Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease.

Partial least squares correlation of multivariate cognitive abilities and local brain structure in children and adolescents.

Intelligent behavior is not a one-dimensional phenomenon. Individual differences in human cognitive abilities might be therefore described by a 'cognitive manifold' of intercorrelated tests from partially independent domains of general intelligence and executive functions. However, the relationship between these individual differences and brain morphology is not yet fully understood. Here we take a multivariate approach to analyzing covariations across individuals in two feature spaces: the low-dimensional space of cognitive ability subtests and the high-dimensional space of local gray matter volume obtained from voxel-based morphometry. By exploiting a partial least squares correlation framework in a large sample of 286 healthy children and adolescents, we identify directions of maximum covariance between both spaces in terms of latent variable modeling. We obtain an orthogonal set of latent variables representing commonalities in the brain-behavior system, which emphasize specific neuronal networks involved in cognitive ability differences. We further explore the early lifespan maturation of the covariance between cognitive abilities and local gray matter volume. The dominant latent variable revealed positive weights across widespread gray matter regions (in the brain domain) and the strongest weights for parents' ratings of children's executive function (in the cognitive domain). The obtained latent variables for brain and cognitive abilities exhibited moderate correlations of 0.46-0.6. Moreover, the multivariate modeling revealed indications for a heterochronic formation of the association as a process of brain maturation across different age groups.

Fronto-cingulate effective connectivity in major depression: a study with fMRI and dynamic causal modeling.

Functional imaging studies are indicating disrupted error monitoring and executive control in a fronto-cingulate network in major depression. However, univariate statistical analyses allow only for a limited assessment of directed neuronal interactions. Therefore, the present study used dynamic causal modeling (DCM) of a fronto-cingulate network to re-analyze the data from a preceding fMRI study in 16 drug-free patients with major depression and 16 healthy controls using the Stroop Color-Word Test (Wagner et al., 2006). In both groups, a significant reciprocal interregional connectivity was found in a cognitive control network including prefrontal cortex (PFC) and dorsal anterior cingulate cortex (ACC). With regard to intrinsic connections we detected a significant difference for dorsal to rostral ACC connectivity between depressive patients and controls in terms of higher connectivity in patients. Additionally, a task by group interaction was observed for the bilinear interaction signaling enhanced task-related input from the dorsal to rostral ACC in subjects with depression. This could be related to the inability of patients to down-regulate rostral ACC activation as observed in the previous univariate analysis. The correlation between interference scores and intrinsic connections from dorsal ACC to dorsolateral PFC (DLPFC) was significant for both groups together, but no significant group differences in correlations could be detected. Thus, the observed relationship between control functions of the dorsal ACC exerted over DLPFC and interference scores appears to be valid in both patients with depression and controls. The findings are consistent with current models of a differential involvement of the fronto-cingulate system in the pathophysiology of major depression.

Volume versus surface-based cortical thickness measurements: A comparative study with healthy controls and multiple sclerosis patients.

The cerebral cortex is a highly folded outer layer of grey matter tissue that plays a key role in cognitive functions. In part, alterations of the cortex during development and disease can be captured by measuring the cortical thickness across the whole brain. Available software tools differ with regard to labor intensity and computational demands. In this study, we compared the computational anatomy toolbox (CAT), a recently proposed volume-based tool, with the well-established surface-based tool FreeSurfer. We observed that overall thickness measures were highly inter-correlated, although thickness estimates were systematically lower in CAT than in FreeSurfer. Comparison of multiple sclerosis (MS) patients with age-matched healthy control subjects showed highly comparable clusters of MS-related thinning for both methods. Likewise, both methods yielded comparable clusters of age-related cortical thinning, although correlations between age and average cortical thickness were stronger for FreeSurfer. Our data suggest that, for the analysis of cortical thickness, the volume-based CAT tool can be regarded a considerable alternative to the well-established surface-based FreeSurfer tool.

Structure-function relationships in the context of reinforcement-related learning: a combined diffusion tensor imaging-functional magnetic resonance imaging study.

In the context of probabilistic learning, previous functional magnetic resonance imaging studies have shown decreasing uncertainty accompanying decreasing neuronal activation in task-relevant networks. Moreover, initial evidence points to a relationship between white matter structure and cognitive performance. Little is known, however, about the structural correlates underlying individual differences in activation and performance in the context of probabilistic learning. This combined functional magnetic resonance imaging-diffusion tensor imaging study aimed at investigating the individual ability to reduce processing resources with decreasing uncertainty in direct relation to individual characteristics in white matter brain structure. Results showed that more successful learners, as compared with less successful learners, exhibited stronger activation decreases with decreasing uncertainty. An increased mean and axial diffusivity in, among others, the inferior and superior longitudinal fasciculus, the posterior part of the cingulum bundle, and the corpus callosum were detectable in less successful learners compared with more successful learners. Most importantly, there was a negative correlation between uncertainty-related activation and diffusivity in a fronto-parieto-striatal network in less successful learners only, indicating a direct relation between diffusivity and the ability to reduce processing resources with decreasing uncertainty. These findings indicate that interindividual variations in white matter characteristics within the normal population might be linked to neuronal activation and critically influence individual learning performance.