Identification and Prediction of Latent Classes of Weight‐loss Strategies Among Women

We apply latent class analysis (LCA) to quantify multidimensional patterns of weight‐loss strategies in a sample of 197 women, and explore the degree to which dietary restraint, disinhibition, and other individual characteristics predict membership in latent classes of weight‐loss strategies. Latent class models were fit to a set of 14 healthy and unhealthy weight‐loss strategies. BMI, weight concern, body satisfaction, depression, dietary disinhibition and restraint, and the interaction of disinhibition and restraint were included as predictors of latent class membership. All analyses were conducted with PROC LCA, a recently developed SAS procedure available for download. Results revealed four subgroups of women based on their history of weight‐loss strategies: No Weight Loss Strategy (10.0%), Dietary Guidelines (26.5%), Guidelines+Macronutrients (39.4%), and Guidelines+Macronutrients+Restrictive (24.2%). BMI, weight concerns, the desire to be thinner, disinhibition, and dietary restraint were all significantly related to weight‐control strategy latent class. Among women with low dietary restraint, disinhibition increases the odds of engaging in any set of weight‐loss strategies vs. none, whereas among medium‐ and high‐restraint women disinhibition increases the odds of use of unhealthy vs. healthy strategies. LCA was an effective tool for organizing multiple weight‐loss strategies in order to identify subgroups of individuals who have engaged in particular sets of strategies over time. This person‐centered approach provides a measure weight‐control status, where the different statuses are characterized by particular combinations of healthy and unhealthy weight‐loss strategies.     

Latent class analysis of a series of 717 patients with vitiligo allows the identification of two clinical subtypes

Non‐segmental vitiligo (NSV) is an enigmatic disease with various clinical courses. To empirically identify underlying subtypes of NSV, we performed latent class analysis (LCA) of 717 consecutive patients with NSV seen between 2006 and 2012 and were analyzed. Median age was 32 yrs (14–45), median age at NSV onset was 18 yrs (8–32), and median NSV duration 5 yrs (0.75–78.5). A two‐class model showed the best fit. Of the 717 patients, 280 (39%) belonged to LC1 and 437 (61%) to LC2. LC1 patients had high probabilities for early disease onset (<12 yrs), halo nevi, family history of premature hair greying, Koebner phenomenon, previous episodes of repigmentation, and family history of vitiligo. By contrast, LC2 patients were characterized by a late disease onset (after or at the age of 12 yrs, median age of 30 yrs) and acrofacial localization without any lesions on trunk or limbs. These two LCA classes (LC1, ‘prepubertal onset’; LC2, ‘post‐pubertal onset’) may help refining results from genome‐wide association studies (GWAS) and allow a more accurate genotype–phenotype correlation and help defining more directed treatment protocols.     

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.     

Measuring hiking specialization and identification of latent profiles of hikers

Previous specialization studies have only measured single items or the sum of responses across dimensions, making it impossible to classify recreationists by their degree of multidimensional specialization. The purpose of the present study is to test a three-dimensional model and its traditional components (i.e., items) of specialization in hikers. Applying a latent profile analysis, the present study examined hikers who shared similar profiles based on multiple dimensions of specialization and classified them based on latent class characteristics and structures. Data from 587 hikers on Namhansanseong Trail in South Korea were analyzed. Four subgroups were identified: novice (32 %), behavior-oriented (18 %), veteran (30 %), and potential veteran (20 %). These groups showed differential patterns in the behavioral and cognitive dimensions of specialization. In particular, the component items of hiking experience and setting experience in the behavioral dimension differed by group.     

Neural implementations of Bayesian inference

Bayesian inference has emerged as a general framework that captures how organisms make decisions under uncertainty. Recent experimental findings reveal disparate mechanisms for how the brain generates behaviors predicted by normative Bayesian theories. Here, we identify two broad classes of neural implementations for Bayesian inference: a modular class, where each probabilistic component of Bayesian computation is independently encoded and a transform class, where uncertain measurements are converted to Bayesian estimates through latent processes. Many recent experimental neuroscience findings studying probabilistic inference broadly fall into these classes. We identify potential avenues for synthesis across these two classes and the disparities that, at present, cannot be reconciled. We conclude that to distinguish among implementation hypotheses for Bayesian inference, we require greater engagement among theoretical and experimental neuroscientists in an effort that spans different scales of analysis, circuits, tasks, and species.     

Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach

BackgroundWhile depression is a frequent psychiatric comorbid condition in diabetes and has significant clinical impact, the syndromal profile of depression and anxiety symptoms has not been examined in detail.AimsTo determine the syndromal pattern of the depression and anxiety spectrum in a large series of patients with type 2 diabetes, as determined using a data-driven approach based on latent class analysis (LCA).MethodType 2 diabetes participants from the observational community-based Fremantle Diabetes Study Phase II underwent assessment of lifetime depression using the Brief Lifetime Depression Scale, the Patient Health Questionnaire 9-item version (PHQ-9) for current depression symptoms, and the Generalized Anxiety Disorder Scale that was specifically developed and validated for this study. The main outcome measure was classes of patients with a specific syndromal profile of depression and anxiety symptoms based on LCA.ResultsLCA identified four classes that were interpreted as “major anxious depression”, “minor anxious depression”, “subclinical anxiety”, and “no anxious depression”. All nine DSM-IV/5 diagnostic criteria for major depression identified a class with a high frequency of major depression. All symptoms of anxiety had similar high probabilities as symptoms of depression for the “major depression-anxiety” class. There were significant differences between classes in terms of history of depression and anxiety, use of psychoactive medication, and diabetes-related variables.ConclusionsPatients with type 2 diabetes show specific profiles of depression and anxiety. Anxiety symptoms are an integral part of major depression in type 2 diabetes. The different classes identified here provide empirically validated phenotypes for future research.     

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Summary Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social science and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this article, we consider multilevel latent class models, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the expectation‐maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less‐efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the obsessive compulsive disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for LCA of multilevel data.     

Identifying patterns of eating and physical activity in children: a latent class analysis of obesity risk

We used latent class analysis (LCA) to identify heterogeneous subgroups with respect to behavioral obesity risk factors in a sample of 4th grade children (n = 997) residing in Southern California. Multiple dimensions assessing physical activity, eating and sedentary behavior, and weight perceptions were explored. A set of 11 latent class indicators were used in the analysis. The final model yielded a five-class solution: "High-sedentary, high-fat/high-sugar (HF/HS) snacks, not weight conscious," "dieting without exercise, weight conscious," "high-sedentary, HF/HS snacks, weight conscious," "active, healthy eating," and "low healthy, snack food, inactive, not weight conscious." The results suggested distinct subtypes of children with respect to obesity-related risk behaviors. Ethnicity, gender, and a socioeconomic status proxy variable significantly predicted the above latent classes. Overweight or obese weight status was determined based on the Centers for Disease Control and Prevention BMI (kg/m2)-for-age-and-sex percentile (overweight, 85th percentile ≤ BMI < 95th percentile; obese, 95th percentile ≤ BMI). The identified latent subgroup membership, in turn, was associated with the children's weight categories. The results suggest that intervention programs could be refined or targeted based on children's characteristics to promote effective pediatric obesity interventions.     

The Association between Cognition and Academic Performance in Ugandan Children Surviving Malaria with Neurological Involvement

Background

The contribution of different cognitive abilities to academic performance in children surviving cerebral insult can guide the choice of interventions to improve cognitive and academic outcomes. This study''s objective was to identify which cognitive abilities are associated with academic performance in children after malaria with neurological involvement.

Methods

62 Ugandan children with a history of malaria with neurological involvement were assessed for cognitive ability (working memory, reasoning, learning, visual spatial skills, attention) and academic performance (reading, spelling, arithmetic) three months after the illness. Linear regressions were fit for each academic score with the five cognitive outcomes entered as predictors. Adjusters in the analysis were age, sex, education, nutrition, and home environment. Exploratory factor analysis (EFA) and structural equation models (SEM) were used to determine the nature of the association between cognition and academic performance. Predictive residual sum of squares was used to determine which combination of cognitive scores was needed to predict academic performance.

Results

In regressions of a single academic score on all five cognitive outcomes and adjusters, only Working Memory was associated with Reading (coefficient estimate = 0.36, 95% confidence interval = 0.10 to 0.63, p<0.01) and Spelling (0.46, 0.13 to 0.78, p<0.01), Visual Spatial Skills was associated with Arithmetic (0.15, 0.03 to 0.26, p<0.05), and Learning was associated with Reading (0.06, 0.00 to 0.11, p<0.05). One latent cognitive factor was identified using EFA. The SEM found a strong association between this latent cognitive ability and each academic performance measure (P<0.0001). Working memory, visual spatial ability and learning were the best predictors of academic performance.

Conclusion

Academic performance is strongly associated with the latent variable labelled “cognitive ability” which captures most of the variation in the individual specific cognitive outcome measures. Working memory, visual spatial skills, and learning together stood out as the best combination to predict academic performance.     

Trajectories of Television Watching from Childhood to Early Adulthood and Their Association with Body Composition and Mental Health Outcomes in Young Adults

IntroductionPrior studies examining longitudinal patterns of television (TV) watching have tended to use analytical approaches which do not allow for heterogeneity in the variation of TV watching over time. In the current study, we used latent class analysis (LCA) to examine the relationships between television watching (from childhood to early adulthood) and body fat percentage (%) and mental health.MethodsData were collected from 2411 participants (50% female) from the Raine Study, a prospective birth cohort study in Australia. Participants were followed up over 15 years and answered questions about hours of TV watching per week at six time-points (5, 8, 10, 14, 17 and 20yrs). Trajectories of television watching were estimated using LCA and appropriate regression models used to test the association of television watching class with percentage body fat (measured by DXA) and mental health (DASS-21) at age 20. Physical activity was used as a covariate.ResultsThree distinct trajectories of TV watching were identified. Class 1 (47.4%) had consistently high (>14 hrs/wk) levels of TV watching, Class 2 (37.9%) was characterised by an increase in TV watching over adolescence and Class 3 (14.7%) had consistently lower (<14 hrs/wk) TV watching over 15 years. Sex was used as an active covariate in the latent class model and was significantly associated with class membership (p<0.001), with females comprising 45%, 47% and 59% of Class 1, 2 and 3 respectively. In females, membership in Class 2 or 3 was associated with lower body fat % at age 20, compared to Class 1 (p<0.001). For males, membership in Class 2 was associated with lower body fat % compared with males in Class 1 (p = 0.026). Membership of TV watching class and mental health were not related (p>0.05).ConclusionsTV watching from childhood to young adulthood appears to be a relatively stable behavior for around two thirds of participants, but not everyone tracks consistently. This study identified a subset of participants with low levels of TV watching in childhood and also that this group, despite an increase in TV watching over adolescence, maintained a lower level of body fat in young adulthood.     

Genetic Diversity in Bradyrhizobium japonicum Serogroup 123 and Its Relation to Genotype-Specific Nodulation of Soybean

The genetic diversity among 20 field isolates of Bradyrhizobium japonicum serogroup 123 was examined by using restriction endonuclease digestions, one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis of total cell proteins, Southern hybridization analysis of nif and nod genes, and intrinsic antibiotic resistance profiles. All of the isolates were previously separated into three broad nodulation classes (low, medium, and high) based on their ability to form symbioses with specific soybean genotypes. Results of our studies indicate that there is a relationship between these three genotype-specific nodulation classes and groupings that have been made based on genomic DNA digestion patterns, sodium dodecyl sulfate-protein profiles, and Southern hybridizations to a nifHD gene probe. Intrinsic antibiotic resistance profiles and nodAB gene hybridizations were not useful in determining interrelationships between isolates and nodulation classes. Southern hybridizations revealed that two of the isolates had reiterated nod genes; however, there was no correlation between the presence of extra nodAB genes and the nodulation classes or symbiotic performance on permissive soybean genotypes. Hybridizations with the nif gene probe indicated that there is a relationship among serogroup, nodulation class, and the physical organization of the genome.     

Patterns of goal attainment among young mothers in a home visiting program

This paper examined heterogeneity in parents’ patterns of goal attainment following home visiting. Young mothers (n = 696) participating in a randomized controlled trial (RCT) evaluation of a statewide home visiting program were classified, using latent class analysis (LCA), according to their pattern of goal attainment (i.e., educational attainment, employment, parenting, personal functioning) two years postpartum. We explored direct and indirect (via social connectedness) associations between program participation and goal attainment. LCA revealed four classes: (a) High Education & Employment/High Parenting & Personal Functioning (n = 286, 41%); (b) High Education & Employment/Low Parenting & Personal Functioning (n = 212, 30%); (c) Low Education & Employment/Low Parenting & Personal Functioning (n = 71, 10%); and (d) Low Education & Employment/High Parenting & Personal Functioning (n = 127, 18%). Home visiting was not directly associated with class membership, but indirectly through social connectedness. This paper contributes to understanding home visiting impacts.     

A general class of pattern mixture models for nonignorable dropout with many possible dropout times

Summary .   In this article we consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times. We propose a marginally specified latent class pattern mixture model. The marginal mean is assumed to follow a generalized linear model, whereas the mean conditional on the latent class and random effects is specified separately. Because the dimension of the parameter vector of interest (the marginal regression coefficients) does not depend on the assumed number of latent classes, we propose to treat the number of latent classes as a random variable. We specify a prior distribution for the number of classes, and calculate (approximate) posterior model probabilities. In order to avoid the complications with implementing a fully Bayesian model, we propose a simple approximation to these posterior probabilities. The ideas are illustrated using data from a longitudinal study of depression in HIV-infected women.     

Patterns of Weight Control Strategies Predict Differences in Women's 4‐Year Weight Gain

The purpose of this study was to explore whether differences in patterns of weight control strategies predict 4‐year weight change among women. Participants (N = 176), were assessed at baseline and biennially on three occasions. Weight control strategies were assessed by the Weight Loss Behavior Scale. Height and weight were measured to calculate BMI. Latent class analysis (LCA) identified groups of women differing in their reported weight control strategies. Repeated measures were employed to examine the relationship between using different types of weight control strategies and weight change before and after adjusting for education, income, and initial BMI. LCA yielded a three‐group solution: use of none (N), healthy (H), and healthy plus unhealthy (H+U) weight control strategies. The N group had the lowest initial BMIs. Women's pattern of weight gain differed by latent group membership after adjusting for covariates: H+U group gained significantly more weight (4.56 kg) than the N group (1.51 kg) and H group (1.02 kg). Similar patterns emerged predicting weight change between years 2 and 4: H+U group gained significantly more weight (2.86 kg) than the H group (0.03 kg) and N group (0.44 kg). H+U weight control group had higher scores on weight concerns, dietary restraint, and eating attitudes than women in the H or N groups. These findings provide evidence that self‐reported weight control attempts do not necessarily lead to large weight gains; rather the amount of weight gain may depend on the type of weight control strategies that women are practicing.     

Developmental Profiles of Eczema,Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies

Background

The term “atopic march” has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level.

Methods and Findings

Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time.Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts.

Conclusions

The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors'' Summary     

Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups

Background

Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.

Methods

Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.

Results

The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001).

Conclusion

The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.     

Tuberculosis Infection among Young Nursing Trainees in South India

Background

Among healthcare workers in developing countries, nurses spend a large amount of time in direct contact with tuberculosis (TB) patients, and are at high risk for acquisition of TB infection and disease. To better understand the epidemiology of nosocomial TB among nurses, we recruited a cohort of young nursing trainees at Christian Medical College, a large, tertiary medical school hospital in Southern India.

Methodology/Principal Findings

Among 535 nursing students enrolled in 2007, 468 gave consent to participate, and 436 underwent two-step tuberculin skin testing (TST). A majority (95%) were females, and almost 80% were under 22 years of age. Detailed TB exposure information was obtained using interviews and clinical log books. Prevalence of latent TB infection (LTBI) was estimated using Bayesian latent class analyses (LCA). Logistic regression analyses were done to determine the association between LTBI prevalence and TB exposure and risk factors. 219 of 436 students (50.2%, 95% CI: 45.4–55.0) were TST positive using the 10 mm or greater cut-off. Based on the LCA, the prevalence of LTBI was 47.8% (95% credible interval 17.8% to 65.6%). In the multivariate analysis, TST positivity was strongly associated with time spent in health care, after adjusting for age at entry into healthcare.

Conclusions

Our study showed a high prevalence of LTBI even in young nursing trainees. With the recent TB infection control (TBIC) policy guidance from the World Health Organization as the reference, Indian healthcare providers and the Indian Revised National TB Control Programme will need to implement TBIC interventions, and enhance capacity for TBIC at the country level. Young trainees and nurses, in particular, will need to be targeted for TBIC interventions.     

The Structure of Co-Occurring Bullying Experiences and Associations with Suicidal Behaviors in Korean Adolescents

Objective

This study had two main goals: to examine the structure of co-occurring peer bullying experiences among adolescents in South Korea from the perspective of victims and to determine the effects of bullying on suicidal behavior, including suicidal ideation and suicide attempts, among adolescents.

Method

This study used data gathered from 4,410 treatment-seeking adolescents at their initial visits to 31 local mental health centers in Gyeonggi Province, South Korea. The structure of peer bullying was examined using latent class analysis (LCA) to classify participants’ relevant experiences. Then, a binomial logistic regression adjusted by propensity scores was conducted to identify relationships between experiences of being bullied and suicidal behaviors.

Results

The LCA of experiences with bullying revealed two distinct classes of bullying: physical and non-physical. Adolescents who experienced physical bullying were 3.05 times more likely to attempt suicide than those who were not bullied. Victims of (non-physical) cyber bullying were 2.94 times more likely to attempt suicide than were those who were not bullied.

Conclusions

Both physical and non-physical bullying were associated with suicide attempts, with similar effect sizes. Schools and mental health professionals should be more attentive than they currently are to non-physical bullying.     

A Latent Class Model with Hidden Markov Dependence for Array CGH Data

Summary Array CGH is a high‐throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class‐specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.     

The organization of taste sensibilities in hamster chorda tympani nerve fibers

Electrophysiological measurements of nerve impulse frequencies were used to explore the organization of taste sensibilities in single fibers of the hamster chorda tympani nerve. Moderately intense taste solutions that are either very similar or easily discriminated were applied to the anterior lingual surface. 40 response profiles or 13 stimulus activation patterns were considered variables and examined with multivariate statistical techniques. Three kinds of response profiles were seen in fibers that varied in their overall sensitivity to taste solutions. One profile (S) showed selectivity for sweeteners, a second (N) showed selectivity for sodium salts, and a third (H) showed sensitivity to salts, acids, and other compounds. Hierarchical cluster analysis indicated that profiles fell into discrete classes. Responses to many pairs of effective stimuli were covariant across profiles within a class, but some acidic stimuli had more idiosyncratic effects. Factor analysis of profiles identified two common factors, accounting for 77% of the variance. A unipolar factor was identified with the N profile, and a bipolar factor was identified with the S profile and its opposite, the H profile. Three stimulus activation patterns were elicited by taste solutions that varied in intensity of effect. Hierarchical cluster analysis indicated that the patterns fell into discrete classes. Factor analysis of patterns identified three common unipolar factors accounting for 82% of the variance. Eight stimuli (MgSO4, NH4Cl, KCl, citric acid, acetic acid, urea, quinine HCl, HCl) selectively activated fibers with H profiles, three stimuli (fructose, Na saccharin, sucrose) selectively activated fibers with S profiles, and two stimuli (NaNO3, NaCl) activated fibers with N profiles more strongly than fibers with H profiles. Stimuli that evoke different patterns taste distinct to hamsters. Stimuli that evoke the same pattern taste more similar. It was concluded that the hundreds of peripheral taste neurons that innervate the anterior tongue play one of three functional roles, providing information about one of three features that are shared by different chemical solutions.