Replay, the default mode network and the cascaded memory systems model

Time-dependent consolidation mechanisms of durable memory in spaced learning

Emerging studies suggest that time-dependent consolidation enables memory stabilization by promoting memory integration and hippocampal-cortical transfer. Compared to massed learning, how time-dependent consolidation contributes to forming durable memory and what neural signatures predict durable memory in spaced learning remain unclear. We recruited 48 participants who underwent either 3-day spaced learning or 1-day massed learning, and both resting-state and task-based fMRI data were collected in multiple delayed tests (i.e., immediate, 1-week, and 1-month). We use representational similarity analysis to assess neural integration and replay in the hippocampus and default mode network (DMN) subsystems. In contrast with massed learning, spaced learning induces higher neural pattern similarity during immediate retrieval only in DMN subsystems. Particularly, the neural pattern similarity in the dorsal-medial DMN (DMNdm) and medial-temporal DMN subsystems predicts the durable memory defined by 1-month delay. Moreover, we find increased neural replay of durable memory in the DMNdm for spaced learning and in the hippocampus for both spaced and massed learning. Our findings suggest that time-dependent consolidation promotes neural integration and replay in the cortex rather than in the hippocampus, which may underlie the formation of durable memory after spaced learning.

Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass cognitive task learning. The strengthening of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.

Highlights

Learning shifts multi-task representations from compositional to conjunctive formatsCortical conjunctions uniquely associate with improved behavior and pattern separationThese conjunctions strengthen over separated learning events and index switch costsSubcortical regions are critical for cross-region binding of task rule information.

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Topography of putative bi-directional interaction between hippocampal sharp-wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

An insight from the default mode network in patients with amnesia following left thalamic infarction involving the mediodorsal nucleus and mammillothalamic tract

The role of the medial part of the thalamus, and in particular the mediodorsal nucleus (MD) and the mammillothalamic tract (MTT), in memory has long been studied, but their contribution remains unclear. While the main functional hypothesis regarding the MTT focuses on memory, some authors postulate that the MD plays a supervisory executive role (indirectly affecting memory retrieval) due to its dense structural connectivity with the prefrontal cortex (PFC). Recently, it has been proposed that the MD, MTT and PFC form part of the DMN the default mode network (DMN). Due to the theoretical presence of MD and MTT in the DMN, we aimed to show the effect of thalamic lesions on functional connectivity (FC) and its putative role in cognitive impairment. We recruited 12 patients with left thalamic infarction and 12 matched healthy controls. They underwent neuropsychological assessment including memory tasks, morphological 3D MRI and resting state fMRI. A ROI-to-ROI method was used for group-level FC analyses. Patients had lesions in the MD and ventrolateral nuclei, with a damaged mammillothalamic tract (MTT) in seven of them. They showed lower performance than controls on verbal memory, executive function and language tests, with more impairment in memory, working memory, semantic verbal fluency and attention in the MTT-damaged patients. Contrast analyses between patients and matched controls showed lower FC in the ventral and dorsal DMN. Correlation analyses (patients and controls pooled) showed i/a positive correlation between memory and DMN, and ii/that MTT volume correlated with decreased functional connectivity in the dorsal DMN, whereas there was no correlation with MD lesion volume. These results suggest that both the memory impairment and the DMN functional change we observed may reflect an effect of the MTT lesion rather than MD damage.

Detection of latent brain states from spontaneous neural activity in the amygdala

The amygdala responds to a large variety of socially and emotionally salient environmental and interoceptive stimuli. The context in which these stimuli occur determines their social and emotional significance. In canonical neurophysiological studies, the fast-paced succession of stimuli and events induce phasic changes in neural activity. During inter-trial intervals, neural activity is expected to return to a stable and featureless level of spontaneous activity, often called baseline. In previous studies we found that context, such as the presence of a social partner, induces brain states that can transcend the fast-paced succession of stimuli and can be recovered from the spontaneous, inter-trial firing rate of neurons. Indeed, the spontaneous firing rates of neurons in the amygdala are different during blocks of gentle grooming touches delivered by a trusted social partner, and during blocks of non-social airflow stimuli delivered by a computer-controlled air valve. Here, we examine local field potentials (LFPs) recorded during periods of spontaneous activity to determine whether information about context can be extracted from these signals. We found that information about social vs. non-social context is present in the local field potential during periods of spontaneous activity between the application of grooming and airflow stimuli, as machine learning techniques can reliably decode context from spectrograms of spontaneous LFPs. No significant differences were detected between the nuclei of the amygdala that receive direct or indirect inputs from areas of the prefrontal cortex known to coordinate flexible, context-dependent behaviors. The lack of nuclear specificity suggests that context-related synaptic inputs arise from a shared source, possibly interoceptive inputs, that signal the physiological state of the body during social and non-social blocks of tactile stimulation.

Intact ultrafast memory consolidation in adults with autism and neurotypicals with autism traits

The processes of learning and memory consolidation are closely interlinked. Therefore, to uncover statistical learning in autism spectrum disorder (ASD), an in-depth examination of memory consolidation is essential. Studies of the last five years have revealed that learning can take place not only during practice but also during micro rest (<1 min) between practice blocks, termed micro offline gains. The concept of micro offline gains refers to performance improvements during short rest periods interspersed with practice, rather than during practice itself. This phenomenon is crucial for the acquisition and consolidation of motor skills and has been observed across various learning contexts. Numerous studies on learning in autism have identified intact learning but there has been no investigation into this fundamental aspect of memory consolidation in autistic individuals to date. We conducted two studies with two different samples: 1) neurotypical adults with distinct levels of autistic traits (N = 166) and 2) ASD-diagnosed adults (NASD = 22, NNTP = 20). Participants performed a well-established probabilistic learning task, allowing us to measure two learning processes separately in the same experimental design: statistical learning (i.e., learning probability-based regularities) and visuomotor performance (i.e., speed-up regardless of probabilities). Here we show considerable individual differences in offline (between blocks) changes during statistical learning and between-blocks improvement during visuomotor performance. However, cumulative evidence from individual studies suggests that the degree of autistic traits and ASD status are not associated with micro offline gains, indicating that, like statistical learning, rapid memory consolidation is intact.

Electroencephalography Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity

Proper names are linguistic expressions referring to unique entities, such as individual people or places. This sets them apart from other words like common nouns, which refer to generic concepts. And yet, despite both being individual entities, one's closest friend and one's favorite city are intuitively associated with very different pieces of knowledge-face, voice, social relationship, autobiographical experiences for the former, and mostly visual and spatial information for the latter. Neuroimaging research has revealed the existence of both domain-general and domain-specific brain correlates of semantic processing of individual entities; however, it remains unclear how such commonalities and similarities operate over a fine-grained temporal scale. In this work, we tackle this question using EEG and multivariate (time-resolved and searchlight) decoding analyses. We look at when and where we can accurately decode the semantic category of a proper name and whether we can find person- or place-specific effects of familiarity, which is a modality-independent dimension and therefore avoids sensorimotor differences inherent among the two categories. Semantic category can be decoded in a time window and with spatial localization typically associated with lexical semantic processing. Regarding familiarity, our results reveal that it is easier to distinguish patterns of familiarity-related evoked activity for people, as opposed to places, in both early and late time windows. Second, we discover that within the early responses, both domain-general (left posterior-lateral) and domain-specific (right fronto-temporal, only for people) neural patterns can be individuated, suggesting the existence of person-specific processes.

Default mode network and dorsal attentional network connectivity changes as neural markers of spinal manipulative therapy in lumbar disc herniation

Spinal manipulative therapy (SMT) has been shown to significantly alleviate pain in patients with lumbar disc herniation (LDH), with its effects closely associated with brain function modulation. This study investigates the neural biomarkers linked to pain relief efficacy following a complete SMT treatment cycle in LDH patients. A total of 59 LDH patients were randomized into two groups: SMT treatment (Group 1, n = 28) and sham treatment (ST) (Group 2, n = 31). A matched healthy control group (Group 3, n = 28) was also included. Functional magnetic resonance imaging (fMRI) was performed on LDH patients at two time points (TPs)-before (TP1) and after (TP2) treatment-while healthy controls were scanned once. Clinical assessments were conducted using the Visual Analogue Scale (VAS) and the Japanese Orthopaedic Association (JOA) scale. Post-treatment results indicated significant improvements in both VAS and JOA scores for Group 1, while the improvement was limited to VAS scores for Group 2. Graph properties analysis revealed notable differences in brain network connectivity between LDH patients and healthy controls, particularly between the left precentral gyrus (left PreCG) and left inferior frontal gyrus, opercular part (left IFGoperc). Enhanced functional connectivity (FC) was observed in Group 1, notably between the right angular gyrus (right ANG) and the left middle orbital gyrus (left ORBmid), with right ANG showing a significant positive correlation with clinical scores. This study identifies the sensorimotor network-salience network are significantly activated in chronic pain among LDH patients. The default mode network-dorsal attention network may serve as key neural biomarkers for the efficacy of SMT treatment in alleviating pain in LDH.

Family lexicon: Using language models to encode memories of personally familiar and famous people and places in the brain

Knowledge about personally familiar people and places is extremely rich and varied, involving pieces of semantic information connected in unpredictable ways through past autobiographical memories. In this work, we investigate whether we can capture brain processing of personally familiar people and places using subject-specific memories, after transforming them into vectorial semantic representations using language models. First, we asked participants to provide us with the names of the closest people and places in their lives. Then we collected open-ended answers to a questionnaire, aimed at capturing various facets of declarative knowledge. We collected EEG data from the same participants while they were reading the names and subsequently mentally visualizing their referents. As a control set of stimuli, we also recorded evoked responses to a matched set of famous people and places. We then created original semantic representations for the individual entities using language models. For personally familiar entities, we used the text of the answers to the questionnaire. For famous entities, we employed their Wikipedia page, which reflects shared declarative knowledge about them. Through whole-scalp time-resolved and searchlight encoding analyses, we found that we could capture how the brain processes one's closest people and places using person-specific answers to questionnaires, as well as famous entities. Overall encoding performance was significant in a large time window (200-800ms). Using spatio-temporal EEG searchlight, we found that we could predict brain responses significantly better than chance earlier (200-500ms) in bilateral temporo-parietal electrodes and later (500-700ms) in frontal and posterior central electrodes. We also found that XLM, a contextualized (or large) language model, provided superior encoding scores when compared with a simpler static language model as word2vec. Overall, these results indicate that language models can capture subject-specific semantic representations as they are processed in the human brain, by exploiting small-scale distributional lexical data.

Explorations of using a convolutional neural network to understand brain activations during movie watching

Background

Naturalistic stimuli, such as videos, can elicit complex brain activations. However, the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations, particularly for higher-level processes such as social interactions.

Objective

We hypothesized that activations in different layers of a convolutional neural network (VGG-16) would correspond to varying levels of brain activation, reflecting the brain's visual processing hierarchy. Additionally, we aimed to explore which brain regions would be linked to the deeper layers of the network.

Methods

This study analyzed functional MRI data from participants watching a cartoon video. Using a pre-trained VGG-16 convolutional neural network, we mapped hierarchical features of the video to different levels of brain activation. Activation maps from various kernels and layers were extracted from video frames, and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain responses.

Results

Lower layers of the network were primarily associated with activations in lower visual regions, although some kernels also unexpectedly showed associations with the posterior cingulate cortex. Deeper layers were linked to more anterior and lateral regions of the visual cortex, as well as the supramarginal gyrus.

Conclusions

This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions, providing valuable insights into how different brain regions respond to varying levels of visual processing.

Topography of putative bidirectional interaction between hippocampal sharp wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across neocortex and the mechanisms enabling it remain unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with widefield imaging of dorsal neocortex, we found spatially and temporally precise bidirectional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability was correlated with UP/DOWN states in mouse default mode network, with highest modulation by RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN→UP transitions, while large-amplitude SWRs were often followed by DOWN states originating in RSC. We explain these electrophysiological results with a model in which HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Motifs of human hippocampal and cortical high frequency oscillations structure processing and memory of naturalistic stimuli

The discrete events of our narrative experience are organized by the neural substrate that underlies episodic memory. This narrative process is segmented into discrete units by event boundaries. This permits a replay process that acts to consolidate each event into a narrative memory. High frequency oscillations (HFOs) are a potential mechanism for synchronizing neural activity during these processes. Here, we use intracranial recordings from participants viewing and freely recalling a naturalistic stimulus. We show that hippocampal HFOs increase following event boundaries and that coincident hippocampal-cortical HFOs (co-HFOs) occur in cortical regions previously shown to underlie event segmentation (inferior parietal, precuneus, lateral occipital, inferior frontal cortices). We also show that event-specific patterns of co-HFOs that occur during event viewing re-occur following the subsequent three event boundaries (in decaying fashion) and also during recall. This is consistent with models that support replay as a mechanism for memory consolidation. Hence, HFOs may coordinate activity across brain regions serving widespread event segmentation, encode naturalistic memory, and bind representations to assemble memory of a coherent, continuous experience.

Repetition dynamically and rapidly increases cortical, but not hippocampal, offline reactivation

No sooner is an experience over than its neural representation begins to be transformed through memory reactivation during offline periods. The lion's share of prior research has focused on understanding offline reactivation within the hippocampus. However, it is hypothesized that consolidation processes involve offline reactivation in cortical regions as well as coordinated reactivation in the hippocampus and cortex. Using fMRI, we presented novel and repeated paired associates to participants during encoding and measured offline memory reactivation for those events during an immediate post-encoding rest period. post-encoding reactivation frequency of repeated and once-presented events did not differ in the hippocampus. However, offline reactivation in widespread cortical regions and hippocampal-cortical coordinated reactivation were significantly enhanced for repeated events. These results provide evidence that repetition might facilitate the distribution of memory representations across cortical networks, a hallmark of systems-level consolidation. Interestingly, we found that offline reactivation frequency in both hippocampus and cortex explained variance in behavioral success on an immediate associative recognition test for the once-presented information, potentially indicating a role of offline reactivation in maintaining these novel, weaker, memories. Together, our findings highlight that endogenous offline reactivation can be robustly and significantly modulated by study repetition.

Neurofeedback training can modulate task-relevant memory replay rate in rats

Hippocampal replay - the time-compressed, sequential reactivation of ensembles of neurons related to past experience - is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories

Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the "here-and-now" was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.

The neurobiology of interoception and affect

Scholars have argued for centuries that affective states involve interoception, or representations of the state of the body. Yet, we lack a mechanistic understanding of how signals from the body are transduced, transmitted, compressed, and integrated by the brains of humans to produce affective states. We suggest that to understand how the body contributes to affect, we first need to understand information flow through the nervous system's interoceptive pathways. We outline such a model and discuss how unique anatomical and physiological aspects of interoceptive pathways may give rise to the qualities of affective experiences in general and valence and arousal in particular. We conclude by considering implications and future directions for research on interoception, affect, emotions, and human mental experiences.

Detection of latent brain states from baseline neural activity in the amygdala

The amygdala responds to a large variety of socially and emotionally salient environmental and interoceptive stimuli. The context in which these stimuli occur determines their social and emotional significance. In canonical neurophysiological studies, the fast-paced succession of stimuli and events induce phasic changes in neural activity. During inter-trial intervals neural activity is expected to return to a stable and relatively featureless baseline. Context, such as the presence of a social partner, or the similarity of trials in a blocked design, induces brain states that can transcend the fast-paced succession of stimuli and can be recovered from the baseline firing rate of neurons. Indeed, the baseline firing rates of neurons in the amygdala change between blocks of trials of gentle grooming touch, delivered by a trusted social partner, and non-social airflow stimuli, delivered by a computer-controlled air valve. In this experimental paradigm, the presence of the groomer alone was sufficient to induce small but significant changes in baseline firing rates. Here, we examine local field potentials (LFP) recorded during these baseline periods to determine whether context was encoded by network dynamics that emerge in the local field potentials from the activity of large ensembles of neurons. We found that machine learning techniques can reliably decode social vs. non-social context from spectrograms of baseline local field potentials. Notably, decoding accuracy improved significantly with access to broad-band information. No significant differences were detected between the nuclei of the amygdala that receive direct or indirect inputs from areas of the prefrontal cortex known to coordinate flexible, context-dependent behaviors. The lack of nuclear specificity suggests that context-related synaptic inputs arise from a shared source, possibly interoceptive inputs that signal the sympathetic- vs. parasympathetic-dominated states characterizing non-social and social blocks, respectively.

The multiscale topological organization of the functional brain network in adolescent PTSD

The experience of an extremely aversive event can produce enduring deleterious behavioral, and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. Although adolescence is a period of great exposure to potentially traumatic events, the effects of trauma during adolescence remain understudied in clinical neuroscience. In this exploratory work, we aim to study the whole-cortex functional organization of 14 adolescents with PTSD using a data-driven method tailored to our population of interest. To do so, we built on the network neuroscience framework and specifically on multilayer (multisubject) community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), a hyper-colocalization between regions belonging to occipital and pericentral regions and hypo-colocalization in middle temporal, posterior-anterior medial, and frontal cortices in the adolescent PTSD group compared to a nontrauma exposed group of adolescents. These preliminary results raise the question of an altered large-scale cortical organization in adolescent PTSD, opening an interesting line of research for future investigations.

Cortical and Subcortical Mechanisms of Orthographic Word-form Learning

We examined the initial stages of orthographic learning in real time as literate adults learned spellings for spoken pseudowords during fMRI scanning. Participants were required to learn and store orthographic word forms because the pseudoword spellings were not uniquely predictable from sound to letter mappings. With eight learning trials per word form, we observed changes in the brain's response as learning was taking place. Accuracy was evaluated during learning, immediately after scanning, and 1 week later. We found evidence of two distinct learning systems-hippocampal and neocortical-operating during orthographic learning, consistent with the predictions of dual systems theories of learning/memory such as the complementary learning systems framework [McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457, 1995]. The bilateral hippocampus and the visual word form area (VWFA) showed significant BOLD response changes over learning, with the former exhibiting a rising pattern and the latter exhibiting a falling pattern. Moreover, greater BOLD signal increase in the hippocampus was associated with better postscan recall. In addition, we identified two distinct bilateral brain networks that mirrored the rising and falling patterns of the hippocampus and VWFA. Functional connectivity analysis revealed that regions within each network were internally synchronized. These novel findings highlight, for the first time, the relevance of multiple learning systems in orthographic learning and provide a paradigm that can be used to address critical gaps in our understanding of the neural bases of orthographic learning in general and orthographic word-form learning specifically.

How does the "default mode" network contribute to semantic cognition?

This review examines whether and how the "default mode" network (DMN) contributes to semantic processing. We review evidence implicating the DMN in the processing of individual word meanings and in sentence- and discourse-level semantics. Next, we argue that the areas comprising the DMN contribute to semantic processing by coordinating and integrating the simultaneous activity of local neuronal ensembles across multiple unimodal and multimodal cortical regions, creating a transient, global neuronal ensemble. The resulting ensemble implements an integrated simulation of phenomenological experience - that is, an embodied situation model - constructed from various modalities of experiential memory traces. These situation models, we argue, are necessary not only for semantic processing but also for aspects of cognition that are not traditionally considered semantic. Although many aspects of this proposal remain provisional, we believe it provides new insights into the relationships between semantic and non-semantic cognition and into the functions of the DMN.

The 'L-factor': Language as a transdiagnostic dimension in psychopathology

Thoughts and moods constituting our mental life incessantly change. When the steady flow of this dynamics diverges in clinical directions, the possible pathways involved are captured through discrete diagnostic labels. Yet a single vulnerable neurocognitive system may be causally involved in psychopathological deviations transdiagnostically. We argue that language viewed as integrating cortical functions is the best current candidate, whose forms of breakdown along its different dimensions are then manifest as symptoms - from prosodic abnormalities and rumination in depression to distortions of speech perception in verbal hallucinations, distortions of meaning and content in delusions, or disorganized speech in formal thought disorder. Spontaneous connected speech provides continuous objective readouts generating a highly accessible bio-behavioral marker with the potential of revolutionizing neuropsychological measurement. This argument turns language into a transdiagnostic 'L-factor' providing an analytical and mechanistic substrate for previously proposed latent general factors of psychopathology ('p-factor') and cognitive functioning ('c-factor'). Together with immense practical opportunities afforded by rapidly advancing natural language processing (NLP) technologies and abundantly available data, this suggests a new era of translational clinical psychiatry, in which both psychopathology and language may be rethought together.

Electroacupuncture Enhances the Functional Connectivity of Limbic System to Neocortex in the 5xFAD Mouse Model of Alzheimer's Disease

Our previous study revealed that acupuncture may exhibit therapeutic effects on Alzheimer's disease (AD) through the activation of metabolism in memory-related brain regions. However, the underlying functional mechanism remains poorly understood and warrants further investigation. In this study, we used resting-state functional magnetic resonance imaging (rsfMRI) to explore the potential effect of electroacupuncture (EA) on the 5xFAD mouse model of AD. We found that the EA group exhibited significant improvements in the number of platforms crossed and the time spent in the target quadrant when compared with the Model group (p < 0.05). The functional connectivity (FC) of left hippocampus (Hip) was enhanced significantly among 12 regions of interest (ROIs) in the EA group (p < 0.05). Based on the left Hip as the seed point, the rsfMRI analysis of the entire brain revealed increased FC between the limbic system and the neocortex in the 5xFAD mice after EA treatment. Additionally, the expression of amyloid-β(Aβ) protein and deposition in the Hip showed a downward trend in the EA group compared to the Model group (p < 0.05). In conclusion, our findings indicate that EA treatment can improve the learning and memory abilities and inhibit the expression of Aβ protein and deposition of 5xFAD mice. This improvement may be attributed to the enhancement of the resting-state functional activity and connectivity within the limbic-neocortical neural circuit, which are crucial for cognition, motor function, as well as spatial learning and memory abilities in AD mice.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

The cognitive reality monitoring network and theories of consciousness

Theories of consciousness abound. However, it is difficult to arbitrate reliably among competing theories because they target different levels of neural and cognitive processing or anatomical loci, and only some were developed with computational models in mind. In particular, theories of consciousness need to fully address the three levels of understanding of the brain proposed by David Marr: computational theory, algorithms and hardware. Most major theories refer to only one or two levels, often indirectly. The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. A central feature of the CRMN is the mapping of a gating network onto the prefrontal cortex, making it a prime coding circuit involved in monitoring the accuracy of one's mental states and distinguishing them from external reality. Because the CRMN builds on the hierarchical and layer structure of the cerebral cortex, it may connect research and findings across species, further enabling concrete computational models of consciousness with new, explicitly testable hypotheses. In sum, we discuss how the CRMN model can help further our understanding of the nature and function of consciousness.

Sleep spindle architecture associated with distinct clinical phenotypes in older adults at risk for dementia

Sleep spindles are a hallmark of non-REM sleep and play a fundamental role in memory consolidation. Alterations in these spindles are emerging as sensitive biomarkers for neurodegenerative diseases of ageing. Understanding the clinical presentations associated with spindle alterations may help to elucidate the functional role of these distinct electroencephalographic oscillations and the pathophysiology of sleep and neurodegenerative disorders. Here, we use a data-driven approach to examine the sleep, memory and default mode network connectivity phenotypes associated with sleep spindle architecture in older adults (mean age = 66 years). Participants were recruited from a specialist clinic for early diagnosis and intervention for cognitive decline, with a proportion showing mild cognitive deficits on neuropsychological testing. In a sample of 88 people who underwent memory assessment, overnight polysomnography and resting-state fMRI, a k-means cluster analysis was applied to spindle measures of interest: fast spindle density, spindle duration and spindle amplitude. This resulted in three clusters, characterised by preserved spindle architecture with higher fast spindle density and longer spindle duration (Cluster 1), and alterations in spindle architecture (Clusters 2 and 3). These clusters were further characterised by reduced memory (Clusters 2 and 3) and nocturnal hypoxemia, associated with sleep apnea (Cluster 3). Resting-state fMRI analysis confirmed that default mode connectivity was related to spindle architecture, although directionality of this relationship differed across the cluster groups. Together, these results confirm a diversity in spindle architecture in older adults, associated with clinically meaningful phenotypes, including memory function and sleep apnea. They suggest that resting-state default mode connectivity during the awake state can be associated with sleep spindle architecture; however, this is highly dependent on clinical phenotype. Establishing relationships between clinical and neuroimaging features and sleep spindle alterations will advance our understanding of the bidirectional relationships between sleep changes and neurodegenerative diseases of ageing.

Explorations of using a convolutional neural network to understand brain activations during movie watching

Neuroimaging studies increasingly use naturalistic stimuli like video clips to trigger complex brain activations, but the complexity of such stimuli makes it difficult to assign specific functions to the resulting brain activations, particularly for higher-level content like social interactions. To address this challenge, researchers have turned to deep neural networks, e.g., convolutional neural networks (CNNs). CNNs have shown success in image recognition due to their different levels of features enabling high performance. In this study, we used pre-trained VGG-16, a popular CNN model, to analyze video data and extract hierarchical features from low-level shallow layers to high-level deeper layers, linking these activations to different levels of activation of the human brain. We hypothesized that activations in different layers of VGG-16 would be associated with different levels of brain activation and visual processing hierarchy in the brain. We were also curious about which brain regions would be associated with deeper convolutional layers in VGG-16. The study analyzed a functional MRI (fMRI) dataset where participants watched the cartoon movie Partly Cloudy. Frames of the videos were fed into VGG-16, and activation maps from different kernels and layers were extracted. Time series of the average activation patterns for each kernel were created and fed into a voxel-wise model to study brain activations. Results showed that lower convolutional layers (1st convolutional layer) were mostly associated with lower visual regions, but some kernels (6, 19, 24, 42, 55, and 58) surprisingly showed associations with activations in the posterior cingulate cortex, part of the default mode network. Deeper convolutional layers were associated with more anterior and lateral portions of the visual cortex (e.g., the lateral occipital complex) and the supramarginal gyrus. Analyzing activation features associated with different brain regions showed the promise and limitations of using CNNs to link video content to brain functions.

Mitigating radiation-induced cognitive toxicity in brain metastases: More questions than answers

The emergence of advanced systemic therapies added to the use of cranial radiation techniques has significantly improved outcomes for cancer patients with multiple brain metastases (BM), leading to a considerable increase in long-term survivors. In this context, the rise of radiation-induced cognitive toxicity (RICT) has become increasingly relevant. In this critical narrative review, we address the controversies arising from clinical trials aimed at mitigating RICT. We thoroughly examine interventions such as memantine, hippocampal avoidance irradiation during BM treatment or in a prophylactic setting, and the assessment of cognitive safety in stereotactic radiosurgery (SRS). Our focus extends to recent neuroscience research findings, emphasizing the importance of preserving not only the hippocampal cortex but also other cortical regions involved in neural dynamic networks and their intricate role in encoding new memories. Despite treatment advancements, effectively managing patients with multiple BM and determining the optimal timing and integration of radiation and systemic treatments remain areas requiring further elucidation. Future trials are required to delineate optimal indications and ensure SRS safety. Additionally, the impact of new systemic therapies and the potential effects of delaying irradiation on cognitive functioning also need to be addressed. Inclusive trial designs, encompassing patients with multiple BM and accounting for diverse treatment scenarios, are essential for advancing effective strategies in managing RICT and the treatment of BM patients.

Converging diencephalic and hippocampal supports for episodic memory

To understand the neural basis of episodic memory it is necessary to appreciate the significance of the fornix. This pathway creates a direct link between those temporal lobe and medial diencephalic sites responsible for anterograde amnesia. A collaboration with Andrew Mayes made it possible to recruit and scan 38 patients with colloid cysts in the third ventricle, a condition associated with variable fornix damage. Complete fornix loss was seen in three patients, who suffered chronic long-term memory problems. Volumetric analyses involving all 38 patients then revealed a highly consistent relationship between mammillary body volume and the recall of episodic memory. That relationship was not seen for working memory or tests of recognition memory. Three different methods all supported a dissociation between recollective-based recognition (impaired) and familiarity-based recognition (spared). This dissociation helped to show how the mammillary body-anterior thalamic nuclei axis, as well as the hippocampus, is vital for episodic memory yet is not required for familiarity-based recognition. These findings set the scene for a reformulation of temporal lobe and diencephalic amnesia. In this revised model, these two regions converge on overlapping cortical areas, including retrosplenial cortex. The united actions of the hippocampal formation and the anterior thalamic nuclei on these cortical areas enable episodic memory encoding and consolidation, impacting on subsequent recall.

Experience sampling reveals the role that covert goal states play in task-relevant behavior

Cognitive neuroscience has gained insight into covert states using experience sampling. Traditionally, this approach has focused on off-task states. However, task-relevant states are also maintained via covert processes. Our study examined whether experience sampling can also provide insights into covert goal-relevant states that support task performance. To address this question, we developed a neural state space, using dimensions of brain function variation, that allows neural correlates of overt and covert states to be examined in a common analytic space. We use this to describe brain activity during task performance, its relation to covert states identified via experience sampling, and links between individual variation in overt and covert states and task performance. Our study established deliberate task focus was linked to faster target detection, and brain states underlying this experience-and target detection-were associated with activity patterns emphasizing the fronto-parietal network. In contrast, brain states underlying off-task experiences-and vigilance periods-were linked to activity patterns emphasizing the default mode network. Our study shows experience sampling can not only describe covert states that are unrelated to the task at hand, but can also be used to highlight the role fronto-parietal regions play in the maintenance of covert task-relevant states.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

A spontaneous neural replay account for involuntary autobiographical memories and déjà vu experiences

Barzykowski and Moulin argue both involuntary autobiographical memories and déjà vu experiences rely on the same involuntary memory retrieval processes but their underlying neurological basis remains unclear. We propose spontaneous neural replay in the default mode network (DMN) and hippocampus as the basis for involuntary autobiographical memories, whereas for déjà vu experiences such transient activation is limited to the DMN.

Recent advances in the neuroscience of spontaneous and off-task thought: implications for mental health

People spend a remarkable 30-50% of awake life thinking about something other than what they are currently doing. These experiences of being "off-task" can be described as spontaneous thought when mental dynamics are relatively flexible. Here we review recent neuroscience developments in this area and consider implications for mental wellbeing and illness. We provide updated overviews of the roles of the default mode network and large-scale network dynamics, and we discuss emerging candidate mechanisms involving hippocampal memory (sharp-wave ripples, replay) and neuromodulatory (noradrenergic and serotonergic) systems. We explore how distinct brain states can be associated with or give rise to adaptive and maladaptive forms of thought linked to distinguishable mental health outcomes. We conclude by outlining new directions in the neuroscience of spontaneous and off-task thought that may clarify mechanisms, lead to personalized biomarkers, and facilitate therapy developments toward the goals of better understanding and improving mental health.

Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices

Episodic memories are thought to be stabilized through the coordination of cortico-hippocampal activity during sleep. However, the timing and mechanism of this coordination remain unknown. To investigate this, we studied the relationship between hippocampal reactivation and slow-wave sleep up and down states of the retrosplenial cortex (RTC) and prefrontal cortex (PFC). We found that hippocampal reactivations are strongly correlated with specific cortical states. Reactivation occurred during sustained cortical Up states or during the transition from up to down state. Interestingly, the most prevalent interaction with memory reactivation in the hippocampus occurred during sustained up states of the PFC and RTC, while hippocampal reactivation and cortical up-to-down state transition in the RTC showed the strongest coordination. Reactivation usually occurred within 150-200 msec of a cortical Up state onset, indicating that a buildup of excitation during cortical Up state activity influences the probability of memory reactivation in CA1. Conversely, CA1 reactivation occurred 30-50 msec before the onset of a cortical down state, suggesting that memory reactivation affects down state initiation in the RTC and PFC, but the effect in the RTC was more robust. Our findings provide evidence that supports and highlights the complexity of bidirectional communication between cortical regions and the hippocampus during sleep.

Causal effects of COVID-19 on structural changes in specific brain regions: a Mendelian randomization study

BACKGROUND

Previous studies have found a correlation between coronavirus disease 2019 (COVID-19) and changes in brain structure and cognitive function, but it remains unclear whether COVID-19 causes brain structural changes and which specific brain regions are affected. Herein, we conducted a Mendelian randomization (MR) study to investigate this causal relationship and to identify specific brain regions vulnerable to COVID-19.

METHODS

Genome-wide association study (GWAS) data for COVID-19 phenotypes (28,900 COVID-19 cases and 3,251,161 controls) were selected as exposures, and GWAS data for brain structural traits (cortical thickness and surface area from 51,665 participants and volume of subcortical structures from 30,717 participants) were selected as outcomes. Inverse-variance weighted method was used as the main estimate method. The weighted median, MR-Egger, MR-PRESSO global test, and Cochran's Q statistic were used to detect heterogeneity and pleiotropy.

RESULTS

The genetically predicted COVID-19 infection phenotype was nominally associated with reduced cortical thickness in the caudal middle frontal gyrus (β = - 0.0044, p = 0.0412). The hospitalized COVID-19 phenotype was nominally associated with reduced cortical thickness in the lateral orbitofrontal gyrus (β = - 0.0049, p = 0.0328) and rostral middle frontal gyrus (β = - 0.0022, p = 0.0032) as well as with reduced cortical surface area of the middle temporal gyrus (β = - 10.8855, p = 0.0266). These causal relationships were also identified in the severe COVID-19 phenotype. Additionally, the severe COVID-19 phenotype was nominally associated with reduced cortical thickness in the cuneus (β = - 0.0024, p = 0.0168); reduced cortical surface area of the pericalcarine (β = - 2.6628, p = 0.0492), superior parietal gyrus (β = - 5.6310, p = 0.0408), and parahippocampal gyrus (β = - 0.1473, p = 0.0297); and reduced volume in the hippocampus (β = - 15.9130, p = 0.0024).

CONCLUSIONS

Our study indicates a suggestively significant association between genetic predisposition to COVID-19 and atrophy in specific functional regions of the human brain. Patients with COVID-19 and cognitive impairment should be actively managed to alleviate neurocognitive symptoms and minimize long-term effects.

Progress on the hippocampal circuits and functions based on sharp wave ripples

Sharp wave ripples (SWRs) are high-frequency synchronization events generated by hippocampal neuronal circuits during various forms of learning and reactivated during memory consolidation and recall. There is mounting evidence that SWRs are essential for storing spatial and social memories in rodents and short-term episodic memories in humans. Sharp wave ripples originate mainly from the hippocampal CA3 and subiculum, and can be transmitted to modulate neuronal activity in cortical and subcortical regions for long-term memory consolidation and behavioral guidance. Different hippocampal subregions have distinct functions in learning and memory. For instance, the dorsal CA1 is critical for spatial navigation, episodic memory, and learning, while the ventral CA1 and dorsal CA2 may work cooperatively to store and consolidate social memories. Here, we summarize recent studies demonstrating that SWRs are essential for the consolidation of spatial, episodic, and social memories in various hippocampal-cortical pathways, and review evidence that SWR dysregulation contributes to cognitive impairments in neurodegenerative and neurodevelopmental diseases.

Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

Structured memory representations develop at multiple time scales in hippocampal-cortical networks

Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Entropy and Cross-Level Orderliness in Light of the Interconnection between the Neural System and Consciousness

Despite recent advances, the origin and utility of consciousness remains under debate. Using an evolutionary perspective on the origin of consciousness, this review elaborates on the promising theoretical background suggested in the temporospatial theory of consciousness, which outlines world-brain alignment as a critical predisposition for controlling behavior and adaptation. Such a system can be evolutionarily effective only if it can provide instant cohesion between the subsystems, which is possible only if it performs an intrinsic activity modified in light of the incoming stimulation. One can assume that the world-brain interaction results in a particular interference pattern predetermined by connectome complexity. This is what organisms experience as their exclusive subjective state, allowing the anticipation of regularities in the environment. Thus, an anticipative system can emerge only in a regular environment, which guides natural selection by reinforcing corresponding reactions and decreasing the system entropy. Subsequent evolution requires complicated, layered structures and can be traced from simple organisms to human consciousness and society. This allows us to consider the mode of entropy as a subject of natural evolution rather than an individual entity.

Recollection and prior knowledge recruit the left angular gyrus during recognition

The human angular gyrus (AG) is implicated in recollection, or the ability to retrieve detailed memory content from a specific episode. A separate line of research examining the neural bases of more general mnemonic representations, extracted over multiple episodes, also highlights the AG as a core region of interest. To reconcile these separate views of AG function, the present fMRI experiment used a Remember-Know paradigm with famous (prior knowledge) and non-famous (no prior knowledge) faces to test whether AG activity could be modulated by both task-specific recollection and general prior knowledge within the same individuals. Increased BOLD activity in the left AG was observed during both recollection in the absence of prior knowledge (recollected > non-recollected or correctly rejected non-famous faces) and when prior knowledge was accessed in the absence of experiment-specific recollection (famous > non-famous correct rejections). This pattern was most prominent for the left AG as compared to the broader inferior parietal lobe. Recollection-related responses in the left AG increased with encoding duration and prior knowledge, despite prior knowledge being incidental to the recognition decision. Overall, the left AG appears sensitive to both task-specific recollection and the incidental access of general prior knowledge, thus broadening our notions of the kinds of mnemonic representations that drive activity in this region.

Neural representations of naturalistic events are updated as our understanding of the past changes

The brain actively reshapes our understanding of past events in light of new incoming information. In the current study, we ask how the brain supports this updating process during the encoding and recall of naturalistic stimuli. One group of participants watched a movie ('The Sixth Sense') with a cinematic 'twist' at the end that dramatically changed the interpretation of previous events. Next, participants were asked to verbally recall the movie events, taking into account the new 'twist' information. Most participants updated their recall to incorporate the twist. Two additional groups recalled the movie without having to update their memories during recall: one group never saw the twist; another group was exposed to the twist prior to the beginning of the movie, and thus the twist information was incorporated both during encoding and recall. We found that providing participants with information about the twist beforehand altered neural response patterns during movie-viewing in the default mode network (DMN). Moreover, presenting participants with the twist at the end of the movie changed the neural representation of the previously-encoded information during recall in a subset of DMN regions. Further evidence for this transformation was obtained by comparing the neural activation patterns during encoding and recall and correlating them with behavioral signatures of memory updating. Our results demonstrate that neural representations of past events encoded in the DMN are dynamically integrated with new information that reshapes our understanding in natural contexts.

Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization

Neutral events preceding emotional experiences can be better remembered, likely by assigning them as significant to guide possible use in future. Yet, the neurobiological mechanisms of how emotional learning enhances memory for past mundane events remain unclear. By two behavioral studies and one functional magnetic resonance imaging study with an adapted sensory preconditioning paradigm, we show rapid neural reactivation and connectivity changes underlying emotion-charged retroactive memory enhancement. Behaviorally, emotional learning retroactively enhanced initial memory for neutral associations across the three studies. Neurally, emotional learning potentiated trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-relevant neocortex. It further induced rapid hippocampal-neocortical functional reorganization supporting such retroactive memory benefit, as characterized by enhanced hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal connectivity from stimulus-relevant neocortex to distributed transmodal prefrontal-parietal areas at post-learning rests. Together, emotional learning retroactively promotes memory integration for past neutral events through stimulating trial-specific reactivation of overlapping representations and reorganization of associated memories into an integrated network to foster its priority for future use.