out than the high-capacity ones. However, when the target and distracting information pertain to the same object (i.e., multiple-featured object), whether the VWM capacity modulates the feature-based filtering remains unknown.

Methodology/Principal Findings

We explored this issue mainly based on one of our recent studies, in which we asked the participants to remember three colors of colored-shapes or colored-landolt-Cs while using two types of task irrelevant information. We found that the irrelevant high-discriminable information could not be filtered out during the extraction of VWM but the irrelevant fine-grained information could be. We added 8 extra participants to the original 16 participants and then split the overall 24 participants into low- and high-VWM capacity groups. We found that regardless of the VWM capacity, the irrelevant high-discriminable information was selected into VWM, whereas the irrelevant fine-grained information was filtered out. The latter finding was further corroborated in a second experiment in which the participants were required to remember one colored-landolt-C and a more strict control was exerted over the VWM capacity.

Conclusions/Significance

We conclude that VWM capacity did not modulate the feature-based filtering in VWM.     

Cortical functional connectivity during the retention of affective pictures in working memory: EEG-source theta coherence analysis

The pattern of cortical functional connectivity in the source space was studied in a group of righthanded adult participants (N = 44:17 women, 27 men, aged M = 29.61 ± 6.45 years). Participants retained the traces of realistic pictures of positive, neutral, and negative emotional valences in their working memory (WM) while performing the same-different task. Within the framework of this task, participants had to compare the initial picture against a target picture that followed after a specified delay. The coherence (COH) between the pairs of cortical sources chosen in advance according to fMRI data was estimated in the theta frequency range for the period preceding the initial stimulus, during the retention of the initial stimulus in WM, and during the rest interval between successive trials. Two distinct sets of functional links were found. The links of the first type that presumably reflected the involvement of sustained attention were between the dorsal anterior cingulate cortex, the prefrontal areas, and temporal areas of the right hemispheres. When compared to the rest period, the links of this type showed strengthening not only during the retention period but also during the period preceding the initial picture. The links of the second type presumably reflected a progressive neocortex-to-hippocampus functional integration with increasing memory load and strengthened exclusively during the retention period. These links were between the parietal, temporal and prefrontal cortices in the lateral surface of both hemispheres with the additional inclusion of the posterior cingulate cortex and the medial parietal cortex in the left hemisphere. The impact of emotional valence on the strength and topography of the functional links of the second type was found. In the left hemisphere, the increase of strength of cortical interaction was more pronounced for the pictures of positive valence than for the pictures of either neutral or negative valences. When compared to the pictures of neutral valence, the retention of pictorial information of both positive and negative valence showed some extraneous integration of the cortical areas for the theta rhythm. This finding might be related to the additional load exerted by emotionally colored pictures onto the mechanisms of short-time retention of visual information.     

Neurodevelopmental changes in working memory and cognitive control

One of the most salient ways in which our behavior changes during childhood and adolescence is that we get better at working towards long-term goals, at ignoring irrelevant information that could distract us from our goals, and at controlling our impulses - in other words, we exhibit improvements in cognitive control. Several recent magnetic resonance imaging studies have examined the developmental changes in brain structure and function that underlie improvements in working memory and cognitive control. Increased recruitment of task-relevant regions in the prefrontal cortex, parietal cortex and striatum over the course of development is associated with better performance in a range of cognitive tasks. Further work is needed to assess the role of experience in shaping the neural circuitry that underlies cognitive control.     

Distributed neural networks of tactile working memory

Microelectrode recordings of cortical activity in primates performing working memory tasks reveal some cortical neurons exhibiting sustained or graded persistent elevations in firing rate during the period in which sensory information is actively maintained in short-term memory. These neurons are called “memory cells”. Imaging and transcranial magnetic stimulation studies indicate that memory cells may arise from distributed cortical networks. Depending on the sensory modality of the memorandum in working memory tasks, neurons exhibiting memory-correlated patterns of firing have been detected in different association cortices including prefrontal cortex, and primary sensory cortices as well.Here we elaborate on neurophysiological experiments that lead to our understanding of the neuromechanisms of working memory, and mainly discuss findings on widely distributed cortical networks involved in tactile working memory.     

Exact neural mass model for synaptic-based working memory

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.     

From cognitive to neural models of working memory

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.     

A dynamic neural network with temporal coding and functional connectivity

A neural network model capable of altering its pattern classifying properties by program input is proposed. Here the “program input” is another source of input besides the pattern input. Unlike most neural network models, this model runs as a deterministic point process of spikes in continuous time; connections among neurons have finite delays, which are set randomly according to a normal distribution. Furthermore, this model utilizes functional connectivity which is dynamic connectivity among neurons peculiar to temporal-coding neural networks with short neuronal decay time constants. Computer simulation of the proposed network has been performed, and the results are considered in light of experimental results shown recently for correlated firings of neurons. Received: 6 December 1996 / Accepted in revised form: 15 September 1997     

Increased memory load-related frontal activation after estradiol treatment in postmenopausal women

Prior research shows that menopause is associated with changes in cognition in some older women. However, how estrogen loss and subsequent estrogen treatment affects cognition and particularly the underlying brain processes responsible for any cognitive changes is less well understood. We examined the ability of estradiol to modulate the manipulation of information in working memory and related brain activation in postmenopausal women. Twenty healthy postmenopausal women (mean age (SD) = 59.13 (5.5)) were randomly assigned to three months of 1 mg oral 17-β estradiol or placebo. At baseline and three months later each woman completed a visual verbal N-back sequential letter test of working memory during functional magnetic resonance imaging (fMRI). The fMRI data showed that women who were treated with estradiol for three months had increased frontal activation during the more difficult working memory load conditions compared to women treated with placebo. Performance on the verbal working memory task showed no difference between estradiol and placebo treated subjects. These data are consistent with prior work showing increases in frontal activation on memory tasks after estrogen treatment. However, this is the first study to show that estrogen-induced increases in brain activity were tied to cognitive load during a verbal working memory task. These data suggest that estradiol treatment effects on cognition may be in part produced through modulation of frontal lobe functioning under difficult task conditions.     

Increased memory load-related frontal activation after estradiol treatment in postmenopausal women

Prior research shows that menopause is associated with changes in cognition in some older women. However, how estrogen loss and subsequent estrogen treatment affects cognition and particularly the underlying brain processes responsible for any cognitive changes is less well understood. We examined the ability of estradiol to modulate the manipulation of information in working memory and related brain activation in postmenopausal women. Twenty healthy postmenopausal women (mean age (SD) = 59.13 (5.5)) were randomly assigned to three months of 1 mg oral 17-β estradiol or placebo. At baseline and three months later each woman completed a visual verbal N-back sequential letter test of working memory during functional magnetic resonance imaging (fMRI). The fMRI data showed that women who were treated with estradiol for three months had increased frontal activation during the more difficult working memory load conditions compared to women treated with placebo. Performance on the verbal working memory task showed no difference between estradiol and placebo treated subjects. These data are consistent with prior work showing increases in frontal activation on memory tasks after estrogen treatment. However, this is the first study to show that estrogen-induced increases in brain activity were tied to cognitive load during a verbal working memory task. These data suggest that estradiol treatment effects on cognition may be in part produced through modulation of frontal lobe functioning under difficult task conditions.     

Dynamic changes in brain functional connectivity during concurrent dual-task performance

This study investigated the spatial, spectral, temporal and functional proprieties of functional brain connections involved in the concurrent execution of unrelated visual perception and working memory tasks. Electroencephalography data was analysed using a novel data-driven approach assessing source coherence at the whole-brain level. Three connections in the beta-band (18-24 Hz) and one in the gamma-band (30-40 Hz) were modulated by dual-task performance. Beta-coherence increased within two dorsofrontal-occipital connections in dual-task conditions compared to the single-task condition, with the highest coherence seen during low working memory load trials. In contrast, beta-coherence in a prefrontal-occipital functional connection and gamma-coherence in an inferior frontal-occipitoparietal connection was not affected by the addition of the second task and only showed elevated coherence under high working memory load. Analysis of coherence as a function of time suggested that the dorsofrontal-occipital beta-connections were relevant to working memory maintenance, while the prefrontal-occipital beta-connection and the inferior frontal-occipitoparietal gamma-connection were involved in top-down control of concurrent visual processing. The fact that increased coherence in the gamma-connection, from low to high working memory load, was negatively correlated with faster reaction time on the perception task supports this interpretation. Together, these results demonstrate that dual-task demands trigger non-linear changes in functional interactions between frontal-executive and occipitoparietal-perceptual cortices.     

Role of connectivity in immune and neural network models: memory development and aging.

In this paper we analyzed how connectivity (defined as number of connections between network elements) can affect the memory capacity of a network-based model of the Immune System (IS) and of a model of the Nervous System (NS) synaptic plasticity (BCM model). The key point is the concept of competition between the characteristic variables that represent the response of such systems to environmental stimuli: the clonal concentrations for the IS, and the neuron responses for the BCM model. The memory states of both systems are characterized by a high selectivity to specific input patterns, reflecting a similar behaviour of their development rules. This selectivity property of memory states can be controlled by changing the degree of the internal connectivity in each system. We can explain the changes occurring in IS memory states during lifespan as due to a reshaping of its internal connectivity. This assumption is in agreement with experimental observations, reporting an increase of IS memory cells during lifespan. The change of connectivity in the BCM model leads to the introduction of quasilocal variables governing the plasticity of groups of synaptic junctions. This could be interpreted as the result of a refinement of neuron internal mechanisms during development, or it could be seen as a different learning rule deriving from the original BCM theory. We argue that connectivity seems to play an important role in a large class of biological systems controlled by competition mechanisms. Moreover, changes in connectivity may lead to changes in memory properties during development and aging.     

Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

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Differential neural activation for updating rule versus stimulus information in working memory

Establishing what information is actively maintained in working memory (WM) and how it is represented and controlled is essential to understanding how such information guides future behavior. WM has traditionally been investigated in terms of the maintenance of stimulus-specific information, such as locations or words. More recently, investigators have emphasized the importance of rules that establish relationships between those stimuli and the pending response. The current study used a mental arithmetic task with fMRI to test whether updating of numbers (i.e., stimuli) and updating of mathematical operations (i.e., rules) in WM relies on the same neural system. Results indicate that, while a common network is activated by both types of updating, rule updating preferentially activates prefrontal cortex while number updating preferentially activates parietal cortex. The results suggest that both numbers and rules are maintained in WM but that they are different types of information that are controlled independently.     

Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions

One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions.     

Investigating the neural basis for functional and effective connectivity. Application to fMRI

Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.