Robert Dahnke

Brain Structural Trajectories Over the Adult Lifespan

The aim of this large‐sample cross‐sectional voxel‐based morphometry (VBM) study of anatomical brain data was to investigate linear and nonlinear age‐related trajectories of grey matter volume in the human brain during the adult lifespan. To date, there are only a few structural brain studies investigating local nonlinear aspects at the voxel level, i.e., without using anatomical ROIs as a priori hypothesis. Therefore, we analyzed 547 T1‐weighted MR images of healthy adult brains with an age range of 19 to 86 years, including 161 scans of subjects with ages 60 and older. We found that the gray matter volume in some regions did not linearly decrease over time, but rather exhibited a delayed decline. Nonlinear age trajectories were observed in the medial temporal lobe regions, the basal ganglia, and parts of the cerebellum. Their trajectories indicated a preservation of grey matter volume during the early adult lifespan. Interestingly, we found nonlinear grey matter structural dynamics specifically in parts of the brain that have been extensively discussed in the context of learning and memory. We propose a hypothesis in relation to the functional role of these brain regions that may explain these results. Hum Brain Mapp 33:2377–2389, 2012.

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.

Cortical thickness and central surface estimation

Several properties of the human brain cortex, e.g., cortical thickness and gyrification, have been found to correlate with the progress of neuropsychiatric disorders. The relationship between brain structure and function harbors a broad range of potential uses, particularly in clinical contexts, provided that robust methods for the extraction of suitable representations of the brain cortex from neuroimaging data are available. One such representation is the computationally defined central surface (CS) of the brain cortex. Previous approaches to semi-automated reconstruction of this surface relied on image segmentation procedures that required manual interaction, thereby rendering them error-prone and complicating the analysis of brains that were not from healthy human adults. Validation of these approaches and thickness measures is often done only for simple artificial phantoms that cover just a few standard cases. Here, we present a new fully automated method that allows for measurement of cortical thickness and reconstructions of the CS in one step. It uses a tissue segmentation to estimate the WM distance, then projects the local maxima (which is equal to the cortical thickness) to other GM voxels by using a neighbor relationship described by the WM distance. This projection-based thickness (PBT) allows the handling of partial volume information, sulcal blurring, and sulcal asymmetries without explicit sulcus reconstruction via skeleton or thinning methods. Furthermore, we introduce a validation framework using spherical and brain phantoms that confirms accurate CS construction and cortical thickness measurement under a wide set of parameters for several thickness levels. The results indicate that both the quality and computational cost of our method are comparable, and may be superior in certain respects, to existing approaches.

The relation of ventromedial prefrontal cortex activity and heart rate fluctuations at rest

Recent studies applying functional magnetic resonance imaging have focused on the description of cerebral substrates of changes in cardiac function during diverse autonomic maneuvers or stressful cognitive tasks. These studies might be limited by the indistinguishable neuronal activity due to cognitive processes, which are known to influence autonomic function, and the ‘baseline’ activity in the central autonomic network. We therefore investigated 26 healthy volunteers in the magnetic resonance scanner to simultaneously obtain functional brain images and RR intervals (intervals between ventricular depolarizations) of the high‐resolution electrocardiogram. The mean RR interval length within each functional scan was computed, which was finally convolved with the canonical hemodynamic response function to obtain a regressor for the functional time series. The resulting individual contrast image indicated a positive covariation of the blood oxygen level‐dependent signal and RR interval length in the ventromedial prefrontal cortex (vmPFC). Furthermore, a reduced mean cross‐approximate entropy value was shown for the interaction between the vmPFC and individual RR intervals. This suggests reduced asynchrony between the heart rate and vmPFC activity in contrast to other brain areas. Our findings confirm data obtained in animals describing the vmPFC as an important forebrain structure of the central autonomic network and an influence of the vmPFC in the cortical generation of efferent vagal activity. This finding needs to be investigated in diseases with known suppression of efferent vagal modulation.

Disrupted white matter integrity of corticopontine-cerebellar circuitry in schizophrenia

Evidence for white matter abnormalities in patients with schizophrenia is increasing. Decreased fractional anisotropy (FA) in interhemispheric commissural fibers as well as long-ranging fronto-parietal association fibers belongs to the most frequent findings. The present study used tract-based spatial statistics to investigate white matter integrity in 35 patients with schizophrenia and 35 healthy volunteers. We found that patients exhibited significantly decreased FA relative to healthy subjects in the corpus callosum, the cerebral peduncle, the left inferior fronto-occipital fasciculus, the anterior thalamic radiation, the right posterior corona radiata, the middle cerebellar peduncle, and the right superior longitudinal fasciculus. Increased FA was detectable in the inferior sections of the corticopontine-cerebellar circuit. Present data indicate extended cortical-subcortical alterations of white matter integrity in schizophrenia using advanced data analysis strategies. They corroborate preceding findings of white matter structural deficits in mainly long-ranging association fibers and provide first evidence for neuroplastic changes in terms of an increased directionality in more inferior fiber tracts.

Models of the aging brain structure and individual decline

The aging brain’s structural development constitutes a spatiotemporal process that is accessible by MR-based computational morphometry. Here we introduce basic concepts and analytical approaches to quantify age-related differences and changes in neuroanatomical images of the human brain. The presented models first address the estimation of age trajectories, then we consider inter-individual variations of structural decline, using a repeated measures design. We concentrate our overview on preprocessed neuroanatomical images of the human brain to facilitate practical applications to diverse voxel- and surface-based structural markers. Together these methods afford analysis of aging brain structure in relation to behavioral, health, or cognitive parameters.

Topological correction of brain surface meshes using spherical harmonics

Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a “fill” or “cut” operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low‐pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self‐intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a “gold standard” surface created by averaging 12 scans of the same brain. Ninety‐three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6–8 min of computation time. Further improvements are discussed, especially regarding the use of the T1‐weighted image to make corrections. Hum Brain Mapp, 2011.

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.

Premature Brain Aging in Baboons Resulting from Moderate Fetal Undernutrition

Contrary to the known benefits from a moderate dietary reduction during adulthood on life span and health, maternal nutrient reduction during pregnancy is supposed to affect the developing brain, probably resulting in impaired brain structure and function throughout life. Decreased fetal nutrition delivery is widespread in both developing and developed countries, caused by poverty and natural disasters, but also due to maternal dieting, teenage pregnancy, pregnancy in women over 35 years of age, placental insufficiency, or multiples. Compromised development of fetal cerebral structures was already shown in our baboon model of moderate maternal nutrient reduction. The present study was designed to follow-up and evaluate the effects of moderate maternal nutrient reduction on individual brain aging in the baboon during young adulthood (4–7 years; human equivalent 14–24 years), applying a novel, non-invasive neuroimaging aging biomarker. The study reveals premature brain aging of +2.7 years (p < 0.01) in the female baboon exposed to fetal undernutrition. The effects of moderate maternal nutrient reduction on individual brain aging occurred in the absence of fetal growth restriction or marked maternal weight reduction at birth, which stresses the significance of early nutritional conditions in life-long developmental programming. This non-invasive MRI biomarker allows further longitudinal in vivo tracking of individual brain aging trajectories to assess the life-long effects of developmental and environmental influences in programming paradigms, aiding preventive and curative treatments on cerebral atrophy in experimental animal models and humans.

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.