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Biomedical subjects

W R Shankle

Publications and source records attributed to W R Shankle.

At least 19 recordsLinked to original sources
PubMed1999

Two-Stage Machine Learning model for guideline development.

We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existing guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The model incorporates learning intermediate concepts (CDRS category scores) in the first stage that then become the feature space for the second stage (global CDRS score). The sample consisted of 678 patients evaluated in the Alzheimer's Disease Research Center at the University of California, Irvine. The demographic variables, functional and cognitive test results used by physicians for the task of dementia severity staging were used as input to the machine learning algorithms. Decision tree learners and rule inducers (C4.5, Cart, C4.5 rules) were selected for our study as they give expressive models, and Naive Bayes was used as a baseline algorithm for comparison purposes. We first learned the six CDRS category scores (memory, orientation, judgement and problem solving, personal care, home and hobbies, and community affairs). These learned CDRS category scores were then used to learn the global CDRS scores. The Two-Stage ML model classified as well as or better than the published inter-rater agreements for both the category and global CDRS scoring by dementia experts. Furthermore, for the most critical distinction, normal versus very mildly impaired, the Two-Stage ML model was 28.1 and 6.6% more accurate than published performances by domain experts. Our study of the CDRS examined one of the largest, most diverse samples in the literature, suggesting that our findings are robust. The Two-Stage ML model also identified a CDRS category, Judgment and Problem Solving, which has low classification accuracy similar to published reports. Since this CDRS category appears to be mainly responsible for misclassification of the global CDRS score when it occurs, further attribute and algorithm research on the Judgment and Problem Solving CDRS score could improve its accuracy as well as that of the global CDRS score.

Algorithms↗
PubMed1999

Approximating dipoles from human EEG activity: the effect of dipole source configuration on dipolarity using single dipole models.

Dipolarity is the goodness-of-fit of the observed potential distribution with one calculated using specific assumptions about the source of the electrical potential distribution. We used computer simulations to examine the effect of different distributions of sources on their resulting dipolarity values. Electric dipoles were placed in a head-shaped model with uniform conductivity using four different dipole configurations (randomly oriented dipoles, a uniform dipole disk layer, a dipole disk layer with uniformly distributed holes, or one with randomly oriented dipoles). The best-fitting single dipole for each configuration was calculated and the dipolarity was computed as the mean squared error of the electrical potential distributions generated by the actual dipole configuration and by the best-fitting single dipole. The simulations show that: 1) a smooth dipole layer with or without holes gives dipolarities above 99.5% even when extended over areas as large as 1256 mm2; 2) randomly oriented dipoles under a smooth dipole layer also give dipolarities above 99.5%; and 3) randomly oriented and distributed dipoles, even if contained in a small portion of the total area, give dipolarities below 93.0%. These simulations show that inhomogeneity (holes) within a dipole disk layer per se do not lower dipolarity; rather, it is the random orientation and distribution of these dipoles which lowers dipolarity. Furthermore, dipolarity is not lowered by such randomly oriented and distributed dipoles when they are beneath a dipole disk layer.

Brain↗
PubMed1999

Cortical atrophy in Alzheimer's disease unmasks electrically silent sulci and lowers EEG dipolarity.

Alzheimer's disease (AD) patients show lower dipolarity (goodness-of-fit) for dipole localizations of alpha or other dominant electroencephalography (EEG) frequency components in the occipital cortex. In the present study, we performed computer simulations to discover which of distributions of dipole activity lower dipolarity in a manner similar to that seen in severe AD. Dipolarity was estimated from simulations of various electric dipole generator configurations within the occipital cortex under conditions of widened cortical sulci (a severely demented AD case) or no sulcal widening (a normal subject). The cortical and scalp surfaces, derived from the subjects' MRI's, were assumed to be uniformly electrically conducting. Randomly placed, nonoverlapping lesions ranging from 1 to 4 mm2 per lesion were used in both the normal and AD models to simulate the electrical effect of neuropathological AD lesions. In both models, dipolarity decreased as total lesion size increased. However, the AD model showed lower dipolarity than the normal model for both individual lesion sizes and for larger total lesion sizes. The larger decline in dipolarity in the AD model appears to be due to sulcal widening which unmasks the effect of lesions buried within sulci. These simulations identify a possible mechanism explaining why sulcally-located neuropathological changes plus progressive cortical atrophy in AD brains (and presumably other cortical disorders producing atrophy) alter EEG patterns and dipolarity differently from normal cortex damaged by similar lesions.

Adult↗
PubMed1998

Developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to 6 years examined by correspondence analysis.

This paper uses correspondence analysis to examine the developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to 72 months. The study is based on data collected by the late J. L. Conel, which consist of over 4 million individual measurements of six microscopic neuroanatomic features for each of six cortical layers in 46 cytoarchitecturally distinct regions. We analyze 1,727 profiles of development over eight age-points (term birth, 1, 3, 6, 15, 24, 48, and 72 postnatal months) resulting from the combinations of neuroanatomic feature, cortical layer, and brain cytoarchitectural region in the Conel data. The profiles for any given combination of feature and layer are found to be remarkably similar in all regions of the brain, and therefore the developmental patterns of different cytoarchitectural regions are not distinguishable from one another. Developmental change is most rapid at the earlier stages; of the total change in profile patterns observed, more than one-third occurs between birth and 6 months, about one-third occurs between 6 and 15 months, and less than one-third occurs between 15 and 72 months. The majority of the variance in developmental profiles is accounted for by the six microscopic, neuroanatomic features. Correspondence analysis shows that Conel's data are highly consistent and reliable.

Aging↗
PubMed1998

Evidence for a postnatal doubling of neuron number in the developing human cerebral cortex between 15 months and 6 years.

The generalization of the finding of no postnatal neurogenesis in non-human primates to humans may be incorrect because: (1) rhesus macaques belong to a superfamily that diverged more than 25 million years ago from the superfamily including the genus Homo; (2) the pulse thymidine labeling method, which demonstrates DNA synthesis rather than mitosis per se, is less reliable than some have assumed. This study examines changes in the number of neurons in a column underneath a cortical surface area of 1 mm2, extending through all cortical layers (mm2-column) for 35 gyri (representing about 73% of the human cerebral cortex) based on the data of J.L. Conel (1939 to 1967). We corrected these data, derived from his measures of cortical neuronal packing density, somal breadth and height, and cortical layer thickness at postnatal ages 0, 1, 3, 6, 15, 24, 48, and 72 months, for shrinkage and stereological errors. In all 35 gyri, neuron number/mm2-column: (1) initially declines (mu = 46% decline, sigma = 8%), 95% of which is due to surface area expansion (mean age of nadir value = 15.8 months); (2) then increases to age 72 months by 70% (mu = 1.7-fold increase, (mu rate = 1.1% per month). Because of a a concomitant 1.3-fold increase in cortical surface from 15 to 72 months, total cortical neuron number increases 2.2-fold. The close agreement between neuron number/mm2-column for Conel's age 72-month data to the corresponding values reported by others for adult human and primate cortex using more modern methods suggests the finding is not an artifact. Neuronal proliferative fate-determining factors provide at least four mechanisms for increasing cortical neuron number postnatally, with or without DNA synthesis.

Aging↗
PubMed1998

Quantitative microscopic anatomy, illustrated by its potential role in furthering understanding of the processes of structuring the developing human cerebral cortex.

In this study, we searched for patterns in selected, quantitative microscopic features of the developing postnatal human cortex for 35 cytoarchitectural areas at eight age points from birth to 72 months. These data come from the largest extant survey of the microscopic features of the developing postnatal human cerebral cortex (JL Conel, 1939-67). In contrast to Jacobson's proposal that cortical surface area increases in proportion to brain weight, Conel's data show that surface area increases as brain weight2/3, with the maximal rate of increase in both brain weight and cortical surface area occurring from 1 to 3 months. We computed the numbers of cortical neurons per cortical layer under 1 mm2 of cortical surface (neurons/layer per mm2 column) and divided these values by the total neuron number/mm2 column. For all areas, these data show plateaux in most of the layers for periods of months to years, often preceded and followed by changes in their neuron number in a sinusoidal fashion. The age points of the maxima and minima of such laminar values differ across the 35 cortical areas, indicating that their sinusoids are phase-shifted with respect to each other. Ranking the six layers in each area at each age point by their neuron number/layer per mm2 column shows that, by 72 months, the first areas to receive thalamic auditory or visual input (primary sensory and unimodal association cortices) have the most neurons in layer 4 and in either layer 3 or 6. In contrast, by 72 months, other areas have the most neurons in layers 3 and 6, with the primary motor cortex reaching this ranking earlier than any other area. For temporal and parietal association areas, layers 2 (short cortico-cortical connections) and 4 (thalamo-cortical connections) have the most neurons from birth to 6 months, whereas layers 3 (long cortico-cortical connections) and 6 (cortico-thalamic connections) have the most neurons by 72 months. The quantitative, statistically non-random patterns demonstrated by our analyses suggest that hierarchical correlations between such structural changes and age-specific behavioral acquisitions exist during the first 72 months of postnatal development.

Cerebral Cortex↗
PubMed1998

Constructing the human cerebral cortex during infancy and childhood: types and numbers of cortical columns and numbers of neurons in such columns at different age-points.

This study examines JL Conel's data on neuron numbers in 35 human cortical areas for eight age points from 0 (birth) to 72 months, to analyze cortical columns, the presumed functional units of the cortex. For each cortical area at each age point, cortical surface divided by the square root of the area's neuron number gives cross-sectional areas with radii ranging from 180 microns at birth to 250 microns at 72 months. For the prefrontal cortex at birth and 48 months, these radii are approximately 2.10 and 1.19 times the longest radial basal dendrites, suggesting similar dimensions between these two measures of column radius. The logarithm of neuron number per cortical area and age point was examined in relation to the Weber-Fechner law governing the relationship between stimulus intensity and perception. A mechanism for this law consistent with the cortical model of Douglas et al. illustrates the importance of local circuit neurons. The cross-sectional areas of hexagonal columns for prefrontal cortex, using as radius, the longest radial extent of layer 5 pyramidal neuron basal dendrites, ranging from 0.013 mm2 at birth to 0.064 mm2 at 48 months, suggests that functional cortical columns increase cross-sectional area during development. These cross-sectional areas are 55-100-fold larger at birth, and 229-277-fold larger at 48 months, than those computed from somal width in prefrontal, layer 5 pyramidal neurons. Comparison of radial extent of pyramidal basal dendrites to their soma-to-soma distances shows that layer 3 pyramidal basal dendrites reach 1.5 and 4.0 other pyramidal neurons at 15 and 60 months, respectively, while layer 5, extra-large pyramidal basal dendrites reach 1.14 and 1.72 other such neurons at birth and 48 months, respectively. If such a relationship holds for other cortical areas, then the Conel data can be used to estimate basal dendrite extent, for which there currently is a paucity of data.

Aging↗
PubMed1998

Simple models for estimating dementia severity using machine learning.

Estimating dementia severity using the Clinical Dementia Rating (CDR) Scale is a two-stage process that currently is costly and impractical in community settings, and at best has an interrater reliability of 80%. Because staging of dementia severity is economically and clinically important, we used Machine Learning (ML) algorithms with an Electronic Medical Record (EMR) to identify simpler models for estimating total CDR scores. Compared to a gold standard, which required 34 attributes to derive total CDR scores, ML algorithms identified models with as few as seven attributes. The classification accuracy varied with the algorithm used with naïve Bayes giving the highest. (76%) The mildly demented severity class was the only one with significantly reduced accuracy (59%). If one groups the severity classes into normal, very mild-to-mildly demented, and moderate-to-severely demented, then classification accuracies are clinically acceptable (85%). These simple models can be used in community settings where it is currently not possible to estimate dementia severity due to time and cost constraints.

Algorithms↗
PubMed1997

A multinomial modeling analysis of memory deficits in Alzheimer's disease and vascular dementia.

Data from the immediate recall task of the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological test battery were disaggregated into nine subject groups and analyzed with traditional statistics as well as with a general processing tree (GPT) model of free recall. The groups represented four levels of severity of Alzheimer's and vascular dementia, as well as a ninth group of healthy elderly controls. It was demonstrated that the patterns of success and failure of recall to individual items across successive trials contained much more information than the marginal trial-to-trial performance scores traditionally used in scoring the test. The GPT model analyzed recall performance in terms of three levels of item storage: unstored, intermediate, and long-term. Associated with the intermediate and long-term storage levels were respective retrieval parameters. Statistical methods enable one to estimate the parameters for each group, and the analyses revealed group differences in long-term storage that were not evident in a statistical analysis of the marginal trial-to-trial performance scores.

Aged↗
PubMed1996

Ishihara test performance and dementia.

Ishihara trail-tracing (TT) and number-naming (NN) tests were administered to a clinical sample of 378 demented patients. Error counts on TT and NN tests were best fit by a negative binomial (overdispersed Poisson) distribution. TT, NN, and combined (TT + NN) error counts were regressed on patient characteristics (sex, age, and education), severity of cognitive impairment (Mini-Mental State Exam: MMSE), dementia stage (Clinical Dementia Rating: CDR), etiology, onset age, and symptom duration in a negative binomial generalized linear model. Patient characteristics, onset age, and symptom duration had no significant effects on any error count. The effects of MMSE, CDR, and etiology, on the other hand, were highly significant and appear to help discriminate vascular dementia from Alzheimer's disease. MMSE (which taps cognitive skills) correlated with both TT and NN errors. CDR (which taps both cognitive and functional skills) correlated only with TT errors and dementia etiology correlated only with NN errors. These distinct correlational patterns reflect differences between the TT and NN tasks (i.e., trail-tracing vs. number-naming) related to specific brain loci and associated functions. This aspect of the phenomenon suggests that Ishihara tests have useful clinical applications in dementia.

Aged↗
PubMed1996

Full-information models for multiple psychometric tests: annualized rates of change in normal aging and dementia.

The rates of change for five widely used psychometric tests were analyzed to compare how much more variance reduction can be achieved using full-information methods relative to the single-equation methods previously used in dementia research. Nondemented controls and subjects with Alzheimer disease (AD), probable/ possible vascular dementia (VD), or mixed dementia (MD) were evaluated. A cohort design was followed, with follow-up of three demented groups and one normal control group; data were analyzed in a multiple-equation regression model estimated with full-information methods. The study was conducted at Alzheimer's Disease Research Center sites at the University of California, Irvine, and at the University of Southern California. In all, 226 patients and controls who had completed initial assessment and at least one annual reassessment were included in the study. Dependent variables were annualized rates of change in the Mini-Mental State Examination (MMSE), the Short-Blessed Dementia Rating Scale (DRS), the Consortium to Establish a Registry for Alzheimer's Disease drawings test (CD), the WAIS-R Block Design test (WRB), and the Boston Naming Test (BNT). Independent variables were dementia severity, diagnosis (AD, VD, MD, or control), sex, age, marital status, education, and age at onset. Full-information methods reduced the variance in the change scores by > or = 25% compared with previous studies. The model's prediction of a test's rate of change was almost entirely due to dementia stage and diagnosis. The effects of other explanatory variables (sex, marital status, age, and education) were weak and statistically insignificant. When the effects of other independent variables were controlled, AD and MD patients were found to decline at significantly faster rates than VD patients. Full-information methods, relative to single-equation methods, substantially reduce the variance of rates of change for multiple psychometric tests. They do so by simultaneously considering the correlated error terms in the regression for each dependent psychometric change score variable. The robustness of these results to minor variations in follow-up time suggests that annualization is a reasonably valid procedure for making change scores comparable. This study's results suggest that change scores in psychometric tests provide information that can be used to aid differential diagnosis. However, the large variances of change scores preclude many other uses. Finally, since standardization of psychometric change scores translates all tests to the same scale (0-100%), standardized change scores are easier to interpret. The analysis of standardized change scores deserves further investigation.

Age of Onset↗
PubMed1995

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

Aged↗
PubMed1995

Acquisition and long-term retention of a fine motor skill in Alzheimer's disease.

This study investigated the acquisition and long-term retention of the rotary pursuit task under varying amounts of practice in 12 moderate-to-severely demented AD patients and 12 healthy older adults. Equal numbers of AD and control subjects were randomly assigned to either 40, 80, or 120 trials of training (40 trials/day) on the rotary pursuit task, followed by 15-trial retention tests 20 min, 2 days, 7 days, and 37 days following the end of practice. Performance improved significantly in both groups during the first 40 trials, while additional practice provided no ensuing positive or negative effects. Further, subjects in both groups showed minimal forgetting across the four retention tests. Therefore, the results demonstrated that AD patients can effectively learn and retain a motor skill for at least 1 month.

Aged↗
PubMed1995

Data analysis for dynamic contrast-enhanced MRI-based cerebral perfusion measurements: correcting for changing cortical CSF volumes.

The time evolution of the histogram (number of pixels versus signal intensity) is used to calculate delta R2 parameters from dynamic contrast-enhanced magnetic resonance (MR) imaging of the brain. This method partially corrects for partial volume effects and is an improvement over the approach using the signal intensity as a function of time when confounding factors such as changing cortical cerebrospinal fluid volumes are involved. The maximum value for delta R2 is found to correlate with relative cerebral blood flow as assessed by xenon inhalation and can be used to discriminate between vascular dementia and healthy volunteers. With this method, the normal range for delta R2 values is found to be the same for both young (19-40 years old) and elderly (65-85 years old) healthy volunteers.

Adult↗
PubMed1995

Low-dose propranolol reduces aggression and agitation resembling that associated with orbitofrontal dysfunction in elderly demented patients.

Although several reports suggest that intermediate to high doses of propranolol (80-160 and 200-600 mg/day) can effectively treat aggressive behavior in dementia, significant side effects can occur at these doses. To minimize these side effects, we treated and followed-up a series of 12 demented patients, whose caregivers sought medical help for their disruptive, aggressive behavior, with low-dose propranolol monotherapy (10-80 mg/day). Assessment measures obtained at baseline and during treatment by caregiver interview included ordinal ratings of aggression severity, the Cohen-Mansfield Agitation Inventory (CMAI), and the California Behavior Questionnaire (CBQ). The aggression ratings showed that low-dose propranolol effectively reduced aggression in eight of 12 patients (67%) within 2 weeks of treatment and remained effective for the duration of follow-up (1 to 14 months). Subscales of the CMAI showed responders to have significant reductions in physical and verbal aggression/agitation and in pacing/wandering. These results suggest that low-dose propranolol should be further studied for treating aggression or agitation in demented patients.

Aged↗
PubMed1994

Cortical CSF volume fluctuations by MRI in brain aging, dementia and hydrocephalus.

Dynamic MR imaging has revealed dramatic fluctuations in the appearance of CSF in the cortical sulci and cortical subarachnoid spaces in aging individuals and patients with hydrocephalus, dementia and Down syndrome in contrast to young healthy volunteers. The changes have been interpreted as volume fluctuations that represent undamped CSF hydrodynamics and have implications with respect to the origin of CSF in the cortical regions and with respect to the similarity between aging and dementia and edematous states of the brain.

Adult↗
PubMed1994

Cardiomyopathy in childhood and adult life, with emphasis on hypertrophic cardiomyopathy.

Over 60 entries in the genetic catalog have cardiomyopathy features--32 autosomal dominant, 35 autosomal recessive and X-linked. Over 40 present in, or can have survival into, adult life. Major clinicopathologic categories of these cardiomyopathic disorders included: sudden death (13 entities); cardiac conduction disturbance important feature; associated myopathy or motor dysfunction; storage diseases with cardiac involvement; cardiac amyloidoses; and, other categories. Genes, abnormality of which can cause hypertrophic cardiomyopathy (HCM), have been identified on chromosomes 1, 14 and 15, the locus on chromosome 14 involving the B-myosin heavy chain gene, but at least one unidentified locus is known to exist and there is a suggestive locus on chromosome 16, so that HCM is not a single disease but a group of disorders with clinicopatholopic similarities. To investigate these aspects of HCM in some detail, sixty-six patients with "sharply demarcated" differential myocardial fiber bundle hypertrophy (DMBH), considered to be of significant degree, from a pediatric autopsy data base of approximately 8,000 cases, were reviewed. Twenty-three of the patients died suddenly, without antecedent significant cardiac dysfunction, seven had clinical congestive heart failure of varying duration, three were stillborn, six showed evidence of aspiration of amniotic sac content (three had history of fetal distress), five had ischemic bowel disease, three (two with clinical cerebral palsy and one with Ondine's curse syndrome) had cerebral atrophy and sclerosis and one had extensive more acute encephalomalacia, and a variety of other major "causes of death" were present. Whether all infants and children with DMBH meeting the criteria used, who do not have congenital heart disease, have dominant hypertrophic cardiomyopathy (HCM) cannot be established by studies of this type, but the "concentration" of a gene or genes for HCM in pediatric autopsy series because the strong effect of HCM on life expectancy is relevant to this possibility. The data raise the question that stillbirth, fetal distress with aspiration of amniotic sac content, ischemic bowel disease and cerebral atrophy and sclerosis may be hitherto underappreciated features of HCM in childhood, and that patients with HCM may be peculiarly liable to die with certain types of septic shock, such as acute meningococcemia. In the material of this study, sudden death was statistically more frequent in females than in males in childhood (p < .029).

Adolescent↗