Heart rate variability and brain imaging
of schizophrenic patients
Name:
Simon Nak
Student nr:
0301590
Supervisor:
drs. J.H. Meijer
UvA Supervisors: dr. A.H. van Stegeren, prof. dr. B.A. Schmand
Location:
Academic Medical Centre (AMC), Psychiatry, Adolescent Clinic.
Abstract
Introduction: Heart rate variability (HRV) has recently been linked to
schizophrenia in several studies. HRV describes the variation in time between
consecutive heartbeats, and is thought to reflect an interplay between the
sympathetic and the parasympathetic branches of the autonomic nervous system
(ANS). High HRV is associated with a healthy ANS and a healthy heart, while in
recent research HRV is found to be significantly reduced in patients with
schizophrenia.
A number of studies have shown that brain regions in schizophrenia patients are
affected, with both diffuse reductions in white and grey matter density and
reductions in specific regions like the cerebellum. In this study we examined
whether brain regions related to HRV regulation have reduced grey matter
density in schizophrenia patients.
Methods: HRV measurements resulted in four variables (root mean of squared
successive interbeat differences: RMSSD, and three frequency variables) that
could be compared for schizophrenic patients and healthy controls. We further
used a questionnaire to measure severity of positive and negative schizophrenic
symptoms. All these variables were included in analyses of MRI scans of the
schizophrenic patients’ brains. We further measured attention using a sustained
attention test.
Results: We found RMSSD to be reduced in schizophrenic patients compared to
controls. Frequency variables, which are linked to sympathetic/parasympathetic
activity, were all higher for the patient group. Furthermore, correlations between
HRV data and both positive and negative symptoms of schizophrenia were found.
MRI analyses showed possible links between HRV and the cerebellum, brain stem
and occipito parietal junction. No correlation between HRV measurements and
sustained attention was found.
Conclusion: In line with earlier studies we found that both HRV and grey matter
density are reduced in schizophrenic patients. In addition we here show a
correlation between HRV and grey matter density and also between HRV and
symptom severity.
1
1. Introduction
Heart rate variability (HRV) describes the variation in time between consecutive
heartbeats, and is thought to reflect an interplay between the sympathetic and
the parasympathetic branches of the autonomic nervous system (ANS).
Activation of the sympathetic system increases heart rate, diverts blood to the
muscles and prepares the body for action by stimulating the release of adrenalin.
Parasympathetic activation slows down heart rate, stimulates digestion and in
general helps to maintain homeostasis in the body.
Humans are under the influence of complicated external and internal
environments. The ANS needs to adapt itself quickly to provide the necessary
arousal responses, and to help the body to return to its resting state in order to
prevent over-activity. Because of this it is necessary for humans to have a
relatively high variability in their heart rate. High HRV is associated with a
healthy ANS and a healthy heart, while low HRV predicts a diverse range of
cardiac problems (Mujica-Parodi et al., 2005) and is shown to be related to
psychopathological disorders such as anxiety (Licht et al., 2009), depression
(Carney et al., 2009) and schizophrenia (see below).
1.1 HRV and schizophrenia
In recent research HRV is found to be significantly reduced in patients with
schizophrenia compared to healthy individuals (Bär et al., 2007, Mujica-Parodi et
al., 2005, Bär et al, 2004). Since reductions in HRV are present in both
medicated and unmedicated patients, it is unlikely to be a side effect of
antipsychotic medication. Instead, it has been suggested that a disease-related
system change exists in the ANS and that perhaps the severity of psychotic
symptoms could be correlated with reduction in heart rate dynamics (Kim et al.,
2004).
Schizophrenic patients have a mortality rate two to three times higher
than the general population. This mortality is attributed to both suicide and
cardiovascular diseases. Besides the influence of patients’ unhealthy lifestyle and
of medication on cardiac health, autonomic dysregulation can possibly play a role
in this as well (Bär et al., 2007). It is therefore important to identify the changes
in the ANS that are related to reduced HRV in schizophrenia.
2
It is still uncertain how autonomic functioning is affected by schizophrenia.
Possibly the cortical-subcortical circuits that modulate the ANS are disturbed
during psychosis (Valkonen-Korhonen et al., 2003). Heart rate is modulated by
the reticular formation in the brainstem (midbrain and pons), and by motor
nuclei in the medulla. The reticular formation itself projects to and receives input
from the hypothalamus. In turn, the hypothalamus is influenced by the limbic
system (Gazzaniga et al., 2002). It is possible that one or more of these areas
are in some way malfunctioning in patients with schizophrenia.
1.2 Schizophrenia and white and grey matter
There are a number of magnetic resonance imaging (MRI) studies in patients
with schizophrenia. For instance, Lim et al. (2006) found significant correlations
between cognitive performance and white matter fractional anisotropy (FA) in
schizophrenics. FA is a measure reflecting directional organisation of the brain.
This is influenced by the magnitude and organisation of white matter tracts and
reflects white matter integrity. A correlation has been found between high scores
on the Positive and Negative Syndrome Scale (PANSS, a measure of
schizophrenic symptoms) and diffuse reduction in white matter integrity (Skelly
et al., 2007). In this study the authors concluded that there is a (negative)
correlation between FA and positive symptoms of schizophrenia.
In addition, a link is suggested between cerebellar dysfunctions and
schizophrenia. In a review article by Martin & Albers (1995) it is assumed that
certain cerebellar regions are important in schizophrenic disorders and that
cerebellar-frontal pathways “correspond well with hypotheses on
neurotransmitter imbalance in schizophrenia”. More recently Bolbecker et al.
(2009) also suggested that symptoms of schizophrenia could be “due to a
dysfunctional modulatory system associated with the cerebellum”.
1.3 HRV and grey matter in schizophrenia
Firstly, it can be concluded from the results above that brain integrity is affected
in schizophrenia. Secondly, in earlier studies schizophrenic patients were found
to have lower HRV than healthy controls. Combining these two findings, we
hypothesized that a correlation between HRV and MRI-changes in schizophrenia
patients might exist. We have found no studies to date that correlated HRV-data
with MRI-data of the brains of schizophrenic patients. For this study we wanted
3
to examine whether changes can be found in brain structures influencing the
ANS (such as brainstem and hypothalamus) and in the cerebellar region, and if
these changes correlate with HRV measures of schizophrenic patients. A
correlation like this would be expected for structures influencing the ANS, but
would be an interesting new finding for the cerebellum. Since earlier research
does report a role for the cerebellum in schizophrenia(as mentioned above), and
since numerous new, modulatory, functions are being attributed to the
cerebellum (Bolbecker et al., 2009), it can be informative to look for such a
correlation. It could mean that the modulatory functions of the cerebellum
include more than just cortical domains.
1.4 Schizophrenia and attention
In addition, cognitive deficits are considered to be a core feature of
schizophrenia. A cognitive dimension that was found to be impaired in
schizophrenia in seven different studies is attention or vigilance (see
Nuechterlein et al., 2004 for a list). Chen et al. (1996) found that schizophrenic
patients performed significantly worse than a control group on a task for
sustained attention performance. They also suggest that “this attention
impairment in schizophrenia cannot be considered as merely secondary to
distraction from psychotic symptoms, nor to lack of motivation as a result of
negative symptoms”. Because of these findings, we chose to test for sustained
attention capacity in addition to the general measurements of schizophrenia
symptoms with the PANSS. In one earlier study by Duschek et al. (2009) a
relation between autonomic cardiovascular control, which is under the influence
of the ANS, and attentional performance was found. As a secondary goal we
performed this study to see if HRV measures are related to attention
performance in a group of schizophrenia patients compared to healthy controls.
4
2. Hypotheses
Five hypotheses can be formulated based on the above.
1. Schizophrenic patients have reduced HRV compared to healthy subjects.
2. HRV in schizophrenic patients is correlated with grey matter integrity in brain
areas associated with the ANS (such as brainstem and limbic system) and in the
cerebellum.
3. HRV is negatively correlated with the severity of schizophrenic symptoms.
4. Symptom severity in schizophrenic patients is correlated with grey matter
reductions.
5. HRV is positively correlated with attention, as a measure of cognitive
performance.
5
3. Methods
Participants
To test these hypotheses, a large amount of data have been collected. We aimed
at testing around 20 schizophrenia patients over a period of four months. The
patients have been recruited from the Adolescent Clinic of the AMC and from the
VIP-team (Vroege Interventie Psychose team – Early Intervention Psychosis
team). All the subjects agreed to participate in a fixed test battery over the
course of two days. The tests used here were part of this larger battery;
instructions and testing situation were identical during all test sessions.
Since this study is part of a larger research project, in which control
subjects will be recruited only in a later stage of the study (so that strict
matching is possible), the necessary testing was performed on a group of 10
people working at the psychiatry department who had already participated in an
MRI-scan during prior studies. This allowed for a reasonable control group to be
created (with approximately the same age as the patient group) in a limited
amount of time. Information about age and gender of the two groups can be
found in the ‘results’ section. The testing period allowed us to gather data from
18 first episode schizophrenia patients and 10 healthy controls.
Variables
Variable 1: Heart Rate Variability
HRV has been measured using a waistband and a wireless USB receiver (Suunto
Memory Belt, Suunto, Amer Sports Corporation, Finland). The receiver
immediately transferred the heart rate data to a computer program (SuuntoView,
Amer Sports Corporation, Finland), which saved the data under the subject’s
number. HRV measurements could be calculated afterwards in an off-line
analysis (see below).
To collect the data all subjects were instructed to sit in an easy chair for
10 minutes while wearing the waistband. Subjects were informed about the
procedure beforehand. A conductive gel was applied between the waistband and
their skin to improve conduction. The gel is harmless and easy to remove without
water. They had been given some reading material and were instructed to move
as little as possible. Subjects were not allowed to drink coffee during one hour
prior to the measurements, since this could possibly influence their heart rate.
6
Subjects were left alone for the period of 10 minutes, allowing them to relax as
much as possible. The test computer was placed in such a way that the subjects
couldn’t see the screen. This was done to make sure no heart rate biofeedback
would take place.
The computer program (Suuntoview) that we used to collect these data
produced four types of output. HRV can be expressed in two ways: with a time
domain analysis, which leads to the first variable, RMSSD (Root Mean of Squared
Successive interbeat Differences: the mean of the differences in milliseconds
between two successive heartbeats, always expressed in a positive number).
Secondly, a frequency domain analysis using Fast Fourier transformation was
carried out, leading to three different indices, namely Very Low Frequency (VLF),
Low Frequency (LF) and High Frequency (HF). VLF is a measure that expresses
the power (i.e. variance of the interbeat interval series in this domain) for the
lowest frequencies, which only becomes visible in an HRV-time diagram after a
(relatively) long time. LF expresses the power with respect to the intermediate
frequencies and HF relates to the highest frequencies. The frequency data of the
HRV are determined by analyzing these frequency domains (VLF: <0.04Hz, LF:
0.04Hz-0.15Hz, HF: 0.15Hz-0.5Hz). In current literature it has been widely
accepted that the HF component of the signal is under the influence of
parasympathetic activity, while the LF and VLF components are determined by
sympathetic activity (e.g. Kim et al., 2004). These measures can be used for a
more accurate interpretation of the possible HRV differences, and the relative
contribution of the sympathetic and the parasympathetic power to the signal.
Variable 2: Schizophrenic symptoms
The Positive and Negative Syndrome Scale (PANSS) has been used to measure
schizophrenic symptoms. The PANSS is a medical scale used for measuring
symptom reduction in schizophrenia patients. It is also widely used to study
other psychotic disorders. The scale has seven positive-symptom items, seven
negative-symptom items and 16 general psychopathology symptom items. Each
item is scored on a seven-point severity scale. Positive symptoms as measured
with the PANSS are delusions, hallucinations, conceptual disorganisation,
excitement, delusion of grandeur, suspicion and hostility. Negative symptoms are
blunted affect, emotional withdrawal, social withdrawal, difficulty in abstract
thinking, lack of spontaneity, and stereotypical thinking (Kay et al., 1987).
7
Variable 3: Sustained attention
As a part of the same test battery all subjects were assessed wit the Matrics
Consensus Cognitive Battery (MATRICS). One of the subtests in this test battery
is the Continuous Performance Test, Identical Pairs version (CPT-IP). This is a
computerized measure of sustained attention, first developed by Barbara
Cornblatt (Cornblatt et al., 1988). The current version involves monitoring of
digits that appear briefly on a computer screen. Every time digits appeared that
were exactly the same as those right before it, subjects had to press the
spacebar as soon as possible. The test exists of three conditions, with
combinations of two, three or four digits appearing on the screen. In earlier
mentioned research by Nuechterlein et al. (2004) the CPT was the most
prominent measure for attention. The CPT was always offered to the subjects at
approximately the same moment during the assessment.
HRV as well as PANSS and CPT-IP measurements were done during the
same day. Other subtests were performed as well, but there was never more
than two hours between measurements of our variables.
Variable 4: Magnetic Resonance Imaging
With MRI, data on the density of white and grey matter in the brains of
schizophrenic subjects have been collected. Most of the participants had already
been scanned for research during the past year, so no new MRI scanning was
needed for this study. The MRI scans have been performed using a 3 Tesla MRI,
Philips Medical System (Philips, Best, The Netherlands).
All MRI data have been processed and analyzed using SPM2 modified for
optimized voxel-based-morphometry (VBM) on a MATLAB platform (The
MathWorks, Inc, Massachusetts). All images have been pre-processed and
checked for artefacts and image corruption before entering statistical analysis.
The actual MRI data processing was done at the AMC by a neuropsychologist
specialized in this kind of analyses. No regions of interest (ROI’s) were defined,
because the areas in our hypotheses are only suggestions and we are interested
in other findings as well. The coordinates of all the neuro-imaging results are
expressed using the Automated Talairach Atlas (Lancaster et al., 2000).
8
Data processing
We started by performing a number of computations to the data. Firstly, we
discarded the first minute of measurements for all subjects to minimize the effect
of differences in activity prior to the HRV assessment. After this, we checked all
data for outliers. For every subject, we discarded all measurements that were
more than three standard deviations away from the mean of that subject. These
computations were done for RMSSD data as well as for VLF, LF and HF data. We
also checked for outliers between the subjects. For two subjects one of their
mean frequency data (LF and HF respectively) were more than three standard
deviations away from the mean for all the subjects. Those two subjects were
discarded from further analyses.
Statistical analyses have been performed using SPSS 16.0 for Windows.
Heart rate and HRV measurements from both healthy subjects and
schizophrenics have been compared using t-tests or an analysis of variance
(ANOVA). The scores in the three levels of the CPT-IP have been compared using
a General Linear Model (GLM) for repeated measures. Bivariate correlational
analyses were performed on all PANSS and HRV data to search for correlations
between PANSS and HRV data. We also checked for equal distributions of age
and gender between the two groups of subjects.
9
4. Results
Control analyses
To determine whether the collected data of subjects in the schizophrenia group
and the control group differed, we started by performing a number of control
analyses. To assess how well the schizophrenia and control group could be
compared, we checked for equal distribution of age and sex. An independent
samples t-test was conducted to compare age in the schizophrenia and the
control group. No significant difference in age was found for schizophrenia and
control subjects ( t(24) = 0.84, p = 0.41 ). A Pearson Chi-Square test was
conducted to check for interdependence between group (schizophrenia or
control) and sex. No inconsistent distribution was found ( chi-square (1, N=26) =
1.21, p = 0.27 ). The specific data are summarized in table 1.
Age
Sex
Schizophrenia group
M = 22.38
SD = 2.78
14 male
2 female
Control group
M = 23.50
SD = 4.06
7 male
3 female
Table 1: mean age and sex distribution for the two groups.
Hypothesis 1: HRV
Comparing mean heart rate and HRV measures for the schizophrenia and the
control group we found a significant difference in mean resting heart rate (in
beats per minute) between the groups, with the schizophrenia (M = 75.19, SD =
9.01) subjects having a higher baseline heart rate than the control (M = 67.21,
SD = 8.60) subjects ( t(24) = -2.24, p < .05 ).
For the RMSSD data (in milliseconds) we found a significant difference
between schizophrenia (M = 50.06, SD = 21.86) and control (M = 85.51, SD =
55.87) subjects ( t(24) = 2.29, p = 0.031 ) as well. The results are also shown in
the graph below (figure 1).
For the frequency domain data (in sec2) we found significant differences
between the schizophrenia and the control group in all domains:
For the VLF data of the schizophrenia (M = 0.20, SD = 0.13) and the control (M
= 0.02, SD = 0.02) group ( t(24) = -4.17, p < 0.001 );
For the LF data of the schizophrenia (M = 0.23, SD = 0.17) and control (M =
0.039, SD = 0.049) subjects ( t(24) = -3.51, p = 0.002 ); and for the third
10
frequency domain, HF, of the schizophrenia (M = 0.07, SD = 0.07) and controls
(M = 0.02, SD = 0.02) ( t(24) = -2.32, p = 0.029 ). These results are shown in
figure 2.
Figure 1: mean RMSSD (ms) and heart rate (beats/minute) for control and schizophrenia
group.
Figure 2: VLF, LF and HF results (sec2) for control and schizophrenia group.
11
Hypothesis 2: HRV and MRI data of schizophrenic patients
We hypothesized there would be a positive correlation between HRV and brain
integrity in specific areas. Using voxel-based-morphometry and the HRV data we
collected, we searched for correlations between grey matter reduction and HRV.
Three significant correlations were found.
For the RMSSD data in the schizophrenia group, we found a significant
correlation (p<0.001, uncorrected) with grey matter density in the right parietal
lobe (Talairach x,y,z = 2, -47, 40). So, the lower the RMSSD of the subjects was,
the larger the reduction in grey matter. This correlation did not survive correction
for multiple comparisons and should therefore be considered as preliminary.
Secondly, the LF data of the schizophrenia group correlated significantly
(p< 0.001, corrected for multiple comparisons) with grey matter density
reduction in the right occipito parietal junction ( x,y,z = 9, -92, -1 till 9, -92 , 13). LF data also correlated significantly (p<0.017) with grey matter density
reduction in the right cerebellum ( x,y,z = 15, -71, -26). See figure 3 for a
picture of the location of the grey matter density reductions. The red arrow gives
the location of the strongest correlation.
12
Figure 3: correlation of LF data of the schizophrenia group and grey matter density
reductions.
Hypothesis 3: HRV and PANSS-scores
We hypothesized that HRV would be negatively correlated with the severity of
symptoms of schizophrenia. Since HRV is represented by four variables in our
research (namely RMSSD, VLF, LF and HF) and PANSS data are divided in three
variables (positive symptoms, negative symptoms and general symptoms), a
bivariate correlational analysis (2-tailed) was performed to determine which
correlations were present. Because we didn’t collect PANSS data for four of the
subjects and two subjects were found to be outliers, the PANSS results for 12
13
subjects were included in the analysis. Of course no control subjects could be
included here, because PANSS data for non-schizophrenic subjects are
meaningless.
Correlations
mean rmssd
Pearson Correlation
PANSS Positive
Total
,030
PANSS
Negative
**
-,749
PANSS
General
-,099
,925
,005
,759
Sig. (2-tailed)
N
mean VLF
12
12
12
Pearson Correlation
,503
-,471
,324
Sig. (2-tailed)
,096
,123
,305
12
N
mean LF
12
12
Pearson Correlation
*
,588
-,396
,538
Sig. (2-tailed)
,044
,202
,071
N
mean HF
12
12
12
Pearson Correlation
,444
-,522
,358
Sig. (2-tailed)
,148
,082
,253
12
12
12
N
**: Correlation is significant at the 0.01 level (2-tailed).
*: Correlation is significant at the 0.05 level (2-tailed).
...: Trend towards a correlation.
Table 2: correlations between HRV measures and PANSS data.
A number of significant correlations were found, which are shown in yellow
in table 2 (trends towards a correlation are shown in green). A negative
correlation between RMSSD and negative schizophrenia symptoms on the PANSS
was found. This means that subjects with lower HRV on average show more
negative symptoms of schizophrenia.
For the VLF data no significant correlations were found, but we can see a
trend towards a positive correlation with positive symptoms on the PANSS. This
would suggest that subjects with higher VLF power have more positive
schizophrenia symptoms.
For the LF data we found a significant positive correlation with positive
symptoms. This suggests that subjects with higher LF power have more positive
schizophrenia symptoms. A trend towards a positive correlation was found for LF
and general symptoms. This suggests that subjects with higher LF power
experience more global psychopathology.
14
Finally, for the HF data we could see a trend towards a negative
correlation with negative symptoms on the PANSS, suggesting that subjects with
higher HF power have less negative schizophrenia symptoms.
Hypothesis 4: PANSS-scores and MRI-data of schizophrenic patients
We hypothesized that there is a correlation between scores on the PANSS and
brain integrity in specific areas. Using voxel-based-morphometry and the PANSS
data we collected, we searched for correlations with grey matter reduction. Two
significant correlations were found.
For the negative symptoms scores on the PANSS, we found a significant
correlation (p<0.001, uncorrected) with grey matter density reduction in the
brain stem ( x,y,z = 0, -17, -41). This is a preliminary result and is not corrected
for multiple comparisons. The positive symptoms scores on the PANSS correlated
significantly (p<0.001, corrected for multiple comparisons) with grey matter
density reduction in the left cerebellum ( x,y,z = -26, -47, -48). See figure 4 for
a picture of the location of the grey matter density reductions. The red arrow
gives the location of the strongest correlation.
15
Figure 4: correlation of positive symptoms on the PANSS and grey matter density
reductions.
Hypothesis 5: HRV and CPT-IP data
Three healthy subjects and one schizophrenia patient did not complete the
sustained attention task (CPT-IP). For the remaining subjects a General Linear
Model (GLM) for repeated measures, with the scores on the three levels of the
test as within subject measures, was performed to determine whether CPT-IP
results differed between the schizophrenia group and the control group. No
significant interaction between group and difficulty level was found ( F = .004, p
16
> .10 ). There could not be found a main effect for group either ( F = .84, p >
.10 ). See figure 5 for a plot of the results.
Figure 5: CPT-IP scores (mean d-prime) for control (1) and schizophrenia (2) group.
We also checked for an effect of age on CPT-IP performance. No significant
effects could be found.
Finally we looked for correlations between HRV data and CPT-IP performance. We
performed a bivariate correlational analysis for the CPT-IP data and RMSSD, VLF,
LF and HF data respectively. No significant correlations were found (see table 3).
Correlations
CPT-IP Raw Score
Pearson Correlation
mean rmssd
mean VLF
mean LF
mean HF
,203
-,084
-,065
-,067
Sig. (2-tailed)
N
,341
,698
,762
,757
24
24
24
24
**. Correlation is significant at the 0.01 level (2-tailed).
Table 3: correlations between CPT-IP data and HRV variables.
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5. Discussion
From the results we can conclude that four of our five hypotheses were
confirmed in this study. Our main result confirms the first hypothesis: Heart rate,
as represented by the RMSSD, of schizophrenia patients that participated in our
research is significantly less variable than that of healthy subjects. The
significant group differences in the frequency data also indicate that there can be
a difference in the activation of sympathetic and parasympathetic nervous
system between the two groups. Similar results had already been reported in
earlier studies (for instance Bär et al., 2007; see also the introduction).
Secondly, we found a number of interesting correlations between HRV and
grey matter integrity in schizophrenic patients. The reductions in grey matter
density were located in the right cerebellum and the right occipito parietal
junction. The localization in the cerebellum is an interesting new finding, which
might give new information on the link between schizophrenia and cerebellar
dysfunctioning that has been found in earlier studies (Bolbecker et al., 2009;
Martin & Albers, 1995). The reductions in the parietal lobe seem harder to
explain. We will further look into this below.
We further looked at the severity of symptoms in the group of
schizophrenic patients. RMSSD appears to be negatively correlated with negative
symptoms of schizophrenia, which shows that low HRV is related to the more
severe cases of schizophrenia. This is a further indication for a link between HRV
and schizophrenia. We also found (nearly) significant positive correlations
between two of the HRV variables (VLF and LF) and positive symptoms of
schizophrenia, a positive correlation between LF and general psychopathological
symptoms, and a trend towards a negative correlation between HF and negative
symptoms. The positive correlation between LF data and positive schizophrenia
symptoms indicates an overactive sympathetic nervous system in patients with
more positive symptoms. A trend in the same direction can be seen for the VLF
data, which are also related to the sympathetic nervous system. This has also
been found by Bär et al. (2005), who showed a positive correlation between
positive symptoms and VLF. The negative correlation between HF data and
negative symptoms would indicate that subjects with a less active
parasympathetic nervous system have more negative symptoms.
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We also looked at correlations between brain integrity and severity of
schizophrenia symptoms. Correlations were found between negative symptoms
and reduced grey matter density in the brain stem (a trend towards a
correlation) and between positive symptoms and grey matter density reduction
in the left cerebellum. As was mentioned above, the localization in the
cerebellum appears to be in line with other research suggesting a link between
schizophrenia and cerebellar dysfunctioning. Since the brain stem is associated
with the ANS (Gazzaniga et al., 2002), the correlation with the PANSS scores
seems to fit our expectations.
Finally, we couldn’t confirm our fifth hypothesis: No correlation was found
between HRV data and sustained attention. Furthermore, schizophrenic patients
did not perform differently on the sustained attention test compared to healthy
subjects.
Before further discussing specific findings, it must be stated that our subject
groups were relatively small. This was unavoidable due to the small scale of the
study itself, but might pose some problems. With patient and control groups of
the size we used, the power of the found correlations can never be very large.
This implies that all our findings should be considered with some caution. They
are however very interesting and promising directions for new research with
larger groups. As to our first finding, the significant difference between RMSSD
data of the two groups is a clear indication that HRV is indeed lower in
schizophrenia patients compared to healthy people (as was found earlier by Bär
et al., 2007, Mujica-Parodi et al., 2005, Bär et al., 2004). It is interesting to
consider what the frequency data represent and consequently what the
significant differences between the two groups mean. It has already been stated
that the HF component of HRV data is influenced by the parasympathetic
autonomic nervous system, while the VLF and LF components are under the
influence of the sympathetic system (Kim et al., 2004; see also Agadzhanyan et
al., 2007). Our finding that the VLF and LF power are higher in schizophrenia
patients would thus mean that their sympathetic nervous system is in some way
overactive. This has already been found in other studies such as Hempel et al.
(2008) and Bär et al. (2007). However, the fact that HF power is also higher in
our patient group compared to healthy controls is a new finding. It would mean
that the parasympathetic nervous system is also overactive in our patients, while
19
in other research HF measurements are found to be lower in schizophrenic
patients (Chang et al., 2009; Hempel et al., 2009; Bär et al., 2008 and 2007;
Mujica-Parodi et al., 2005). Two things can be said about this. Firstly, both overand underactivity of the autonomic nervous system result in autonomic
disturbances and thus dysregulation. Therefore both results can be seen as
indications that autonomic dysregulation plays a role in schizophrenia. Secondly,
the mean age of the schizophrenia patients and controls (22.4 and 23.5
respectively) is much lower than that of the subjects in most other research (e.g.
Mujica-Parodi et al., 2005: 36.5 and 34.0; Bär et al. 2007: 31.6 and 33.8). This
leads us to suggest that the parasympathetic nervous system is
overcompensating in early cases of schizophrenia, resulting in the higher HF
measurements found in our patient group. We hypothesize that the
parasympathetic system activity drops (decompensates) as schizophrenia
progresses, resulting in the findings reported in much of the research on older
subjects. The same might be true on a smaller scale for the sympathetic nervous
system. We recommend more research to be performed on early cases of
schizophrenia to either confirm or reject our hypothesis. It would also seem
useful to investigate the role of medication and its influence on HRV in new
research.
We have not found any research available that looked for a correlation between
HRV and grey matter density in schizophrenic patients. Therefore the correlation
between LF data and reduction of grey matter in the right cerebellum is a new
finding. In an already mentioned review article by Martin & Albers (1995) a
number of studies that support a link between cerebellar dysfunctions and
schizophrenia are discussed. Bolbecker et al. (2009) suggest a dysfunctional
modulatory system associated with the cerebellum as a cause of symptoms of
schizophrenia. They propose that abnormalities in the cerebellum result in
processing deficits in functional domains (such as working memory, executive
control, attention and timing) over which the cerebellum exerts modulatory
influence. This idea is supported by topographic projections of bidirectional
information streams that connect the cerebellum and motor, prefrontal, and
posterior parietal cortex (Schmahmann, 2004), which leads Bolbecker et al. to
suggest that the cerebellum integrates information from different functional
domains that can be disturbed in schizophrenia. Elsewhere, Andreasen et al.
20
(2008) speak of a cortico-cerebellar-thalamic-cortical circuit in which the
cerebellum plays an important role and modulates cortical activity. The
correlation we found suggests a link between the sympathetic nervous system
and the cerebellum, which is very different from the (cortical) functions
mentioned above. We could however hypothesize that our findings mean that the
modulatory role of the cerebellum also influences autonomic functioning in
schizophrenic patients. In future research it could be very informative to
compare a number of autonomic functions (such as heart functioning, digestive
functioning and/or glandular functioning) with cerebellar functioning of
schizophrenic patients and healthy controls. This could give us more information
about the role of the cerebellum in autonomic functioning.
The correlation with an area in the right occipito parietal junction is
difficult to explain on a functional level, because there is no clearly defined
function known for this area. One fMRI-study by Pernet et al. (2004) found the
right occipito parietal junction to play a role in a visual discrimination task, but
this does not give us direct information about the exact function of the area. New
research using MRI technology might give more specific information about the
causes and effects of reduced integrity in this specific area.
We did not find a correlation between HRV and grey matter density in the
reticular formation or the hypothalamus, something which might have been
expected since those areas directly or indirectly modulate heart rate (Gazzaniga
et al., 2002). Apparently these areas are not significantly reduced in our patient
group. This might mean that they are not responsible for the reduced HRV or
that the grey matter reductions in these areas are too subtle to be found in our
small patient group.
Concerning our third hypothesis in which we expected a relation between HRV
and symptoms of schizophrenia, we found this to be true for negative symptoms
but not for positive symptoms. However, the correlations between frequency
data and symptoms of schizophrenia make an interesting contribution to our
findings on frequency measurements mentioned above. As was already stated
they seem to indicate an even more active sympathetic nervous system in
patients with more positive symptoms. A similar result was found by Bär et al.
(2005). It must however be said that especially VLF measurements, which take
more time than LF and HF, can be influenced by the patients’ movements. Even
21
though they were sitting down and instructed to move as little as possible, some
patients might have looked around or stretched, thereby influencing their VLF
heart rate data. It could even be hypothesized that patients with more positive
symptoms are more active, which would be an alternative explanation for our
finding.
The trend in the correlation between HF data and negative symptoms
appears to indicate that subjects with more negative symptoms have a less
active parasympathetic nervous system. The same was concluded in a somewhat
different way by Boettger et al. (2006) who found a significant correlation
between the peak decay in the HF domain and scores on the Scale for the
Assessment of Negative Symptoms (SANS). Our result might seem to be not
entirely compatible with our earlier results showing that the HF power of HRV is
higher in schizophrenic patients than in healthy controls. However, we show here
that within the patients group, who on average have higher parasympathetic
activity than the controls, the patients with the most negative symptoms have a
reduced parasympathetic activity. This could lead us to hypothesize that the
parasympathetic nervous system of these patients is already decompensating,
but of course this remains speculative until more research is done on the HF
component in schizophrenic patients, who then should be followed over time in a
longitudinal design.
Furthermore, we looked at correlations between brain integrity and level of
symptoms of schizophrenia. We found a correlation with a region in the left
cerebellum. Again this can be seen as support for the role of the cerebellum in
schizophrenia, which has been mentioned earlier. The fact that this correlation
mainly concerns positive symptoms of schizophrenia could speculatively be an
indication of a malfunctioning cortical modulation by the cerebellum, since
positive symptoms of schizophrenia involve numerous cortical functions.
Consider for example Conceptual Disorganization, Suspiciousness and Hostility
that are all largely functions of the frontal cortex, which in turn is influenced by
the cerebellum (Schmahmann, 2004).
The second correlation, between negative symptoms of schizophrenia and
reduced grey matter density in the brain stem, might be seen as supportive of
our first hypothesis, since the brain stem is directly associated with heart rate
and autonomic functioning (Gazzaniga et al., 2002). Although negative
22
symptoms did also show a strong correlation with RMSSD, we did not find a
direct connection between density reduction in the brainstem and HRV measures.
Based on these results we hypothesize that positive and negative
symptoms have different physiological origins, but this needs further research.
An interesting approach might be to select patients on the kind of symptoms
they exhibit and than compare them using MRI technology. Following our present
results we might expect patients with mainly positive symptoms to show more
density reductions in the cerebellum than patients with mostly negative
symptoms.
Finally, we could not find a relation between HRV measures and level of attention
in this group of relatively young subjects. We hypothesize that this indicates that
HRV is a marker for schizophrenia, which can be seen separately from other
(cognitive) processes in schizophrenia. However, since we did not find any
difference in attention between patients and controls, an alternative explanation
might be that our schizophrenia group, with only relatively young first-episode
patients, does not exhibit very strong cognitive deficits anyhow (yet). It could be
informative to follow our patient group over time to detect possible future
changes in attention performance (or other areas).
In conclusion, we can say that this study has furthered our knowledge of the
relation between HRV, grey matter density and symptom severity in
schizophrenic patients. We confirmed most previous findings on this subject,
while we also proposed some new ideas regarding our relatively young patient
group. HRV is lower in patients with schizophrenia and this decrease in variability
is related to both symptom severity and reductions in grey matter density.
Future studies should be directed at longitudinal research with larger groups of
early schizophrenia cases. This could give us more knowledge about possible
changes over time in sympathetic and parasympathetic activity. Combined with
MRI technology such research might also help to understand the role of grey
matter reduction in different schizophrenic symptoms. Finally, research on the
function of the cerebellum in healthy people and in schizophrenic patients is
necessary to further our understanding of the modulatory functions of this brain
area.
23
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