C.H. Kasess

Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies.

Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.

The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling.

Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.

Fully exploratory network ICA (FENICA) on resting-state fMRI data.

Independent component analysis (ICA) is one of the most valuable explorative methods for analyzing resting-state networks (RSNs) in fMRI, representing a data-driven approach that enables decomposition of high-dimensional data into discrete components. Extensions to a group-level suffer from the drawback of evaluating single-subject resting-state components of interest either using a predefined spatial template or via visual inspection. FENICA introduced in the context of group ICA methods is based solely on spatially consistency across subjects directly reflecting similar networks. Therefore, group data can be processed without further visual inspection of the single-subject components or the definition of a template (Schöpf et al., 2009). In this study FENICA was applied to fMRI resting-state data from 28 healthy subjects resulting in eight group RSNs. These RSNs resemble the spatial patterns of the following previously described networks: (1) visual network, (2) default mode network, (3) sensorimotor network, (4) dorsolateral prefrontal network, (5) temporal prefrontal network, (6) basal ganglia network, (7) auditory processing network, and (8) working memory network. This novel analysis approach for identifying spatially consistent networks across a group of subjects does not require manual or template-based selection of single-subject components and, therefore, offers a truly explorative procedure of assessing RSNs.

Model-free fMRI group analysis using FENICA.

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects.

Platelet Serotonin Transporter Function Predicts Default-Mode Network Activity

Background The serotonin transporter (5-HTT) is abundantly expressed in humans by the serotonin transporter gene SLC6A4 and removes serotonin (5-HT) from extracellular space. A blood-brain relationship between platelet and synaptosomal 5-HT reuptake has been suggested, but it is unknown today, if platelet 5-HT uptake can predict neural activation of human brain networks that are known to be under serotonergic influence. Methods A functional magnetic resonance study was performed in 48 healthy subjects and maximal 5-HT uptake velocity (Vmax) was assessed in blood platelets. We used a mixed-effects multilevel analysis technique (MEMA) to test for linear relationships between whole-brain, blood-oxygen-level dependent (BOLD) activity and platelet Vmax. Results The present study demonstrates that increases in platelet Vmax significantly predict default-mode network (DMN) suppression in healthy subjects independent of genetic variation within SLC6A4. Furthermore, functional connectivity analyses indicate that platelet Vmax is related to global DMN activation and not intrinsic DMN connectivity. Conclusion This study provides evidence that platelet Vmax predicts global DMN activation changes in healthy subjects. Given previous reports on platelet-synaptosomal Vmax coupling, results further suggest an important role of neuronal 5-HT reuptake in DMN regulation.

Multiple serial picture presentation with millisecond resolution using a three-way LC-shutter-tachistoscope

Throughout recent years there has been an increasing interest in studying unconscious visual processes. Such conditions of unawareness are typically achieved by either a sufficient reduction of the stimulus presentation time or visual masking. However, there are growing concerns about the reliability of the presentation devices used. As all these devices show great variability in presentation parameters, the processing of visual stimuli becomes dependent on the display-device, e.g. minimal changes in the physical stimulus properties may have an enormous impact on stimulus processing by the sensory system and on the actual experience of the stimulus. Here we present a custom-built three-way LC-shutter-tachistoscope which allows experimental setups with both, precise and reliable stimulus delivery, and millisecond resolution. This tachistoscope consists of three LCD-projectors equipped with zoom lenses to enable stimulus presentation via a built-in mirror-system onto a back projection screen from an adjacent room. Two high-speed liquid crystal shutters are mounted serially in front of each projector to control the stimulus duration. To verify the intended properties empirically, different sequences of presentation times were performed while changes in optical power were measured using a photoreceiver. The obtained results demonstrate that interfering variabilities in stimulus parameters and stimulus rendering are markedly reduced. Together with the possibility to collect external signals and to send trigger-signals to other devices, this tachistoscope represents a highly flexible and easy to set up research tool not only for the study of unconscious processing in the brain but for vision research in general.

P02-38 - Biological alterations during remission of major depressive disorder

Introduction The natural course of Major Depressive Disorder (MDD) encompasses the occurrence of alternating intervals of major depressive episodes and remission. While several abnormalities in neural circuits related to acute MDD have been identified, the neural mechanisms underlying stable remission remain obscure. Objectives Acute MDD is characterized by increased amygdala and subgenual anterior cingulate cortex (sACC) activation and decreased connectivity between the amygdala and the sACC. Consequently, we expect those regions to be affected during remission. Aims To determine whether active counter-regulatory mechanisms are implicated in the maintenance of full remission once antidepressant treatment has been discontinued. Methods Functional and structural magnetic resonance imaging was used to measure brain activation and volume of the amygdala and the sACC. Images were obtained from 38 healthy subjects without any psychiatric life-time diagnosis and 38 gender-matched drug-free remitted MDD patients. Furthermore, correlation analyses were performed with clinical variables. Results Patients with rMDD exhibited lower activation in the amygdala and the sACC and increased functional coupling between the amygdala and sACC compared to controls. This connectivity was particularly pronounced in patients characterized by a long cumulated time of depressive episodes. Similarly, structural connectivity results showed increased association between the amygdala and sACC volume in rMDD patients compared to controls. Conclusions Remitted MDD is related to neural alterations within a neural circuit encompassing the amygdala and the sACC compared to controls. These findings suggest active counter-regulatory mechanisms likely contributing to the maintenance of remission once treatment has been discontinued.

FC07-06 - Differences in the modulatory role of escitalopram and citalopram revealed by effective connectivity analysis

Introduction Citalopram is a widely applied SSRI in patients suffering from affective disorder. It is a racemic mixture of the S- and R-enantiomer of citalopram, consisting of equal parts of S-citalopram and R-citalopram, respectively. It has been shown that the inhibitory potency in serotonin reuptake of S-citalopram is much higher compared to R-citalopram, and it is assumed that S-citalopram is the main carrier of the antidepressant effect. Objectives Here we investigated the effects of the two SSRIs Citalopram (50% S-, 50% R-citalopram) and Escitalopram (100% S-citalopram) on brain networks during emotion processing using pharmacological functional magnetic resonance imaging (fMRI) and dynamic causal modelling (DCM), an advanced tool to investigate functional integration between different brain regions. Methods Our results are based on a placebo-controlled, randomized, double-blind, cross-over pharmacological study in 16 healthy subjects during three fMRI scanning sessions performing a facial emotional discrimination paradigm (Windischberger, Neuroimage, 2010). 32 models of pharmacological modulation within the amygdalar-parahippocampal-orbitofrontal network were analysed using Bayesian Model Averaging (BMA) as implemented in SPM8. Results S-citalopram showed statistically significant modulatory effects on forward amygdala-orbitofrontal and bidirectional amygdala-parahippocampal connections. No significant modulatory effects of R-citalopram were found. Conclusions This is the first fMRI study that showed stimulus-specific differential effects of the two enantiomeres R- and S-citalopram at the neural connectivity level. Our results corroborate studies in rats where escitalopram-induced increases in extracellular serotonin levels were found attenuated when R-citalopram was coinjected. Taken together this might explain the response differences between study drugs as demonstrated in previous clinical trials.