Martin Weygandt

Investigation of mindfulness meditation practitioners with voxel-based morphometry

Mindfulness meditators practice the non-judgmental observation of the ongoing stream of internal experiences as they arise. Using voxel-based morphometry, this study investigated MRI brain images of 20 mindfulness (Vipassana) meditators (mean practice 8.6 years; 2 h daily) and compared the regional gray matter concentration to that of non-meditators matched for sex, age, education and handedness. Meditators were predicted to show greater gray matter concentration in regions that are typically activated during meditation. Results confirmed greater gray matter concentration for meditators in the right anterior insula, which is involved in interoceptive awareness. This group difference presumably reflects the training of bodily awareness during mindfulness meditation. Furthermore, meditators had greater gray matter concentration in the left inferior temporal gyrus and right hippocampus. Both regions have previously been found to be involved in meditation. The mean value of gray matter concentration in the left inferior temporal gyrus was predictable by the amount of meditation training, corroborating the assumption of a causal impact of meditation training on gray matter concentration in this region. Results suggest that meditation practice is associated with structural differences in regions that are typically activated during meditation and in regions that are relevant for the task of meditation.

The role of neural impulse control mechanisms for dietary success in obesity

Deficits in impulse control are discussed as key mechanisms for major worldwide health problems such as drug addiction and obesity. For example, obese subjects have difficulty controlling their impulses to overeat when faced with food items. Here, we investigated the role of neural impulse control mechanisms for dietary success in middle-aged obese subjects. Specifically, we used a food-specific delayed gratification paradigm and functional magnetic resonance imaging to measure eating-related impulse-control in middle-aged obese subjects just before they underwent a twelve-week low calorie diet. As expected, we found that subjects with higher behavioral impulse control subsequently lost more weight. Furthermore, brain activity before the diet in VMPFC and DLPFC correlates with subsequent weight loss. Additionally, a connectivity analysis revealed that stronger functional connectivity between these regions is associated with better dietary success and impulse control. Thus, the degree to which subjects can control their eating impulses might depend on the interplay between control regions (DLPFC) and regions signaling the reward of food (VMPFC). This could potentially constitute a general mechanism that also extends to other disorders such as drug addiction or alcohol abuse.

Diagnosing different binge-eating disorders based on reward-related brain activation patterns

This study addresses how visual food cues are encoded in reward related brain areas and whether this encoding might provide information to differentiate between patients suffering from eating disorders [binge‐eating disorder (BED) and bulimia nervosa (BN)], overweight controls (C‐OW), and normal‐weight controls (C‐NW). Participants passively viewed pictures of food stimuli and neutral stimuli in a cue reactivity design. Two classification analyses were conducted. First, we used multivariate pattern recognition techniques to decode the category of a currently viewed picture from local brain activity patterns. In the second analysis, we applied an ensemble classifier to predict the clinical status of subjects (BED, BN, C‐OW, and C‐NW) based on food‐related brain response patterns. The left insular cortex separated between food and neutral contents in all four groups. Patterns in the right insular cortex provided a maximum diagnostic accuracy for the separation of BED patients and C‐NW (86% accuracy, P < 10−5, 82% sensitivity, and 90% specificity) as well as BN patients and C‐NW (78% accuracy, P = 0.001, 86% sensitivity, and 70% specificity). The right ventral striatum separated maximally between BED patients and C‐OW (71% accuracy, P = 0.013, 59% sensitivity, and 82% specificity). The right lateral orbitofrontal cortex separated maximally between BN patients and C‐OW (86% accuracy, P < 10−4, 79% sensitivity, and 94% specificity). The best differential diagnostic separation between BED and BN patients was obtained in the left ventral striatum (84% accuracy, P < 10−3, 82% sensitivity, and 86% specificity). Our results indicate that pattern recognition techniques are able to contribute to a reliable differential diagnosis of BN and BED. Hum Brain Mapp 33:2135–2146, 2012.

Multi-scale classification of disease using structural MRI and wavelet transform.

Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i.e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.

MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas

Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsingremitting type) in lesioned areas, areas of normalappearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers∗

This study investigates the prediction of mild cognitive impairment‐to‐Alzheimers disease (MCI‐to‐AD) conversion based on extensive multimodal data with varying degrees of missing values.

Emotion modulates the effects of endogenous attention on retinotopic visual processing

A fundamental challenge for organisms is how to focus on perceptual information relevant to current goals while remaining able to respond to goal-irrelevant stimuli that signal potential threat. Here, we studied how visual threat signals influence the effects of goal-directed spatial attention on the retinotopic distribution of processing resources in early visual cortex. We used a combined blocked and event-related functional magnetic resonance imaging paradigm with target displays comprising diagonal pairs of intact and scrambled faces presented simultaneously in the four visual field quadrants. Faces were male or female and had fearful or neutral emotional expressions. Participants attended covertly to a pair of two diagonally opposite stimuli and performed a gender-discrimination task on the attended intact face. In contrast to the fusiform face area, where attention and fearful emotional expression had additive effects, neural responses to attended and unattended fearful faces were indistinguishable in early retinotopic visual areas: When attended, fearful face expression did not further enhance responses, whereas when unattended, fearful expression increased responses to the level of attended face stimuli. Remarkably, the presence of fearful stimuli augmented the enhancing effect of attention on retinotopic responses to neutral faces in remote visual field locations. We conclude that this redistribution of neural activity in retinotopic visual cortex may serve the purpose of allocating processing resources to task-irrelevant threat-signaling stimuli while at the same time increasing resources for task-relevant stimuli as required for the maintenance of goal-directed behavior.

Increasing the spatial resolution and sensitivity of magnetic resonance elastography by correcting for subject motion and susceptibility-induced image distortions

To improve the resolution of elasticity maps by adapting motion and distortion correction methods for phase‐based magnetic resonance imaging (MRI) contrasts such as magnetic resonance elastography (MRE), a technique for measuring mechanical tissue properties in vivo.

Real-time fMRI pattern-classification using artificial neural networks

Background: Cerebral vasospasm is a major complication of aneurysmal subarachnoid hemorrhage (SAH) and may cause delayed ischemic neurological deficits (DIND). Nimodipine improves the clinical outcome following SAH. A spasmolytic effect of systemic nimodipine administration, however, has not yet been reported. Methods: We prospectively monitored 20 patients with aneurysmal SAH for the occurrence and severity of cerebral vasospasm by use of transcranial Doppler and duplex ultrasound. All subjects received oral applications of nimodipine. Upon development of vasospasm, the oral medication was replaced by intravenous nimodipine (48 mg/day). Results: Seventeen of the 20 patients developed cerebral vasospasm. Replacement of oral nimodipine by intravenous nimodipine was associated with a significant reduction of peak systolic flow velocities (PSV) in spastic but not in non-spastic cerebral vessels ( 22.3 ± 3.9% vs. 4.4 ± 4.6%; p < 0.01). In some cases oralization of intravenous treatment led to a relapse of vasospasm (PSV +46.5 ± 6.6% vs. +0.2 ± 58%; p < 0.001). Conclusion: Intravenous but not oral application of nimodipine reduces the severity of cerebral vasospasms following aneurysmal SAH. Future research may elucidate the impact of the pharmaceutical form on the frequency of vasospastic ischemic lesions in patients with SAH.

Brain activity, regional gray matter loss, and decision-making in multiple sclerosis:

Background: Decision-making (DM) abilities deteriorate with multiple sclerosis (MS) disease progression which impairs everyday life and is thus clinically important. Objective: To investigate the underlying neurocognitive processes and their relation to regional gray matter (GM) loss induced by MS. Methods: We used a functional magnetic resonance imaging (fMRI) Iowa Gambling Task to measure DM-related brain activity in 36 MS patients and 21 healthy controls (HC). From this activity, we determined neural parameters of two cognitive stages, a deliberation (“choice”) period preceding a choice and a post-choice (“feedback”) stage reporting decision outcomes. These measures were related to DM separately in intact and damaged GM areas as determined by a voxel-based morphometry analysis. Results: Severely affected patients (with high lesion burden) showed worse DM-learning than HC (t = −1.75, p = 0.045), moderately affected (low lesion burden) did not. Activity in the choice stage in intact insular (t = 4.60, pFamily-Wise Error [FWE] corrected = 0.034), anterior cingulate (t = 4.50, pFWE = 0.044), and dorsolateral prefrontal areas (t = 4.43, pFWE = 0.049) and in insular areas with GM loss (t = 3.78, pFWE = 0.011) was positively linked to DM performance across patients with severe tissue damage and HC. Furthermore, activity in intact orbitofrontal areas was positively linked to DM-learning during the feedback stage across these participants (t = 4.49, pFWE = 0.032). During none of the stages, moderately affected patients showed higher activity than HC, which might have indicated preserved DM due to compensatory activity. Conclusion: We identified dysregulated activity linked to impairment in specific cognitive stages of reward-related DM. The link of brain activity and impaired DM in areas with MS-induced GM loss suggests that this deficit might be tightly coupled to MS neuropathology.