20 years after “The ontogeny of human memory: A cognitive neuroscience perspective,” where are we?

2015 ◽  
Vol 39 (4) ◽  
pp. 293-303 ◽  
Author(s):  
Adeline Jabès ◽  
Charles A Nelson
Keyword(s):  
Cognitive Neuroscience ◽  
Explicit Memory ◽  
Structural Changes ◽  
Current Knowledge ◽  
Hippocampal Formation ◽  
Human Memory ◽  
Brain Regions ◽  
Memory Functions ◽  
Current Article ◽  
The Brain

In 1995, Nelson published a paper describing a model of memory development during the first years of life. The current article seeks to provide an update on the original work published 20 years ago. Specifically, we review our current knowledge on the relation between the emergence of explicit memory functions throughout development and the maturation of associated brain regions. It is now well established that the brain regions subserving explicit memory functions (i.e. the hippocampal formation) are far from mature at birth, and exhibit important and gradual structural changes during childhood and beyond. Accordingly, explicit memory functions develop progressively. While some functions are present shortly after birth (formerly proposed as pre-explicit memory), others exhibit protracted developmental profiles during the first years of life. We examine the link between the emergence of different memory functions and the maturation of specific hippocampal circuits.

scholarly journals The cognitive neuroscience of memory: perspectives from neuroimaging research

1997 ◽  
Vol 352 (1362) ◽  
pp. 1689-1695 ◽  
Author(s):  
◽  
Daniel L. Schacter
Keyword(s):  
Cognitive Neuroscience ◽  
Explicit Memory ◽  
Hippocampal Formation ◽  
Subsequent Performance ◽  
Cognitive Neuroscience Of Memory ◽  
Pet Scanning ◽  
Conscious Recollection ◽  
Cortical Regions ◽  
Past Experiences ◽  
The Brain

Cognitive neuroscience approaches to memory attempt to elucidate the brain processes and systems that are involved in different forms of memory and learning. This paper examines recent research from brain-damaged patients and neuroimaging studies that bears on the distinction between explicit and implicit forms of memory. Explicit memory refers to conscious recollection of previous experiences, whereas implicit memory refers to the non-conscious effects of past experiences on subsequent performance and behaviour. Converging evidence suggests that an implicit form of memory known as priming is associated with changes in posterior cortical regions that are involved in perceptual processing; some of the same regions may contribute to explicit memory. The hippocampal formation and prefrontal cortex also play important roles in explicit memory. Evidence is presented from recent PET scanning studies that suggests that frontal regions are associated with intentional strategic efforts to retrieve recent experiences, whereas the hippocampal formation is associated with some aspect of the actual recollection of an event.

scholarly journals Experience, but not age, is associated with volumetric mushroom body expansion in solitary alkali bees

2021 ◽  
pp. jeb.238899
Author(s):  
Mallory A. Hagadorn ◽  
Makenna M. Johnson ◽  
Adam R. Smith ◽  
Marc A. Seid ◽  
Karen M. Kapheim
Keyword(s):  
Structural Changes ◽  
Evolutionary Relationship ◽  
Brain Regions ◽  
Mushroom Bodies ◽  
Order Processing ◽  
Social Species ◽  
Evolution Of Sociality ◽  
Foraging Experience ◽  
Brain Changes ◽  
The Brain

In social insects, changes in behavior are often accompanied by structural changes in the brain. This neuroplasticity may come with experience (experience-dependent) or age (experience-expectant). Yet, the evolutionary relationship between neuroplasticity and sociality is unclear, because we know little about neuroplasticity in the solitary relatives of social species. We used confocal microscopy to measure brain changes in response to age and experience in a solitary halictid bee (Nomia melanderi). First, we compared the volume of individual brain regions among newly-emerged females, laboratory females deprived of reproductive and foraging experience, and free-flying, nesting females. Experience, but not age, led to significant expansion of the mushroom bodies—higher-order processing centers associated with learning and memory. Next, we investigated how social experience influences neuroplasticity by comparing the brains of females kept in the laboratory either alone or paired with another female. Paired females had significantly larger olfactory regions of the mushroom bodies. Together, these experimental results indicate that experience-dependent neuroplasticity is common to both solitary and social taxa, whereas experience-expectant neuroplasticity may be an adaptation to life in a social colony. Further, neuroplasticity in response to social chemical signals may have facilitated the evolution of sociality.

Swearing and the brain

2018 ◽  
pp. 107-139 ◽  
Author(s):  
Shlomit Ritz Finkelstein
Keyword(s):  
Alzheimer’S Disease ◽  
Tourette Syndrome ◽  
Clinical Data ◽  
Brain Injuries ◽  
Current Knowledge ◽  
Brain Regions ◽  
Future Research ◽  
Social Inhibition ◽  
Previous Knowledge ◽  
The Brain

This chapter explores and summarizes the current knowledge about the neurophysiological substrata of the utterance of expletives—its brain regions, pathways, and neurotransmitters, and its interaction with hormones. The chapter presents clinical data that have been gathered directly from patients of aphasia, Tourette syndrome, Alzheimer’s disease, and brain injuries—all are disorders often accompanied with expletives. It also discusses the possible relations between swearing and aggression, swearing and pain, and swearing and social inhibition in the population at large. Finally, the chapter examines the clinical data and the data gathered from the population at large within one frame, and proposes two hypotheses that can serve as possible directions for future research about the biological substrata of swearing. No previous knowledge of the brain is assumed.

scholarly journals The Presence of Human Herpesvirus 6 in the Brain in Health and Disease

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1520
Author(s):  
Gabriel Santpere ◽  
Marco Telford ◽  
Pol Andrés-Benito ◽  
Arcadi Navarro ◽  
Isidre Ferrer
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Human Herpesvirus ◽  
Brain Regions ◽  
Specific Cell ◽  
Human Herpesvirus 6 ◽  
Detection Techniques ◽  
Herpesvirus 6 ◽  
The Brain

The human herpesvirus 6 (HHV‐6) ‐A and ‐B are two dsDNA beta‐herpesviruses infectingalmost the entire worldwide population. These viruses have been implicated in multipleneurological conditions in individuals of various ages and immunological status, includingencephalitis, epilepsy, and febrile seizures. HHV‐6s have also been suggested as playing a role inthe etiology of neurodegenerative diseases such as multiple sclerosis and Alzheimer’s disease. Theapparent robustness of these suggested associations is contingent on the accuracy of HHV‐6detection in the nervous system. The effort of more than three decades of researching HHV‐6 in thebrain has yielded numerous observations, albeit using variable technical approaches in terms oftissue preservation, detection techniques, sample sizes, brain regions, and comorbidities. In thisreview, we aimed to summarize current knowledge about the entry routes and direct presence ofHHV‐6 in the brain parenchyma at the level of DNA, RNA, proteins, and specific cell types, inhealthy subjects and in those with neurological conditions. We also discuss recent findings relatedto the presence of HHV‐6 in the brains of patients with Alzheimer’s disease in light of availableevidence.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

A cognitive neuroscience review of the aetiology of ADHD

2018 ◽  
Keyword(s):  
Cognitive Neuroscience ◽  
Imaging Techniques ◽  
Brain Regions ◽  
Detailed Understanding ◽  
The Brain

A simple neurological explanation has yet to identify an aetiology and pathogenesis of the disorder.  However, advancements in imaging techniques should help to give a more detailed understanding of the brain regions that are different to those without ADHD.

scholarly journals Astroglial Isopotentiality and Calcium-Associated Biomagnetic Field Effects on Cortical Neuronal Coupling

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 439
Author(s):  
Marcos Martinez-Banaclocha
Keyword(s):  
Current Knowledge ◽  
Critical Role ◽  
Brain Regions ◽  
Local Magnetic Field ◽  
Field Potentials ◽  
Coherent Integration ◽  
Cognitive Faculties ◽  
Synaptic Neurotransmission ◽  
New Research ◽  
The Brain

Synaptic neurotransmission is necessary but does not sufficiently explain superior cognitive faculties. Growing evidence has shown that neuron–astroglial chemical crosstalk plays a critical role in the processing of information, computation, and memory. In addition to chemical and electrical communication among neurons and between neurons and astrocytes, other nonsynaptic mechanisms called ephaptic interactions can contribute to the neuronal synchronization from different brain regions involved in the processing of information. New research on brain astrocytes has clearly shown that the membrane potential of these cells remains very stable among neighboring and distant astrocytes due to the marked bioelectric coupling between them through gap junctions. This finding raises the possibility that the neocortical astroglial network exerts a guiding template modulating the excitability and synchronization of trillions of neurons by astroglial Ca2+-associated bioelectromagnetic interactions. We propose that bioelectric and biomagnetic fields of the astroglial network equalize extracellular local field potentials (LFPs) and associated local magnetic field potentials (LMFPs) in the cortical layers of the brain areas involved in the processing of information, contributing to the adequate and coherent integration of external and internal signals. This article reviews the current knowledge of ephaptic interactions in the cerebral cortex and proposes that the isopotentiality of cortical astrocytes is a prerequisite for the maintenance of the bioelectromagnetic crosstalk between neurons and astrocytes in the neocortex.

scholarly journals Visual stability

2011 ◽  
Vol 366 (1564) ◽  
pp. 468-475 ◽  
Author(s):  
David Melcher
Keyword(s):  
Cognitive Neuroscience ◽  
State Of The Art ◽  
Brain Regions ◽  
Clinical Neurology ◽  
Visual Stability ◽  
Retinal Motion ◽  
Brain Circuits ◽  
The World ◽  
The Brain

Our vision remains stable even though the movements of our eyes, head and bodies create a motion pattern on the retina. One of the most important, yet basic, feats of the visual system is to correctly determine whether this retinal motion is owing to real movement in the world or rather our own self-movement. This problem has occupied many great thinkers, such as Descartes and Helmholtz, at least since the time of Alhazen. This theme issue brings together leading researchers from animal neurophysiology, clinical neurology, psychophysics and cognitive neuroscience to summarize the state of the art in the study of visual stability. Recently, there has been significant progress in understanding the limits of visual stability in humans and in identifying many of the brain circuits involved in maintaining a stable percept of the world. Clinical studies and new experimental methods, such as transcranial magnetic stimulation, now make it possible to test the causal role of different brain regions in creating visual stability and also allow us to measure the consequences when the mechanisms of visual stability break down.

scholarly journals Neuroscience Research on Human Visual Path Integration: Empirical Overview and Strategic Considerations

2021 ◽  
Author(s):  
Jimmy Y. Zhong
Keyword(s):  
Path Integration ◽  
Hippocampal Formation ◽  
Brain Activity ◽  
Brain Regions ◽  
Neural Basis ◽  
Age Related ◽  
Neuroscience Research ◽  
Integration Strategies ◽  
The Impact ◽  
The Brain

Over the past two decades, many neuroimaging studies have attempted uncover the brain regions and networks involved in path integration and identify the underlying neurocognitive mechanisms. Although these studies made inroads into the neural basis of path integration, they have yet to offer a full disclosure of the functional specialization of the brain regions supporting path integration. In this paper, I reviewed notable neuroscientific studies on visual path integration in humans, identified the commonalities and discrepancies in their findings, and incorporated fresh insights from recent path integration studies. Specifically, this paper presented neuroscientific studies performed with virtual renditions of the triangle/path completion task and addressed whether or not the hippocampus is necessary for human path integration. Based on studies that showed evidence supporting and negating the involvement of the hippocampal formation in path integration, this paper introduces the proposal that the use of different path integration strategies may determine the extent to which the hippocampus and entorhinal cortex are engaged during path integration. To this end, recent studies that investigated the impact of different path integration strategies on behavioral performance and functional brain activity were discussed. Methodological concerns were raised with feasible recommendations for improving the experimental design of future strategy-related path integration studies, which can cover cognitive neuroscience research on age-related differences in the role of the hippocampal formation in path integration and Bayesian modelling of the interaction between landmark and self-motion cues. The practical value of investigating different path integration strategies was also discussed briefly from a biomedical perspective.

scholarly journals Musical skills can be decoded from magnetic resonance images

2020 ◽  
Author(s):  
Tuomas Puoliväli ◽  
Tuomo Sipola ◽  
Anja Thiede ◽  
Marina Kliuchko ◽  
Brigitte Bogert ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Structure ◽  
Structural Changes ◽  
Musical Instrument ◽  
Structural Features ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Brain

AbstractLearning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions encompassing the whole cortex. Using a supervised machine learning technique called support vector machines, we achieved significant (κ = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding an acquired skill from magnetic resonance images of brain structure is feasible to some extent. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely overlapped with earlier findings, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude.

Sign in / Sign up

Export Citation Format

Share Document