Predict Group Membership Research Articles

Synergistic effects of students’ mathematics and science motivational beliefs on achievement, and their determinants

BackgroundStudents’ mathematics and science motivational beliefs are crucial determinants of their school academic achievement in math and science. The current study aimed to identify the group memberships of students’ motivational beliefs in math and science, which are closely related. Furthermore, this study probed the predictive effects of individual students’ experiences at school on forming group membership. We also tested the mean differences of the identified latent groups in math and science achievement.ResultsUsing latent profile analysis modeling, we examined data from 3857 Korean eighth-grade students participating in the 2019 Trends in International Mathematics and Science Study. The theoretical rationale and supplementary statistical indices showed a five-group membership as the optimal solution. The five groups are high motivation, medium motivation, low math/high science motivation, low motivation, and very low motivation. Students’ sense of school belonging was the most crucial predictor in forming group membership, whereas perceived student bullying did not predict group membership. Finally, students in distinct motivational belief groups exhibited differences in their math and science achievements.ConclusionsThis study identified five subgroups of students based on their distinct motivational beliefs in math and science, and variations in their association with achievements. In terms of policy development and intervention, it is important to nurture students’ sense of school belonging. This study advances motivational theories in science, technology, engineering, and mathematics education, and provides practical suggestions for improving educational practices to enhance student math and science motivational beliefs.

Intraindividual Variability in Attention-Deficit/Hyperactivity Disorder: An Ex-Gaussian Approach.

Higher intraindividual variability (IIV) of response times is consistently noted in children with attention-deficit/hyperactivity disorder (ADHD). The current study investigated whether an ex-Gaussian estimate of IIV in children ages 6-13 years-old could differentiate between children with and without ADHD. Children completed a computerized go/no-go task to estimate trial-by-trial IIV and a continuous performance test (CPT) to estimate inattention and hyperactivity/impulsivity. Parents completed questionnaires assessing inattention and hyperactive/impulsive behaviors. IIV, commission errors, and attention problems as rated by parents were significantly greater in the ADHD group. Groups did not differ on errors of omission, but IIV was predictive of omission errors and parent ratings of inattention and hyperactivity/impulsivity. IIV predicted group membership (ADHD vs Control) whereas errors of omission did not. However, IIV did not improve diagnostic accuracy when parent ratings were used, such that parent ratings were superior at determining diagnosis. Current results support the use of IIV, based on the ex-Gaussian approach, as an objective measure of attention problems over omission errors on sustained attention CPT-type tasks. Additionally, while parent ratings of attention impairment remain the best predictor of ADHD diagnostic status, IIV may be helpful in determining when further assessment is required in the absence of those ratings.

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.

The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Heterogeneity of health-related quality of life after mild traumatic brain injury with systemic injury: a cluster analytic approach

Purpose Health-related quality of life (HRQoL) is a key component of evaluating outcome after mild traumatic brain injury (mTBI). As outcome is heterogeneous following mTBI, it is relevant to examine individual differences in HRQoL. This study investigated whether multiple homogenous subgroups could be meaningfully identified, 10 weeks after hospitalised mTBI with systemic injury, on the basis of HRQoL profiles. Methods Ninety-one adults were assessed for HRQoL, pain, fatigue, sleep quality, psychological distress, cognition and post-concussion symptoms. Results Cluster analyses revealed three separate subgroups based on physical, mental, social and energy HRQoL. One group (42%) demonstrated normative levels of HRQoL on all subdomains. The remaining two groups demonstrated significantly reduced HRQoL on all subdomains. These groups had equivalently poor mental, social and energy HRQoL, but the smallest group (27%) had significantly poorer physical HRQoL. Multinomial logistic regression revealed that pain significantly and independently predicted group membership for the particularly poor physical HRQoL group. Fatigue was the only significant independent predictor of group membership for the remaining group with reduced HRQoL. Conclusion These findings suggest more than 50% of hospitalised individuals with mTBI and systemic injury, have reduced HRQoL, 10 weeks after mTBI. Pain and fatigue warrant clinical attention in these individuals. IMPLICATIONS FOR REHABILITATION Mild traumatic brain injury is a common event that has been shown to be associated with persistently reduced health-related quality of life in approximately 50% of individuals 6 to 12 months after injury. Health-related quality of life likely varies between individuals after injury Relative to the “normal” population, most individuals in this cohort of individuals with mTBI and systemic injury had reduced mental, social and energy quality of life 10 weeks after injury. Fatigue and pain are important factors in reduced health-related quality of life after mTBI with systemic injury. Further research is needed to determine whether these fatigue and pain issues are related to mTBI-factors, such as headache, and/or related to systemic injury factors, which are common in the mTBI population.

A Latent Profile Analysis of Substance Use and Post-Traumatic Stress on Substance Use Treatment Outcomes Among People Involved with the Justice System

This study used latent profile analysis to classify legally-involved persons in substance use treatment into mutually exclusive groups based on their clinical presentation of substance use and post-traumatic stress. Predictors of group membership were tested, and group classification was evaluated as a predictor of engagement in substance use treatment. There was a significant amount of variability in substance use and post-traumatic stress symptomatology at the start of treatment. Clients’ symptoms were classified into four groups: (1) high substance use, low trauma, (2) low substance use high trauma, (3) high substance use, high trauma, and (4) low substance use, low trauma. Psychological distress was the primary predictor of group classification and profile membership was differentially related to treatment engagement. Together, these results can be used to inform screening tools, assessment protocols, and adaptive treatment models to better serve people in the legal system experiencing comorbid substance use and post-traumatic stress.

Latent Profiles of Coronavirus-Related Health Literacy in Chinese Residents and Its Related Predictors.

This study aimed to explore the latent profiles of coronavirus-related health literacy among Chinese residents and investigate whether some predictive factors are linked with different latent subgroups of coronavirus-related health literacy. This study was a cross-sectional survey conducted among Chinese residents beginning in April 2020. A total of 1610 Chinese residents aged 15 to 69 years were involved and were asked to complete the questionnaire about coronavirus-related health literacy, demographic information, and COVID-19 pandemic-related information. Examined the latent profiles of coronavirus-related health literacy and explored the influencing factors of different profiles of coronavirus-related health literacy. The results showed that there were 3 latent subgroups, namely, high coronavirus-related health literacy (45.53%), medium coronavirus-related health literacy (47.76%), and low coronavirus-related health literacy (6.71%). Logistic regression analysis showed that gender, education, and the frequency of exposure to news about the pandemic could predict group membership. Most Chinese residents have medium coronavirus-related health literacy. Education is an important factor that has a long-term stable influence on coronavirus-related health literacy, and the frequency of exposure to news about the pandemic is a factor that can improve coronavirus-related health literacy in a short time.

Using the MMPI-2-RF, IOP-29, IOP-M, and FIT in the In-Person and Remote Administration Formats: A Simulation Study on Feigned mTBI.

Our study compared the impact of administering Symptom Validity Tests (SVTs) and Performance Validity Tests (PVTs) in in-person versus remote formats and assessed different approaches to combining validity test results. Using the MMPI-2-RF, IOP-29, IOP-M, and FIT, we assessed 164 adults, with half instructed to feign mild traumatic brain injury (mTBI) and half to respond honestly. Within each subgroup, half completed the tests in person, and the other half completed them online via videoconferencing. Results from 2 ×2 analyses of variance showed no significant effects of administration format on SVT and PVT scores. When comparing feigners to controls, the MMPI-2-RF RBS exhibited the largest effect size (d = 3.05) among all examined measures. Accordingly, we conducted a series of two-step hierarchical logistic regression models by entering the MMPI-2-RF RBS first, followed by each other SVT and PVT individually. We found that the IOP-29 and IOP-M were the only measures that yielded incremental validity beyond the effects of the MMPI-2-RF RBS in predicting group membership. Taken together, these findings suggest that administering these SVTs and PVTs in-person or remotely yields similar results, and the combination of MMPI and IOP indexes might be particularly effective in identifying feigned mTBI.

Developmental trajectories of conduct problems and time-varying peer problems: the Bergen child study

BackgroundWhile it is increasingly acknowledged that conduct problems and peer problems often co-occur in development, less is known about the ways in which peer problems may alter the developmental course of conduct problems for distinct subgroups.MethodsUsing data from a large population-based study in Norway (the Bergen Child Study/youth@hordaland; 47.4% males), we estimated group-based trajectories of conduct problems and the presence of time-varying peer problems on the developmental progression of conduct problems between seven and 19 years of age. Risk factors for group membership were also examined.ResultsA 3-group model of conduct problems best fit the data (non-engagers, low-engagers, moderate-stable). The presence of peer problems increased the estimated level of conduct problems for both the low-engagers and moderate-stable groups across adolescence. No differences in conduct problems were observed when peer problems were present in childhood or preadolescence for these two groups, nor for the non-engagers group at any point. Being male, having lower perceived economic wellbeing, and lower levels of parental education predicted group membership for the moderate-stable group, whilst lower paternal education predicted membership for the low-engagers group.ConclusionsSupport for developmental ‘turning points’ was found, suggesting that adolescence is a particularly salient time for those with conduct problems. In particular, the presence of peer problems can increase observed conduct problems at this stage in development.

Diagnostic Accuracy of Grammaticality and Utterance Length in Bilingual Children.

The purpose of this study was to investigate the diagnostic accuracy of two measures derived from spontaneous language samples, mean length of utterance in words (MLUw) and percentage of grammatical utterances (PGU), in identifying developmental language disorder (DLD) in Spanish-English bilingual children. We examined two approaches: best language and total language. The participants in this study included 74 Spanish-English bilingual children with (n = 36) and without (n = 38) DLD. Language samples were elicited through a story retell and story generation task using Frog wordless picture books in English and Spanish. Stories were transcribed and coded using the Systematic Analysis of Language Samples (Miller & Iglesias, 2020) to extract MLUw and PGU in both languages. Logistic regression analyses suggested that a model that included PGU, MLUw, and age achieved the best diagnostic accuracy in predicting group membership. Both approaches, best language and total language, had fair diagnostic accuracy. In combination, PGU and MLUw seem to be useful diagnostic tools to differentiate bilingual children with and without DLD. Clinical implications and usability are discussed.

Predicting the chance for being at‐risk of AD based on smartphone data obtained during an everyday wayfinding task in the real world

AbstractBackgroundPatients with Alzheimer’s disease (AD) often show problems in spatial navigation. However, spatial navigation is rarely investigated in real world settings and in people at early disease stages. Capitalizing on recent advancements in digital technologies, we measured the performance of healthy younger (YA) and older adults (OA), as well as older adults with subjective cognitive decline (SCD) while they solved a smartphone‐based wayfinding task.MethodUsing the novel smartphone application “Explore”, we tracked GPS data and other behavioral indicators in 24 YAs, 25 OAs, and 23 SCDs, while they were asked to find points of interest on the medical campus in Magdeburg. In our analyses, we first quantified similarities between the GPS trajectories and identified wayfinding styles using k‐medoids clustering. Next, we extracted navigation profiles with aggregated performance measures as input features (e.g., wayfinding distance and number of help function calls) in a latent profile analysis. Mixed effect models were fitted to evaluate group differences for each performance measure separately. We then fed the performance measure that showed the largest groups differences into a multinomial logistic regression model to predict age and diagnostic group in unknown users via leave‐one‐out cross‐validation.ResultThe GPS data could be clustered into three wayfinding styles. However, the correspondence to our groups was rather low (Figure 1a). In contrast, navigation profiles obtained from the latent profile analysis showed a higher correspondence (Figure 1b). Particularly the number of brief stops during navigation differed between the groups, all p = .006 (Figure 2), and predicted age group, p = .015, and diagnostic group above chance, p = .004 (Figure 3). The leave‐one‐out cross‐validation confirmed the successful prediction of group membership in unknown participants, accuracy = 0.57 (chance level 0.33).ConclusionHere, we show how information about the cognitive health status of an individual can be inferred from smartphone‐data, obtained during a brief episode of a frequently performed everyday behavior. In particular, the number of short stops during wayfinding could be used to distinguish between YA, OA, and SCDs. Hence, real‐world navigation data, obtained from mobile devices, might help to identify individuals at increased risk for developing AD.

Predicting the chance for being at‐risk of AD based on smartphone data obtained during an everyday wayfinding task in the real world

AbstractBackgroundPatients with Alzheimer’s disease (AD) often show problems in spatial navigation. However, spatial navigation is rarely investigated in real world settings and in people at early disease stages. Capitalizing on recent advancements in digital technologies, we measured the performance of healthy younger (YA) and older adults (OA), as well as older adults with subjective cognitive decline (SCD) while they solved a smartphone‐based wayfinding task.MethodUsing the novel smartphone application “Explore”, we tracked GPS data and other behavioral indicators in 24 YAs, 25 OAs, and 23 SCDs, while they were asked to find points of interest on the medical campus in Magdeburg. In our analyses, we first quantified similarities between the GPS trajectories and identified wayfinding styles using k‐medoids clustering. Next, we extracted navigation profiles with aggregated performance measures as input features (e.g., wayfinding distance and number of help function calls) in a latent profile analysis. Mixed effect models were fitted to evaluate group differences for each performance measure separately. We then fed the performance measure that showed the largest groups differences into a multinomial logistic regression model to predict age and diagnostic group in unknown users via leave‐one‐out cross‐validation.ResultThe GPS data could be clustered into three wayfinding styles. However, the correspondence to our groups was rather low (Figure 1a). In contrast, navigation profiles obtained from the latent profile analysis showed a higher correspondence (Figure 1b). Particularly the number of brief stops during navigation differed between the groups, all p ≤ .006 (Figure 2), and predicted age group, p = .015, and diagnostic group above chance, p = .004 (Figure 3). The leave‐one‐out cross‐validation confirmed the successful prediction of group membership in unknown participants, accuracy = 0.57 (chance level 0.33).ConclusionHere, we show how information about the cognitive health status of an individual can be inferred from smartphone‐data, obtained during a brief episode of a frequently performed everyday behavior. In particular, the number of short stops during wayfinding could be used to distinguish between YA, OA, and SCDs. Hence, real‐world navigation data, obtained from mobile devices, might help to identify individuals at increased risk for developing AD.

Comparative Study of K-NN, Naive Bayes and SVM for Face Expression Classification Techniques

Abstract Classification is a data mining technique used to predict group membership for data instances within a given dataset. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. This comparative study focuses on the application of K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machines (SVM) classification techniques in the context of face expression recognition. The study aims to evaluate and compare the performance of these techniques in accurately classifying facial expressions, their advantages and disadvantages. Facial expression recognition is an important area in computer vision and emotion analysis. It involves identifying and classifying different facial expressions such as happiness, sadness, anger, fear, surprise and disgust. Accurate classification of facial expressions is crucial for various applications, including human-computer interaction, affective computing and psychological research. K-NN is a non-parametric algorithm that classifies data based on the majority class of its K-nearest neighbors. Naive Bayes is a probabilistic algorithm that makes classification decisions based on the Bayes theorem and assumes independence between features. SVM is a machine learning algorithm that constructs a hyperplane to separate different classes of data. Dataset such as Affectnet is used to conduct the study including labeled facial expression images. Three algorithms are tested on this dataset to evaluate their performance in correctly classifying facial expressions. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the classification performance of each technique. Software used for conducting the experiments is Python.

Detecting early Alzheimer’s disease using neuropsychological assessment and structural neuroimaging

AbstractBackgroundAlzheimer’s disease (AD) can be challenging to diagnose early as cognitive changes are mild. Neuropsychological assessment, in contrast to cognitive screening or isolated cognitive tests, are more sensitive to the mild cognitive changes in early AD. Magnetic resonance imaging (MRI) can detect early structural brain changes in AD, particularly entorhinal cortex, transentorhinal cortex, and hippocampal atrophy. Combining neuropsychological assessment with MRI examination may yield improved ability to detect early AD. The current study examined combining neuropsychological and MRI measures to detect patients with mild cognitive impairment (MCI) in a memory clinic sample.MethodNeuropsychological assessment and entorhinal cortex, transentorhinal cortex, and hippocampal volume measurement were conducted in memory clinic patients, including 38 with memory symptoms, 16 with MCI, and 16 with dementia due to AD, and in 40 healthy controls. Backward stepwise multinomial logistic regression was used to compare the neuropsychological measures and MRI measures.ResultAmong the neuropsychological measures, memory and processing speed were the most important measures that contributed to group membership prediction. All MRI measures contributed to group membership prediction. The neuropsychological measures were more accurate than the MRI measures at detecting patients with MCI. Combining the MRI measures with the neuropsychological measures did not improve group membership prediction.ConclusionNeuropsychological assessment is an important tool to support MCI diagnosis. MRI measures contribute little diagnostic value over and above neuropsychological assessment. Further advancement in deriving MRI‐based measurements is required to achieve clinical utility.

Females evaluated for autism: characteristics and co-occurring and differential DSM-5 diagnoses

Objective: While sex differences in autism spectrum disorder (ASD) have been identified in areas such as neurocognitive functioning, behavior patterns, and diagnostic criteria, less work has focused on differences within females referred for ASD evaluation, including those who did not go on to receive a diagnosis. This study examined psychological and behavioral characteristics and co-occurring and differential DSM-5 diagnoses between pediatric female participants who received an ASD diagnosis (ASD+) and those who did not (ASD-). Method: Data on cognitive functioning, adaptive functioning, internalizing symptoms, externalizing symptoms, and ADOS-2 scores were analyzed among 137 3- to 20-year-old patients. The sample was divided into two age groups (ages 3–8 and ages 9–20) for analyses of between-group differences (ASD+ vs. ASD-) and predictors of group membership. Results: Females in the ASD+ group were significantly younger, had lower cognitive scores, lower internalizing and externalizing symptoms, and had higher Autism Diagnostic Observation Schedule-2 (ADOS-2) scores than those in the ASD- group. ADOS-2 scores were also the only significant predictor of ASD group membership across age groups. The ASD+ group had a higher percentage of intellectual disability while the ASD- group had higher percentages of anxiety disorder, attention-deficit/hyperactivity disorder, and disruptive behavior disorders. Conclusions: Psychological and behavioral presentations among females referred for ASD evaluation varied with age and ASD diagnostic groups. These results highlight potential female differences in ASD referrals and identification of ASD and the need to improve care for females in consideration of demographic factors.

89 Detecting Feigned Cognitive Impairment Using Pupillometry on the Warrington Recognition Memory Test for Words

Objective:Traditional methods of assessing performance validity have numerous weaknesses, among them, results can be consciously manipulated by examinees who wish to feign cognitive impairment. This study tested the ability of pupillary dilation patterns during a performance validity test (PVT) to enhance diagnostic accuracy in discriminating true from feigned impairment of traumatic brain injury (TBI). Pupillometry provides information about physiological and psychological processes related to cognitive load, familiarity, and deception and is outside of conscious control. Patrick, Rapport, Kanser, Hanks, and Bashem (2021) established proof of concept for the utility of pupillometry with PVTs applied to the Test of Memory Malingering (TOMM). This study replicated and extended this work by evaluating the incremental utility of pupillary-derived indices on the Warrington Recognition Memory Test for Words (RMT).Participants and Methods:Participants included 214 adults in three groups: adults with bona fide TBI (TBI; n = 51) healthy comparisons instructed to perform their best (HC; n = 72), and healthy adults instructed and incentivized to simulate cognitive impairment due to TBI (SIM; n = 91). Moreover, this study examined pupillary pattern differences among successful (i.e., failed < 1 PVT and performed impaired on cognitive tests) and unsuccessful (i.e., failed > 2 PVTs or did not score impaired on a cognitive test) SIM, including SIM who did and did not fail the RMT. The RMT was administered in the context of a comprehensive neuropsychological battery. Indices included two pure pupil dilation (PD) indices: a simple measure of baseline arousal (PD-Baseline) and a nuanced measure of dynamic engagement (PD-Range). A pupillo-behavioral index was also evaluated: Dilation-response inconsistency (DRI) captured the frequency with which examinees displayed a pupillary familiarity response to the correct answer but selected the unfamiliar stimulus (incorrect answer).Results:The results generally replicated Patrick et al. (2021), as all three indices were useful in discriminating between groups and provided incremental utility to traditional accuracy scores. PD-Baseline appeared sensitive to oculomotor dysfunction due to TBI (i.e., increasing accurate identification of that group); adults with TBI displayed significantly lower chronic arousal as compared to the two groups of healthy adults (SIM, HC). In fact, the TBI group showed significantly lower PD-Baseline than both unsuccessful simulators who were detected as feigners and successful simulators who passed PVTs but effectively feigned TBI on other tests. Dynamic engagement (PD-Range) yielded a hierarchical structure such that SIM were more dynamically engaged than TBI followed by HC. As predicted, simulators engaged in DRI significantly more frequently than other groups. Moreover, DRI added unique information to RMT accuracy in classifying unsuccessful simulators from all other groups. Each of these three pupillary indices showed large effect sizes, and logistic regressions indicated that each contributed unique variance in predicting group membership on one or more of the paired contrasts (i.e., SIM-TBI, SIM-HC, HC-TBI).Conclusions:Taken together, the findings support continued research on the application of pupillometry to performance validity assessment: Pupillometry provided unique information in enhancing classification accuracy beyond traditional PVT accuracy scores. Overall, the findings highlight the promise of biometric indices in multimethod assessments of performance validity.

2 Cognitive Heterogeneity and Risk of Progression in Data-Driven Subtle Cognitive Decline Phenotypes

Objective:There is increasing recognition of cognitive and pathological heterogeneity in early-stage Alzheimer’s disease and other dementias. Data-driven approaches have demonstrated cognitive heterogeneity in those with mild cognitive impairment (MCI), but few studies have examined this heterogeneity and its association with progression to MCI/dementia in cognitively unimpaired (CU) older adults. We identified cluster-derived subgroups of CU participants based on comprehensive neuropsychological data and compared baseline characteristics and rates of progression to MCI/dementia or a Dementia Rating Scale (DRS) of <129 across subgroups.Participants and Methods:A hierarchical cluster analysis was conducted using 11 baseline neuropsychological test scores from 365 CU participants in the UCSD Shiley-Marcos Alzheimer’s Disease Research Center (age M=71.93 years, SD=7.51; 55.9% women; 15.6% Hispanic/Latino/a/x/e). A discriminate function analysis was then conducted to test whether the individual neuropsychological scores predicted cluster-group membership. Cox regressions examined the risk of progression to consensus diagnosis of MCI or dementia, or to DRS score <129, by cluster group.Results:Cluster analysis identified 5 groups: All-Average (n=139), Low-Visuospatial (n=46), Low-Executive (n=51), Low-Memory/Language (n=83), and Low-All Domains (n=46). The discriminant function analysis using the neuropsychological measures to predict group membership into these 5 clusters correctly classified 85.2% of the participants. Subgroups had unique demographic and clinical characteristics. Relative to the All-Average group, the Low-Visuospatial (hazard ratio [HR] 2.39, 95% CI [1.03, 5.56], p=.044), Low-Memory/Language (HR 4.37, 95% CI [2.24, 8.51], p<.001), and Low-All Domains (HR 7.21, 95% CI [3.59, 14.48], p<.001) groups had greater risk of progression to MCI/dementia. The Low-Executive group was also twice as likely to progress to MCI/dementia compared to the AllAverage group, but did not statistically differ (HR 2.03, 95% CI [0.88,4.70], p=.096). A similar pattern of results was found for progression to DRS score <129, with the Low-Executive (HR 2.82, 95% CI [1.26, 6.29], p=.012), Low-Memory/Language (HR 3.70, 95% CI [1.80, 7.56], p<.001) and Low-All Domains (HR 5.79, 95% CI [2.74, 12.27], p<.001) groups at greater risk of progression to a DRS score <129 than the All-Average group. The Low-Visuospatial group was also twice as likely to progress to DRS <129 compared to the All-Average group, but did not statistically differ (HR 2.02, 95% CI [0.80, 5.06], p=.135).Conclusions:Our results add to a growing literature documenting heterogeneity in the earliest cognitive and pathological presentations associated with Alzheimer’s disease and related disorders. Participants with subtle memory/language, executive, and visuospatial weaknesses all declined at faster rates than the All-Average group, suggesting that there are multiple pathways and/or unique subtle cognitive decline profiles that ultimately lead to a diagnosis of MCI/dementia. These results have important implications for early identification of individuals at risk for MCI/dementia. Given that the same classification approach may not be optimal for everyone, determining profiles of subtle cognitive difficulties in CU individuals and implementing neuropsychological test batteries that assess multiple cognitive domains may be a key step towards an individualized approach to early detection and fewer missed opportunities for early intervention.

Parenting Factors Predicting Longitudinal Fast-Food Consumption From Adolescence

Adolescence has been identified as a critical developmental period predicting adult health. Despite this knowledge, few studies have examined the relationship between parenting/family variables occurring during adolescence and future food consumption patterns. The present study examined the predictive nature of family dinners and parent control over food in relation to longitudinal fast-food consumption beginning in adolescence and across the transition into adulthood. Data came from The National Longitudinal Study of Adolescent to Adult Health. A group-based modeling technique identified distinct participant groups with similar trajectories of fast-food consumption across waves II-IV (1994-2008). Parent control over food and family dinner frequency were used as predictors of group membership in the final models. Four separate trajectories of fast-food consumption frequency emerged. Both parent control over food and family dinner frequency during adolescence were related to trajectory group membership, acting as protective factors against spikes in fast-food consumption. The observed relationships highlight key areas for positive change in parental involvement in children’s food choices and future health interventions to decrease fast-food consumption during this critical developmental period.

Can you find it? Novel oddity detection task for the early detection of Alzheimer's disease.

We aimed to develop a measure to specifically assess the functioning of the perirhinal cortex (PRC), a brain structure affected very early in Alzheimer's disease (AD) pathology. In this novel task, participants were shown arrays of six complex figures and had to identify the "odd-one." The pilot study included 50 normal controls (NCs) and 50 patients in very early stages of AD. Participants completed the task and received MRI scanning. Best differentiating items were determined and applied in a validation study including 25 NCs, 27 early-stage AD patients, and 26 patients with major depression. Logistic regression models investigated if task performance predicted group membership. Task performance was then related to whole-brain gray matter integrity. As proof of concept, cortical thickness values of four regions of interest (ROIs; e.g., medial PRC and entorhinal cortex [ERC]) were compared between the groups. The associations of task performance and cortical thickness of the ROIs were investigated using linear models. Task performance showed good discriminative ability between early-stage AD patients and NCs. Whole-brain analyses revealed four significant clusters (p < .001) with peak voxels in parahippocampal regions including PRC and ERC. ROI analyses showed distinctly reduced cortical thickness in the AD group compared to both other groups in the medial PRC and ERC (p ≤ .001). Task performance modeled by ROI cortical thickness did not achieve significant results. Although further validation is needed, especially with age-matched participant groups, these findings indicate that the task detects early cognitive impairment related to AD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Call for Methodological Reflexivity in Researching Language Testing and Migration

Abstract A growing scholarly literature in a subfield of applied linguistics focuses on language testing and language requirements for migrants. We sought to understand what theoretical paradigms and methodological approaches have framed this research and propose future research directions. To this end, we conducted a systematic review of articles on language testing and migration published between 2001 and 2022 in the applied linguistics field. For each of the 46 studies which met inclusion/exclusion criteria, we randomly selected a control article from the same journals. Both the test corpus and the control corpus were double coded for theoretical and methodological characteristics and analysed using contingency tables, Mann–Whitney tests, and forward stepwise logistic regression to predict group membership. The findings revealed that literature on language testing and migration is highly reliant on critical theory and less reliant on features associated with empirical methods. We suggest that research in this domain would benefit from methodological reflexivity (i.e. questioning prevalent assumptions and employing theoretical variety) and that complementary approaches could lead to a more nuanced research consensus and promote research-policy collaboration.

A geometric morphometric analysis of variation in Australian frog ilia and taxonomic interpretations.

Anurans including frogs and toads exhibit an ilium that is often regarded as taxonomically diagnostic. The ilium, one of the three paired bones that make up the pelvic girdle, has been important in the fossil record for identifying anuran morphotypes. Osteological collections for Australian frogs are rare in herpetological museums, and skeletonizing whole-bodied specimens requires destroying soft tissue morphology which is valuable to anuran specialists working on living species. Computed tomography scans provide the opportunity to study anuran osteology without the loss of soft tissues. Our study, based on microcomputed tomography scans of extant Australian frogs from the public repository Morphosource and from museum collections focuses on the morphological differences between Australian frogs at the familial and generic levels using geometric morphometrics to compare the diagnostic shape of the ilium. Principal component analysis (PCA) and canonical variate analysis (CVA) were conducted to assess differences in the ilium between supraspecific groups of Australian frogs. The canonical variates analysis accurately predicted group membership (i.e., the correct family) with up to 76.2% success for cross-validated predictions and 100% of original group predictions. While the sample was limited to familial and generic level analyses, our research shows that ilial morphology in Australian frogs is taxonomically informative. This research provides a guide for identifying Australian anurans, including fossils, as well as new information relevant to considerations about their phylogenetic relationships, and the potential use of the fossil record to enhance efforts to conserve threatened living frog species.