Age-dependent differences in human brain activity using a face- and location-matching task: An fMRI study

Purpose: To evaluate the differences of cortical activation patterns in young and elderly healthy subjects for object and spatial visual processing using a face- and location-matching task. Materials and Methods: We performed a face- and a location-matching task in 15 young (mean age: 28 +/- 9 years) and 19 elderly (mean age: 71 +/- 6 years) subjects. Each experiment consisted of 7 blocks alternating between activation and control condition. For face matching, the subjects had to indicate whether two displayed faces were identical or different. For location matching, the subjects had to press a button whenever two objects had an identical position. For control condition, we used a perception task with abstract images. Functional imaging was performed on a 1.5-tesla scanner using an EPI sequence. Results: In the face-matching task, the young subjects showed bilateral (right 1 left) activation in the occipito-temporal pathway (occipital gyrus, inferior and middle temporal gyrus). Predominantly right hemispheric activations were found in the fusiform gyrus, the right dorsolateral prefrontal cortex (inferior and middle frontal gyrus) and the superior parietal gyrus. In the elderly subjects, the activated areas in the right fronto-lateral cortex increased. An additional activated area could be found in the medial frontal gyrus (right > left). In the location-matching task, young subjects presented increased bilateral (right > left) activation in the superior parietal lobe and precuneus compared with face matching. The activations in the occipito-temporal pathway, in the right fronto-lateral cortex and the fusiform gyrus were similar to the activations found in the face-matching task. In the elderly subjects, we detected similar activation patterns compared to the young subjects with additional activations in the medial frontal gyrus. Conclusion: Activation patterns for object-based and spatial visual processing were established in the young and elderly healthy subjects. Differences between the elderly and young subjects could be evaluated, especially by using a face-matching task. Copyright (c) 2007 S. Karger AG, Basel

fMRI of reward processing in a community-based longitudinal study

Up to 40% of youth with autism spectrum disorder (ASD) also suffer from anxiety, and this comorbidity is linked with significant functional impairment. However, the mechanisms of this overlap are poorly understood. We investigated the interplay between ASD traits and anxiety during reward processing, known to be affected in ASD, in a community sample of 1472 adolescents (mean age=14.4 years) who performed a modified monetary incentive delay task as part of the Imagen project. Blood-oxygen-level dependent (BOLD) responses to reward anticipation and feedback were compared using a 2x2 analysis of variance test (ASD traits: low/high; anxiety symptoms: low/high), controlling for plausible covariates. In addition, we used a longitudinal design to assess whether neural responses during reward processing predicted anxiety at 2-year follow-up. High ASD traits were associated with reduced BOLD responses in dorsal prefrontal regions during reward anticipation and negative feedback. Participants with high anxiety symptoms showed increased lateral prefrontal responses during anticipation, but decreased responses following feedback. Interaction effects revealed that youth with combined ASD traits and anxiety, relative to other youth, showed high right insula activation when anticipating reward, and low right-sided caudate, putamen, medial and lateral prefrontal activations during negative feedback (all clusters PFWE<0.05). BOLD activation patterns in the right dorsal cingulate and right medial frontal gyrus predicted new-onset anxiety in participants with high but not low ASD traits. Our results reveal both quantitatively enhanced and qualitatively distinct neural correlates underlying the comorbidity between ASD traits and anxiety. Specific neural responses during reward processing may represent a risk factor for developing anxiety in ASD youth

Association between cognitive performance and cortical glucose metabolism in patients with mild Alzheimer's disease

Background: Neuronal and synaptic function in Alzheimer's disease (AD) is measured in vivo by glucose metabolism using positron emission tomography (PET). Objective: We hypothesized that neuronal activation as measured by PET is a more sensitive index of neuronal dysfunction than activity during rest. We investigated if the correlations between dementia severity as measured with the Mini Mental State Examination (MMSE) and glucose metabolism are an artifact of brain atrophy. Method: Glucose metabolism was measured using {[}F-18]fluorodeoxyglucose PET during rest and activation due to audiovisual stimulation in 13 mild to moderate AD patients (MMSE score >= 17). PET data were corrected for brain atrophy. Results: In the rest condition, glucose metabolism was correlated with the MMSE score primarily within the posterior cingulate and parietal lobes. For the activation condition, additional correlations were within the primary and association audiovisual areas. Most local maxima remained significant after correcting for brain atrophy. Conclusion: PET activity measured during audiovisual stimulation was more sensitive to functional alterations in glucose metabolism in AD patients compared to the resting PET. The association between glucose metabolism and MMSE score was not dependent on brain atrophy. Copyright (C) 2005 S. Karger AG, Basel

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: Testing gene × environment moderation and mediation

Background. It has not yet been determined if the commonly reported cannabispsychosis association is limited to individuals with pre-existing genetic risk for psychotic disorders.Methods. We examined whether the relationship between polygenic risk score for schizophrenia (PRS-Sz) and psychotic-like experiences (PLEs), as measured by the Community Assessment of Psychic Experiences-42 (CAPE-42) questionnaire, is mediated or moderated by lifetime cannabis use at 16 years of age in 1740 of the individuals of the European IMAGEN cohort. Secondary analysis examined the relationships between lifetime cannabis use, PRS-Sz and the various sub-scales of the CAPE-42. Sensitivity analyses including covariates, including a PRS for cannabis use, were conducted and results were replicated using data from 1223 individuals in the Dutch Utrecht cannabis cohort.Results. PRS-Sz significantly predicted cannabis use (p = 0.027) and PLE (p = 0.004) in the IMAGEN cohort. In the full model, considering PRS-Sz and covariates, cannabis use was also significantly associated with PLE in IMAGEN (p = 0.007). Results remained consistent in the Utrecht cohort and through sensitivity analyses. Nevertheless, there was no evidence of a mediation or moderation effects.Conclusions. These results suggest that cannabis use remains a risk factor for PLEs, over and above genetic vulnerability for schizophrenia. This research does not support the notion that the cannabis-psychosis link is limited to individuals who are genetically predisposed to psychosis and suggests a need for research focusing on cannabis-related processes in psychosis that cannot be explained by genetic vulnerability

Are psychotic-like experiences related to a discontinuation of cannabis consumption in young adults?

Objective: To assess changes in cannabis use in young adults as a function of psychotic-like experiences. Method: Participants were initially recruited at age 14 in high schools for the longitudinal IMAGEN study. All measures presented here were assessed at follow-ups at age 19 and at age 22, respectively. Perceived stress was only assessed once at age 22. Ever users of cannabis (N = 552) gave qualitative and quantitative information on cannabis use and psychotic-like experiences using the Community Assessment of Psychic Experiences (CAPE). Of those, nearly all n = 549 reported to have experienced at least one psychotic experience of any form at age 19. Results: Mean cannabis use increased from age 19 to 22 and age of first use of cannabis was positively associated with a change in cannabis use between the two time points. Change in cannabis use was not significantly associated with psychotic-like experiences at age 19 or 22. In exploratory analysis, we observed a positive association between perceived stress and the experience of psychotic experiences at age 22. Conclusion: Age of first use of cannabis influenced trajectories of young cannabis users with later onset leading to higher increase, whereas the frequency of psychotic-like experiences was not associated with a change in cannabis use. The observed association between perceived stress and psychotic-like experiences at age 22 emphasizes the importance of stress experiences in developing psychosis independent of cannabis use

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all PFDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|rho| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|rho = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|rho| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives