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When gathering valued goods, risk and reward are often coupled and escalate over time, for instance, during foraging, trading, or gambling. This escalating frame requires agents to continuously balance expectations of reward against those of risk. To address how the human brain dynamically computes these tradeoffs, we performed whole-brain fMRI while healthy young individuals engaged in a sequential gambling task. Participants were repeatedly confronted with the option to continue with throwing a die to accumulate monetary reward under escalating risk, or the alternative option to stop to bank the current balance. Within each gambling round, the accumulation of gains gradually increased reaction times for “continue” choices, indicating growing uncertainty in the decision to continue. Neural activity evoked by “continue” choices was associated with growing activity and connectivity of a cortico-subcortical “braking” network that positively scaled with the accumulated gains, including pre-supplementary motor area (pre-SMA), inferior frontal gyrus, caudate, and subthalamic nucleus (STN). The influence of the STN on continue-evoked activity in the pre-SMA was predicted by interindividual differences in risk-aversion attitudes expressed during the gambling task. Furthermore, activity in dorsal anterior cingulate cortex (ACC) reflected individual choice tendencies by showing increased activation when subjects made nondefault “continue” choices despite an increasing tendency to stop, but ACC activity did not change in proportion with subjective choice uncertainty. Together, the results implicate a key role of dorsal ACC, pre-SMA, inferior frontal gyrus, and STN in computing the trade-off between escalating reward and risk in sequential decision-making.SIGNIFICANCE STATEMENTUsing a paradigm where subjects experienced increasing potential rewards coupled with increasing risk, this study addressed two unresolved questions in the field of decision-making: First, we investigated an “inhibitory” network of regions that has so far been investigated with externally cued action inhibition. In this study, we show that the dynamics in this network under increasingly risky decisions are predictive of subjects' risk attitudes. Second, we contribute to a currently ongoing debate about the anterior cingulate cortex's role in sequential foraging decisions by showing that its activity is related to making nondefault choices rather than to choice uncertainty.

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.

Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a 'brain map' derived from the classification model. In this study we focus on the relative influence of model regularization parameter choices on both the model generalization, the reliability of the spatial patterns extracted from the classification model, and the ability of the resulting model to identify relevant brain networks defining the underlying neural encoding of the experiment. For a support vector machine, logistic regression and Fisher's discriminant analysis we demonstrate that selection of model regularization parameters has a strong but consistent impact on the generalizability and both the reproducibility and interpretable sparsity of the models for both @?"2 and @?"1 regularization. Importantly, we illustrate a trade-off between model spatial reproducibility and prediction accuracy. We show that known parts of brain networks can be overlooked in pursuing maximization of classification accuracy alone with either @?"2 and/or @?"1 regularization. This supports the view that the quality of spatial patterns extracted from models cannot be assessed purely by focusing on prediction accuracy. Our results instead suggest that model regularization parameters must be carefully selected, so that the model and its visualization enhance our ability to interpret the brain.

Adaptive actions build on internal probabilistic models of possible outcomes that are tuned according to the errors of their predictions when experiencing an actual outcome. Prediction errors (PEs) inform choice behavior across a diversity of outcome domains and dimensions, yet neuroimaging studies have so far only investigated such signals in singular experimental contexts. It is thus unclear whether the neuroanatomical distribution of PE encoding reported previously pertains to computational features that are invariant with respect to outcome valence, sensory domain, or some combination of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum and frontopolar cortex coded increasingly positive PEs, whereas dorsal anterior cingulate cortex (dACC) traced increasingly negative PEs, irrespectively of the outcome dimension. Individual reversal behavior was unaffected by context manipulations and was predicted by activity in dACC and right inferior frontal gyrus (IFG). The stronger the response to negative PEs in these areas, the lower was the tendency to reverse choice behavior in response to negative events, suggesting that these regions enforce a rule-based strategy across outcome dimensions. Outcome valence influenced PE-related activity in left amygdala, IFG, and dorsomedial prefrontal cortex, where activity selectively scaled with increasingly positive PEs in the reward-seeking but not punishment-avoidance context, irrespective of sensory domain. Left amygdala displayed an additional influence of sensory domain. In the context of avoiding punishment, amygdala activity increased with increasingly negative PEs, but only for facial stimuli, indicating an integration of outcome valence and sensory domain during probabilistic choices.

Introduction Transcranial weak current stimulation (tCS) are a range of brain stimulation methods in which current is applied to the brain through surface electrodes mounted on the scalp, with the goal of promoting cortical excitability in a certain brain region. However, electric fields are governed by the Laplace equation, which result in physical constraints on how the electric field will be distributed in the tissue and to which extent its distribution can be controlled by manipulation of the external electrode configuration and applied current strengths. Methods In order to assess the inherent limits on tCS focality, we set up various constrained optimization problems to find electrode positions and current combinations which maximize either the field strength at a selected target or stimulation focality, while keeping the injected current within safe limits. Simulations were performed in both simple 3-layered spherical models as well as realistic head models, generated by the SimNIBS package ( www.simnibs.org ). Results The human head can be approximated as a piecewise-constant conductor, with constant conductivity in each tissue and boundaries between tissues. In such a conductor, it can be shown that due to the inherent properties of the Laplace equation, the maximal electrical field in any of the constant conductivity domains must be in its boundaries. This means that focal stimulation of deep cortical targets is not feasible under normal circumstances. Also, for limited amounts of current, our optimization results show that there is a natural trade-off between field focality and intensity at a target in tCS. That is, if we optimize the stimulation so that it is maximally focal, intensity at the target is lost, and vice versa. Conclusion While optimization procedures can substantially improve tCS targeting in terms of focality, the electric fields produced by those techniques are inherently subject to physical constraints, which fundamentally limit the focality and selectivity of stimulation, in particular for deeper targets. Download : Download high-res image (486KB) Download : Download full-size image

Purpose: The developing visual cortex has a strong potential to undergo plastic changes. Little is known about the potential of the ageing visual cortex to express plasticity. A pertinent question is whether therapeutic interventions can trigger plastic changes in the ageing visual cortex by restoring vision. Methods: Twelve patients aged 50–85 years underwent structural high-resolution T1-weighted MRI of the whole brain 2 days and 6 weeks after unilateral cataract surgery. Voxel-based morphometry (VBM) based on T1-weighted magnetic resonance imaging (MRI) was employed to test whether cataract surgery induces a regional increase in grey matter in areas V1 and V2 of the visual cortex. Results: In all patients, cataract surgery immediately improved visual acuity, contrast sensitivity and mean sensitivity in the visual field of the operated eye. The improvement in vision was stable throughout the 6 weeks after operation. VBM revealed a regional expansion of grey matter volume in area V2 contralateral to the operated eye during the 6-week period after surgery. Individual increases in grey matter were predicted by the symmetry in visual acuity between the operated eye and nonoperated eye. The more symmetrical visual acuity became after unilateral cataract surgery, the more pronounced was the grey matter increase in visual cortex. Conclusion: The data suggest that cataract surgery triggered a use-dependent structural plasticity in V2 presumably through improved binocular integration of visual input from both eyes. We conclude that activity-dependent cortical plasticity is preserved in the ageing visual cortex and may be triggered by restoring impaired vision.

The adult visual cortex maintains a substantial potential for plasticity in response to a change in visual input. For instance, transcranial magnetic stimulation (TMS) studies have shown that binocular deprivation (BD) increases the cortical excitability for inducing phosphenes with TMS. Here, we employed TMS to trace plastic changes in adult visual cortex before, during, and after 48 h of monocular deprivation (MD) of the right dominant eye. In healthy adult volunteers, MD-induced changes in visual cortex excitability were probed with paired-pulse TMS applied to the left and right occipital cortex. Stimulus-response curves were constructed by recording the intensity of the reported phosphenes evoked in the contralateral visual field at range of TMS intensities. Phosphene measurements revealed that MD produced a rapid and robust decrease in cortical excitability relative to a control condition without MD. The cortical excitability returned to preinterventional baseline levels within 3 h after the end of MD. The results show that in contrast to the excitability increase in response to BD, MD acutely triggers a reversible decrease in visual cortical excitability. This shows that the pattern of visual deprivation has a substantial impact on experience-dependent plasticity of the human visual cortex.

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.

We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. Thus, the algorithm models neural activity as a linear superposition of components with a fixed time course that may vary across either trials or space in its overall intensity and latency. Its utility is demonstrated on simulated data as well as actual EEG, and fMRI data. We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.

Optic neuritis (ON) is the first clinical manifestation in ∼20% of patients with multiple sclerosis (MS). The inflammation and demyelination of the optic nerve are characterized by symptomatic visual impairment and retrobulbar pain, and associated with decreased visual acuity, decreased colour and contrast sensitivity, delayed visual evoked potentials and visual field defects. Spontaneous recovery of vision typically occurs within weeks or months after onset, depending on the resolution of inflammation, remyelination, restoration of conduction in axons which persist demyelinated and neuronal plasticity in the cortical and subcortical visual pathways. To assess where recovery takes place along the visual pathway, visual activation was studied in the lateral geniculate nucleus (LGN), the main thalamic relay nucleus in the visual pathway and in three areas of the visual cortex: the lateral occipital complexes (LOC), V1 and V2. We conducted a longitudinal functional magnetic resonance imaging (fMRI) study of regions of interest (ROI) of activation in LGN and visual cortex in 19 patients with acute ON at onset, 3 and 6 months from presentation. With fMRI we measured the activation in the ROIs and compared activation during monocular stimulation of the affected and unaffected eye. In the acute phase the activation of LGN during visual stimulation of the affected eye was significantly reduced (P

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.

The neural correlates of clearly perceived visual stimuli have been reported previously in contrast to unperceived stimuli, but it is uncertain whether intermediate or graded perceptual experiences correlate with different patterns of neural activity. In this study, the subjective appearance of briefly presented visual stimuli was rated individually by subjects with respect to perceptual clarity: clear, vague or no experience of a stimulus. Reports of clear experiences correlated with activation in a widespread network of brain areas, including parietal cortex, prefrontal cortex, premotor cortex, supplementary motor areas, insula and thalamus. The reports of graded perceptual clarity were reflected in graded neural activity in a network comprising the precentral gyrus, intraparietal sulcus, basal ganglia and the insula. In addition, the reports of vague experiences demonstrated unique patterns of activation. Different degrees of perceptual clarity were reflected both in the degree to which activation was found within parts of the network serving a clear conscious percept, and additional unique activation patterns for different degrees of perceptual clarity. Our findings support theories proposing the involvement of a widespread network of brain areas during conscious perception.

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.

We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure in the population.

In this paper we investigate how the Infinite Relational Model can be used to infer functional groupings of the human striatum using resting state fMRI data from 30 healthy subjects. The Infinite Relational Model is a non-parametric Bayesian method for infering community structure in complex networks. We visualize the solution found by performing evidence accumulation clustering on the maximum a posterior solutions found in 100 runs of the sampling scheme. The striatal groupings found are symmetric between hemispheres indicating that the model is able to group voxels across hemispheres, which are involved in the same neural computations. The reproducibility of the groupings found are assessed by calculating mutual information between half splits of the subject sample for various hyperparameter values. Finally, the model's ability to predict unobserved links is assessed by randomly treating links and non-links in the graphs as missing. We find that the model is performing well above chance for all subjects.

Sex-hormone fluctuations may increase risk for developing depressive symptoms and alter emotional processing as supported by observations in menopausal and pre- to postpartum transition. In this double-blinded, placebo-controlled study, we used blood−oxygen level dependent functional magnetic resonance imaging (fMRI) to investigate if sex-steroid hormone manipulation with a gonadotropin-releasing hormone agonist (GnRHa) influences emotional processing. Fifty-six healthy women were investigated twice: at baseline (follicular phase of menstrual cycle) and 16±3 days post intervention. At both sessions, fMRI-scans during exposure to faces expressing fear, anger, happiness or no emotion, depressive symptom scores and estradiol levels were acquired. The fMRI analyses focused on regions of interest for emotional processing. As expected, GnRHa initially increased and subsequently reduced estradiol to menopausal levels, which was accompanied by an increase in subclinical depressive symptoms relative to placebo. Women who displayed larger GnRHa-induced increase in depressive symptoms had a larger increase in both negative and positive emotion-elicited activity in the anterior insula. When considering the post-GnRHa scan only, depressive responses were associated with emotion-elicited activity in the anterior insula and amygdala. The effect on regional activity in anterior insula was not associated with the estradiol net decline, only by the GnRHa-induced changes in mood. Our data implicate enhanced insula recruitment during emotional processing in the emergence of depressive symptoms following sex-hormone fluctuations. This may correspond to the emotional hypersensitivity frequently experienced by women postpartum.

The shift invariant multi-linear model based on the CandeComp/PARAFAC (CP) model denoted ShiftCP has proven useful for the modeling of latency changes in trial based neuroimaging data [17]. In order to facilitate component interpretation we presently extend the shiftCP model such that the extracted components can be constrained to pertain to predefined frequency ranges such as alpha, beta and gamma activity. To infer the number of components in the model we propose to apply automatic relevance determination by imposing priors that define the range of variation of each component of the shiftCP model and learning the hyper-parameters of these priors during model estimation.

To overcome poor signal-to-noise ratios in neuroimaging, data sets are often acquired over repeated trials that form a three-way array of space×time×trials. As neuroimaging data contain multiple inter-mixed signal components blind signal separation and decomposition methods are frequently invoked for exploratory analysis and as a preprocessing step for signal detection. Most previous component analyses have avoided working directly with the tri-linear structure, but resorted to bi-linear models such as ICA, PCA, and NMF. Multi-linear decomposition can exploit consistency over trials and contrary to bi-linear decomposition render unique representations without additional constraints. However, they can degenerate if data does not comply with the given multi-linear structure, e.g., due to time-delays. Here we extend multi-linear decomposition to account for general temporal modeling within a convolutional representation. We demonstrate how this alleviates degeneracy and helps to extract physiologically plausible components. The resulting convolutive multi-linear decomposition can model realistic trial variability as demonstrated in EEG and fMRI data.

There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging.

Despite the importance of vision for spatial navigation, blind subjects retain the ability to represent spatial information and to move independently in space to localize and reach targets. However, the neural correlates of navigation in subjects lacking vision remain elusive. We therefore used functional MRI (fMRI) to explore the cortical network underlying successful navigation in blind subjects. We first trained congenitally blind and blindfolded sighted control subjects to perform a virtual navigation task with the tongue display unit (TDU), a tactile-to-vision sensory substitution device that translates a visual image into electrotactile stimulation applied to the tongue. After training, participants repeated the navigation task during fMRI. Although both groups successfully learned to use the TDU in the virtual navigation task, the brain activation patterns showed substantial differences. Blind but not blindfolded sighted control subjects activated the parahippocampus and visual cortex during navigation, areas that are recruited during topographical learning and spatial representation in sighted subjects. When the navigation task was performed under full vision in a second group of sighted participants, the activation pattern strongly resembled the one obtained in the blind when using the TDU. This suggests that in the absence of vision, cross-modal plasticity permits the recruitment of the same cortical network used for spatial navigation tasks in sighted subjects.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (www.nitrc.org/projects/fcon_1000/ .

Non-negative matrix factorization (NMF), i.e. V ap WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based representation. The NMF decomposition is not in general unique and a part based representation not guaranteed. However, imposing sparseness both improves the uniqueness of the decomposition and favors part based representation. Sparseness in the form of attaining as many zero elements in the solution as possible is appealing from a conceptional point of view and corresponds to minimizing reconstruction error with an L0 norm constraint. In general, solving for a given L0 norm is an NP hard problem thus convex relaxation to regularization by the L1 norm is often considered, i.e., minimizing (1/2||V - WH||F 2 + lambda||H||1).An open problem is to control the degree of sparsity lambda imposed. We here demonstrate that a full regularization path for the L1 norm regularized least squares NMF for fixed W can be calculated at the cost of an ordinary least squares solution based on a modification of the least angle regression and selection (LARS) algorithm forming a non-negativity constrained LARS (NLARS). With the full regularization path, the L1 regularization strength lambda that best approximates a given L0 can be directly accessed and in effect used to control the sparsity of H. The MATLAB code for the NLARS algorithm is available for download.

Udgivelsesdato: 2008-Aug-15 Patients with optic neuritis (ON) undergo cortical and subcortical neuroplasticity as revealed by functional magnetic resonance imaging (fMRI). However, the effect of the heterogeneity of scotomas has not been adequately addressed previously. We introduce a new method of modelling scotomas in fMRI, to reveal a clearer pattern of neuroplasticity, across a mixed patient population. A longitudinal fMRI-study of visual function in 19 ON patients examined at four timepoints between presentation and 6 months was performed. Four different models were compared. The first model included the four different examination timepoints as separate explanatory variables without adjustment for visual field defects. The second model also included covariates reflecting subject-specific deviations in visual field defect from the average group value of the Humphrey mean deviation (HMD) at each examination timepoint. In the third and fourth models the four examination timepoints were not modelled explicitly, but entered vicariously through the associated changes in the HMD for each subject that marked their individual recovery. The results show that the third and fourth models were more sensitive to geniculate and visual cortical neuroplasticity during recovery. Moreover, inferences from the fourth model can be extended to the general population of patients recovering from ON. In conclusion, we present a method of accommodating subject-specific differences between patients with acute ON by inclusion of an HMD-index. This method is sensitive to the processes of neuroplasticity whilst the generalisation of inferences makes the method suitable for future studies of treatment.

Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be induced by the Doppler effect. However, the model is also relevant for biomedical data analysis where the sources are given by compound intensities over time and the onset of the profiles have different delays to the sensors. A simple algorithm based on multiplicative updates is derived and it is demonstrated how the algorithm correctly identifies the components of a synthetic data set. Matlab implementation of the algorithm and a demonstration data set is available.

Illusory motion can be generated by successively flashing a stationary visual stimulus in two spatial locations separated by several degrees of visual angle. In appropriate conditions, the apparent motion is indistinguishable from real motion: The observer experiences a luminous object traversing a continuous path from one stimulus location to the other through intervening positions where no physical stimuli exist. The phenomenon has been extensively investigated for nearly a century but little is known about its neurophysiological foundation. Here we present images of activations in the primary visual cortex in response to real and apparent motion. The images show that during apparent motion, a path connecting the cortical representations of the stimulus locations is filled in by activation. The activation along the path of apparent motion is similar to the activation found when a stimulus is presented in real motion between the two locations.

Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution.

In the Bayesian modeling framework there is a close relation between regularization and the prior distribution over parameters. For prior distributions in the exponential family, we show that the optimal hyper-parameter, i.e., the optimal strength of regularization, satisfies a simple relation: The expectation of the regularization function, i.e., takes the same value in the posterior and prior distribution. We present three examples: two simulations, and application in fMRI neuroimaging.

Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation. Here, we explore the fact that a shift τ in the time domain corresponds to a multiplication of e-iωτin the frequency domain. Using this property an algorithm in the case of sources≤sensors allowing arbitrary mixing and delays is developed. The algorithm is based on the following steps: 1) Find a subspace of shifted sources. 2) Resolve shift and rotation ambiguity by information maximization in the complex domain. The algorithm is proven to correctly identify the components of synthetic data. However, the problem is prone to local minima and difficulties arise especially in the presence of large delays and high frequency sources. A Matlab implementation can be downloaded from [1].

Slice-wise motion tracking during combined electroencephalography (EEG) and echo planar imaging (EPI) is developed. Using gradient-induced noise on the EEG for tracking, no interleaved navigator modules or additional hardware is needed. The motion parameters are determined after a calibration and training scan. The method is explored in a phantom and in vivo.

Exploratory analysis of functional MRI data aims at revealing known as well as unknown properties in a data-driven manner devoid of hypotheses on the time course of the hemodynamic response. This uncommitted approach usually precedes confirmatory modeling and may point to unexpected results that otherwise would be lost. Common approaches include clustering methods, principal component analysis (PCA) and in particular independent component analysis (ICA). ICA assumes that the measured activity patterns consist of linear combinations of a set of statistically independent source signals. Under favorable circumstances, one of more of these signals describe activation patterns, while others model noise and other nuisance factors. This work introduces a competing method for fMRI analysis known as sparse principal component analysis (SPCA). We argue that SPCA is less committed than ICA and show that similar results, with better suppression of noise, are obtained.

PARAFAC2 is a widely applicable method often used for analyzing multi-way chromatographic data. We recently proposed a probabilistic framework for PARAFAC2[1]. The probabilistic formulations allow for a principled way of determining the number of latent components as well as modeling heteroscedastic noise. In this work we present a summary of the probabilistic PARAFAC2 models and their properties by revisiting the previous results of the analyzed data sets in a concise fashion.

The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noise due to respiration and cardiac pulsation and residual movement artefacts not accounted for by rigid body registration. These contributions give rise to temporal autocorrelation in the residuals of the fMRI signal and invalidate the statistical analysis as the errors are no longer independent. The low-frequency drift is often removed by high-pass filtering, and other effects are typically modelled as an autoregressive (AR) process. In this paper, we propose an alternative approach: Nuisance Variable Regression (NVR). By inclusion of confounding effects in a general linear model (GLM), we first confirm that the spatial distribution of the various fMRI noise sources is similar to what has already been described in the literature. Subsequently, we demonstrate, using diagnostic statistics, that removal of these contributions reduces first and higher order autocorrelation as well as non-normality in the residuals, thereby improving the validity of the drawn inferences. In addition, we also compare the performance of the NVR method to the whitening approach implemented in SPM2.