Brain Topogr DOI 10.1007/s10548-014-0413-3
ORIGINAL PAPER
Altered Functional Connectivity Patterns of the Insular Subregions in Psychogenic Nonepileptic Seizures Rong Li • Kai Liu • Xujing Ma • Zhiqiang Li Xujun Duan • Dongmei An • Qiyong Gong • Dong Zhou • Huafu Chen
•
Received: 17 June 2014 / Accepted: 20 October 2014 Ó Springer Science+Business Media New York 2014
Abstract Neuroimaging studies have demonstrated that psychogenic nonepileptic seizures (PNES) are characterized by unstable cognitive-emotional and motor system, which is engaged in hyperactivity of limbic regions and sensorimotor area. The insula, which is a part of the limbic system, includes various subregions with some distinct connectivity patterns separately. However, whether these insular subregions show different connectivity patterns respectively in PNES remains largely unknown. We aimed to investigate the functional connectivity (FC) of insular subregions in PNES and extend the understanding of the complex pathophysiological mechanisms of this disease. A resting-state FC based on the insular subregions were conducted in 18 patients and 20 healthy controls. We examined the differences in FC values between PNES patients and controls using two sample t test. Our results showed patients had significantly stronger FC between insular subregions and sensorimotor network, lingual
Electronic supplementary material The online version of this article (doi:10.1007/s10548-014-0413-3) contains supplementary material, which is available to authorized users. R. Li K. Liu X. Ma Z. Li X. Duan H. Chen (&) Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610041, People’s Republic of China e-mail: [email protected] D. An D. Zhou Department of Neurology, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China Q. Gong Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China
gyrus, superior parietal gyrus and putamen, which suggested a hyperlink pattern of insular subregions involved in abnormal emotion regulation, cognitive processes and motor function in PNES. Pearson correlation analysis between the mean FC values within abnormal regions and the frequency of PNES further indicated PNES exhibited abnormal functional organization whose stressful emotion of patients have great direct influence on their motor functions. The differentially impaired functional connectivity patterns of insular subregions might provide new insights into the complex neurological mechanism of PNES. Keywords PNES fMRI Functional connectivity Insular subregions Frequency
Introduction Psychogenic nonepileptic seizures (PNES) are observable paroxysmal changes in behavior, sensation, motor activity, cognitive processing and autonomic functions, which resemble epileptic seizures while not related to electrographic ictal discharges (Baslet 2011, 2012). PNES are frequently mistaken for epileptic seizures, which have serious consequences such as the side effects of antiepileptic drugs, considerable delay in proper treatment and a huge economic burden for patients. So far, recent researches suggest a multifactorial basis for PNES are of interest. Among the postulated factors, emotional, psychogenic or social factors may have caused the seizure (Krumholz and Niedermeyer 1983; Reuber 2008). Meanwhile, PNES shared many underlying psychological mechanisms with dissociation and conversion disorder patients (Reuber et al. 2003; Guz et al. 2004).
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Although the mechanism in the aetiology of PNES is complex and still mysterious, a plenty of existing theories have started to shed light on the pathophysiology of PNES (Vuilleumier et al. 2001; Montoya et al. 2006). Baslet (2011) puts forward a model for PNES which suggests an alteration in the influence and interaction of brain areas related to emotion processes onto other areas involved in sensorimotor and cognitive function. Etiologically,PNES have been hypothesized to dysfunction in processing of psychological or trauma-related stress (Lesser 2003; Uliaszek et al. 2012), which manifested an unstable cognitive-emotional attention system and accompanied a range of somatic symptoms. Specifically, as the unconscious production of neurologic symptoms that are generally associated with emotional stressors or conflicts, PNES along with other conversion disorders have been hypothesized that they affect the abnormal inhibition of motor systems by limbic regions or impairments of motor conceptualization (Burgmer et al. 2006; de Lange et al. 2007). Increasing attention has been paid to resting-state functional magnetic resonance imaging (fMRI) since the first study of Biswal et al. (1995). Up to now, resting-state fMRI has been conducted in patients with many psychiatric and neurological disease, such as depression (Guo et al. 2011; Liu e al. 2012), schizophrenia (Hoptman et al. 2012; Guo e al. 2014) and obsessive compulsive disorder (Peng et al. 2014). In addition, A recent work using resting-state fMRI identified increased functional connectivity (FC) between regions involved in emotion (insula) and motor planning (precentral sulcus) in patients with PNES, suggesting that emotions strongly influenced motor function bypass executive control (van der Kruijs et al. 2012). Van der Kruijs et al. (2014) later found PNES showed increased coactivation of insular and subcallosal cortex in the resting-state network. Integrating these information, PNES are characterized by an abnormal networks engaging limbic regions and other brain areas, and the insula has begun to show promise as an important locus in PNES (Voon et al. 2010a, b). As a part of the limbic system, insula is a heterogeneous area that involves multimodal functions, including emotion regulation, visceral sensory perception, self-awareness and sensorimotor processing (Craig 2009). Numerous studies employing a diverse range of methodological approaches (Kurth et al. 2010b; Cauda et al. 2011) appear to converge on the functional parcellation of the insula into distinct functional subregions (Deen et al. 2011). These include a ventrol anterior region related to chemosensory and socioemotional processing, a dorsal anterior region related to higher cognitive processing, and a posterior region associated with pain and sensorimotor processing. Considering the insular heterogeneity and its association with PNES neuropathology, it is important to ascertain the intrinsic
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resting-state functional connectivity (RSFC) patterns of the insular subregions in PNES. Thus, we hypothesized that functional connections of insular subregions involved in emotion, cognitive processes and sensorimotor were abnormal in patients with PNES. In this study, to test the hypotheses, we conducted an FC analysis between insular subregions and the whole-brain in PNES patients based on the insular segmentation masks that were proposed in the prior literature (Deen et al. 2011). Then, we explored dysfunctional connectivity among the insular subregions involved in emotion, cognitive processes and sensorimotor function in PNES. In Addition, we further speculated that these abnormal functional connectivity were associated with the clinical features of PNES.
Methods Participants The subjects came from our previous studies (Ding et al. 2013). Twenty patients with PNES (7 males, mean age: 19.65 ± 7.56 years) and 20 controls (8 males, mean age: 21.85 ± 1.70 years) were enrolled in the study. PNES was diagnosed by experienced neurologists using clinical descriptions of symptoms and long-term video-EEG monitoring according to the recent recommendations (Benbadis et al. 2004; Devinsky et al. 2011). Only patients with a diagnosis of definite PNES were included in this study. The inclusion criteria included: (1) at least one single typical episode was recorded by video EEG, and EEG did not show any epileptiform discharge or ictal slowing; (2) patients have no history of neurological disease; (3) patients have no obvious abnormality in routine structural MRI examinations. The exclusion criteria were: (1) patients with neurological comorbidity (e.g. epilepsy); (2) patients with malingering, or any psychiatric disorders (e.g. mood and anxiety disorders, schizophrenia and psychosis). Here, the diagnosis of malingering or psychiatric disorders was determined by two attending psychiatrists using the Structured Clinical Interview for DSM-IV (SCID)-Patients Version and their scores on the Positive and Negative Syndrome Scale, Hamilton Anxiety Rating Scale and Hamilton Depression Rating Scale. Four of 20 patients were taking antiepileptic drugs before the diagnosis of PNES. All drugs were discontinued at least 2 weeks prior to MRI examination. The demographic and clinical characteristics for all patients are presented in Table 1. PNES frequency was assessed according to the patient or their family member reports. The median frequency was 2.5 per month (with a range of one per week to eight per week). Two patients have a long duration of disease with 18 years, because of
Brain Topogr Table 1 Demographic and clinical characteristics of PNES Patient
Age
Gender
Duration
Frequency (times/m)
Type of symptoms
Previous treatmenta
1
34 y
F
18 y
2
Unresponsiveness/eye closure
None
2
17 y
F
2y
8
Unresponsiveness/eye closure/hyperventilation/bod rigidity
None
3
38 y
F
18 y
3
Unresponsiveness/hypermotor EX
None
4
23 y
F
8y
1
Unresponsiveness/eye closure/hyperventilation/bod rigidity
None
5
13 y
F
1m
5
Unresponsiveness/eye closure
None
6
20 y
M
2y
2
Unresponsiveness
VPA
7
14 y
F
2m
3
Unresponsiveness
None
8
17 y
F
2m
4
Unresponsiveness/eye closure/bod rigidity/trembling EX
None VPA
9
14 y
M
5m
5
Unresponsiveness/vocalization
10
16 y
F
2y
0.3
Unresponsiveness/hyperventilation/hypermotor EX
None
11 12
17 y 21 y
F F
4m 8m
4 4
Unresponsiveness/hyperventilation Unresponsiveness/hypermotor EX
None None
13
21 y
M
1m
1
Eye closure/hyperventilation/bod rigidity
None
14
13 y
M
1y
1
Unresponsiveness/eye closure/hyperventilation
None
15
13 y
F
1y
0.5
Unresponsiveness/eye closure/hyperventilation
None
16
35 y
M
15 d
4
Unresponsiveness/eye closure/hypermotor EX
None
17
16 y
F
3y
4
Unresponsiveness
CBZ
18
20 y
M
2y
0.5
Unresponsiveness/hypermotor EX
VPA
19
13 y
M
1m
1
Unresponsiveness
None
20
18 y
F
7m
2
Unresponsiveness/eye closure
None
F female, M male, d day, m month, y year, hypermotor EX hypermotor movements of the extremities, trembling EX trembling of the extremities, VPA valproate, CBZ carbamazepine a
All the drugs were discontinued at least 2 weeks before MRI examination
which the two patients were excluded from the analysis to ensure patients’ illness condition as similar as possible. The control subjects had neither neurologic/psychiatric disorders evaluated by using the SCID-Non-Patient Version, nor took any psychotropic medication within the past 6 months. All participants gave informed consent to participate in the investigation, which received ethical approval by the local Ethics Committee of West China Hospital. Data Acquisition MRI was performed on a 3T Siemens Trio scanner (Erlangen, Germany). Participants were instructed to rest with their eyes closed but not fall asleep, and keep their heads still. Meanwhile, Foam pads and earplugs were used to reduce head motion and scanner noise. The resting-state fMRI was obtained by using an echo planar imaging sequence for a total of 205 volumes with following protocols: TR = 2,000 ms; TE = 30 ms; flip angle = 90°; field of view = 240 9 240 mm2; acquisition matrix = 64 9 64; voxel size = 3.75 9 3.75 9 5 mm3, no slice gap; 30 axial slices. Structural MRI data was acquired using a three-dimensional (3D) T1-weighted spoiled gradient echo sequence with the following parameters: TR = 20 ms; TE = 3.69 ms; flip
angle = 12°; field of view = 250 9 250 mm2; acquisition matrix = 320 9 320; voxel size = 0.78 9 0.78 9 1 mm3, no slice gap, 128 sagittal slices. Additionally, the diffusion tensor images (DTI) were collected using a single-shot spinecho planar imaging sequence in contiguous axial planes covering the whole-brain. Each volume consisted of 50 contiguous axial slices (TR = 6,800 ms; TE = 93 ms; flip angle = 90°; field of view = 240 9 240 mm2; acquisition matrix = 128 9 128; voxel size = 1.88 9 1.88 9 3 mm3, no slice gap). At each slice position, except for S0 (b = 0 s/mm2), a single b-value (b = 1,000 s/mm2) was applied to 20 non-collinear gradient directions. Data Preprocessing Functional images preprocessing was carried out by using the Statistical Parametric Mapping software (SPM8, http:// www.fil.ion.ucl.ac.uk/spm). The first five volumes were discarded because of instability of the initial MRI signal and adaptation of participants to the circumstance. The remaining 200 functional images were first corrected for the acquisition time delay among different slices, and then were realigned to the first volume for head motion correction. The time course of head motion was obtained by estimating the translation in each direction and the rotation
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in angular motion on each axis for all 200 consecutive volumes. There were no subjects with movement greater than 1.5 mm translation or 1.5° rotation (Liu et al. 2013b). The fMRI data were further spatially warped into a standard stereotaxic space at a resolution of 3 9 3 9 3 mm3, using the Montreal Neurological Institute (MNI) echoplanar imaging (EPI) template in SPM8. Then the resultant normalized functional data were smoothed by convolution with an 8-mm full-width half-maximum Gaussian kernel to reduce spatial noise. Definition of Insular Subregions Regions of interest (ROIs) for the left and right insular subregions were selected (of approximately 47–125 voxels) for all the subjects. We created three insular subregions in each hemisphere using cluster according to the previous literature (Deen et al. 2011), in which the insular lobe based on clustering of FC patterns were subdivided. The bilateral insula subregions were defined anatomically by drawing insular gray matter on the Montreal Neurological Institute (MNI) 152 standard brain (Deen et al. 2011). The limits of the insula were taken to be the anterior, superior, and inferior periinsular sulci (Ture et al. 1999; Nanetti et al. 2009).The cluster analysis revealed three subregions of the insula, for both left and right ROIs: ventral anterior insula (vAI), dorsal anterior insula (dAI), and posterior insula (PI). Each voxel in the insular ROIs (converted to 3-mm resolution) was used as a seed in a whole-brain functional connectivity analysis in both PNES patients and controls groups. Functional Connectivity The whole-brain functional connectivity maps for each of the insular subregions were investigated using the Pearson correlation (Fox et al. 2009; Liu et al. 2013a).To perform FC analysis, time series were extracted from each ROI. Each time series was further corrected for the effect of six head motion parameters by linear regression to reduce the influence of head motion (Fox et al. 2009) and removed confounding effects of physiological noise. Additionally, the averaged signals from the specific cerebrospinal fluid and white matter masks were regressed based on the previous resting state fMRI studies (Liu et al. 2014; Bettus et al. 2009; Fair et al. 2008). Subsequently, the resulting time series were filtered by applying a bandpass filter (0.01–0.08 Hz) to reduce the effects of lowfrequency drift and high frequency noise. The residuals signal constituted the regional time series of ROI for further analyses. The steps to obtain the FC maps are as following: (1) Extract the time series of every voxel in the whole-brain. (2) Calculate the correlation coefficients between the averaged time course of all the voxels in the ROIs and every other voxel’s time course in the whole-brain separately. (3) The
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resulting r values were converted using Fisher’s r-to-z transformation (Waites et al. 2006) to improve the Gaussianity of their distribution. For each subject, we obtained 6 z-score maps that represented the RSFC patterns of the 6 insular subregions (three for each hemisphere). Structural Connectivity We did a DTI analysis to investigate whether the FC abnormalities are accompanied by structural (white matter) abnormalities. Firstly, insular subregions and FC ROIs for the DTI analysis were derived from the normalized MNI space (the details of these brain regions see Supplementary Table S3). Then, whole brain fiber tracking was performed in native diffusion space to reveal whether the insular subregions and altered functional connectivity regions were anatomically connected or not. The details of DTI deterministic tractography analysis are as following. For DTI, eddy current distortions and head motions were corrected using FMRIB’s Diffusion Toolbox (FSL 4.1; http://www.fmrib.ox.ac.uk/ fsl). For each subject, whole brain fiber tracking (an interpolated streamline propagation algorithm) was performed in the DTI native space using TrackVis (Schmahmann et al. 2007). Path tracing proceeded until either the fractional anisotropy (FA)was lower than 0.15 or the angle between the current and the previous path segment was higher than 35°(Liao et al. 2011). As five pair of ROIs (vAI and SMA, postcentral gyrus, lingual gyrus, dAI and superior parietal gyrus L, PI and putamen) were derived from the normalized MNI space, the inverse transformation of the spatial normalization were applied to acquire the ROIs in the native space of DTI (Greicius et al. 2009; Long et al. 2014). More specifically, the inverse transformation (T-1) was applied to the eight ROIs in the normalized MNI, resulting in the subject-specific ROIs in the native space of DTI. Fiber bundles connecting each pair of ROIs were then extracted from the total collection of brain fibers. This was done by a three-step procedure. First, an initial ROI was selected, and the tracts that reached the first ROI were chosen from all fibers. A second ROI was then retrieved from the rest of the ROIs. Only those tracts that reached the second ROI were picked from the resulting tracts of the previous step. Statistical Analysis For each group and each seed ROI, individual z values maps were analyzed with a random effect one-sample t test to determine the FC spatial pattern of the insluar subregions, stringently thresholded using family-wise error (FWE) correction for multiple comparisons (p \ 0.001). To compare the differences of the FC between the PNES patients and control groups, two sample t test with age, gender and duration of disease as covariates to be regressed
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was performed. The resulting statistical FC maps were corrected for multiple comparisons to a significant level of p \ 0.05 (combined height threshold of p \ 0.01 and a minimum cluster size of 66 voxels), using the AlphaSim program in the REST software (http://resting-fmri.source forge.net). Furthermore, in order to investigate the links between the FC values and the clinical features of PNES, we calculated the Pearson correlation between mean z values within abnormal regions and the average number of episodes that patients experienced for a month. The regions of altered FC in the voxel-wise statistics were used for the correlation analysis. Correlations between mean z values within these regions and the frequency of PNES episodes were analyzed. The threshold of p \ 0.05 was considered to be significant for these analyses.
right dVI and PI yielded significant group differences in connectivity values (p \ 0.05, AlphaSim corrected), which are illustrated in Fig. 1 (region details see in Table 2). The other three seed regions did not show significant statistical differences. For the left vAI seed, compared to the controls, PNES patients had stronger functional connectivity in the following regions: the right lingual gyrus, left postcentral gyrus and bilateral SMA. Additionally, for the right seeds, both right dAI and PI, we observed corresponding high FC related to left superior parietal gyrus and left putamen in patients with PNES. However, we did not detect any direct fiber tracts that passed through the insular subregions to the altered FC regions in either PNES groups or healthy controls. Correlations Between Functional Connectivity Values and the Frequency of PNES
Results Spatial Pattern of Functional Connectivity We first detected the functional connectivity pattern of ROI in insula. As showed in Supplementary Figs. S1 and S2, the one-sample t tests revealed a typical spatial pattern in each insular subregion in both controls and PNES patients(the one-sample activated region details are showed in Supplementary Tables S1, S2). The vAI seed, was functionally connected with the bilateral insula, anterior and middle cingulate gyrus, supplementary motor area (SMA) and part of frontal and temporal gyrus. The dAI seed was functionally connected with much of insula, as well as a series of regions involved in control network (Dosenbach et al. 2007) including the anterior and middle cingulate gyrus, SMA, precentral gyrus, supramarginal gyrus and superior parietal gyrus. The PI-seeded functional connectivity revealed correlations with the bilateral insula, SMA, much of the precentral and postcentral gyrus, putamen, as well as the cingulate gyrus and the superior temporal gyrus. The results demonstrated a distinct functional connectivity pattern of the insular subregions, which had been emphasized in the prior studies (Deen et al. 2011; Kurth et al. 2010a). Particularly, the patient group showed obvious stronger connectivity in the superior parietal gyrus, postcentral gyrus, the lingual gyrus and the bilateral SMA compared to the controls. Differences in Functional Connectivity Between PNES Patients and Controls We examined the differences in FC values between PNES patients and control subjects based on the six seed insular subregions. The FC maps based on the ROIs of left vAI,
To explore whether the frequency of PNES were associated with FC values, Pearson correlation analyses were conducted. As presented in Fig. 2, the mean Z values of bilateral SMA based on the left vAI seed in PNES patients were significantly positively correlated with the frequency of PNES episodes (p \ 0.05). No significant correlation was detected when other seeds were considered.
Discussion By employing RSFC analysis to fMRI data of PNES and healthy controls, we observed a broad range of hyperlink in PNES based on the insular seeds. Significantly stronger functional connections were detected between the regions involved in motor cortex, putamen, superior parietal gyrus, lingual gyrus and the insular subregions, which suggested that the PNES was characterized by the abnormal emotion, cognitive processes and motor system. Additionally, FC values between the bilateral SMA and the vAI seed were positively correlated with the frequency of PNES, suggesting that the abnormal functional organization whose emotion has a great influence on motor function may predispose or facilitate PNES. Overall, these findings have important implications for the underlying pathophysiological mechanisms of PNES and add the new evidence for the hyperlink of insula and other brain regions. Hyperlink Between vAI and Sensorimotor Network Previous studies demonstrated that conversion disorder had abnormal inhibition of motor systems affected by limbic regions (Burgmer et al. 2006). Particularly, in the first study using resting-state fMRI on patients with PNES, van
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Fig. 1 Regions of significant stronger functional connectivity for insluar seeds in PNES patients compared to controls (AlphaSim corrected p \ 0.01, cluster size = 66). The bar on the bottom right of each activated map indicates the t value of the activation level.
Table 2 demonstrates the details of the significantly differently regions in PNES. L left, R right, vAI ventral anterior insula, dAI dorsal anterior insula, PI posterior insula
der Kruijs et al. (2012) reported PNES had a stronger FC between insula and sensorimotor cortex. As we conducted the FC analysis in PNES patients by dividing insula into three subreigons, higher FC values between region related to emotion processing (left vAI) and sensorimotor network (left postcentral gyrus and bilateral SMA) have been found in PNES patients. The seed-based findings were consisted with the previous results indicating that compared with healthy controls, PNES patients were more likely to be influenced by the emotional upset and result in involuntary behavioural patterns. Several prior findings indicated vAI played an important role in the processing of emotion (Dupont et al. 2003) and our studies may thus suggest the abnormal emotional regions of PNES were located in the
vAI, instead of entire insula, which might provide more accurate evidences for the pathology of PNES. In addition, the function of the SMA is implicated in self-initiated actions as well as in unconscious motor inhibition (Picard and Strick 2003). The existence of such an abnormal emotion–sensorimotor system is further supported by the manifestations that PNES is accompanied by somatoform symptoms such as a range of involuntary behavioral patterns and exhibits a failure in the coordination and balance of different mental and somatic function (Baslet 2011; Landgrebe et al. 2008). Actually, for most patients, these involuntary behavioural patterns are the avoidant coping strategies to deal with stressful emotion experiences (Magaudda et al. 2011). Combining the results,
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Brain Topogr Table 2 The significantly differences of FC among the insular seeds between controls and PNES patients Region name
Hem
BA
Coordinates X
Y
Z
Peak t value
vAI_L Supplementary motor area
L
6
Postcentral gyrus
R L
6 6
Lingual gyrus
R
18
-3
-8
63
4.02
6 -6 -48 -24
51 27
3.92 4.25
15
-69
0
4.88
L
-30
-69
54
3.97
R
-27
0
9
4.2
dAI_R Superior parietal gyrus PI_R Putamen
Results were corrected at a significant level of p \ 0.05 as well as cluster size [66 voxels by using AlphaSim. The coordinates were showed in the Montreal Neurological Institute (MNI) standard space FC functional connectivity, PNES psychogenic non-epileptic seizures, Hem hemisphere, BA Brodmann’s area, L left, R right, vAI ventral anterior insula, dVI dorsal anterior insula, pI posterior insula
it is reasonable to conclude that an abnormality of emotion–sensorimotor network is a potential factor that result in the occurrence of PNES. Furthermore, altered connectivity between vAI and bilateral SMA was correlated with frequency of PNES, suggesting the abnormal functional organization whose emotion has a great influence on motor function may predispose to or facilitate PNES episodes. Selectively modulating the skewed balance between limbic system and motor cortex may prevent PNES events. Hyperlink Between PI and Putamen in PNES Evidences had been found that the PI was functionally connected to primary and secondary motor and
somatosensory cortices (Deen et al. 2011). Though the putamen was concluded, it has no specialization. It works in conjunction with many other structures to control types of motor skills such as motor performance and tasks, motor preparation, specifying amplitudes of movement and the selection of movement (Alexander and Crutcher 1990; Griffiths et al. 1994). Brain imaging studies reporting PNESrelated disorders showed decreased or excessive activity in motor areas during motor task (Stone et al. 2007). Accordingly, the increased functional connectivity we found between the PI and putamen in PNES patients could be interpreted as PNES always accompanying movementrelated dysfunction involved in motor skills and tasks on the basis of aberrant emotion–sensorimotor system. Further motor task-related studies are needed to investigate the mechanism of abnormal limb movement function in patients with PNES. Hyperlink Between dAI and Parietal Lobe in PNES We observed PNES patients had an abnormal functional connectivity between the right dAI and the left parietal lobe. A stronger FC was also found in patients with PNES between the insula and the parietal cortex in the previous fMRI studies (van der Kruijs et al. 2012). The dAI was found to be functionally related to a set of regions previously described as a cognitive network (Dosenbach et al. 2007). Several researchers have demonstrated the specific role of parietal lobe in spatial orientation, sensory information processing and subsequent action organization (Fogassi et al. 2005). Given these evidences, sensory information in the parietal lobe could influence cognitive function and executive control in PNES patients through a pathway derived from this dAI-parietal circuit. Not uncommonly, individuals with PNES present with
Fig. 2 Scatterplots for the correlation between the frequency of PNES per month and altered functional connectivity respectively in a the left supplementary motor area (SMA_L, r = 0.594, p = 0.0093), b the right supplementary motor area (SMA_R, r = 0.609, p = 0.0073)
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Fig. 3 Circuitry for altered functional connectivity involved in emotion, cognitive and motor network in PNES patients. We found the insular subregions (vAI, dAI, PI) have a significantly higher connectivity within motor (postcentral gyrus/SMA and putamen), visual memory (lingual gyrus) and sensory process (parietal lobe) regions. Particularly the circuitry highlights the influence of emotion (vAI) over sensorimotor (postcentral gyrus and SMA) function in
patients with PNES. It appears that in PNES patients emotion experiences in the past can directly result in the involuntary behavioural movement while the healthy people can control their motor functions without the influence of stressful emotions. vAI ventral anterior insula, dAI dorsal anterior insula, PI posterior insula, SMA supplementary motor area
subjective cognitive complaints were beyond their cognitive alteration during the attacks (Baslet 2011). In fact, patients might perform a series of cognitive defects by partially or completely losing their integrative capacity for certain mental or somatic actions due to the past traumatic experiences.
this network can explain the fact PNES patients exhibit a failure in the coordination and balance of different mental and somatic functions. It is worth mentioning that our previous study (Ding et al. 2013) revealed that PNES exhibited altered nodal characteristics of structural network in several brain regions, including insula. These inconsistent findings may be due to the different methodologies as we defined 6 insular subregions as ROIs when constructing the structural connectivity. Different from the current study, Ding et al. computed the structural network at a regional level with 90 regions from the automated anatomical labeling (AAL) atlas. Moreover, we did not detect any fiber tracts that passed through the subregions of the insula to the altered FC regions, which is consistent with previous studies (Cloutman et al. 2012; Cerliani et al. 2012). Tractography seeds derived from the anterior insula were mainly linked with limbic and paralimbic regions and anterior parts of the inferior frontal gyrus, while seeds derived from the posterior and dorsal-middle insular regions were associated with a network focusing on posterior temporal cortices. Thus, these findings indicated that the altered functional connectivity in the patients was directly associated with the disease rather than the structural connectivity abnormalities.
Hyperlink Between vAI and the Right Lingual Gyrus It is interesting to note that we identified an increased functional connection between the left vAI and the right lingual gyrus in patients with PNES. The lingual gyrus is a structure in the visual cortex that is linked to visual memory and facial recognition (Kapur et al. 1995). A taskrelated study indicated that PNES patients show relatively increased avoidance tendencies or related motor responses for angry faces (Bakvis et al. 2011). Our finding of the presence of higher FC in vAI and lingual gyrus in PNES patients might reflect an adaptation for long-term social threat cues and display increased response and greater attention to negative emotion stimuli. Combining the results of our study, the altered functional connectivity of insular subregions suggested that an underlying pathophysiological mechanisms for PNES where the network involved in emotion regulation, cognitive processes and sensorimotor display an abnormal interactions and disturbances. Figure 3 displays the circuitry for hyperlink network involved in emotion, cognitive process and motor in PNES patients. It appears that for PNES patients the stressful emotion experiences in the past have great direct influence on their motor functions bypassing the cognitive information processing and eventually cause the occurrence of PNES. The dysfunction in
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Limitations Our findings need also to be considered in light of several limitations. First, the small sample size may reduce statistical power. Second, we didn’t carry out any subgroup analyses since our sample size was small. Larger sample studies are needed to investigate different subtypes of
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PNES and comprehensively examine these functional abnormalities. Finally, the ROIs were selected according to the mask definitions available from the literature without using anatomical information which has a certain apriority. In this regard, the selection of the insular subregions, as well as the seed size in the functional connectivity analysis should be considered.
Conclusion This is the first study to investigate the RSFC patterns in PNES patients by employing the insular subregions as seeds. We have identified abnormal FC of insular subregions in PNES and show that the hyperlink patterns of abnormalities varies in different insular subregions, which involved in emotion regulation, cognitive processes and motor function in PNES. Furthermore, the correlation between the altered FC values and the frequency of PNES episodes suggests the abnormal functional organization whose stressful emotion of patients have great direct influence on their motor functions. The differentially impaired functional connectivity patterns of insular subregions might provide new insights into the complex neurological mechanism of PNES. Acknowledgments This work was supported by 973 Project 2012CB517901, and by the Natural Science Foundation of China, Grant Nos. 61035006, 61125304, and by the Specialized Research Fund for the Doctoral Program of Higher Education of China 20120185110028. The authors have no financial relationships to disclose.
References Alexander GE, Crutcher MD (1990) Preparation for movement: neural representations of intended direction in three motor areas of the monkey. J Neurophysiol 64(1):133–150 Bakvis P, Spinhoven P, Zitman FG, Roelofs K (2011) Automatic avoidance tendencies in patients with psychogenic non-epileptic seizures. Seizure 20(8):628–634. doi:10.1016/j.seizure.2011.06. 006 Baslet G (2011) Psychogenic non-epileptic seizures: a model of their pathogenic mechanism. Seizure 20(1):1–13. doi:10.1016/j.sei zure.2010.10.032 Baslet G (2012) Psychogenic nonepileptic seizures: a treatment review. What have we learned since the beginning of the millennium? Neuropsychiatr Dis Treat 8:585–598. doi:10.2147/ NDT.S32301 Benbadis SR, Siegrist K, Tatum WO, Heriaud L, Anthony K (2004) Short-term outpatient EEG video with induction in the diagnosis of psychogenic seizures. Neurology 63(9):1728–1730 Bettus G, Guedj E, Joyeux F, Confort-Gouny S, Soulier E, Laguitton V, Cozzone PJ, Chauvel P, Ranjeva JP, Bartolomei F, Guye M (2009) Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp 30(5):1580–1591. doi:10.1002/hbm.20625
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541 Burgmer M, Konrad C, Jansen A, Kugel H, Sommer J, Heindel W, Ringelstein EB, Heuft G, Knecht S (2006) Abnormal brain activation during movement observation in patients with conversion paralysis. Neuroimage 29(4):1336–1343. doi:10. 1016/j.neuroimage.2005.08.033 Cauda F, D’Agata F, Sacco K, Duca S, Geminiani G, Vercelli A (2011) Functional connectivity of the insula in the resting brain. Neuroimage 55(1):8–23. doi:10.1016/j.neuroimage.2010.11.049 Cerliani L, Thomas RM, Jbabdi S, Siero JC, Nanetti L, Crippa A, Gazzola V, D’Arceuil H, Keysers C (2012) Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex. Hum Brain Mapp 33(9):2005–2034. doi:10.1002/hbm.21338 Cloutman LL, Binney RJ, Drakesmith M, Parker GJ, Lambon Ralph MA (2012) The variation of function across the human insula mirrors its patterns of structural connectivity: evidence from in vivo probabilistic tractography. Neuroimage 59(4):3514–3521. doi:10.1016/j.neuroimage.2011.11.016 Craig AD (2009) How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci 10(1):59–70. doi:10.1038/ nrn2555 de Lange FP, Roelofs K, Toni I (2007) Increased self-monitoring during imagined movements in conversion paralysis. Neuropsychologia 45(9):2051–2058. doi:10.1016/j.neuropsychologia. 2007.02.002 Deen B, Pitskel NB, Pelphrey KA (2011) Three systems of insular functional connectivity identified with cluster analysis. Cereb Cortex 21(7):1498–1506. doi:10.1093/cercor/bhq186 Devinsky O, Gazzola D, LaFrance WC Jr (2011) Differentiating between nonepileptic and epileptic seizures. Nat Rev Neurol 7(4):210–220. doi:10.1038/nrneurol.2011.24 Ding JR, An D, Liao W, Li J, Wu GR, Xu Q, Long Z, Gong Q, Zhou D, Sporns O, Chen H (2013) Altered functional and structural connectivity networks in psychogenic non-epileptic seizures. PLoS One 8(5):e63850. doi:10.1371/journal.pone.0063850 Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL, Petersen SE (2007) Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci USA 104(26):11073–11078. doi:10.1073/pnas.0704320104 Dupont S, Bouilleret V, Hasboun D, Semah F, Baulac M (2003) Functional anatomy of the insula: new insights from imaging. Surg Radiol Anat 25(2):113–119. doi:10.1007/s00276-0030103-4 Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL (2008) The maturing architecture of the brain’s default network. Proc Natl Acad Sci USA 105(10):4028–4032. doi:10.1073/pnas.0800376105 Fogassi L, Ferrari PF, Gesierich B, Rozzi S, Chersi F, Rizzolatti G (2005) Parietal lobe: from action organization to intention understanding. Science 308(5722):662–667. doi:10.1126/ science.1106138 Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101(6):3270–3283. doi:10.1152/jn.90777.2008 Greicius MD, Supekar K, Menon V, Dougherty RF (2009) Restingstate functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 19(1):72–78. doi:10. 1093/cercor/bhn059 Griffiths PD, Perry RH, Crossman AR (1994) A detailed anatomical analysis of neurotransmitter receptors in the putamen and caudate in Parkinson’s disease and Alzheimer’s disease. Neurosci Lett 169(1–2):68–72
123
Brain Topogr Guo WB, Liu F, Xue ZM, Yu Y, Ma CQ, Tan CL, Sun XL, Chen JD, Liu ZN, Xiao CQ, Chen HF, Zhao JP (2011) Abnormal neural activities in first-episode, treatment-naive, short-illness-duration, and treatment-response patients with major depressive disorder: a resting-state fMRI study. J Affect Disord 135(1–3):326–331. doi:10.1016/j.jad.2011.06.048 Guo W, Liu F, Liu J, Yu L, Zhang J, Zhang Z, Xiao C, Zhai J, Zhao J (2014) Abnormal causal connectivity by structural deficits in first-episode, drug-naive schizophrenia at rest. Schizophr Bull. doi:10.1093/schbul/sbu126 Guz H, Doganay Z, Ozkan A, Colak E, Tomac A, Sarisoy G (2004) Conversion and somatization disorders: dissociative symptoms and other characteristics. J Psychosom Res 56(3):287–291. doi:10.1016/S0022-3999(03)00069-2 Hoptman MJ, Zuo XN, D’Angelo D, Mauro CJ, Butler PD, Milham MP, Javitt DC (2012) Decreased interhemispheric coordination in schizophrenia: a resting state fMRI study. Schizophr Res 141(1):1–7. doi:10.1016/j.schres.2012.07.027 Kapur N, Friston KJ, Young A, Frith CD, Frackowiak RS (1995) Activation of human hippocampal formation during memory for faces: a PET study. Cortex 31(1):99–108 Krumholz A, Niedermeyer E (1983) Psychogenic seizures: a clinical study with follow-up data. Neurology 33(4):498–502 Kurth F, Eickhoff SB, Schleicher A, Hoemke L, Zilles K, Amunts K (2010a) Cytoarchitecture and probabilistic maps of the human posterior insular cortex. Cereb Cortex 20(6):1448–1461. doi:10. 1093/cercor/bhp208 Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB (2010b) A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 214(5–6):519–534. doi:10.1007/s00429-0100255-z Landgrebe M, Barta W, Rosengarth K, Frick U, Hauser S, Langguth B, Rutschmann R, Greenlee MW, Hajak G, Eichhammer P (2008) Neuronal correlates of symptom formation in functional somatic syndromes: a fMRI study. Neuroimage 41(4):1336–1344. doi:10. 1016/j.neuroimage.2008.04.171 Lesser RP (2003) Treatment and outcome of psychogenic nonepileptic seizures. Epilepsy Curr 3(6):198–200. doi:10.1046/j.15357597.2003.03601.x Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Wang Z, Tan Q, Lu G, Chen H (2011) Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 32(6):883–895. doi:10.1002/hbm. 21076 Liu F, Guo W, Fouche JP, Wang Y, Wang W, Ding J, Zeng L, Qiu C, Gong Q, Zhang W, Chen H (2013a) Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. doi:10.1007/s00429-013-0641-4 Liu F, Guo W, Liu L, Long Z, Ma C, Xue Z, Wang Y, Li J, Hu M, Zhang J, Du H, Zeng L, Liu Z, Wooderson SC, Tan C, Zhao J, Chen H (2013b) Abnormal amplitude low-frequency oscillations in medication-naive, first-episode patients with major depressive disorder: a resting-state fMRI study. J Affect Disord 146(3):401–406. doi:10.1016/j.jad.2012.10.001 Liu F, Hu M, Wang S, Guo W, Zhao J, Li J, Xun G, Long Z, Zhang J, Wang Y, Zeng L, Gao Q, Wooderson SC, Chen J, Chen H (2012) Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients with late-life depression: a resting-state fMRI study. Prog Neuropsychopharmacol Biol Psychiatry 39(2):326–331 Liu F, Xie B, Wang Y, Guo W, Fouche JP, Long Z, Wang W, Chen H, Li M, Duan X, Zhang J, Qiu M (2014) Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topogr. doi:10.1007/s10548-014-0386-2
123
Long Z, Duan X, Wang Y, Liu F, Zeng L, Zhao JP, Chen H (2014) Disrupted structural connectivity network in treatment-naive depression. Prog Neuropsychopharmacol Biol Psychiatry 56C, 18–26. doi:10.1016/j.pnpbp.2014.07.007 Magaudda A, Gugliotta SC, Tallarico R, Buccheri T, Alfa R, Lagana A (2011) Identification of three distinct groups of patients with both epilepsy and psychogenic nonepileptic seizures. Epilepsy Behav 22(2):318–323. doi:10.1016/j.yebeh.2011.07.005 Montoya A, Price BH, Lepage M (2006) Neural correlates of ‘functional’ symptoms in neurology. Funct Neurol 21(4):193–197 Nanetti L, Cerliani L, Gazzola V, Renken R, Keysers C (2009) Group analyses of connectivity-based cortical parcellation using repeated k-means clustering. Neuroimage 47(4):1666–1677. doi:10.1016/j.neuroimage.2009.06.014 Peng ZW, Xu T, He QH, Shi CZ, Wei Z, Miao GD, Jing J, Lim KO, Zuo XN, Chan RC (2014) Default network connectivity as a vulnerability marker for obsessive compulsive disorder. Psychol Med 44(7):1475–1484. doi:10.1017/S0033291713002250 Picard N, Strick PL (2003) Activation of the supplementary motor area (SMA) during performance of visually guided movements. Cereb Cortex 13(9):977–986 Reuber M (2008) Psychogenic nonepileptic seizures: answers and questions. Epilepsy Behav 12(4):622–635. doi:10.1016/j.yebeh. 2007.11.006 Reuber M, House AO, Pukrop R, Bauer J, Elger CE (2003) Somatization, dissociation and general psychopathology in patients with psychogenic non-epileptic seizures. Epilepsy Res 57(2–3):159–167. doi:10.1016/j.eplepsyres.2003.11.004 Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, de Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(Pt 3):630–653. doi:10.1093/ brain/awl359 Stone J, Zeman A, Simonotto E, Meyer M, Azuma R, Flett S, Sharpe M (2007) FMRI in patients with motor conversion symptoms and controls with simulated weakness. Psychosom Med 69(9):961–969. doi:10.1097/PSY.0b013e31815b6c14 Ture U, Yasargil DC, Al-Mefty O, Yasargil MG (1999) Topographic anatomy of the insular region. J Neurosurg 90(4):720–733 Uliaszek AA, Prensky E, Baslet G (2012) Emotion regulation profiles in psychogenic non-epileptic seizures. Epilepsy Behav 23(3):364–369. doi:10.1016/j.yebeh.2012.01.009 van der Kruijs SJ, Bodde NM, Vaessen MJ, Lazeron RH, Vonck K, Boon P, Hofman PA, Backes WH, Aldenkamp AP, Jansen JF (2012) Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures. J Neurol Neurosurg Psychiatr 83(3):239–247. doi:10.1136/jnnp-2011-300776 van der Kruijs SJ, Jagannathan SR, Bodde NM, Besseling RM, Lazeron RH, Vonck KE, Boon PA, Cluitmans PJ, Hofman PA, Backes WH, Aldenkamp AP, Jansen JF (2014) Resting-state networks and dissociation in psychogenic non-epileptic seizures. J Psychiatr Res 54:126–133. doi:10.1016/j.jpsychires.2014.03.010 Voon V, Brezing C, Gallea C, Ameli R, Roelofs K, LaFrance WC Jr, Hallett M (2010a) Emotional stimuli and motor conversion disorder. Brain 133(Pt 5):1526–1536. doi:10.1093/brain/awq054 Voon V, Gallea C, Hattori N, Bruno M, Ekanayake V, Hallett M (2010b) The involuntary nature of conversion disorder. Neurology 74(3):223–228. doi:10.1212/WNL.0b013e3181ca00e9 Vuilleumier P, Chicherio C, Assal F, Schwartz S, Slosman D, Landis T (2001) Functional neuroanatomical correlates of hysterical sensorimotor loss. Brain 124(Pt 6):1077–1090 Waites AB, Briellmann RS, Saling MM, Abbott DF, Jackson GD (2006) Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann Neurol 59(2):335–343. doi:10.1002/ ana.20733
ORIGINAL PAPER
Altered Functional Connectivity Patterns of the Insular Subregions in Psychogenic Nonepileptic Seizures Rong Li • Kai Liu • Xujing Ma • Zhiqiang Li Xujun Duan • Dongmei An • Qiyong Gong • Dong Zhou • Huafu Chen
•
Received: 17 June 2014 / Accepted: 20 October 2014 Ó Springer Science+Business Media New York 2014
Abstract Neuroimaging studies have demonstrated that psychogenic nonepileptic seizures (PNES) are characterized by unstable cognitive-emotional and motor system, which is engaged in hyperactivity of limbic regions and sensorimotor area. The insula, which is a part of the limbic system, includes various subregions with some distinct connectivity patterns separately. However, whether these insular subregions show different connectivity patterns respectively in PNES remains largely unknown. We aimed to investigate the functional connectivity (FC) of insular subregions in PNES and extend the understanding of the complex pathophysiological mechanisms of this disease. A resting-state FC based on the insular subregions were conducted in 18 patients and 20 healthy controls. We examined the differences in FC values between PNES patients and controls using two sample t test. Our results showed patients had significantly stronger FC between insular subregions and sensorimotor network, lingual
Electronic supplementary material The online version of this article (doi:10.1007/s10548-014-0413-3) contains supplementary material, which is available to authorized users. R. Li K. Liu X. Ma Z. Li X. Duan H. Chen (&) Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610041, People’s Republic of China e-mail: [email protected] D. An D. Zhou Department of Neurology, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China Q. Gong Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, People’s Republic of China
gyrus, superior parietal gyrus and putamen, which suggested a hyperlink pattern of insular subregions involved in abnormal emotion regulation, cognitive processes and motor function in PNES. Pearson correlation analysis between the mean FC values within abnormal regions and the frequency of PNES further indicated PNES exhibited abnormal functional organization whose stressful emotion of patients have great direct influence on their motor functions. The differentially impaired functional connectivity patterns of insular subregions might provide new insights into the complex neurological mechanism of PNES. Keywords PNES fMRI Functional connectivity Insular subregions Frequency
Introduction Psychogenic nonepileptic seizures (PNES) are observable paroxysmal changes in behavior, sensation, motor activity, cognitive processing and autonomic functions, which resemble epileptic seizures while not related to electrographic ictal discharges (Baslet 2011, 2012). PNES are frequently mistaken for epileptic seizures, which have serious consequences such as the side effects of antiepileptic drugs, considerable delay in proper treatment and a huge economic burden for patients. So far, recent researches suggest a multifactorial basis for PNES are of interest. Among the postulated factors, emotional, psychogenic or social factors may have caused the seizure (Krumholz and Niedermeyer 1983; Reuber 2008). Meanwhile, PNES shared many underlying psychological mechanisms with dissociation and conversion disorder patients (Reuber et al. 2003; Guz et al. 2004).
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Although the mechanism in the aetiology of PNES is complex and still mysterious, a plenty of existing theories have started to shed light on the pathophysiology of PNES (Vuilleumier et al. 2001; Montoya et al. 2006). Baslet (2011) puts forward a model for PNES which suggests an alteration in the influence and interaction of brain areas related to emotion processes onto other areas involved in sensorimotor and cognitive function. Etiologically,PNES have been hypothesized to dysfunction in processing of psychological or trauma-related stress (Lesser 2003; Uliaszek et al. 2012), which manifested an unstable cognitive-emotional attention system and accompanied a range of somatic symptoms. Specifically, as the unconscious production of neurologic symptoms that are generally associated with emotional stressors or conflicts, PNES along with other conversion disorders have been hypothesized that they affect the abnormal inhibition of motor systems by limbic regions or impairments of motor conceptualization (Burgmer et al. 2006; de Lange et al. 2007). Increasing attention has been paid to resting-state functional magnetic resonance imaging (fMRI) since the first study of Biswal et al. (1995). Up to now, resting-state fMRI has been conducted in patients with many psychiatric and neurological disease, such as depression (Guo et al. 2011; Liu e al. 2012), schizophrenia (Hoptman et al. 2012; Guo e al. 2014) and obsessive compulsive disorder (Peng et al. 2014). In addition, A recent work using resting-state fMRI identified increased functional connectivity (FC) between regions involved in emotion (insula) and motor planning (precentral sulcus) in patients with PNES, suggesting that emotions strongly influenced motor function bypass executive control (van der Kruijs et al. 2012). Van der Kruijs et al. (2014) later found PNES showed increased coactivation of insular and subcallosal cortex in the resting-state network. Integrating these information, PNES are characterized by an abnormal networks engaging limbic regions and other brain areas, and the insula has begun to show promise as an important locus in PNES (Voon et al. 2010a, b). As a part of the limbic system, insula is a heterogeneous area that involves multimodal functions, including emotion regulation, visceral sensory perception, self-awareness and sensorimotor processing (Craig 2009). Numerous studies employing a diverse range of methodological approaches (Kurth et al. 2010b; Cauda et al. 2011) appear to converge on the functional parcellation of the insula into distinct functional subregions (Deen et al. 2011). These include a ventrol anterior region related to chemosensory and socioemotional processing, a dorsal anterior region related to higher cognitive processing, and a posterior region associated with pain and sensorimotor processing. Considering the insular heterogeneity and its association with PNES neuropathology, it is important to ascertain the intrinsic
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resting-state functional connectivity (RSFC) patterns of the insular subregions in PNES. Thus, we hypothesized that functional connections of insular subregions involved in emotion, cognitive processes and sensorimotor were abnormal in patients with PNES. In this study, to test the hypotheses, we conducted an FC analysis between insular subregions and the whole-brain in PNES patients based on the insular segmentation masks that were proposed in the prior literature (Deen et al. 2011). Then, we explored dysfunctional connectivity among the insular subregions involved in emotion, cognitive processes and sensorimotor function in PNES. In Addition, we further speculated that these abnormal functional connectivity were associated with the clinical features of PNES.
Methods Participants The subjects came from our previous studies (Ding et al. 2013). Twenty patients with PNES (7 males, mean age: 19.65 ± 7.56 years) and 20 controls (8 males, mean age: 21.85 ± 1.70 years) were enrolled in the study. PNES was diagnosed by experienced neurologists using clinical descriptions of symptoms and long-term video-EEG monitoring according to the recent recommendations (Benbadis et al. 2004; Devinsky et al. 2011). Only patients with a diagnosis of definite PNES were included in this study. The inclusion criteria included: (1) at least one single typical episode was recorded by video EEG, and EEG did not show any epileptiform discharge or ictal slowing; (2) patients have no history of neurological disease; (3) patients have no obvious abnormality in routine structural MRI examinations. The exclusion criteria were: (1) patients with neurological comorbidity (e.g. epilepsy); (2) patients with malingering, or any psychiatric disorders (e.g. mood and anxiety disorders, schizophrenia and psychosis). Here, the diagnosis of malingering or psychiatric disorders was determined by two attending psychiatrists using the Structured Clinical Interview for DSM-IV (SCID)-Patients Version and their scores on the Positive and Negative Syndrome Scale, Hamilton Anxiety Rating Scale and Hamilton Depression Rating Scale. Four of 20 patients were taking antiepileptic drugs before the diagnosis of PNES. All drugs were discontinued at least 2 weeks prior to MRI examination. The demographic and clinical characteristics for all patients are presented in Table 1. PNES frequency was assessed according to the patient or their family member reports. The median frequency was 2.5 per month (with a range of one per week to eight per week). Two patients have a long duration of disease with 18 years, because of
Brain Topogr Table 1 Demographic and clinical characteristics of PNES Patient
Age
Gender
Duration
Frequency (times/m)
Type of symptoms
Previous treatmenta
1
34 y
F
18 y
2
Unresponsiveness/eye closure
None
2
17 y
F
2y
8
Unresponsiveness/eye closure/hyperventilation/bod rigidity
None
3
38 y
F
18 y
3
Unresponsiveness/hypermotor EX
None
4
23 y
F
8y
1
Unresponsiveness/eye closure/hyperventilation/bod rigidity
None
5
13 y
F
1m
5
Unresponsiveness/eye closure
None
6
20 y
M
2y
2
Unresponsiveness
VPA
7
14 y
F
2m
3
Unresponsiveness
None
8
17 y
F
2m
4
Unresponsiveness/eye closure/bod rigidity/trembling EX
None VPA
9
14 y
M
5m
5
Unresponsiveness/vocalization
10
16 y
F
2y
0.3
Unresponsiveness/hyperventilation/hypermotor EX
None
11 12
17 y 21 y
F F
4m 8m
4 4
Unresponsiveness/hyperventilation Unresponsiveness/hypermotor EX
None None
13
21 y
M
1m
1
Eye closure/hyperventilation/bod rigidity
None
14
13 y
M
1y
1
Unresponsiveness/eye closure/hyperventilation
None
15
13 y
F
1y
0.5
Unresponsiveness/eye closure/hyperventilation
None
16
35 y
M
15 d
4
Unresponsiveness/eye closure/hypermotor EX
None
17
16 y
F
3y
4
Unresponsiveness
CBZ
18
20 y
M
2y
0.5
Unresponsiveness/hypermotor EX
VPA
19
13 y
M
1m
1
Unresponsiveness
None
20
18 y
F
7m
2
Unresponsiveness/eye closure
None
F female, M male, d day, m month, y year, hypermotor EX hypermotor movements of the extremities, trembling EX trembling of the extremities, VPA valproate, CBZ carbamazepine a
All the drugs were discontinued at least 2 weeks before MRI examination
which the two patients were excluded from the analysis to ensure patients’ illness condition as similar as possible. The control subjects had neither neurologic/psychiatric disorders evaluated by using the SCID-Non-Patient Version, nor took any psychotropic medication within the past 6 months. All participants gave informed consent to participate in the investigation, which received ethical approval by the local Ethics Committee of West China Hospital. Data Acquisition MRI was performed on a 3T Siemens Trio scanner (Erlangen, Germany). Participants were instructed to rest with their eyes closed but not fall asleep, and keep their heads still. Meanwhile, Foam pads and earplugs were used to reduce head motion and scanner noise. The resting-state fMRI was obtained by using an echo planar imaging sequence for a total of 205 volumes with following protocols: TR = 2,000 ms; TE = 30 ms; flip angle = 90°; field of view = 240 9 240 mm2; acquisition matrix = 64 9 64; voxel size = 3.75 9 3.75 9 5 mm3, no slice gap; 30 axial slices. Structural MRI data was acquired using a three-dimensional (3D) T1-weighted spoiled gradient echo sequence with the following parameters: TR = 20 ms; TE = 3.69 ms; flip
angle = 12°; field of view = 250 9 250 mm2; acquisition matrix = 320 9 320; voxel size = 0.78 9 0.78 9 1 mm3, no slice gap, 128 sagittal slices. Additionally, the diffusion tensor images (DTI) were collected using a single-shot spinecho planar imaging sequence in contiguous axial planes covering the whole-brain. Each volume consisted of 50 contiguous axial slices (TR = 6,800 ms; TE = 93 ms; flip angle = 90°; field of view = 240 9 240 mm2; acquisition matrix = 128 9 128; voxel size = 1.88 9 1.88 9 3 mm3, no slice gap). At each slice position, except for S0 (b = 0 s/mm2), a single b-value (b = 1,000 s/mm2) was applied to 20 non-collinear gradient directions. Data Preprocessing Functional images preprocessing was carried out by using the Statistical Parametric Mapping software (SPM8, http:// www.fil.ion.ucl.ac.uk/spm). The first five volumes were discarded because of instability of the initial MRI signal and adaptation of participants to the circumstance. The remaining 200 functional images were first corrected for the acquisition time delay among different slices, and then were realigned to the first volume for head motion correction. The time course of head motion was obtained by estimating the translation in each direction and the rotation
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in angular motion on each axis for all 200 consecutive volumes. There were no subjects with movement greater than 1.5 mm translation or 1.5° rotation (Liu et al. 2013b). The fMRI data were further spatially warped into a standard stereotaxic space at a resolution of 3 9 3 9 3 mm3, using the Montreal Neurological Institute (MNI) echoplanar imaging (EPI) template in SPM8. Then the resultant normalized functional data were smoothed by convolution with an 8-mm full-width half-maximum Gaussian kernel to reduce spatial noise. Definition of Insular Subregions Regions of interest (ROIs) for the left and right insular subregions were selected (of approximately 47–125 voxels) for all the subjects. We created three insular subregions in each hemisphere using cluster according to the previous literature (Deen et al. 2011), in which the insular lobe based on clustering of FC patterns were subdivided. The bilateral insula subregions were defined anatomically by drawing insular gray matter on the Montreal Neurological Institute (MNI) 152 standard brain (Deen et al. 2011). The limits of the insula were taken to be the anterior, superior, and inferior periinsular sulci (Ture et al. 1999; Nanetti et al. 2009).The cluster analysis revealed three subregions of the insula, for both left and right ROIs: ventral anterior insula (vAI), dorsal anterior insula (dAI), and posterior insula (PI). Each voxel in the insular ROIs (converted to 3-mm resolution) was used as a seed in a whole-brain functional connectivity analysis in both PNES patients and controls groups. Functional Connectivity The whole-brain functional connectivity maps for each of the insular subregions were investigated using the Pearson correlation (Fox et al. 2009; Liu et al. 2013a).To perform FC analysis, time series were extracted from each ROI. Each time series was further corrected for the effect of six head motion parameters by linear regression to reduce the influence of head motion (Fox et al. 2009) and removed confounding effects of physiological noise. Additionally, the averaged signals from the specific cerebrospinal fluid and white matter masks were regressed based on the previous resting state fMRI studies (Liu et al. 2014; Bettus et al. 2009; Fair et al. 2008). Subsequently, the resulting time series were filtered by applying a bandpass filter (0.01–0.08 Hz) to reduce the effects of lowfrequency drift and high frequency noise. The residuals signal constituted the regional time series of ROI for further analyses. The steps to obtain the FC maps are as following: (1) Extract the time series of every voxel in the whole-brain. (2) Calculate the correlation coefficients between the averaged time course of all the voxels in the ROIs and every other voxel’s time course in the whole-brain separately. (3) The
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resulting r values were converted using Fisher’s r-to-z transformation (Waites et al. 2006) to improve the Gaussianity of their distribution. For each subject, we obtained 6 z-score maps that represented the RSFC patterns of the 6 insular subregions (three for each hemisphere). Structural Connectivity We did a DTI analysis to investigate whether the FC abnormalities are accompanied by structural (white matter) abnormalities. Firstly, insular subregions and FC ROIs for the DTI analysis were derived from the normalized MNI space (the details of these brain regions see Supplementary Table S3). Then, whole brain fiber tracking was performed in native diffusion space to reveal whether the insular subregions and altered functional connectivity regions were anatomically connected or not. The details of DTI deterministic tractography analysis are as following. For DTI, eddy current distortions and head motions were corrected using FMRIB’s Diffusion Toolbox (FSL 4.1; http://www.fmrib.ox.ac.uk/ fsl). For each subject, whole brain fiber tracking (an interpolated streamline propagation algorithm) was performed in the DTI native space using TrackVis (Schmahmann et al. 2007). Path tracing proceeded until either the fractional anisotropy (FA)was lower than 0.15 or the angle between the current and the previous path segment was higher than 35°(Liao et al. 2011). As five pair of ROIs (vAI and SMA, postcentral gyrus, lingual gyrus, dAI and superior parietal gyrus L, PI and putamen) were derived from the normalized MNI space, the inverse transformation of the spatial normalization were applied to acquire the ROIs in the native space of DTI (Greicius et al. 2009; Long et al. 2014). More specifically, the inverse transformation (T-1) was applied to the eight ROIs in the normalized MNI, resulting in the subject-specific ROIs in the native space of DTI. Fiber bundles connecting each pair of ROIs were then extracted from the total collection of brain fibers. This was done by a three-step procedure. First, an initial ROI was selected, and the tracts that reached the first ROI were chosen from all fibers. A second ROI was then retrieved from the rest of the ROIs. Only those tracts that reached the second ROI were picked from the resulting tracts of the previous step. Statistical Analysis For each group and each seed ROI, individual z values maps were analyzed with a random effect one-sample t test to determine the FC spatial pattern of the insluar subregions, stringently thresholded using family-wise error (FWE) correction for multiple comparisons (p \ 0.001). To compare the differences of the FC between the PNES patients and control groups, two sample t test with age, gender and duration of disease as covariates to be regressed
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was performed. The resulting statistical FC maps were corrected for multiple comparisons to a significant level of p \ 0.05 (combined height threshold of p \ 0.01 and a minimum cluster size of 66 voxels), using the AlphaSim program in the REST software (http://resting-fmri.source forge.net). Furthermore, in order to investigate the links between the FC values and the clinical features of PNES, we calculated the Pearson correlation between mean z values within abnormal regions and the average number of episodes that patients experienced for a month. The regions of altered FC in the voxel-wise statistics were used for the correlation analysis. Correlations between mean z values within these regions and the frequency of PNES episodes were analyzed. The threshold of p \ 0.05 was considered to be significant for these analyses.
right dVI and PI yielded significant group differences in connectivity values (p \ 0.05, AlphaSim corrected), which are illustrated in Fig. 1 (region details see in Table 2). The other three seed regions did not show significant statistical differences. For the left vAI seed, compared to the controls, PNES patients had stronger functional connectivity in the following regions: the right lingual gyrus, left postcentral gyrus and bilateral SMA. Additionally, for the right seeds, both right dAI and PI, we observed corresponding high FC related to left superior parietal gyrus and left putamen in patients with PNES. However, we did not detect any direct fiber tracts that passed through the insular subregions to the altered FC regions in either PNES groups or healthy controls. Correlations Between Functional Connectivity Values and the Frequency of PNES
Results Spatial Pattern of Functional Connectivity We first detected the functional connectivity pattern of ROI in insula. As showed in Supplementary Figs. S1 and S2, the one-sample t tests revealed a typical spatial pattern in each insular subregion in both controls and PNES patients(the one-sample activated region details are showed in Supplementary Tables S1, S2). The vAI seed, was functionally connected with the bilateral insula, anterior and middle cingulate gyrus, supplementary motor area (SMA) and part of frontal and temporal gyrus. The dAI seed was functionally connected with much of insula, as well as a series of regions involved in control network (Dosenbach et al. 2007) including the anterior and middle cingulate gyrus, SMA, precentral gyrus, supramarginal gyrus and superior parietal gyrus. The PI-seeded functional connectivity revealed correlations with the bilateral insula, SMA, much of the precentral and postcentral gyrus, putamen, as well as the cingulate gyrus and the superior temporal gyrus. The results demonstrated a distinct functional connectivity pattern of the insular subregions, which had been emphasized in the prior studies (Deen et al. 2011; Kurth et al. 2010a). Particularly, the patient group showed obvious stronger connectivity in the superior parietal gyrus, postcentral gyrus, the lingual gyrus and the bilateral SMA compared to the controls. Differences in Functional Connectivity Between PNES Patients and Controls We examined the differences in FC values between PNES patients and control subjects based on the six seed insular subregions. The FC maps based on the ROIs of left vAI,
To explore whether the frequency of PNES were associated with FC values, Pearson correlation analyses were conducted. As presented in Fig. 2, the mean Z values of bilateral SMA based on the left vAI seed in PNES patients were significantly positively correlated with the frequency of PNES episodes (p \ 0.05). No significant correlation was detected when other seeds were considered.
Discussion By employing RSFC analysis to fMRI data of PNES and healthy controls, we observed a broad range of hyperlink in PNES based on the insular seeds. Significantly stronger functional connections were detected between the regions involved in motor cortex, putamen, superior parietal gyrus, lingual gyrus and the insular subregions, which suggested that the PNES was characterized by the abnormal emotion, cognitive processes and motor system. Additionally, FC values between the bilateral SMA and the vAI seed were positively correlated with the frequency of PNES, suggesting that the abnormal functional organization whose emotion has a great influence on motor function may predispose or facilitate PNES. Overall, these findings have important implications for the underlying pathophysiological mechanisms of PNES and add the new evidence for the hyperlink of insula and other brain regions. Hyperlink Between vAI and Sensorimotor Network Previous studies demonstrated that conversion disorder had abnormal inhibition of motor systems affected by limbic regions (Burgmer et al. 2006). Particularly, in the first study using resting-state fMRI on patients with PNES, van
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Fig. 1 Regions of significant stronger functional connectivity for insluar seeds in PNES patients compared to controls (AlphaSim corrected p \ 0.01, cluster size = 66). The bar on the bottom right of each activated map indicates the t value of the activation level.
Table 2 demonstrates the details of the significantly differently regions in PNES. L left, R right, vAI ventral anterior insula, dAI dorsal anterior insula, PI posterior insula
der Kruijs et al. (2012) reported PNES had a stronger FC between insula and sensorimotor cortex. As we conducted the FC analysis in PNES patients by dividing insula into three subreigons, higher FC values between region related to emotion processing (left vAI) and sensorimotor network (left postcentral gyrus and bilateral SMA) have been found in PNES patients. The seed-based findings were consisted with the previous results indicating that compared with healthy controls, PNES patients were more likely to be influenced by the emotional upset and result in involuntary behavioural patterns. Several prior findings indicated vAI played an important role in the processing of emotion (Dupont et al. 2003) and our studies may thus suggest the abnormal emotional regions of PNES were located in the
vAI, instead of entire insula, which might provide more accurate evidences for the pathology of PNES. In addition, the function of the SMA is implicated in self-initiated actions as well as in unconscious motor inhibition (Picard and Strick 2003). The existence of such an abnormal emotion–sensorimotor system is further supported by the manifestations that PNES is accompanied by somatoform symptoms such as a range of involuntary behavioral patterns and exhibits a failure in the coordination and balance of different mental and somatic function (Baslet 2011; Landgrebe et al. 2008). Actually, for most patients, these involuntary behavioural patterns are the avoidant coping strategies to deal with stressful emotion experiences (Magaudda et al. 2011). Combining the results,
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Brain Topogr Table 2 The significantly differences of FC among the insular seeds between controls and PNES patients Region name
Hem
BA
Coordinates X
Y
Z
Peak t value
vAI_L Supplementary motor area
L
6
Postcentral gyrus
R L
6 6
Lingual gyrus
R
18
-3
-8
63
4.02
6 -6 -48 -24
51 27
3.92 4.25
15
-69
0
4.88
L
-30
-69
54
3.97
R
-27
0
9
4.2
dAI_R Superior parietal gyrus PI_R Putamen
Results were corrected at a significant level of p \ 0.05 as well as cluster size [66 voxels by using AlphaSim. The coordinates were showed in the Montreal Neurological Institute (MNI) standard space FC functional connectivity, PNES psychogenic non-epileptic seizures, Hem hemisphere, BA Brodmann’s area, L left, R right, vAI ventral anterior insula, dVI dorsal anterior insula, pI posterior insula
it is reasonable to conclude that an abnormality of emotion–sensorimotor network is a potential factor that result in the occurrence of PNES. Furthermore, altered connectivity between vAI and bilateral SMA was correlated with frequency of PNES, suggesting the abnormal functional organization whose emotion has a great influence on motor function may predispose to or facilitate PNES episodes. Selectively modulating the skewed balance between limbic system and motor cortex may prevent PNES events. Hyperlink Between PI and Putamen in PNES Evidences had been found that the PI was functionally connected to primary and secondary motor and
somatosensory cortices (Deen et al. 2011). Though the putamen was concluded, it has no specialization. It works in conjunction with many other structures to control types of motor skills such as motor performance and tasks, motor preparation, specifying amplitudes of movement and the selection of movement (Alexander and Crutcher 1990; Griffiths et al. 1994). Brain imaging studies reporting PNESrelated disorders showed decreased or excessive activity in motor areas during motor task (Stone et al. 2007). Accordingly, the increased functional connectivity we found between the PI and putamen in PNES patients could be interpreted as PNES always accompanying movementrelated dysfunction involved in motor skills and tasks on the basis of aberrant emotion–sensorimotor system. Further motor task-related studies are needed to investigate the mechanism of abnormal limb movement function in patients with PNES. Hyperlink Between dAI and Parietal Lobe in PNES We observed PNES patients had an abnormal functional connectivity between the right dAI and the left parietal lobe. A stronger FC was also found in patients with PNES between the insula and the parietal cortex in the previous fMRI studies (van der Kruijs et al. 2012). The dAI was found to be functionally related to a set of regions previously described as a cognitive network (Dosenbach et al. 2007). Several researchers have demonstrated the specific role of parietal lobe in spatial orientation, sensory information processing and subsequent action organization (Fogassi et al. 2005). Given these evidences, sensory information in the parietal lobe could influence cognitive function and executive control in PNES patients through a pathway derived from this dAI-parietal circuit. Not uncommonly, individuals with PNES present with
Fig. 2 Scatterplots for the correlation between the frequency of PNES per month and altered functional connectivity respectively in a the left supplementary motor area (SMA_L, r = 0.594, p = 0.0093), b the right supplementary motor area (SMA_R, r = 0.609, p = 0.0073)
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Fig. 3 Circuitry for altered functional connectivity involved in emotion, cognitive and motor network in PNES patients. We found the insular subregions (vAI, dAI, PI) have a significantly higher connectivity within motor (postcentral gyrus/SMA and putamen), visual memory (lingual gyrus) and sensory process (parietal lobe) regions. Particularly the circuitry highlights the influence of emotion (vAI) over sensorimotor (postcentral gyrus and SMA) function in
patients with PNES. It appears that in PNES patients emotion experiences in the past can directly result in the involuntary behavioural movement while the healthy people can control their motor functions without the influence of stressful emotions. vAI ventral anterior insula, dAI dorsal anterior insula, PI posterior insula, SMA supplementary motor area
subjective cognitive complaints were beyond their cognitive alteration during the attacks (Baslet 2011). In fact, patients might perform a series of cognitive defects by partially or completely losing their integrative capacity for certain mental or somatic actions due to the past traumatic experiences.
this network can explain the fact PNES patients exhibit a failure in the coordination and balance of different mental and somatic functions. It is worth mentioning that our previous study (Ding et al. 2013) revealed that PNES exhibited altered nodal characteristics of structural network in several brain regions, including insula. These inconsistent findings may be due to the different methodologies as we defined 6 insular subregions as ROIs when constructing the structural connectivity. Different from the current study, Ding et al. computed the structural network at a regional level with 90 regions from the automated anatomical labeling (AAL) atlas. Moreover, we did not detect any fiber tracts that passed through the subregions of the insula to the altered FC regions, which is consistent with previous studies (Cloutman et al. 2012; Cerliani et al. 2012). Tractography seeds derived from the anterior insula were mainly linked with limbic and paralimbic regions and anterior parts of the inferior frontal gyrus, while seeds derived from the posterior and dorsal-middle insular regions were associated with a network focusing on posterior temporal cortices. Thus, these findings indicated that the altered functional connectivity in the patients was directly associated with the disease rather than the structural connectivity abnormalities.
Hyperlink Between vAI and the Right Lingual Gyrus It is interesting to note that we identified an increased functional connection between the left vAI and the right lingual gyrus in patients with PNES. The lingual gyrus is a structure in the visual cortex that is linked to visual memory and facial recognition (Kapur et al. 1995). A taskrelated study indicated that PNES patients show relatively increased avoidance tendencies or related motor responses for angry faces (Bakvis et al. 2011). Our finding of the presence of higher FC in vAI and lingual gyrus in PNES patients might reflect an adaptation for long-term social threat cues and display increased response and greater attention to negative emotion stimuli. Combining the results of our study, the altered functional connectivity of insular subregions suggested that an underlying pathophysiological mechanisms for PNES where the network involved in emotion regulation, cognitive processes and sensorimotor display an abnormal interactions and disturbances. Figure 3 displays the circuitry for hyperlink network involved in emotion, cognitive process and motor in PNES patients. It appears that for PNES patients the stressful emotion experiences in the past have great direct influence on their motor functions bypassing the cognitive information processing and eventually cause the occurrence of PNES. The dysfunction in
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Limitations Our findings need also to be considered in light of several limitations. First, the small sample size may reduce statistical power. Second, we didn’t carry out any subgroup analyses since our sample size was small. Larger sample studies are needed to investigate different subtypes of
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PNES and comprehensively examine these functional abnormalities. Finally, the ROIs were selected according to the mask definitions available from the literature without using anatomical information which has a certain apriority. In this regard, the selection of the insular subregions, as well as the seed size in the functional connectivity analysis should be considered.
Conclusion This is the first study to investigate the RSFC patterns in PNES patients by employing the insular subregions as seeds. We have identified abnormal FC of insular subregions in PNES and show that the hyperlink patterns of abnormalities varies in different insular subregions, which involved in emotion regulation, cognitive processes and motor function in PNES. Furthermore, the correlation between the altered FC values and the frequency of PNES episodes suggests the abnormal functional organization whose stressful emotion of patients have great direct influence on their motor functions. The differentially impaired functional connectivity patterns of insular subregions might provide new insights into the complex neurological mechanism of PNES. Acknowledgments This work was supported by 973 Project 2012CB517901, and by the Natural Science Foundation of China, Grant Nos. 61035006, 61125304, and by the Specialized Research Fund for the Doctoral Program of Higher Education of China 20120185110028. The authors have no financial relationships to disclose.
References Alexander GE, Crutcher MD (1990) Preparation for movement: neural representations of intended direction in three motor areas of the monkey. J Neurophysiol 64(1):133–150 Bakvis P, Spinhoven P, Zitman FG, Roelofs K (2011) Automatic avoidance tendencies in patients with psychogenic non-epileptic seizures. Seizure 20(8):628–634. doi:10.1016/j.seizure.2011.06. 006 Baslet G (2011) Psychogenic non-epileptic seizures: a model of their pathogenic mechanism. Seizure 20(1):1–13. doi:10.1016/j.sei zure.2010.10.032 Baslet G (2012) Psychogenic nonepileptic seizures: a treatment review. What have we learned since the beginning of the millennium? Neuropsychiatr Dis Treat 8:585–598. doi:10.2147/ NDT.S32301 Benbadis SR, Siegrist K, Tatum WO, Heriaud L, Anthony K (2004) Short-term outpatient EEG video with induction in the diagnosis of psychogenic seizures. Neurology 63(9):1728–1730 Bettus G, Guedj E, Joyeux F, Confort-Gouny S, Soulier E, Laguitton V, Cozzone PJ, Chauvel P, Ranjeva JP, Bartolomei F, Guye M (2009) Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp 30(5):1580–1591. doi:10.1002/hbm.20625
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541 Burgmer M, Konrad C, Jansen A, Kugel H, Sommer J, Heindel W, Ringelstein EB, Heuft G, Knecht S (2006) Abnormal brain activation during movement observation in patients with conversion paralysis. Neuroimage 29(4):1336–1343. doi:10. 1016/j.neuroimage.2005.08.033 Cauda F, D’Agata F, Sacco K, Duca S, Geminiani G, Vercelli A (2011) Functional connectivity of the insula in the resting brain. Neuroimage 55(1):8–23. doi:10.1016/j.neuroimage.2010.11.049 Cerliani L, Thomas RM, Jbabdi S, Siero JC, Nanetti L, Crippa A, Gazzola V, D’Arceuil H, Keysers C (2012) Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex. Hum Brain Mapp 33(9):2005–2034. doi:10.1002/hbm.21338 Cloutman LL, Binney RJ, Drakesmith M, Parker GJ, Lambon Ralph MA (2012) The variation of function across the human insula mirrors its patterns of structural connectivity: evidence from in vivo probabilistic tractography. Neuroimage 59(4):3514–3521. doi:10.1016/j.neuroimage.2011.11.016 Craig AD (2009) How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci 10(1):59–70. doi:10.1038/ nrn2555 de Lange FP, Roelofs K, Toni I (2007) Increased self-monitoring during imagined movements in conversion paralysis. Neuropsychologia 45(9):2051–2058. doi:10.1016/j.neuropsychologia. 2007.02.002 Deen B, Pitskel NB, Pelphrey KA (2011) Three systems of insular functional connectivity identified with cluster analysis. Cereb Cortex 21(7):1498–1506. doi:10.1093/cercor/bhq186 Devinsky O, Gazzola D, LaFrance WC Jr (2011) Differentiating between nonepileptic and epileptic seizures. Nat Rev Neurol 7(4):210–220. doi:10.1038/nrneurol.2011.24 Ding JR, An D, Liao W, Li J, Wu GR, Xu Q, Long Z, Gong Q, Zhou D, Sporns O, Chen H (2013) Altered functional and structural connectivity networks in psychogenic non-epileptic seizures. PLoS One 8(5):e63850. doi:10.1371/journal.pone.0063850 Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL, Petersen SE (2007) Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci USA 104(26):11073–11078. doi:10.1073/pnas.0704320104 Dupont S, Bouilleret V, Hasboun D, Semah F, Baulac M (2003) Functional anatomy of the insula: new insights from imaging. Surg Radiol Anat 25(2):113–119. doi:10.1007/s00276-0030103-4 Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL (2008) The maturing architecture of the brain’s default network. Proc Natl Acad Sci USA 105(10):4028–4032. doi:10.1073/pnas.0800376105 Fogassi L, Ferrari PF, Gesierich B, Rozzi S, Chersi F, Rizzolatti G (2005) Parietal lobe: from action organization to intention understanding. Science 308(5722):662–667. doi:10.1126/ science.1106138 Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101(6):3270–3283. doi:10.1152/jn.90777.2008 Greicius MD, Supekar K, Menon V, Dougherty RF (2009) Restingstate functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 19(1):72–78. doi:10. 1093/cercor/bhn059 Griffiths PD, Perry RH, Crossman AR (1994) A detailed anatomical analysis of neurotransmitter receptors in the putamen and caudate in Parkinson’s disease and Alzheimer’s disease. Neurosci Lett 169(1–2):68–72
123
Brain Topogr Guo WB, Liu F, Xue ZM, Yu Y, Ma CQ, Tan CL, Sun XL, Chen JD, Liu ZN, Xiao CQ, Chen HF, Zhao JP (2011) Abnormal neural activities in first-episode, treatment-naive, short-illness-duration, and treatment-response patients with major depressive disorder: a resting-state fMRI study. J Affect Disord 135(1–3):326–331. doi:10.1016/j.jad.2011.06.048 Guo W, Liu F, Liu J, Yu L, Zhang J, Zhang Z, Xiao C, Zhai J, Zhao J (2014) Abnormal causal connectivity by structural deficits in first-episode, drug-naive schizophrenia at rest. Schizophr Bull. doi:10.1093/schbul/sbu126 Guz H, Doganay Z, Ozkan A, Colak E, Tomac A, Sarisoy G (2004) Conversion and somatization disorders: dissociative symptoms and other characteristics. J Psychosom Res 56(3):287–291. doi:10.1016/S0022-3999(03)00069-2 Hoptman MJ, Zuo XN, D’Angelo D, Mauro CJ, Butler PD, Milham MP, Javitt DC (2012) Decreased interhemispheric coordination in schizophrenia: a resting state fMRI study. Schizophr Res 141(1):1–7. doi:10.1016/j.schres.2012.07.027 Kapur N, Friston KJ, Young A, Frith CD, Frackowiak RS (1995) Activation of human hippocampal formation during memory for faces: a PET study. Cortex 31(1):99–108 Krumholz A, Niedermeyer E (1983) Psychogenic seizures: a clinical study with follow-up data. Neurology 33(4):498–502 Kurth F, Eickhoff SB, Schleicher A, Hoemke L, Zilles K, Amunts K (2010a) Cytoarchitecture and probabilistic maps of the human posterior insular cortex. Cereb Cortex 20(6):1448–1461. doi:10. 1093/cercor/bhp208 Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB (2010b) A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 214(5–6):519–534. doi:10.1007/s00429-0100255-z Landgrebe M, Barta W, Rosengarth K, Frick U, Hauser S, Langguth B, Rutschmann R, Greenlee MW, Hajak G, Eichhammer P (2008) Neuronal correlates of symptom formation in functional somatic syndromes: a fMRI study. Neuroimage 41(4):1336–1344. doi:10. 1016/j.neuroimage.2008.04.171 Lesser RP (2003) Treatment and outcome of psychogenic nonepileptic seizures. Epilepsy Curr 3(6):198–200. doi:10.1046/j.15357597.2003.03601.x Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Wang Z, Tan Q, Lu G, Chen H (2011) Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 32(6):883–895. doi:10.1002/hbm. 21076 Liu F, Guo W, Fouche JP, Wang Y, Wang W, Ding J, Zeng L, Qiu C, Gong Q, Zhang W, Chen H (2013a) Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. doi:10.1007/s00429-013-0641-4 Liu F, Guo W, Liu L, Long Z, Ma C, Xue Z, Wang Y, Li J, Hu M, Zhang J, Du H, Zeng L, Liu Z, Wooderson SC, Tan C, Zhao J, Chen H (2013b) Abnormal amplitude low-frequency oscillations in medication-naive, first-episode patients with major depressive disorder: a resting-state fMRI study. J Affect Disord 146(3):401–406. doi:10.1016/j.jad.2012.10.001 Liu F, Hu M, Wang S, Guo W, Zhao J, Li J, Xun G, Long Z, Zhang J, Wang Y, Zeng L, Gao Q, Wooderson SC, Chen J, Chen H (2012) Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients with late-life depression: a resting-state fMRI study. Prog Neuropsychopharmacol Biol Psychiatry 39(2):326–331 Liu F, Xie B, Wang Y, Guo W, Fouche JP, Long Z, Wang W, Chen H, Li M, Duan X, Zhang J, Qiu M (2014) Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topogr. doi:10.1007/s10548-014-0386-2
123
Long Z, Duan X, Wang Y, Liu F, Zeng L, Zhao JP, Chen H (2014) Disrupted structural connectivity network in treatment-naive depression. Prog Neuropsychopharmacol Biol Psychiatry 56C, 18–26. doi:10.1016/j.pnpbp.2014.07.007 Magaudda A, Gugliotta SC, Tallarico R, Buccheri T, Alfa R, Lagana A (2011) Identification of three distinct groups of patients with both epilepsy and psychogenic nonepileptic seizures. Epilepsy Behav 22(2):318–323. doi:10.1016/j.yebeh.2011.07.005 Montoya A, Price BH, Lepage M (2006) Neural correlates of ‘functional’ symptoms in neurology. Funct Neurol 21(4):193–197 Nanetti L, Cerliani L, Gazzola V, Renken R, Keysers C (2009) Group analyses of connectivity-based cortical parcellation using repeated k-means clustering. Neuroimage 47(4):1666–1677. doi:10.1016/j.neuroimage.2009.06.014 Peng ZW, Xu T, He QH, Shi CZ, Wei Z, Miao GD, Jing J, Lim KO, Zuo XN, Chan RC (2014) Default network connectivity as a vulnerability marker for obsessive compulsive disorder. Psychol Med 44(7):1475–1484. doi:10.1017/S0033291713002250 Picard N, Strick PL (2003) Activation of the supplementary motor area (SMA) during performance of visually guided movements. Cereb Cortex 13(9):977–986 Reuber M (2008) Psychogenic nonepileptic seizures: answers and questions. Epilepsy Behav 12(4):622–635. doi:10.1016/j.yebeh. 2007.11.006 Reuber M, House AO, Pukrop R, Bauer J, Elger CE (2003) Somatization, dissociation and general psychopathology in patients with psychogenic non-epileptic seizures. Epilepsy Res 57(2–3):159–167. doi:10.1016/j.eplepsyres.2003.11.004 Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, de Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(Pt 3):630–653. doi:10.1093/ brain/awl359 Stone J, Zeman A, Simonotto E, Meyer M, Azuma R, Flett S, Sharpe M (2007) FMRI in patients with motor conversion symptoms and controls with simulated weakness. Psychosom Med 69(9):961–969. doi:10.1097/PSY.0b013e31815b6c14 Ture U, Yasargil DC, Al-Mefty O, Yasargil MG (1999) Topographic anatomy of the insular region. J Neurosurg 90(4):720–733 Uliaszek AA, Prensky E, Baslet G (2012) Emotion regulation profiles in psychogenic non-epileptic seizures. Epilepsy Behav 23(3):364–369. doi:10.1016/j.yebeh.2012.01.009 van der Kruijs SJ, Bodde NM, Vaessen MJ, Lazeron RH, Vonck K, Boon P, Hofman PA, Backes WH, Aldenkamp AP, Jansen JF (2012) Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures. J Neurol Neurosurg Psychiatr 83(3):239–247. doi:10.1136/jnnp-2011-300776 van der Kruijs SJ, Jagannathan SR, Bodde NM, Besseling RM, Lazeron RH, Vonck KE, Boon PA, Cluitmans PJ, Hofman PA, Backes WH, Aldenkamp AP, Jansen JF (2014) Resting-state networks and dissociation in psychogenic non-epileptic seizures. J Psychiatr Res 54:126–133. doi:10.1016/j.jpsychires.2014.03.010 Voon V, Brezing C, Gallea C, Ameli R, Roelofs K, LaFrance WC Jr, Hallett M (2010a) Emotional stimuli and motor conversion disorder. Brain 133(Pt 5):1526–1536. doi:10.1093/brain/awq054 Voon V, Gallea C, Hattori N, Bruno M, Ekanayake V, Hallett M (2010b) The involuntary nature of conversion disorder. Neurology 74(3):223–228. doi:10.1212/WNL.0b013e3181ca00e9 Vuilleumier P, Chicherio C, Assal F, Schwartz S, Slosman D, Landis T (2001) Functional neuroanatomical correlates of hysterical sensorimotor loss. Brain 124(Pt 6):1077–1090 Waites AB, Briellmann RS, Saling MM, Abbott DF, Jackson GD (2006) Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann Neurol 59(2):335–343. doi:10.1002/ ana.20733
