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Background: Electroencephalography (EEG) and single-pulse transcranial magnetic stimulation (spTMS) of the primary motor hand area (M1-HAND) have been combined to explore whether the instantaneous expression of pericentral mu-rhythm drives fluctuations in corticomotor excitability, but this line of research has yielded diverging results.Objectives: To re-assess the relationship between the mu-rhythm power expressed in left pericentral cortex and the amplitude of motor potentials (MEP) evoked with spTMS in left M1-HAND.Methods: 15 non-preselected healthy young participants received spTMS to the motor hot spot of left M1-HAND. Regional expression of mu-rhythm was estimated online based on a radial source at motor hotspot and informed the timing of spTMS which was applied either during epochs belonging to the highest or lowest quartile of regionally expressed mu-power. Using MEP amplitude as dependent variable, we computed a linear mixed-effects model, which included mu-power and mu-phase at the time of stimulation and the inter-stimulus interval (ISI) as fixed effects and subject as a random effect. Mu-phase was estimated by post-hoc sorting of trials into four discrete phase bins. We performed a follow-up analysis on the same EEG-triggered MEP data set in which we isolated mu-power at the sensor level using a Laplacian montage centered on the electrode above the M1-HAND.Results: Pericentral mu-power traced as radial source at motor hot spot did not significantly modulate the MEP, but mu-power determined by the surface Laplacian did, showing a positive relation between mu-power and MEP amplitude. In neither case, there was an effect of mu-phase on MEP amplitude.Conclusion: The relationship between cortical oscillatory activity and cortical excitability is complex and minor differences in the methodological choices may critically affect sensitivity.

Research in primates and rodents ascribes the striatum a critical role in integrating elementary movements into unitary action sequences through reinforcement-based learning. Yet it remains to be shown whether the human striatum represents action sequence-specific information. Young right-handed volunteers underwent functional magnetic resonance imaging while they performed four discrete finger sequences with their right hand, consisting of five button presses. Specific finger sequences could be discriminated based on the distributed activity patterns in left and right striatum, but not by average differences in single-voxel activity. Multiple bilateral clusters in putamen and caudate nucleus belonging to motor, associative, parietal and limbic territories contributed to classification sensitivity. The results show that individual finger movement sequences are widely represented in human striatum, supporting functional integration rather than segregation. The findings are compatible with the idea that the basal ganglia simultaneously integrate motor, associative and limbic aspects in the control of complex overlearned behaviour.

BackgroundParkinson’s disease (PD) leads to a loss of neuromelanin positive, noradrenergic neurons in the locus coeruleus (LC) which has been implicated in non-motor dysfunction. “Neuromelanin sensitive” magnetic resonance imaging (MRI) has emerged as a promising tool for mapping the structural integrity of LC in vivo.ObjectivesTo identify spatial patterns of structural LC disintegration in PD and regions in the LC where structural disintegration is associated with specific non-motor dysfunctions.Methods42 patients with PD and 24 age-matched healthy volunteers underwent ultra-high field MRI of the LC using a “neuromelanin sensitive” magnetization transfer weighted (MTw) sequence. The contrast-to-noise ratio of the MTw signal (CNRMTw) served as an estimate of structural integrity, slice- and voxel-wise analyses of CNRMTw were performed to map the spatial pattern of structural disintegration, complemented by Principal Component Analysis (PCA). We also tested for correlations between CNRMTw and the severity of non-motor symptoms.ResultsMean CNRMTw of LC was reduced in patients relative to controls. The attenuation of CNRMTw was not uniformly expressed in LC, but confined to the middle and caudal LC. CNRMTw attenuation in caudal LC scaled with the orthostatic drop in systolic blood pressure and apathy ratings. PCA identified a bilaterally expressed component that was more weakly expressed in patients. This component was characterized by a gradual change in CNRMTw along the rostro-caudal and dorso-ventral axes of the nucleus. The individual expression score of this component reflected the overall severity of non-motor symptoms.ConclusionPD related structural disintegration of LC mainly affects its caudal part and may determine the individual expression of specific non-motor symptoms such as orthostatic dysregulation or apathy.

Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within‐site and between‐site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting‐state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within‐site generalizability of the classification framework in the main data set using cross‐validation. Then, we trained a model in the main data set and investigated between‐site generalization in the validated data set using external validation. Finally, recognizing the poor between‐site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between‐site classification performance. Cross‐validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within‐site cross‐validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.

BackgroundThe motor potentials evoked by transcranial magnetic stimulation (TMS) over the motor hand area (M1-HAND) show substantial inter-trial variability. Pericentral mu-rhythm oscillations, might contribute to inter-trial variability. Recent studies targeting mu-activity based on real-time electroencephalography (EEG) reported an influence of mu-power and mu-phase on the amplitude of motor evoked potentials (MEPs) in a preselected group with strong pericentral mu-activity. Other studies that determined mu-power or mu-phase based on post-hoc trial sorting according in non-preselected individuals were largely negative.ObjectivesTo reassess if cortico-spinal activity is modulated by the mu-rhythm, we applied single-pulse TMS to the M1-HAND conditional on the phase of the intrinsically expressed pericentral mu-rhythm in 14 non-preselected healthy young participants.MethodsTMS was given at 0, 90, 180, and 270 degrees of the mu-phase. Based on the absence of effects of mu-phase or mu-power when analyzing the mean MEP amplitudes, we also computed a linear mixed effects model, which included mu-phase, mu-power, inter-stimulus interval (ISIs) as fixed effects, treating the subject factor as a random effect.ResultsMixed model analysis revealed a significant effect of mu-power and ISI, but no effect of mu-phase and no interactions. MEP amplitude scaled linearly with lower mu-power or longer ISIs, but these modulatory effects were very small relative to inter-trial MEP variability.ConclusionOur largely negative results are in agreement with previous offline TMS-EEG studies and point to a possible influence of ISI. Future research needs to clarify under which circumstances the responsiveness of human the M1-HAND to TMS depends on the synchronicity with mu-power and mu-phase.HighlightsPhase-triggered TMS at four distinct phases of the ongoing mu-oscillations is technically feasible in non-preselected young volunteersTargeting the ongoing mu-activity did not reveal consistent modulatory effect of mu-phase on corticospinal excitability in a non-preselected groupMixed-effects analysis revealed a weak but significant effect of pre-stimulus mu-power and ISI on corticospinal excitability

Endoscopic surgery requires skilled bimanual use of complex instruments that extend the peri-personal workspace. To delineate brain structures involved in learning such surgical skills, 48 medical students without surgical experience were randomly assigned to five training sessions on a virtual-reality endoscopy simulator or to a non-training group. Brain activity was probed with functional MRI while participants performed endoscopic tasks. Repeated task performance in the scanner was sufficient to enhance task-related activity in left ventral premotor cortex (PMv) and the anterior Intraparietal Sulcus (aIPS). Simulator training induced additional increases in task-related activation in right PMv and aIPS and reduced effective connectivity from left to right PMv. Skill improvement after training scaled with stronger task-related activation of the lateral left primary motor hand area (M1-HAND). The results suggest that a bilateral fronto-parietal grasping network and left M1-HAND are engaged in bimanual learning of tool-based manipulations in an extended peri-personal space.

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

The experimental manipulation of neural activity by neurostimulation techniques overcomes the inherent limitations of correlative recordings, enabling the researcher to investigate causal brain-behavior relationships. But only when stimulation and recordings are combined, the direct impact of the stimulation on neural activity can be evaluated. In humans, this can be achieved non-invasively through the concurrent combination of transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI). Concurrent TMS-fMRI allows the assessment of the neurovascular responses evoked by TMS with excellent spatial resolution and full-brain coverage. This enables the functional mapping of both local and remote network effects of TMS in cortical as well as deep subcortical structures, offering unique opportunities for basic research and clinical applications. The purpose of this review is to introduce the reader to this powerful tool. We will introduce the technical challenges and state-of-the art solutions and provide a comprehensive overview of the existing literature and the available experimental approaches. We will highlight the unique insights that can be gained from concurrent TMS-fMRI, including the state-dependent assessment of neural responsiveness and inter-regional effective connectivity, the demonstration of functional target engagement, and the systematic evaluation of stimulation parameters. We will also discuss how concurrent TMS-fMRI during a behavioral task can help to link behavioral TMS effects to changes in neural network activity and to identify peripheral co-stimulation confounds. Finally, we will review the use of concurrent TMS-fMRI for developing TMS treatments of psychiatric and neurological disorders and suggest future improvements for further advancing the application of concurrent TMS-fMRI.

Background: Psychological traumatic experiences can lead to posttraumatic stress disorder (PTSD). Secondary psychotic symptoms are not common but may occur. Objectives: Since psychotic symptoms of schizophrenia have been related to aberrant reward processing in the striatum, using the same paradigm we investigate whether the same finding extends to psychotic and anhedonic symptoms in PTSD. Methods: A total of 70 male refugees: 18 PTSD patients with no secondary psychotic symptoms (PTSD-NSP), 21 PTSD patients with secondary psychotic symptoms (PTSD-SP), and 31 healthy controls (RHC) were interviewed and scanned with functional magnetic resonance imaging (fMRI) during a monetary incentive delay task. Using region of interest analysis of the prefrontal cortex and ventral striatum, we investigated reward-related activity. Results: Compared to RHC, participants with PTSD had decreased neural activity during monetary reward. Also, participants with PTSD-SP exhibited decreased activity in the associative striatum relative to participants with PTSD-NSP during processing of motivational reward anticipation which correlated with severity of psychotic symptoms. However, the difference between the two PTSD groups disappeared when PTSD severity and trauma exposure were accounted for. Conclusions: Anhedonia and secondary psychotic symptoms in PTSD are characterized by dysfunctional reward consumption and anticipation processing, respectively. The latter may reflect a mechanism by which abnormal reward signals in the basal ganglia facilitates psychotic symptoms across psychiatric conditions.

BackgroundElectroencephalography (EEG) and single-pulse transcranial magnetic stimulation (spTMS) of the primary motor hand area (M1-HAND) have been combined to explore whether the instantaneous expression of pericentral mu-rhythm drives fluctuations in corticomotor excitability, but this line of research has yielded diverging results.ObjectivesTo re-assess the relationship between the mu-rhythm power expressed in left pericentral cortex and the amplitude of motor potentials (MEP) evoked with spTMS in left M1-HAND.Methods15 non-preselected healthy young participants received spTMS to the motor hot spot of left M1-HAND. Regional expression of mu-rhythm was estimated online based on a radial source at motor hotspot and informed the timing of spTMS which was applied either during epochs belonging to the highest or lowest quartile of regionally expressed mu-power. Using MEP amplitude as dependent variable, we computed a linear mixed-effects model, which included mu-power and mu-phase at the time of stimulation and the inter-stimulus interval (ISI) as fixed effects and subject as a random effect. Mu-phase was estimated by post-hoc sorting of trials into four discrete phase bins. We performed a follow-up analysis on the same EEG-triggered MEP data set in which we isolated mu-power at the sensor level using a Laplacian montage centered on the electrode above the M1-HAND.ResultsPericentral mu-power traced as radial source at motor hot spot did not significantly modulate the MEP, but mu-power determined by the surface Laplacian did, showing a positive relation between mu-power and MEP amplitude. In neither case, there was an effect of mu-phase on MEP amplitude.ConclusionThe relationship between cortical oscillatory activity and cortical excitability is complex and minor differences in the methodological choices may critically affect sensitivity.

Highlights • The effect of first dopaminergic treatment on the reward circuit in PD is unclear. • Many ON/OFF designs suffer from long-lasting dopaminergic effects (multiple days). • We tested reward processing in dopa-naive PD before and after treatment initiation. • Dopaminergic drugs increased reward-related hippocampus activity in PD. • The relationship with development of neuropsychiatric symptoms remains unknown., Background Parkinson’s disease is associated with severe nigro-striatal dopamine depletion, leading to motor dysfunction and altered reward processing. We previously showed that drug-naïve patients with Parkinson’s disease had a consistent attenuation of reward signalling in the mesolimbic and mesocortical system. Here, we address the neurobiological effects of dopaminergic therapy on reward sensitivity in the mesolimbic circuitry, and how this may contribute to neuropsychiatric symptoms. Objectives We tested the hypothesis that (1) dopaminergic treatment would restore the attenuated, mesolimbic and mesocortical responses to reward; and (2) restoration of reward responsivity by dopaminergic treatment would predict motor performance and the emergence of impulse control symptoms. Methods In 11 drug-naïve Parkinson patients, we prospectively assessed treatment-induced changes in reward processing before, and eight weeks after initiation of monotherapy with dopamine agonists. They were compared to 10 non-medicated healthy controls who were also measured longitudinally. We used whole-brain functional magnetic resonance imaging at 3 Tesla to assess the reward responsivity of the brain to monetary gains and losses, while participants performed a simple consequential gambling task. Results In patients, dopaminergic treatment improved clinical motor symptoms without significantly changing task performance. Dopamine agonist therapy induced a stronger reward responsivity in the right hippocampus with higher doses being less effective. None of the patients developed impulse control disorders in the follow-up period of four years. Conclusions Short-term treatment with first-ever dopaminergic medication partially restores deficient reward-related processing in the hippocampus in de novo Parkinson’s disease.

Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

BackgroundSymptoms of anhedonia are often central to posttraumatic stress disorder (PTSD), but it is unclear how anhedonia is affected by processes induced by reliving past traumatic memories.MethodsSixty-nine male refugees (PTSD = 38) were interviewed and scanned with functional magnetic resonance imaging while viewing positive, neutral and Scrambled Pictures after being read personalized scripts evoking an emotionally neutral memory and a traumatic memory. We further measured postprovocation state symptoms, physiological measures and PTSD symptoms. We tested whether neural activity associated with positive picture viewing in participants with PTSD was differentially affected by symptom provocation compared to controls.ResultsFor the pictures > scrambled contrast (Positive contrast), PTSD participants had significantly less activity than controls in fusiform gyrus, right inferior temporal gyrus and left middle occipital gyrus. The Positive contrast activity in fusiform gyrus scaled negatively with anhedonia symptoms in PTSD participants after controlling for total PTSD severity. Relative to the emotionally Neutral Script, the Trauma Script decreased positive picture viewing activity in posterior cingulate cortex, precuneus and left calcarine gyrus, but there was no difference between PTSD participants and controls.ConclusionsWe found reduced responsiveness of higher visual processing of emotionally positive pictures in PTSD. The significant correlation found between positive picture viewing activity and anhedonia suggests the reduced responsiveness to be due to the severity of anhedonia.

Highlights • Parkinson's patients with and without impulse control disorders gamble similarly. • Patients with ICD have reduced activity in inhibitory control regions. • STN and striatum activity scales with risk in patients without ICD but not with ICD. • Dopamine therapy differently affects the groups' neural activity and connectivity., Dopaminergic treatment may impair the ability to suppress impulsive behaviours in patients with Parkinson's disease, triggering impulse control disorders. It is unclear how dopaminergic medication affects the neural networks that contribute to withholding inappropriate actions. To address this question, we mapped task-related brain activity with whole-brain functional magnetic resonance imaging at 3 Tesla in 26 patients with Parkinson’s disease. Patients performed a sequential gambling task while being ON and OFF their regular dopaminergic treatment. During a gambling round, patients repeatedly decided between the option to continue with gambling and accumulate more monetary reward under increasing risk or the option to bank the current balance and start a new round. 13 patients had an impulse control disorder (ICD + group). These patients did not differ in risk-taking attitude during sequential gambling from 13 patients without impulse control disorder (ICD - group), but they displayed differences in gambling-related activity in cortico-subcortical brain areas supporting inhibitory control. First, the ICD + group showed reduced “continue-to-gamble” activity in right inferior frontal gyrus and subthalamic nucleus. Second, the individual risk-attitude scaled positively with “continue-to-gamble” activity in right subthalamic nucleus and striatum in the ICD - group only. Third, ICD + patients differed in their functional neural responses to dopaminergic treatment from ICD - patients: dopaminergic therapy reduced functional connectivity between inferior frontal gyrus and subthalamic nucleus during “continue-to-gamble” decisions and attenuated striatal responses towards accumulating reward and risk. Together, the medication-independent (trait) and medication-related (state) differences in neural activity may set a permissive stage for the emergence of impulse control disorders during dopamine replacement therapy in Parkinson’s disease.

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Comparing electric field simulations from individualized head models against in-vivo recordings is important for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. This also helps to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines, against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that ignoring uncertainty in the simulations leads to a strong bias in the estimated linear relationship between simulated and measured fields. In addition, even though the simulations between the pipelines differ notably, this is not reflected in the correlation with the measurements. We discuss potential reasons for this apparent mismatch and propose a new Bayesian regression analysis of the data that yields unbiased estimates enabling robust conclusions to be reached.

A patient with motor conversion disorder presented with a functional paresis of the left hand. After exclusion of structural brain damage, she was repeatedly examined with whole-brain functional magnetic resonance imaging, while she performed visually paced finger-tapping tasks. The dorsal premotor cortex showed a bilateral deactivation in the acute-subacute phase. Recovery from unilateral hand paresis was associated with a gradual increase in task-based activation of the dorsal premotor cortex bilaterally. The right medial prefrontal cortex displayed the opposite pattern, showing initial task-based activation that gradually diminished with recovery. The inverse dynamics of premotor and medial prefrontal activity over time were found during unimanual finger-tapping with the affected and non-affected hand as well as during bimanual finger-tapping. These observations suggest that reduced premotor and increased medial prefrontal activity reflect an effector-independent cortical dysfunction in conversion paresis which gradually disappears in parallel with clinical remission of paresis. The results link the medial prefrontal and dorsal premotor areas to the generation of intentional actions. We hypothesise that an excessive ‘veto’ signal generated in medial prefrontal cortex along with decreased premotor activity might constitute the functional substrate of conversion disorder. This notion warrants further examination in a larger group of affected patients.

Phasic dopamine release from mid-brain dopaminergic neurons is thought to signal errors of reward prediction (RPE). If reward maximisation is to maintain homeostasis, then the value of primary rewards should be coupled to the homeostatic errors they remediate. This leads to the prediction that RPE signals should be configured as a function of homeostatic state and thus diminish with the attenuation of homeostatic error. To test this hypothesis, we collected a large volume of functional MRI data from five human volunteers on four separate days. After fasting for 12 hours, subjects consumed preloads that differed in glucose concentration. Participants then underwent a Pavlovian cue-conditioning paradigm in which the colour of a fixation-cross was stochastically associated with the delivery of water or glucose via a gustometer. This design afforded computation of RPE separately for better- and worse-than expected outcomes during ascending and descending trajectories of serum glucose fluctuations. In the parabrachial nuclei, regional activity coding positive RPEs scaled positively with serum glucose for both ascending and descending glucose levels. The ventral tegmental area and substantia nigra became more sensitive to negative RPEs when glucose levels were ascending. Together, the results suggest that RPE signals in key brainstem structures are modulated by homeostatic trajectories of naturally occurring glycaemic flux, revealing a tight interplay between homeostatic state and the neural encoding of primary reward in the human brain.

Functional magnetic resonance imaging (fMRI) is widely used to measure brain function during various cognitive states. However, it remains a challenge to obtain low-rank models of functional networks in fMRI that have interpretable latent features and generalize across groups of subjects, due to significant intersubject variability in the signal structure and noise. Group-level modeling is typically performed using component decompositions such as independent component analysis (ICA), which represent data as a linear combination of latent brain patterns, or using clustering models, where data are assumed to be generated by a set of “prototype” time series. Archetypal analysis (AA) provides a promising alternative, combining the advantages of component-model flexibility with highly interpretable latent “archetypes” (similar to cluster-model prototypes). To date, AA has not been applied to group-level fMRI; a major limitation is that it does not generalize to multi-subject datasets, which may have significant variations in blood oxygenation-level-dependent signal and heteroscedastic noise. We develop multi-subject AA (MS-AA), which accounts for group-level data by assuming that archetypal temporal profiles have a common latent generator across subjects, ensuring that the temporal components are derived from a consistent set of brain regions. In addition, the model accounts for noise heteroscedasticity by modeling subject- and voxel-specific noise variance. This provides a novel approach to group-level modeling and an alternative to preexisting methods that account for inter-subject variability by extracting individual maps as a postprocessing step (e.g., dual-regression ICA), or assuming spatial dependency of maps across subjects (e.g., independent vector analysis). MS-AA shows robust performance when modelling archetypes for a motor task experiment. The procedure extracts a “seed map” across subjects, used to provide brain parcellations with subject-specific temporal profiles. Our approach thus decomposes multisubject fMRI data into distinct interpretable component archetypes that may help to model both consistent group-level measures of fMRI data and individual variability.

The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.

Pathological gambling is an addictive disorder characterized by an irresistible urge to gamble despite severe consequences. One of the hallmarks of pathological gambling is maladaptive and highly risky decision-making, which has been linked to dysregulation of reward-related brain regions such as the ventral striatum. However, previous studies have produced contradictory results regarding the implication of this network, revealing either hypo- or hypersensitivity to monetary gains and losses. One possible explanation is that the gambling brain might be misrepresenting the benefits and costs when weighting the potential outcomes, and not the gains and losses per se. To address this issue, we investigated whether pathological gambling is associated with abnormal brain activity during decisions that weight the utility of possible gains against possible losses. Pathological gamblers and healthy human subjects underwent functional magnetic resonance imaging while they accepted or rejected mixed gain/loss gambles with fifty–fifty chances of winning or losing. Contrary to healthy individuals, gamblers showed a U-shaped response profile reflecting hypersensitivity to the most appetitive and most aversive bets in an executive cortico-striatal network including the dorsolateral prefrontal cortex and caudate nucleus. This network is concerned with the evaluation of action–outcome contingencies, monitoring recent actions and anticipating their consequences. The dysregulation of this specific network, especially for extreme bets with large potentials consequences, offers a novel understanding of the neural basis of pathological gambling in terms of deficient associations between gambling actions and their financial impact.

Background Levodopa-induced dyskinesias are a common side effect of dopaminergic therapy in PD, but their neural correlates remain poorly understood. Objectives This study examines whether dyskinesias are associated with abnormal dopaminergic modulation of resting-state cortico-striatal connectivity. Methods Twelve PD patients with peak-of-dose dyskinesias and 12 patients without dyskinesias were withdrawn from dopaminergic medication. All patients received a single dose of fast-acting soluble levodopa and then underwent resting-state functional magnetic resonance imaging before any dyskinesias emerged. Levodopa-induced modulation of cortico-striatal resting-state connectivity was assessed between the putamen and the following 3 cortical regions of interest: supplementary motor area, primary sensorimotor cortex, and right inferior frontal gyrus. These functional connectivity measures were entered into a linear support vector classifier to predict whether an individual patient would develop dyskinesias after levodopa intake. Linear regression analysis was applied to test which connectivity measures would predict dyskinesia severity. Results Dopaminergic modulation of resting-state connectivity between the putamen and primary sensorimotor cortex in the most affected hemisphere predicted whether patients would develop dyskinesias with a specificity of 100% and a sensitivity of 91% (P < .0001). Modulation of resting-state connectivity between the supplementary motor area and putamen predicted interindividual differences in dyskinesia severity (R2 = 0.627, P = .004). Resting-state connectivity between the right inferior frontal gyrus and putamen neither predicted dyskinesia status nor dyskinesia severity. Conclusions The results corroborate the notion that altered dopaminergic modulation of cortico-striatal connectivity plays a key role in the pathophysiology of dyskinesias in PD. © 2016 International Parkinson and Movement Disorder Society

Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. The measurements also help to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that modeling differences in the pipelines lead to notable differences in the simulated electric field distributions that are often large enough to change the conclusions regarding the dose distribution and strength in the brain. Specifically, differences in the automatic segmentations of the head anatomy from structural magnetic resonance images are a major factor contributing to the observed field differences. However, the differences in the simulated fields are not reflected in the comparison between the simulations and intra-cranial measurements. This apparent mismatch is partly explained by the noisiness of the intra-cranial measurements, which renders comparisons between the methods inconclusive. We further demonstrate that a standard regression analysis, which ignores uncertainties in the simulations, leads to a strong bias in the estimated linear relationship between simulated and measured fields. Ignoring this bias leads to the incorrect conclusion that the models systematically misestimate the field strength in the brain. We propose a new Bayesian regression analysis of the data that yields unbiased parameter estimates, along with their uncertainties, and gives further insights to the fit between simulations and measurements. Specifically, the unbiased results give only weak support for systematic misestimations of the fields by the models.

Understanding the origins of intrinsic time-varying functional connectivity remains a challenge in the neuroimaging community. However, some associations between dynamic functional connectivity (dFC) and behavioral traits have been observed along with gender differences. We propose a permutation testing framework to investigate dynamic differences between groups of subjects. In particular, we investigate differences in fractional occupancy, state persistency and the full transition probability matrix. We demonstrate our framework on resting state functional magnetic resonance imaging data from 820 healthy young adults from the Human Connectome Project considering two prominent dFC models, namely sliding-window k-means and the Gaussian hidden Markov model. The variables showing consistent significant dynamic differences were limited to gender and the degree of motion in the scanner. We observe for the data considered that a large sample size (here 500 subjects) is needed to to draw reliable conclusions about the significance of those variables. Our results point to dynamic features providing limited information with regard to behavioral traits despite a relatively large sample size.

Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate different emission models in the hidden Markov model framework, each representing certain assumptions about dynamic changes in the brain. We evaluate each model by how well they can discriminate between schizophrenic patients and healthy controls based on a group independent component analysis of resting-state functional magnetic resonance imaging data. We find that simple emission models without full covariance matrices can achieve similar classification results as the models with more parameters. This raises questions about the predictability of dynamic functional connectivity in comparison to simpler dynamic features when used as biomarkers. However, we must stress that there is a distinction between characterization and classification, which has to be investigated further.

Transcranial brain stimulation (TBS) techniques such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and others have seen a strong increase as tools in therapy and research within the last 20 years. In order to precisely target the stimulation, it is important to accurately model the individual head anatomy of a subject. Of particular importance is accurate reconstruction of the skull, as it has the strongest impact on the current pathways due to its low conductivity. Thus providing automated tools, which can reliably reconstruct the anatomy of the human head from magnetic resonance (MR) scans would be highly valuable for the application of transcranial stimulation methods. These head models can also be used to inform source localization methods such as EEG and MEG. Automated segmentation of the skull from MR images is, however, challenging as the skull emits very little signal in MR. In order to avoid topological defects, such as holes in the segmentations, a strong model of the skull shape is needed. In this paper we propose a new shape model for skull segmentation based on the so-called convolutional restricted Boltzmann machines (cRBMs). Compared to traditionally used lower-order shape models, such as pair-wise Markov random fields (MRFs), the cRBMs model local shapes in larger spatial neighborhoods while still allowing for efficient inference. We compare the skull segmentation accuracy of our approach to two previously published methods and show significant improvement.

In neuroimaging, it has become evident that models of dynamic functional connectivity (dFC), which characterize how intrinsic brain organization changes over time, can provide a more detailed representation of brain function than traditional static analyses. Many dFC models in the literature represent functional brain networks as a meta-stable process with a discrete number of states; however, there is a lack of consensus on how to perform model selection and learn the number of states, as well as a lack of understanding of how different modeling assumptions influence the estimated state dynamics. To address these issues, we consider a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data. Examining several prominent models of dFC (in their probabilistic formulations) we demonstrate our framework on synthetic data, and apply it on two real-world examples: a face recognition EEG experiment and resting-state fMRI. Our results evidence that both EEG and fMRI are better characterized using dynamic modeling approaches than by their static counterparts, but we also demonstrate that one must be cautious when interpreting dFC because parameter settings and modeling assumptions, such as window lengths and emission models, can have a large impact on the estimated states and consequently on the interpretation of the brain dynamics.

People are more sensitive to losses than to equivalent gains when making financial decisions. We used functional magnetic resonance imaging (fMRI) to illuminate how the amygdala contributes to loss aversion. The blood oxygen level dependent (BOLD) response of the amygdala was mapped while healthy individuals were responding to 50/50 gambles with varying potential gain and loss amounts. Overall, subjects demanded twice as high potential gain as loss to accept a gamble. The individual level of loss aversion was expressed by the decision boundary, i.e., the gain-loss ratio at which subjects accepted and rejected gambles with equal probability. Amygdala activity increased the more the gain-loss ratio deviated from the individual decision boundary showing that the amygdala codes action value. This response pattern was more strongly expressed in loss aversive individuals, linking amygdala activity with individual differences in loss aversion. Together, the results show that the amygdala signals subjective appetitiveness or aversiveness of gain-loss ratios at the time of choice. (C) 2015 Elsevier Ltd. All rights reserved.

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained to solve visual recognition tasks. As a working example of transfer learning, a system able to accurately classify sleep stages in new unseen patients is presented. Evaluations in a widely-used publicly available dataset favourably compare to state-of-the-art results, while providing a framework for visual interpretation of outcomes., 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on Machine Learning for Signal Processing

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

IntroductionTraumatic brain injury (TBI) is considered one of the most pervasive causes of disability in people under the age of 45. TBI often results in disorders of consciousness, and clinical assessment of the state of consciousness in these patients is challenging due to the lack of behavioural responsiveness. Functional neuroimaging offers a means to assess these patients without the need for behavioural signs, indicating that brain connectivity plays a major role in consciousness emergence and maintenance. However, little is known regarding how changes in connectivity during recovery from TBI accompany changes in the level of consciousness. Here, we aim to combine cutting-edge neuroimaging techniques to follow changes in brain connectivity in patients recovering from severe TBI.Methods and analysisA multimodal, longitudinal assessment of 30 patients in the subacute stage after severe TBI will be made comprising an MRI session combined with electroencephalography (EEG), a positron emission tomography session and a transcranial magnetic stimulation (TMS) combined with EEG (TMS/EEG) session. A group of 20 healthy participants will be included for comparison. Four sessions for patients and two sessions for healthy participants will be planned. Data analysis techniques will focus on whole-brain, both data-driven and hypothesis-driven, connectivity measures that will be specific to the imaging modality.Ethics and disseminationThe project has received ethical approval by the local ethics committee of the Capital Region of Denmark and by the Danish Data Protection. Results will be published as original research articles in peer-reviewed journals and disseminated in international conferences. None of the measurements will have any direct clinical impact on the patients included in the study but may benefit future patients through a better understanding of the mechanisms underlying the recovery process after TBI.Trial registration number:NCT02424656; Pre-results.

Movement artifacts compromise image quality and may interfere with interpretation, especially in magnetic resonance imaging (MRI) applications with low signal-to-noise ratio such as functional MRI or diffusion tensor imaging, and when imaging small lesions. High image resolution has high sensitivity to motion artifacts and often prolongs scan time that again aggravates movement artifacts. During the scan fast imaging techniques and sequences, optimal receiver coils, careful patient positioning, and instruction may minimize movement artifacts. Physiological noise sources are motion from respiration, flow and pulse coupled to cardiac cycles, from the swallowing reflex and small spontaneous head movements. Par example, in resting-state functional MRI spontaneous neuronal activity adds 1-2% of signal change, even under optimal conditions signal contributions from physiological noise remain a considerable fraction hereof. Movement tracking during imaging may allow for prospective correction or postprocessing steps separating signal and noise.

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered., Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 17

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks., Comment: 4 pages, 3 figures, 1 table, IEEE 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

Olfaction and gustation contribute both to the appreciation of food flavours. Although acquired loss of smell has profound consequences on the pleasure of eating, food habits and body weight, less is known about the impact of congenital olfactory impairment on gustatory processing. Here we examined taste identification accuracy and its neural correlates using functional magnetic resonance imaging (fMRI) in 12 congenitally olfactory impaired individuals and 8 normosmic controls. Results showed that taste identification was worse in congenitally olfactory impaired compared to control subjects. The fMRI results demonstrated that olfactory impaired individuals had reduced activation in medial orbitofrontal cortex (mOFC) relative to normosmic subjects while tasting. In addition, olfactory performance as measured with the Sniffin' Sticks correlated positively with taste-induced blood-oxygen-level dependent (BOLD) signal increases in bilateral mOFC and anterior insula. Our data provide a neurological underpinning for the reduced taste perception in congenitally olfactory impaired individuals.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study changes in long-range functional brain connectivity in multiple sclerosis (MS). Yet little is known about how MS affects functional brain connectivity at the local level. Here we studied 42 patients with MS and 30 matched healthy controls with whole-brain rs-fMRI at 3 T to examine local functional connectivity. Using the Kendall's Coefficient of Concordance, regional homogeneity of blood-oxygen-level-dependent (BOLD)-signal fluctuations was calculated for each voxel and used as a measure of local connectivity. Patients with MS showed a decrease in regional homogeneity in the upper left cerebellar hemisphere in lobules V and VI relative to healthy controls. Similar trend changes in regional homogeneity were present in the right cerebellar hemisphere. The results indicate a disintegration of regional processing in the cerebellum in MS. This might be caused by a functional disruption of cortico-ponto-cerebellar and spino-cerebellar inputs, since patients with higher lesion load in the left cerebellar peduncles showed a stronger reduction in cerebellar homogeneity. In patients, two clusters in the left posterior cerebellum expressed a reduction in regional homogeneity with increasing global disability as reflected by the Expanded Disability Status Scale (EDSS) score or higher ataxia scores. The two clusters were mainly located in Crus I and extended into Crus II and the dentate nucleus but with little spatial overlap. These findings suggest a link between impaired regional integration in the cerebellum and general disability and ataxia., Highlights • Multiple sclerosis (MS) leads to reduced regional cerebellar functional connectivity. • Reduced cerebellar connectivity correlates with lesion load of cerebellar peduncles. • Regional cerebellar connectivity is inversely correlated with clinical disability. • The findings suggest a disintegration of regional cerebellar processing in MS.

Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging, the current study investigated whether WM-dependent modulation of effective connectivity in this network is affected in a group of first-episode schizophrenia (FES) patients compared with similarly performing healthy participants during a verbal n-back task. Dynamic causal modeling (DCM) of the coupling between regions (left inferior frontal gyrus (IFG), left inferior parietal lobe (IPL), and primary visual area) identified in a psychophysiological interaction (PPI) analysis was performed to characterize effective connectivity during the n-back task. The PPI analysis revealed that the connectivity between the left IFG and left IPL was modulated by WM and that this modulation was reduced in FES patients. The subsequent DCM analysis confirmed this modulation by WM and found evidence that FES patients had reduced forward connectivity from IPL to IFG. These findings provide evidence for impaired WM modulation of frontoparietal effective connectivity in the early phase of schizophrenia, even with intact WM performance, suggesting a failure of context-sensitive coupling in the schizophrenic brain.

Resting-state functional MRI (rs-fMRI) of the brain has been successfully used to identify altered functional connectivity in the motor network in multiple sclerosis (MS).1 In clinically stable patients with MS, we recently demonstrated increased coupling between the basal ganglia and the motor network.1 Accordingly, rs-fMRI in MS is particularly suited to investigate functional reorganisation of the motor network in the remission phase after a relapse because the resting-state connectivity pattern is not influenced by interindividual differences in motor ability and task performance. In this prospective rs-fMRI study, we mapped acute changes in resting-state motor connectivity in 12 patients with relapsing forms of MS presenting with an acute relapse involving an upper limb paresis. Previous functional MRI (fMRI) studies have shown that the activation of sensorimotor areas was stronger and more widespread in the brain of patients with MS compared to healthy controls and increased proportionally with the extent of MS-related brain damage.2 We therefore hypothesised that a motor relapse involving paresis of the upper limbs would trigger an acute compensatory increase in motor resting-state connectivity and that the compensatory increase in functional connectivity would decrease over the following days or weeks in proportion to the degree of clinical remission. ### Participants We studied 12 patients with MS presenting with acute motor deficits involving paresis of the left (n=5) or right (n=7) arm. MRI and neurological examination including Expanded Disability Status Scale (EDSS) score was performed twice, at inclusion and at follow-up.3 The relapse was treated with a three-day course of intravenous methylprednisolone 1 g daily which was initiated after the first scan. Written informed consent was obtained from all patients prior to any examination, and all protocols were approved by the local scientific ethical committee (protocol no. KF01–131/03). Clinical characteristics are listed in table 1 (see online supplementary material …

Background: Multiple sclerosis (MS) impairs signal transmission along cortico-cortical and cortico-subcortical connections, affecting functional integration within the motor network. Functional magnetic resonance imaging (fMRI) during motor tasks has revealed altered functional connectivity in MS, but it is unclear how much motor disability contributed to these abnormal functional interaction patterns. Objective: To avoid any influence of impaired task performance, we examined disease-related changes in functional motor connectivity in MS at rest. Methods: A total of 42 patients with MS and 30 matched controls underwent a 20-minute resting-state fMRI session at 3 Tesla. Independent component analysis was applied to the fMRI data to identify disease-related changes in motor resting-state connectivity. Results: Patients with MS showed a spatial expansion of motor resting-state connectivity in deep subcortical nuclei but not at the cortical level. The anterior and middle parts of the putamen, adjacent globus pallidus, anterior and posterior thalamus and the subthalamic region showed stronger functional connectivity with the motor network in the MS group compared with controls. Conclusion: MS is characterised by more widespread motor connectivity in the basal ganglia while cortical motor resting-state connectivity is preserved. The expansion of subcortical motor resting-state connectivity in MS indicates less efficient funnelling of neural processing in the executive motor cortico-basal ganglia-thalamo-cortical loops.

Fatigue is a common and highly disabling symptom of multiple sclerosis. Patients experience an effort-independent general subjective feeling of fatigue as well as excessive fatigability when engaging in physical or mental activity. Previous research using functional magnetic resonance imaging (fMRI) has revealed heterogeneous findings, but some evidence implicates the motor system. To identify brain correlates of fatigue, 44 mildly impaired patients with relapsing-remitting multiple sclerosis and 25 age- and gender-matched healthy controls underwent functional magnetic resonance imaging at 3 Tesla, while they performed alternating blocks of rest and a non-fatiguing precision grip task. We investigated neural correlates of fatigue using the motor subscore of Fatigue Scale for Motor and Cognitive Functions (FSMCMOTOR) using the bilateral motor cerebellum, putamen, and dorsal premotor cortex as regions of interest. Patients and healthy controls performed the grip force task equally well without being fatigued. In patients, task-related activity in lobule VI of right motor cerebellum changed in proportion with individual FSMCMOTOR scores. In right dorsal premotor cortex, linear increases in activity across consecutive task blocks scaled with individual FSMCMOTOR scores in healthy controls, but not in patients. In premotor and dorsomedial prefrontal areas, patients were impaired at upscaling task-related activity the more they were affected by motor fatigue. The results support the notion that increased sensorimotor processing in the cerebellum contributes to the experience of motor fatigue and fatigability in multiple sclerosis. Additionally, downscaling of motivational input or sensorimotor processing in prefrontal and premotor areas may constitute an additional pathophysiological factor.

Introduction The large trial-to-trial variability of MEP amplitude in response to TMS may be caused by fluctuations of the endogenous brain state at the time of stimulation. The phase of pericentral m-oscillations is thought to reflect pulsed inhibition at the positive peak, and facilitation (increased excitability) at the negative peak (Jensen and Mazaheri, 2010). Using state-informed EEG-TMS of the left motor hand area, we targeted the pericentral μ-rhythm at four different phases of the endogenous m-oscillations to investigate the state dependency of stimulation. Methods We used real-time EEG analysis to perform state informed TMS, triggering single TMS pulses at a specific phase of the intrinsically expressed pericentral m-rhythm. Endogenous m- activity in the dominant left primary motor hand area was determined based on radial source projection using subject-specific head models and the EEG signal recorded from 63 surface electrodes. Corticospinal excitability was assessed by recording the motor evoked potential (MEP) of the right FDI muscle. Mean MEP amplitudes were compared for stimulation at low (positive peak, 0°) and high (trough, 180°) excitability states, as well as for rising (90°) and falling (270°) phase of the μ-oscillations (60 MEPs per condition). We investigated phase-dependent changes in MEP amplitude, using both biphasic (16 healthy young subjects; 14 females; age range: 20–34 years) and monophasic (15 healthy young subjects; 12 females; age range: 20–39 years) TMS. No pre-selection of subjects was performed to test the applicability of our setup to a general sample. Results Our state-informed open-loop EEG-TMS setup enabled successful state-informed TMS, hitting the desired phase with a mean absolute error of 48.6° across all trials. Using the MEP amplitude, we tested for differences between high (trough) and low (peak) excitability states, as well as between the rising and falling phase of the μ-oscillation. Neither monophasic TMS nor biphasic TMS yielded significant phase-dependent differences in mean MEP amplitude (two-sided t-tests, p > 0.4 for all comparisons). Conclusion In a non-preselected sample of healthy young individuals, we did not observe any consistent modulation in mean MEP amplitude depending on the phase of the endogenous m-oscillation at the time of stimulation. Stringent pre-selection of subjects who have a strong and stable pericentral μ-rhythm or a restriction of TMS to periods during which μ-power is even higher, might reveal a significant modulation of MEP amplitude. Acknowledgements: Funded by the Novo Nordisk Foundation Interdisciplinary Synergy Program 2014 (“Biophysically adjusted state-informed cortex stimulation (BASICS); NNF14OC0011413).

Introduction In recent years, there has been an increasing interest of using concurrent EEG-TMS to perform state informed brain stimulation to decrease the trial by trial variability. This is methodologically challenging because it requires real-time acquisition and fast analysis of EEG data. We present a framework for online analysis of EEG to enable state-informed TMS, more specifically, to trigger TMS pulses based on the phase of endogenous brain oscillations. The framework is based on open source software and written in Python and doesn’t require a specialized real-time computer. Methods We consider EEG data sampled at 5 kHz across 63-channels using a NeurOne EEG amplifier. The framework ensures minimal latency while circumventing issues of dropped samples by an efficient asynchronous analysis loop running in a separate process. In the current setting the analysis loop determines the phase of the pericentral μ -oscillation using the following steps: (1) Acquire a 500 ms data window. (2) EEG source projection (subject specific head model). (3) Linear detrending. (4) Phase estimation (continuous Morlet Wavelet transform). (5) Project phase estimate to the end of window. (6) Generate trigger if criteria for stimulation are met. Since a TMS pulse will subsequently contaminate the EEG signal with a large artifact, TMS is only performed for half of the estimated triggers, leaving the remaining for determining the performance. For phase estimation the Morlet wavelet was chosen due to its ability to handle non-stationary data. We targeted four phases (0°, 90°, 180° and 270°) in 15 subjects in a frequency band of 4 Hz centered on the subject μ -frequency. For each subject, we performed 60 stimulations for each phase. Results We were able to estimate the pericentral μ -phase with short latency. The analysis loop was updated with an average rate of more than 1500 Hz (Intel i7-4770 CPU). Therefore, the processing latency was negligible compared to the ∼ 3 ms latency of EEG data from the amplifier. To determine the performance, we analyzed the non-stimulated triggers using a centered 500 ms time window. The intended phase was hit with a mean absolute error of 48.6° across all subjects. We mainly attribute this variability to the fact that pericentral μ -power had often diminished at the intended stimulation time complicating estimation of the phase. Conclusion We present a framework capable of targeting the individual phase of the pericentral μ -rhythm with an acceptable accuracy using standard computer hardware. In practice, quite long windows are needed to stabilize frequency and amplitude estimation, and while an instantaneous phase estimate is produced by projecting to the end of the window, it is difficult to ensure that the oscillation is not diminished at the stimulation time. The presented framework readily generalizes to estimation of other states estimated from EEG data and other stimulation settings. Acknowledgements: Funded by the Novo Nordisk Foundation Interdisciplinary Synergy Program 2014 “BASICS”; NNF14OC0011413.

Functional Magnetic Resonance Imaging has become a central measuring modality to quantify functional activiation of the brain in both task and rest. Most analysis used to quantify functional activation requires supervised approaches as employed in statistical parametric mapping (SPM) to extract maps of task induced functional activations. This requires strong knowledge and assumptions on the BOLD response as a function of activitation while smoothing in general enhances the statistical power but at the cost of spatial resolution. We propose a fully unsupervised approach for the extraction of task activated functional units in multi-subject fMRI data that exploits that regions of task activation are consistent across subjects and can be more reliably inferred than regions that are not activated. We develop a non-parametric Gaussian mixture model that apriori assumes activations are smooth using a Gaussian Process prior while assuming the segmented functional maps are the same across subjects but having individual time-courses and noise variances. To improve inference we propose an enhanced split-merge procedure. We find that our approach well extracts the induced activity of a finger tapping fMRI paradigm with maps that well corresponds to a supervised group SPM analysis. We further find interesting regions that are not activated time locked to the paradigm. Demonstrating that we in a fully unsupervised manner are able to extract the task-induced activations forms a promising framework for the analysis of task fMRI and resting-state data in general where strong knowledge of how the task induces a BOLD response is missing.

Objective Transcranial magnetic stimulation (TMS) can be used to map the corticomotor representations of hand muscles in the precentral motor-cortex (PMC). The spatial peak of the corticomotor representations is often not located in the primary motor cortex (M1HAND), but shows an anterior shift towards the dorsal premotor cortex (PMd). Here we used magnetic resonance imaging (MRI) to test the hypothesis that the PMC shows different structural and functional properties in individuals with a clear “premotor” representation compared to individuals with a preponderant “primary-motor” representation of hand muscles. Methods MRI-measurements and neuronavigated-TMS were performed on twenty-four volunteers (mean age: 24.3 ± 0.9 SE, 12 women). Participants underwent structural MRI to evaluate cortical thickness and curvature of the PMC. We also performed fMRI to evaluate precentral functional activation in the hand-knob during a simple motor task. Sulcus-shape based TMS-mapping was used to obtain mediolateral and posterior-anterior corticomotor excitability profiles of the left abductor-digiti-minimi and first-dorsal-interosseus muscles. Results In 14 out of 24 individuals (58%), TMS mapping disclosed a clear spatial peak in the stimulation lines overlying the PMd, whereas the remaining 10 subjects (42%) showed maximal motor responses more posteriorly along M1HAND. During fMRI, the “premotor” group displayed a stronger task-related activation in the PMd relative to the “primary-motor” group ( p = 0.002). No difference between two groups was evident for curvature and cortical thickness. Conclusion The results confirm that many individuals have a more premotor corticomotor representation of small hand muscles when measured with TMS. This premotor phenotype in terms of corticomotor representation is associated with a stronger premotor activation during simple movements. This association supports the notion of two distinct functional phenotypes of corticomotor hand representations in human PMC: a primary motor and a premotor phenotype.

It is critical for survival to quickly respond to environmental stimuli with the most appropriate action. This task becomes most challenging when response tendencies induced by relevant and irrelevant stimulus features are in conflict, and have to be resolved in real time. Inputs from the pre-supplementary motor area (pre-SMA) and inferior frontal gyrus (IFG) to the subthalamic nucleus (STN) are thought to support this function, but the connectivity and causality of these regions in calibrating motor control has not been delineated. In this study, we combined off-line noninvasive brain stimulation and functional magnetic resonance imaging, while young healthy human participants performed a modified version of the Simon task. We show that impairing pre-SMA function by noninvasive brain stimulation improved control over impulsive response tendencies, but only when participants were explicitly rewarded for fast and accurate responses. These effects were mediated by enhanced activation and connectivity of the IFG-STN pathway. These results provide causal evidence for a pivotal role of the IFG-STN pathway during action control. Additionally, they suggest a parallel rather than hierarchical organization of the pre-SMA-STN and IFG-STN pathways, since interruption of pre-SMA function can enhance IFG-STN connectivity and improve control over inappropriate responses.KEYWORDS:Simon task; TMS; fMRI; motor control; pre-SMA; reward

Objective To characterize the relationship between motor resting-state connectivity of the dorsal pre-motor cortex (PMd) and clinical disability in patients with multiple sclerosis (MS). Materials and methods A total of 27 patients with relapsing–remitting MS (RR-MS) and 15 patients with secondary progressive MS (SP-MS) underwent functional resting-state magnetic resonance imaging. Clinical disability was assessed using the Expanded Disability Status Scale (EDSS). Independent component analysis was used to characterize motor resting-state connectivity. Multiple regression analysis was performed in SPM8 between the individual expression of motor resting-state connectivity in PMd and EDSS scores including age as covariate. Separate post hoc analyses were performed for patients with RR-MS and SP-MS. Results The EDSS scores ranged from 0 to 7 with a median score of 4.3. Motor resting-state connectivity of left PMd showed a positive linear relation with clinical disability in patients with MS. This effect was stronger when considering the group of patients with RR-MS alone, whereas patients with SP-MS showed no increase in coupling strength between left PMd and the motor resting-state network with increasing clinical disability. No significant relation between motor resting-state connectivity of the right PMd and clinical disability was detected in MS. Conclusions The increase in functional coupling between left PMd and the motor resting-state network with increasing clinical disability can be interpreted as adaptive reorganization of the motor system to maintain motor function, which appears to be limited to the relapsing–remitting stage of the disease.

Stroke remains the most frequent cause of handicap in adult life and according to the WHO the second cause of death in the Western world. In the peracute phase, intravenous thrombolysis and in some cases endovascular therapy may induce early revascularization and hereby improve prognosis. However, only up to 20-25% of patients are eligible to causal treatment. Further, care in a specialized stroke unit improves prognosis in all patients independent of age and stroke severity. Even when it is not possible to prevent tissue loss, the surviving brain areas of functional brain networks have a substantial capacity to reorganize after a focal ischemic (or hemorrhagic) brain lesion. This functional reorganization contributes to functional recovery after stroke. Functional magnetic resonance imaging (fMRI) provides a valuable tool to capture the spatial and temporal activity changes in response to an acute ischemic lesion. Task-related as well as resting-state fMRI have been successfully applied to elucidate post-stroke remodeling of functional brain networks. This includes regional changes in neuronal activation as well as distributed changes in functional brain connectivity. Since fMRI is readily available and does not pose any adverse effects, repeated fMRI measurements provide unprecedented possibilities to prospectively assess the time course of reorganization in functional neural networks after stroke and relate the temporospatial dynamics of reorganization at the systems level to functional recovery. Here we review the current status and future perspectives of fMRI as a means of studying functional brain reorganization after stroke. We summarize (a) how fMRI has advanced our knowledge regarding the recovery mechanisms after stroke, and (b) how fMRI has been applied to document the effects of therapeutical interventions on post-stroke functional reorganization.

Since working memory deficits in schizophrenia have been linked to negative symptoms, we tested whether features of the one could predict the treatment outcome in the other. Specifically, we hypothesized that working memory-related functional connectivity at pre-treatment can predict improvement of negative symptoms in antipsychotic-treated patients. Fourteen antipsychotic-naive patients with first-episode schizophrenia were clinically assessed before and after 7 months of quetiapine monotherapy. At baseline, patients underwent functional magnetic resonance imaging while performing a verbal n-back task. Spatial independent component analysis identified task-modulated brain networks. A linear support vector machine was trained with these components to discriminate six patients who showed improvement in negative symptoms from eight non-improvers. Classification accuracy and significance was estimated by leave-one-out cross-validation and permutation tests, respectively. Two frontoparietal and one default mode network components predicted negative symptom improvement with a classification accuracy of 79% (p = 0.003). Discriminating features were found in the frontoparietal networks but not the default mode network. These preliminary data suggest that functional patterns at baseline can predict negative symptom treatment–response in schizophrenia. This information may be used to stratify patients into subgroups thereby facilitating personalized treatment.

The study of (Libet, 1985) gave rise to active discussion among scientists over the nature of the will and volition, suggesting that intention to perform voluntary action can be predicted from prior neural activity. (Brass and Haggart, 2008) proposed three different classes of voluntary decisions: ”what” type of action to perform, “when” to act, and “whether” to act or not. Those distinct decisions might involve different neural pathways and anatomical regions, including medial pFC, ACC, preSMA and SMA, PMC, and parietal cortex. In our study, we repeatedly confront participants with the three classes of decisions in a natural, yet still strictly controlled experimental setup, involving navigating a car through a virtual environment. For each participant we acquired high-resolution EEG data with 128-channel Biosemi ActiveTwo system, structural MR brain image (3T Philips), and recorded electrode coordinates with Localite neuro-navigation system. We demonstrate electrophysiological differences in activation of brain regions related to different classes of decisions, in terms of spatial distribution and time-frequency modulation. The event-related modulation of EEG signals, along with subject-specific T1 images, session-specific electrode coordinates, and set of spatial filters are then used to localize decision-relevant neuroanatomical sources distributed over frontal and posterior cortical regions.