Remapping of memory encoding and retrieval networks: insights from neuroimaging in primates

Neural circuits for retrospective and prospective introspection for the past, present and future in macaque monkeys and humans

For animals, including humans, to have self-awareness, the ability to reflect on one's own perceptions and cognitions, which is known as metacognition, and an understanding of consistency of the self from the past to the present and into the future based on metacognition is essential. Through the mediation of self-consciousness, animals are thought to be able to proactively act to change their environment rather than passively responding to changes in their environment. However, it has not been known whether animals have self-awareness, and, if so, how it is implemented neurobiologically. In this review article, I introduce our studies examining the neural basis of metacognitive abilities for past, present, and future actions in macaque monkeys and humans, and explore the evolutionary origins of self-awareness.

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Trettenbrein (2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb's (1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein's (2016) critiques of the synaptic theory in light of Hebb's theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb's theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of "intelligent" robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.

A simplified computational memory model from information processing

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

Neurocognitive effects of estrogens across the adult lifespan in nonhuman primates: State of knowledge and new perspectives

This article is part of a Special Issue "Estradiol and cognition". This review discusses the unique contribution of nonhuman primate research to our understanding of the neurocognitive effects of estrogens throughout the adult lifespan in females. Mounting evidence indicates that estrogens affect many aspects of hippocampal, prefrontal and cholinergic function in the primate brain and the underlying mechanisms are beginning to be elucidated. In addition, estrogens may also influence cognitive function indirectly, via the modulation of other systems that impact cognition. We will focus on the effects of estrogens on sleep and emphasize the need for primate models to better understand these complex interactions. Continued research with nonhuman primates is essential for the development of therapies that are optimal for the maintenance of women's cognitive health throughout the lifespan.

Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques

Neuroimaging and neurophysiology have revealed that multiple areas in the prefrontal cortex (PFC) are activated in a specific memory task, but severity of impairment after PFC lesions is largely different depending on which activated area is damaged. The critical relationship between lesion sites and impairments has not yet been given a clear mechanistic explanation. Although recent works proposed that a whole-brain network contains hubs that play integrative roles in cortical information processing, this framework relying on an anatomy-based structural network cannot account for the vulnerable locus for a specific task, lesioning of which would bring impairment. Here, we hypothesized that (i) activated PFC areas dynamically form an ordered network centered at a task-specific “functional hub” and (ii) the lesion-effective site corresponds to the “functional hub,” but not to a task-invariant “structural hub.” To test these hypotheses, we conducted functional magnetic resonance imaging experiments in macaques performing a temporal contextual memory task. We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns. Using a novel simulated-lesion method based on support vector machine, we estimated severity of impairment after lesioning of each area, which accorded well with a known dissociation in contextual memory impairment in macaques (impairment after lesioning in area 9/46d, but not in area 8Ad). The predicted severity of impairment was proportional to the network “hubness” of the virtually lesioned area in the task-evoked directed connectivity network, rather than in the anatomical network known from tracer studies. Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs. These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

Patterns of whole-brain activity while macaques perform a memory retrieval task show that the task-specific functional hub in the dynamic cortical network predicts the task-specific consequences of brain damage better than a task-invariant structural hub does.

Patients with lesions in the front part of the brain’s frontal lobe—the prefrontal cortex—suffer from severe memory deficits. Neuroimaging and neurophysiology studies have revealed that multiple areas in the prefrontal cortex are activated during a specific memory task. However, the severity of the memory deficit after a lesion in the prefrontal cortex largely depends on which of the activated areas is damaged; lesions in only a fraction of the activated areas actually lead to memory deficits. It is currently unknown why some activated areas are “lesion effective” and others are not. Here, by using functional magnetic resonance imaging (fMRI) to measure macaque whole-brain activity during a memory task, we found that the activated areas and the task-specific functional connectivity among them formed a hierarchical network centered on a hub. The task-specific “functional hub” in this dynamic network accurately corresponds to the well-documented lesion-effective site and avoids the neighboring non-lesion-effective sites. Quantitatively, the predicted severity of memory impairment is proportional to the network “hubness” of the lesioned area in the functional network, rather than in the anatomical network, which is statically determined by axonal projection patterns. Our results suggest that the areas of the prefrontal cortex dynamically shape a hub-centric network, reallocating the lesion-effective site apart from the static anatomical hubs depending on the cognitive requirements of the specific memory task.