A Resting-State Functional MRI Study in Patients with Vestibular Migraine During Interictal Period

Materials And Methods

Subjects

We recruited 39 right-handed patients (18 VM, of which 3 had a past history of migraine with vestibular aura, and 21 migraine without aura (MWoA) patients) from the outpatient clinic of neurology department of our hospital from September 2018 to May 2019. All patients were enrolled according to the diagnostic criteria of ICHD-3. The enrolled patients with MWoA had no vestibular symptoms and patients with VM were enrolled according to the definite criteria for VM. The subjects were aged between 18 and 60 years. All patients were in attack-free state for three days prior to MRI scanning, the day of MRI scanning, and three days after MRI scanning (Wang et al., 2019). In addition, they were not taking any medications to prevent migraine, vertigo or dizziness. The control group consisted of 21 healthy right-handed subjects with no significant differences in gender, age, or educational level from the enrolled patients. The HCs had no pathologic pain, including chronic pain, and no family history of migraine. None of the research subjects reported other neurological, psychiatric, endocrine, cardio-cerebrovascular diseases and other major system diseases. All subjects did not have peripheral vestibular dysfunction according to videonystagmography (VNG) recordings with caloric testing. In addition, participants who abused substances (such as alcohol, nicotine) that affected test results were excluded.

No brain structural abnormalities, such as T2 hyperintensities in deep white matter, were found in the enrolled subjects through conventional MRI scanning.

MRI acquisition

All subjects whose heads were fixed with sponge pads were supine on a 3.0T GE scanner, and a conventional eight-channel quadrature head coil was used for MRI scanning to obtain images. In order to avoid the adverse effect of machine noise on the results, subjects needed to insert earplugs in their ears. In addition, subjects were required to stay awake and close their eyes without systematic thinking.

Prior to the functional run, the brain structural images of each subject were generated by a three-dimensional T1-weighted magnetization prepared rapid gradient echo (3D T1-MPRAGE) sequence with following parameters: repetition time (TR) = 5.5 ms, echo time (TE) = 1.8 ms, flip angle = 9°, slice thickness = 1.0 mm, 256 slices, matrix size = 256 × 256, voxel size=0.9 mm × 0.9 mm × 0.9 mm, field of view (FOV) = 256mm × 256 mm. Two experienced radiologists examined all participants’ brain structure information to rule out the possibility of subclinical lesions.

Next, functional blood-oxygen level-dependent (BOLD) signals were obtained using an echo-planar imaging sequence with following parameters: TR = 2000 ms, TE = 35 ms, flip angle = 90°, slice thickness = 3.5 mm, FOV = 256mm × 256 mm, matrix size = 64 × 64, 64slices.

Data preprocessing

Statistical Parametric Mapping 12 (SPM12) software was used for data preprocessing and statistical analyses. The first 10 volumes of each subject's scanned image were discarded for subjects’ adaptation to the scanning environment and scanner calibration (Hoptman et al., 2010). Then the remaining volumes were corrected for acquisition delay between slices and aligned to the first image of each session for motion correction (Xue et al., 2013). The functional image data of subjects were imported into DARTEL standard space template for spatial normalization with a resampling voxel size of 3 mm × 3 mm × 3 mm. The subjects’ head should not move more than 1 mm or rotate 1° in any direction. Next, the 6-mm full-width at half maximum Gaussian kernel was used to spatially smooth the images in order to improve the signal to noise ratio of the functional images and reduce the influence of noise.

Finally, imaging data were temporally filtered (band pass, 0.01–0.08 Hz) to obtain the low-frequency resting-state functional magnetic resonance signal (Xue et al., 2013).

ALFF analysis

Amplitude of low frequency fluctuation (ALFF) analyses were performed by using RESTplus V1.22 software. Fast Fourier transform (FFT) was used to transform the preprocessed filtered time series into frequency domain to obtain the power spectrum. The averaged square root of each voxel in the range of 0.01–0.08 Hz was taken as the ALFF value. For standardization, the ALFF of each voxel was divided by the global average of the ALFF value (Xue et al., 2013).

ROI-based FC analysis

Before performing functional connectivity (FC) analysis, multiple sources of false variance were removed from the preprocessed data by linear regression, including head motion parameters, global average BOLD signals and average BOLD signals of ventricular and white matter regions. Region of interest (ROI) method was adopted for FC analysis. The brain region with the largest differentia in ALFF comparison was selected as ROI to calculate the whole brain and its FC, and fisher-z transformation was conducted to obtain the FC map of each subject. Finally, analysis of covariance was used to quantitatively compare the FC of brain regions in the three groups.

Statistics

The data of demographics and clinical characteristics were expressed by χ̅ ±s and percentage. Fisher exact test was used to analyze the difference among the categorical data, and at the same time, student's t-test and analysis of variance were used to analyze the difference of the continuous variables. These data were analyzed using SPSS 22.0.

The comparisons of ALFF and FC maps which had smoothed among three groups were assessed using analysis of covariance in SPM12, including age and sex as covariates. At the same time, ALFF survived with FDR correction. Then, the Bonferroni method was used for multiple comparisons, and the P-value threshold was less than 0.05(double tails). Finally, the results were displayed with xjView and MRIcron software.

The ALFF values of the VM group corresponding to the ROI were extracted and correlated with the clinical characteristics of patients with VM. P < 0.05 was considered statistically significant.

Results

Demographics and clinical characteristics are summarized in Table 1. There was no difference among the three groups in terms of age (p = 0.915), gender (p = 0.922), and education level (p = 0.954).

Table 1

Demographics and patient characteristics
	VM	MWoA	HCs
Number of subjects	18	21	21
Age (years)	36.17 ± 8.65	36.81 ± 11.61	36.15 ± 12.11
Gender (male/female)	3/15	4/17	5/16
education level (years)	12.11 ± 2.47	11.86 ± 2.74	12.00 ± 2.51
VM disease duration (years)	6.56 ± 3.36	NA	NA
Migraine disease duration (years)	12.67 ± 7.62	11.57 ± 7.32	NA
Attack frequency per month	2.72 ± 2.23	1.86 ± 1.20	NA
NA: not applicable; VM: vestibular migraine; MWoA: migraine without aura; HCs: healthy controls.

The results of ALFF analysis among the three groups by analysis of covariance are summarized in Table 2 and shown in Fig. 1. The ALFF values of right putamen and right lingual gyrus were not completely equal (p < 0.05, FDR corrected, cluster extent = 10 voxels). The results of the Bonferroni multiple comparisons are shown in Table 3. Compared with healthy control subjects, the patients with VM and the patients with MWoA showed significant ALFF decrease in the right putamen (P < 0.05), and significant increase in the right lingual gyrus (P < 0.05). What’s more, compared with MWoA patients, patients with VM showed significant ALFF increase in the right lingual gyrus (P < 0.05). In addition, we found that ALFF values in the right putamen was negatively correlated with the duration of migraine and the frequency of migraine attacks (P < 0.05). The results are shown in Table 4.

Table 2

Regions showing significant ALFF differences among VM patients, MWoA patients, and HCs (p < 0.05, FDR corrected, cluster extent = 10 voxels)
Brain regions (AAL)	Cluster extent (number of voxels)	Peak MNI coordinates (X Y Z)					F Peak intensity
Putamen_R	38	30		12	-9		15.9024
Lingual_R	11	27	-54			-3	14.7124
AAL: Anatomical Automatic Labeling; L: left; R: right; MNI: Montreal Neurological Institute; Putamen_R: right putamen; Lingual_R: right lingual gyrus.

Table 3

Regions showing significant ALFF differences between patients and controls as well as between different groups of migraineurs using the Bonferroni multiple comparisons (p < 0.05).
Brain regions (AAL)	Group 1	Group 2	Mean Difference (Group 1 – Group 2)	P value
Putamen_R	VM	HCs	-0.457417	0.000
Putamen_R	MWoA	HCs	-0.353476	0.000
Lingual_R	VM	HCs	0.994779	0.000
Lingual_R	VM	MWoA	0.496900	0.030
Lingual_R	MWoA	HCs	0.497879	0.023

Table 4

The correlations between ALFF values of patients with VM in the right putamen and patients’ clinical characteristics
	χ̅ ±s	Correlation coefficient ®	P
the frequency of migraine attacks	3.62 ± 3.48	-0.529	0.035
the duration of migraine	13.25 ± 8.31	-0.517	0.040

FC of brain regions in the patients with VM and subjects in two other groups were calculated using the right putamen as the ROI. The results of FC analysis among the three groups by analysis of covariance are summarized in Table 5 and shown in Fig. 2. The FC values of the right cerebellum, the left dorsolateral superior frontal gyrus and the left median cingulate and paracingulate gyri were not completely equal (p < 0.001, cluster extent = 5 voxels). The results of the Bonferroni multiple comparisons are shown in Table 6. Compared with healthy volunteers, patients with VM showed significant FC increase among the right cerebellum, the left dorsolateral superior frontal gyrus and the right putamen (P < 0.05) but significant FC decrease among the left median cingulate, the paracingulate gyri, and the right putamen (P < 0.05). Compared with MWoA patients, patients with VM showed significant FC increase in the right cerebellum and in the right putamen (P < 0.05) but significant FC decrease among the left median cingulate, the paracingulate gyri, and the right putamen (P < 0.05). Compared with HCs, patients with MWoA only showed significant FC increase in the left dorsolateral superior frontal gyrus and in the right putamen (P < 0.05).

Table 5

Regions showing significant FC differences among VM patients, MWoA patients, and HCs (p < 0.001, cluster extent = 5 voxels)
Brain regions (AAL)	Cluster extent (number of voxels)	Peak MNI coordinates (X Y Z)					F Peak intensity
Cerebelum_6_R	19	9		-60	-24		11.4059
Frontal_Sup_L	7	-15	63			24	10.2476
Cingulum_Mid_L	5	-12	-42			48	9.4814
Cerebelum_6_R: right cerebellum; Frontal_Sup_L: left dorsolateral superior frontal gyrus; Cingulum_Mid_L: left median cingulate and paracingulate gyri.

Table 6

Regions showing significant FC differences between patients and controls as well as between different groups of migraineurs using the Bonferroni multiple comparisons (p < 0.05).
Brain regions (AAL)	Group 1	Group 2	Mean Difference (Group 1 – Group 2)	P value
Cerebelum_6_R	VM	HCs	0.291095	0.000
Cerebelum_6_R	VM	MWoA	0.240688	0.001
Frontal_Sup_L	MWoA	HCs	0.237301	0.000
Frontal_Sup_L	VM	HCs	0.194002	0.004
Cingulum_Mid_L	VM	HCs	-0.189849	0.004
Cingulum_Mid_L	VM	MWoA	-0.249289	0.000

Discussion

Resting-state functional MRI (fMRI) which is a noninvasive imaging technique measures low-frequency fluctuations in BOLD signals (Fox and Raichle, 2007). Cortical and subcortical areas of spatially distinct and functionally related groups will show related spontaneous fluctuations during the resting state, composed of intrinsic functional networks of the human brain (Seeley et al., 2009). Changes in internal functional networks will affect performance of tasks in real life (Fox et al., 2007; (Hesselmann et al., 2008). In addition, the resting-state method has been widely used to reveal the typical and atypical functional structures inherent in the brain (Fox and Raichle, 2007; Greicius, 2008). Changes in characteristics at rest can be used as indicators of the progression of various diseases, such as heroin addiction (Yuan et al., 2010; Yuan et al., 2010; Yuan et al., 2010), Alzheimer’s disease (Greicius et al., 2004) and schizophrenia (Lui et al., 2009).

Most resting-state fMRI studies use FC to study temporal relationships between internal fluctuations observed in spatially distinct brain regions. However, there are few local features of spontaneous brain activity observed in specific areas by the method of FC. As a complement to the method of FC, the ALFF (Zang et al., 2007) method which measures regional spontaneous neuronal activity during resting-state fMRI is also widely used. In this study, we evaluated regional changes in both regional spontaneous neuronal activity and corresponding brain circuits during rest.

ALFF

In this study, we identified several brain regions showing differences among the three groups during the resting state. “Balance triple” refers to the visual, proprioceptive and vestibular systems that maintain the balance of the human body. The primary visual cortex, as well as the lingual gyrus and lateral geniculate body, belongs to the medial visual cortex, which is responsible for processing visual information (Beckmann et al., 2005; Mantini et al., 2007). This study showed that the ALFF values of lingual gyrus in patients with VM were significantly higher than that in patients with MWoA and HCs, which indicated that the spontaneous neuronal activity of lingual gyrus in patients with VM under resting state was enhanced and the excitability was increased. It may reflect the changes in the integration function of visual information in the brain of patients with VM, which is most likely an adaptive change in response to repeated vertigo. The caudate nucleus and the lenticular nucleus are important components of the basal ganglia involved in the regulation of pain. In the study, the function of basal ganglia in the most patients with VM and MWoA was abnormal. Most scholars believe that the basal ganglia need to be involved in pain regulation due to repeated headaches, and the basal ganglia may be damaged during the process, which leads to a decrease in the ability of pain regulation. And with the increase of frequency and duration of attacks, this decline is more obvious.

As one of the most commonly used analytical methods of FC, the ROI correlation analysis method has been widely used in the research of brain sensation, cognitive and emotion. In this study, the ALFF values of the three groups of subjects were calculated, and it was found that the brain area with the most significant difference was the right putamen. Therefore, it was designated as the ROI, and ALFF and FC were combined to describe the functional characteristics between brain regions. The ALFF combined with FC method has been widely used in a variety of brain diseases and provides important information for understanding these diseases, such as Alzheimer’s disease (Sorg et al., 2007), heroin addiction (Yuan et al., 2010; Yuan et al., 2010; Yuan et al., 2010; Jiang et al., 2011), schizophrenia (Hoptman et al., 2010; Zhou et al., 2007) and depression (Anand et al., 2005).

The vestibular center also includes the subtentorial brainstem and cerebellar associated nucleus mass. Some scholars (Marcelli et al., 2009) have conducted fMRI studies on the vestibule of patients with cold water stimulation. They found that the brain areas with neuronal activity changes mainly included the insular cortex, thalamus, brainstem and cerebellum. The sensorimotor components associated with balance include the brainstem pathway, which produces motor responses to the body (such as the vestibule-oculomotor reflex) and visceral (the vestibular sympathetic and parasympathetic nerves) (Balaban and Yates, 2004; Holstein et al., 2011). These vestibular sensorimotor responses are regulated by cerebellum (Balaban and Yates, 2004; Ito, 1984). Patients with VM did show impaired central integration of semicircular canal rotation signal and otolithic organ gravity signal center (Wang and Lewis, 2016). Some studies have found that some patients with VM had lesions at the caudal part of the cerebellum, so the correct processing of otolitic information couldn’t be carried out, and patients with VM would be highly sensitive to head movement (Espinosa-Sanchez and Lopez-Escamez, 2015). The most common abnormality of eye movement in patients with VM is central positional nystagmus(Radtke et al., 2012), which may suggest that the cerebellum inhibits the release of vestibular effect (Bronstein and Lempertt, 2007). Some studies suggested that central positional nystagmus was the result of lesions at nodules and lingual lobe of cerebellum which made vestibular maladjustment (Choi et al., 2014). Therefore, cerebellar dysfunction may be involved in the pathogenesis of VM. Previous study found that functional imaging at the onset of VM showed bilateral cerebellar activation (Shin et al., 2014). Cerebellar hyperactivity during the onset and interictal stages of patients with VM was considered to be an adaptive mechanism to inhibit hyperactivity of the vestibular system (Jeong et al., 2015). VM is closely linked to motion sickness (Akdal et al., 2015), and even during the interictal stages, patients with VM are less tolerant of riding a carousel, a car, or even watching a 3D movie. Changes in the functional activity of the cerebellum in patients with VM found in this study might partially explain this phenomenon, suggesting that VM was not a simple episode disease. In functional imaging studies, it was found that the brain regions involved in pain information processing mainly included the anterior cingulate cortex, prefrontal cortex, insula, cerebellum, brainstem and somatosensory cortex (Yu et al., 2011). The cerebellum, as part of the pain matrix, is involved in pain processing and regulation (Yu et al., 2011). In animals and humans, the deep cerebellar nucleus process harmful stimuli and participate in pain perception and suppression through the relationship with the brainstem nucleus and thalamus. When cerebellar activity is forcibly enhanced, the pain threshold will increase, suggesting that cerebellum’s anti-nociceptive effect is enhanced (Pereira et al., 2017). The cerebellum also exhibits emotional processing functions (Klingner et al., 2013), which indicates that changes in cerebellar function can easily lead to adverse emotions. Unfavorable emotions are important factors that can trigger VM and MWoA. When the emotional function of cerebellum is abnormal, it may induce VM attacks.

Dorsolateral superior frontal gyrus is an important part of limbic system and an important part of emotional pathway of pain perception. This study found that the FC between the cerebellum, dorsolateral superior frontal gyrus and ROI of patients with VM were enhanced, and the FC between the dorsolateral superior frontal gyrus and ROI was enhanced in patients with MWoA. It suggested that patients with VM and MWoA had suffered from painful stimulation for a long time, and their endogenous analgesic mechanisms were adjusted, which might be used to process and modify painful emotional responses, or to reduce pain perception and cognition, so as to reduce input of pain signals.

Median cingulate and paracingulate gyri are not only involved in the attention and emotional response to pain, but also in the subjective perception of pain and its cognition and memory (Bluhm et al., 2007). They are also important brain regions that form the default network (Uckner et al., 2008). In a comparative study (Liu et al., 2006) of HCs and patients with chronic pain, a high concentration of opioid receptor binding sites was observed in the cingulate gyrus, suggesting that median cingulate and paracingulate gyri played an important role in the formation and regulation of pain sensation. In this study, FC of the median cingulate and paracingulate gyri and ROI was weakened in patients with VM, which might be the result of adaptation of nervous system. The attenuation of the nervous system itself can not only reduce the pain response and emotional response, but also reduce the subjective perception of pain.

The pathogenesis of VM is not clear. Imbalance of excitement or inhibition of various sensory information, vestibular information and pain signals may lead to the occurrence of vestibular migrane (Espinosa-Sanchez and Lopez-Escamez, 2015). The study found that patients with VM had reduced activity in multiple vestibular functional areas, while other vestibular functional areas showed compensatory increased activity, and activity of pain-related brain areas changed similar to patients with migraine. In patients with VM, abnormal brain functional networks associated with the right putamen were observed. All of these findings are likely due to adaptive changes in the brain regions of patients with VM.

References

Von BM, Lempert T (2016) Vestibular migraine. Handb Clin Neurol 137:301–316
Lempert T, Olesen J, Furman J, Waterston J, Seemungal B, Carey J, Bisdorff A, Versino M, Evers S, Newman-Toker D (2012) Vestibular migraine: diagnostic criteria. J Vestib Res 22:167–172
Headache Classification Committee of the International Headache Society (IHS) (2018) The International Classification of Headache Disorders, 3rd edition. Cephalalgia. 38, 1-211
Obermann M (2017) Editorial: Vestibular migraine. Front Neurol 8:213
Power L, Shute W, Mcowan B, Murray K, Szmulewicz D (2018) Clinical characteristics and treatment choice in vestibular migraine. J Clin Neuro 52:50–53
Espinosa-Sanchez JM, Lopez-Escamez JA (2015) New insights into pathophysiology of vestibular migraine. Front Neurol 6:1–6
Messina R, Rocca MA, Colombo B, Teggi R, Falini A, Comi G, Filippi M (2017) Structural brain abnormalities in patients with vestibular migraine. J Neurol 264:295–303
Obermann M, Wurthmann S, Steinberg BS, Theysohn N, Diener HC, Naegel S (2014) Central vestibular system modulation in vestibular migraine. Cephalalgia 34:1053–1061
Russo A, Marcelli V, Esposito F, Corvino V, Marcuccio L, Giannone A, Conforti R, Marciano E, Tedeschi G, Tessitore A (2014) Abnormal thalamic function in patients with vestibular migraine. Neurology 82:2120–2126
Teggi R, Colombo B, Rocca MA, Bondi S, Messina R, Comi G, Filippi M (2016) A review of recent literature on functional MRI and personal experience in two cases of definite vestibular migraine. Neurol Sci 37:1399–1402
Wang S, Wang H, Zhao D, Liu X, Yan W, Wang M, Zhao R,2019. Grey matter changes in patients with vestibular migraine. Clin. Radiol. 74, 898.e1-898.e5
Hoptman MJ, Zuo XN, Butler PD, Javitt DC, D’Angelo D, Mauro CJ, Milham MP (2010) Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study. Schizophr Res 117:13–20
Xue T, Yuan K, Cheng P, Zhao L, Zhao L, Yu D, Dong T, Karen M, Gong Q, Qin W, Tian J (2013) Alterations of regional spontaneous neuronal activity and corresponding brain circuit changes during resting state in migraine without aura. NMR Biomed 26:1051–1058
Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711
Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD (2009) Neurodegenerative diseases target large-scale human brain networks. Neuron 62:42–52
Fox MD, Snyder AZ, Vincent JL, Raichle ME (2007) Intrinsic fluctuations within cortical systems account for intertrial variability in human Behavior. Neuron 56:171–184
Hesselmann G, Kell CA, Eger E, Kleinschmidt A (2008) Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proc. Natl. Acad. Sci. 105, 10984–10989
Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430
Yuan K, Qin W, Dong M, Liu J, Liu P, Zhang Y, Sun J, Wang W, Wang Y, Li Q (2010) Combining spatial and temporal information to explore restingstate networks changes in abstinent heroin-dependent individuals. Neurosci Lett 475:20–24
Yuan K, Qin W, Dong M, Liu J, Sun J, Liu P, Zhang Y, Wang W, Wang Y, Li Q, Zhao L, Deneen KM, Liu Y, Gold MS, Tian J (2010) Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals. Neurosci Lett 482:101–105
Yuan K, Qin W, Liu JX, Guo QA, Dong MH, Sun JB, Zhang Y, Liu P, Wang W, Wang YR, Li QA, Yang WC, Deneen KM, Gold MS, Liu YJ, Tian J (2010) Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals. Neurosci Lett 477:37–42
Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. 101, 4637–4642
Lui S, Deng W, Huang XQ, Jiang LJ, Ma XH, Chen HF, Zhang TJ, Li XL, Li DM, Zou L, Tang HH, Zhou XHJ, Mechelli A, Collier DA, Sweeney JA, Li T, Gong QY (2009) Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am Psychiatric Assoc 166:196–205
Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, Tian LX, Jiang TZ, Wang YF (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29:83–91
Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360:1001–1013
Mantini D, Perrucci MG, Del, Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. 104, 13170–13175
Sorg C, Riedl V, Muhlau M, Calhoun VD, Eichele T, Laer L, Drzezga A, Forstl H, Kurz A, Zimmer C, Wohlschlager AM (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc. Natl. Acad. Sci. 104, 18760–18765
Jiang G, Qiu Y, Zhang X, Han LJ, Lv XF, Li L, Lin C, Zhuo F, Hu S, Tian J (2011) Amplitude low-frequency oscillation abnormalities in the heroin users: A resting state fMRI study. Neuroimage 57:149–154
Zhou Y, Liang M, Tian L, Wang K, Hao Y, Liu H, Liu Z, Jiang T (2007) Functional disintegration in paranoid schizophrenia using resting-state fMRI. Schizophr Res 97:194–205
Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, Mathews VP, Kalnin A, Lowe MJ (2005) Activity and Connectivity of Brain Mood Regulating Circuit in Depression: A Functional Magnetic Resonance Study. Biol Psychiatry 57:1079–1088
Marcelli V, Esposito F, Aragri A, Furia T, Riccardi P, Tosetti M, Biagi L, Marciano E, Di, Salle F (2009) Spatio-temporal pattern of vestibular information processing after brief caloric stimulation. Eur J Radiol 70:312–316
Balaban C, Yates BJ (2004) Vestibulo-autonomic interactions: a teleologic perspective. In: Highstein SN, Fay RR, Popper AN (eds) Springer handbook of auditory research: the vestibular system. Springer-Verlag, New York, pp 286–342
Holstein GR, Friedrich VL, Kang T, Kukielka E, Martinelli GP (2011) Direct projections from the caudal vestibular nuclei to the ventrolateral medulla in the rat. Neuroscience 175:104–117
Ito M (1984) The cerebellum and neural control. Raven Press, New York
Wang J, Lewis RF (2016) Contribution of intravestibular sensory conflict to motion sickness and dizziness in migraine disorders. J Neurophysiol 116:1586–1591
Radtke A, Brevern M, Neuhauser H, Hottenrott T, Lempert T (2012) Vestibular migraine: Long-term follow-up of clinical symptoms and vestibulo-cochlear findings. Neurology 79:1607–1614
Bronstein M, Lempertt (2007) Dizziness: A practical approach to diagnosis and management, 93. Cambridge University Press, Cambridge, pp 149–150
Choi JY, Kim JH, Kim HJ, Glasauer S, Kim JS (2015) Central paroxysmal positional nystagmus: Characteristics and possible mechanisms. Neurology 84:2238–2246
Shin JH, Kim YK, Kim HJ (2014) Altered brain metabolism in vestibular migraine: comparison of interictal and ictal findings. Cephalalgia 34:58–67
Jeong SH, Oh SY, Kim HJ, Koo JW, Kim JS (2010) Vestibular dysfunction in migraine: effects of associated vertigo and motion sickness. J Neurol 257:905–912
Akdal G, Baykan B, Karli N (2015) Population-based study of vestibular symptoms in migraineurs. Acta oto-laryngologica 135:435
Yu D, Yuan K, Zhao L, Zhao L, Dong M, Liu P, Wang G, Liu J, Sun J, Zhou G, Deneena K, Liang F, Qin W, Tian J (2011) Regional homogeneity abnormalities in patients with interictal migraine without aura: a resting-state study. NMR Biomed 25:806–812
Pereira M, Rafiq B, Chowdhury E, Babayev J, Boo H, Metwaly R, Sandilyaa P, Chusida E, Battaglia F (2017) Anodal cerebellar tDCS modulates lower extremity pain perception. NeuroRehabilitation 40:195–200
Klingner CM, Volk GF, Flatz C, Brodoehl S, Dieterich M, Witte OW, Guntinas-Lichius O (2013) Components of vestibular cortical function. Behav Brain Res 236:194–199
Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld R, Theberge J, Schaefer B, Williamson P (2007) Spontaneous Low-Frequency Fluctuations in the BOLD Signal in Schizophrenic Patients: Anomalies in the Default Network. Schizophr Bull 33:1004–1012
Uckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38
Liu H, Liu Z, Liang M, Hao Y, Tan L, Kuang F, Yi Y, Xu L, Jiang T (2006) Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study. Neuro Report 17:19–22

A Resting-State Functional MRI Study in Patients with Vestibular Migraine During Interictal Period

Abstract

Introduction