Combining constraint-induced movement therapy and action-observation training in children with unilateral cerebral palsy: a randomized controlled trial

BACKGROUND

Upper limb (UL) deficits in children with unilateral cerebral palsy (uCP) have traditionally been targeted with motor execution treatment models, such as modified Constraint-Induced Movement Therapy (mCIMT). However, new approaches based on a neurophysiological model such as Action-Observation Training (AOT) may provide new opportunities for enhanced motor learning. The aim of this study is to describe a randomised controlled trial (RCT) protocol investigating the effects of an intensive treatment model, combining mCIMT and AOT compared to mCIMT alone on UL function in children with uCP. Additionally, the role of neurological factors as potential biomarkers of treatment response will be analysed.

METHODS

An evaluator-blinded RCT will be conducted in 42 children aged between 6 and 12 years. Before randomization, children will be stratified according to their House Functional Classification Scale, age and type of corticospinal tract wiring. A 2-week day-camp will be set up in which children receive intensive mCIMT therapy for 6 hours a day on 9 out of 11 consecutive days (54 h) including AOT or control condition (15 h). During AOT, these children watch video sequences showing goal-directed actions and subsequently execute the observed actions with the more impaired UL. The control group performs the same actions after watching computer games without human motion. The primary outcome measure will be the Assisting Hand Assessment. Secondary outcomes comprise clinical assessments across body function, activity and participation level of the International Classification of Function, Disability and Health. Furthermore, to quantitatively evaluate UL movement patterns, a three-dimensional motion analysis will be conducted. UL function will be assessed at baseline, immediately before and after intervention and at 6 months follow up. Brain imaging comprising structural and functional connectivity measures as well as Transcranial Magnetic Stimulation (TMS) to evaluate corticospinal tract wiring will be acquired before the intervention.

DISCUSSION

This paper describes the methodology of an RCT with two main objectives: (1) to evaluate the added value of AOT to mCIMT on UL outcome in children with uCP and (2) to investigate the role of neurological factors as potential biomarkers of treatment response.

TRIAL REGISTRATION

NCT03256357 registered on 21st August 2017 (retrospectively registered).

Stochastic resonance enhances the rate of evidence accumulation during combined brain stimulation and perceptual decision-making

Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence. It is often assumed that such decisions are degraded by adding noise to a stimulus, or to the neural systems involved in the decision making process itself. But it has been suggested that adding an optimal amount of noise can, under appropriate conditions, enhance the quality of subthreshold signals in nonlinear systems, a phenomenon known as stochastic resonance. Here we asked whether perceptual decisions made by human observers obey these stochastic resonance principles, by adding noise directly to the visual cortex using transcranial random noise stimulation (tRNS) while participants judged the direction of coherent motion in random-dot kinematograms presented at the fovea. We found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below perceptual threshold, but not when they were well below or above threshold. We modelled the data under a drift diffusion framework, and showed that bilateral tRNS selectively increased the drift rate parameter, which indexes the rate of evidence accumulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and to show that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.

Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1-/y) and contactin-associated (CNTNAP2-/-) knockout mice. Young Fmr1-/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2-/- mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes.

The eWrist - A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation

Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Training wrist extensor function and detecting unwanted movement strategies in an EMG-controlled visuomotor task

Stroke patients often suffer from severe upper limb paresis. Rehabilitation treatment typically targets motor impairments as early as possible, however, muscular contractions, particularly in the wrist and fingers, are often too weak to produce overt movements, making the initial phase of rehabilitation training difficult. Here we propose a new training tool whereby electromyographic (EMG) activity is measured in the wrist extensors and serves as a proxy of voluntary corticomotor drive. We used the Myo armband to develop a proportional EMG controller which allowed volunteers to perform a simple visuomotor task by modulating wrist extensor activity. In this preliminary study six healthy participants practiced the task for one session (144 trials), which resulted in a significant reduction of the average trial time required to move and hold a cursor in different target zones (p < 0.001, ANOVA), indicating skill learning. Additionally, we implemented an EMG based classifier to distinguish between the desired movement strategy and unwanted alternatives. Validation of the classifier indicated that accuracy for detecting rest, wrist extension and unwanted strategies was 92.5 + 6.9% (M + SD) across all participants. When performing the motor task the classification algorithm flagged 4.3 + 3.5% of the trials as 'unwanted strategies', even in healthy subjects. We also report initial feedback from a survey submitted to two chronic stroke patients to inquire about feasibility and acceptance of the general setup by patients.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Improving the quality of combined EEG-TMS neural recordings: Introducing the coil spacer

BACKGROUND

In the last decade, interest in combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) approaches has grown substantially. Aside from the obvious artifacts induced by the magnetic pulses themselves, separate and more sinister signal disturbances arise as a result of contact between the TMS coil and EEG electrodes.

NEW METHOD

Here we profile the characteristics of these artifacts and introduce a simple device - the coil spacer - to provide a platform allowing physical separation between the coil and electrodes during stimulation.

RESULTS

EEG data revealed high amplitude signal disturbances when the TMS coil was in direct contact with the EEG electrodes, well within the physiological range of viable EEG signals. The largest artifacts were located in the Delta and Theta frequency range, and standard data cleanup using independent components analysis (ICA) was ineffective due to the artifact's similarity to real brain oscillations.

COMPARISON WITH EXISTING METHOD

While the current best practice is to use a large coil holding apparatus to fixate the coil 'hovering' over the head with an air gap, the spacer provides a simpler solution that ensures this distance is kept constant throughout testing.

CONCLUSIONS

The results strongly suggest that data collected from combined TMS-EEG studies with the coil in direct contact with the EEG cap are polluted with low frequency artifacts that are indiscernible from physiological brain signals. The coil spacer provides a cheap and simple solution to this problem and is recommended for use in future simultaneous TMS-EEG recordings.

Neural processing of biological motion in autism: An investigation of brain activity and effective connectivity

The superior temporal sulcus (STS) forms a key region for social information processing and disruptions of its function have been associated with socio-communicative impairments characteristic of autism spectrum disorders (ASD). Task-based fMRI was applied in 15 adults with ASD and 15 matched typical-controls (TC) to explore differences in activity and effective connectivity of STS while discriminating either 'intact' versus 'scrambled' biological motion point light displays (explicit processing) or responding to a color-change while the 'intact' versus 'scrambled' nature of the stimulus was irrelevant for the task (implicit processing). STS responded stronger to 'intact' than 'scrambled' stimuli in both groups, indicating that the basic encoding of 'biological' versus 'non-biological' motion seems to be intact in ASD. Only in the TC-group however, explicit attention to the biological motion content induced an augmentation of STS-activity, which was not observed in the ASD-group. Overall, these findings suggest an inadequacy to recruit STS upon task demand in ASD, rather than a generalized alteration in STS neural processing. The importance of attention orienting for recruiting relevant neural resources was further underlined by the observation that connectivity between STS and medial prefrontal cortex (mPFC), a key region in attention regulation, effectively modulated STS-recruitment in the ASD-group.

Detecting large-scale networks in the human brain using high-density electroencephalography

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

Disrupted prediction errors index social deficits in autism spectrum disorder

Social deficits are a core symptom of autism spectrum disorder; however, the perturbed neural mechanisms underpinning these deficits remain unclear. It has been suggested that social prediction errors—coding discrepancies between the predicted and actual outcome of another’s decisions—might play a crucial role in processing social information. While the gyral surface of the anterior cingulate cortex signalled social prediction errors in typically developing individuals, this crucial social signal was altered in individuals with autism spectrum disorder. Importantly, the degree to which social prediction error signalling was aberrant correlated with diagnostic measures of social deficits. Effective connectivity analyses further revealed that, in typically developing individuals but not in autism spectrum disorder, the magnitude of social prediction errors was driven by input from the ventromedial prefrontal cortex. These data provide a novel insight into the neural substrates underlying autism spectrum disorder social symptom severity, and further research into the gyral surface of the anterior cingulate cortex and ventromedial prefrontal cortex could provide more targeted therapies to help ameliorate social deficits in autism spectrum disorder.

Deep sleep maintains learning efficiency of the human brain

It is hypothesized that deep sleep is essential for restoring the brain’s capacity to learn efficiently, especially in regions heavily activated during the day. However, causal evidence in humans has been lacking due to the inability to sleep deprive one target area while keeping the natural sleep pattern intact. Here we introduce a novel approach to focally perturb deep sleep in motor cortex, and investigate the consequences on behavioural and neurophysiological markers of neuroplasticity arising from dedicated motor practice. We show that the capacity to undergo neuroplastic changes is reduced by wakefulness but restored during unperturbed sleep. This restorative process is markedly attenuated when slow waves are selectively perturbed in motor cortex, demonstrating that deep sleep is a requirement for maintaining sustainable learning efficiency.

Deep sleep is hypothesized to restore the brain's capacity to learn. Here the authors provide causal evidence by specifically perturbing slow wave activity over the motor cortex during NREM sleep in humans and demonstrate a reduction in neurophysiological markers of plasticity and capacity for motor learning.

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ = 34 s (partial Spearman correlation ρ=0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: ρ=-0.43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

Monetary, Food, and Social Rewards Induce Similar Pavlovian-to-Instrumental Transfer Effects

Multiple types of reward, such as money, food or social approval, are capable of driving behavior. However, most previous investigations have only focused on one of these reward classes in isolation, as such it is not clear whether different reward classes have a unique influence on instrumental responding or whether the subjective value of the reward, rather than the reward type per se, is most important in driving behavior. Here, we investigate behavior using a well-established reward paradigm, Pavlovian-to-instrumental transfer (PIT), and three different reward types: monetary, food and social rewards. The subjective value of each reward type was matched using a modified Becker-DeGroot-Marschak (BDM) auction where subjective reward value was expressed through physical effort using a bimanual grip force task. We measured the influence of reward-associated stimuli on how participants distributed forces between hands when reaching a target effort range on the screen bimanually and on how much time participants spent in this target range. Participants spent significantly more time in the target range (15% ± 2% maximal voluntary contraction) when a stimulus was presented that was associated with a reward used during instrumental conditioning or Pavlovian conditioning compared to a stimulus associated with a neutral outcome (i.e., general PIT). The strength of the PIT effect was modulated by subjective value (i.e., individuals who showed a stronger PIT effect rated the value of rewards more highly), but not by reward type, demonstrating that stimuli of all reward types were able to act as appetitive reinforcers and influenced instrumental responding, when matched to the same subjective reward value. This is the first demonstration that individually matched monetary, food and social rewards are equally effective as appetitive reinforcers in PIT. These findings strengthen the hypotheses that the subjective value is crucial for how much reward-associated stimuli influence behavior.

Food-Predicting Stimuli Differentially Influence Eye Movements and Goal-Directed Behavior in Normal-Weight, Overweight, and Obese Individuals

Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues as for example food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behavior but it is currently unknown, whether this effect differs between normal-weight, overweight, and obese individuals. Here, we investigate this question by using a Pavlovian-to-instrumental transfer (PIT) task in normal-weight (N = 20), overweight (N = 17), and obese (N = 17) individuals. Furthermore, we applied eye tracking during Pavlovian conditioning to measure the participants' conditioned response as a proxy of the incentive salience of the predicted reward. Our results show that the goal-directed behavior of overweight individuals was more strongly influenced by food-predicting cues (i.e., stronger PIT effect) than that of normal-weight and obese individuals (p < 0.001). The weight groups were matched for age, gender, education, and parental education. Eye movements during Pavlovian conditioning also differed between weight categories (p < 0.05) and were used to categorize individuals based on their fixation style into "high eye index" versus "low eye index" as well. Our main finding was that the fixation style exhibited a complex interaction with the weight category. Furthermore, we found that normal-weight individuals of the group "high eye index" had higher body mass index within the healthy range than individuals of the group "low eye index" (p < 0.001), but this relationship was not found within in the overweight or obese groups (p > 0.646). Our findings are largely consistent with the incentive sensitization theory predicting that overweight individuals are more susceptible to food-related cues than normal-weight controls. However, this hypersensitivity might be reduced in obese individuals, possibly due to habitual/compulsive overeating or differences in reward valuation.

Beyond Autism: Introducing the Dialectical Misattunement Hypothesis and a Bayesian Account of Intersubjectivity

Drawing on sociocultural theories and Bayesian accounts of brain function, in this article we construe psychiatric conditions as disorders of social interaction to fully account for their complexity and dynamicity across levels of description and temporal scales. After an introduction of the theoretical underpinnings of our integrative approach, we take autism spectrum conditions (ASC) as a paradigm example and discuss how neurocognitive hypotheses can be translated into a Bayesian formulation, i.e., in terms of predictive processing and active inference. We then argue that consideration of individuals (even within a Bayesian framework) will not be enough for a comprehensive understanding of psychiatric conditions and consequently put forward the dialectical misattunement hypothesis, which views psychopathology not merely as disordered function within single brains but also as a dynamic interpersonal mismatch that encompasses various levels of description. Moving from a mere comparison of groups, i.e., "healthy" persons versus "patients," to a fine-grained analysis of social interactions within dyads and groups of individuals will open new avenues and may allow to avoid an overly neurocentric scope in psychiatric research as well as help to reduce social exclusion.

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for autism spectrum disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.