Principal Component Regression predicts functional responses across individuals

International audienceInter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes

Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults

Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively “Stable” or “Declining” based on ≥1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R2 = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R2 = .285; C index = .787), whereas the addition of EM did not (R2 = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer\u27s disease. (JINS, 2012, 18, 1–11

Prediction of Cognitive Decline in Healthy Older Adults using fMRI

Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three neuropsychological measures. Using logistic regression, demographic variables (age, years of education, gender) and family history of dementia did not predict future cognitive decline. Greater fMRI activity, absence of an APOE ε4 allele, and larger hippocampal volume were associated with reduced likelihood of cognitive decline. The most effective combination of predictors involved fMRI brain activity and APOE ε4 status. Brain activity measured from task-activated fMRI, in combination with APOE ε4 status, was successful in identifying cognitively intact individuals at greatest risk for developing cognitive decline over a relatively brief time period. These results have implications for enriching prevention clinical trials designed to slow AD progression

Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical modeling approach to multivariate functional observations. Within this familiar setting, we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry, and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al.(2014) recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing (The Model, TM). Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise (Tong et al., 2008)

Cognitive support at encoding attenuates age differences in recollective experience among adults of lower frontal lobe function

Free recall and recollective experience were investigated in relation to neuropsychological measures of frontal lobe function (FLF) among 105 healthy adults divided into three age groups; young (M = 21.82 years), young-old (M = 64.69 years), and old-old (M = 70.69 years). Participants were tested on free recall and recognition of word lists in each of two study conditions. In the first, semantically related words were organizable into one of four taxonomic categories, whereas in the second (random) condition, words were semantically unrelated. Results in respect of free recall showed memory performance was inferior with increasing age, lower FLF, and random encoding condition. There were no interactions involving those variables. With regard to recollective experience, a similar pattern of results was obtained. However, analyses also identified a significant interaction, suggesting old-old adults of lower FLF to exhibit poorer recollective experience. This interaction was significantly modified when semantic organization was available at study. Recognition measures classified as familiar did not vary as a function of age, neuropsychological function, or encoding condition. The results are consistent with the view that autonoetic consciousness, supported by the neural systems of the prefrontal cortex, underpins recollective experience. Further, among older adults, cognitive support at encoding attenuates the detrimental effects of individual differences in those neural systems, in relation to recognition performance

Religion and health : the application of a cognitive-behavioural framework

The empirical examination of the relationship between religion and health has often lacked theoretical direction. The aim of the study was to examine the relationship between dimensions of religiosity and health within the context of James and Wells’ cognitive-behavioural framework of religion. A community sample of 177 UK adults completed measures of religious orientation, religious coping, and prayer activity alongside the SF-36 Health Survey. Consistent with the cognitive-behavioural framework of religion, intrinsic religiosity and meditative prayer scores accounted for unique variance in both physical and mental health scores over a number of religious measures. These findings suggest the potential usefulness and importance of a cognitive-behavioural framework to understand the relationship between religion (as measured by meditative prayer and intrinsic religiosity) and health

Self-Pity: Exploring the Links to Personality, Control Beliefs, and Anger

Self-pity is a frequent response to stressful events. So far, however, empirical research has paid only scant attention to this subject. The present article aims at exploring personality characteristics associated with individual differences in feeling sorry for oneself. Two studies with N = 141 and N = 161 university students were conducted, employing multidimensional measures of personality, control beliefs, anger, loneliness, and adult attachment. With respect to personality, results showed strong associations of self-pity with neuroticism, particularly with the depression facet. With respect to control beliefs, individuals high in self-pity showed generalized externality beliefs, seeing themselves as controlled by both chance and powerful others. With respect to anger expression, self-pity was primarily related to anger-in. Strong connections with anger rumination were also found. Furthermore, individuals high in self-pity reported emotional loneliness and ambivalent-worrisome attachments. Finally, in both studies, a strong correlation with gender was found, with women reporting more self-pity reactions to stress than men. Findings are discussed with respect to how they support, extend, and qualify the previous literature on self-pity, and directions for future empirical research are pointed out

Linking Aboveground Traits to Root Traits and Local Environment: Implications of the Plant Economics Spectrum.

The plant economics spectrum proposes that ecological traits are functionally coordinated and adapt along environmental gradients. However, empirical evidence is mixed about whether aboveground and root traits are consistently linked and which environmental factors drive functional responses. Here we measure the strength of relationships between aboveground and root traits, and examine whether community-weighted mean trait values are adapted along gradients of light and soil fertility, based on the seedling censuses of 57 species in a subtropical forest. We found that aboveground traits were good predictors of root traits; specific leaf area, dry matter, nitrogen and phosphorus content were strongly correlated with root tissue density and specific root length. Traits showed patterns of adaptation along the gradients of soil fertility and light; species with fast resource-acquisitive strategies were more strongly associated with high soil phosphorus, potassium, openness, and with low nitrogen, organic matter conditions. This demonstrates the potential to estimate belowground traits from known aboveground traits in seedling communities, and suggests that soil fertility is one of the main factors driving functional responses. Our results extend our understanding of how ecological strategies shape potential responses of plant communities to environmental change