Annette Horstmann

Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections.

Recent findings in neuroscience suggest that adult brain structure changes in response to environmental alterations and skill learning. Whereas much is known about structural changes after intensive practice for several months, little is known about the effects of single practice sessions on macroscopic brain structure and about progressive (dynamic) morphological alterations relative to improved task proficiency during learning for several weeks. Using T1-weighted and diffusion tensor imaging in humans, we demonstrate significant gray matter volume increases in frontal and parietal brain areas following only two sessions of practice in a complex whole-body balancing task. Gray matter volume increase in the prefrontal cortex correlated positively with subjects performance improvements during a 6 week learning period. Furthermore, we found that microstructural changes of fractional anisotropy in corresponding white matter regions followed the same temporal dynamic in relation to task performance. The results make clear how marginal alterations in our ever changing environment affect adult brain structure and elucidate the interrelated reorganization in cortical areas and associated fiber connections in correlation with improvements in task performance.

Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain

Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Googles PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.

Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia

Introduction Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separately and on combined information from different imaging modalities to improve the detection and differentiation of different types of dementia. Methods Patients with clinically diagnosed Alzheimers disease (AD: n = 21), with frontotemporal lobar degeneration (FTLD: n = 14) and control subjects (n = 13) underwent both [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) scanning and magnetic resonance imaging (MRI), together with clinical and behavioral assessment. FDG-PET and MRI data were commonly processed to get a precise overlap of all regions in both modalities. Support vector machine classification was applied with varying parameters separately for both modalities and to combined information obtained from MR and FDG-PET images. ROIs were extracted from comprehensive systematic and quantitative meta-analyses investigating both disorders. Results Using single-modality whole-brain and ROI information FDG-PET provided highest accuracy rates for both, detection and differentiation of AD and FTLD compared to structural information from MRI. The ROI-based multimodal classification, combining FDG-PET and MRI information, was highly superior to the unimodal approach and to the whole-brain pattern classification. With this method, accuracy rate of up to 92% for the differentiation of the three groups and an accuracy of 94% for the differentiation of AD and FTLD patients was obtained. Conclusion Accuracy rate obtained using combined information from both imaging modalities is the highest reported up to now for differentiation of both types of dementia. Our results indicate a substantial gain in accuracy using combined FDG-PET and MRI information and suggest the incorporation of such approaches to clinical diagnosis and to differential diagnostic procedures of neurodegenerative disorders.

Obesity-Related Differences between Women and Men in Brain Structure and Goal-Directed Behavior

Gender differences in the regulation of body-weight are well documented. Here, we assessed obesity-related influences of gender on brain structure as well as performance in the Iowa Gambling Task. This task requires evaluation of both immediate rewards and long-term outcomes and thus mirrors the trade-off between immediate reward from eating and the long-term effect of overeating on body-weight. In women, but not in men, we show that the preference for salient immediate rewards in the face of negative long-term consequences is higher in obese than in lean subjects. In addition, we report structural differences in the left dorsal striatum (i.e., putamen) and right dorsolateral prefrontal cortex for women only. Functionally, both regions are known to play complimentary roles in habitual and goal-directed control of behavior in motivational contexts. For women as well as men, gray matter volume correlates positively with measures of obesity in regions coding the value and saliency of food (i.e., nucleus accumbens, orbitofrontal cortex) as well as in the hypothalamus (i.e., the brains central homeostatic center). These differences between lean and obese subjects in hedonic and homeostatic control systems may reflect a bias in eating behavior toward energy-intake exceeding the actual homeostatic demand. Although we cannot infer from our results the etiology of the observed structural differences, our results resemble neural and behavioral differences well known from other forms of addiction, however, with marked differences between women and men. These findings are important for designing gender-appropriate treatments of obesity and possibly its recognition as a form of addiction.

Sex-Dependent Influences of Obesity on Cerebral White Matter Investigated by Diffusion-Tensor Imaging

Several studies have shown that obesity is associated with changes in human brain function and structure. Since women are more susceptible to obesity than men, it seems plausible that neural correlates may also be different. However, this has not been demonstrated so far. To address this issue, we systematically investigated the brains white matter (WM) structure in 23 lean to obese women (mean age 25.5 y, std 5.1 y; mean body mass index (BMI) 29.5 kg/m2, std 7.3 kg/m2) and 26 lean to obese men (mean age 27.1 y, std 5.0 y; mean BMI 28.8 kg/m2, std 6.8 kg/m2) with diffusion-weighted magnetic resonance imaging (MRI). There was no significant age (p>0.2) or BMI (p>0.7) difference between female and male participants. Using tract-based spatial statistics, we correlated several diffusion parameters including the apparent diffusion coefficient, fractional anisotropy (FA), as well as axial (λ∥) and radial diffusivity (λ⊥) with BMI and serum leptin levels. In female and male subjects, the putative axon marker λ∥ was consistently reduced throughout the corpus callosum, particularly in the splenium (r = −0.62, p<0.005). This suggests that obesity may be associated with axonal degeneration. Only in women, the putative myelin marker λ⊥ significantly increased with increasing BMI (r = 0.57, p<0.005) and serum leptin levels (r = 0.62, p<0.005) predominantly in the genu of the corpus callosum, suggesting additional myelin degeneration. Comparable structural changes were reported for the aging brain, which may point to accelerated aging of WM structure in obese subjects. In conclusion, we demonstrate structural WM changes related to an elevated body weight, but with differences between men and women. Future studies on obesity-related functional and structural brain changes should therefore account for sex-related differences.

Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies

FDG-PET ([18F]fluorodeoxyglucose positron emission tomography) is frequently used to improve the differential diagnosis of dementia. However, a fundamental methodological issue of the reference area for the intensity normalization procedure is still unsolved. Here, we systematically compared the two most commonly used normalization methods to the cerebral and to the cerebellar metabolic rate for glucose with regard to detection and differentiation of dementia syndromes. FDG-PET imaging was performed on 19 subjects with early Alzheimers disease, 13 subjects with early frontotemporal lobar degeneration and 10 subjects complaining of memory impairment, which had not been confirmed by comprehensive clinical testing. Images were normalized to either the cerebral or the cerebellar metabolic rate for glucose. Differences in relative regional glucose metabolism were assessed by voxelwise comparison. Analysis using the two normalization procedures revealed remarkable differential effects. Whereas cerebellar normalization was superior in identifying dementia patients in comparison to control subjects, cerebral normalization showed better results for differential diagnosis between types of dementia. These effects were shown for both, Alzheimers disease and frontotemporal lobar degeneration. Relative hypermetabolism in comparison to the control group was only detected in both kinds of dementia using global normalization. The results indicate that normalization has a decisive impact on diagnostic accuracy in dementia. While cerebellar normalization seems to be more sensitive for early diagnosis, cerebral global normalization might be superior for differential diagnostic purposes in dementia syndromes.

Reward processing in obesity, substance addiction and non-substance addiction

Similarities and differences between obesity and addiction are a prominent topic of ongoing research. We conducted an activation likelihood estimation meta‐analysis on 87 studies in order to map the functional magnetic resonance imaging (fMRI) response to reward in participants with obesity, substance addiction and non‐substance (or behavioural) addiction, and to identify commonalities and differences between them. Our study confirms the existence of alterations during reward processing in obesity, non‐substance addiction and substance addiction. Specifically, participants with obesity or with addictions differed from controls in several brain regions including prefrontal areas, subcortical structures and sensory areas. Additionally, participants with obesity and substance addictions exhibited similar blood‐oxygen‐level‐dependent fMRI hyperactivity in the amygdala and striatum when processing either general rewarding stimuli or the problematic stimuli (food and drug‐related stimuli, respectively). We propose that these similarities may be associated with an enhanced focus on reward – especially with regard to food or drug‐related stimuli – in obesity and substance addiction. Ultimately, this enhancement of reward processes may facilitate the presence of compulsive‐like behaviour in some individuals or under some specific circumstances. We hope that increasing knowledge about the neurobehavioural correlates of obesity and addictions will lead to practical strategies that target the high prevalence of these central public health challenges.

Body weight status, eating behavior, sensitivity to reward/punishment, and gender: Relationships and interdependencies

Behavioral and personality characteristics are factors that may jointly regulate body weight. This study explored the relationship between body mass index (BMI) and self-reported behavioral and personality measures. These measures included eating behavior (based on the Three-Factor Eating Questionnaire; Stunkard and Messick, 1985), sensitivity to reward and punishment (based on the Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scales) (Carver and White, 1994) and self-reported impulsivity (based on the Barratt Impulsiveness Scale-11; Patton et al., 1995). We found an inverted U-shaped relationship between restrained eating and BMI. This relationship was moderated by the level of disinhibited eating. Independent of eating behavior, BIS and BAS responsiveness were associated with BMI in a gender-specific manner with negative relationships for men and positive relationships for women. Together, eating behavior and BIS/BAS responsiveness accounted for a substantial proportion of BMI variance (men: ∼25%, women: ∼32%). A direct relationship between self-reported impulsivity and BMI was not observed. In summary, our results demonstrate a system of linear and non-linear relationships between the investigated factors and BMI. Moreover, body weight status was not only associated with eating behavior (cognitive restraint and disinhibition), but also with personality factors not inherently related to an eating context (BIS/BAS). Importantly, these relationships differ between men and women.

Peptide hormones regulating appetite—focus on neuroimaging studies in humans

In recent years, knowledge about hormonal feedback from the gastrointestinal tract and adipose tissue has increased tremendously. Peptide hormones modulating hunger have been intensively studied, mostly in animals but increasingly also in humans. The first therapeutic agents, such as GLP‐1 analogues, are in successful clinical use for T2D and may beneficially affect hunger and reduce weight. Data from in vitro studies and animals provide detailed insight into regulatory mechanisms leading to peptide secretion and receptor bindings, as well as to the distribution of receptors involved in different parts of the body. With neuroimaging techniques human brain structures have been identified that play a role in hunger, satiety and eating behaviour. These include the primary gustatory (insular) and olfactory (pyriform) cortex and regions with a highly permeable blood‐brain barrier (hypothalamus, brain stem), which facilitates humoral input via gut peptides and leptin. In addition, cerebral networks involved in higher cognitive functions, especially those relevant to reward, pleasure and also addiction (ventral and dorsal striatum, amygdala, orbitofrontal cortex (OFC), prefrontal cortex (PFC)) were shown to be involved. First indications of direct influences of peptide hormones on these networks have become available from neuroimaging studies administrating synthetic PYY, ghrelin and leptin. Insulin also appears to play an important role as a central satiety hormone, and evidence indicating the possibility of central insulin resistance in obesity is available. Copyright