Mapping sequence structure in the human lateral entorhinal cortex

Remembering event sequences is central to episodic memory and presumablysupported by the hippocampal-entorhinal region. We previously demonstrated that thehippocampus maps spatial and temporal distances between events encountered along a routethrough a virtual city (Deuker et al., 2016), but the content of entorhinal mnemonic representationsremains unclear. Here, we demonstrate that multi-voxel representations in the anterior-lateralentorhinal cortex (alEC) — the human homologue of the rodent lateral entorhinal cortex —specifically reflect the temporal event structure after learning. Holistic representations of thesequence structure related to memory recall and the timeline of events could be reconstructedfrom entorhinal multi-voxel patterns. Our findings demonstrate representations of temporalstructure in the alEC; dovetailing with temporal information carried by population signals in thelateral entorhinal cortex of navigating rodents and alEC activations during temporal memoryretrieval. Our results provide novel evidence for the role of the alEC in representing time forepisodic memory

Structuring time in human lateral entorhinal cortex

Episodic memories consist of event information linked to spatio-temporal context. Notably, the hippocampus is involved in the encoding, representation and retrieval of temporal relations that comprise a context, but it remains largely unclear how coding for elapsed time arises in the hippocampal-entorhinal region. The entorhinal cortex (EC), the main cortical input structure of the hippocampus, has been hypothesized to provide temporal tags for memories via contextual drift and recent evidence demonstrates that time can be decoded from population activity in the rodent lateral EC. Here, we use fMRI to show that the anterior-lateral EC (alEC), the human homologue region of rodent lateral EC, maps the temporal structure of events. Participants acquired knowledge about temporal and spatial relationships between object positions - dissociated via teleporters - along a fixed route through a virtual city. Multi-voxel pattern similarity in alEC changed through learning to reflect elapsed time between event memories. Furthermore, we reconstructed the temporal structure of object relationships from alEC pattern similarity change. In contrast to the hippocampus, which maps the subjective time between event memories in this task, the temporal map in alEC reflected the objective time elapsed between events. Our findings provide evidence for the notion that alEC represents the temporal structure of memories, putatively derived from slowly-varying population signals during learning. Further, our findings suggest a dissociation between objective and subjective temporal maps in EC and hippocampus; thereby providing novel evidence for the role of the hippocampal-entorhinal region in representing time for episodic memory

Structuring time: The hippocampus constructs sequence memories that generalize temporal relations across experiences

The hippocampal-entorhinal region supports memory for episodic details, such as temporal relations of sequential events, and mnemonic constructions combining experiences for inferential reasoning. However, it is unclear whether hippocampal event memories reflect temporal relations derived from mnemonic constructions, event order, or elapsing time, and whether these sequence representations generalize temporal relations across similar sequences. Here, participants mnemonically constructed times of events from multiple sequences using infrequent cues and their experience of passing time. After learning, event representations in the anterior hippocampus reflected temporal relations based on constructed times. Temporal relations were generalized across sequences, revealing distinct representational formats for events from the same or different sequences. Structural knowledge about time patterns, abstracted from different sequences, biased the construction of specific event times. These findings demonstrate that mnemonic construction and the generalization of relational knowledge combine in the hippocampus, consistent with the simulation of scenarios from episodic details and structural knowledge

Mnemonic construction and representation of temporal structure in the hippocampal formation

The hippocampal-entorhinal region supports memory for episodic details, such as temporal relations of sequential events, and mnemonic constructions combining experiences for inferential reasoning. However, it is unclear whether hippocampal event memories reflect temporal relations derived from mnemonic constructions, event order, or elapsing time, and whether these sequence representations generalize temporal relations across similar sequences. Here, participants mnemonically constructed times of events from multiple sequences using infrequent cues and their experience of passing time. After learning, event representations in the anterior hippocampus reflected temporal relations based on constructed times. Temporal relations were generalized across sequences, revealing distinct representational formats for events from the same or different sequences. Structural knowledge about time patterns, abstracted from different sequences, biased the construction of specific event times. These findings demonstrate that mnemonic construction and the generalization of relational knowledge combine in the hippocampus, consistent with the simulation of scenarios from episodic details and structural knowledge

Grid-cell representations in mental simulation

Anticipating the future is a key motif of the brain, possibly supported by mental simulation of upcoming events. Rodent single-cell recordings suggest the ability of spatially tuned cells to represent subsequent locations. Grid-like representations have been observed in the human entorhinal cortex during virtual and imagined navigation. However, hitherto it remains unknown if grid-like representations contribute to mental simulation in the absence of imagined movement. Participants imagined directions between building locations in a large-scale virtual-reality city while undergoing fMRI without re-exposure to the environment. Using multi-voxel pattern analysis, we provide evidence for representations of absolute imagined direction at a resolution of 30° in the parahippocampal gyrus, consistent with the head-direction system. Furthermore, we capitalize on the six-fold rotational symmetry of grid-cell firing to demonstrate a 60° periodic pattern-similarity structure in the entorhinal cortex. Our findings imply a role of the entorhinal grid-system in mental simulation and future thinking beyond spatial navigation

Boosting Long-term Memory via Wakeful Rest: Intentional Rehearsal is not Necessary, Automatic Consolidation is Sufficient.

<div><p>People perform better on tests of delayed free recall if learning is followed immediately by a short wakeful rest than by a short period of sensory stimulation. Animal and human work suggests that wakeful resting provides optimal conditions for the consolidation of recently acquired memories. However, an alternative account cannot be ruled out, namely that wakeful resting provides optimal conditions for intentional rehearsal of recently acquired memories, thus driving superior memory. Here we utilised non-recallable words to examine whether wakeful rest boosts long-term memory, even when new memories could not be rehearsed intentionally during the wakeful rest delay. The probing of non-recallable words requires a recognition paradigm. Therefore, we first established, via Experiment 1, that the rest-induced boost in memory observed via free recall can be replicated in a recognition paradigm, using concrete nouns. In Experiment 2, participants heard 30 non-recallable non-words, presented as ‘foreign names in a bridge club abroad’ and then either rested wakefully or played a visual spot-the-difference game for 10 minutes. Retention was probed via recognition at two time points, 15 minutes and 7 days after presentation. As in Experiment 1, wakeful rest boosted recognition significantly, and this boost was maintained for at least 7 days. Our results indicate that the enhancement of memory via wakeful rest is <i>not</i> dependent upon intentional rehearsal of learned material during the rest period. We thus conclude that consolidation is <i>sufficient</i> for this rest-induced memory boost to emerge. We propose that wakeful resting allows for superior memory consolidation, resulting in stronger and/or more veridical representations of experienced events which can be detected via tests of free recall and recognition.</p></div

Unmasking selective path integration deficits inAlzheimer’s disease risk carriers

Alzheimer’s disease (AD) manifests with progressive memory loss and spatial disorientation. Neuropathological studies suggest early AD pathology in the entorhinal cortex (EC) of young adults at genetic risk for AD (APOE4-carriers). Because the EC harbors grid cells, a likely neural substrate of path integration (PI), we examined PI performance in APOE4-carriers during a virtual navigation task. We report a selective impairment in APOE4-carriers specifically when recruitment of compensatory navigational strategies via supportive spatial cues was disabled. A separate fMRI study revealed that PI performance was associated with the strength of entorhinal grid-like representations when no compensatory strategies were available, suggesting grid cell dysfunction as a mechanistic explanation for PI deficits in APOE4-carriers. Furthermore, posterior cingulate/retrosplenial cortex was involved in the recruitment of compensatory navigational strategies via supportive spatial cues. Our results provide evidence for selective PI deficits in AD risk carriers, decades before potential disease onset

Age-related changes in neural functional connectivity and its behavioral relevance

<p>Abstract</p> <p>Background</p> <p>Resting-state recordings are characterized by widely distributed networks of coherent brain activations. Disturbances of the default network - a set of regions that are deactivated by cognitive tasks and activated during passive states - have been detected in age-related disorders such as Alzheimer's or Parkinson's disease but alterations in the course of healthy aging still need to be explored.</p> <p>Results</p> <p>Using magnetoencephalography (MEG), the present study investigated how age-related functional resting-state brain connectivity links to cognitive performance in healthy aging in fifty-three participants ranging in age from 18 to 89 years. A beamforming technique was used to reconstruct the brain activity in source space and the interregional coupling was investigated using partial directed coherence (PDC). We found significant age-related alterations of functional resting-state connectivity. These are mainly characterized by reduced information input into the posterior cingulum/precuneus region together with an enhanced information flow to the medial temporal lobe. Furthermore, higher inflow in the medial temporal lobe subsystem was associated with weaker cognitive performance whereas stronger inflow in the posterior cluster was related to better cognitive performance.</p> <p>Conclusion</p> <p>This is the first study to show age-related alterations in subsystems of the resting state network that are furthermore associated with cognitive performance.</p

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest