Summary
Tinnitus
is a common disorder characterized by ringing in the ear in the absence
of sound. Converging evidence suggests that tinnitus pathophysiology
involves damage to peripheral and/or central auditory pathways. However,
whether auditory system dysfunction is sufficient to explain chronic
tinnitus is unclear, especially in light of evidence implicating other
networks, including the limbic system. Using functional magnetic
resonance imaging and voxel-based morphometry, we assessed
tinnitus-related functional and anatomical anomalies in auditory and
limbic networks. Moderate hyperactivity was present in the primary and
posterior auditory cortices of tinnitus patients. However, the nucleus
accumbens exhibited the greatest degree of hyperactivity, specifically
to sounds frequency-matched to patients’ tinnitus. Complementary
structural differences were identified in ventromedial prefrontal
cortex, another limbic structure heavily connected to the nucleus
accumbens. Furthermore, tinnitus-related anomalies were intercorrelated
in the two limbic regions and between limbic and primary auditory areas,
indicating the importance of auditory-limbic interactions in tinnitus
Introduction
Tinnitus
is a common hearing disorder characterized by a “phantom sensation” of
ringing or buzzing in one’s ear in the absence of an external sound
source. Although many people experience transient tinnitus-like symptoms
as a result of brief loud-noise exposure (e.g., a rock concert) or
stress, for an estimated 5–15% of the population tinnitus can become
chronic and detrimental to quality of life (Eggermont and Roberts, 2004; Heller, 2003; Henry et al., 2005). With an even higher prevalence of tinnitus in expanding demographics, including aging individuals and recent war veterans (Department of Veterans Affairs, 2008; Henry et al., 2005), proper diagnosis and treatment of tinnitus are of growing concern.
Despite
its high prevalence, there is little consensus regarding the
neurophysiological origin of tinnitus. Most researchers agree that
tinnitus can be linked to changes at one or more points along the
peripheral and central auditory pathways (Eggermont and Roberts, 2004; Jastreboff, 1990; Møller, 2003; Rauschecker et al., 2010).
Indeed, human brain imaging studies have identified tinnitus-related
dysfunction in auditory areas, including the inferior colliculus (Melcher et al., 2000) and auditory cortex (Giraud et al., 1999; Lockwood et al., 1998; Plewnia et al., 2007; Reyes et al., 2002).
In addition, a link between tinnitus and reorganization of central
tonotopic maps has been suggested, based on MEG studies in humans (Mühlnickel et al., 1998; Weisz et al., 2005; Wienbruch et al., 2006) and electrophysiological investigations of animals subjected to acoustic trauma (Eggermont and Komiya, 2000; Irvine et al., 2003; Rajan et al., 1993).
Many have proposed that these changes in the central auditory system
result from damage to the auditory periphery; however, some cases of
tinnitus without significant hearing loss seem to indicate that central
auditory system dysfunction can stem from other etiologies, like head or
neck injury (Henry et al., 2005; Levine et al., 2003) or may reflect the limitations of standard audiometry (Weisz et al., 2006). Conversely, peripheral hearing loss does not always lead to tinnitus (Hoffman and Reed, 2004).
While
it seems, therefore, that auditory system dysfunction is necessary for
tinnitus to occur, it is unclear whether auditory system damage alone is
sufficient to cause chronic tinnitus, or whether additional mechanisms
outside auditory-sensory regions may be involved. Clinicians have noted a
relationship between tinnitus and emotional state (Dobie, 2003; Sullivan et al., 1988), which has led some researchers to propose that the limbic system may play a role in modulating or perpetuating tinnitus (Jastreboff, 1990; Rauschecker et al., 2010).
Indeed, the lifetime incidence of clinical depression in tinnitus
patients is estimated to be more than twice that of the national average
(~35% vs. ~15%, respectively; Folmer et al., 1999), and treatment regimens that include forms of cognitive-behavioral therapy have been shown to be effective for some patients (Jastreboff, 2007; Robinson et al., 2008).
However, empirical evidence of limbic system involvement in tinnitus is
sparse, and these few studies that report limbic involvement implicate
disparate sites: e.g., amygdala (Mirz et al., 2000; Shulman et al., 1995), hippocampus (Landgrebe et al., 2009; Lockwood et al., 1998), basal ganglia (Cheung and Larson, 2010; Lowry et al., 2004), and subcallosal regions (Mühlau et al., 2006). Thus, the exact nature of limbic system involvement in chronic tinnitus, if any, has yet to be elucidated.
In
the current study, we use magnetic resonance imaging (MRI) to test our
recent proposal that chronic tinnitus involves compromised limbic
regulation of aberrant auditory system activity (Rauschecker et al. 2010).
Using functional MRI (fMRI), we compared sound-evoked activity in
individuals with and without tinnitus, in a corticostriatal limbic
network as well as auditory cortex and thalamus. To assess potential
differences in the grey and white matter of tinnitus patients’ brains,
we used voxel-based morphometry (VBM) analyses of high-resolution
structural MRI, again focusing on limbic and auditory brain regions. If
tinnitus pathophysiology does indeed involve impaired auditory-limbic
interaction, then the strength of any limbic marker of tinnitus we
identify should correlate with stimulus-evoked hyperactivity in the
auditory system. Thus, the current study constitutes a first critical
test of our previous model. Ultimately, we hoped to determine the nature
of neural anomalies in tinnitus, improving our understanding of this
common disorder and informing future treatments.
Results
Neural hyperactivity in tinnitus patients
During
fMRI scans, auditory stimuli of several frequencies were presented: one
matched in frequency to each patient’s tinnitus (TF-matched; see
Methods), and others within 2 octaves above or below the TF-matched
stimulus. In this way, each tinnitus patient, and their
“stimulus-matched” control participant, heard a custom set of stimuli
based on the frequency of the patient’s tinnitus sensation (Suppl. Table 1). We thus compared levels of stimulus-evoked function in individuals with and without tinnitus (Table 1).
Table 1
Participant characteristics
When
presented with TF-matched stimuli, tinnitus patients demonstrated
higher fMRI signal than controls in the ventral striatum, specifically
the nucleus accumbens (NAc; p(corr) < 0.05, Figure 1A,B).
Though a similar trend was present for all stimulus frequencies in
separate ROI analyses, these differences were not significant (p(corr)
> 0.05, Bonferroni-corrected for the number of tests performed,
i.e., 5). Thus, NAc hyperactivity in tinnitus patients appeared to be
specific for the tinnitus frequency. Examining pairwise correlations
between NAc activity and age or hearing loss clearly shows that these
variables had no effect on group differences in fMRI signal (Figure 1C,D). Indeed, NAc hyperactivity in tinnitus patients was present in the single-voxel analysis (Figure 1A), in which hearing loss was a “nuisance” covariate, as well as in a separate ROI analysis, in which age was a covariate: t(20) = 5.34, p = 0.00004. Additionally, NAc hyperactivity persisted in an ROI analysis restricted to the four youngest patients (t(13) = 4.98, p = 0.0003), where age and hearing loss were equivalent between groups (age: t(13) = 0.99, p = 0.34; mean hearing loss: t(13) = 0.64, p = 0.53).
Figure 1
Hyperactivity
in tinnitus patients was localized to the ventral striatum near the
nucleus accumbens (center of gravity: X,Y,Z = −16, 6, −0.5; volume = 108
mm3). A. Voxels exhibiting significant (p(corr) < 0.05) between-groups differences (more ...)
In
an analysis restricted to voxels within the auditory cortex and medial
geniculate nuclei (MGN; a “masked analysis”, as defined in Methods),
tinnitus patients exhibited greater fMRI signal than controls in
bilateral posterior superior temporal gyri and sulci (p < 0.01, k > 108 mm3).
Hyperactivity in posterior superior temporal cortex (pSTC) was
significant at the single-voxel level for all stimulus frequencies
except the lowest (Table 2, tFigure 2A). However, in an ROI comprised of voxels exhibiting significant between-groups differences for any stimulus frequency (Figure 2B), a similar trend was observed for the lowest stimulus frequencies ((20) = 2.49, p
= 0.02). Tinnitus patients also demonstrated increased signal in
response to TF-matched stimuli in left medial Heschl’s gyrus (mHG, Table 2, Figure 2A) at the single-voxel level. This hyperactivity in mHG, the likely location of primary auditory cortex (Penhune et al., 1996; Rademacher et al., 2001), was not significant for other stimulus conditions (Figure 2C).
Again, mean hearing loss (a “nuisance” covariate in the above analyses)
and age did not affect these results; an additional ROI analysis
restricted to the four youngest patients yielded hyperactivity for
TF-matched stimuli (pSTC: t(13) = 4.05, p = 0.001; mHG: t(13) = 3.37, p
= 0.005). In addition, hyperactivity in mHG was still apparent when
comparing fMRI signal in tinnitus patients on TF-matched trials against
fMRI signal in controls on all stimulus trials (ROI analysis, t(20) = 2.11, p = 0.048). No differences in fMRI signal were seen between groups in any MGN voxels at any stimulus frequency.
Table 2
Masked fMRI analysis of auditory cortex and MGN
Figure 2
In
a masked analysis restricted to auditory cortex and thalamus,
hyperactivity in tinnitus patients was demonstrated in auditory cortex. A. Voxels that demonstrated between-groups differences in fMRI signal (p < 0.01, k > 108 mm3) are (more ...)
Anatomical anomalies in the brains of tinnitus patients
In
VBM analyses, significant differences in anatomical images were seen
between groups in the subcallosal region, in ventromedial prefrontal
cortex (vmPFC; t > 4.65 p < 0.0001, Figure 3A).
For both modulated and unmodulated grey matter (GM) images (interpreted
as GM amount and concentration, respectively), tinnitus patients
exhibited significantly reduced signal intensity (Figure 3A,B).
Tinnitus patients demonstrated a corresponding increase in vmPFC signal
intensity in unmodulated white matter (WM) images as well (Figure 3A,B), which can be interpreted as an increase in WM concentration in this region relative to other types of tissue.
Figure 3
Structural
differences between tinnitus patients and control participants were
identified in ventromedial prefrontal cortex (vmPFC). A. Voxels demonstrating significant differences in VBM values between groups are shown on group-averaged anatomical images. (more ...)
These
effects appear to be independent of age and total GM or WM volume;
these factors were used as covariates in all VBM analyses. Additionally,
these between-groups differences persisted when mean hearing loss was
entered as a covariate in ROI analyses as well (GM amount: t = 4.70, p < 0.0001; GM concentration: t = 5.76, p < 0.00001; WM concentration: t = 7.14, p
< 0.00001). Thus, anatomical differences were not related to
measurable hearing loss. Examination of pairwise scatterplots of
anatomical effects and age or hearing loss (Figure 3C,D)
shows little relationship between group differences in VBM measures and
these variables, and additional ROI analyses comparing the youngest
patients and control participants yield similar results (GM amount,
patients < controls: t(13) = 4.84, p = 0.0003; GM concentration, patients < controls: t(13) = 4.68, p = 0.0004; WM concentration, patients > controls: t(13) = 4.97, p = 0.0003).
In
a masked analysis restricted to voxels within auditory-sensory regions,
including auditory cortex, MGN, and IC, no significant differences were
found between tinnitus patients and controls (p > 0.01).
Structure-function correspondence in tinnitus-related regions
In
a masked VBM analysis restricted to NAc voxels that demonstrated a
significant functional difference between participant groups, there was
no significant corresponding anatomical difference (p >
0.01). Similarly, in a masked fMRI analysis restricted to vmPFC voxels
that demonstrated significant anatomical between-groups differences, we
saw no significant functional difference between tinnitus patients and
controls (p > 0.01). So, no single brain region exhibited both structural and functional differences.
There was, however, a correlation between NAc fMRI signal and vmPFC VBM values in tinnitus patients (r = 0.73, t(8) = 2.99, p
= 0.02; outlier removed, see Methods), such that patients with the
highest degree of NAc hyperactivity also had correspondingly greater
anatomical differences (i.e., decreases in GM concentration and amount,
with increased WM amount compared to controls; Figure 4A). This relationship was not present in control participants (r = −0.03, t(9) = −0.10, p
= 0.919). Moreover, there was moderate correspondence between limbic
abnormalities and primary auditory cortex hyperactivity in tinnitus
patients (NAc × mHG: r = 0.51, t(8) = 1.67, p = 0.13, Figure 4B; vmPFC × mHG: r = 0.61, t(8) = 2.17, p = 0.06, Figure 4C). Correlations between limbic and posterior auditory areas were not significant (NAc × pSTC; r = 0.17, t(8) = 0.49, p = 0.64, Figure 4D; vmPFC × pSTC: r = 0.42, t(8) = 1.30, p = 0.23, Figure 4E), nor was activity in primary and posterior auditory cortex related (mHG × pSTC: r = −0.13, t(8) = 0.38, p = 0.72, Figure 4F).
This suggests that the degree of functional and structural differences
in the limbic system (i.e., NAc and vmPFC, respectively) and primary
auditory cortex may be directly related in tinnitus patients.
Figure 4
Correlations
between functional and anatomical markers are displayed. Data
corresponding to NAc, mHG, and pSTC reflect fMRI signal during
TF-matched trials. Global VBM values in vmPFC reflect the mean
difference in modulated and unmodulated grey matter (more ...)
Discussion
In
this paper, we report both functional and structural markers of chronic
tinnitus in limbic and auditory regions of the human brain. The most
robust of these tinnitus-related differences were located in limbic
areas previously shown to evaluate the significance of stimuli (Kable and Glimcher, 2009),
including the nucleus accumbens (NAc; part of the ventral striatum) as
well as the ventromedial prefrontal cortex (vmPFC). In tinnitus
patients, the NAc exhibited hyperactivity specifically for stimuli
matched to each patient’s tinnitus frequency (i.e., TF-matched).
Corresponding anatomical differences were identified in the vmPFC, which
is strongly connected to the ventral striatum (Di Martino et al., 2008; Ferry et al., 2000).
Indeed, the magnitude of these effects in NAc and vmPFC were related in
the current study, suggesting that these regions play a similar role in
tinnitus pathology. Within auditory cortex, we noted hyperactivity in
mHG, the likely location of primary auditory cortex (Penhune et al., 1996; Rademacher et al., 2001),
and posterior superior temporal cortex (pSTC), a secondary auditory
region. This increased activity in tinnitus patients was present for all
stimuli in pSTC; however, hyperactivity in mHG was restricted to
TF-matched stimuli and was positively correlated with tinnitus-related
limbic abnormalities as well. Overall, our data suggest that both
auditory and limbic regions are involved in tinnitus, and that
interactions between the limbic corticostriatal network and primary
auditory cortex may be the key to understanding chronic tinnitus.
Limbic system contributions to tinnitus
Many
have proposed a role for the limbic system in tinnitus pathology;
however, the exact nature of limbic contributions to tinnitus is
unknown. We have previously proposed that chronic tinnitus is caused by a
compromised limbic corticostriatal circuit, which results in disordered
evaluation of the tinnitus sensation’s perceptual relevance and, thus,
disordered gain control of the tinnitus percept (Mühlau et al., 2006; Rauschecker et al., 2010).
The same corticostriatal network has been implicated in evaluation of
reward, emotion, and aversiveness in other domains as well (Bar, 2009; Blood et al., 1999; Breiter et al., 2001; Kable and Glimcher, 2009; Ressler and Mayberg, 2007; Sotres-Bayon and Quirk, 2010).
This suggests that the corticostriatal circuit is part of a general
“appraisal network,” determining which sensations are important, and
ultimately affecting how (or whether) those sensations are experienced.
In the current study, we provide evidence that these structures,
specifically the NAc and vmPFC, do indeed differ in the brains of
individuals with tinnitus.
The vmPFC and NAc are part of a
canonical cortico-striatal-thalamic circuit, in which vmPFC exerts
excitatory influence on the NAc, among other structures (Figure 5) (Divac et al., 1987; Ferry et al., 2000; Jayaraman, 1980). The reductions in vmPFC GM-markers we report are consistent with reduced functional output of vmPFC in tinnitus patients (Schlee et al., 2009). However, although vmPFC markers and NAc hyperactivity are clearly related (Figure 4),
the exact nature of this relationship remains to be determined.
Increased NAc activity could reflect disinhibition of NAc resulting from
decreased vmPFC input to local inhibitory interneurons, though it may
also reflect aberrant auditory activity (i.e., tinnitus or TF-matched
stimulus) entering the limbic system via the amygdala. Positive
correlations between NAc and mHG activity support both hypotheses;
future research regarding connectivity between these structures in
tinnitus patients are needed to shed light on these issues.
Additionally, measuring possible up- or down-regulation of
neurotransmitter receptors and/or transporters in these structures could
be a target for future studies.
Figure 5
Schematic
of proposed auditory-limbic interactions in tinnitus. Sensory input
originates subcortically and enters both auditory and limbic circuits
via the medial geniculate nucleus (MGN). Under normal circumstances, the
limbic system may identify a sensory (more ...)
Regardless
of its origin, we argue that NAc hyperactivity indicates appraisal of
the perceptual relevance of the tinnitus sensation (and/or perhaps the
aversiveness of TF-matched stimuli), with the ultimate objective of
affecting perception. VmPFC also projects to the thalamic reticular
nucleus (TRN), including its auditory division (Zikopoulos and Barbas, 2006), which is in a position to inhibit (or modulate) communication between auditory cortex and MGN (Figure 5).
Thus, inefficient vmPFC output could prevent inhibition of the tinnitus
signal at the MGN. As such, positive correlation between the magnitude
of vmPFC anomalies and NAc/mHG activity may indicate some preservation
of function: Those patients with greater amounts/concentrations of GM in
vmPFC exhibit less hyperactivity in NAc and mHG, thus reflecting a
relatively greater ability of the vmPFC to exert an inhibitory influence
on the auditory system.
Auditory system contributions to tinnitus
Tinnitus
patients demonstrated increased auditory cortical activation in
response to sound in our study. Specifically, medial Heschl’s gyrus
(mHG) exhibited hyperactivity in response to TF-matched stimuli, and
posterior superior temporal cortex (pSTC) was hyperactive across all
stimulus frequencies tested.
Most theories regarding tinnitus pathophysiology involve dysfunction of the central auditory system (Eggermont and Roberts, 2004; Jastreboff, 1990; Møller, 2003).
However, precise characterization of this process has been complicated
by several factors. Potential sites of tinnitus generation are likely to
include parts of the auditory pathway that are thought to process
relatively simple (i.e., tinnitus-like) stimuli. Thus in our study,
sound-evoked hyperactivity in mHG is a likely candidate, given that it
typically coincides with primary auditory cortex (Rademacher et al., 2001).
However, hyperactivity or dysfunction in one auditory region may merely
be a consequence of a tinnitus signal generated elsewhere in the
auditory pathway. Indeed, although tinnitus-related dysfunction has been
previously identified in primary auditory cortex (Sun et al., 2009), other auditory regions have been implicated as well (Eggermont and Roberts, 2004; Melcher et al., 2000).
Moreover, the location and nature of dysfunction that ultimately
generates the chronic tinnitus percept may differ from the site and
nature of initial damage, which itself may vary across patients (Henry et al, 2005).
Therefore, research concentrating on the exact mechanisms that generate
the tinnitus signal within the auditory pathways, whether an increase
in baseline activity (Eggermont and Roberts, 2004), reorganization of frequency maps (Eggermont and Komiya, 2000; Irvine et al., 2003; Mühlnickel et al., 1998; Rajan et al., 1993; Weisz et al., 2005; Wienbruch et al., 2006),
or some other mechanism, is needed. This is of particular importance
given that, although studying stimulus-evoked neural activity is
informative, it may not be equivalent to measuring activity
corresponding to the tinnitus itself, since sound can have variable
effects on patients’ tinnitus sensations (Tyler et al., 2008).
For these purposes, studying individuals with intermittent tinnitus, or
using imaging techniques that are able to measure metabolic activity
directly (e.g., PET), may be particularly useful.
Several
human imaging studies of tinnitus have reported elevated activity in
pSTC in association with the tinnitus sensation itself, when tinnitus
loudness was modulated either through administration of lidocaine (Reyes et al., 2002) or by facial movements (a relatively rare tinnitus subtype; Giraud et al., 1999; Lockwood et al., 1998). Though its exact role is debated, posterior auditory cortex is thought to subserve relatively complex auditory functions (Griffiths and Warren, 2002; Rauschecker and Scott, 2009),
making it an unlikely first site for the generation of tinnitus
sensations. Instead, pSTC hyperactivity could reflect the patients’ need
to separate the tinnitus signal from the remainder of the acoustic
environment. This would be consistent with evidence indicating that
posterior auditory cortex is involved in the segregation of multiple
auditory signals (i.e., the “cocktail party” problem; Alain et al., 2005; Wilson et al., 2007; Wong et al., 2008).
For patients in our study, successful task performance depended upon
their ability to separate the tinnitus sensation from auditory
stimulation; this was not the case for control participants, who did not
experience tinnitus. In fact, one could argue that the separation of
multiple acoustic signals is a constant concern for tinnitus patients,
and therefore is relevant even for those studies not involving
concurrent auditory tasks or stimuli (Giraud et al., 1999; Lockwood et al., 1998; Plewnia et al., 2007; Reyes et al., 2002).
Technical considerations: hearing loss and age
Hearing
loss and age did not affect any tinnitus-related neural markers we
identified in this study. However, both hearing loss and age have been
important topics in the field of tinnitus research. The prevalence of
tinnitus increases with age, presumably due to increased incidences of
hearing loss (Heller 2003; Eggermont and Roberts 2004).
Hearing loss can be interpreted as a correlate of peripheral or central
auditory system damage and/or dysfunction, the latter of which is a
critical component of all current theories of tinnitus pathophysiology.
However, audiometry of even an extended range of frequencies (i.e., >
8 kHz) may not capture all types of auditory system dysfunction (e.g., Weisz et al., 2006).
Certainly, controlling for the possible influence of age and
audiometrically measurable hearing loss is critical to tinnitus
research, as we have attempted to do in our study through careful
examination of single subject data and covariate analyses. However,
restriction of participant samples along these dimensions is not a
preferable solution to this problem. It is likely to be those neural
markers that are shared across patients of different ages and hearing
profiles that are most indicative of tinnitus pathophysiology, and
therefore may be most likely to lead to effective treatments.
Conclusions: Limbic-auditory interactions in tinnitus
In
our opinion, the key to understanding tinnitus pathophysiology lies in
understanding how the auditory and limbic systems interact.
The present
study reports, for the first time, functional differences in the NAc of
patients with chronic tinnitus. Furthermore, this hyperactivity in NAc
correlates with the magnitude of structural changes in the vmPFC in
these same patients.
We conclude, therefore, that a dysregulation of
limbic and auditory networks may be at the heart of chronic tinnitus.
A
complete understanding and ultimate cure of tinnitus may depend on a
detailed understanding of the nature and basis of this dysregulation.
Given the paucity of effective treatments for tinnitus, this field of
research is in need of new and testable ideas, and the model we propose
will certainly benefit and evolve from future research.
For example,
although we report moderate correlations between functional activity in
primary auditory cortex and limbic regions in tinnitus patients,
additional studies are needed to directly assess the nature of
connectivity between these and other limbic and auditory regions.
We
have proposed topographic inhibitory influence of the thalamic reticular
nucleus (TRN) on auditory thalamic (i.e., MGN) transmission as a
candidate noise-cancellation site in this network (Mühlau et al., 2006; Rauschecker et al., 2010);
however, further research is needed to test the site(s) of
limbic-auditory interaction relevant for tinnitus, particularly in
animal models of tinnitus.
Limbic corticostriatal
structures (i.e., vmPFC and NAc) have also been linked to disordered
appraisal of hedonic state in drug addiction (Ahmed and Koob, 1998) and emotional state in mood disorders (Mayberg, 1997).
Both these conditions are associated with structural abnormalities in vmPFC (Drevets et al., 1997; Koenigs and Grafman, 2009; Tanabe et al., 2009)
similar to the ones we report in individuals with chronic tinnitus.
Adjacent mPFC and cingulate structures, along with other limbic regions,
have also been implicated in chronic pain (DaSilva et al., 2008; Geha et al., 2008; Kuchinad et al., 2007),
which too may involve the inability to suppress unwanted sensory
signals.
Converging evidence regarding common mechanisms shared between
these and similar disorders will further our understanding of the limbic
system and its influence on perception.
Tinnitus, as a relatively
circumscribed condition, may facilitate better understanding of limbic
dysregulation in many of these disorders.
Methods
Participants
Twenty-two
volunteers (11 tinnitus patients, 6 female; 11 controls, 7 female) were
recruited from the Georgetown University Medical Center community and
gave informed written consent to participate in this study. Tinnitus
patients ranged widely in age (20–64 yrs; SD = 16.0 yrs) and were on
average 44.4 years old; the mean age of control participants was 23.0
years (SD = 3.3, Table 1).
Participants reported no history of neurological disorders, though one
tinnitus patient reported a diagnosis of clinical depression at the time
of the study, for which he was taking antidepressants. Data collected
from this participant did not differ appreciably from that of other
patients; this participant’s data have been noted when possible in
Tables and Figures. No other participants reported a history of mood
disorders.
Patients reported having chronic tinnitus,
which we defined as being present either constantly or intermittently
for at least 6 months (mean = 9.7 years, SD = 17.6 years). Self-reported
severity of tinnitus impact was measured on a scale roughly comparable
to the Tinnitus Handicap Inventory (THI) (Newman et al., 1996). Its outcome varied across patients, but was generally mild-to-moderate (Suppl. Table 2).
Patients reported no history of severe hyperacusis or phonophobia, and
in a short survey reported limited or no sensitivity to noise (Suppl. Table 2).
Neither tinnitus severity nor noise sensitivity scores correlated with
the magnitude of neural tinnitus-markers we report (data not shown), and
are therefore not discussed here.
Audiological examination
All
participants underwent audiological testing to determine hearing
levels. Pure tones ranging from 250 Hz to 12 kHz were presented to each
ear until the threshold of detection was reached. Two control
participants were tested at a more conventional range of frequencies
(250 Hz to 8 kHz in octave steps). Using a relatively strict
classification scheme, all but three participants (two controls and one
tinnitus patient) exhibited some degree of hearing loss at one or more
of the tested frequencies (Suppl. Figure 1).
Eleven participants (4 tinnitus patients) exhibited a mild or moderate
hearing loss at one or more frequencies (20–40 dB or 40–60 dB above
threshold, respectively), and eight participants (6 tinnitus patients)
demonstrated severe loss in at least one tested frequency (60–90 dB
above threshold). No participants showed profound hearing loss at any
frequency (> 90 dB above threshold).
Tinnitus patients
underwent additional audiological testing to find the best match to the
perceived frequency of their tinnitus. Patients initially identified
the pure tone from the audiological examination that best matched the
center frequency of their tinnitus sensation. Then, subsequent pure
tones were presented in neighboring frequencies until a match was
identified. All patients reported having a tinnitus sensation with a
clearly definable pitch. Tinnitus frequencies ranged from 150 Hz to 12
kHz (Table 1), but were generally high (mean = 6,083 Hz, SD = 4,100 Hz).
Stimulus construction and presentation
Stimuli
consisted of band-passed white noise (BPN) bursts with 0.167 octave
bandwidth, and were presented in trains at 3 Hz for 6 s per trial. BPN
center frequencies were dependent on the best match of the tinnitus
frequency of each patient; they were either matched to the tinnitus
frequency, or were 0.5, 1, or 2 octaves above or below the tinnitus
frequency. To ensure that stimuli remained within normal hearing range
(i.e., below 20 kHz, Suppl Table 1),
center frequencies were adjusted in some cases to accommodate instances
of high-frequency tinnitus sensations. For each tinnitus patient, a
“stimulus-matched” control participant completed the experiment with the
same range of stimulus frequencies.
During scans, stimuli were presented via in-ear electrostatic headphones (Stax),
constructed to have a relatively flat frequency response up to 20 kHz
(±4 dB). Stimuli were first adjusted to a comfortable volume determined
by the subject in the scanner environment (~60–65 dB SPL), with
attenuation of ambient noise provided by ear defenders (~26 dB SPL
reduction, Bilsom). Then, stimulus level was adjusted in a
stimulus-specific manner to reflect each participant’s detection
threshold at each frequency in the scanner. These adjustments were not
made for two tinnitus patients and their stimulus-matched controls.
Participants
were asked to perform an “oddball” task during the fMRI experiment. On
8% of trials, BPN stimulus trains were interrupted by a short period of
silence. On these target trials, participants were instructed to respond
via button press. On nontarget trials, participants were not to make
any response. Data associated with less than 80% accuracy on this task
were excluded from further analysis. Eighteen participants (9 patients)
completed this task; the remaining four (2 patients) were asked to
listen attentively to intact BPN stimulus trains and make no response.
Image acquisition and processing
Images
were acquired using a 3.0 Tesla Siemens Trio scanner. Two sets of
functional echo-planar images (EPI) were acquired using a
sparse-sampling paradigm: repetition time (TR) = 10 s, TR delay = 7.72
ms, echo time (TE) = 36 ms, flip angle = 90°, 25 axial slices, 1.5 × 1.5
× 1.9 mm3 resolution. A high-resolution anatomical scan
(MPRAGE) was also performed for each subject: TR = 2,300 ms, TE = 2.94
ms, inversion time (TI) = 900 ms, flip angle = 9°, 160 sagittal slices,
matrix size 256 × 256 mm2, 1 × 1 × 1 mm3
resolution. Data for four participants (2 patients) were acquired using
nearly identical sequences with the following differences: EPI, TR = 12
s, TR delay = 9.72 ms; MPRAGE, TR = 1600 ms, TE = 4.38 ms, TI = 640 ms,
flip angle 15°. The field of view of functional EPI images was
restricted to auditory cortex, subcortical structures superior to the
midbrain (i.e., including MGN but not inferior colliculi), and ventral
prefrontal cortex. A standard field of view encompassing the entire
brain was used for anatomical images.
Functional imaging analyses were completed using BrainVoyager QX (Brain Innovation, Inc).
Functional images from each run were corrected for motion in six
directions, relieved of linear trend, high-pass filtered at 3 Hz, and
spatially smoothed using a 6-mm full-width-at-half-maximum (FWHM)
Gaussian filter. Data were then coregistered with anatomical images, and
interpolated into Talairach space (Talairach and Tournoux, 1988) at 3 × 3 × 3 mm3 resolution.
Voxel-based morphometry (VBM) analyses were completed using SPM8 (Wellcome Trust Centre for Neuroimaging).
Anatomical images were corrected for intensity bias, spatially
normalized, and segmented into white matter, grey matter, and
cerebrospinal fluid using tissue probability maps (International Consortium for Brain Mapping).
Grey and white matter images were then modulated to reflect the degree
of local deformation applied during spatial normalization, and smoothed
using a 12-mm FWHM Gaussian filter. All images were thresholded at 0.20
probability of tissue classification. This yielded four types of
anatomical images for use in subsequent VBM analyses: unmodulated grey,
unmodulated white, modulated grey, and modulated white matter images.
Umodulated images are thought to reflect the concentration (or
“density”) of a tissue class relative to other tissues, while data from
modulated images are argued to reflect the amount (or “volume”) of a
particular tissue class in a given anatomical area (Ashburner and Friston, 2000).
Interpretation of voxel-based morphometry (VBM) results is not always straightforward. Ashburner and Friston (2000)
explain that unmodulated, segmented images (i.e., images not adjusted
to reflect the degree of warping during spatial normalization) reflect
the concentration of a tissue type in a given area relative to other
tissue types. This is often referred to as tissue “density”. Thus,
values along tissue borders are complementary as they are blurred during
smoothing, which may partially explain, e.g., corresponding decreases
in GM concentration and increases in WM concentration within a single
area. Note also that VBM concentrations (unmodulated values) have not
been directly linked to cellular make-up or density thus far. VBM values
adjusted for the degree of deformation applied during spatial
normalization (i.e., modulated values) reflect the total amount of a
tissue type in a given region (Ashburner and Friston, 2000).
Although these modulated values are often interpreted as a proxy for
“volume,” direct measurements (e.g., of cortical thickness) would be
necessary to confirm volumetric differences in a given region.
Statistical analyses
Functional images
Group
analyses using the general linear model (GLM) were executed in single
voxels and in regions of interest (ROIs), in order to assess the
relationship between fMRI signal and our experimental manipulations
(i.e., regressors; Friston et al., 1995)
using BrainVoyager. Trials were binned based on their relationship to
the tinnitus frequency (TF) into trials in which: 1) BPN center
frequency (BPNCF) was more than 0.5 octaves below TF, 2) BPNCF was less than or equal to 0.5 octaves below TF, 3) BPNCF matched TF, 4) BPNCF was less than or equal to 0.5 octaves above TF, and 5) BPNCF
was more than 0.5 octaves above TF. These five stimulus conditions were
entered as GLM regressors, along with “confound” regressors
corresponding to task oddball trials and subject identity (to reduce the
influence of inter-subject variability). Single-subject beta maps were
generated for each of five stimulus conditions, which were then used to
assess between-group differences in function using Analyses of
Covariance (ANCOVAs). Participant group (i.e., tinnitus patients vs.
controls) and mean hearing loss (mHL) were entered as a between-subjects
factor and covariate, respectively. Single-voxel thresholds were chosen
(p < 0.001); maps were then corrected for cluster volume at p(corr) < 0.05 using Montecarlo simulations (a means of estimating the rate of false positive voxels; Forman et al., 1995). Single-voxel thresholds were reduced to p(uncorr) < 0.01, k > 108 mm3 in masked analyses (below).
Anatomical images
Single-voxel
GLM analyses assessed anatomical differences between tinnitus patients
and controls, with compensation for unequal variance between groups in
SPM8. T-tests were performed across groups, and both age and total grey
or white matter volume were entered as confound covariates. A
single-voxel (i.e., voxel-wise) threshold was chosen of t > 4.65, p < 0.0001; cluster volume was greater than 80 mm3. Single-voxel thresholds were reduced to p
< 0.01 in masked analyses. All single-voxel VBM analyses were
performed in the same resolution as the tissue probability maps used for
segmentation (2 × 2 × 2 mm3).
Mask and ROI creation
A
mask of the auditory system was created for both functional and
anatomical analyses. Auditory cortex was defined by selecting those
functional voxels in superior temporal cortex that survived a sounds
> silence contrast with a single-voxel threshold of t > 2.58, p(uncorr) < 0.01, k > 4 (group data). The MGN were defined using the WFU Pick Atlas (Lancaster et al., 2000; Maldjian et al., 2003),
dilated by 1 mm, and then flipped to create a symmetrical mask in both
hemispheres. Additional masks were created using significant clusters
from both functional and anatomical analyses. Masks were transferred
between programs via image files (ANALYZE format), which were then
adjusted to the appropriate format in BrainVoyager or SPM. Coordinate
conversions between Talairach and MNI spaces were done using a
well-accepted nonlinear transform (http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach).
Correlation analyses
Pairwise
correlations between mean fMRI signal or VBM values were performed for
ROIs exhibiting significant between-groups differences using the
statistical tests described above. Cook’s d tests were used to assess
the influence of potential outliers on the resulting correlation
statistics. Data points from a single participant, Patient #7, had
Cook’s d values close to 1.0 (a commonly used benchmark for identifying
potential outliers) for 4 out of 6 pairwise tests (Suppl. Table 3).
Therefore, we computed correlations both with and without this subject
included. Excluding this potential outlier significantly affected only
one pairwise correlation (Figure 4C), and strengthened other correlations already apparent when including this outlier (Figure 4A,B).
Supplementary Material
Click here to view.(355K, pdf)
Acknowledgments
We
wish to thank Jeremy Purcell, Kenta Takagaki, and Anne Fieger for their
technical assistance. This work was funded by the National Institutes
of Health (Grants RC1-DC010720 to J.P.R. and F31-DC008921 to A.M.L.),
Skirball Foundation, Tinnitus Research Initiative, and Tinnitus Research
Consortium.
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Neuron. Author manuscript; available in PMC 2012 January 13.
Published in final edited form as:
PMCID: PMC3092532
NIHMSID: NIHMS270452
Imagen: Proyecto ADN
