Widespread ripples synchronize human cortical activity during sleep,waking, and memory recall

Declarative memory encoding, consolidation, and retrieval require the integration of elements encoded in widespread cortical locations. The mechanism whereby such “binding” of different components of mental events into unified representations occurs is unknown. The “binding-by-synchrony” theory proposes that distributed encoding areas are bound by synchronous oscillations enabling enhanced communication. However, evidence for such oscillations is sparse. Brief high-frequency oscillations (“ripples”) occur in the hippocampus and cortex and help organize memory recall and consolidation. Here, using intracranial recordings in humans, we report that these ∼70-ms-duration, 90-Hz ripples often couple (within ±500 ms), co-occur (≥ 25-ms overlap), and, crucially, phase-lock (have consistent phase lags) between widely distributed focal cortical locations during both sleep and waking, even between hemispheres. Cortical ripple co-occurrence is facilitated through activation across multiple sites, and phase locking increases with more cortical sites corippling. Ripples in all cortical areas co-occur with hippocampal ripples but do not phase-lock with them, further suggesting that cortico-cortical synchrony is mediated by cortico-cortical connections. Ripple phase lags vary across sleep nights, consistent with participation in different networks. During waking, we show that hippocampo-cortical and cortico-cortical coripples increase preceding successful delayed memory recall, when binding between the cue and response is essential. Ripples increase and phase-modulate unit firing, and coripples increase high-frequency correlations between areas, suggesting synchronized unit spiking facilitating information exchange. co-occurrence, phase synchrony, and high-frequency correlation are maintained with little decrement over very long distances (25 cm). Hippocampo-cortico-cortical coripples appear to possess the essential properties necessary to support binding by synchrony during memory retrieval and perhaps generally in cognition.

Ripples are brief high-frequency oscillations that have been well-studied in the rodent hippocampus during non-rapid eye movement sleep (NREM), when they mark the replay of events from the previous waking period, and are critical for memory consolidation in the cortex (1–4). Recently, ripples were found in rat association cortex but not primary sensory or motor cortices during sleep, with increased coupling to hippocampal ripples in sleep following learning (5). An earlier study reported ripples in waking and sleeping cat cortex, especially NREM (6). In humans, cortical ripples have recently been identified during waking and were more frequently found in lateral temporal than in rolandic cortex. Hippocampal sharpwave-ripple occurrence and ripple coupling between parahippocampal gyrus and temporal association cortex increase preceding memory recall in humans (7, 8), possibly facilitating replay of cortical neuron firing sequences established during encoding (9). In rats, ripples co-occur between hippocampus and ∼1 mm² of parietal cortex in sleep following learning (5), in mice, ripples propagate from the hippocampus to retrosplenial cortex (10), and in cats, ripple co-occurrence is reportedly limited to short distances (6).We recently reported, using human intracranial recordings, that ∼70-ms-long, ∼90-Hz ripples are ubiquitous in all regions of the cortex during NREM as well as waking (11). During waking, cortical ripples occur on local high-frequency activity peaks. During sleep, cortical ripples typically occur on the cortical down-to-upstate transition, often with 10- to 16-Hz cortical sleep spindles, and local unit firing patterns consistent with generation by pyramidal-interneuron feedback. We found that cortical ripples group cofiring within the window of spike-timing-dependent plasticity. These findings are consistent with cortical ripples contributing to memory consolidation during NREM in humans.While there is thus an emerging appreciation that hippocampal and cortical ripples have an important role in human and rodent memory, nothing is known of the network properties of cortical ripples. Specifically, it is not known if ripples co-occur or phase-synchronize between cortical sites and, if so, whether this is affected by distance or correlated with the reconstruction of declarative memories. These would be critical properties for cortical ripples to participate in the binding of different elements of memories that are represented in disparate cortical areas, the essence of hippocampus-dependent memory (12).The binding of disparate elements of a memory is a specific case of a more general problem of how the various contents of a mental event are united into a single experience. Most often addressed is how different visual qualities of an object (e.g., color, shape, location, and texture) are associated with each other (13), but the “binding problem” generalizes to how the contents of awareness are unified in a single stream of consciousness (14). Modern accounts often rely on hierarchical and multimodal convergence. However, cortical processing is distributed, and it would be difficult to represent the combinatorial possibilities contained in all potential experiences with convergence, leading to the suggestion that temporal synchrony binds cortical areas (15). This hypothesis was first supported by phase-locked unit firing and local field potentials (LFPs) at 40 to 60 Hz evoked by simple visual stimuli in the anesthetized cat primary visual cortex at distances <7 mm (16). Although some further studies found similar results in other cortical areas, behaviors, and species, as would be expected under the binding-by-synchrony hypothesis (17, 18), others have been less successful (19). Synchronous high-gamma oscillations have also been criticized as providing no mechanism for neuronal interaction beyond generic activation (19, 20).Here, using human intracranial stereoelectroencephalography (SEEG) recordings, we find that ripples co-occur and, remarkably, phase-synchronize across all lobes and between both hemispheres, with little decrement, even at long distances. Furthermore, ripple co-occurrence is enhanced between cortical sites as well as between the cortex and hippocampus preceding successful delayed recall. Corippling was progressively above that expected as it involved a larger proportion of sites, and this led to progressively stronger phase locking. Single-unit firing increased during, and phase-locked to, cortical ripples, providing a basic requirement for ripples to enhance communication via gain modulation and coincidence detection. Enhanced communication was supported by our finding of increased high-frequency correlation between even distant corippling regions. These characteristics suggest that distributed, phase-locked cortical ripples possess the properties that may allow them to help integrate different components of a memory. More generally, ripples may help to “bind” different aspects of a mental event encoded in widespread cortical areas into a coherent representation.     

An assessment of fixed-capacity models of visual working memory

Visual working memory is often modeled as having a fixed number of slots. We test this model by assessing the receiver operating characteristics (ROC) of participants in a visual-working-memory change-detection task. ROC plots yielded straight lines with a slope of 1.0, a tell-tale characteristic of all-or-none mnemonic representations. Formal model assessment yielded evidence highly consistent with a discrete fixed-capacity model of working memory for this task.     

Sleep,Brain, and Cognition Special Feature: Sleep deprivation and hippocampal ripple disruption after one-session learning eliminate memory expression the next day

Memory reactivation during non–rapid-eye-movement ripples is thought to communicate new information to a systems-wide network and thus can be a key player mediating the positive effect of sleep on memory consolidation. Causal experiments disrupting ripples have only been performed in multiday training paradigms, which decrease but do not eliminate memory performance, and no comparison with sleep deprivation has been made. To enable such investigations, we developed a one-session learning paradigm in a Plusmaze and show that disruption of either sleep with gentle handling or hippocampal ripples with electrical stimulation impaired long-term memory. Furthermore, we detected hippocampal ripples and parietal high-frequency oscillations after different behaviors, and a bimodal frequency distribution in the cortical events was observed. Faster cortical high-frequency oscillations increased after normal learning, a change not seen in the hippocampal ripple-disruption condition, consistent with these having a role in memory consolidation.

Sleep is important for memory consolidation (1). Critical network interactions associated with systems consolidation are thought to occur during sleep (2, 3). Specifically, reactivation of previous experiences during hippocampal ripples (HPC-R) during sleep is a long-known phenomenon that has been proposed to enable systems consolidation (4, 5). While early studies provided correlative links between HPC-R, memory reactivations, and memory performance (6, 7), it is only over the last decade that several studies started to explore and reveal a causal relationship. They use closed-loop ripple disruption, through which ripples during either rest/sleep (i.e., targeting memory consolidation) or wakefulness (e.g., targeting memory retrieval, working memory, planning, or consolidation) are detected on-line and disrupted, and test if this can alter subsequent memory performance (8). However, closed-loop approaches have been applied in multiday memory paradigms for only 1 h posttraining and, while memory performance was impaired, it was still above chance in the disruption groups (9, 10). Further, while ripple activity has been proposed to be the main mechanism during sleep enabling consolidation, ripple-disruption effects have not yet been directly compared with sleep-deprivation effects on memory consolidation. To examine whether disruption of ripples or sleep deprivation is needed for consolidation, a one-session learning paradigm that leads to long-term memory expression is needed.To test this, we developed a one-session learning paradigm in a Plusmaze that results in long-term memory. We compared new goal location learning with different behaviors in the event arena (11, 12): (1) a nonlearning “Baseline” condition; (2) Foraging, a mix of open-field foraging and track running with small chocolate rewards spread along a track; and (3) Novelty, exploration of a new environment with very novel cues/textures.     

Expectation effects in working memory training

There is a growing body of research focused on developing and evaluating behavioral training paradigms meant to induce enhancements in cognitive function. It has recently been proposed that one mechanism through which such performance gains could be induced involves participants’ expectations of improvement. However, no work to date has evaluated whether it is possible to cause changes in cognitive function in a long-term behavioral training study by manipulating expectations. In this study, positive or negative expectations about cognitive training were both explicitly and associatively induced before either a working memory training intervention or a control intervention. Consistent with previous work, a main effect of the training condition was found, with individuals trained on the working memory task showing larger gains in cognitive function than those trained on the control task. Interestingly, a main effect of expectation was also found, with individuals given positive expectations showing larger cognitive gains than those who were given negative expectations (regardless of training condition). No interaction effect between training and expectations was found. Exploratory analyses suggest that certain individual characteristics (e.g., personality, motivation) moderate the size of the expectation effect. These results highlight aspects of methodology that can inform future behavioral interventions and suggest that participant expectations could be capitalized on to maximize training outcomes.

There is a great deal of current scientific interest as to whether and/or how basic cognitive skills can be improved via dedicated behavioral training (1–3). This potential, if realized, could lead to substantial real-world impact. Indeed, effective training paradigms would have significant value not only for populations that show deficits in cognitive skills (e.g., individuals diagnosed with Attention Deficit Hyperactivity Disorder [ADHD] or Alzheimer’s disease and related dementias) but also, for the general public, where core cognitive capacities underpin success in both academic and professional contexts (4–6). These possible translational applications, paired with an emerging understanding of how to best unlock neuroplastic change across the life span (7, 8), have spurred hundreds of behavioral intervention studies over the past few decades. While the results have not been uniformly positive (perhaps not surprising given the massive heterogeneity in theoretical approach, methods, etc.), multiple meta-analyses suggest that it is possible for cognitive functions to be improved via some forms of dedicated behavioral training (9–11). However, while these basic science results provide optimism that real-world gains could be realized [and in fact, real-world gain is already being realized in some spheres, such as a Food and Drug Administration (FDA)–cleared video game–based treatment supplement for ADHD (12, 13)], concerns have been raised as to whether those interventions that have produced positive outcomes are truly working via the proposed mechanisms or through other nonspecific third-variable mechanisms. Several factors have been proposed to explain improvements in behavioral interventions, including selective attrition, contextual factors, regression to the mean, and practice effects to name a few (14). Here, we focus on whether expectation-based (i.e., placebo) mechanisms can explain improvements in cognitive training (15–17).In other domains, such as in clinical trials in the pharmaceutical domain for instance, expectation-based mechanisms are typically controlled for by making the experimental treatment and the control treatment perceptually indistinguishable (e.g., both might be clear fluids in an intravenous bag or a white unmarked pill). Because perceptual characteristics cannot be used to infer condition, this methodology is meant to ensure that expectations are matched between the experimental and control groups (both in terms of the expectations that the participants have and in terms of the expectations that the research team members who interact with the participants have). Under ideal circumstances, the use of such a “double-unaware” design ensures that expectations cannot be an explanatory mechanism underlying any differences between the groups’ outcomes [note that we use the double-unaware terminology in lieu of the more common “double-blind” terminology, which can be seen as ableist (18)].It is unclear whether most pharmaceutical trials do, in fact, truly meet the double-unaware standard (e.g., despite being perceptually identical, active and control treatments nonetheless often produce different patterns of side effects that could be used to infer condition) (19, 20). Yet, meeting the double-unaware standard is particularly difficult in the case of cognitive training interventions (16). Here, there is simply no way to make the experimental and control interventions perceptually indistinguishable while at the same time, ensuring that the experimental condition contains an “active ingredient” that the control condition lacks. In behavioral interventions, no matter what the active ingredient may be, it will necessarily produce a difference in look and feel as compared with a training condition that lacks the ingredient.Researchers designing cognitive training trials, therefore, typically attempt to utilize experimental and control conditions that, while differing in the proposed active ingredient, will nonetheless produce similar expectations about the likely outcomes (16, 21–24). This type of matching process, however, is inherently difficult as it is not always clear what expectations will be induced by a given type of experience. Consistent with this, there is reason to believe that expectations have not always been successfully matched. In multiple cases, despite attempts to match expectations across conditions, participants in behavioral intervention studies have nonetheless indicated the belief that the true active training task will produce more cognitive gains than the control task (25–27). Critically, the data as to whether differential expectations in these cases actually, in turn, influence the observed outcomes are decidedly mixed. In some cases, participant expectations differed between training and control conditions, and these expectations were at least partially related to differences in behavior (25). In other cases, participants expected to improve but did not show any actual improvements in cognitive skill (28), or the degree to which they improved was unrelated to their stated expectations (29).Regardless of the mixed nature of the data thus far, there is increasing consensus that training studies should 1) attempt to match the expectations generated by their experimental and control treatment conditions, 2) measure the extent to which this matching is successful and if the matching was not successful, and 3) evaluate the extent to which differential expectations explain differences in outcome (16, 30). Yet, such methods are not ideal with respect to getting to the core question of whether expectation-based mechanisms can, in fact, alter performance on cognitive tasks in the context of cognitive intervention studies in the first place. Indeed, there is a growing body of work suggesting that self-reported expectations do not necessarily fully reflect the types of predictions being generated by the brain (e.g., it is possible to produce placebo analgesia effects even in the absence of self-reported expectation of pain relief) (31, 32). Instead, addressing this question would entail purposefully maximizing the differences in expectations between groups (i.e., rather than attempting to minimize differential expectations and then, measuring the possible impact if the differences were not eliminated, as is done in most cognitive training studies).One key question then is how to maximize such expectations. In general, in those domains that have closely examined placebo effects, expectations are typically induced through two broad routes: an explicit route and an associative route. In the explicit route, as given by the name, participants are explicitly told what behavioral changes they should expect (e.g., “this pill will improve your symptoms” or “this cognitive training will improve your cognition”) (33). In the associative learning route, participants are made to experience a behavioral change associated with expected outcomes (e.g., feeling improvements of symptoms or gains in cognition) through some form of deception (34). For example, in an explicit expectation induction study, participants may first have a hot temperature probe applied to their skin, after which they are asked to rate their pain level. An inert cream is then applied that is explicitly described as an analgesic before the hot temperature probe is reapplied. If participants indicate less pain after the cream is applied, this is taken as evidence of an explicit expectation effect. In the associative expectation version, the study progresses identically as above except that when the hot temperature probe is applied the second time, it is at a physically lower temperature than it was initially (participants are not made aware of this fact). This is meant to create an associative pairing between the cream and a reduction in experienced pain (i.e., not only are they told that the cream will reduce their pain, they are provided “evidence” that the cream works as described). If then, after reapplying the cream and applying the hot temperature probe a third time (this time at the same temperature setting as the first application), if participants indicate even less pain than in the explicit condition, this is taken as evidence of an associative expectation effect. It remains to be clarified how associative learning approaches may be best applied to cognitive training; however, we suggest here that a reasonable approach to this would be to provide test sessions where test items are manipulated to provide participants with an experience where they perceive that they are performing better, or worse in the case of a nocebo, than they did at the initial test session. Notably, while there are cases where strong placebo effects have been induced via only explicit (35) or only associative methods (36), in general, the most consistent and robust effects have been induced when a combination of these methods has been utilized (37–39).Within the cognitive training field, the corresponding literature is quite sparse. Few studies have deliberately attempted to create differences in participant expectations, and of those, all have used the explicit expectation route alone, have implemented the manipulation in the context of rather short interventions (e.g., utilizing 20 min of “training” within a single session rather than the multiple hours that are typically implemented in actual training studies), or both. Of these, the results are again at best mixed, with one study suggesting that expectations alone can result in a positive impact on cognitive measures (40), while others have found no such effects (33, 41, 42). Given this critical gap in knowledge, here we examined the impact of manipulations deliberately designed to maximize the presence of differential expectations in the context of a long-term cognitive training study.     

Specific responses of human hippocampal neurons are associated with better memory

A population of human hippocampal neurons has shown responses to individual concepts (e.g., Jennifer Aniston) that generalize to different instances of the concept. However, recordings from the rodent hippocampus suggest an important function of these neurons is their ability to discriminate overlapping representations, or pattern separate, a process that may facilitate discrimination of similar events for successful memory. In the current study, we explored whether human hippocampal neurons can also demonstrate the ability to discriminate between overlapping representations and whether this selectivity could be directly related to memory performance. We show that among medial temporal lobe (MTL) neurons, certain populations of neurons are selective for a previously studied (target) image in that they show a significant decrease in firing rate to very similar (lure) images. We found that a greater proportion of these neurons can be found in the hippocampus compared with other MTL regions, and that memory for individual items is correlated to the degree of selectivity of hippocampal neurons responsive to those items. Moreover, a greater proportion of hippocampal neurons showed selective firing for target images in good compared with poor performers, with overall memory performance correlated with hippocampal selectivity. In contrast, selectivity in other MTL regions was not associated with memory performance. These findings show that a substantial proportion of human hippocampal neurons encode specific memories that support the discrimination of overlapping representations. These results also provide previously unidentified evidence consistent with a unique role of the human hippocampus in orthogonalization of representations in declarative memory.A cornerstone of memory is the ability to discriminate among similar events (e.g., remembering where one parked his/her car today as opposed to yesterday). To discriminate and retrieve similar memories effectively, it is necessary to maintain separation of their neural representations. While the entire medial temporal lobe (MTL) is crucial for the formation of new declarative memories for facts and events (1, 2), focal hippocampal lesions can lead to selective deficits in recognition memory whereby discrimination of previously learned items from novel similar items is impaired (3, 4). Consistent with these findings, the hippocampus is thought to orthogonalize or separate overlapping information to support memory specificity (5). Results supporting this idea come from human fMRI studies showing that the blood-oxygenated level dependent (BOLD) signal in the combined area of CA3 and dentate gyrus (CA3DG) of the hippocampus differentiates between old (targets) and novel similar (lure) images (6, 7). However, because the hippocampal BOLD response does not always reflect underlying single neuron activity (8), intracranial recordings from single neurons in humans can be more directly informative. For example, single neurons within the human hippocampus have been found to significantly increase in firing rate to varying photographs of an individual face (e.g., Jennifer Aniston; refs. 9–12), suggesting that hippocampal neurons may participate in concept representations that are not specific to a single stimulus. It is unknown whether a different population of hippocampal neurons exists that are selective for specific stimuli (i.e., a particular photograph of a face) and whether activity in these neurons supports the role of the hippocampus in discrimination of overlapping memory representations.The current study sought to determine whether the neuronal code reflecting the creation of specific memories could be reflected at the single neuron level in humans. With the rare opportunity to work with patients undergoing clinical evaluation for possible surgical therapy, we were able to record human MTL neurons while subjects were engaged in a hippocampal-dependent memory task requiring the discrimination of studied targets from similar unstudied lures. We hypothesized that firing in a population of hippocampal neurons would reflect memory specificity by firing in a selective manner that discriminates previously learned targets from similar lures. We predicted that firing rate increases that were specific to targets would not generalize to similar lures. Given the suggested role of the hippocampus in pattern separation for memory, we predicted that the specificity of hippocampal firing would be related to the participant’s performance on the discrimination task. We further hypothesized that this relationship would be specific to hippocampal neurons, whereas the firing pattern of neurons in other medial temporal lobe regions which we assessed, including the entorhinal cortex, parahippocompal cortex, and amygdala, would not relate to performance on a test of discrimination ability in memory.     

Multiple component networks support working memory in prefrontal cortex

Lateral prefrontal cortex (PFC) is regarded as the hub of the brain’s working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two modes encode early and late forms of information storage, whereas the third mode encodes response preparation. Neurons that reflect these modes are concentrated at different anatomical locations in PFC and exhibit distinct patterns of coordinated firing rates and spike timing during WM, consistent with distinct networks. These findings support multiple component models of WM and consequently predict distinct failures that could contribute to neurologic dysfunction.High-level cognition depends on the ability to translate stored information about recent experience into a behaviorally appropriate response, an ability known as working memory (WM). WM relies on a storage process that actively maintains information and a control process that manipulates stored information to support the selection and preparation of a contingent response (1–3). The neural mechanisms that support WM involve networks that are broadly distributed throughout the brain (4–7) and rely heavily on the prefrontal cortex (PFC) for normal operation (6–9). However, the degree to which WM is supported by a single distributed network or multiple specialized network components in PFC remains unclear (6, 10, 11), hindering progress in the search for neurocognitive therapies to treat disorders of cognition (12).Persistent spiking activity is commonly thought to reflect the mechanistic basis of WM in PFC (13–16). This activity manifests in different ways, including time-varying neuronal responses that decay, ramp up, or are stable in time during memory delays. Although such a diversity of responses could reflect distinct modes of persistent activity, it has long been a standard practice to treat all persistently active neurons in PFC as representative of a single composite WM function that supports the maintenance and manipulation of information necessary for memory-guided behavior (14, 17–19). The implicit assumption that the representations of stored information and contingent responses overlap at the neural circuit level contrasts with an alternate view, which suggests that PFC primarily encodes the selection and preparation of responses (6, 10, 11). This difference highlights the need to directly investigate the circuit-level organization of storage and response preparation-related activity in PFC.We address this problem here, using a simple manipulation of WM in concert with large-scale recordings from neurons across lateral PFC of macaque monkeys. By mapping neural activity during memory and visual delays of the same oculomotor delayed response (ODR) task, we show that WM is composed of three anatomically specific modes of persistent activity. The first two modes specifically encode early and late forms of memory storage, and the third mode predicts behavioral variability after the delay, consistent with response preparation. We then offer multiple convergent lines of evidence that the neural populations that support these three modes are organized with distinct spatiotemporal profiles in PFC. These results suggest that information storage and the preparation of contingent responses are supported by functionally specialized networks in PFC.     

Age-related changes in working memory and the ability to ignore distraction

A weakened ability to effectively resist distraction is a potential basis for reduced working memory capacity (WMC) associated with healthy aging. Exploiting data from 29,631 users of a smartphone game, we show that, as age increases, working memory (WM) performance is compromised more by distractors presented during WM maintenance than distractors presented during encoding. However, with increasing age, the ability to exclude distraction at encoding is a better predictor of WMC in the absence of distraction. A significantly greater contribution of distractor filtering at encoding represents a potential compensation for reduced WMC in older age.The number of items that can be held in working memory (WM) declines with increasing age (1). Our ability to effectively exclude distractors is one basis for this limited working memory capacity (WMC) (2, 3), with impaired inhibitory processing of distraction contributing to an age-related reduction in WM performance (4). A specific impairment in suppressing distractor representations in older adults has been linked to reduced WMC (5). Typically distractors are presented either with the items to be remembered (encoding distraction, ED, e.g., 6, 7) or while these items are held in mind (delay distraction, DD, e.g., 5, 8). We recently highlighted a distinction between the effects of these two types of distraction in younger adults (9). Although greater WMC is associated with an enhanced ability to exclude distractors in both cases, each makes a unique contribution to WMC (9). Here we examine the well-known age-related reduction in WMC. Previous work has identified an age-related delay in ED filtering (7) and an early age-related deficit in DD suppression (8). We directly compare the age-related decline in ED and DD to assess whether an ability to ignore a distraction at encoding or at delay provides the best predictor of general WMC.We obtained data from 29,631 users of a smartphone game (part of The Great Brain Experiment, www.thegreatbrainexperiment.com), a platform that has enabled us to replicate a range of laboratory studies (9, 10). Using this medium we implemented a WM task to enable us to directly compare the effects of age on WM in the absence of distractors (no distraction, ND; Fig. 1A), when distractors are presented at encoding (ED; Fig. 1B) and when distractors are presented during maintenance (DD; Fig. 1C). This large subject pool enabled us to consider data from six age groups (18–24 y: n = 7,658; 25–29 y: n = 5,702; 30–39 y: n = 8,225; 40–49 y: n = 4,667; 50–59 y: n = 2,359; and 60–69 y: n = 1,020). For each condition the number of items to be remembered (WM load) increased as a function of performance until either eight trials had been completed or a participant failed two successive trials of a given WM load. Data were excluded from participants who failed a “load 2” trial in any condition. For each condition, the participant’s score represents the maximum number of items for which they could report all items successfully, representing their WMC.Open in a separate windowFig. 1.The smartphone game. Red circles are presented simultaneously, followed by a delay of 1 s. Participants should then indicate the positions of the red circles. (A) No distraction (ND) condition; only red circles are shown. (B) Encoding distraction (ED) condition; two yellow circles (distractors) are presented with the red circles. (C) Delay distraction (DD) condition; two yellow circles (distractors) are presented during the delay.     

Plasma metabolites were associated with spatial working memory in major depressive disorder

Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.     

Local generation of multineuronal spike sequences in the hippocampal CA1 region

Sequential activity of multineuronal spiking can be observed during theta and high-frequency ripple oscillations in the hippocampal CA1 region and is linked to experience, but the mechanisms underlying such sequences are unknown. We compared multineuronal spiking during theta oscillations, spontaneous ripples, and focal optically induced high-frequency oscillations (“synthetic” ripples) in freely moving mice. Firing rates and rate modulations of individual neurons, and multineuronal sequences of pyramidal cell and interneuron spiking, were correlated during theta oscillations, spontaneous ripples, and synthetic ripples. Interneuron spiking was crucial for sequence consistency. These results suggest that participation of single neurons and their sequential order in population events are not strictly determined by extrinsic inputs but also influenced by local-circuit properties, including synapses between local neurons and single-neuron biophysics.A hypothesized hallmark of cognition is self-organized sequential activation of neuronal assemblies (1). Self-organized neuronal sequences have been observed in several cortical structures (2–5) and neuronal models (6–7). In the hippocampus, sequential activity of place cells (8) may be induced by external landmarks perceived by the animal during spatial navigation (9) and conveyed to CA1 by the upstream CA3 region or layer 3 of the entorhinal cortex (10). Internally generated sequences have been also described in CA1 during theta oscillations in memory tasks (4, 11), raising the possibility that a given neuronal substrate is responsible for generating sequences at multiple time scales. The extensive recurrent excitatory collateral system of the CA3 region has been postulated to be critical in this process (4, 7, 12, 13).The sequential activity of place cells is “replayed” during sharp waves (SPW) in a temporally compressed form compared with rate modulation of place cells (14–20) and may arise from the CA3 recurrent excitatory networks during immobility and slow wave sleep. The SPW-related convergent depolarization of CA1 neurons gives rise to a local, fast oscillatory event in the CA1 region (“ripple,” 140–180 Hz; refs. 8 and 21). Selective elimination of ripples during or after learning impairs memory performance (22–24), suggesting that SPW ripple-related replay assists memory consolidation (12, 13). Although the local origin of the ripple oscillations is well demonstrated (25, 26), it has been tacitly assumed that the ripple-associated, sequentially ordered firing of CA1 neurons is synaptically driven by the upstream CA3 cell assemblies (12, 15), largely because excitatory recurrent collaterals in the CA1 region are sparse (27). However, sequential activity may also emerge by local mechanisms, patterned by the different biophysical properties of CA1 pyramidal cells and their interactions with local interneurons, which discharge at different times during a ripple (28–30). A putative function of the rich variety of interneurons is temporal organization of principal cell spiking (29–32). We tested the “local-circuit” hypothesis by comparing the probability of participation and sequential firing of CA1 neurons during theta oscillations, natural spontaneous ripple events, and “synthetic” ripples induced by local optogenetic activation of pyramidal neurons.     

Mechanisms and plasticity of chemogenically induced interneuronal suppression of principal cells

How do firing patterns in a cortical circuit change when inhibitory neurons are excited? We virally expressed an excitatory designer receptor exclusively activated by a designer drug (Gq-DREADD) in all inhibitory interneuron types of the CA1 region of the hippocampus in the rat. While clozapine N-oxide (CNO) activation of interneurons suppressed firing of pyramidal cells, unexpectedly the majority of interneurons also decreased their activity. CNO-induced inhibition decreased over repeated sessions, which we attribute to long-term synaptic plasticity between interneurons and pyramidal cells. Individual interneurons did not display sustained firing but instead transiently enhanced their activity, interleaved with suppression of others. The power of the local fields in the theta band was unaffected, while power at higher frequencies was attenuated, likely reflecting reduced pyramidal neuron spiking. The incidence of sharp wave ripples decreased but the surviving ripples were associated with stronger population firing compared with the control condition. These findings demonstrate that DREADD activation of interneurons brings about both short-term and long-term circuit reorganization, which should be taken into account in the interpretation of chemogenic effects on behavior.

The chemogenetic technology DREADD (designer receptors exclusively activated by designer drugs) is a widely used experimental method to control neuronal activity with an exogenous receptor that is engineered to respond selectively to an injectable agonist (1–4). In contrast to traditional pharmacology, chemogenetic techniques are both generalizable and specific because a receptor–agonist combination can be used for cell type-specific activation or inhibition of different neural populations in any brain region (4). The most prevalent DREADD platform exploits the human muscarinic receptors hM3Dq and hM4Di that are not activated by endogenous neurotransmitters but via the “designer drug” clozapine N-oxide (CNO). Subsequent experiments identified that the pharmacological actuator of CNO in the brain is the metabolically derived clozapine, arising from systemic CNO administration (4, 5). A recent improvement of the method introduced an ion channel-based platform for more potent neuronal activation and silencing that is controlled by pharmacologically selective actuator modules (6). Because of their easy use and assumed selective action, chemogenetic tools have become popular in animal research, and there is growing interest in developing chemogenetic techniques for clinical therapeutics (3).DREADD techniques have the advantage of activating or suppressing neurons over longer time periods, allowing for testing the contribution of specific neuron classes to behavior. However, it is unlikely that CNO brings about sustained activation or suppression in all target neurons uniformly and continuously because activation of interconnected inhibitory neuron populations often brings about unpredictable effects (7–11). Long-lasting excitation or inhibition of neurons typically induces synaptic plasticity and homeostatic regulations (10–13) but such potential circuit modifications have not yet been examined in connection with DREADD.The distal-less homeobox 5 and 6 (Dlx5/6) genes are specifically and transiently expressed by all forebrain GABAergic interneurons during embryonic development (14) and the recombinant adeno-associated virus (rAAV-hDLX) restricts gene expression to GABAergic interneurons in several species tested (15). Using rAAV-hDLX-Gq-DREADD in the hippocampus allowed us to investigate the mechanisms of chemogenetic modulation of interneuronal activity in behaving rats. CNO activation of all types of interneurons in the CA1 hippocampal region leads to a paradoxical decrease of the overall firing of many interneurons, coupled with a several-fold decrease of pyramidal cell firing. Interneurons did not display uniform sustained firing but, instead, enhanced activity of subgroups was interleaved with suppression of others. The sustained suppression of pyramidal cell activity was often interrupted by population bursts underlying sharp wave ripples. During such events, spiking of pyramidal cells was enhanced compared with control conditions. These findings demonstrate that DREADD activation of interneurons leads to dynamic circuit reorganization, which should be considered in the interpretation of chemogenic mechanisms in behavior (5, 16–21).     

Age-related top-down suppression deficit in the early stages of cortical visual memory processing

In this study, electroencephalography (EEG) was used to examine the relationship between two leading hypotheses of cognitive aging, the inhibitory deficit and the processing speed hypothesis. We show that older adults exhibit a selective deficit in suppressing task-irrelevant information during visual working memory encoding, but only in the early stages of visual processing. Thus, the employment of suppressive mechanisms are not abolished with aging but rather delayed in time, revealing a decline in processing speed that is selective for the inhibition of irrelevant information. EEG spectral analysis of signals from frontal regions suggests that this results from excessive attention to distracting information early in the time course of viewing irrelevant stimuli. Subdividing the older population based on working memory performance revealed that impaired suppression of distracting information early in the visual processing stream is associated with poorer memory of task-relevant information. Thus, these data reconcile two cognitive aging hypotheses by revealing that an interaction of deficits in inhibition and processing speed contributes to age-related cognitive impairment.     

基底节损伤患者视—空间工作记忆反应时间研究

目的探讨基底节损伤患者视—空间工作记忆反应时间的改变。方法对25例基底节损伤患者及25例与其人口学资料相匹配的健康对照者,采用视觉面孔和视觉空间的延迟匹配任务,并对上述2组进行视—空间工作记忆测试。结果基底节损伤组患者的视觉面孔及空间工作记忆反应时间分别为（2 314.9±400.4）、（1 844.2±381.9）m s,对照组分别为（1 871.8±621.2）、（1 389.6±368.2）m s,2组比较差异均有统计学意义（P均〈0.01）。结论基底节在工作记忆环路中承担着重要的连接功能,基底节损伤可表现为工作记忆执行障碍,且左、右侧基底节均参与了执行过程。     

Correlated memory defects and hippocampal dendritic spine loss after acute stress involve corticotropin-releasing hormone signaling

Stress affects the hippocampus, a brain region crucial for memory. In rodents, acute stress may reduce density of dendritic spines, the location of postsynaptic elements of excitatory synapses, and impair long-term potentiation and memory. Steroid stress hormones and neurotransmitters have been implicated in the underlying mechanisms, but the role of corticotropin-releasing hormone (CRH), a hypothalamic hormone also released during stress within hippocampus, has not been elucidated. In addition, the causal relationship of spine loss and memory defects after acute stress is unclear. We used transgenic mice that expressed YFP in hippocampal neurons and found that a 5-h stress resulted in profound loss of learning and memory. This deficit was associated with selective disruption of long-term potentiation and of dendritic spine integrity in commissural/associational pathways of hippocampal area CA3. The degree of memory deficit in individual mice correlated significantly with the reduced density of area CA3 apical dendritic spines in the same mice. Moreover, administration of the CRH receptor type 1 (CRFR₁) blocker NBI 30775 directly into the brain prevented the stress-induced spine loss and restored the stress-impaired cognitive functions. We conclude that acute, hours-long stress impairs learning and memory via mechanisms that disrupt the integrity of hippocampal dendritic spines. In addition, establishing the contribution of hippocampal CRH–CRFR₁ signaling to these processes highlights the complexity of the orchestrated mechanisms by which stress impacts hippocampal structure and function.     

Amphetamine modulates brain signal variability and working memory in younger and older adults

Better-performing younger adults typically express greater brain signal variability relative to older, poorer performers. Mechanisms for age and performance-graded differences in brain dynamics have, however, not yet been uncovered. Given the age-related decline of the dopamine (DA) system in normal cognitive aging, DA neuromodulation is one plausible mechanism. Hence, agents that boost systemic DA [such as d-amphetamine (AMPH)] may help to restore deficient signal variability levels. Furthermore, despite the standard practice of counterbalancing drug session order (AMPH first vs. placebo first), it remains understudied how AMPH may interact with practice effects, possibly influencing whether DA up-regulation is functional. We examined the effects of AMPH on functional-MRI–based blood oxygen level-dependent (BOLD) signal variability (SD_BOLD) in younger and older adults during a working memory task (letter n-back). Older adults expressed lower brain signal variability at placebo, but met or exceeded young adult SD_BOLD levels in the presence of AMPH. Drug session order greatly moderated change–change relations between AMPH-driven SD_BOLD and reaction time means (RT_mean) and SDs (RT_SD). Older adults who received AMPH in the first session tended to improve in RT_mean and RT_SD when SD_BOLD was boosted on AMPH, whereas younger and older adults who received AMPH in the second session showed either a performance improvement when SD_BOLD decreased (for RT_mean) or no effect at all (for RT_SD). The present findings support the hypothesis that age differences in brain signal variability reflect aging-induced changes in dopaminergic neuromodulation. The observed interactions among AMPH, age, and session order highlight the state- and practice-dependent neurochemical basis of human brain dynamics.Human brain signals are characteristically variable and dynamic, at a variety of timescales and levels of analysis (1, 2). For decades, age-related cognitive deficits have been conceptualized as due to various forms of “noisy,” inefficient neural processing (3, 4). However, the preponderance of available neuroimaging work on brain signal variability and aging indicates that healthy, higher performing, younger adults typically express more signal variability across trials and time in a variety of cortical regions relative to older, poorer performers (2, 5–7). Theoretical and computational explanations of this finding include notions such as flexibility/adaptability, dynamic range, Bayesian optimality, and multistability (2), but empirically supported mechanisms for age and performance-graded differences in human brain signal dynamics are not yet available. Dopamine (DA) neuromodulation may provide one such mechanism.DA neuromodulation is a leading mechanistic candidate for age-related cognitive losses (8–10). Concurrent with substantia nigra and ventral tegmental DA neuron loss, D₁ and D₂ receptor densities reduce notably from early to late adulthood across various subcortical and cortical regions (9–11). However, it is not yet known whether DA affects brain signal variability in relation to age and performance. DA is generally considered a neuromodulator supporting both system stability (e.g., signal “fidelity,” “precision,” and “signal-to-noise ratio”) and flexibility/adaptability (8–10, 12–14). Single-unit and multiunit computational models demonstrate that simulated aging-related DA losses can yield more inefficient stimulus detection, lower average neuronal firing pattern, and dedifferentiation of neural responses in the face of varying stimuli (8). Importantly, adding noise to “older” DA-degraded neurons can improve their relatively poor stimulus detection properties (15). This neurocomputational result highlights the potential benefits of explicitly boosting dynamics in DA-degraded, aging neural systems.DA may influence in vivo brain dynamics in a variety of ways. DA neurons exhibit dominant low-frequency tonic firing patterns along with intermittent phasic bursts (16), resulting in moment-to-moment variation in neural signaling. At millisecond and second levels, DA release also operates via shorter and longer-term facilitation and depression (e.g., so-called “kick-and-relax” dynamics, 17, 18) that affect subsequent DA-dependent spike dynamics. Mouse data highlight that DA-deficient animals display a complete lack of phasic bursting activity (variable on and off periods of spike trains), exhibiting only single spikes with long interspike intervals (19); notably although, bursting activity in these DA deficient animals can be restored toward normal levels via DA agonism (19). In addition, in line with the contention that brain signal variability can index a healthy neural system (2), animal models indicate that trial-to-trial variability in DA release appears to increase, rather than decrease, with increasing task proficiency (20). From this work, it is plausible that DA may also affect in vivo brain signal variability and its cognitive correlates in humans. Given that normal aging is associated with DA decline and poorer cognitive performance, and that poorer cognitive performance often characterizes generalized aging-related reductions in brain signal variability, lower age-related brain signal variability could reflect DA system degradation. Accordingly, we predicted that pharmacological agents that boost systemic DA, such as amphetamine (AMPH), would restore deficient signal variability levels in older adults. Extant studies have examined the effect of DA agonists on average blood oxygen level-dependent (BOLD) signal responses and cognition (13, 21, 22); however, the effects of DA agonists on BOLD signal variability and cognition, and the moderation of these effects by adult age, have not been investigated thus far.In the present study, we examine the multivariate effects of AMPH on BOLD signal variability and cognitive performance in younger and older adults during an n-back working memory (WM) task. Given impoverished signal variability (5–7) and DA levels (9, 10) in normal aging, we predicted that older adults’ BOLD variability, on average, would increase more on AMPH relative to placebo than that of younger adults. We used mixed modeling to examine whether AMPH-related changes in SD_BOLD predict AMPH-related changes in performance within individuals, and whether a higher average level of SD_BOLD coincides with higher average performance across individuals. In particular, we hypothesized that older adults (typified by lower DA, less BOLD variability, and lower cognitive performance) would show increased BOLD variability and improved cognitive performance under AMPH. Furthermore, recent evidence suggests that drug session order may influence the robustness of drug-related changes in performance (e.g., ref. 23); we thus explored whether administration order (i.e., AMPH first vs. placebo first) would moderate the effects of AMPH on SD_BOLD, cognitive performance, or both, thus pointing to state- and practice-dependent aspects of human brain dynamics, and adult age differences therein.     

Sleep,Brain, and Cognition Special Feature: Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal–subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.     

From the Cover: Inability to suppress salient distractors predicts low visual working memory capacity

According to contemporary accounts of visual working memory (vWM), the ability to efficiently filter relevant from irrelevant information contributes to an individual’s overall vWM capacity. Although there is mounting evidence for this hypothesis, very little is known about the precise filtering mechanism responsible for controlling access to vWM and for differentiating low- and high-capacity individuals. Theoretically, the inefficient filtering observed in low-capacity individuals might be specifically linked to problems enhancing relevant items, suppressing irrelevant items, or both. To find out, we recorded neurophysiological activity associated with attentional selection and active suppression during a competitive visual search task. We show that high-capacity individuals actively suppress salient distractors, whereas low-capacity individuals are unable to suppress salient distractors in time to prevent those items from capturing attention. These results demonstrate that individual differences in vWM capacity are associated with the timing of a specific attentional control operation that suppresses processing of salient but irrelevant visual objects and restricts their access to higher stages of visual processing.Each day, human observers perform numerous tasks that require temporary storage of information about objects in the surrounding visual environment. Laboratory studies have revealed substantial variability across neurologically healthy adults in the ability to keep such visuospatial information in mind (1–4). Originally, this variability was attributed to individual differences in the capacity of visual working memory (vWM). According to this account, the maximum amount of information that can be entered into vWM at one time, or the number of “slots” available to store the information, varies across individuals (3, 5–8). Other contemporary accounts, however, relate the individual differences in vWM performance to variability in attentional control, as well as capacity (9–12). One such attention-based perspective holds that when faced with multiple visual objects, low-capacity individuals have difficulty filtering relevant from irrelevant information (11–15). More specifically, this filtering-efficiency hypothesis proposes that attention regulates the flow of sensory information to the limited-capacity vWM system and that consuming capacity with task-irrelevant information effectively reduces storage capacity for task-relevant items. This hypothesis helps to explain why low-capacity individuals sometimes store more items in vWM than do high-capacity individuals: whereas high-capacity individuals encode only task-relevant items, low-capacity individuals encode irrelevant items along with task-relevant items (15).Although there is mounting evidence for the filtering-efficiency hypothesis, little is known about the precise mechanism responsible for controlling access to vWM or how its operation differs in low- and high-capacity individuals. Theoretically, filtering can be achieved by enhancing the representation of a to-be-remembered item or by suppressing the representation of a to-be-ignored item (16). Accordingly, the inefficient filtering observed in low-capacity individuals might be linked to problems enhancing relevant items, problems suppressing irrelevant items, or both. Precise characterization of individual differences in filtering efficiency requires not only a method for determining what items gain access to vWM but also a method for isolating processes associated with the two diametrically opposed facets of filtering. Behavioral measures (e.g., negative priming) have been used to study the link between attention and vWM capacity (17, 18), but given the difficulty in linking such measures to specific processes (e.g., perceptual inhibition, memory retrieval), existing behavioral results do not clearly indicate whether individual differences in capacity are related to selective enhancement or suppression.Researchers have started to develop event-related potential (ERP) methods to determine how attention-filtering capabilities vary as a function of vWM capacity. In one pair of studies (19, 20), participants were cued in advance to attend to the location of an impending visual target that was accompanied by at least one distractor item on the same side of fixation (with an equal number of items on the opposite side of fixation). After a brief interval, bilateral “probe” stimuli were presented to assess the spatial gradient of attention. ERPs elicited by the probes were used to compute an attention-gradient index, which was positive when attention was tightly focused at the target location and was near zero when attention was broadly distributed across the items in the cued hemifield. Low-capacity individuals were found to have a broader distribution of attention than high-capacity individuals. This finding could indicate that low-capacity individuals are unable to prevent the inadvertent capture of attention by nearby distractors (19), to boost the target’s representation over and above those of nearby distractors, or to maintain a tight focus of attention at the cued location before the appearance of the target display. At present, it is impossible to distinguish between these alternatives in part because the attention-gradient index that was used did not isolate target-selection and distractor-suppression processes separately.In the present study, we recorded ERPs during a unidimensional variant of the additional singleton search paradigm and isolated specific components known to reflect stimulus selection (N2pc) and active suppression (distractor positivity, P_D). The N2pc, an enhanced negative potential observed contralateral to attended targets, is a well-known electrophysiological index of attentional selection that emerges over the posterior scalp 180–200 ms after the appearance of a search array (21, 22). In contrast, the P_D is an enhanced positive potential observed contralateral to task-irrelevant distractors in the same time interval (23, 24). Two key pieces of evidence indicate that the P_D is associated with an active suppression process. First, the P_D is present when observers must carefully inspect another task-relevant item (target) but is absent when observers merely have to detect the target (23). Second, the amplitude of the P_D is predictive of the speed with which participants respond to a target on a trial-by-trial basis, with faster response times (i.e., less distraction) associated with larger P_D amplitudes (24, 25). These findings indicate that the visual system resolves attentional competition in demanding identification tasks by suppressing potentially distracting items, but that the ability to suppress, and thus to prevent distraction, varies across trials.Armed with these two electrophysiological indices of attention, we asked whether individuals with higher vWM capacities are better able to select items of interest or to suppress potentially distracting items. Participants searched multiitem displays for a prespecified color singleton while attempting to ignore other, task-irrelevant color singletons that could appear in the same displays. Each display contained eight or nine same-color nontargets, one yellow target, and on distractor-present trials, one red or blue distractor (Fig. 1). The color of the nontargets was varied (all green or all orange) to disentangle distractor salience from distractor color. Specifically, the red distractor was the most salient singleton against green nontargets, whereas the blue distractor was the most salient singleton against orange nontargets (this was confirmed in a behavioral pilot experiment; SI Results). Target- and distractor-related ERPs were measured separately for individuals with low, medium, and high vWM capacities to determine whether the attentional deficits associated with low capacity are attributable to difficulties selecting an object of interest, actively suppressing irrelevant objects, or both.Open in a separate windowFig. 1.ERPs elicited by displays containing a midline target and a lateral distractor for each nontarget condition. Time 0 reflects the onset of the search display, and negative voltage deflections are plotted above the x-axis, by convention. Waveforms were recorded over the lateral occipital scalp (electrodes PO7 and PO8). (A) ERPs recorded contralateral and ipsilateral to a low-salience distractor. (B) ERPs recorded contralateral and ipsilateral to a high-salience distractor.