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At least 127 records · Page 7Linked to original sources

PubMed1997

Cognitive maps beyond the hippocampus.

We present a conceptual framework for the role of the hippocampus and its afferent and efferent structures in rodent navigation. Our proposal is compatible with the behavioral, neurophysiological, anatomical, and neuropharmacological literature, and suggests a number of practical experiments that could support or refute it. We begin with a review of place cells and how the place code for an environment might be aligned with sensory cues and updated by self-motion information. The existence of place fields in the dark suggests that location information is maintained by path integration, which requires an internal representation of direction of motion. This leads to a consideration of the organization of the rodent head direction system, and thence into a discussion of the computational structure and anatomical locus of the path integrator. If the place code is used in navigation, there must be a mechanism for selecting an action based on this information. We review evidence that the nucleus accumbens subserves this function. From there, we move to interactions between the hippocampal system and the environment, emphasizing mechanisms for learning novel environments and for aligning the various subsystems upon re-entry into familiar environments. We conclude with a discussion of the relationship between navigation and declarative memory.

Acetylcholine↗

PubMed1997

LTP and spatial learning--where to next?

Hebb suggested, in 1949, that memories could be stored by forming associative connections between neurons if the criterion for increasing the connection strength between them be that they were active simultaneously. Much attention has been devoted towards trying to determine a) if there is a physiological substrate of such a rule, and b) if so, whether the phenomenon participates in real-life memory formation. The discovery of the electrically induced increase in synaptic strength known as long-term potentiation (LTP), in the early 1970s, demonstrated that a neural version of the Hebb rule could be observed under laboratory conditions in the hippocampus, a structure important for some types of learning. However, a quarter of a century later, the evidence linking LTP to learning and memory is still contradictory. The purpose of the present article is to review and assess the types of approach that have been taken in trying to determine whether hippocampal synaptic plasticity participates in memory formation.

Animals↗

PubMed1999

Encoding and retrieval in human medial temporal lobes: an empirical investigation using functional magnetic resonance imaging (fMRI).

The precise functional role of the hippocampus in human episodic memory is an unresolved question though it has recently been suggested that distinct medial temporal lobe (MTL) regions are involved in encoding and retrieval operations respectively. For example, a recent meta-analysis of positron emission tomography (PET) literature has suggested a rostral-caudal functional division in the medial temporal lobes (MTL), with rostral MTL mediating encoding and caudal MTL retrieval operations. However, a review of the combined PET and fMRI literature, reported in the present issue, while noting systematic discrepancies between PET and fMRI, reaches a conclusion that posterior MTL is involved in encoding. Here we present fMRI data, from a modified artificial grammar learning paradigm, that examines two questions concerning the functional role of the hippocampus, and related MTL structures in episodic memory. Firstly, we test a hypothesis that anterior hippocampus is activated during encoding and that this response is greater for novel items. Secondly, we test whether increasing familiarity with stimulus material is associated with a posterior MTL neural response. Our empirical findings support both hypotheses in that we demonstrate a left anterior hippocampal response sensitive to encoding demands and a posterior parahippocampal response sensitive to retrieval demands. Furthermore, we show that both anterior and posterior hippocampal responses are modulated to the degree to which stimuli can be assimilated into a meaningful rule-based framework.

Adult↗

PubMed1999

Analysis of information transmission in the Schaffer collaterals.

Hippocampal region CA1 seems from comparative studies to be particularly important in the primate brain, in addition to being crucial to memory function. Thus, it is an extremely appropriate place to begin a quantitative investigation of the information representation and transmission capabilities of cerebral neural networks. In this study, a mathematical model of the Schaffer collateral projection from CA3 to CA1 is described. From the model, the amount of information that can be conveyed by the Schaffer collaterals is calculated, i.e., the information that a pattern of firing in CA1 conveys about a pattern of firing in CA3, because of the connections between them. The calculation is performed analytically for an arbitrary probability distribution describing the pattern of CA3 firing and then solved numerically for particular input distributions. The effect of a number of issues on the information conveyed is examined. Consideration of the effect of the amount of analog resolution of firing rates in the patterns of activity in CA3 confirmed information transmission to be most efficient for binary codes, to a degree that depends on the sparseness of activity. For very sparse codes, a binary code allows more information to be received even in absolute terms, but for more distributed codes, slightly more information can be received by CA1 by making use of analog resolution. The pattern of convergence of connections from CA3 to CA1 is examined, i.e., the spatial distribution of the number of connections each CA1 neuron receives. It is found that the effect of the difference between a uniform convergence model and a proposed real convergence pattern (Bernard and Wheal, Hippocampus 1994;4:497-529) is minimal. The effect of the ratio of expansion between CA3 and CA1 due to the relative numbers of neurons in these two areas is studied. The Schaffer collaterals in all mammalian species reported in the literature seem to operate in a regime in which there is at least the scope for efficient transfer of information. In addition, the effect of topography (with respect to the transverse hippocampal axis) in the Schaffer collateral connectivity is examined. In the absence of spatial correlations, topography is found to have essentially no effect on information transmission. If spatial correlations in firing were present in CA3 (which, however, would be less efficient for memory storage in the recurrent collaterals), information transmission would be maximized by matching the topographic spread to the spatial scale of correlation.

Animals↗

PubMed1998

Pharmacotherapy for people with Alzheimer's disease: a Markov-cycle evaluation of five years' therapy using donepezil.

This article combines data from a clinical trial of donepezil with costing figures to evaluate expected direct costs of care over 5 years after diagnosis of Alzheimer's disease (AD) for patients aged 75 years and over at diagnosis. A Markov model simulates the progression of elderly persons through changing levels of severity. The model compares three treatment regimes for each of two patient groups; mild AD at start of treatment; moderate AD at start of treatment. Patients are followed until 5 years after the start of the treatment. Despite the acquisition costs, use of donepezil is approximately cost-neutral for both 5 mg and 10 mg treatment groups and for patients initially at either mild or moderate states of illness. Expected costs are slightly higher than for the placebo group, but higher expenditure on drugs is partly offset by lower costs of care consequent on treated patients not declining as rapidly as those untreated. The model showed that donepezil patients spent less time in the state of severe dementia, where costs of care are higher. Sensitivity analysis on key assumptions demonstrated that expected costs were highly dependent on discount rate and, more significantly, on the mortality rate.

Aged↗

PubMed1999

Parameter estimation from Rician-distributed data sets using a maximum likelihood estimator: application to T1 and perfusion measurements.

General expressions are presented to calculate the maximum likelihood (ML) estimator and corresponding Fisher matrix for Rician-distributed data sets. This estimator results in the most precise, unbiased estimations of T1 from magnitude data sets, even when low signal-to-noise ratios (<6) are present. By optimizing the sample point distributions for inversion-recovery experiments, a 32% increase in precision of the estimated T1 is obtained, compared with a linear sampling scheme. Perfusion rates are estimated from combined data sets of the slice- and nonslice-selective inversion-recovery experiments, as obtained with the flow-sensitive alternating inversion recovery (FAIR) technique. The ML estimator for the combined data set results in the most precise, unbiased estimations of the perfusion rate. Error analysis shows that very high signal-to-noise ratios are required for precise estimation of perfusion rates from FAIR experiments.

Animals↗

PubMed2000

Dynamic models in fMRI.

Most statistical methods for assessing activated voxels in fMRI experiments are based on correlation or regression analysis. In this context, the main assumptions are that the baseline can be described by a few known basis functions or variables and that the effect of the stimulus, i.e., the activation, stays constant over time. As these assumptions are in many cases neither necessary nor correct, a new dynamic approach that does not depend on those assumptions will be presented. This allows for simultaneous nonparametric estimation of the baseline and, as an important feature, of time-varying effects of stimulation. This method of estimating the stimulus related areas of the brain furthermore provides the possibility to analyze the temporal and spatial evolution of the activation within an fMRI experiment.

Algorithms↗

PubMed2000

Psychometric intelligence differences and brain function.

Psychometric intelligence attracts a converging consensus about its phenotypic structure. Mental ability test scores have proven predictive validity. However, although individual differences in mental abilities can be measured, they are not understood. A long-standing aim of the 'London School' of British psychologists, since Galton and Spearman, is to understand the origins of psychometric intelligence differences in terms of individual differences in brain processes. The history of this research is described, as is the rise in interest since the 1970s. The first problem, met since antiquity, is to discover the relevant levels of brain function. Thus, aspects of brain function that 'explain' psychometric intelligence differences are sought at psychometric, cognitive, psychophysical, physiological, neurochemical and genetic levels. The growing points and dead-ends within each of these levels are identified. Special attention is given to research that crosses levels of description of brain function. Two types of multi-level brain function research are discussed, 'correlational' and 'circumstantial/experimental,' and examples of each are described. Illustrating both approaches, there is a detailed account of research on inspection time that discusses how psychometric intelligence-brain process correlations at one level (psychophysical) may be expanded using event-related potentials, psychopharmacology and functional magnetic resonance imaging.

Brain↗

PubMed2000

Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results.

Hippocampal-neocortical interactions in memory have typically been characterized within the "standard model" of memory consolidation. In this view, memory storage initially requires hippocampal linking of dispersed neocortical storage sites, but over time this need dissipates, and the hippocampal component is rendered unnecessary. This change in function over time is held to account for the retrograde amnesia (RA) gradients often seen in patients with hippocampal damage. Recent evidence, however, calls this standard model into question, and we have recently proposed a new approach, the "multiple memory trace" (MMT) theory. In this view, hippocampal ensembles are always involved in storage and retrieval of episodic information, but semantic (gist) information can be established in neocortex, and will survive damage to the hippocampal system if enough time has elapsed. This approach accounts more readily for the very long RA gradients often observed in amnesia. We report the results of analytic and connectionist simulations that demonstrate the feasibility of MMT. We also report a neuroimaging study showing that retrieval of very remote (25-year-old) memories elicits as much activation in hippocampus as retrieval of quite recent memories. Finally, we report new data from the study of patients with temporal lobe damage, using more sensitive measures than previously the case, showing that deficits in both episodic and spatial detail can be observed even for very remote memories. Overall, these findings indicate that the standard model of memory consolidation, which views the hippocampus as having only a temporary role in memory, is wrong. Instead, the data support the view that for episodic and spatial detail the hippocampal system is always necessary.

Epilepsy, Temporal Lobe↗

PubMed2000

Computational principles of learning in the neocortex and hippocampus.

We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly, using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion. We summarize the results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, and we point to directions of current development.

Hippocampus↗

PubMed2000

Physiological patterns in the hippocampo-entorhinal cortex system.

The anatomical connectivity and intrinsic properties of entorhinal cortical neurons give rise to ordered patterns of ensemble activity. How entorhinal ensembles form, interact, and accomplish emergent processes such as memory formation is not well-understood. We lack sufficient understanding of how neuronal ensembles in general can function transiently and distinctively from other neuronal ensembles. Ensemble interactions are bound, foremost, by anatomical connectivity and temporal constraints on neuronal discharge. We present an overview of the structure of neuronal interactions within the entorhinal cortex and the rest of the hippocampal formation. We wish to highlight two principle features of entorhinal-hippocampal interactions. First, large numbers of entorhinal neurons are organized into at least two distinct high-frequency population patterns: gamma (40-100 Hz) frequency volleys and ripple (140-200 Hz) frequency volleys. These patterns occur coincident with other well-defined electrophysiological patterns. Gamma frequency volleys are modulated by the theta cycle. Ripple frequency volleys occur on each sharp wave event. Second, these patterns occur dominantly in specific layers of the entorhinal cortex. Theta/gamma frequency volleys are the principle pattern observed in layers I-III, in the neurons that receive cortical inputs and project to the hippocampus. Ripple frequency volleys are the principle population pattern observed in layers V-VI, in the neurons that receive hippocampal output and project primarily to the neocortex. Further, we will highlight how these ensemble patterns organize interactions within distributed forebrain structures and support memory formation.

Afferent Pathways↗

PubMed1993

How does a serial, integrated and very limited stream of consciousness emerge from a nervous system that is mostly unconscious, distributed, parallel and of enormous capacity?

Much of the nervous system can be viewed as a massively parallel, distributed system of highly specialized but unconscious processors. Conscious experience on the other hand is traditionally viewed as a serial stream that integrates different sources of information but is limited to only one internally consistent content at any given moment. Global Workspace theory suggests that conscious experience emerges from a nervous system in which multiple input processors compete for access to a broadcasting capability; the winning processor can disseminate its information globally throughout the brain. Global workspace architectures have been widely employed in computer systems to integrate separate modules when they must work together to solve a novel problem or to control a coherent new response. The theory articulates a series of increasingly complex models, able to account for more and more evidence about conscious functioning, from perceptual consciousness to conscious problem-solving, voluntary control of action, and directed attention. Global Workspace theory is consistent with, but not reducible to, other theories of limited-capacity mechanisms. Global workspace architectures must show competition for input to a neural global workspace and global distribution of its output. Brain structures that are demonstrably required for normal conscious experience can carry out these two functions. The theory makes testable predictions, especially for newly emerging, high-speed brain imaging technology.

Consciousness↗

PubMed1993

Integrated cortical field model of consciousness.

The idea that there is a localized module or limited capacity mechanism in the brain that subserves consciousness is wrong. Awareness is a product of the activity of widely distributed neuronal assemblies that represent diverse aspects of experience. Central to a representation's entry into consciousness is its integration into the currently dominant pattern of central neuronal activity (dominant focus). A representation anywhere in the forebrain could on one occasion enter consciousness and on another remain outside it, depending on whether it is, perhaps by temporal coherence of discharge of cell assemblies, integrated into the dominant focus. There is no privileged locus or 'internal eye' for the benefit of which input is elaborated and toward which information must be transported. When a perceptual decision is made there need be no re-enactment ('filling in') of the appearance in question. Nor is there a 'finish line', the crossing of which determines the perceived sequence of events. Neuropsychological syndromes that involve unawareness of a perceptual domain illustrate the explanatory value of this integrated cortical field model of consciousness. Awareness cannot be conceptualized as separate from the neural activity of which it is the subjective concomitant. Being aware is what it is like to have a particular pattern of neuronal activity. To regard consciousness as arising from brain activity by some esoteric transformation is misconceived.

Awareness↗

PubMed2007

Schizophrenic-like neurocognitive deficits in children and adolescents with 22q11 deletion syndrome.

22q11.2 Deletion Syndrome (22q11DS) is the most common genetic microdeletion syndrome affecting humans. The syndrome is associated with general cognitive impairments and specific deficits in visual-spatial ability, non-verbal reasoning, and planning skills. 22q11DS is also associated with behavioral and psychiatric abnormalities, including a markedly elevated risk for schizophrenia. Research findings indicate that people with schizophrenia, as well as those identified as schizoptypic, show specific cognitive deficits in the areas of sustained attention, executive functioning, and verbal working memory. The present study examined such schizophrenic-like cognitive deficits in children and adolescents with 22q11DS (n = 26) and controls (n = 25) using a cross-sectional design. As hypothesized, 22q11DS participants exhibited deficits in intelligence, achievement, sustained attention, executive functioning, and verbal working memory compared to controls. Furthermore, deficits in attention and executive functioning were more pronounced in the 22q11DS sample relative to general cognitive impairment. These findings suggest that the same pattern of neuropsychological impairment seen in patients with schizophrenia is present in non-psychotic children identified as at-risk for the development of schizophrenia based on a known genetic risk marker.

Adolescent↗

PubMed1987

Risk factors for ischemic stroke: a prospective study in Rochester, Minnesota.

A cohort of 1,804 residents of Rochester, Minnesota, who were at least 50 years old, free of stroke, and who underwent examination at the Mayo Clinic in 1960, was followed for 13 years. During this period, there were 110 first ischemic strokes and 616 deaths without stroke. The time of onset, if available, or the time of diagnosis of potential risk factors was determined for all patients during the study and was used to construct a proportional hazards model of time to occurrence of stroke with time-dependent risk factors. The model included 8 risk factors (2 fixed and 6 time-dependent). For these, the individual relative risks are: 1.6 for age (per 10 years), 2.0 for males, 4.0 for definite hypertension, 3.9 for transient ischemic attacks, 2.2 for hypertensive heart disease, 2.2 for coronary heart disease, 1.7 for congestive heart failure, and 1.7 for diabetes mellitus. Atrial fibrillation was not a significant risk factor using time-dependent multivariate analysis.

Brain Ischemia↗

PubMed1992

Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer's disease.

A higher prevalence of dementia in individuals with fewer years of education has suggested that education may protect against Alzheimer's disease (AD). We tested whether individuals with more years of education have a more advanced AD before it is clinically evident. As a measure of pathophysiological severity, we quantified regional cerebral blood flow (rCBF), by the 133Xenon inhalation technique; a specific pattern of flow reduction in the parietotemporal cortex corresponds to AD pathology. In 3 groups of patients with probable AD, matched for clinical measures of dementia severity but with varying levels of education, whole-cortex mean flows were comparable. However, the parietotemporal perfusion deficit was significantly greater in the group with the highest level of education, indicating that AD was more advanced in this group. We conclude that education or its covariates or both may provide a reserve that compensates for the neuropathological changes of AD and delays the onset of its clinical manifestations.

Alzheimer Disease↗

PubMed2006

Nonlinear local electrovascular coupling. I: A theoretical model.

Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data.

Brain↗

PubMed2006

Testing effective connectivity changes with structural equation modeling: what does a bad model tell us?

Structural equation modeling (SEM) is a statistical method that can assess changes in effective connectivity across tasks or between groups. In its initial application to neuroimaging data, anatomical connectivity provided the constraints to decompose interregional covariances to estimate effective connections. There have been concerns expressed, however, with the validity of interpreting effective connections for a model that does not adequately fit the data. We sought to address this concern by creating two population networks with different patterns of effective connectivity, extracting three samples sizes (N = 100, 60, 20), and then assessing whether the ability to detect effective connectivity differences depended on absolute model fit. Four scenarios were assessed: (1) elimination of a region showing no task differences; (2) elimination of connections with no task differences; (3) elimination of connections that carried task differences, but could be expressed through alternative indirect routes; (4) elimination of connections that carried task differences, and could not be expressed through indirect routes. We were able to detect task differences in all four cases, despite poor absolute model fit. In scenario 3, total effects captured the overall task differences even though the direct effect was no longer present. In scenario 4, task differences that were included in the model remained, but the missing effect was not expressed. In conclusion, it seems that when independent information (e.g., anatomical connectivity) is used to define the causal structure in SEM, inferences about task- or group-dependent changes are valid regardless of absolute model fit.

Algorithms↗