Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Sleep improves the consolidation of both declarative and non-declarative memories. Diekelmann and Born discuss the potential mechanisms through which slow wave sleep and rapid eye movement sleep support system and synaptic consolidation. Sleep has been identified as a state that optimizes the consolidation of newly acquired information in memory, depending on the specific conditions of learning and the timing of sleep. Consolidation during sleep promotes both quantitative and qualitative changes of memory representations. Through specific patterns of neuromodulatory activity and electric field potential oscillations, slow-wave sleep (SWS) and rapid eye movement (REM) sleep support system consolidation and synaptic consolidation, respectively. During SWS, slow oscillations, spindles and ripples — at minimum cholinergic activity — coordinate the re-activation and redistribution of hippocampus-dependent memories to neocortical sites, whereas during REM sleep, local increases in plasticity-related immediate-early gene activity — at high cholinergic and theta activity — might favour the subsequent synaptic consolidation of memories in the cortex.

The memory function of sleep

Mini Mental State

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

https://www.zora.uzh.ch/id/eprint/77770/1/physiol_rev_93.pdf

About sleep's role in memory

A fundamental question in memory research is how our brains can form enduring memories. In humans, memories of everyday life depend initially on the medial temporal lobe system, including the hippocampus. As these memories mature, they are thought to become increasingly dependent on other brain regions such as the cortex. Little is understood about how new memories in the hippocampus are transformed into remote memories in cortical networks. However, recent studies have begun to shed light on how remote memories are organized in the cortex, and the molecular and cellular events that underlie their consolidation.

/pdf/the-organization-of-recent-and-remote-memories-3ix3ec7bmc.pdf

The organization of recent and remote memories

The function of rapid-eye-movement (REM) sleep is still unknown. One prevailing hypothesis suggests that REM sleep is important in processing memory traces. Here, using positron emission tomography (PET) and regional cerebral blood flow measurements, we show that waking experience influences regional brain activity during subsequent sleep. Several brain areas activated during the execution of a serial reaction time task during wakefulness were significantly more active during REM sleep in subjects previously trained on the task than in non-trained subjects. These results support the hypothesis that memory traces are processed during REM sleep in humans.

Experience-dependent changes in cerebral activation during human REM sleep

http://leadserv.u-bourgogne.fr/files/publications/000327-implicit-sequence-learning-in-children.pdf

Implicit sequence learning in children

This PET study is concerned with the what, where, and how of implicit sequence learning. In contrast with previous studies imaging the serial reaction time (SRT) task, the sequence of successive locations was determined by a probabilistic finite-state grammar. The implicit acquisition of statistical relationships between serially ordered elements (i.e., what) was studied scan by scan, aiming to evidence the brain areas (i.e., where) specifically involved in the implicit processing of this core component of sequential higher-order knowledge. As behavioural results demonstrate between- and within-subjects variability in the implicit acquisition of sequential knowledge through practice, functional PET data were modelled using a random-effect model analysis (i.e., how) to account for both sources of behavioural variability. First, two mean condition images were created per subject depending on the presence or not of implicit sequential knowledge at the time of each of the 12 scans. Next, direct comparison of these mean condition images provided the brain areas involved in sequential knowledge processing. Using this approach, we have shown that the striatum is involved in more than simple pairwise associations and that it has the capacity to process higher-order knowledge. We suggest that the striatum is not only involved in the implicit automatization of serial information through prefrontal cortex-caudate nucleus networks, but also that it plays a significant role for the selection of the most appropriate responses in the context created by both the current and previous stimuli, thus contributing to better efficiency and faster response preparation in the SRT task.

/pdf/striatum-forever-despite-sequence-learning-variability-a-3o79143fy6.pdf

Striatum forever, despite sequence learning variability: a random effect analysis of PET data.

Associative chunk strength in artificial grammar learning.

OBJECTIVE: Schizophrenia impairs performance on explicit, but not implicit, memory tasks, indicating that conscious awareness at retrieval is a critical determinant of impaired memory. The authors investigated implicit learning, i.e., knowledge acquisition in the absence of conscious awareness, in patients with schizophrenia. METHOD: An artificial grammar learning task was used to assess implicit learning in 48 patients with schizophrenia and 24 healthy comparison subjects. The subjects were first presented with letter strings that were generated according to the rules of a finite-state grammar paradigm. They were then required to indicate whether new letter strings were “grammatical,” depending on whether or not the strings corresponded to the rules. IQ, working memory, explicit memory, verbal fluency, and speed of processing were also assessed. RESULTS: Patients performed significantly worse than the comparison subjects on cognitive tasks that assessed episodic memory, verbal fluency, working memory, an...

https://ajp.psychiatryonline.org/doi/pdfplus/10.1176/appi.ajp.158.6.944

Thierry Meulemans

Papers

Experience-dependent changes in cerebral activation during human REM sleep

Implicit sequence learning in children

Striatum forever, despite sequence learning variability: a random effect analysis of PET data.

Associative chunk strength in artificial grammar learning.

Intact Implicit Learning in Schizophrenia