Recalled through this day but forgotten next week?-retrieval activity predicts durability of partly consolidated memories.

Even partly consolidated memories can be forgotten given sufficient time, but the brain activity associated with durability of episodic memory at different time scales remains unclear. Here, we aimed to identify brain activity associated with retrieval of partly consolidated episodic memories that continued to be remembered in the future. Forty-nine younger (20 to 38 years; 25 females) and 43 older adults (60 to 80 years, 25 females) were scanned with functional magnetic resonance imaging during associative memory retrieval 12 h post-encoding. Twelve hours is sufficient to allow short-term synaptic consolidation as well as early post-encoding replay to initiate memory consolidation. Successful memory trials were classified into durable and transient source memories based on responses from a memory test ~6 d post-encoding. Results demonstrated that successful retrieval of future durable vs. transient memories was supported by increased activity in a medial prefrontal and ventral parietal area. Individual differences in activation as well as the subjective vividness of memories during encoding were positively related to individual differences in memory performance after 6 d. The results point to a unique and novel aspect of brain activity supporting long-term memory, in that activity during retrieval of memories even after 12 h of consolidation contains information about potential for long-term durability.

Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding.

Systems consolidation of new experiences into lasting episodic memories involves hippocampal-neocortical interactions. Evidence of this process is already observed during early post-encoding rest periods, both as increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific patterns following intensive encoding tasks. We investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. We observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimuli encountered during memory encoding. Post-encoding modulations were manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement exceeds perceptual aspects. Reinstatement of encoding patterns was not observed in resting-state scans collected 12 h later, nor when using other candidate seed regions. The similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans involves a spectrum of cognitive processes engaged during the experience of an event. There were no age effects, suggesting that upregulation of hippocampal-neocortical connectivity represents a general phenomenon seen across the adult lifespan.

Hippocampal-cortical functional connectivity during memory encoding and retrieval.

Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.

Whole-brain connectivity during encoding: age-related differences and associations with cognitive and brain structural decline.

There is a limited understanding of age differences in functional connectivity during memory encoding. In the present study, a sample of cognitively healthy adult participants (n = 488, 18-81 years), a subsample of whom had longitudinal cognitive and brain structural data spanning on average 8 years back, underwent functional magnetic resonance imaging while performing an associative memory encoding task. We investigated (1) age-related differences in whole-brain connectivity during memory encoding; (2) whether encoding connectivity patterns overlapped with the activity signatures of specific cognitive processes, and (3) whether connectivity associated with memory encoding related to longitudinal brain structural and cognitive changes. Age was associated with lower intranetwork connectivity among cortical networks and higher internetwork connectivity between networks supporting higher level cognitive functions and unimodal and attentional areas during encoding. Task-connectivity between mediotemporal and posterior parietal regions-which overlapped with areas involved in mental imagery-was related to better memory performance only in older age. The connectivity patterns supporting memory performance in older age reflected preservation of thickness of the medial temporal cortex. The results are more in accordance with a maintenance rather than a compensation account.

Reduced Hippocampal-Striatal Interactions during Formation of Durable Episodic Memories in Aging.

Encoding of durable episodic memories requires cross-talk between the hippocampus and multiple brain regions. Changes in these hippocampal interactions could contribute to age-related declines in the ability to form memories that can be retrieved after extended time intervals. Here we tested whether hippocampal-neocortical- and subcortical functional connectivity (FC) observed during encoding of durable episodic memories differed between younger and older adults. About 48 younger (20-38 years; 25 females) and 43 older (60-80 years; 25 females) adults were scanned with fMRI while performing an associative memory encoding task. Source memory was tested ~20 min and ~6 days postencoding. Associations recalled after 20 min but later forgotten were classified as transient, whereas memories retained after long delays were classified as durable. Results demonstrated that older adults showed a reduced ability to form durable memories and reduced hippocampal-caudate FC during encoding of durable memories. There was also a positive relationship between hippocampal-caudate FC and higher memory performance among the older adults. No reliable age group differences in durable memory-encoding activity or hippocampal-neocortical connectivity were observed. These results support the classic theory of striatal alterations as one cause of cognitive decline in aging and highlight that age-related changes in episodic memory extend beyond hippocampal-neocortical connections.

A Single Mechanism for Global and Selective Response Inhibition under the Influence of Motor Preparation.

In our everyday behavior, we frequently cancel one movement while continuing others. Two competing models have been suggested for the cancellation of such specific actions: (1) the abrupt engagement of a unitary global inhibitory mechanism followed by reinitiation of the continuing actions; or (2) a balance between distinct global and selective inhibitory mechanisms. To evaluate these models, we examined behavioral and physiological markers of proactive control, motor preparation, and response inhibition using a combination of behavioral task performance measures, electromyography, electroencephalography, and motor evoked potentials elicited with transcranial magnetic stimulation. Healthy human participants of either sex performed two versions of a stop signal task with cues incorporating proactive control: a unimanual task involving the initiation and inhibition of a single response, and a bimanual task involving the selective stopping of one of two prepared responses. Stopping latencies, motor evoked potentials, and frontal ß power (13-20 Hz) did not differ between the unimanual and bimanual tasks. However, evidence for selective proactive control before stopping was manifest in the bimanual condition as changes in corticomotor excitability, µ (9-14 Hz), and ß (15-25 Hz) oscillations over sensorimotor cortex. Together, our results favor the recruitment of a single inhibitory stopping mechanism with the net behavioral output depending on the levels of action-specific motor preparation.SIGNIFICANCE STATEMENT Response inhibition is a core function of cognitive flexibility and movement control. Previous research has suggested separate mechanisms for selective and global inhibition, yet the evidence is inconclusive. Another line of research has examined the influence of preparation for action stopping, or what is called proactive control, on stopping performance, yet the neural mechanisms underlying this interaction are unknown. We combined transcranial magnetic stimulation, electroencephalography, electromyography, and behavioral measures to compare selective and global inhibition models and to investigate markers of proactive control. The results favor a single inhibitory mechanism over separate selective and global mechanisms but indicate a vital role for preceding motor activity in determining whether and which actions will be stopped.

Frontal-midline theta reflects different mechanisms associated with proactive and reactive control of inhibition.

Reactive control of response inhibition is associated with a right-lateralised cortical network, as well as frontal-midline theta (FM-theta) activity measured at the scalp. However, response inhibition is also governed by proactive control processes, and how such proactive control is reflected in FM-theta activity and associated neural source activity remains unclear. To investigate this, simultaneous recordings of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data was performed while participants performed a cued stop-signal task. The cues (0%, 25% or 66%) indicated the likelihood of an upcoming stop-signal in the following trial. Results indicated that participants adjusted their behaviour proactively, with increasing go-trial reaction times following increasing stop-signal probability, as well as modulations of both go-trial and stop-trial accuracies. Target-locked theta activity was higher in stop-trials than go-trials and modulated by probability. At the single-trial level, cue-locked theta was associated with shorter reaction-times, while target-locked theta was associated with both faster reaction times and higher probability of an unsuccessful stop-trial. This dissociation was also evident at the neural source level, where a joint ICA revealed independent components related to going, stopping and proactive preparation. Overall, the results indicate that FM-theta activity can be dissociated into several mechanisms associated with proactive control, response initiation and response inhibition processes. We propose that FM-theta activity reflects both heightened preparation of the motor control network, as well as stopping-related processes associated with a right lateralized cortical network.

Differences in unity: The go/no-go and stop signal tasks rely on different mechanisms.

Response inhibition refers to the suppression of prepared or initiated actions. Typically, the go/no-go task (GNGT) or the stop signal task (SST) are used interchangeably to capture individual differences in response inhibition. On the one hand, factor analytic and conjunction neuroimaging studies support the association of both tasks with a single inhibition construct. On the other hand, studies that directly compare the two tasks indicate distinct mechanisms, corresponding to action restraint and cancellation in the GNGT and SST, respectively. We addressed these contradictory findings with the aim to identify the core differences in the temporal dynamics of the functional networks that are recruited in both tasks. We extracted the time-courses of sensory, motor, attentional, and cognitive control networks by group independent component (G-ICA) analysis of electroencephalography (EEG) data from both tasks. Additionally, electromyography (EMG) from the responding effector muscles was recorded to detect the timing of response inhibition. The results indicated that inhibitory performance in the GNGT may be comparable to response selection mechanisms, reaching peripheral muscles at around 316 â€‹ms. In contrast, inhibitory performance in the SST is achieved via biasing of the sensorimotor system in preparation for stopping, followed by fast sensory, motor and frontal integration during outright stopping. Inhibition can be detected at the peripheral level at 140 â€‹ms after stop stimulus presentation. The GNGT and the SST therefore seem to recruit widely different neural dynamics, implying that the interchangeable use of superficially similar inhibition tasks in both basic and clinical research is unwarranted.

A Tutorial Review on Multi-subject Decomposition of EEG.

Over the last years we saw a steady increase in the relevance of big neuroscience data sets, and with it grew the need for analysis tools capable of handling such large data sets while simultaneously extracting properties of brain activity that generalize across subjects. For functional magnetic resonance imaging, multi-subject or group-level independent component analysis provided a data-driven approach to extract intrinsic functional networks, such as the default mode network. Meanwhile, this methodological framework has been adapted for the analysis of electroencephalography (EEG) data. Here, we provide an overview of the currently available approaches for multi-subject data decomposition as applied to EEG, and highlight the characteristics of EEG that warrant special consideration. We further illustrate the importance of matching one's choice of method to the data characteristics at hand by guiding the reader through a set of simulations. In sum, algorithms for group-level decomposition of EEG provide an innovative and powerful tool to study the richness of functional brain networks in multi-subject EEG data sets.

The Temporal Dynamics of Response Inhibition and their Modulation by Cognitive Control.

Behavioral adjustments require interactions between distinct modes of cognitive control and response inhibition. Hypothetically, fast and global inhibition is exerted in the reactive control mode, whereas proactive control enables the preparation of inhibitory pathways in advance while relying on the slower selective inhibitory system. We compared the temporal progression of inhibition in the reactive and proactive control modes using simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. A selective stop signal task was used where go stimuli required bimanual responses, but only one hand's response had to be suppressed in stop trials. Reactive and proactive conditions were incorporated by non-informative and informative cues, respectively. In 47% of successful stop trials, subthreshold EMG activity was detected that was interrupted as early as 150 ms after stop stimulus presentation, indicating that inhibition occurs much earlier than previously thought. Inhibition latencies were similar across the reactive and proactive control modes. The EMG of the responding hand in successful selective stop trials indicated a global suppression of ongoing motor actions in the reactive condition, and less inhibitory interference on the ongoing actions in the proactive condition. Group-level second order blind separation (SOBI) was applied to the EEG to dissociate temporally overlapping event-related potentials. The components capturing the N1 and N2 were larger in the reactive than the proactive condition. P3 activity was distributed across four components, three of which were augmented in the proactive condition. Thus, although EEG indices were modulated by the control mode, the inhibition latency remained unaffected.

Partial response electromyography as a marker of action stopping.

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.

Inhibitory Control and the Structural Parcelation of the Right Inferior Frontal Gyrus.

The right inferior frontal gyrus (rIFG) has most strongly, although not exclusively, been associated with response inhibition, not least based on covariations of behavioral performance measures and local gray matter characteristics. However, the white matter microstructure of the rIFG as well as its connectivity has been less in focus, especially when it comes to the consideration of potential subdivisions within this area. The present study reconstructed the structural connections of the three main subregions of the rIFG (i.e., pars opercularis, pars triangularis, and pars orbitalis) using diffusion tensor imaging, and further assessed their associations with behavioral measures of inhibitory control. The results revealed a marked heterogeneity of the three subregions with respect to the pattern and extent of their connections, with the pars orbitalis showing the most widespread inter-regional connectivity, while the pars opercularis showed the lowest number of interconnected regions. When relating behavioral performance measures of a stop signal task to brain structure, the data indicated an association between the dorsal opercular connectivity and the go reaction time and the stopping accuracy.

The P300 as marker of inhibitory control - Fact or fiction?

Inhibitory control, i.e., the ability to stop or suppress actions, thoughts, or memories, represents a prevalent and popular concept in basic and clinical neuroscience as well as psychology. At the same time, it is notoriously difficult to study as successful inhibition is characterized by the absence of a continuously quantifiable direct behavioral marker. It has been suggested that the P3 latency, and here especially its onset latency, may serve as neurophysiological marker of inhibitory control as it correlates with the stop signal reaction time (SSRT). The SSRT estimates the average stopping latency, which itself is unobservable since no overt response is elicited in successful stop trials, based on differences in the distribution of go reaction times and the delay of the stop-relative to the go-signal in stop trials. In a meta-analysis and an independent electroencephalography (EEG) experiment, we found that correlations between the P3 latency and the SSRT are indeed replicable, but also unspecific. Not only does the SSRT also correlate with the N2 latency, but both P3 and N2 latency measures show similar or even higher correlations with other behavioral parameters such as the go reaction time or stopping accuracy. The missing specificity of P3-SSRT correlations, together with the general pattern of associations, suggests that these manifest effects are driven by underlying latent processes other than inhibition, such as behavioral adaptations in context of performance monitoring operations.

tDCS over the inferior frontal gyri and visual cortices did not improve response inhibition.

The ability to cancel an already initiated response is central to flexible behavior. While several different behavioral and neural markers have been suggested to quantify the latency of the stopping process, it remains unclear if they quantify the stopping process itself, or other supporting mechanisms such as visual and/or attentional processing. The present study sought to investigate the contributions of inhibitory and sensory processes to stopping latency markers by combining transcranial direct current stimulation (tDCS), electroencephalography (EEG) and electromyography (EMG) recordings in a within-participant design. Active and sham tDCS were applied over the inferior frontal gyri (IFG) and visual cortices (VC), combined with both online and offline EEG and EMG recordings. We found evidence that neither of the active tDCS condition affected stopping latencies relative to sham stimulation. Our results challenge previous findings suggesting that anodal tDCS over the IFG can reduce stopping latency and demonstrates the necessity of adequate control conditions in tDCS research. Additionally, while the different putative markers of stopping latency showed generally positive correlations with each other, they also showed substantial variation in the estimated latency of inhibition, making it unlikely that they all capture the same construct exclusively.

Strategy switches in proactive inhibitory control and their association with task-general and stopping-specific networks.

Prior information about the likelihood of a stop-signal pre-activates networks associated with response inhibition in both go- and stop-trials. How such prior information modulates the neural mechanisms enacting response inhibition is only poorly understood. To investigate this, a cued stop-signal task (with cues indicating stopping probabilities of 0%, 25% or 66%) was implemented in combination with functional magnetic resonance imaging (fMRI) data acquisition. Specifically, we focused on the effect of proactive inhibitory control as reflected in the activity of regions known to regulate response inhibition. Further, modulatory activity profiles in three different sub-regions of the right inferior frontal area were investigated. Behavioural results revealed an adaptation of task strategies through proactive control, with a possible gain for efficient inhibition at high stopping probabilities. The imaging data indicate that this adaption was supported by different regions traditionally involved in the stopping network. While the right inferior parietal cortex (IPC), right middle frontal gyrus (MFG), right inferior frontal gyrus (rIFG) pars triangularis, and left anterior insula all showed increased go-trial activity in the 0% condition compared to the 25% condition, the pre-supplementary motor area (pre-SMA), anterior midcingulate cortex (aMCC), right anterior insula, and the rIFG pars opercularis showed a more stopping-specific pattern, with stronger stop-trial activity in the 66% condition than in the 25% condition. Furthermore, activity in inferior frontal sub-regions correlated with behavioural changes, where more pronounced response slowing was associated with stronger activity increases from low to high stopping probabilities. Notably, the different right inferior frontal sub-regions showed different activity patterns in response to proactive inhibitory control modulations, supporting the idea of a functional dissociation within this area. Specifically, while the pars opercularis and the right insula showed stopping-related modulations of activity, the rIFG pars triangularis exhibited modulations only in go-trials with strong adaptions to fast responding or proactive slowing. Overall, the results indicate that proactive inhibitory control results in the switching of task or strategy modes, either favouring fast responding or stopping, and that these strategical adaptations are governed by an interplay of different regions of the stopping network.

Sleep deprivation differentially affects subcomponents of cognitive control.

STUDY OBJECTIVES: Although sleep deprivation has long been known to negatively affect cognitive performance, the exact mechanisms through which it acts and what cognitive domains are affected most is still disputed. The current study provides a theory-driven approach to examine and explain the detrimental effects of sleep loss with a focus on attention and cognitive control. METHODS: Twenty-four participants (12 females; age: 24 ± 3 years) completed the experiment that involved laboratory-controlled over-night sleep deprivation and two control conditions, namely, a normally rested night at home and a night of sleep in the laboratory. Using a stop signal task in combination with electroencephalographic recordings, we dissociated different processes contributing to task performance such as sustained attention, automatic or bottom-up processing, and strategic or top-down control. At the behavioral level, we extracted reaction times, response accuracy, and markers of behavioral adjustments (post-error and post-stop slowing), whereas at the neural level event-related potentials (ERP) found in context of response inhibition (N2/P3) and error monitoring (ERN/Pe) were obtained. RESULTS: It was found that 24 hr of sleep deprivation resulted in declined sustained attention and reduced P300 and Pe amplitudes, demonstrating a gradual breakdown of top-down control. In contrast, N200 and ERN as well as the stop-signal reaction time showed higher resilience to sleep loss signifying the role of automatic processing. CONCLUSIONS: These results support the notion that sleep deprivation is more detrimental to cognitive functions that are relatively more dependent on mental effort and/or cognitive capacity, as opposed to more automatic control processes.