White matter changes compromise prefrontal cortex function in healthy elderly individuals.
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Changes in memory function in elderly individuals are often attributed to dysfunction of the prefrontal cortex (PFC). One mechanism for this dysfunction may be disruption of white matter tracts that connect the PFC with its anatomical targets. Here, we tested the hypothesis that white matter degeneration is associated with reduced prefrontal activation. We used white matter hyperintensities (WMH), a magnetic resonance imaging (MRI) finding associated with cerebrovascular disease in elderly individuals, as a marker for white matter degeneration. Specifically, we used structural MRI to quantify the extent of WMH in a group of cognitively normal elderly individuals and tested whether these measures were predictive of the magnitude of prefrontal activity (fMRI) observed during performance of an episodic retrieval task and a verbal working memory task. We also examined the effects of WMH located in the dorsolateral frontal regions with the hypothesis that dorsal PFC WMH would be strongly associated with not only PFC function, but also with areas that are anatomically and functionally linked to the PFC in a task-dependent manner. Results showed that increases in both global and regional dorsal PFC WMH volume were associated with decreases in PFC activity. In addition, dorsal PFC WMH volume was associated with decreased activity in medial temporal and anterior cingulate regions during episodic retrieval and decreased activity in the posterior parietal and anterior cingulate cortex during working memory performance. These results suggest that disruption of white matter tracts, especially within the PFC, may be a mechanism for age-related changes in memory functioning. (+info)
A logic model for the integration of mental health into chronic disease prevention and health promotion.
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Mental illnesses such as depression or anxiety affect an individual's ability to undertake health-promoting behaviors. Chronic diseases can have a profound impact on an individual's mental health; in turn, mental health status affects an individual's ability to participate in treatment and recovery. A group of mental health and public health professionals convened to develop a logic model for addressing mental health as it relates to chronic disease prevention and health promotion. The model provides details on inputs, activities, and desired outcomes, and the designers of the model welcome input from other mental health and public health practitioners. (+info)
Microparadigms: chains of collective reasoning in publications about molecular interactions.
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We analyzed a very large set of molecular interactions that had been derived automatically from biological texts. We found that published statements, regardless of their verity, tend to interfere with interpretation of the subsequent experiments and, therefore, can act as scientific "microparadigms," similar to dominant scientific theories [Kuhn, T. S. (1996) The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago)]. Using statistical tools, we measured the strength of the influence of a single published statement on subsequent interpretations. We call these measured values the momentums of the published statements and treat separately the majority and minority of conflicting statements about the same molecular event. Our results indicate that, when building biological models based on published experimental data, we may have to treat the data as highly dependent-ordered sequences of statements (i.e., chains of collective reasoning) rather than unordered and independent experimental observations. Furthermore, our computations indicate that our data set can be interpreted in two very different ways (two "alternative universes"): one is an "optimists' universe" with a very low incidence of false results (<5%), and another is a "pessimists' universe" with an extraordinarily high rate of false results (>90%). Our computations deem highly unlikely any milder intermediate explanation between these two extremes. (+info)
Relating derived relations as a model of analogical reasoning: reaction times and event-related potentials.
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The current study aimed to test a Relational Frame Theory (RFT) model of analogical reasoning based on the relating of derived same and derived difference relations. Experiment 1 recorded reaction time measures of similar-similar (e.g., "apple is to orange as dog is to cat") versus different-different (e.g., "he is to his brother as chalk is to cheese") derived relational responding, in both speed-contingent and speed-noncontingent conditions. Experiment 2 examined the event-related potentials (ERPs) associated with these two response patterns. Both experiments showed similar-similar responding to be significantly faster than different-different responding. Experiment 2 revealed significant differences between the waveforms of the two response patterns in the left-hemispheric prefrontal regions; different-different waveforms were significantly more negative than similar-similar waveforms. The behavioral and neurophysiological data support the RFT prediction that, all things being equal, similar-similar responding is relationally "simpler" than, and functionally distinct from, different-different analogical responding. The ERP data were fully consistent with findings in the neurocognitive literature on analogy. These findings strengthen the validity of the RFT model of analogical reasoning and supplement the behavior-analytic approach to analogy based on the relating of derived relations. (+info)
On aims and methods in the neuroimaging of derived relations.
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Ingenious and seemingly powerful technologies have been developed recently that enable the visualization in some detail of events in the brain concomitant upon the ongoing behavioral performance of a human participant. Measurement of such brain events offers at the very least a new set of dependent variables in relation to which the independent variables familiarly manipulated in the operant laboratory may be explored. Two related paradigms in which a start has been made in such research concern the derivation of novel or emergent relations from a baseline set of trained relations, and include the phenomenon of transitive inference (TI), observed in studies of stimulus equivalence (SE) and serial learning (SL) or seriation. This paper reviews some published and forthcoming neuroimaging studies of these and related phenomena, and considers how this line of research both demands and represents a welcome synthesis between types of question and levels of explanation in behavioral science that often have been seen as antithetical. (+info)
Knowledge discovery from structured mammography reports using inductive logic programming.
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The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself. (+info)
Two DL-based methods for auditing medical terminological systems.
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Medical terminological systems (TSs) play an increasingly important role in health care by supporting recording, retrieval and analysis of patient information. As the size and complexity of TSs are growing, the need arises for means to audit them, i.e. verify and maintain (logical) consistency and (semantic) correctness of their contents. In this paper we describe two methods based on description logics (DLs) for the audit of TSs. One method uses non-primitive definitions to detect concepts with equivalent definitions. The other method is characterized by stringent assumptions that are made about concept definitions, in order to detect inconsistent definitions. We discuss the possibility of applying these methods to the Foundational Model of Anatomy (FMA) to demonstrate the potentials and pitfalls of these methods. We show that the methods are complementary, and can indeed improve the contents of medical TSs. (+info)
Use of description logic classification to reason about consequences of penetrating injuries.
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The consequences of penetrating injuries can be complex, including abnormal blood flow through the injury channel and functional impairment of organs if arteries supplying them have been severed. Determining the consequences of such injuries can be posed as a classification problem, requiring a priori symbolic knowledge of anatomy. We hypothesize that such symbolic knowledge can be modeled using ontologies, and that the reasoning task can be accomplished using knowl-edge representation in description logics (DL) and automatic classification. We demonstrate the capabilities of automated classification using the Web Ontology Language (OWL) to reason about the consequences of penetrating injuries. We created in OWL a knowledge model of chest and heart anatomy describing the heart structure and the surrounding anatomic compartments, as well as the perfusion of regions of the heart by branches of the coronary arteries. We then used a domain-independent classifier to infer ischemic regions of the heart as well as anatomic spaces containing ectopic blood secondary to the injuries. Our results highlight the advantages of posing reasoning problems as a classification task, and lever-aging the automatic classification capabilities of DL to create intelligent applications. (+info)