Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Tinnitus: A Large VBM-EEG Correlational Study

  • Sven Vanneste ,

    ven.vanneste@utdallas.edu

    Affiliations Department of Translational Neuroscience, Faculty of Medicine, University of Antwerp, Antwerp, Belgium, School for Behavioral & Brain Sciences, University of Texas at Dallas, Dallas, Texas, United States of America

  • Paul Van De Heyning,

    Affiliations Department of Translational Neuroscience, Faculty of Medicine, University of Antwerp, Antwerp, Belgium, ENT Department, University Hospital Antwerp, Antwerp, Belgium

  • Dirk De Ridder

    Affiliation Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand

Tinnitus: A Large VBM-EEG Correlational Study

  • Sven Vanneste, 
  • Paul Van De Heyning, 
  • Dirk De Ridder
PLOS
x

Abstract

A surprising fact in voxel-based morphometry (VBM) studies performed in tinnitus is that not one single region is replicated in studies of different centers. The question then rises whether this is related to the low sample size of these studies, the selection of non-representative patient subgroups, or the absence of stratification according to clinical characteristics. Another possibility is that VBM is not a good tool to study functional pathologies such as tinnitus, in contrast to pathologies like Alzheimer’s disease where it is known the pathology is related to cell loss. In a large sample of 154 tinnitus patients VBM and QEEG (Quantitative Electroencephalography) was performed and evaluated by a regression analysis. Correlation analyses are performed between VBM and QEEG data. Uncorrected data demonstrated structural differences in grey matter in hippocampal and cerebellar areas related to tinnitus related distress and tinnitus duration. After control for multiple comparisons, only cerebellar VBM changes remain significantly altered. Electrophysiological differences are related to distress, tinnitus intensity, and tinnitus duration in the subgenual anterior cingulate cortex, dorsal anterior cingulate cortex, hippocampus, and parahippocampus, which confirms previous results. The absence of QEEG-VBM correlations suggest functional changes are not reflected by co-occurring structural changes in tinnitus, and the absence of VBM changes (except for the cerebellum) that survive correct statistical analysis in a large study population suggests that VBM might not be very sensitive for studying tinnitus.

Introduction

At some point in life most people will experience an unexplainable sound, often described as ringing or roaring, in their ears/head which has no apparent external sound source[1]. This phantom sound is also called tinnitus, and is defined as the perception of a sound in the absence of any external sound source. Tinnitus has been related to listening to loud music [2], sudden sensorineural hearing loss [3], use of medication [4] or other causes. Typically, this sensation is reversible and subsides after a few seconds or sometimes after a few days. Although most incidences of tinnitus are temporary, chronic subjective tinnitus occurs in 10–15% of the general population [5] and severely disrupts quality of life in about 2.4% [6], causing considerable distress involving sleep deprivation [7], depression [8], cognitive problems [9], and work impairment [5].

Analogous to neuropathic pain, tinnitus is related to hyperactivity [1; 10; 11] of the central auditory nervous system. Research on functional imaging also provides evidence that non-auditory brain regions are associated with tinnitus [12] such as the insula [13], anterior cingulate cortex [14; 15], posterior cingulate cortex [15], parahippocampal area [16], and the dorsolateral prefrontal cortex [15; 17; 18; 19]. However, the obtained results of functional neuroimaging are not straightforward as different studies report differences in which non-auditory brain areas are involved.

Apart from functional changes, structural changes in grey matter have been shown to be involved in the pathogenesis of tinnitus [20; 21; 22; 23; 24]. Using voxel-based morphometry (VBM) grey matter changes were shown in tinnitus patients in comparison to control subjects in both the auditory and limbic systems. It was shown that in the thalamus grey matter increases while in the subcallosal frontal cortex grey matter decreases in tinnitus patients [22]. Grey matter decreases have also been shown in the right inferior colliculus and the left hippocampus for tinnitus patients relative to controls [20; 25]. However, grey matter also decreases in individuals with hearing loss without tinnitus in the anterior cingulate and superior medial frontal gyri in comparison to patients with tinnitus and hearing loss [21]. This was further explored by Melcher et al. revealing that the subcallosal brain regions negatively correlate with supra-clinical frequencies (>8 kHz), but not with tinnitus [26]. These findings question the relationship between tinnitus and the structural changes. In addition, it is important to note that most VBM results in tinnitus populations were not obtained using whole brain analysis, but only on supplementary region of interest analyses.

After reviewing both the functional and structural studies together, it is quite surprising that the different studies do not converge to the same results. Low sample size of these studies, the selection of non-representative patient subgroups, or the absence of stratification according to clinical characteristics may explain the lack of agreement between the two studies. Another possibility is that VBM is not an ideal tool to study functional pathologies such as tinnitus, in contrast to pathologies like Alzheimer’s disease where it is known the pathology is related to cell loss [27; 28]. Preliminary evidence by functional imaging studies using QEEG (Quantitative Electroencephalography: i.e. computerized software based analysis of the digitally recorded EEG data, initially limited to Fourier transforms quantitatively expressing the amount of delta, theta, alpha, beta and gamma band activity, but now also extending to Hilbert or wavelet transforms, independent component analyses, source analysis methods etc.) and MEG indicate that depending on the clinical characteristics, the neurophysiological mechanism differs in tinnitus patients. It was demonstrated that depending on the tinnitus lateralization [29], tinnitus sound [30], tinnitus accompanied with distress or not [14; 15], and tinnitus duration [31; 32], different brain areas are involved.

The aim of this study is to verify whether VBM reflects structural changes that might accompany functional changes in tinnitus. This is done by verifying whether (1) Structural (VBM) and electrophysiological changes (QEEG) are correlated, and (2) specific functional and structural changes in the brain of tinnitus patients depend on differences in tinnitus characteristics explaining the variability in previously published data. We opt to use QEEG above fMRI as (1) QEEG measures brain activity more directly, while fMRI indirectly measures using BOLD, (2) fMRI inherently generates noise from the scanner (up to 130 dB) [33] which is problematic when performing research in tinnitus patients, and (3) results reported on tinnitus using fMRI are mainly region of interest analyses and task related, while QEEG measures spontaneous resting state activity. Combining VBM and QEEG has previously been applied in epilepsy research which has shown that combining methods improves the understanding of the pathophysiology of epilepsy: focal discharges may arise from areas of structural abnormality [34; 35]. Therefore, we combine a VBM analysis on MRI data with source localized QEEG in a large group of tinnitus patients.

Material and Methods

Patients

One hundred-and-fifty-four tinnitus patients (102 males and 52 females) with a mean age of 50.24 years (Sd = 14.28 years) and a mean tinnitus duration of 5.30 years (Sd = 4.02 years) were selected from the multidisciplinary Tinnitus Research Initiative (TRI) Clinic of the University Hospital of Antwerp, Belgium. Individuals with pulsatile tinnitus, Ménière’s disease, otosclerosis, chronic headache, neurological disorders such as brain tumors, and individuals being treated for mental disorders were not included in the study in order to obtain a more homogeneous sample. In addition, patients with multiple percepts (e.g. perceiving both a pure tone and narrow band noise tinnitus) or with a broadband percept were not included in the study. A combination of a pure tone and noise-like percept is rare and would add an extra condition to the regression analysis. The study was approved by the local ethical committee (Antwerp University Hospital) and was in accordance with the declaration of Helsinki. All patients signed a written informed consent.

All patients were interviewed to determine their perceived location of the tinnitus (unilateral or bilateral), tinnitus duration, and for qualities of their unique tinnitus tone (pure tone like tinnitus or noise-like tinnitus). In addition, all patients were screened for the extent of hearing loss using a pure tone audiometry in accordance with British Society of Audiology procedures at .125 kHz, .25 kHz, .5 kHz, 1 kHz, 2 kHz, 3 kHz, 4 kHz, 6 kHz and 8 kHz [36] (see S1 Fig.). Tinnitus patients were tested for the tinnitus frequency using tinnitus matching analysis. In unilateral tinnitus patients, the tinnitus analysis was performed contralaterally to the tinnitus ear. In bilateral tinnitus patients, tinnitus analysis was performed contralaterally to the worst tinnitus ear. The tinnitus matching analysis consisted of the assessment of the tinnitus pitch and loudness. Depending whether a patient perceives a pure tone or narrow band noise, a 1 kHz pure tone or a narrow band noise around 1kHz (1/3 of an octave above and below the center frequency) was presented contralateral to the (worst) tinnitus ear at 10 dB above the patient’s hearing threshold in that ear. The pitch was adjusted until the patient judged the sound to resemble his/her tinnitus most correctly. The loudness of this tone was then adjusted in a similar way until it corresponded to the patient’s specific tinnitus loudness as well. The tinnitus loudness (dB SL) was computed by subtracting the absolute tinnitus loudness (dB HL) with the auditory threshold at that frequency [37; 38]. Only patients able to successfully perform pitch and loudness matching were included in the study.

A numeric rating scale (NRS) for loudness (‘How loud is your tinnitus?’: 0 = no tinnitus and 10 = as loud as imaginable’) was assessed in addition to the Dutch translation of the Tinnitus Questionnaire validated by Meeus et al. [39] which measures tinnitus related distress. This scale is comprised of 52 items and is a well-established measure for the assessment of a broad spectrum of tinnitus-related psychological complaints. The TQ measures emotional distress, cognitive distress, intrusiveness, auditory perceptual difficulties, sleep disturbances, and somatic complaints. As previously mentioned, the global TQ score can be computed to measure the general level of psychological and psychosomatic distress. In several studies, this measure has been shown to be a reliable and valid instrument in different countries [40; 41]. A 3-point scale is given for all items, ranging from ‘true’ (2 points) to ‘partly true’ (1 point), and ‘not true’ (0 points). The total score (from 0–84) was computed according to standard criteria published in previous work [39; 41; 42]. For the clinical and demographic characterization of the sample see Table 1.

Data acquisition

1. VBM

Images were acquired using a 3.0 Tesla Siemens Trio scanner. A high resolution scan (MPRAGE) was performed for each subject. TR = 2300 ms, TE = 2.94 ms, inversion time (TI) = 900 ms, flip angle = 9°, 160 sagittal slices, matrix size 256 x 256 mm2, 1 x 1 x 1 mm3 resolution.

Voxel based morphometry (VBM) is based on high-resolution structural 3D MR images, transformed into a common stereotactic space and is designed to seek significant regional differences by applying voxel-wise statistics in the context of Gaussian random fields [43].

2. QEEG data collection

QEEG recordings (Mitsar-201, NovaTech http://www.novatecheeg.com/) were obtained in a quiet and dimly lit room with each participant sitting upright on a small but comfortable chair. Participants were requested to refrain from alcohol consumption 24 hours prior to recording, and from caffeinated beverages consumption on the day of recording. The actual recording lasted approximately 5 min. The QEEG was sampled with 19 electrodes in the standard 10–20 International placement referenced to linked ears and impedances were checked to remain below 5 kΩ. Data was collected with the patient’s eyes-closed (sampling rate = 1024Hz, band passed 0.15–200Hz). This method is already applied in previous research [15; 32].

Data processing

1. VBM

Statistical parametric software (SPM8, Welcome Trust Center for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/software/spm8) was used to analyze the data. Image preprocessing was performed following an optimized-VBM-protocol by using VBM8 (http://dbm.neuro.uni-jena.de/vbm.htlm) with default parameters. All images were partitioned into grey and white matter and cerebrospinal fluid (CSF). Images were bias-corrected, tissue classified, and registered using linear (12-parameter affine) and non-linear transformations (warping), within a unified model [44]. Subsequently, analyses were performed on the density of the gray matter (GM) segments, which were multiplied by the non-linear components derived from the normalization matrix in order to preserve actual GM and WM values locally (modulated GM and WM volumes). The resulting grey matter images were smoothed with a Gaussian kernel of 8 mm full width at half maximum (FWHM).

For the region of interest analysis, we used a mask of cortical and subcorticortical regions that were obtained with QEEG. In order to maintain compatibility of the results obtained with QEEG and MRI, we used the corresponding Brodmann areas as defined in Montreal-Neurological-Institute (MNI)-coordinate space with the WFU-Pickatlas [45].

Anatomical labeling of significant clusters was done by means of the anatomical automatic labeling toolbox (AAL) [46].

2. QEEG & Source Localization

Data was resampled to 128 Hz, band-pass filtered (fast Fourier transform filter) to 2–44 Hz, and subsequently transposed into Eureka! Software [47] where it was plotted and carefully inspected for artifacts (i.e. eye blinks, eye movements, teeth clenching, body movement, or ECG artifact) which were removed. Using a fast Fourier transform for the different frequency bands, the power for the delta (2–3.5 Hz), theta (4–7.5 Hz), alpha (8–12Hz), beta (13–30 Hz) and gamma (30.5–44 Hz) were computed.

Standardized low-resolution brain electromagnetic tomography (sLORETA) was used to estimate the intracerebral electrical sources that generated the scalp-recorded activity in each of the five frequency bands [48]. sLORETA computes electric neuronal activity as current density (A/m²) without assuming a predefined number of active sources. The sLORETA solution space consists of 6,239 voxels (voxel size: 5 x 5 x 5 mm). Computations were made in a realistic head model, and were restricted to cortical grey matter and hippocampi, as defined by digitized MNI152 template [49]. Scalp electrode coordinates on the MNI brain are derived from the international 5% system [50]. Thus, sLORETA images represent the standardized electric activity at each voxel in neuroanatomic Montreal Neurological Institute (MNI) space as the exact magnitude of the estimated current density. Anatomical labels as Brodmann areas are also reported using MNI space, with correction to Talairach space [51].

In addition, the log-transformed electrical current density was averaged across all voxels belonging to the region of interest that were obtained for the whole-brain analysis for the specific frequency band.

Statistical analyses

1. VBM

For the statistical analysis, we excluded all voxels with a grey matter value below 0.1 (maximum value: 1) to avoid possible edge effects around the border between grey and white matter, and to include only voxels with sufficient grey matter proportion. A whole brain multiple regression analysis was conducted using age, gender, tinnitus type, tinnitus lateralization, tinnitus related distress, tinnitus loudness, tinnitus duration, tinnitus frequency, and tinnitus sensation level as independent variables to test for any association with changes in grey matter. These independent variables were selected based on previous research which demonstrated that these different characteristics have an influence on brain activity or connectivity in tinnitus; namely age [52], gender [53], tinnitus lateralization [16; 17], tinnitus sound type [30], tinnitus loudness [54], distress [15; 55], tinnitus duration [31; 32], frequency and sensation level [21]. We included both the tinnitus loudness as well as tinnitus sensation level, because the tinnitus loudness is a subjective measure of how loud the patient perceives the tinnitus, and the tinnitus sensation level is a more objective measure of the tinnitus including a control for hearing loss at the tinnitus frequency. The tinnitus loudness matching in dB sensation level is calculated by subtracting the hearing loss in dB from the intensity of the presented sound in dB HL in order to compensate for hearing loss at the tinnitus frequency. In view of the fact that the tinnitus normally matches the area of hearing loss [56], this implies that hearing loss is controlled for. Both measures in our population do not correlate (r = .06, n.s.).

In addition to the whole brain analysis, we performed a region of interest analysis using a mask of cortical and subcortical regions in the auditory system previously used [20; 21]. We used the same anatomical mask wherein the region of interest encompassed the ventral and dorsal cochlear nuclei (sphere radius, 5 mm; Montreal-Neurological-Institute (MNI)-coordinates, ±10, −38, −45), superior olivary complex (sphere radius, 5 mm; MNI-coordinates, ±13, −35, −41), inferior colliculus (sphere radius, 5 mm; MNI-coordinates, ±6, −33, −11), medial geniculate nucleus (sphere radius, 8 mm; MNI coordinates, ±17, −24, −2), and the primary and secondary auditory cortices corresponding to Brodmann areas 41, 42, and 22 (defined with the WFU-Pick Atlas [45]). The main advantage of using a priori region of interest testing over whole-brain analysis is that it limits type I errors by reducing the number of statistical tests to only a few regions of interest. Again, a multiple regression analysis was conducted with age, gender, tinnitus type, tinnitus lateralization, tinnitus related distress, tinnitus loudness, tinnitus duration, tinnitus frequency, and tinnitus sensation level as independent variables to test for any association with changes in grey matter within the regions of interest.

To verify the obtained VBM results we applied a supplementary analysis for which we assigned each subject randomly to one of two smaller groups and conducted a similar analysis (split-half analysis).

In addition, we also performed three simple regression analyses on respectively distress, duration and the tinnitus loudness as separate independent variables and test for any association with changes in grey matter. We selected these three variables as previous research has shown that these parameters play an important role in tinnitus [57]. It is important to verify whether similar results can be obtained for different tinnitus characteristics separately. To some extent, results within the integrative model can be biased due to intercorrelations between the different tinnitus characteristics. Therefore, we conducted additional analyses including only one regressor each time (tinnitus distress, loudness, and duration).

In addition to the previously mentioned analyses we also applied a simple regression on the hearing loss to further confirm previous research findings on hearing loss and verify whether hearing loss does have a great impact on grey matter. In addition we applied a multiple regression analysis with hearing loss, tinnitus loudness, tinnitus distress, and tinnitus duration as independent variables to compare what the influence is from respectively hearing loss and the tinnitus characteristics (loudness distress and duration).

For all the analyses the statistical significance was set at p < .001 uncorrected and p < .05 false discovery rate (FDR) corrected at voxel–level and the minimum contiguous cluster size at 20 voxels, except when the effect was strong, the cluster size was set at 50 voxels (i.e. for hearing loss)

2. Source localization

sLORETA was used to perform a voxel-by-voxel analysis (comprising 6239 voxels) for the different frequency bands to regress of the current density distribution to identify potential differences in brain electrical activity. Nonparametric statistical analyses of functional sLORETA images (statistical nonparametric mapping: SnPM) were performed for each contrast using sLORETA’s built-in voxel wise randomization tests (5000 permutations) and employing a F-statistic with a threshold P < 0.05. As explained by Nichols and Holmes, the statistical nonparametric mapping method does not rely on an assumption of a Gaussian distribution for the validity and corrects for all multiple comparisons (i.e. for the collection of test performed for all voxels and for all frequency bands) by employing a locally pooled (smoothed) variance estimate the outperforms of the comparable statistical parametric mapping [58; 59]. A multiple regression analysis was conducted with age, gender, tinnitus type, tinnitus lateralization, tinnitus related distress, tinnitus loudness, tinnitus duration, tinnitus frequency, tinnitus sensation level and hearing loss as independent variables. We also applied a simple regression on the hearing loss to further confirm previous research findings on hearing loss the QEEG analysis.

3. The combination of source localized QEEG and VBM

A whole brain analysis correlating activity of QEEG and VBM was conducted, as well as region of interest analysis on the regions obtained with source localized QEEG to increase spatial accuracy. That is the dorsal and subgenual anterior cingulate cortex, the parahippocampus and auditory cortex for specific frequency obtained. These regions of interest were then correlated using a Pearson correlation to the same region of interest on the VBM data. Thalamic and cerebellum region of interests were not included in this subsection as this is not possible to locate with source localized QEEG.

Results

VBM

1. Integrative model: Tinnitus

The whole brain analysis showed only a significant effect for the grey matter density changes after correction at voxel–level for tinnitus distress (Z = 4.29, pcorrected = .02), tinnitus loudness (Z = 4.22, pcorrected = .03), and tinnitus duration (Z = 4.25, pcorrected = .04) (see Table 2 for overview). These results demonstrated that the higher patient’s score on tinnitus distress, tinnitus loudness, and tinnitus duration, the more grey matter density was decreased (see Fig. 1; uncorrected results see S1 Text & S1 Table).

thumbnail
Fig 1. Grey matter concentration for (A) for tinnitus lateralization.

Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 2 & S2 Table). (B) Grey matter concentration increases for tinnitus distress. Grey matter increase projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 2 & S2 Table). (C) Grey matter concentration decreases for tinnitus distress. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 2 & S2 Table). (D) Grey matter concentration decreases for tinnitus loudness Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 2 & S2 Table). (E) Grey matter concentration decreases for tinnitus distress. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 2 & S2 Table). (F) Grey matter concentration decreases for hearing loss. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 3 & S3 Table).

https://doi.org/10.1371/journal.pone.0115122.g001

thumbnail
Table 2. Local maxima from the different contrasts highlighting grey matter differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness, Tinnitus duration, Tinnitus frequency and Tinnitus sensation level (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t002

To verify the VBM results we conducted an additional analysis for which we assigned each subject randomly to one of two smaller groups and conducted a similar analysis (see S2 Table). Again our results demonstrated that the higher the patient’s score on tinnitus distress, tinnitus loudness and tinnitus duration, the more grey matter density was decreased mainly in the cerebellum. However, these findings are only present when they remain uncorrected. After correction no significant results were obtained.

We also conducted a region of interest analysis for the entire auditory system. We detected a smaller grey matter density for the unilateral tinnitus patients in comparison to the bilateral tinnitus patients in the right primary auditory cortex, which is in association with tinnitus lateralization (Z = 2.86, p = .002 uncorrected). However, no other changes were obtained in respectively the ventral and dorsal cochlear nuclei, inferior colliculus, medial geniculate nucleus as well as the primary and secondary auditory cortices in relation to age, gender, tinnitus type, tinnitus lateralization, tinnitus related distress, tinnitus loudness, tinnitus duration, tinnitus frequency and tinnitus sensation level.

2. Integrative model: Tinnitus and hearing loss

The whole brain analysis revealed a significant effect for the grey matter density changes after correction at voxel–level for hearing, (see Table 3 for overview). These results demonstrated that the more hearing loss patients have the more grey matter density was decreased in the auditory cortex (Z = 4.86, pcorrected = .007) and thalamus (Z = 4.77, pcorrected = .02). No effects were obtained for the tinnitus characteristics (see Fig. 1, uncorrected results see S2 Text & S3 Table).

thumbnail
Table 3. Local maxima from the different contrasts highlighting grey matter differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness, Tinnitus duration, Tinnitus frequency and Tinnitus sensation level and hearing loss (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t003

Since the different variables included in the integrative model are to some extent intercorrelated which could bias the results, we conducted an additional analysis including only one regressor. We applied this method with tinnitus distress, tinnitus duration and tinnitus loudness as regressors on the whole brain. We also applied a similar method for hearing loss as previous research already indicated that hearing loss might be a more important variable to explain the grey matter density in tinnitus patients.

3. Single regression model: Tinnitus Distress

When applying a whole brain analysis with tinnitus distress as the single regressor, a significant effect for the cerebellar activity (Z = 5.48, pcorrected < .001) after correction at voxel–level, again reveals a decrease in grey matter density analogous with more distress (see Table 4; see Fig. 2A). However, several other areas where also shown to have a decrease in grey matter in correlation with more distress such as in the cerebellum, the dorsal lateral prefrontal cortex, the posterior cingulate cortex, the inferior temporal cortex, and the posterior middle temporal cortex. However, these findings were significantly uncorrected.

thumbnail
Fig 2. Grey matter concentration decreases for (A) tinnitus duration.

Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see S2 Table). (B) Grey matter concentration decreases for NRS loudness. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see S3 Table). (C) Grey matter concentration decreases for tinnitus duration. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see S4 Table). (D) Grey matter concentration decreases for hearing loss. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .05 (corrected) at both voxel and cluster level. Coordinates of peak voxel (see Table 4). (E) Grey matter concentration decreases for hearing loss controlling for tinnitus characteristics (distress, loudness and duration). Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .05 (corrected) at both voxel and cluster level. Coordinates of peak voxel (see S5 Table). (F) Grey matter concentration decreases for tinnitus characteristics controlling for hearing loss. Grey matter decrease projected onto a T1-weighted image. Maximum intensity projection with a threshold of p < .001 (uncorrected) at both voxel and cluster level. Coordinates of peak voxel (see S6 Table).

https://doi.org/10.1371/journal.pone.0115122.g002

thumbnail
Table 4. Local maxima from the different contrasts highlighting grey matter differences for TQ (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t004

4. Single regression model: Tinnitus Duration

When applying a whole brain analysis with duration as the single regressor, no significant effect could be obtained after correction at voxel–level (see Table 5; see Fig. 2B). However, uncorrected demonstrates a significant effect in the right parahippocampus and the superior temporal pole areas revealing a decrease in grey matter in relationship with tinnitus duration.

thumbnail
Table 5. Local maxima from the different contrasts highlighting grey matter differences for duration (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t005

5. Single regression model: Tinnitus Loudness

A whole brain analysis with tinnitus loudness as the single regressor indicated no significant effect could be obtained after correction at voxel–level (see Table 6; see Fig. 2C). However, uncorrected demonstrates a significant effect in the dorsal lateral prefrontal cortex, caudate nucleus, inferior temporal cortex, cerebellum, and putamen revealing a decrease in grey matter in relationship with an increase in tinnitus loudness.

thumbnail
Table 6. Local maxima from the different contrasts highlighting grey matter differences for loudness (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t006

6. Single regression model: Hearing Loss

When applying a whole brain analysis with hearing loss as the single regressor, several significant effects were obtained after correction at voxel–level, revealing a decrease in grey matter density associated with an increase in hearing loss (see Table 7; see Fig. 2D). Decrease in grey matter was obtained in the cerebellum, the ventral lateral prefrontal cortex, the somatosensory cortex, the auditory cortex, the posterior cingulate cortex, and the superior parietal cortex.

thumbnail
Table 7. Local maxima from the different contrasts highlighting grey matter differences for hearing loss controlling for the tinnitus characteristics (Distress, Loudness and Duration) (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t007

7. A comparison between hearing loss and tinnitus characteristics (Distress, Loudness and Duration).

A whole brain analysis including in the contrast hearing loss and the tinnitus characteristics as covariates revealed a significant effect after correction at voxel-level, demonstrating a decrease in grey matter density (see Table 8; Fig. 2E). Decrease in grey matter was obtained in the cerebellum (Z = 7.14, pcorrected < .001), ventral lateral prefrontal cortex (Z = 6.26, pcorrected = .01), somatosensory cortex (Z = 6.13, pcorrected = .01), auditory cortex (Z = 5.88, pcorrected = .02), posterior cingulate cortex (Z = 5.85, pcorrected = .02), superior parietal cortex (Z = 5.61, pcorrected = .05), and crus I (Z = 5.48, pcorrected = .05).

thumbnail
Table 8. Local maxima from the different contrasts highlighting grey matter differences for tinnitus characteristics (Distress, Loudness and Duration) controlling for hearing loss (N = 154).

https://doi.org/10.1371/journal.pone.0115122.t008

A similar analysis including in the contrast of the different tinnitus characteristics and hearing loss as a covariates revealed a significant effect uncorrected, however after correction the effect disappeared (see Table 9; Fig. 2F). Decrease in grey matter was obtained in the cerebellum (Z = 3.01 puncorrected < .001), angular gyrus (Z = 3.19 puncorrected < .001), insula (Z = 2.88 puncorrected < .001) and middle temporal cortex (Z = 3.29 puncorrected < .001).

thumbnail
Table 9. Local maxima from the different contrasts highlighting activity differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness and duration (N = 154) for the source localized QEEGs.

https://doi.org/10.1371/journal.pone.0115122.t009

Source localized QEEG

A regressions analysis revealed a significant effect (p < .05) for tinnitus related distress, tinnitus loudness and tinnitus duration (See Table 10). A closer look shows a positive relation between tinnitus related distress and respectively the subgenual anterior cingulate cortex, the dorsal anterior cingulate cortex and the hippocampus for the alpha frequency as well as between tinnitus related distress and the hippocampus for the beta frequency (see Fig. 3A-B). Tinnitus loudness has a positive relationship with the left parahippocamal area in the gamma frequency (see Fig. 3C). In addition, tinnitus duration is positively related to increased theta activity within the dorsal anterior cingulate cortex (see Fig. 3D). In addition we applied a single regression analysis to verify whether we could observe a similar result for tinnitus duration (see S2 Fig.), tinnitus related distress (see S3 Fig.), and tinnitus loudness (see S4 Fig.). A simple regression analysis including hearing loss did not yield a significant result.

thumbnail
Fig 3. Significant results for current density amplitude analysis in the (A) alpha frequency band.

sLORETA current source density in the alpha band correlated positively with tinnitus distress in the subgenual anterior cingulate, dorsal anterior cingulate and the hippocampus. (B) Significant results for current density amplitude analysis in the beta frequency band.in the beta in the dorsal anterior cingulate cortex. This image shows significant results only. (C) Significant results for current density amplitude analysis in the gamma frequency band. sLORETA current source density in the gamma band correlated positively with tinnitus loudness in the left parahippocampal area. This image shows significant results only. (D) Significant results for current density amplitude analysis in the theta frequency band. sLORETA current source density in the theta band correlated positively with tinnitus duration in the dorsal anterior cingulate cortex area This image shows significant results only.

https://doi.org/10.1371/journal.pone.0115122.g003

thumbnail
Table 10. Pearson correlation between the region of interest on the sturctural (VBM) and functional (source localized EEG).

https://doi.org/10.1371/journal.pone.0115122.t010

The combination of source localized QEEG and VBM

A whole brain analysis between the structural data obtained by MRI and functional data obtained by QEEG yielded no significant effect. In addition, no significant effects were obtained between the regions of interest on source localized QEEGs and the region of interest on the VBM data (r = between -06 and .12) (see Table 10).

Discussion

The aim of this study is to verify whether VBM reflects structural changes that might accompany functional changes in tinnitus. However no correlations could be found between functional and structural changes. In epilepsy it has been shown that focal discharges occur at the areas of structural brain changes, suggesting these structural changes might be causally related to the epilepsy. However in tinnitus, even though structural changes have been described, they do not seem to be related to the oscillatory changes that have been linked to tinnitus. One explanation could be that the structural changes seen in tinnitus relate more to hearing loss, which frequently accompanies tinnitus, than to the tinnitus per se. Another explanation could be that structural changes induce oscillatory changes at a distance, change synchrony or modify connectivity rather than activity.

From a methodological point of view EEG and VBM have nothing in common. Source analyzed EEG can record and correctly localize cortical activity, although it excludes activity from deep nuclei, the brainstem, and/or the cerebellum, whereas VBM is anatomically unrestricted. Although the neural elements in EEG are relatively well defined, it is unclear what the anatomical basis for VBM actually is. VBM likely evaluates structural changes, EEG records functional changes. So, although both techniques could be complementary, this study did not find any correlation between the results of the two used techniques. This could also be related to the fact that they measure unrelated properties of tinnitus. This raises the question whether the VBM technique is ideally suited to study pathologies related to functional changes like tinnitus. No studies have been able to replicate other centers’ results. One option for the variability is selection of the patients, which can bias the obtained results as it has been shown that many brain areas are involved in tinnitus, depending on the investigated tinnitus characteristic.

This study detected structural differences in grey matter within auditory, hippocampal, thalamic, and cerebellar areas that are related to specific tinnitus characteristics. However, these effects were mainly uncorrected. In contrast, significant results were obtained for hearing loss in the cerebellum, ventral lateral prefrontal cortex, somatosensory cortex, auditory cortex, thalamus, posterior cingulate cortex, and superior parietal cortex. These latter results remained after correction for multiple comparisons. In an integrative model including all tinnitus characteristics and hearing loss only an effect for hearing loss was found, demonstrating a decrease in grey matter density in the auditory cortex and the thalamus. These findings corroborate previous research indicating that grey matter decreases can be explained by the hearing loss and not by the tinnitus [21; 26]. Functional (QEEG) differences were obtained for tinnitus distress, tinnitus intensity, and tinnitus duration in the subgenual and dorsal anterior cingulate cortex, hippocampus and the parahippocampus. This is consistent with previously published data [14; 15], suggesting this population is representative and comparable with previously studied populations.

One structural finding that was consistently present, after correction for multiple comparisons, was the changes within the cerebellum. However this effect did not remain if we included hearing loss in the model. That is, tinnitus distress, tinnitus loudness and tinnitus duration are negatively correlated with grey matter within the cerebellum. This was further confirmed by a split-half reliability analysis that again revealed that the higher the patient scored on tinnitus distress, tinnitus loudness, and tinnitus duration the more grey matter density was decreased mainly in the cerebellum. The cerebellum has previously been associated with higher-order functions [60; 61], as well as auditory sensory processing [62]. Cerebellar involvement in auditory processing could be related to auditory prediction, i.e. the cerebellum detects relevant changes in auditory events, such as sound onset, and predicts when the next event is going to occur [63]. It is strange that there are no direct functional connections between the primary auditory cortex and the cerebellum [64], but animal research in cats demonstrated anatomical connections between the cochlear nuclei and parts of the cerebellum that might be the anatomical basis for auditory sensory input to the cerebellum [65]. Recently, it was shown in animals that the paraflocculus of the cerebellum also plays a role in tinnitus [66]. In humans, functional cerebellar anomalies in conjunction with self-reported tinnitus has been documented [67], and PET studies have shown cerebellar activation in gaze evoked tinnitus [68] as well as right cerebellar activation during tinnitus perception in deaf patients with tinnitus and a cochlear implant [69]. In addition, it was shown that aversive sounds mimicking tinnitus presented to subjects without tinnitus also showed regional cerebral blood flow changes in the cerebellum [70].

A recent paper by Schecklmann and collaborators [71] could only find an uncorrected significant correlation between tinnitus related distress and a decrease in grey matter volume within the auditory cortex and insula after correction from potential cofounders such as age, gender, and audiometric parameters [71]. In contrast we found after correction for multiple comparisons only cerebellar changes survive statistical analysis, in contrast to the QEEG data in this study. The discrepancies between previous and current structural results could be related to the variability of clinical characteristics of the population studied. The results of this study indeed reveal that tinnitus lateralization, tinnitus distress, tinnitus loudness and tinnitus duration all influence the outcome. Therefore it is important to take these different characteristics into account when looking at structural (and functional) differences in the brain of tinnitus patients. Thus one should control for most characteristics important in tinnitus when analyzing a specific tinnitus characteristic, similar to what has been done for QEEG studies [15; 30; 32; 72]. However high interindividual anatomical variability for certain brain areas with respect to gyration and angulation may constitute a disadvantage for VBM approaches, = because interindividual anatomical variability is likely to translate into regionally fluctuating statistical power to detect group differences [73]. Thus, our data indicate that minor differences in populations with regard to tinnitus characteristics may account for differing results.

Another possibility for obtaining different results between labs might be different MRI systems (e.g. 1.5 vs. 3 Tesla) with different image acquisitions and data analysis. This explanation is unlikely as a previous study [20] tried to closely follow the protocol of another study [22] with only slight modifications and could not replicate the results even after lowering the statistical threshold, and defining specific regions of interest [20; 22]. Nevertheless, differences in machinery and acquisitions could contribute to differences in findings.

The question then is whether VBM is useful in evaluating functional pathologies such as tinnitus? Arguments against the use of VBM are the following: (1) Only the cerebellum survives statistically correct analysis by multiple comparisons, but not if controlled for hearing loss. (2) TMS of the auditory cortex in healthy controls evokes VBM changes without clinical changes [74], (3) It is not known what VBM really measures at a cellular level. (4) Between centres results are not reproducible, and (5) it remains to be elucidated by interventional studies whether the observed correlations reflect causal relations or are purely epiphenomena. Moreover, we analysed only the effect of selected clinical characteristics. Other clinically relevant factors may influence brain activity as well. The effects detected in the present study were rather moderate (liberal significance threshold, big sample size …).

Nevertheless, some arguments in favour of VBM can also be given. (1) Results in the different studies converge in the sense that the brain areas showing structural VBM changes occur in brain areas already implicated in tinnitus via functional imaging studies. (2) One centre has been able to replicate its own results [22; 23] (3) it is possible that tinnitus subgroups with similar characteristics might differ in their underlying neurobiological mechanism [75].

In most cases phantom sounds are the result of a lack of auditory input due to deafferentation as a consequence of a noise trauma or presbyacusis (i.e. age-related hearing loss) [10]. The VBM results obtained in previous research on tinnitus might not be directly related to tinnitus related factors, but by hearing loss as such. This supports the results of the Husain et al. (2011) where the most significant structural changes appeared in the group with hearing loss without tinnitus and there were no statistically significant differences between the tinnitus with hearing loss group and the normal hearing group. In addition, we showed that the effect of hearing loss was still present after performing regression analysis without specific tinnitus characteristics. Regression for the tinnitus characteristics without hearing loss generated no effect that survived correction. These findings were further confirmed by Melcher et al. (2012) revealing no statistical difference in grey matter comparing a tinnitus group with a control group, all with normal hearing at standard clinical frequencies (< 8 Hz). However, they did find a negative correlation between VBM and hearing thresholds at supra-clinical frequencies (< 8Hz). Previous research on structural changes in tinnitus evaluated a very heterogeneous group. While Mühlau et al. (2006) and Landgrebe et al. (2009) included no tinnitus patients with hearing loss of more than 25 dB HL nor did they report any kind of noise trauma, chronic, noise exposure or hyperacusis. Schneider et al. (2009) and Leaver et al. (2011) included tinnitus patients with a varying degree of hearing loss. This is further in line with several recent findings that have demonstrated structural changes due to aging or musical ability, but they did not mention tinnitus. That is, a relationship was revealed between the cortical neuroanatomy of cognitive brain regions and spoken word processing in the older adults [76]. Above that, increased gray matter volume of left pars opercularis in musicians correlated positively with years of musical performance [77].

Future research should confirm our findings using different functional imaging techniques such as positron emission tomography (PET), and magnetoencephalography (MEG), since QEEG has a poorer spatial resolution in comparison to those two. Nevertheless the advantages of using QEEG lie in its quiet operation, relatively inexpensive usage and better temporal resolution with respect to PET and fMRI. The sLORETA source analysis methodology has received considerable validation from studies combining LORETA with other more established localization methods, such as functional Magnetic Resonance Imaging (fMRI) [78; 79], and Positron Emission Tomography (PET) [80; 81; 82]. Further sLORETA validation has been based on findings obtained from invasive, implanted depth electrodes, in which case there are several studies in epilepsy [83; 84] and cognitive ERPs [85]. It is worth emphasizing that deep cortical structures such as the anterior cingulate cortex [86], and mesial temporal lobes [87] can be correctly localized with these methods. However, deeper structures such as the thalamus and cerebellum could not be included in our analysis due to the limitations of the source localized QEEG. As such the results should be interpreted with care. In addition, further research might also include secondary measure that might have an additional effect on the outcome such as hyperacusis, etc. as reported be in previous research [88].

In conclusion, functional changes as demonstrated by source localized QEEG are not reflected by associated structural changes. Specific tinnitus characteristics are also not reflected by structural changes. The VBM results obtained on tinnitus might not be directly related to tinnitus related factors, but instead to hearing loss.

Supporting Information

S1 Fig. The mean audiogram overall tinnitus patients.

https://doi.org/10.1371/journal.pone.0115122.s001

(DOCX)

S2 Fig. Significant results for current density amplitude analysis in the alpha and beta frequency band.

sLORETA current source density in the alpha1 (8–12 Hz) band correlated positively with tinnitus distress in the subgenual anterior cingulate, dorsal anterior cingulate and the hippocampus and in the beta (13–30 Hz) in the dorsal anterior cingulate cortex. This image shows significant results only.

https://doi.org/10.1371/journal.pone.0115122.s002

(DOCX)

S3 Fig. Significant results for current density amplitude analysis in the gamma frequency band.

sLORETA current source density in the gamma (30.5–44 Hz) band correlated positively with tinnitus loudness in the left parahippocampal area. This image shows significant results only.

https://doi.org/10.1371/journal.pone.0115122.s003

(DOCX)

S4 Fig. Significant results for current density amplitude analysis in the theta frequency band.

sLORETA current source density in the theta (4–7.5 Hz) band correlated positively with tinnitus duration in the dorsal anterior cingulate cortex area This image shows significant results only.

https://doi.org/10.1371/journal.pone.0115122.s004

(DOCX)

S1 Table. Local maxima from the different contrasts highlighting grey matter differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness, Tinnitus duration, Tinnitus frequency and Tinnitus sensation level (N = 154).

https://doi.org/10.1371/journal.pone.0115122.s005

(DOCX)

S2 Table. Split-half reliability analysis: Local maxima from the different contrasts highlighting grey matter differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness, Tinnitus duration, Tinnitus frequency and Tinnitus sensation level (N = 154).

https://doi.org/10.1371/journal.pone.0115122.s006

(DOCX)

S3 Table. Local maxima from the different contrasts highlighting grey matter differences for tinnitus type, tinnitus lateralization, TQ, Vas loudness, Tinnitus duration, Tinnitus frequency and Tinnitus sensation level (N = 154).

https://doi.org/10.1371/journal.pone.0115122.s007

(DOCX)

S1 Text. Results integrative model for tinnitus (uncorrected).

https://doi.org/10.1371/journal.pone.0115122.s008

(DOCX)

S2 Text. Results integrative model for tinnitus and hearing loss (uncorrected).

https://doi.org/10.1371/journal.pone.0115122.s009

(DOCX)

Author Contributions

Conceived and designed the experiments: SV DDR. Performed the experiments: SV DDR. Analyzed the data: SV DDR. Contributed reagents/materials/analysis tools: SV DDR. Wrote the paper: SV PVDH DDR.

References

  1. 1. Eggermont JJ, Roberts LE (2004) The neuroscience of tinnitus. Trends Neurosci 27: 676–682. pmid:15474168
  2. 2. Axelsson A, Prasher D (2000) Tinnitus induced by occupational and leisure noise. Noise Health 2: 47–54. pmid:12689461
  3. 3. Schreiber BE, Agrup C, Haskard DO, Luxon LM (2010) Sudden sensorineural hearing loss. Lancet 375: 1203–1211. pmid:20362815
  4. 4. Dille MF, Konrad-Martin D, Gallun F, Helt WJ, Gordon JS, et al. (2010) Tinnitus onset rates from chemotherapeutic agents and ototoxic antibiotics: results of a large prospective study. J Am Acad Audiol 21: 409–417. pmid:20701838
  5. 5. Heller AJ (2003) Classification and epidemiology of tinnitus. Otolaryngol Clin North Am 36: 239–248. pmid:12856294
  6. 6. Axelsson A, Ringdahl A (1989) Tinnitus—a study of its prevalence and characteristics. Br J Audiol 23: 53–62. pmid:2784987
  7. 7. Cronlein T, Langguth B, Geisler P, Hajak G (2007) Tinnitus and insomnia. Prog Brain Res 166: 227–233. pmid:17956787
  8. 8. Dobie RA (2003) Depression and tinnitus. Otolaryngol Clin North Am 36: 383–388. pmid:12856305
  9. 9. Hallam RS, McKenna L, Shurlock L (2004) Tinnitus impairs cognitive efficiency. Int J Audiol 43: 218–226. pmid:15250126
  10. 10. Salvi RJ, Wang J, Ding D (2000) Auditory plasticity and hyperactivity following cochlear damage. Hear Res 147: 261–274. pmid:10962190
  11. 11. Kaltenbach JA, Afman CE (2000) Hyperactivity in the dorsal cochlear nucleus after intense sound exposure and its resemblance to tone-evoked activity: a physiological model for tinnitus. Hear Res 140: 165–172. pmid:10675644
  12. 12. Vanneste S, De Ridder D (2012) The auditory and non-auditory brain areas involved in tinnitus. An emergent property of multiple parallel overlapping subnetworks. Front Syst Neurosci 6: 31. pmid:22586375
  13. 13. van der Loo E, Congedo M, Vanneste S, De Heyning PV, De Ridder D (2011) Insular lateralization in tinnitus distress. Auton Neurosci 165: 191–194. pmid:21889914
  14. 14. De Ridder D, Vanneste S, Congedo M (2011) The distressed brain: a group blind source separation analysis on tinnitus. PLoS One 6: e24273. pmid:21998628
  15. 15. Vanneste S, Plazier M, der Loo E, de Heyning PV, Congedo M, et al. (2010) The neural correlates of tinnitus-related distress. Neuroimage 52: 470–480. pmid:20417285
  16. 16. Vanneste S, de Heyning PV, Ridder DD (2011) Contralateral parahippocampal gamma-band activity determines noise-like tinnitus laterality: a region of interest analysis. Neuroscience.
  17. 17. Smits M, Kovacs S, de Ridder D, Peeters RR, van Hecke P, et al. (2007) Lateralization of functional magnetic resonance imaging (fMRI) activation in the auditory pathway of patients with lateralized tinnitus. Neuroradiology 49: 669–679. pmid:17404721
  18. 18. Mirz F, Gjedde A, Ishizu K, Pedersen CB (2000) Cortical networks subserving the perception of tinnitus—a PET study. Acta Otolaryngol Suppl 543: 241–243. pmid:10909031
  19. 19. Schlee W, Mueller N, Hartmann T, Keil J, Lorenz I, et al. (2009) Mapping cortical hubs in tinnitus. BMC Biol 7: 80. pmid:19930625
  20. 20. Landgrebe M, Langguth B, Rosengarth K, Braun S, Koch A, et al. (2009) Structural brain changes in tinnitus: grey matter decrease in auditory and non-auditory brain areas. Neuroimage 46: 213–218. pmid:19413945
  21. 21. Husain FT, Medina RE, Davis CW, Szymko-Bennett Y, Simonyan K, et al. (2011) Neuroanatomical changes due to hearing loss and chronic tinnitus: a combined VBM and DTI study. Brain Res 1369: 74–88. pmid:21047501
  22. 22. Muhlau M, Rauschecker JP, Oestreicher E, Gaser C, Rottinger M, et al. (2006) Structural brain changes in tinnitus. Cereb Cortex 16: 1283–1288. pmid:16280464
  23. 23. Leaver AM, Renier L, Chevillet MA, Morgan S, Kim HJ, et al. (2011) Dysregulation of limbic and auditory networks in tinnitus. Neuron 69: 33–43. pmid:21220097
  24. 24. Mahoney CJ, Rohrer JD, Goll JC, Fox NC, Rossor MN, et al. (2011) Structural neuroanatomy of tinnitus and hyperacusis in semantic dementia. J Neurol Neurosurg Psychiatry.
  25. 25. Boyen K, Langers DR, de Kleine E, van Dijk P (2012) Gray matter in the brain: Differences associated with tinnitus and hearing loss. Hear Res.
  26. 26. Melcher JR, Knudson IM, Levine RA (2012) Subcallosal brain structure: Correlation with hearing threshold at supra-clinical frequencies (>8 kHz), but not with tinnitus. Hear Res.
  27. 27. Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV (2011) Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies. Neurobiol Aging 32: 1733–1741. pmid:20005012
  28. 28. Li J, Pan P, Huang R, Shang H (2011) A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer's disease. Neurosci Biobehav Rev.
  29. 29. Vanneste S, Plazier M, van der Loo E, Van de Heyning P, De Ridder D (2010) The difference between uni- and bilateral auditory phantom percept. Clin Neurophysiol.
  30. 30. Vanneste S, Plazier M, van der Loo E, Van de Heyning P, De Ridder D (2010) The differences in brain activity between narrow band noise and pure tone tinnitus. PLoS One 5: e13618. pmid:21048975
  31. 31. Schlee W, Hartmann T, Langguth B, Weisz N (2009) Abnormal resting-state cortical coupling in chronic tinnitus. BMC Neurosci 10: 11. pmid:19228390
  32. 32. Vanneste S, van de Heyning P, De Ridder D (2011) The neural network of phantom sound changes over time: a comparison between recent-onset and chronic tinnitus patients. Eur J Neurosci 34: 718–731. pmid:21848924
  33. 33. Johnsrude IS, Giraud AL, Frackowiak RS (2002) Functional imaging of the auditory system: the use of positron emission tomography. Audiol Neurootol 7: 251–276. pmid:12232496
  34. 34. Betting LE, Li LM, Lopes-Cendes I, Guerreiro MM, Guerreiro CA, et al. (2010) Correlation between quantitative EEG and MRI in idiopathic generalized epilepsy. Hum Brain Mapp 31: 1327–1338. pmid:20082332
  35. 35. Koepp MJ, Woermann FG (2005) Imaging structure and function in refractory focal epilepsy. Lancet Neurol 4: 42–53. pmid:15620856
  36. 36. British Society of Audiology (2008) Recommended procedure:pure tone air and bone conduction threshold audiometry with and without masking and determination of uncomfortable loudness levels. Available: http://www.thebsa.org.uk/docs/RecPro/PTA.pdf.
  37. 37. Meeus O, Heyndrickx K, Lambrechts P, De Ridder D, Van de Heyning P (2009) Phase-shift treatment for tinnitus of cochlear origin. Eur Arch Otorhinolaryngol.
  38. 38. Meeus O, De Ridder D, Van de Heyning P (2011) Administration of the Combination Clonazepam-Deanxit as Treatment for Tinnitus. Otol Neurotol.
  39. 39. Meeus O, Blaivie C, Van de Heyning P (2007) Validation of the Dutch and the French version of the Tinnitus Questionnaire. B-ENT 3 Suppl 7: 11–17. pmid:18228680
  40. 40. McCombe A, Baguley D, Coles R, McKenna L, McKinney C, et al. (2001) Guidelines for the grading of tinnitus severity: the results of a working group commissioned by the British Association of Otolaryngologists, Head and Neck Surgeons, 1999. Clin Otolaryngol Allied Sci 26: 388–393. pmid:11678946
  41. 41. Hiller W, Goebel G (1992) A psychometric study of complaints in chronic tinnitus. J Psychosom Res 36: 337–348. pmid:1593509
  42. 42. Hiller W, Goebel G, Rief W (1994) Reliability of self-rated tinnitus distress and association with psychological symptom patterns. Br J Clin Psychol 33 (Pt 2): 231–239.
  43. 43. Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11: 805–821. pmid:10860804
  44. 44. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26: 839–851. pmid:15955494
  45. 45. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19: 1233–1239. pmid:12880848
  46. 46. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273–289. pmid:11771995
  47. 47. Congedo M (2002) EureKa! (Version 3.0) [Computer Software]. Knoxville, TN: NovaTech EEG Inc. Freeware available at http://www.NovaTechEEG.
  48. 48. Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24 Suppl D: 5–12. pmid:12575463
  49. 49. Fuchs M, Kastner J, Wagner M, Hawes S, Ebersole JS (2002) A standardized boundary element method volume conductor model. Clin Neurophysiol 113: 702–712. pmid:11976050
  50. 50. Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34: 1600–1611. pmid:17207640
  51. 51. Brett M, Johnsrude IS, Owen AM (2002) The problem of functional localization in the human brain. Nat Rev Neurosci 3: 243–249. pmid:11994756
  52. 52. Schlee W, Leirer V, Kolassa IT, Weisz N, Elbert T (2012) Age-related changes in neural functional connectivity and its behavioral relevance. BMC Neurosci 13: 16. pmid:22333511
  53. 53. Vanneste S, Joos K, De Ridder D (2012) Prefrontal cortex based sex differences in tinnitus perception: same tinnitus intensity, same tinnitus distress, different mood. PLoS One 7: e31182. pmid:22348053
  54. 54. van der Loo E, Gais S, Congedo M, Vanneste S, Plazier M, et al. (2009) Tinnitus intensity dependent gamma oscillations of the contralateral auditory cortex. PLoS One 4: e7396. pmid:19816597
  55. 55. Vanneste S, Congedo M, De Ridder D (2014) Pinpointing a highly specific pathological functional connection that turns phantom sound into distress. Cereb Cortex 24: 2268–2282. pmid:23632885
  56. 56. Norena A, Micheyl C, Chery-Croze S, Collet L (2002) Psychoacoustic characterization of the tinnitus spectrum: implications for the underlying mechanisms of tinnitus. Audiol Neurootol 7: 358–369. pmid:12401967
  57. 57. De Ridder D, Elgoyhen AB, Romo R, Langguth B (2011) Phantom percepts: tinnitus and pain as persisting aversive memory networks. Proc Natl Acad Sci U S A 108: 8075–8080. pmid:21502503
  58. 58. Holmes AP, Blair RC, Watson JD, Ford I (1996) Nonparametric analysis of statistic images from functional mapping experiments. J Cereb Blood Flow Metab 16: 7–22. pmid:8530558
  59. 59. Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15: 1–25. pmid:11747097
  60. 60. Schmahmann JD (1991) An emerging concept. The cerebellar contribution to higher function. Arch Neurol 48: 1178–1187. pmid:1953406
  61. 61. Ramnani N (2006) The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci 7: 511–522. pmid:16791141
  62. 62. Petacchi A, Laird AR, Fox PT, Bower JM (2005) Cerebellum and auditory function: an ALE meta-analysis of functional neuroimaging studies. Hum Brain Mapp 25: 118–128. pmid:15846816
  63. 63. Schwartze M, Tavano A, Schroger E, Kotz SA (2011) Temporal aspects of prediction in audition: Cortical and subcortical neural mechanisms. Int J Psychophysiol 83: 200–207. pmid:22108539
  64. 64. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT (2011) The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol 106: 2322–2345. pmid:21795627
  65. 65. Huang CM, Liu G, Huang R (1982) Projections from the cochlear nucleus to the cerebellum. Brain Res 244: 1–8. pmid:7116161
  66. 66. Brozoski TJ, Ciobanu L, Bauer CA (2007) Central neural activity in rats with tinnitus evaluated with manganese-enhanced magnetic resonance imaging (MEMRI). Hear Res 228: 168–179. pmid:17382501
  67. 67. Shulman A, Strashun A (1999) Descending auditory system/cerebellum/tinnitus. Int Tinnitus J 5: 92–106. pmid:10753427
  68. 68. Lockwood AH, Wack DS, Burkard RF, Coad ML, Reyes SA, et al. (2001) The functional anatomy of gaze-evoked tinnitus and sustained lateral gaze. Neurology 56: 472–480. pmid:11222790
  69. 69. Osaki Y, Nishimura H, Takasawa M, Imaizumi M, Kawashima T, et al. (2005) Neural mechanism of residual inhibition of tinnitus in cochlear implant users. Neuroreport 16: 1625–1628. pmid:16189467
  70. 70. Mirz F, Gjedde A, Sodkilde-Jrgensen H, Pedersen CB (2000) Functional brain imaging of tinnitus-like perception induced by aversive auditory stimuli. Neuroreport 11: 633–637. pmid:10718327
  71. 71. Schecklmann M, Lehner A, Poeppl TB, Kreuzer PM, Rupprecht R, et al. (2013) Auditory cortex is implicated in tinnitus distress: a voxel-based morphometry study. Brain Struct Funct 218: 1061–1070. pmid:23435735
  72. 72. Vanneste S, Plazier M, van der Loo E, Van de Heyning P, De Ridder D (2011) The difference between uni- and bilateral auditory phantom percept. Clin Neurophysiol 122: 578–587. pmid:20801079
  73. 73. Tisserand DJ, van Boxtel MP, Pruessner JC, Hofman P, Evans AC, et al. (2004) A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cereb Cortex 14: 966–973. pmid:15115735
  74. 74. May A, Hajak G, Ganssbauer S, Steffens T, Langguth B, et al. (2007) Structural brain alterations following 5 days of intervention: dynamic aspects of neuroplasticity. Cereb Cortex 17: 205–210. pmid:16481564
  75. 75. Moller AR (2007) Tinnitus: presence and future. Prog Brain Res 166: 3–16. pmid:17956767
  76. 76. Wong PC, Ettlinger M, Sheppard JP, Gunasekera GM, Dhar S (2010) Neuroanatomical characteristics and speech perception in noise in older adults. Ear Hear 31: 471–479. pmid:20588117
  77. 77. Abdul-Kareem IA, Stancak A, Parkes LM, Sluming V (2011) Increased gray matter volume of left pars opercularis in male orchestral musicians correlate positively with years of musical performance. J Magn Reson Imaging 33: 24–32. pmid:21182117
  78. 78. Mulert C, Jager L, Schmitt R, Bussfeld P, Pogarell O, et al. (2004) Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage 22: 83–94. pmid:15109999
  79. 79. Vitacco D, Brandeis D, Pascual-Marqui R, Martin E (2002) Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Hum Brain Mapp 17: 4–12. pmid:12203683
  80. 80. Dierks T, Jelic V, Pascual-Marqui RD, Wahlund L, Julin P, et al. (2000) Spatial pattern of cerebral glucose metabolism (PET) correlates with localization of intracerebral EEG-generators in Alzheimer's disease. Clin Neurophysiol 111: 1817–1824. pmid:11018498
  81. 81. Pizzagalli DA, Oakes TR, Fox AS, Chung MK, Larson CL, et al. (2004) Functional but not structural subgenual prefrontal cortex abnormalities in melancholia. Mol Psychiatry 9: 325, 393–405. pmid:14699431
  82. 82. Zumsteg D, Wennberg RA, Treyer V, Buck A, Wieser HG (2005) H2(15)O or 13NH3 PET and electromagnetic tomography (LORETA) during partial status epilepticus. Neurology 65: 1657–1660. pmid:16301501
  83. 83. Zumsteg D, Lozano AM, Wennberg RA (2006) Depth electrode recorded cerebral responses with deep brain stimulation of the anterior thalamus for epilepsy. Clin Neurophysiol 117: 1602–1609. pmid:16759907
  84. 84. Zumsteg D, Lozano AM, Wieser HG, Wennberg RA (2006) Cortical activation with deep brain stimulation of the anterior thalamus for epilepsy. Clin Neurophysiol 117: 192–207. pmid:16364686
  85. 85. Volpe U, Mucci A, Bucci P, Merlotti E, Galderisi S, et al. (2007) The cortical generators of P3a and P3b: a LORETA study. Brain Res Bull 73: 220–230. pmid:17562387
  86. 86. Pizzagalli D, Pascual-Marqui RD, Nitschke JB, Oakes TR, Larson CL, et al. (2001) Anterior cingulate activity as a predictor of degree of treatment response in major depression: evidence from brain electrical tomography analysis. Am J Psychiatry 158: 405–415. pmid:11229981
  87. 87. Zumsteg D, Lozano AM, Wennberg RA (2006) Mesial temporal inhibition in a patient with deep brain stimulation of the anterior thalamus for epilepsy. Epilepsia 47: 1958–1962. pmid:17116040
  88. 88. Adjamian P, Sereda M, Hall DA (2009) The mechanisms of tinnitus: perspectives from human functional neuroimaging. Hear Res 253: 15–31. pmid:19364527