Statistical flattening of MEG beamformer images

We propose a method of correction for multiple comparisons in MEG beamformer based Statistical Parametric Maps (SPMs). We introduce a modification to the minimum-variance beamformer, in which beamformer weights and SPMs of source-power change are computed in distinct steps. This approach allows the calculation of image smoothness based on the computed weights alone. In the first instance we estimate image smoothness by looking at local spatial correlations in residual images generated using random data; we then go on to show how the smoothness of the SPM can be obtained analytically by measuring the correlations between the adjacent weight vectors. In simulations we show that the smoothness of the SPM is highly inhomogeneous and depends on the source strength. We show that, for the minimum variance beamformer, knowledge of image smoothness is sufficient to allow for correction of the multiple comparison problem. Per-voxel threshold estimates, based on the voxels extent (or cluster size) in flattened space, provide accurate corrected false positive error rates for these highly inhomogeneously smooth images.     

Accuracy and applications of group MEG studies using cortical source locations estimated from participants' scalp surfaces

We contend that powerful group studies can be conducted using magnetoencephalography (MEG), which can provide useful insights into the approximate distribution of the neural activity detected with MEG without requiring magnetic resonance imaging (MRI) for each participant. Instead, a participant's MRI is approximated with one chosen as a best match on the basis of the scalp surface from a database of available MRIs. Because large inter-individual variability in sulcal and gyral patterns is an inherent source of blurring in studies using grouped functional activity, the additional error introduced by this approximation procedure has little effect on the group results, and offers a sufficiently close approximation to that of the participants to yield a good indication of the true distribution of the grouped neural activity. T1-weighted MRIs of 28 adults were acquired in a variety of MR systems. An artificial functional image was prepared for each person in which eight 5 x 5 x 5 mm regions of brain activation were simulated. Spatial normalisation was applied to each image using transformations calculated using SPM99 with (1) the participant's actual MRI, and (2) the best matched MRI substituted from those of the other 27 participants. The distribution of distances between the locations of points using real and substituted MRIs had a modal value of 6 mm with 90% of cases falling below 12.5 mm. The effects of this approach on real grouped SAM source imaging of MEG data in a verbal fluency task are also shown. The distribution of MEG activity in the estimated average response is very similar to that produced when using the real MRIs.     

Deriving frequency‐dependent spatial patterns in MEG‐derived resting state sensorimotor network: A novel multiband ICA technique

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band‐limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass‐filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well‐characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single‐band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779–791, 2017. © 2016 Wiley Periodicals, Inc.     

MEG source estimation from mesio-basal temporal areas in a child with a porencephalic cyst

BACKGROUND: A child whose left temporal lobe contained mesial, anterior and basal structures but lacked superio-lateral cortex had intractable epilepsy secondary to a porencephalic cyst. Magnetoencephalography (MEG) shows equivalent current dipoles (ECDs) as dipole modeling for temporal lobe epilepsy rather than in an exact location. AIM: We hypothesized that the magnetic fields generated by the epileptic discharges in mesio-basal temporal areas could be detected by MEG without interference from the superio-lateral temporal cortices. METHODS: We analyzed MEG spikes using single dipole analysis and synthetic aperture magnetometry (SAM), and compared with EEG spike topography. RESULTS: Two MEG ECDs corresponding to T3 spikes localized to the anterior mesio-basal temporal region with vertical orientation. Sixteen MEG ECDs corresponding to T5 spikes localized to the middle to posterior mesio-basal temporal region with vertical orientation. SAM revealed maximum current density at hippocampus and anterior fusiform gyrus for T3 spikes, and at posterior hippocampus and fusiform gyrus for T5 spikes. CONCLUSION: Vertically oriented ECDs were obtained without superio-lateral temporal cortices because of temporo-parieto-occipital porencephalic cyst. The absence of superio-lateral temporal cortices, prominent temporal EEG spikes, less prominent MEG spikes, and mesio-basal SAM spikes indicated that the vertically oriented ECDs were projected directly from the mesio-basal temporal region.     

Cortical compensation associated with dysphagia caused by selective degeneration of bulbar motor neurons

According to recent neuroimaging studies, swallowing is processed within multiple regions of the human brain. In contrast to this, little is known about the cortical contribution and compensatory mechanisms produced by impaired swallowing. In the present study, we therefore investigated the cortical topography of volitional swallowing in patients with X-linked bulbospinal neuronopathy (Kennedy disease, KD). Eight dysphagic patients with genetically proven KD and an age-matched healthy control group were studied by means of whole-head magnetoencephalography using a previously established swallowing paradigm. Analysis of data was carried out with synthetic aperture magnetometry (SAM). The group analysis of individual SAM results was performed using a permutation test. KD patients showed significantly larger swallow-related activation of the bilateral primary sensorimotor cortex than healthy controls. In contrast to the control group, in KD patients the maximum activity was located in the right sensorimotor cortex. Furthermore, while in nondysphagic subjects a previously described time-dependent shift from the left to the right hemisphere was found during the one second of most pronounced swallow-related muscle activity, KD patients showed a strong right hemispheric activation in each time segment analyzed. Since the right hemisphere has an established role in the coordination of the pharyngeal phase of swallowing, the stronger right hemispheric activation observed in KD patients indicates cortical compensation of pharyngeal phase dysphagia.     

Magnetoencephalographic gamma power reduction in patients with schizophrenia during resting condition

Objective: The “default network” represents a baseline condition of brain function and is of interest in schizophrenia research because its component brain regions are believed to be aberrant in the disorder. We hypothesized that magnetoencephalographic (MEG) source localization analysis would reveal abnormal resting activity within particular frequency bands in schizophrenia. Experimental Design: Eyes‐closed resting state MEG signals were collected for two comparison groups. Patients with schizophrenia (N = 38) were age‐gender matched with healthy control subjects (N = 38), and with a group of unmedicated unaffected siblings of patients with schizophrenia (N = 38). To localize 3D‐brain regional differences, synthetic aperture magnetometry was calculated across established frequency bands as follows: delta (0.9–4 Hz), theta (4–8 Hz), alpha (8–14 Hz), beta (14–30 Hz), gamma (30–80 Hz), and super‐gamma (80–150 Hz). Principle Observations: Patients with schizophrenia showed significantly reduced activation in the gamma frequency band in the posterior region of the medial parietal cortex. As a group, unaffected siblings of schizophrenia patients also showed significantly reduced activation in the gamma bandwidth across similar brain regions. Moreover, using the significant region for the patients and examining the gamma band power gave an odds ratio of 6:1 for reductions of two standard deviations from the mean. This suggests that the measure might be the basis of an intermediate phenotype. Conclusions: MEG resting state analysis adds to the evidence that schizophrenic patients experience this condition very differently than healthy controls. Whether this baseline difference relates to network abnormalities remains to be seen. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.     

Sensitivity of beamformer source analysis to deficiencies in forward modeling

Beamforming approaches have recently been developed for the field of electroencephalography (EEG) and magnetoencephalography (MEG) source analysis and opened up new applications within various fields of neuroscience. While the number of beamformer applications thus increases fast-paced, fundamental methodological considerations, especially the dependence of beamformer performance on leadfield accuracy, is still quite unclear. In this article, we present a systematic study on the influence of improper volume conductor modeling on the source reconstruction performance of an EEG-data based synthetic aperture magnetometry (SAM) beamforming approach. A finite element model of a human head is derived from multimodal MR images and serves as a realistic volume conductor model. By means of a theoretical analysis followed by a series of computer simulations insight is gained into beamformer performance with respect to reconstruction errors in peak location, peak amplitude, and peak width resulting from geometry and anisotropy volume conductor misspecifications, sensor noise, and insufficient sensor coverage. We conclude that depending on source position, sensor coverage, and accuracy of the volume conductor model, localization errors up to several centimeters must be expected. As we could show that the beamformer tries to find the best fitting leadfield (least squares) with respect to its scanning space, this result can be generalized to other localization methods. More specific, amplitude, and width of the beamformer peaks significantly depend on the interaction between noise and accuracy of the volume conductor model. The beamformer can strongly profit from a high signal-to-noise ratio, but this requires a sufficiently realistic volume conductor model.     

A geometric correction scheme for spatial leakage effects in MEG/EEG seed‐based functional connectivity mapping

Spatial leakage effects are particularly confounding for seed‐based investigations of brain networks using source‐level electroencephalography (EEG) or magnetoencephalography (MEG). Various methods designed to avoid this issue have been introduced but are limited to particular assumptions about its temporal characteristics. Here, we investigate the usefulness of a model‐based geometric correction scheme (GCS) to suppress spatial leakage emanating from the seed location. We analyze its properties theoretically and then assess potential advantages and limitations with simulated and experimental MEG data (resting state and auditory‐motor task). To do so, we apply Minimum Norm Estimation (MNE) for source reconstruction and use variation of error parameters, statistical gauging of spatial leakage correction and comparison with signal orthogonalization. Results show that the GCS has a local (i.e., near the seed) effect only, in line with the geometry of MNE spatial leakage, and is able to map spatially all types of brain interactions, including linear correlations eliminated after signal orthogonalization. Furthermore, it is robust against the introduction of forward model errors. On the other hand, the GCS can be affected by local overcorrection effects and seed mislocation. These issues arise with signal orthogonalization too, although significantly less extensively, so the two approaches complement each other. The GCS thus appears to be a valuable addition to the spatial leakage correction toolkits for seed‐based FC analyses in source‐projected MEG/EEG data. Hum Brain Mapp 36:4604–4621, 2015. © 2015 Wiley Periodicals, Inc .     

Gamma oscillations in the primary motor cortex studied with MEG

In recent years, there has been a growing interest on the role of gamma band (>30 Hz) neural oscillations in motor control, although the function of this activity in motor control is unknown clearly. With the goal of discussing the high frequency sources non-invasively and precisely during unilateral index finger movement, we investigated gamma band oscillations in 20 right-handed normal adults with magnetoencephalography (MEG). The results showed that gamma band activity appeared only during finger movement. Nineteen subjects displayed consistently contralateral event-related synchronization (C-ERS) within high gamma band (70–150 Hz) in primary motor cortex (M1) of both hemispheres. Interestingly, 15 subjects displayed ipsilateral event-related desynchronization (I-ERD) and C-ERS within broad gamma band (30–150 Hz). The locations of the broad gamma band I-ERD and C-ERS revealed hemispherical symmetry in M1. These findings demonstrate that there are consistent high gamma C-ERS and inconsistent low gamma I-ERD during a simple finger movement in the motor cortex. This study provides new evidence for the use of high gamma frequency oscillations as biomarkers in the analyses of functional brain activity and the localization of the motor cortex.     

ROI and phobias: The effect of ROI approach on an ALE meta‐analysis of specific phobias

About 90% of fMRI findings on specific phobias (SP) include analysis of region of interest (ROI). This approach characterized by higher sensitivity may produce inflated results, particularly when findings are aggregated in meta‐analytic maps. Here, we conducted a systematic review and activation likelihood estimation (ALE) meta‐analysis on SP, testing the impact of the inclusion of ROI‐based studies. ALE meta‐analyses were carried out either including ROI‐based results or focusing on whole‐brain voxelwise studies exclusively. To assess the risk of bias in the neuroimaging field, we modified the Newcastle–Ottawa Scale (NOS) and measured the reliability of fMRI findings. Of the 31 selected investigations (564 patients and 485 controls) one‐third did not motivate ROI selection: five studies did not report an explicit rationale, whereas four did not cite any specific reference in this regard. Analyses including ROI‐based studies revealed differences between phobics and healthy subjects in several regions of the limbic circuit. However, when focusing on whole‐brain analysis, only the anterior midcingulate cortex differentiated SP from controls. Notably, 13 studies were labeled with low risk of bias according to the adapted NOS. The inclusion of ROI‐based results artificially inflates group differences in fMRI meta‐analyses. Moreover, a priori, well‐motivated selection of ROIs is desirable to improve quality and reproducibility in SP neuroimaging studies. Lastly, the use of modified NOS may represent a valuable way to assess and evaluate biases in fMRI studies: “low risk” of bias was reported for less than half of the included studies, indicating the need for better practices in fMRI.     

A framework for the design of flexible cross‐talk functions for spatial filtering of EEG/MEG data: DeFleCT

Brain activation estimated from EEG and MEG data is the basis for a number of time‐series analyses. In these applications, it is essential to minimize “leakage” or “cross‐talk” of the estimates among brain areas. Here, we present a novel framework that allows the design of flexible cross‐talk functions (DeFleCT), combining three types of constraints: (1) full separation of multiple discrete brain sources, (2) minimization of contributions from other (distributed) brain sources, and (3) minimization of the contribution from measurement noise. Our framework allows the design of novel estimators by combining knowledge about discrete sources with constraints on distributed source activity and knowledge about noise covariance. These estimators will be useful in situations where assumptions about sources of interest need to be combined with uncertain information about additional sources that may contaminate the signal (e.g. distributed sources), and for which existing methods may not yield optimal solutions. We also show how existing estimators, such as maximum‐likelihood dipole estimation, L2 minimum‐norm estimation, and linearly‐constrained minimum variance as well as null‐beamformers, can be derived as special cases from this general formalism. The performance of the resulting estimators is demonstrated for the estimation of discrete sources and regions‐of‐interest in simulations of combined EEG/MEG data. Our framework will be useful for EEG/MEG studies applying time‐series analysis in source space as well as for the evaluation and comparison of linear estimators. Hum Brain Mapp 35:1642–1653, 2014. © 2013 Wiley Periodicals, Inc.     

A penny for your thoughts! patterns of fMRI activity reveal the content and the spatial topography of visual mental images

Visual mental imagery is a complex process that may be influenced by the content of mental images. Neuropsychological evidence from patients with hemineglect suggests that in the imagery domain environments and objects may be represented separately and may be selectively affected by brain lesions. In the present study, we used functional magnetic resonance imaging (fMRI) to assess the possibility of neural segregation among mental images depicting parts of an object, of an environment (imagined from a first‐person perspective), and of a geographical map, using both a mass univariate and a multivariate approach. Data show that different brain areas are involved in different types of mental images. Imagining an environment relies mainly on regions known to be involved in navigational skills, such as the retrosplenial complex and parahippocampal gyrus, whereas imagining a geographical map mainly requires activation of the left angular gyrus, known to be involved in the representation of categorical relations. Imagining a familiar object mainly requires activation of parietal areas involved in visual space analysis in both the imagery and the perceptual domain. We also found that the pattern of activity in most of these areas specifically codes for the spatial arrangement of the parts of the mental image. Our results clearly demonstrate a functional neural segregation for different contents of mental images and suggest that visuospatial information is coded by different patterns of activity in brain areas involved in visual mental imagery. Hum Brain Mapp 36:945–958, 2015. © 2014 Wiley Periodicals, Inc.     

Focusing effects of L‐dopa in Parkinson's disease

Previous fMRI motor studies in Parkinson's disease (PD) have suggested that L ‐dopa may “normalize” areas of hypo‐ and hyperactivity. However, results from these studies, which were largely based on analyzing BOLD signal amplitude, have been conflicting. Examining only amplitude changes at distinct loci may thus be inadequate in fully capturing the activation changes induced by L ‐dopa. In this article, we extended prior analyses on the effects of L ‐dopa by investigating both amplitude and spatial changes of brain activation before and after L ‐dopa. Ten subjects with PD, both on and off medication, and ten healthy, age‐matched controls performed a visuo‐motor tracking task in which they sinusoidally squeezed a bulb at 0.25, 0.5, and 0.75 Hz. This task was contrasted with static squeezing to generate fMRI activation maps. To investigate the effects of L ‐dopa, we examined the amplitude and spatial variance of the BOLD response within anatomically‐defined regions of interest (ROIs). L ‐dopa had significant main effects on the amplitude of BOLD signal in bilateral primary motor cortex and left SMA. In contrast, L ‐dopa‐mediated spatial changes were apparent in bilateral cerebellar hemispheres, M1, SMA, and right prefrontal cortex. Moreover, L ‐dopa appeared to normalize the spatial distribution of ROI activation in PD to that of the controls. Specifically, L ‐dopa had a “focusing” effect on activity—an effect more pronounced than the typically‐measured fMRI amplitude changes. This observation is consistent with modeling studies, which demonstrated that dopamine increases the signal‐to‐noise ratio at the neuronal level with a resultant focusing of representations at the macroscopic level. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.     

Evidencing a place for the hippocampus within the core scene processing network

Functional neuroimaging studies have identified several “core” brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene‐sensitivity in the HC relative to these other “core” regions. Here, we characterised the frequency and consistency of individual scene‐preferential responses within these regions by analysing a large dataset (n = 51) in which participants performed a one‐back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently‐defined anatomical ROIs to individual‐level functional data across different voxel‐wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene‐related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap “peaks” were close to those identified in group‐level analyses (particularly for TOS and HC), inter‐individual consistency varied across regions and statistical thresholds. The inter‐regional and inter‐individual variability revealed by these analyses has implications for how scene‐sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779–3794, 2016. © 2016 Wiley Periodicals, Inc .     

Localizing the distributed language network responsible for the N400 measured by MEG during auditory sentence processing

We studied auditory sentence comprehension using magnetoencephalography while subjects listened to sentences whose correctness they had to judge subsequently. The localization and the time course of brain electrical activity during processing of correct and semantically incorrect sentences were estimated by computing a brain surface current density within a cortical layer for both conditions. Finally, a region of interest (ROI) analysis was conducted to determine the time course of specific locations. A magnetic N400 was present in six spatially different ROIs. Semantic anomalies caused an exclusive involvement of the ventral portion of the left inferior frontal gyrus (BA 47) and left pars triangularis (BA 45). The anterior parts of the superior (BA 22) and inferior (BA 20/21) temporal gyri bilaterally were activated by both conditions. The activation for the correct condition, however, peaked earlier in both left temporal regions (approximately 32 ms). In general, activation due to semantic violations was more pronounced, started later, and lasted longer as compared to correct sentences. The findings reveal a clear left-hemispheric dominance during language processing indicated firstly by the mere number of activated regions (four in the left vs. two in the right hemisphere) and secondly by the observed specificity of the left inferior frontal ROIs to semantic violations. The temporal advantage observed for the correct condition in the left temporal regions is supporting the notion that the established context eases the processing of the final word. Semantically incorrect words that do not fit into the context result in longer integration times.     

A computer electron microscope plotter for mapping spatial distributions in biological tissues

We have designed a computer-based electron microscope plotting system which maps the locations of organelles in tissue specimens and analyzes their distribution. The system includes: (1) two optical incremental shaft encoders which translate stage drive rotation to electrical pulses; (2) a Display/Control unit used to convert encoder pulses to binary code for computer input; (3) two 16-bit parallel interfaces for transferring data to the computer; (4) a Hewlett-Packard 9845T microcomputer, used to control data input and to store, graph, and analyze the plots. The software for the plotter is written in enhanced BASIC.The plotter system is driven by 4 programs called Trace, Plot, Analyze, and Density. The Trace program “draws” an outline of the edges of the tissue. The Plot program maps the positions of profiles within the tissue. The Analyze program compares trace and plot data and calculates the depth and medial-lateral distance of each plotted profile from the surfaces of the tissue. The Density program sorts and counts profile types, measures surface areas, and calculates profile densities. Commercial statistical software is used to analyze the data. Our laboratory uses the system to map the spatial distribution of synapses and neurons in the central nervous system. The plotting system will also be of value in other areas of neurobiology research.     

Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data‐driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex‐valued resting‐state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post‐ICA phase de‐ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel‐wise F‐test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.     

The attention network of the human brain: relating structural damage associated with spatial neglect to functional imaging correlates of spatial attention

Functional imaging studies of spatial attention regularly report activation of the intraparietal sulcus (IPS) and dorsal premotor cortex including the frontal eye fields (FEF) in tasks requiring overt or covert shifting of attention. In contrast, lesion-overlap studies of patients with spatial neglect – a syndrome characterized by severe impairments of spatial attention – show that the critical damage concerns more ventral regions, comprising the inferior parietal lobule, the temporal–parietal junction (TPJ), and the superior temporal gyrus. We performed voxel-based lesion-symptom mapping of 29 right-hemisphere stroke patients, using several performance indices derived from a cueing task as measures of spatial attention. In contrast to previous studies, we focused our analyses on eight regions of interest defined according to results of previous functional imaging studies. A direct comparison of neglect with control patients revealed that neglect was associated with damage to the TPJ, the middle frontal gyrus, and the posterior IPS. The latter region was also a significant predictor of the degree of contralesional slowing of target detection and the extent to which ipsilesional distracters captured attention of neglect patients. Finally, damage to the FEF and posterior IPS was negatively correlated with the tendency of neglect patients to orient attention toward behaviourally relevant distracters. These findings support the results of functional imaging studies of spatial attention and provide evidence for a network account of neglect, according to which attentional selection of relevant environmental stimuli and the reorienting of attention result from dynamic interactions between the IPS, the dorsal premotor cortex, and the TPJ.     

The neural substrates for atypical planning and execution of word production in stuttering

Using an fMRI-based classification approach and the structural equation modeling (SEM) method, this study examined the neural bases of atypical planning and execution processes involved in stuttering. Twelve stuttering speakers and 12 controls were asked to name pictures under different conditions (single-syllable, multi-syllable, or repeated-syllable) in the scanner. The contrasts between conditions provided information about planning and execution processes. The classification analysis showed that, as compared to non-stuttering controls, stuttering speakers’ atypical planning of speech was evident in their neural activities in the bilateral inferior frontal gyrus (IFG) and right putamen and their atypical execution of speech was evident in their activations in the right cerebellum and insula, left premotor area (PMA), and angular gyrus (AG). SEM results further revealed two parallel neural circuits—the basal ganglia-IFG/PMA circuit and the cerebellum-PMA circuit—that were involved in atypical planning and execution processes of stuttering, respectively. The AG appeared to be involved in the interface of atypical planning and execution in stuttering. These results are discussed in terms of their implications to the theories about stuttering and to clinical applications.