Standard fMRI Research Articles

P.1.j.015 The effects of brain radiotherapy on facial emotion recognition and association with hippocampal and frontal dosimetry

Large, longitudinal, multi-center MR neuroimaging studies require comprehensive quality assurance (QA) protocols for assessing the general quality of the compiled data, indicating potential malfunctions in the scanning equipment, and evaluating inter-site differences that need to be accounted for in subsequent analyses.We describe the implementation of a QA protocol for functional magnet resonance imaging (fMRI) data based on the regular measurement of an MRI phantom and an extensive variety of currently published QA statistics. The protocol is implemented in the MACS (Marburg-Münster Affective Disorders Cohort Study, http://for2107.de/), a two-center research consortium studying the neurobiological foundations of affective disorders. Between February 2015 and October 2016, 1214 phantom measurements have been acquired using a standard fMRI protocol. Using 444 healthy control subjects which have been measured between 2014 and 2016 in the cohort, we investigate the extent of between-site differences in contrast to the dependence on subject-specific covariates (age and sex) for structural MRI, fMRI, and diffusion tensor imaging (DTI) data.We show that most of the presented QA statistics differ severely not only between the two scanners used for the cohort but also between experimental settings (e.g. hardware and software changes), demonstrate that some of these statistics depend on external variables (e.g. time of day, temperature), highlight their strong dependence on proper handling of the MRI phantom, and show how the use of a phantom holder may balance this dependence. Site effects, however, do not only exist for the phantom data, but also for human MRI data. Using T1-weighted structural images, we show that total intracranial (TIV), grey matter (GMV), and white matter (WMV) volumes significantly differ between the MR scanners, showing large effect sizes. Voxel-based morphometry (VBM) analyses show that these structural differences observed between scanners are most pronounced in the bilateral basal ganglia, thalamus, and posterior regions. Using DTI data, we also show that fractional anisotropy (FA) differs between sites in almost all regions assessed. When pooling data from multiple centers, our data show that it is a necessity to account not only for inter-site differences but also for hardware and software changes of the scanning equipment. Also, the strong dependence of the QA statistics on the reliable placement of the MRI phantom shows that the use of a phantom holder is recommended to reduce the variance of the QA statistics and thus to increase the probability of detecting potential scanner malfunctions.

E21 Resting-state Functional Mri: Studying Default Mode Network In Healthy Controls Versus Early Manifest Patients With Huntington's Disease

Background Huntington’s disease (HD) is characterised by a progressive neuronal cell loss in the brain, which leads to impairment of various brain functions. Resting-state fMRI measures spontaneous low-frequency fluctuations of the BOLD signal and helps to investigate functional architecture of the brain. It allows to identify a number of resting-state networks (RSN) – brain regions with synchronous spontaneous BOLD signal fluctuations. One of the most stable RSNs is default mode network (DMN), which is thought to be linked to integration of cognitive and emotional processing and to mind-wandering. Objective To assess pattern of activation within DMN in healthy controls in comparison with early manifest HD patients. Methods Ten right-handed early manifest HD mutation carriers (including 4 females, mean age 32.0 years) of stages 1 and 2 (Shoulson and Fahn, 1979) and sixteen right-handed healthy controls (including 9 females, mean age 41.4 years) underwent 1.5 T MRI scanning. Participants were instructed to lie still and relaxed with closed eyes, not to think about anything in particular, and not to fall asleep. Standard MRI scans and resting-state fMRI scans were acquired. A group independent component analysis (ICA) was used to identify pattern of DMN. Obtained data was processed using GIFT 2.0, SPM5, and SPM8 (Matlab 2011 and 2013) software. Gender and age were taken as covariates in data analysis. Results Using ICA, we identified DMN activation pattern both in healthy controls and early manifest HD mutation carriers. With a two-sample t-test we found a decreased activation within DMN in early manifest HD mutation carriers in comparison with healthy controls. We observed significantly reduced activation (p Conclusions Our findings may indicate the ongoing neurodegenerative process. We are working on a further analysis of clinical data and on linking it with resting-state fMRI data to find any possible correlations.

NEUROFEEDBACK USING FUNCTIONAL SPECTROSCOPY.

Neurofeedback based on real-time measurement of the blood oxygenation level-dependent (BOLD) signal has potential for treatment of neurological disorders and behavioral enhancement. Commonly employed methods are based on functional magnetic resonance imaging (fMRI) sequences that sacrifice speed and accuracy for whole-brain coverage, which is unnecessary in most applications. We present multi-voxel functional spectroscopy (MVFS): a system for computing the BOLD signal from multiple volumes of interest (VOI) in real-time that improves speed and accuracy of neurofeedback. MVFS consists of a functional spectroscopy (FS) pulse sequence, a BOLD reconstruction component, a neural activation estimator, and a stimulus system. The FS pulse sequence is a single-voxel, magnetic resonance spectroscopy sequence without water suppression that has been extended to allow acquisition of a different VOI at each repetition and real-time subject head motion compensation. The BOLD reconstruction component determines the T2* decay rate, which is directly related to BOLD signal strength. The neural activation estimator discounts nuisance signals and scales the activation relative to the amount of ROI noise. Finally, the neurofeedback system presents neural activation-dependent stimuli to experimental subjects with an overall delay of less than 1s. Here we present the MVFS system, validation of certain components, examples of its usage in a practical application, and a direct comparison of FS and echo-planar imaging BOLD measurements. We conclude that in the context of realtime BOLD imaging, MVFS can provide superior accuracy and temporal resolution compared with standard fMRI methods.

Aging associated changes in the motor control of ankle movements in the brain

Although age-related gait changes have been well characterized, little is known regarding potential functional changes in central motor control of distal lower limb movements with age. We hypothesized that there are age-related changes in brain activity associated with the control of repetitive ankle movements, an element of gait feasible for study with functional magnetic resonance imaging. We analyzed standardized functional magnetic resonance imaging data from 102 right-foot dominant healthy participants aged 20–83 years for age-associated effects using FSL and a meta-analysis using coordinate-based activation likelihood estimation. For the first time, we have confirmed age-related changes in brain activity with this gait-related movement of the lower limb in a large population. Increasing age correlated strongly with increased movement-associated activity in the cerebellum and precuneus. Given that task performance did not vary with age, we interpret these changes as potentially compensatory for other age-related changes in the sensorimotor network responsible for control of limb function.

Head Motion Parameters in fMRI Differ Between Patients with Mild Cognitive Impairment and Alzheimer Disease Versus Elderly Control Subjects

Motion artifacts are a well-known and frequent limitation during neuroimaging workup of cognitive decline. While head motion typically deteriorates image quality, we test the hypothesis that head motion differs systematically between healthy controls (HC), amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD) and consequently might contain diagnostic information. This prospective study was approved by the local ethics committee and includes 28 HC (age 71.0±6.9years, 18 females), 15 aMCI (age 67.7±10.9years, 9 females) and 20 AD (age 73.4±6.8years, 10 females). Functional magnetic resonance imaging (fMRI) at 3T included a 9min echo-planar imaging sequence with 180 repetitions. Cumulative average head rotation and translation was estimated based on standard fMRI preprocessing and compared between groups using receiver operating characteristic statistics. Global cumulative head rotation discriminated aMCI from controls [p<0.01, area under curve (AUC) 0.74] and AD from controls (p<0.01, AUC 0.73). The ratio of rotation z versus y discriminated AD from controls (p<0.05, AUC 0.71) and AD from aMCI (p<0.05, AUC of 0.75). Head motion systematically differs between aMCI/AD and controls. Since motion is not random but convoluted with diagnosis, the higher amount of motion in aMCI and AD as compared to controls might be a potential confounding factor for fMRI group comparisons. Additionally, head motion not only deteriorates image quality, yet also contains useful discriminatory information and is available for free as a "side product" of fMRI data preprocessing.

Investigating the Properties of the Hemodynamic Response Function after Mild Traumatic Brain Injury

Abstract Although several functional magnetic resonance imaging (fMRI) studies have been conducted in human models of mild traumatic brain injury (mTBI), to date no studies have explicitly examined how injury may differentially affect both the positive phase of the hemodynamic response function (HRF) as well as the post-stimulus undershoot (PSU). Animal models suggest that the acute and semi-acute stages of mTBI are associated with significant disruptions in metabolism and to the microvasculature, both of which could impact on the HRF. Therefore, fMRI data were collected on a cohort of 30 semi-acute patients with mTBI (16 males; 27.83±9.97 years old; 13.00±2.18 years of education) and 30 carefully matched healthy controls (HC; 16 males; 27.17±10.08 years old; 13.37±2.31 years of education) during a simple sensory-motor task. Patients reported increased cognitive, somatic, and emotional symptoms relative to controls, although no group differences were detected on traditional neuropsychological examination. There were also no differences between patients with mTBI and controls on fMRI data using standard analytic techniques, although mTBI exhibited a greater volume of activation during the task qualitatively. A significant Group×Time interaction was observed in the right supramarginal gyrus, bilateral primary and secondary visual cortex, and the right parahippocampal gyrus. The interaction was the result of an earlier time-to-peak and positive magnitude shift throughout the estimated HRF in patients with mTBI relative to HC. This difference in HRF shape combined with the greater volume of activated tissue may be indicative of a potential compensatory mechanism to injury. The current study demonstrates that direct examination and modeling of HRF characteristics beyond magnitude may provide additional information about underlying neuropathology that is not available with more standard fMRI analyses.

FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox

We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.

Advances in functional magnetic resonance imaging of the human brainstem

The brainstem is of tremendous importance for our daily survival, and yet the functional relationships between various nuclei, their projection targets, and afferent regulatory areas remain poorly characterized. The main reason for this lies in the sub-optimal performance of standard neuroimaging methods in this area. In particular, fMRI signals are much harder to detect in the brainstem region compared to cortical areas. Here we describe and validate a new approach to measure activation of brainstem nuclei in humans using standard fMRI sequences and widely available tools for statistical image processing. By spatially restricting an independent component analysis to an anatomically defined brainstem mask, we excluded those areas from the analysis that were strongly affected by physiological noise. This allowed us to identify for the first time intrinsic connectivity networks in the human brainstem and to map brainstem–cortical connectivity purely based on functionally defined regions of interest.

Functional Connectivity and Brain Activation: A Synergistic Approach

Traditional functional magnetic resonance imaging (fMRI) studies exploit endogenous brain activity for mapping brain activation during "periodic" cognitive/emotional challenges or brain functional connectivity during the "resting state". Previous studies demonstrated that these approaches provide a limited view of brain function which can be complemented by each other. We hypothesized that graph theory functional connectivity density (FCD) mapping would demonstrate regional FCD decreases between resting-state scan and a continuous "task-state" scan. Forty-five healthy volunteers underwent functional connectivity MRI during resting-state as well as a continuous visual attention task, and standard fMRI with a blocked version of the visual attention task. High-resolution data-driven FCD mapping was used to measure task-related connectivity changes without a priori hypotheses. Results demonstrate that task performance was associated with FCD decreases in brain regions weakly activated/deactivated by the task. Furthermore, a pronounced negative correlation between blood oxygen level-dependent-fMRI activation and task-related FCD decreases emerged across brain regions that also suggest the disconnection of task-irrelevant networks during task performance. The correlation between improved accuracy and stronger FCD decreases further suggests the disconnection of task-irrelevant networks during task performance. Functional connectivity can potentiate traditional fMRI studies and offer a more complete picture of brain function.

Parallel EPI artifact correction (PEAC) for N/2 ghost suppression in neuroimaging applications

Echo Planar Imaging (EPI) is a neuroimaging tool for clinical practice and research investigation. Due to odd-even echo phase inconsistencies, however, EPI suffers from Nyquist N/2 ghost artifacts. In standard neuroimaging protocols, EPI artifacts are suppressed using phase correction techniques that require reference data collected from a reference scan. Because reference-scan based techniques are sensitive to subject motion, EPI performance is sub-optimal in neuroimaging applications. In this technical note, we present a novel EPI data processing technique which we call Parallel EPI Artifact Correction (PEAC). By introducing an implicit data constraint associated with multi-coil sensitivity in parallel imaging, PEAC converts phase correction into a constrained problem that can be resolved using an iterative algorithm. This enables “reference-less” EPI that can improve neuroimaging performance. In the presented work, PEAC is investigated using a standard functional magnetic resonance imaging (fMRI) protocol with multi-slice 2D EPI. It is demonstrated that PEAC can suppress ghost artifacts as effectively as the standard reference-scan based phase correction technique used on a clinical MRI system. We also found that PEAC can achieve dynamic phase correction when motion occurs.

Structural brain changes after 4 wk of unilateral strength training of the lower limb

Strength training enhances muscular strength and neural drive, but the underlying neuronal mechanisms remain unclear. This study used magnetic resonance imaging (MRI) to identify possible changes in corticospinal tract (CST) microstructure, cortical activation, and subcortical structure volumes following unilateral strength training of the plantar flexors. Mechanisms underlying cross-education of strength in the untrained leg were also investigated. Young, healthy adult volunteers were assigned to training (n = 12) or control (n = 9) groups. The 4 wk of training consisted of 16 sessions of 36 unilateral isometric plantar flexions. Maximum voluntary isometric contraction torque was tested pre- and posttraining. MRI investigation included a T1-weighted scan, diffusion tensor imaging and functional MRI. Probabilistic fiber tracking of the CST was performed on the diffusion tensor imaging images using a two-regions-of-interest approach. Fractional anisotropy and mean diffusivity were calculated for the left and right CST in each individual before and after training. Standard functional MRI analyses and volumetric analyses of subcortical structures were also performed. Maximum voluntary isometric contraction significantly increased in both the trained and untrained legs of the training group, but not the control group. A significant decrease in mean diffusivity was found in the left CST following strength training of the right leg. No significant changes were detected in the right CST. No significant changes in cortical activation were observed following training. A significant reduction in left putamen volume was found after training. This study provides the first evidence for strength training-related changes in white matter and putamen in the healthy adult brain.

Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial.

BackgroundApproximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD.Methods/DesignThe international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n = 102), test the findings in the second half, and then extend the analyses to the total sample.Trial registrationInternational Study to Predict Optimized Treatment - in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849.

Defining the habenula in human neuroimaging studies.

Recently there has been renewed interest in the habenula; a pair of small, highly evolutionarily conserved epithalamic nuclei adjacent to the medial dorsal (MD) nucleus of the thalamus. The habenula has been implicated in a range of behaviours including sleep, stress and pain, and studies in non-human primates have suggested a potentially important role in reinforcement processing, putatively via its effects on monoaminergic neurotransmission. Over the last decade, an increasing number of neuroimaging studies have reported functional responses in the human habenula using functional magnetic resonance imaging (fMRI). However, standard fMRI analysis approaches face several challenges in isolating signal from this structure because of its relatively small size, around 30 mm3 in volume. In this paper we offer a set of guidelines for locating and manually tracing the habenula in humans using high-resolution T1-weighted structural images. We also offer recommendations for appropriate pre-processing and analysis of high-resolution functional magnetic resonance imaging (fMRI) data such that signal from the habenula can be accurately resolved from that in surrounding structures.

Autism spectrum disorder, functional MRI and MR spectroscopy: possibilities and challenges

BackgroundIn this article we provide an overview of the use of the functional magnetic resonance imaging (fMRI) and MR spectroscopy (MRS) in studies of autism spectrum disorders (ASD). We moreover provide preliminary data using these measures in cases of children with ASD and healthy controls. A hypothesis was that ASD children would show aberrant brain activation in the prefrontal and parietal cortex in an oddball stimulus situation, with predictable and unpredictable deviant tone stimuli, as an index of resistance to change in the ASD children. We also hypothesized that glutamate and GABA metabolite levels would differ between the two groups.MethodsfMRI images were acquired from a GE Signa HDx 3T MR scanner, as were the MRS data. Behavioral data were acquired as response accuracy to the deviant tone stimulus. The tone stimuli were presented in a standard fMRI ON-OFF box-car paradigim.ResultsThe fMRI results showed reduced brain activation in the ASD cases compared to the controls, preferably in the inferior and superior frontal gyrus, posterior temporal lobe, and superior and inferior parietal lobule. These areas make up an effort mode network (EMN), being activated in response to cognitive effort. The MRS results also showed differences between the groups.DiscussionThe results are discussed in a theoretical framework of resistance to unexpected changes in the environment in ASD children, and how this could have a neurobiological underpinning. The results are also discussed in relation to the brain-gut link, and the possibility that ASD may have a microbial link. A limitation with the study is the few cases reported and the preliminary quality of the results.

Decoding information in the human hippocampus: A user's guide

Multi-voxel pattern analysis (MVPA), or ‘decoding’, of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus.

Building virtual reality fMRI paradigms: A framework for presenting immersive virtual environments

The advantage of using a virtual reality (VR) paradigm in fMRI is the possibility to interact with highly realistic environments. This extends the functions of standard fMRI paradigms, where the volunteer usually has a passive role, for example, watching a simple movie paradigm without any stimulus interactions. From that point of view the combined usage of VR and real-time fMRI offers great potential to identify underlying cognitive mechanisms such as spatial navigation, attention, semantic and episodic memory, as well as neurofeedback paradigms. However, the design and the implementation of a VR stimulus paradigm as well as the integration into an existing MR scanner framework are very complex processes. To support the modeling and usage of VR stimuli we developed and implemented a VR stimulus application based on C++. This software allows the fast and easy presentation of VR environments for fMRI studies without any additional expert knowledge. Furthermore, it provides for the reception of real-time data analysis values a bidirectional communication interface. In addition, the internal plugin interface enables users to extend the functionality of the software with custom programmed C++ plugins. The VR stimulus framework was tested in several performance tests and a spatial navigation study. According to the post-experimental interview, all subjects described immersive experiences and a high attentional load inside the artifical environment. Results from other VR spatial memory studies confirm the neuronal activation that was detected in parahippocampal areas, cuneus, and occipital regions.

NEUROCIRCUITRY OF GENERALIZED ANXIETY DISORDER IN ADOLESCENTS: A PILOT FUNCTIONAL NEUROIMAGING AND FUNCTIONAL CONNECTIVITY STUDY

Dysfunction of neural systems responsible for the processing of emotional stimuli is hypothesized to be involved in the pathophysiology of generalized anxiety disorder (GAD) in adolescents. We used standard fMRI and functional connectivity analyses to examine the functional neurocircuitry of GAD in adolescents. Ten adolescents with GAD and 10 healthy comparison subjects underwent fMRI while performing a continuous performance task with emotional and neutral distractors. Standard event-related voxel-wise fMRI and steady-state functional connectivity analyses were performed. Increased activation was observed in the left medial prefrontal cortex and right ventrolateral prefrontal cortex (VLPFC) in response to emotional images compared to neutral imagines in youth with GAD. Connectivity analyses using the right VLPFC seed region suggested decreased connectivity between this region and the bilateral medial prefrontal cortex. Connectivity analyses using the right amygdala seed region revealed decreased correlation with the posterior cingulate cortex in adolescents with GAD. The left amygdala seed region demonstrated increased connectivity with the ipsilateral precuneus in youth with GAD compared to healthy subjects. In addition to increased activation of the medial prefrontal cortex and right VLPFC, we observed altered connectivity between the amygdala or VLPFC and regions, which subserve mentalization (e.g. posterior cingulate cortex, precuneus, and medial prefrontal cortex). This suggests that structures that regulate emotion and affect interact abnormally with key structures that are involved in mentalization, a process known to be disrupted in GAD.

Identifying FMRI model violations with Lagrange multiplier tests.

The standard modeling framework in functional magnetic resonance imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialized software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as nonlinearity, nonstationarity and validity of the common double gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples.

Cognitive States from Brain Images : SVM Approach

As the field of functional human brain mapping has matured, it has become apparent that a comprehensive understanding of the human brain, and its relationship with cognition, will require a quantitative assessment of individual differences in both brain function and structure. To assess brain structure, accurate classification of magnetic resonance images needed. In recent years, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. As in other modern empirical sciences, this new instrumentation has led to a flood of new data and a corresponding need for new data analysis methods. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. In this paper we discuss the analysis of fMRI data, from the angle of support vector machine classification for analysis of complex, multivariate data.

Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis

Complex bimanual motor learning causes specific changes in activation across brain regions. However, there is little information on how motor learning changes the functional connectivity between these regions, and whether this is influenced by different sensory feedback modalities. We applied graph-theoretical network analysis (GTNA) to examine functional networks based on motor-task-related fMRI activations. Two groups learned a complex 90° out-of-phase bimanual coordination pattern, receiving either visual or auditory feedback. 3T fMRI scanning occurred before (day 0) and after (day 5) training. In both groups, improved motor performance coincided with increased functional network connectivity (increased clustering coefficients, higher number of network connections and increased connection strength, and shorter communication distances). Day×feedback interactions were absent but, when examining network metrics across all examined brain regions, the visual group had a marginally better connectivity, higher connection strength, and more direct communication pathways. Removal of feedback had no acute effect on the functional connectivity of the trained networks. Hub analyses showed an importance of specific brain regions not apparent in the standard fMRI analyses. These findings indicate that GTNA can make unique contributions to the examination of functional brain connectivity in motor learning.