Steffen Bollmann

Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD)

OBJECTIVE Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. METHODS This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. RESULTS ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHD children were not classified convincingly with these markers. CONCLUSIONS Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. SIGNIFICANCE Spectral biomarkers may have both diagnostic and prognostic value.

Developmental changes in gamma-aminobutyric acid levels in attention-deficit/hyperactivity disorder

While the neurobiological basis and developmental course of attention-deficit/hyperactivity disorder (ADHD) have not yet been fully established, an imbalance between inhibitory/excitatory neurotransmitters is thought to have an important role in the pathophysiology of ADHD. This study examined the changes in cerebral levels of GABA+, glutamate and glutamine in children and adults with ADHD using edited magnetic resonance spectroscopy. We studied 89 participants (16 children with ADHD, 19 control children, 16 adults with ADHD and 38 control adults) in a subcortical voxel (children and adults) and a frontal voxel (adults only). ADHD adults showed increased GABA+ levels relative to controls (P=0.048), while ADHD children showed no difference in GABA+ in the subcortical voxel (P>0.1), resulting in a significant age by disorder interaction (P=0.026). Co-varying for age in an analysis of covariance model resulted in a nonsignificant age by disorder interaction (P=0.06). Glutamine levels were increased in children with ADHD (P=0.041), but there was no significant difference in adults (P>0.1). Glutamate showed no difference between controls and ADHD patients but demonstrated a strong effect of age across both groups (P<0.001). In conclusion, patients with ADHD show altered levels of GABA+ in a subcortical voxel which change with development. Further, we found increased glutamine levels in children with ADHD, but this difference normalized in adults. These observed imbalances in neurotransmitter levels are associated with ADHD symptomatology and lend new insight in the developmental trajectory and pathophysiology of ADHD.

The PhysIO toolbox for modeling physiological noise in fMRI data

BACKGROUND Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Echo time-dependent quantitative susceptibility mapping contains information on tissue properties

Magnetic susceptibility is a physical property of matter that varies depending on chemical composition and abundance of different molecular species. Interest is growing in mapping of magnetic susceptibility in the human brain using magnetic resonance imaging techniques, but the influences affecting the mapped values are not fully understood.

Subcortical Glutamate Mediates the Reduction of Short-Range Functional Connectivity with Age in a Developmental Cohort

Marked changes in brain physiology and structure take place between childhood and adulthood, including changes in functional connectivity and changes in the balance between main excitatory and inhibitory neurotransmitters glutamate (Glu) and GABA. The balance of these neurotransmitters is thought to underlie neural activity in general and functional connectivity networks in particular, but so far no studies have investigated the relationship between human development related differences in these neurotransmitters and concomitant changes in functional connectivity. GABA+/H2O and Glu/H2O levels were acquired in a group of healthy children, adolescents, and adults in a subcortical (basal ganglia) region, as well as in a frontal region in adolescents and adults. Our results showed higher GABA+/Glu with age in both the subcortical and the frontal voxel, which were differentially associated with significantly lower Glu/H2O with age in the subcortical voxel and by significantly higher GABA+/H2O with age in the frontal voxel. Using a seed-to-voxel analysis, we were further able to show that functional connectivity between the putamen (seed) and other subcortical structures was lower with age. Lower subcortical Glu/H2O with age mediated the lower connectivity in the dorsal putamen. Based on these results, and the potential role of Glu in synaptic pruning, we suggest that lower Glu mediates a reduction of local connectivity during human development.

Age-dependent and -independent changes in attention-deficit/hyperactivity disorder (ADHD) during spatial working memory performance

Abstract Objectives: Attention-deficit/hyperactivity disorder (ADHD) has been associated with spatial working memory as well as frontostriatal core deficits. However, it is still unclear how the link between these frontostriatal deficits and working memory function in ADHD differs in children and adults. This study examined spatial working memory in adults and children with ADHD, focussing on identifying regions demonstrating age-invariant or age-dependent abnormalities. Methods: We used functional magnetic resonance imaging to examine a group of 26 children and 35 adults to study load manipulated spatial working memory in patients and controls. Results: In comparison to healthy controls, patients demonstrated reduced positive parietal and frontostriatal load effects, i.e., less increase in brain activity from low to high load, despite similar task performance. In addition, younger patients showed negative load effects, i.e., a decrease in brain activity from low to high load, in medial prefrontal regions. Load effect differences between ADHD and controls that differed between age groups were found predominantly in prefrontal regions. Age-invariant load effect differences occurred predominantly in frontostriatal regions. Conclusions: The age-dependent deviations support the role of prefrontal maturation and compensation in ADHD, while the age-invariant alterations observed in frontostriatal regions provide further evidence that these regions reflect a core pathophysiology in ADHD.

Effects of Steroid Hormones on Sex Differences in Cerebral Perfusion

Sex differences in the brain appear to play an important role in the prevalence and progression of various neuropsychiatric disorders, but to date little is known about the cerebral mechanisms underlying these differences. One widely reported finding is that women demonstrate higher cerebral perfusion than men, but the underlying cause of this difference in perfusion is not known. This study investigated the putative role of steroid hormones such as oestradiol, testosterone, and dehydroepiandrosterone sulphate (DHEAS) as underlying factors influencing cerebral perfusion. We acquired arterial spin labelling perfusion images of 36 healthy adult subjects (16 men, 20 women). Analyses on average whole brain perfusion levels included a multiple regression analysis to test for the relative impact of each hormone on the global perfusion. Additionally, voxel-based analyses were performed to investigate the sex difference in regional perfusion as well as the correlations between local perfusion and serum oestradiol, testosterone, and DHEAS concentrations. Our results replicated the known sex difference in perfusion, with women showing significantly higher global and regional perfusion. For the global perfusion, DHEAS was the only significant predictor amongst the steroid hormones, showing a strong negative correlation with cerebral perfusion. The voxel-based analyses revealed modest sex-dependent correlations between local perfusion and testosterone, in addition to a strong modulatory effect of DHEAS in cortical, subcortical, and cerebellar regions. We conclude that DHEAS in particular may play an important role as an underlying factor driving the difference in cerebral perfusion between men and women.

Accelerated mapping of magnetic susceptibility using 3D planes‐on‐a‐paddlewheel (POP) EPI at ultra‐high field strength

With the advent of ultra‐high field MRI scanners in clinical research, susceptibility based MRI has recently gained increasing interest because of its potential to assess subtle tissue changes underlying neurological pathologies/disorders. Conventional, but rather slow, three‐dimensional (3D) spoiled gradient‐echo (GRE) sequences are typically employed to assess the susceptibility of tissue. 3D echo‐planar imaging (EPI) represents a fast alternative but generally comes with echo‐time restrictions, geometrical distortions and signal dropouts that can become severe at ultra‐high fields.

A GPU-accelerated Performance Optimized RAP-MUSIC Algorithm for Real-Time Source Localization

C. Dinh, Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany, christoph.dinh@tu-ilmenau.de J. Rühle, Department of Mathematics and Computer Sciences, Friedrich Schiller University Jena, Jena, Germany, johannes.ruehle@tu-ilmenau.de S. Bollmann, Zentrum für MR-Forschung, Kinderspital Zürich, Zurich, Switzerland, steffen.bollmann@kispi.uzh.ch J. Haueisen, Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Germany, jens.haueisen@tu-ilmenau.de D. Güllmar, Department of Radiology, Jena University Hospital, Jena, Germany, daniel.guellmar@med.uni-jena.de

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements. The mapping of magnetic susceptibility in vivo has gained broad interest in several fields of science and medicine because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium. Thereby, QSM can also reveal pathological changes of these key components in devastating diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. As QSM requires the solution of an ill-posed field-to-source-inversion, current techniques utilize manual optimization of regularization parameters to balance between smoothing, artifacts and quantification accuracy. We trained a fully convolutional deep neural network - DeepQSM - to invert the magnetic dipole kernel convolution. This network is capable of solving the ill-posed field-to-source inversion on real-world in vivo MRI phase data without the need for manual parameter tuning, which proves that this network has generalized the underlying mathematical principle of the dipole inversion. We demonstrate that DeepQSM’s susceptibility maps enable identification of deep brain substructures that are not visible in MRI phase data and provide information on their respective magnetic tissue properties. We illustrate DeepQSM’s clinical relevance in a patient with multiple sclerosis showing its sensitivity to white matter lesions. In summary, DeepQSM can be used to determine the composition of myelin sheets of nerve fibers in the brain, and to assess quantitative information on iron homeostasis and its dysregulation, and will subsequently contribute to a better understanding of these biological processes in health and disease.