- Email: [email protected]
Predictability of depression severity based on posterior alpha oscillations
Accepted Manuscript Predictability of depression severity based on posterior alpha oscillations H. Jiang, T. Popov, P. Jylänki, K. Bi, Z. Yao, Q. Lu, O. Jensen, M.A.J. van Gerven PII: DOI: Reference:
S1388-2457(16)00003-1 http://dx.doi.org/10.1016/j.clinph.2015.12.018 CLINPH 2007714
To appear in:
Clinical Neurophysiology
Accepted Date:
29 December 2015
Please cite this article as: Jiang, H., Popov, T., Jylänki, P., Bi, K., Yao, Z., Lu, Q., Jensen, O., van Gerven, M.A.J., Predictability of depression severity based on posterior alpha oscillations, Clinical Neurophysiology (2016), doi: http://dx.doi.org/10.1016/j.clinph.2015.12.018
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Predictability of depression severity based on posterior alpha oscillations H. Jianga,b, T. Popovb, P. Jylänkib, K. Bic, Z. Yaoa,d*, Q. Luc*, O. Jensenb, M. A. J. van Gervenb
a
Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing
210029, China b
Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE, Nijmegen, The
Netherlands c
Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast
University, Nanjing 210096, China d
Medical College of Nanjing University, 22 Hankou Road, Nanjing 210093, China
Corresponding authors:
Zhijian Yao Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China Tel:
+86 25 82296252
E-mail: [email protected]
Qing Lu Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast University, Nanjing 210096, China Tel:
+86 25 83795549
E-mail: [email protected]
Highlights • • •
Distinct oscillatory activity differences between major depressive disorder (MDD) and healthy controls (HC) were identified. Posterior alpha power was found to be negatively related to depression severity. Bayesian linear regression provides a quantitative and objective estimation of depression severity.
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Abstract Objective: We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. Methods: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillations power. Results: In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively. Conclusions: Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. Significance: The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process.
Keywords: MEG, resting state, major depressive disorder, Bayesian linear regression.
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1. Introduction It has been suggested that neuronal oscillations shape the functional architecture of the brain by temporally coordinating information both within and across neural systems (Buzsáki et al., 2004). As such, neuronal oscillations are a useful tool to investigate healthy and disrupted cognitive processes (Watson et al., 2015). In contemporary psychiatric research, it is of importance to move from subjective ratings towards more objective approaches when evaluating psychiatric disease (Insel et al., 2010). Evidence-based quantitative approaches based on neurobiological measures can be of value for diagnosis, prognosis and the assessment of treatment effects (Stephan et al., 2014). Indeed, emerging researches suggest that neural oscillations are disrupted in major depressive disorder (MDD), for instance. These disruptions play a critical role not only in symptomatology but also in deficits of cognition (Fingelkurts et al., 2015). Several studies have reported abnormal neuronal activity in MDD when comparing to healthy controls (HC). Converging evidence suggests that MDD is characterized by activity changes in both anterior and posterior hub regions (Fingelkurts et al., 2015). In anterior hub regions, models of depression have linked left and right frontal cortex to positive and negative affect systems respectively (Smart et al., 2015). This is supported by a frontal alpha asymmetry during resting state in depressed patients where right frontal alpha activity was stronger than left frontal alpha activity (Henriques et al., 1990). However, stability of frontal alpha asymmetry differs across groups and was not reliable in depression patients during different retest phases (Debener et al., 2000). Moreover, changes in asymmetry across assessments did not predict depression severity well (Allen et al., 2004). This raises doubts whether frontal alpha asymmetry allows for reliable prediction of depression severity. Apart from alpha power measures, depressed patients were found to have decreased centro-frontal theta activity (Volf et al., 2002) and increased frontal beta activity (Flor-Henry et al., 2004) compared to controls. In posterior hub regions, the alpha rhythm has been proposed to modulate top-down control or active inhibition in posterior regions (Jensen et al., 2010). Various attention and memory studies support this notion (Haegens et al., 2011, Jiang et al., 2015b). In addition, pre-stimulus alpha activity is important for guiding behavior since it determines the brain state prior to visual input (Busch et al., 2009, Weisz et al., 2014). In relation to MDD patients, the association between the BDNF Val66Met polymorphism, resting state EEG alpha power, and depression severity were examined in a large cohort (Zoon et al., 2013). Posterior alpha power during rest was found to inversely relate to depression severity in a reliable manner. 3 Jiang et al
The present study had two goals. First, we aimed to discriminate MDD from HC by computing the spectral characteristics of the MEG data during resting state conditions. The classical frequency bands in the theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz) and gamma (30-75 Hz) range were chosen for subsequent analysis. The second goal was to more closely evaluate the link between oscillatory activity and depression severity. This was realized by incorporating spatial (whole brain) and spectral (frequency specific) information. A Bayesian linear regression model was applied in order to demonstrate that knowledge derived from electrophysiology can be used to predict depression severity. 2. Methods 2.1. Subjects Twenty-two medication free MDD patients were recruited from Nanjing Brain Hospital, China. Twenty-two age and gender-matched HC participants were recruited via advertisements. At subject screening, the diagnosis of MDD patients was conducted by professional psychiatrists using the Hamilton Depression Rating Scale (HAM-D) and the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). Subjects with other neurological or psychiatric disorders, drug or alcohol abuse and suicidal ideation or behavior were excluded from the study. All subjects were informed with written consent forms and a local ethics committee approved the study. Table 1 summarizes the demographic information of the present sample. 2.2. Data acquisition Eyes closed continuous recordings were collected from all subjects with a CTF275 MEG system at 300 Hz sampling rate. The duration of the recording was 4 minutes. Head coils were placed at the naison, left and right pre-auricular points to localize head position. Structural (T1-weighted) anatomical images were obtained by a GE 1.5T system using a high-resolution, 3D gradientecho pulse sequence. During MR acquisition, we placed earplugs with vitamin E in the ear canals to allow offline MRI and MEG co-registration. 2.3. Data preprocessing and spectral analysis Offline analysis was performed with the FieldTrip toolbox (Oostenveld et al., 2011). First, the complete recording session was divided into 2 s epochs with 1 s overlap. Epochs that contained large jumps were rejected based on visual inspection. A logistic infomax Independent Component Analysis (ICA) was then applied to remove eye moment and cardiac artifacts (Bell et al., 1995). After artifact rejection, we calculated spectral power from 1 to 75 Hz in steps of 0.5 Hz using a Hanning taper. Spectral power was computed for each epoch and then averaged at the sensor level. To make sensor level and source level data more comparable, the planar gradient for each sensor was estimated using a nearest neighbor interpolation (Hämäläinen et 4 Jiang et al
al., 1993). Finally, we normalized each frequency power bin by dividing the sum of the power estimates in all frequency bins from 1 to 75 Hz at each sensor. 2.4. Source reconstruction We used a Dynamic Imaging of Coherent Sources (DICS) beamforming method to identify source activity (Gross et al., 2001). After MRI and MEG data co-registration, a realistically shaped single-shell head model was constructed from each individual’s anatomical MRI. Each brain was divided into grid points with a 1 cm resolution and further warped to the MNI template using the SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm). Then, we calculated lead fields for each grid. In order to reduce the depth bias, the leadfield was further normalized by the sum of squares of the elements in the leadfield matrix (Fuchs et al., 1999). Next, spatial filters were constructed for each grid point based on lead fields and cross-spectral density matrices. The spatial filter minimizes the source power at a given location under a unit-gain constraint. This constraint enforces that if a source with unit amplitude projects to the sensors via the leadfield, the reconstructed signal amplitude at that location should equal to one as well when applying the spatial filter. Thus, the spatial filters allow us to estimate the source activity at any given location in the brain independent of all other possible locations. This is achieved by multiplying the spatial filters with the axial gradiometer data at each grid point across the whole brain. 2.5. Statistical analysis We applied a nonparametric cluster-based permutation test to statistically evaluate group differences while controlling for multiple comparisons (Maris et al., 2007). At each frequency band of interest, mean band power was calculated. This was done by averaging the power in each frequency bin (between 4-8 Hz for the theta band, 8-14 Hz for the alpha band and 14-30 Hz for the beta band) separately. At the sensor level, the test statistics were obtained by independent two-sample t-tests between MDD and HC groups on the mean power of individual frequency band. All t-values exceeding an a priori threshold (p<0.05) became cluster candidates. At the source level, we used normalized difference ((MDD-HC)⁄(MDD+HC)) as the test statistic. The logic was that beamforming seeks to make sources as focal as possible and we aimed to identify the maximum source activation difference. Source cluster candidates were identified by thresholding the observed normalized difference values at the 95th percentile of this distribution (two-tailed test). Then, source and sensor cluster candidates formed clusters based on spatial adjacency and the sum of statistical values over grid points or sensors were defined as the cluster statistic. To obtain the reference cluster distribution, we randomized the data across the two groups 1000 times. For each randomization, maximum cluster statistic value was
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used to create the reference distribution. Observed cluster values exceeding the 95th percentile of the reference distribution were considered significant (two-tailed, p<0.05). 2.6. Bayesian linear regression Bayesian linear regression was used to study the predictive power of the MEG features derived from different sensors and frequency bands. The linear regression model can be written as = ∑ , + +
(1)
where = 1, … , is the subject index, is the observed HAM-D score for subject , , is the th covariate (e.g., th channel) related to subject , is the regression coefficient for the th covariate, is a bias term and is additive Gaussian noise with unknown variance . Because the number of input variables can be large compared to the number of observations (e.g., d=275 if alpha power from all the sensors is included), we used independent Gaussian prior distributions ~0, exp
(2)
where is a real-valued relevance hyperparameter that controls the prior variance and hence also the magnitude of coefficient . If a certain input feature is irrelevant for predicting the HAM-D score within the training observations, then the corresponding relevance hyperparameter tends to get smaller values during model training and effectively prunes the associated coefficient out of the model. Markov chain Monte Carlo was used to infer the unknown model parameters , , and . The final prediction for the HAM-D score was obtained by computing the average prediction over the different hyperparameter values corresponding to different model structures. The model was implemented using the Bayesian modeling software package GPstuff (Vanhatalo et al., 2013). 3. Results 3.1. MDD patients exhibit less frontal theta and posterior alpha power compared to healthy controls Our first analysis focused on group differences of normalized power in the theta band (4-8 Hz) between MDD and healthy controls (Fig. 1). A strong frontal theta activity was evident in both groups (Fig. 1A and 1B) and might reflect eye-induced artifacts. The statistical evaluation showed a significant group difference on the basis of a fronto-temporal sensor cluster (Fig. 1C), reflecting decreased theta power in MDD patients compared to healthy controls (Fig. 1D). Source reconstruction revealed that the difference was confined mainly to superior frontal gyrus, middle frontal gyrus, precentral gyrus and supplementary motor area (Fig. 1E). 6 Jiang et al
Next, the evaluation focused on the alpha band activity (8-14 Hz), comparing MDD with healthy controls (Fig. 2). Both groups showed a clear peak in the spectrum at ~10Hz (Fig. 2D) over posterior areas (MDD patients in Fig. 2A; healthy controls in Fig. 2B). A non-parametric permutation analysis resulted in clusters of sensors located over occipital-temporal-frontal regions when assessing significant group differences (Fig. 2C), reflecting decreased alpha power in MDD patients compared to healthy controls (Fig. 2D). Sources reflecting this group difference effect were mostly located in the occipital lobe, cuneus and paracentral lobule (Fig. 2E). In addition, we checked the individual alpha peak frequency difference between MDD and HC. No significant differences between these two groups were identified (p=0.48, two-sample t- tests). 3.2. MDD patients exhibit stronger frontal beta power compared to healthy controls Figures 3A and 3B illustrate centro-parietal beta band activity (14-30 Hz) in both groups. The statistical evaluation suggested that that MDD group was characterized by stronger beta band power as compared to healthy controls (Figs. 3C and 3D). Subsequent source analysis confirmed MDD’s stronger beta band activity predominantly in the supplementary motor area, precentral gyrus, postcentral gyrus, inferior frontal gyrus and middle frontal gyrus (Fig. 3E). In short, in comparison to healthy controls, the pattern of spontaneous brain activity in MDD patients can be described as a decrease of frontal theta and parietal alpha power with a concomitant increase in beta power confined to frontal cortex. There were no group differences in frequency bands above 30 Hz. In the following section, a potential neural correlate of depression severity shall be considered. 3.3. Alpha activity is associated with depression severity The relationship between oscillatory activity and depression severity is illustrated in Fig. 4. The corresponding oscillatory band power was averaged over sensors identified in Fig. 1C, Fig. 2C and Fig. 3C respectively. Although a weak negative correlation in the theta band (r = -0.08; p=0.80, Fig. 4A) and a weak positive correlation in the beta band (r = 0.16; p=0.43, Fig. 4C) were observed, these were not significant. However, we found a significant negative correlation between alpha power and depression severity represented by HAM-D value (r = -0.65, p=0.001; Fig. 4B). This was still significant after Bonferroni correction (N = 3 frequency bands; p=0.03). A homogeneity test confirmed that the slopes were significantly different for the slope of the regression of HAM-D on alpha versus HAM-D on theta (F = 6.9, p=0.012) as well as for the slope of the regression of HAM-D on alpha versus HAM-D on beta (F = 11.8, p=0.001). Thus, the observed relationship between the neural data and depression severity was specific to the alpha band activity. While we used a standard range for the alpha band activity in this study, we also checked whether there was a systematic relationship between individual alpha peak frequency and depression severity. No significant correlation was found (Spearman correlation, 7 Jiang et al
r= 0.09, p=0.68). Although a strong correlation between alpha power and depression severity was observed, this does not directly translate into an objective prediction of depression severity based on neuronal data. In the following session, this objective will be met by using a state-of-the-art Bayesian linear regression method. 3.4. Bayesian linear regression on oscillatory alpha power predicts depression severity A Bayesian linear regression model was used to predict individual MDD severity (Fig. 5). To this end, power values in the frequency bands of interest were calculated and averaged per subject over each MEG sensor and used as inputs to the Bayesian linear regression model. To evaluate the Bayesian linear regression model, we used a 12-fold cross-validation. This approach essentially partitioned the MDD group of participants with a unique HAM-D value into 12 subgroups. Of the 12 subgroups, 11 subgroups were used to train the model and the remaining one group was retained to test and validate the model. This procedure was then repeated 12 times. Figure 5 illustrates the Bayesian linear regression model predictive performance based on theta, alpha and beta power respectively. In Fig. 5A, the diagonal line would represent an exact match between subjective HAM-D values as estimated by clinical experts and objective HAM-D values as estimated by our model. Above the diagonal line, the subjective HAM-D values were higher than the objective HAM-D values and vice versa for values below the diagonal line. In line with the results from the previous section, there was no significant predictive performance from theta activity (Fig. 5A, left panel). However, there was a significant correlation between predicted HAM-D values and professional HAM-D values in the alpha band (r = 0.68, p=0.0005; Fig. 5A, middle panel) as well as the beta band (r = 0.56, p=0.007; Fig. 5A, right panel). Figure 5B shows the correlation between predicted HAM-D values and theta, alpha and beta power respectively. We found a significant negative correlation in the alpha band (r = -0.57, p=0.006) but not in the theta band (r=-0.4, p=0.07) and the beta band (r=0.32, p=0.14). Taken together, this provides the potential to support MDD diagnosis in a clinical setting. That is, we can use our trained Bayesian linear regression model to predict depression severity after recording MEG resting state data in putative patients. Assessment by a psychiatrist can be further compared to model predictions. If there is a substantial difference in assessment, this could be either due to under-performance of the model or misdiagnosis by the psychiatrist, warranting closer monitoring. 4. Discussion We here demonstrate altered spontaneous oscillatory activity in MDD patients. Reduced posterior alpha activity was shown to be negatively correlated with depression severity, reflecting a core neurophysiological relation with clinical symptoms in MDD. Furthermore, it was shown that, using a Bayesian linear regression model applied to the neural data, it is 8 Jiang et al
possible to reliably predict HAM-D values for MDD patients. This provides a quantitative and objective measure of estimating depression severity, which may be useful clinically for early diagnosis of MDD as well as monitoring disease progression in personalized treatment. 4.1. Disrupted oscillatory activity in MDD Although we found decreased theta activity in centro-frontal areas and increased centro-frontal beta activity in MDD, these did not show a significant correlation with depression severity. No gamma activity (30-75 Hz) difference was observed between MDD and healthy controls. This might be explained by the fact that gamma activity is normally only reliably elicited by presenting external stimuli such as oriented gratings and natural images (Hoogenboom et al., 2006, Brunet et al., 2015). Hence, it might be difficult to extract gamma activity during the resting state in our case. Moreover, the identified alpha activity differences between MDD patients and HC were widespread in occipital-temporal-frontal sites. However, on the basis of the current source analysis and effects of volume conduction contributing to observed topographies at the sensor level, we consider the strongest effect attributed to posterior regions. It should be pointed out that other related EEG studies on depression report increased frontal theta, global alpha and beta power, as well as less occipito-parietal theta and global delta power in MDD patients (Fingelkurts et al., 2015). This is somewhat incongruent with our findings. At the same time, other studies have reported decreased delta, theta and alpha activity in MDD patients (Volf et al., 2002), as well as an inverse relationship between alpha activity and depression severity (Zoon et al., 2013). These discrepancies might be explained by heterogeneity across samples. Studies vary in terms of medication, subtypes of MDD, age and gender. These factors should be examined more explicitly in the future. Research has also revealed differences within MDD populations. For instance, MDD Met-carriers had lower global absolute alpha power in the eyes closed condition compared with Val-carriers (Zoon et al., 2013). Frontal alpha asymmetries have been investigated as biomarkers of depression for a long time (Allen et al., 2004). However, attempts to demonstrate a consistent correlation between frontal alpha asymmetry and depression severity thus far have failed due to the temporal instability of alpha asymmetry findings relative to depressive state (Smart et al., 2015). Posterior alpha activity has also been reported to be negatively associated with depression severity, indicating its critical role in the pathogenesis of MDD (Zoon et al., 2013). These findings were replicated in the current study. In previous work, posterior alpha power has also been negatively associated with cortical excitability (Romei et al., 2008). Thus, decreased posterior alpha power indicates an alpha synchronization deficit and enhanced neuronal excitability (Niedermeyer, 1997). In 9 Jiang et al
relation to the present findings, this would imply that attenuated posterior alpha activity in MDD patients may link to increased arousal in posterior brain regions, possibly reflecting prolonged stress during MDD psychopathology development (Heller et al., 1998). 4.2. Diagnostic precision in psychiatric disorders In psychiatry, diagnosis is inevitably subjective because it typically relies on self-reports and experts’ experiences. In order to induce changes in patient diagnosis and care, the National Institute of Mental Health developed the Research Domain Criteria (RDoC). In particular, the RDoC initiative aims to provide novel ways of qualifying psychiatric disorders based on both neurobiological and behavioral measures (Insel et al., 2010, Cuthbert et al., 2013). Although behavioral, physiological and neuroimaging work on MDD or other mental illness has seen significant advances, direct translation of basic research into clinical applications remains rare. We argue that machine learning techniques could play a key role here since they are instrumental for detecting representative patterns among vast amounts of neural data, offering individualized predictions (Lemm et al., 2011, Helmstaedter, 2015). In the present report, application of a Bayesian linear regression model allowed for an enhanced prediction of depression severity, compared to what can be achieved by conventional analysis. For instance, a significant HAM-D prediction performance based on beta band power was achieved which was not reflected in the correlation analysis. These findings offer new opportunities for evidence-based diagnosis and monitoring of MDD patients. 4.3. Study Limitations There are a few limitations to our study. First, the overall sample was relatively small. It would therefore be valuable to replicate our findings on large cohorts. Future work will also need to examine whether changes in alpha power reflect treatment effects. This could be investigated by testing if posterior alpha activity in MDD patients normalizes to the level of healthy subjects after successful treatment. The reliability and generalizability of the predictions provided by the Bayesian linear regression model also deserve further investigation. Furthermore, results in the current study were obtained using MEG which allowed us to identify the areas in which the oscillatory activity differed in healthy controls compared to MDD patients. While this information is important for understanding which regions are functioning differently in MDD patients, it is not required for estimating depression severity per se. In future work, it would be of practical interest to investigate if EEG data alone also allows for estimating depression severity. Lastly, in addition to the difference in power, connectivity analysis showed higher functional connectivity in MDD patients compared with healthy controls in a broad frequency range (Fingelkurts et al., 2007, Leuchter et al., 2012, Quraan et al., 2014). These differences were most notable in the theta, alpha, and beta bands. The stronger functional coupling in 10 Jiang et al
depressed patients could reflect altered neural communication (Menon, 2011). This also implies that patterns of functional connectivity that might distinguish between depressed patients and healthy controls should be further examined in the future. Moreover, functional connectivity can extend to cross-frequency coupling (CFC) because neuronal oscillations at different frequency bands interact with each other (Jensen et al., 2007, Jiang et al., 2015a). CFC appears to serve a functional role in neuronal computation and communication (Canolty et al., 2010). A recent study in Parkinson’s disease demonstrated large clinically relevant changes in betagamma coupling in the motor cortex and subthalamic nucleus pathway with deep brain stimulation (de Hemptinne et al., 2013). Therefore, future studies are also needed to disentangle potential CFC alterations at the network level in MDD patients. 5. Conclusions The current study provides evidence for how disrupted resting-state posterior alpha activity contributes to characteristic clinical manifestations in MDD and introduced a compelling model that provides an objective individualized MDD severity prediction. This proposed framework could potentially lead to innovations in diagnosis, monitoring and treatment of MDD patients.
Conflict of interest The authors reported no biomedical financial interests or potential conflicts of interest.
Acknowledgements The work was supported by the grants of: National High Technology Research and Development Program of China (863 Program) (2015AA020509); The National Natural Science Foundation of China (81371522, 61372032); Jiangsu Clinical Medicine Technology Foundation (BL2012052, BL2014009).
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Table and Figure Legends
Table 1. Demographic information for MDD patients and HC subjects. We did not find differences between the two groups in terms of age and education at p<0.05. Fig. 1. Theta band activity (4-8 Hz) for MDD patients versus healthy controls. Mean theta power was averaged between 4-8 Hz. (A) Topography of mean theta power in MDD patients. (B) Topography of mean theta power in healthy controls. (C) Scalp topography of the group difference in the theta power. T-values were assessed by independent two-sample t-tests between MDD and HC groups on the mean theta band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD patients and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different negative clusters were identified in superior frontal gyrus, middle frontal gryus, precentral gyrus and supplementary motor area (p<0.05).
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Fig. 2. Alpha band activity (8-14 Hz) for MDD patients versus healthy controls. Mean alpha power was averaged between 8-14 Hz. (A) Topography of mean alpha power in MDD patients. (B) Topography of mean alpha power in healthy controls. (C) Scalp topography of the group difference in the alpha power. T-values were assessed by independent two-sample t-tests between MDD and HC groups on the mean alpha band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD patients and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different negative clusters were identified in the occipital lobe, cuneus and paracentral lobule (p<0.05). Fig. 3. Beta band activity (14-30 Hz) for MDD patients versus healthy controls. Mean beta power was averaged between 14-30 Hz. (A) Topography of mean beta power scalp topography in MDD patients. (B) Topography of mean beta power in healthy controls. (C) Scalp topography of the group difference in the beta band power. T-values were assessed by independent twosample t-tests between MDD and HC groups on the mean beta band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different positive clusters were identified in supplementary motor area, precentral gyrus, postcentral gyrus, inferior frontal gyrus and middle frontal gyrus (p<0.05). Fig. 4. Spearman correlation between HAM-D value and mean theta band (A), alpha band (B) and beta band (C) power respectively. The corresponding oscillatory band power was averaged over sensors identified in Fig. 1C, Fig. 2C and Fig. 3C respectively. Fig. 5. HAM-D values estimated by the Bayesian linear regression model based on theta (left panels), alpha (middle panels) and beta (right panels) power respectively. (A) HAM-D values as predicted by the Bayesian linear regression model. Predictive performance was evaluated by Spearman correlation between predicted HAM-D values and professionally evaluated HAM-D values. The numbers represented the MDD patient’s ID. (B) Relationship between predicted HAM-D values and ranked individual frequency band power. The error bar represents the credible interval covering 40% of the posterior confidence interval. The red stars represent professionally evaluated HAM-D values. The black line shows the regression line between predicted HAM-D values and individual band power.
15 Jiang et al
B 0.06
0
Normalized power
A
0
t value
6
−6
Normalized power
D
C
MDD HC
0.025 0.015 0.005 5
E
-0.3 0 0.3 Normalized difference
15 25 35 Frequency (Hz)
B 0.04
0
Normalized power
A
0
t value
4
−4
Normalized power
D
C
MDD HC
0.05 0.03 0.01 5
15 25 35 Frequency (Hz)
E
-0.4 0 0.4 Normalized difference
B 0.015
0 D
0 −4
t value
4
Normalized power
C
Normalized power
A
0.035
MDD HC
0.025 0.015 0.005 5
15 25 35 Frequency (Hz)
E
-0.2 0 0.2 Normalized difference
(A)
(C)
(B) r=−0.06; p=0.80
r=−0.65; p=0.001
r=0.18; p=0.43
HAM-D value
35 30 25 20 15 0.005 0.015 0.025 Normalized theta power
0.01 0.03 0.05 Normalized alpha power
0.005 0.01 0.015 Normalized beta power
A
Professional HAM-D value
r=0.38 ; p=0.08 35 20
25
8
20
22 8 4 1 6 14 18 17 2 3 12 9 21 15 5 11 10 13 16 7
16
19
20
r=0.56 ; p=0.007
20 22 4 8 71 18 6 14 17 3 12 29 5 15 13 21 10 11
22
1 7 4 6 18 14 3 12 17 2 15 9 5 21 11 13 10 16
15 10
r=0.68 ; p=0.0005
19
30
40
10
20
19
30
40
10
20
30
Model estimated HAM-D value Model estimated HAM-D value
B r=−0.40; p=0.07
r=−0.57 ; p=0.006
r=0.32 ; p=0.14
45 35 25 15 5
0
5 10 15 20 Rank of theta power
0
5 10 15 20 Rank of alpha power
0
5 10 15 20 Rank of beta power
40
MDD patients (n = 22)
HC (n = 22)
Age (years)
33.3±7.8
29.9±7.1
Gender (Male/Female)
11/11
12/10
Education (years)
13.7±1.6
14.7±1.5
HAM-D value
26.4±4.1
-
16 Jiang et al
S1388-2457(16)00003-1 http://dx.doi.org/10.1016/j.clinph.2015.12.018 CLINPH 2007714
To appear in:
Clinical Neurophysiology
Accepted Date:
29 December 2015
Please cite this article as: Jiang, H., Popov, T., Jylänki, P., Bi, K., Yao, Z., Lu, Q., Jensen, O., van Gerven, M.A.J., Predictability of depression severity based on posterior alpha oscillations, Clinical Neurophysiology (2016), doi: http://dx.doi.org/10.1016/j.clinph.2015.12.018
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Predictability of depression severity based on posterior alpha oscillations H. Jianga,b, T. Popovb, P. Jylänkib, K. Bic, Z. Yaoa,d*, Q. Luc*, O. Jensenb, M. A. J. van Gervenb
a
Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing
210029, China b
Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE, Nijmegen, The
Netherlands c
Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast
University, Nanjing 210096, China d
Medical College of Nanjing University, 22 Hankou Road, Nanjing 210093, China
Corresponding authors:
Zhijian Yao Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China Tel:
+86 25 82296252
E-mail: [email protected]
Qing Lu Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast University, Nanjing 210096, China Tel:
+86 25 83795549
E-mail: [email protected]
Highlights • • •
Distinct oscillatory activity differences between major depressive disorder (MDD) and healthy controls (HC) were identified. Posterior alpha power was found to be negatively related to depression severity. Bayesian linear regression provides a quantitative and objective estimation of depression severity.
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Abstract Objective: We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. Methods: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillations power. Results: In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively. Conclusions: Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. Significance: The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process.
Keywords: MEG, resting state, major depressive disorder, Bayesian linear regression.
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1. Introduction It has been suggested that neuronal oscillations shape the functional architecture of the brain by temporally coordinating information both within and across neural systems (Buzsáki et al., 2004). As such, neuronal oscillations are a useful tool to investigate healthy and disrupted cognitive processes (Watson et al., 2015). In contemporary psychiatric research, it is of importance to move from subjective ratings towards more objective approaches when evaluating psychiatric disease (Insel et al., 2010). Evidence-based quantitative approaches based on neurobiological measures can be of value for diagnosis, prognosis and the assessment of treatment effects (Stephan et al., 2014). Indeed, emerging researches suggest that neural oscillations are disrupted in major depressive disorder (MDD), for instance. These disruptions play a critical role not only in symptomatology but also in deficits of cognition (Fingelkurts et al., 2015). Several studies have reported abnormal neuronal activity in MDD when comparing to healthy controls (HC). Converging evidence suggests that MDD is characterized by activity changes in both anterior and posterior hub regions (Fingelkurts et al., 2015). In anterior hub regions, models of depression have linked left and right frontal cortex to positive and negative affect systems respectively (Smart et al., 2015). This is supported by a frontal alpha asymmetry during resting state in depressed patients where right frontal alpha activity was stronger than left frontal alpha activity (Henriques et al., 1990). However, stability of frontal alpha asymmetry differs across groups and was not reliable in depression patients during different retest phases (Debener et al., 2000). Moreover, changes in asymmetry across assessments did not predict depression severity well (Allen et al., 2004). This raises doubts whether frontal alpha asymmetry allows for reliable prediction of depression severity. Apart from alpha power measures, depressed patients were found to have decreased centro-frontal theta activity (Volf et al., 2002) and increased frontal beta activity (Flor-Henry et al., 2004) compared to controls. In posterior hub regions, the alpha rhythm has been proposed to modulate top-down control or active inhibition in posterior regions (Jensen et al., 2010). Various attention and memory studies support this notion (Haegens et al., 2011, Jiang et al., 2015b). In addition, pre-stimulus alpha activity is important for guiding behavior since it determines the brain state prior to visual input (Busch et al., 2009, Weisz et al., 2014). In relation to MDD patients, the association between the BDNF Val66Met polymorphism, resting state EEG alpha power, and depression severity were examined in a large cohort (Zoon et al., 2013). Posterior alpha power during rest was found to inversely relate to depression severity in a reliable manner. 3 Jiang et al
The present study had two goals. First, we aimed to discriminate MDD from HC by computing the spectral characteristics of the MEG data during resting state conditions. The classical frequency bands in the theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz) and gamma (30-75 Hz) range were chosen for subsequent analysis. The second goal was to more closely evaluate the link between oscillatory activity and depression severity. This was realized by incorporating spatial (whole brain) and spectral (frequency specific) information. A Bayesian linear regression model was applied in order to demonstrate that knowledge derived from electrophysiology can be used to predict depression severity. 2. Methods 2.1. Subjects Twenty-two medication free MDD patients were recruited from Nanjing Brain Hospital, China. Twenty-two age and gender-matched HC participants were recruited via advertisements. At subject screening, the diagnosis of MDD patients was conducted by professional psychiatrists using the Hamilton Depression Rating Scale (HAM-D) and the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). Subjects with other neurological or psychiatric disorders, drug or alcohol abuse and suicidal ideation or behavior were excluded from the study. All subjects were informed with written consent forms and a local ethics committee approved the study. Table 1 summarizes the demographic information of the present sample. 2.2. Data acquisition Eyes closed continuous recordings were collected from all subjects with a CTF275 MEG system at 300 Hz sampling rate. The duration of the recording was 4 minutes. Head coils were placed at the naison, left and right pre-auricular points to localize head position. Structural (T1-weighted) anatomical images were obtained by a GE 1.5T system using a high-resolution, 3D gradientecho pulse sequence. During MR acquisition, we placed earplugs with vitamin E in the ear canals to allow offline MRI and MEG co-registration. 2.3. Data preprocessing and spectral analysis Offline analysis was performed with the FieldTrip toolbox (Oostenveld et al., 2011). First, the complete recording session was divided into 2 s epochs with 1 s overlap. Epochs that contained large jumps were rejected based on visual inspection. A logistic infomax Independent Component Analysis (ICA) was then applied to remove eye moment and cardiac artifacts (Bell et al., 1995). After artifact rejection, we calculated spectral power from 1 to 75 Hz in steps of 0.5 Hz using a Hanning taper. Spectral power was computed for each epoch and then averaged at the sensor level. To make sensor level and source level data more comparable, the planar gradient for each sensor was estimated using a nearest neighbor interpolation (Hämäläinen et 4 Jiang et al
al., 1993). Finally, we normalized each frequency power bin by dividing the sum of the power estimates in all frequency bins from 1 to 75 Hz at each sensor. 2.4. Source reconstruction We used a Dynamic Imaging of Coherent Sources (DICS) beamforming method to identify source activity (Gross et al., 2001). After MRI and MEG data co-registration, a realistically shaped single-shell head model was constructed from each individual’s anatomical MRI. Each brain was divided into grid points with a 1 cm resolution and further warped to the MNI template using the SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm). Then, we calculated lead fields for each grid. In order to reduce the depth bias, the leadfield was further normalized by the sum of squares of the elements in the leadfield matrix (Fuchs et al., 1999). Next, spatial filters were constructed for each grid point based on lead fields and cross-spectral density matrices. The spatial filter minimizes the source power at a given location under a unit-gain constraint. This constraint enforces that if a source with unit amplitude projects to the sensors via the leadfield, the reconstructed signal amplitude at that location should equal to one as well when applying the spatial filter. Thus, the spatial filters allow us to estimate the source activity at any given location in the brain independent of all other possible locations. This is achieved by multiplying the spatial filters with the axial gradiometer data at each grid point across the whole brain. 2.5. Statistical analysis We applied a nonparametric cluster-based permutation test to statistically evaluate group differences while controlling for multiple comparisons (Maris et al., 2007). At each frequency band of interest, mean band power was calculated. This was done by averaging the power in each frequency bin (between 4-8 Hz for the theta band, 8-14 Hz for the alpha band and 14-30 Hz for the beta band) separately. At the sensor level, the test statistics were obtained by independent two-sample t-tests between MDD and HC groups on the mean power of individual frequency band. All t-values exceeding an a priori threshold (p<0.05) became cluster candidates. At the source level, we used normalized difference ((MDD-HC)⁄(MDD+HC)) as the test statistic. The logic was that beamforming seeks to make sources as focal as possible and we aimed to identify the maximum source activation difference. Source cluster candidates were identified by thresholding the observed normalized difference values at the 95th percentile of this distribution (two-tailed test). Then, source and sensor cluster candidates formed clusters based on spatial adjacency and the sum of statistical values over grid points or sensors were defined as the cluster statistic. To obtain the reference cluster distribution, we randomized the data across the two groups 1000 times. For each randomization, maximum cluster statistic value was
5 Jiang et al
used to create the reference distribution. Observed cluster values exceeding the 95th percentile of the reference distribution were considered significant (two-tailed, p<0.05). 2.6. Bayesian linear regression Bayesian linear regression was used to study the predictive power of the MEG features derived from different sensors and frequency bands. The linear regression model can be written as = ∑ , + +
(1)
where = 1, … , is the subject index, is the observed HAM-D score for subject , , is the th covariate (e.g., th channel) related to subject , is the regression coefficient for the th covariate, is a bias term and is additive Gaussian noise with unknown variance . Because the number of input variables can be large compared to the number of observations (e.g., d=275 if alpha power from all the sensors is included), we used independent Gaussian prior distributions ~0, exp
(2)
where is a real-valued relevance hyperparameter that controls the prior variance and hence also the magnitude of coefficient . If a certain input feature is irrelevant for predicting the HAM-D score within the training observations, then the corresponding relevance hyperparameter tends to get smaller values during model training and effectively prunes the associated coefficient out of the model. Markov chain Monte Carlo was used to infer the unknown model parameters , , and . The final prediction for the HAM-D score was obtained by computing the average prediction over the different hyperparameter values corresponding to different model structures. The model was implemented using the Bayesian modeling software package GPstuff (Vanhatalo et al., 2013). 3. Results 3.1. MDD patients exhibit less frontal theta and posterior alpha power compared to healthy controls Our first analysis focused on group differences of normalized power in the theta band (4-8 Hz) between MDD and healthy controls (Fig. 1). A strong frontal theta activity was evident in both groups (Fig. 1A and 1B) and might reflect eye-induced artifacts. The statistical evaluation showed a significant group difference on the basis of a fronto-temporal sensor cluster (Fig. 1C), reflecting decreased theta power in MDD patients compared to healthy controls (Fig. 1D). Source reconstruction revealed that the difference was confined mainly to superior frontal gyrus, middle frontal gyrus, precentral gyrus and supplementary motor area (Fig. 1E). 6 Jiang et al
Next, the evaluation focused on the alpha band activity (8-14 Hz), comparing MDD with healthy controls (Fig. 2). Both groups showed a clear peak in the spectrum at ~10Hz (Fig. 2D) over posterior areas (MDD patients in Fig. 2A; healthy controls in Fig. 2B). A non-parametric permutation analysis resulted in clusters of sensors located over occipital-temporal-frontal regions when assessing significant group differences (Fig. 2C), reflecting decreased alpha power in MDD patients compared to healthy controls (Fig. 2D). Sources reflecting this group difference effect were mostly located in the occipital lobe, cuneus and paracentral lobule (Fig. 2E). In addition, we checked the individual alpha peak frequency difference between MDD and HC. No significant differences between these two groups were identified (p=0.48, two-sample t- tests). 3.2. MDD patients exhibit stronger frontal beta power compared to healthy controls Figures 3A and 3B illustrate centro-parietal beta band activity (14-30 Hz) in both groups. The statistical evaluation suggested that that MDD group was characterized by stronger beta band power as compared to healthy controls (Figs. 3C and 3D). Subsequent source analysis confirmed MDD’s stronger beta band activity predominantly in the supplementary motor area, precentral gyrus, postcentral gyrus, inferior frontal gyrus and middle frontal gyrus (Fig. 3E). In short, in comparison to healthy controls, the pattern of spontaneous brain activity in MDD patients can be described as a decrease of frontal theta and parietal alpha power with a concomitant increase in beta power confined to frontal cortex. There were no group differences in frequency bands above 30 Hz. In the following section, a potential neural correlate of depression severity shall be considered. 3.3. Alpha activity is associated with depression severity The relationship between oscillatory activity and depression severity is illustrated in Fig. 4. The corresponding oscillatory band power was averaged over sensors identified in Fig. 1C, Fig. 2C and Fig. 3C respectively. Although a weak negative correlation in the theta band (r = -0.08; p=0.80, Fig. 4A) and a weak positive correlation in the beta band (r = 0.16; p=0.43, Fig. 4C) were observed, these were not significant. However, we found a significant negative correlation between alpha power and depression severity represented by HAM-D value (r = -0.65, p=0.001; Fig. 4B). This was still significant after Bonferroni correction (N = 3 frequency bands; p=0.03). A homogeneity test confirmed that the slopes were significantly different for the slope of the regression of HAM-D on alpha versus HAM-D on theta (F = 6.9, p=0.012) as well as for the slope of the regression of HAM-D on alpha versus HAM-D on beta (F = 11.8, p=0.001). Thus, the observed relationship between the neural data and depression severity was specific to the alpha band activity. While we used a standard range for the alpha band activity in this study, we also checked whether there was a systematic relationship between individual alpha peak frequency and depression severity. No significant correlation was found (Spearman correlation, 7 Jiang et al
r= 0.09, p=0.68). Although a strong correlation between alpha power and depression severity was observed, this does not directly translate into an objective prediction of depression severity based on neuronal data. In the following session, this objective will be met by using a state-of-the-art Bayesian linear regression method. 3.4. Bayesian linear regression on oscillatory alpha power predicts depression severity A Bayesian linear regression model was used to predict individual MDD severity (Fig. 5). To this end, power values in the frequency bands of interest were calculated and averaged per subject over each MEG sensor and used as inputs to the Bayesian linear regression model. To evaluate the Bayesian linear regression model, we used a 12-fold cross-validation. This approach essentially partitioned the MDD group of participants with a unique HAM-D value into 12 subgroups. Of the 12 subgroups, 11 subgroups were used to train the model and the remaining one group was retained to test and validate the model. This procedure was then repeated 12 times. Figure 5 illustrates the Bayesian linear regression model predictive performance based on theta, alpha and beta power respectively. In Fig. 5A, the diagonal line would represent an exact match between subjective HAM-D values as estimated by clinical experts and objective HAM-D values as estimated by our model. Above the diagonal line, the subjective HAM-D values were higher than the objective HAM-D values and vice versa for values below the diagonal line. In line with the results from the previous section, there was no significant predictive performance from theta activity (Fig. 5A, left panel). However, there was a significant correlation between predicted HAM-D values and professional HAM-D values in the alpha band (r = 0.68, p=0.0005; Fig. 5A, middle panel) as well as the beta band (r = 0.56, p=0.007; Fig. 5A, right panel). Figure 5B shows the correlation between predicted HAM-D values and theta, alpha and beta power respectively. We found a significant negative correlation in the alpha band (r = -0.57, p=0.006) but not in the theta band (r=-0.4, p=0.07) and the beta band (r=0.32, p=0.14). Taken together, this provides the potential to support MDD diagnosis in a clinical setting. That is, we can use our trained Bayesian linear regression model to predict depression severity after recording MEG resting state data in putative patients. Assessment by a psychiatrist can be further compared to model predictions. If there is a substantial difference in assessment, this could be either due to under-performance of the model or misdiagnosis by the psychiatrist, warranting closer monitoring. 4. Discussion We here demonstrate altered spontaneous oscillatory activity in MDD patients. Reduced posterior alpha activity was shown to be negatively correlated with depression severity, reflecting a core neurophysiological relation with clinical symptoms in MDD. Furthermore, it was shown that, using a Bayesian linear regression model applied to the neural data, it is 8 Jiang et al
possible to reliably predict HAM-D values for MDD patients. This provides a quantitative and objective measure of estimating depression severity, which may be useful clinically for early diagnosis of MDD as well as monitoring disease progression in personalized treatment. 4.1. Disrupted oscillatory activity in MDD Although we found decreased theta activity in centro-frontal areas and increased centro-frontal beta activity in MDD, these did not show a significant correlation with depression severity. No gamma activity (30-75 Hz) difference was observed between MDD and healthy controls. This might be explained by the fact that gamma activity is normally only reliably elicited by presenting external stimuli such as oriented gratings and natural images (Hoogenboom et al., 2006, Brunet et al., 2015). Hence, it might be difficult to extract gamma activity during the resting state in our case. Moreover, the identified alpha activity differences between MDD patients and HC were widespread in occipital-temporal-frontal sites. However, on the basis of the current source analysis and effects of volume conduction contributing to observed topographies at the sensor level, we consider the strongest effect attributed to posterior regions. It should be pointed out that other related EEG studies on depression report increased frontal theta, global alpha and beta power, as well as less occipito-parietal theta and global delta power in MDD patients (Fingelkurts et al., 2015). This is somewhat incongruent with our findings. At the same time, other studies have reported decreased delta, theta and alpha activity in MDD patients (Volf et al., 2002), as well as an inverse relationship between alpha activity and depression severity (Zoon et al., 2013). These discrepancies might be explained by heterogeneity across samples. Studies vary in terms of medication, subtypes of MDD, age and gender. These factors should be examined more explicitly in the future. Research has also revealed differences within MDD populations. For instance, MDD Met-carriers had lower global absolute alpha power in the eyes closed condition compared with Val-carriers (Zoon et al., 2013). Frontal alpha asymmetries have been investigated as biomarkers of depression for a long time (Allen et al., 2004). However, attempts to demonstrate a consistent correlation between frontal alpha asymmetry and depression severity thus far have failed due to the temporal instability of alpha asymmetry findings relative to depressive state (Smart et al., 2015). Posterior alpha activity has also been reported to be negatively associated with depression severity, indicating its critical role in the pathogenesis of MDD (Zoon et al., 2013). These findings were replicated in the current study. In previous work, posterior alpha power has also been negatively associated with cortical excitability (Romei et al., 2008). Thus, decreased posterior alpha power indicates an alpha synchronization deficit and enhanced neuronal excitability (Niedermeyer, 1997). In 9 Jiang et al
relation to the present findings, this would imply that attenuated posterior alpha activity in MDD patients may link to increased arousal in posterior brain regions, possibly reflecting prolonged stress during MDD psychopathology development (Heller et al., 1998). 4.2. Diagnostic precision in psychiatric disorders In psychiatry, diagnosis is inevitably subjective because it typically relies on self-reports and experts’ experiences. In order to induce changes in patient diagnosis and care, the National Institute of Mental Health developed the Research Domain Criteria (RDoC). In particular, the RDoC initiative aims to provide novel ways of qualifying psychiatric disorders based on both neurobiological and behavioral measures (Insel et al., 2010, Cuthbert et al., 2013). Although behavioral, physiological and neuroimaging work on MDD or other mental illness has seen significant advances, direct translation of basic research into clinical applications remains rare. We argue that machine learning techniques could play a key role here since they are instrumental for detecting representative patterns among vast amounts of neural data, offering individualized predictions (Lemm et al., 2011, Helmstaedter, 2015). In the present report, application of a Bayesian linear regression model allowed for an enhanced prediction of depression severity, compared to what can be achieved by conventional analysis. For instance, a significant HAM-D prediction performance based on beta band power was achieved which was not reflected in the correlation analysis. These findings offer new opportunities for evidence-based diagnosis and monitoring of MDD patients. 4.3. Study Limitations There are a few limitations to our study. First, the overall sample was relatively small. It would therefore be valuable to replicate our findings on large cohorts. Future work will also need to examine whether changes in alpha power reflect treatment effects. This could be investigated by testing if posterior alpha activity in MDD patients normalizes to the level of healthy subjects after successful treatment. The reliability and generalizability of the predictions provided by the Bayesian linear regression model also deserve further investigation. Furthermore, results in the current study were obtained using MEG which allowed us to identify the areas in which the oscillatory activity differed in healthy controls compared to MDD patients. While this information is important for understanding which regions are functioning differently in MDD patients, it is not required for estimating depression severity per se. In future work, it would be of practical interest to investigate if EEG data alone also allows for estimating depression severity. Lastly, in addition to the difference in power, connectivity analysis showed higher functional connectivity in MDD patients compared with healthy controls in a broad frequency range (Fingelkurts et al., 2007, Leuchter et al., 2012, Quraan et al., 2014). These differences were most notable in the theta, alpha, and beta bands. The stronger functional coupling in 10 Jiang et al
depressed patients could reflect altered neural communication (Menon, 2011). This also implies that patterns of functional connectivity that might distinguish between depressed patients and healthy controls should be further examined in the future. Moreover, functional connectivity can extend to cross-frequency coupling (CFC) because neuronal oscillations at different frequency bands interact with each other (Jensen et al., 2007, Jiang et al., 2015a). CFC appears to serve a functional role in neuronal computation and communication (Canolty et al., 2010). A recent study in Parkinson’s disease demonstrated large clinically relevant changes in betagamma coupling in the motor cortex and subthalamic nucleus pathway with deep brain stimulation (de Hemptinne et al., 2013). Therefore, future studies are also needed to disentangle potential CFC alterations at the network level in MDD patients. 5. Conclusions The current study provides evidence for how disrupted resting-state posterior alpha activity contributes to characteristic clinical manifestations in MDD and introduced a compelling model that provides an objective individualized MDD severity prediction. This proposed framework could potentially lead to innovations in diagnosis, monitoring and treatment of MDD patients.
Conflict of interest The authors reported no biomedical financial interests or potential conflicts of interest.
Acknowledgements The work was supported by the grants of: National High Technology Research and Development Program of China (863 Program) (2015AA020509); The National Natural Science Foundation of China (81371522, 61372032); Jiangsu Clinical Medicine Technology Foundation (BL2012052, BL2014009).
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Jensen O, Mazaheri A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci. 2010;4:186. Jiang H, Bahramisharif A, van Gerven MAJ, Jensen O. Measuring directionality between neuronal oscillations of different frequencies. NeuroImage. 2015a;118:359-67. Jiang H, van Gerven MA, Jensen O. Modality-specific alpha modulations facilitate long-term memory encoding in the presence of distracters. J Cogn Neurosci. 2015b;27:583-92. Lemm S, Blankertz B, Dickhaus T, Müller KR. Introduction to machine learning for brain imaging. NeuroImage 2011;56:387-99. Leuchter AF, Cook IA, Hunter AM, Cai C, Horvath S. Resting-state quantitative electroencephalography reveals increased neurophysiologic connectivity in depression. PLoS One. 2012;7:e32508. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Meth 2007;164:177-90. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15:483-506. Niedermeyer E. Alpha rhythms as physiological and abnormal phenomena. Int J Psychophysiol 1997;26:31-49. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011:156869. Quraan MA, Protzner AB, Daskalakis ZJ, Giacobbe P, Tang CW, Kennedy SH, et al. EEG power asymmetry and functional connectivity as a marker of treatment effectiveness in DBS surgery for depression. Neuropsychopharmacol 2014;39:1270-81. Romei V, Rihs T, Brodbeck V, Thut G. Resting electroencephalogram alpha-power over posterior sites indexes baseline visual cortex excitability. Neuroreport. 2008;19:203-8. Smart OL, Tiruvadi VR, Mayberg HS. Multimodal approaches to define network oscillations in depression. Biol Psychiatry. 2015;77:1061-70. Stephan KE, Mathys C. Computational approaches to psychiatry. Curr Opin Neurobiol 2014;25:85-92. Vanhatalo J, Riihimäki J, Hartikainen J, Jylänki P, Tolvanen V, Vehtari A. GPstuff: Bayesian modeling with Gaussian Processes. J Mach Learn Res. 2013;14:1175-9. Volf NV, Passynkova NR. EEG mapping in seasonal affective disorder. J Affect Disorders 2002;72:61-9. Watson BO, Buzsáki G. Neural syntax in mental disorders. Biol Psychiatry. 2015;77:998-1000. Weisz N, Wuhle A, Monittola G, Demarchi G, Frey J, Popov T, et al. Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception. Proc Natl Acad Sci U S A. 2014;111:E417-25. Zoon HFA, Veth CPM, Arns M, Drinkenburg WHIM, Talloen W, Peeters PJ, et al. EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with Major Depressive Disorder. J Clin Neurophysiol 2013;30:261-7.
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Table and Figure Legends
Table 1. Demographic information for MDD patients and HC subjects. We did not find differences between the two groups in terms of age and education at p<0.05. Fig. 1. Theta band activity (4-8 Hz) for MDD patients versus healthy controls. Mean theta power was averaged between 4-8 Hz. (A) Topography of mean theta power in MDD patients. (B) Topography of mean theta power in healthy controls. (C) Scalp topography of the group difference in the theta power. T-values were assessed by independent two-sample t-tests between MDD and HC groups on the mean theta band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD patients and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different negative clusters were identified in superior frontal gyrus, middle frontal gryus, precentral gyrus and supplementary motor area (p<0.05).
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Fig. 2. Alpha band activity (8-14 Hz) for MDD patients versus healthy controls. Mean alpha power was averaged between 8-14 Hz. (A) Topography of mean alpha power in MDD patients. (B) Topography of mean alpha power in healthy controls. (C) Scalp topography of the group difference in the alpha power. T-values were assessed by independent two-sample t-tests between MDD and HC groups on the mean alpha band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD patients and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different negative clusters were identified in the occipital lobe, cuneus and paracentral lobule (p<0.05). Fig. 3. Beta band activity (14-30 Hz) for MDD patients versus healthy controls. Mean beta power was averaged between 14-30 Hz. (A) Topography of mean beta power scalp topography in MDD patients. (B) Topography of mean beta power in healthy controls. (C) Scalp topography of the group difference in the beta band power. T-values were assessed by independent twosample t-tests between MDD and HC groups on the mean beta band power. Dots mark sensors belonging to significant clusters as identified by a cluster permutation test (p<0.05). (D) Power spectrum for MDD and healthy controls respectively. These were obtained by averaging over the sensors marked in C. (E) Source reconstruction of the group difference. Statistically significant different positive clusters were identified in supplementary motor area, precentral gyrus, postcentral gyrus, inferior frontal gyrus and middle frontal gyrus (p<0.05). Fig. 4. Spearman correlation between HAM-D value and mean theta band (A), alpha band (B) and beta band (C) power respectively. The corresponding oscillatory band power was averaged over sensors identified in Fig. 1C, Fig. 2C and Fig. 3C respectively. Fig. 5. HAM-D values estimated by the Bayesian linear regression model based on theta (left panels), alpha (middle panels) and beta (right panels) power respectively. (A) HAM-D values as predicted by the Bayesian linear regression model. Predictive performance was evaluated by Spearman correlation between predicted HAM-D values and professionally evaluated HAM-D values. The numbers represented the MDD patient’s ID. (B) Relationship between predicted HAM-D values and ranked individual frequency band power. The error bar represents the credible interval covering 40% of the posterior confidence interval. The red stars represent professionally evaluated HAM-D values. The black line shows the regression line between predicted HAM-D values and individual band power.
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B 0.06
0
Normalized power
A
0
t value
6
−6
Normalized power
D
C
MDD HC
0.025 0.015 0.005 5
E
-0.3 0 0.3 Normalized difference
15 25 35 Frequency (Hz)
B 0.04
0
Normalized power
A
0
t value
4
−4
Normalized power
D
C
MDD HC
0.05 0.03 0.01 5
15 25 35 Frequency (Hz)
E
-0.4 0 0.4 Normalized difference
B 0.015
0 D
0 −4
t value
4
Normalized power
C
Normalized power
A
0.035
MDD HC
0.025 0.015 0.005 5
15 25 35 Frequency (Hz)
E
-0.2 0 0.2 Normalized difference
(A)
(C)
(B) r=−0.06; p=0.80
r=−0.65; p=0.001
r=0.18; p=0.43
HAM-D value
35 30 25 20 15 0.005 0.015 0.025 Normalized theta power
0.01 0.03 0.05 Normalized alpha power
0.005 0.01 0.015 Normalized beta power
A
Professional HAM-D value
r=0.38 ; p=0.08 35 20
25
8
20
22 8 4 1 6 14 18 17 2 3 12 9 21 15 5 11 10 13 16 7
16
19
20
r=0.56 ; p=0.007
20 22 4 8 71 18 6 14 17 3 12 29 5 15 13 21 10 11
22
1 7 4 6 18 14 3 12 17 2 15 9 5 21 11 13 10 16
15 10
r=0.68 ; p=0.0005
19
30
40
10
20
19
30
40
10
20
30
Model estimated HAM-D value Model estimated HAM-D value
B r=−0.40; p=0.07
r=−0.57 ; p=0.006
r=0.32 ; p=0.14
45 35 25 15 5
0
5 10 15 20 Rank of theta power
0
5 10 15 20 Rank of alpha power
0
5 10 15 20 Rank of beta power
40
MDD patients (n = 22)
HC (n = 22)
Age (years)
33.3±7.8
29.9±7.1
Gender (Male/Female)
11/11
12/10
Education (years)
13.7±1.6
14.7±1.5
HAM-D value
26.4±4.1
-
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