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Lateral Differences in the Default Mode Network in Healthy Controls and Schizophrenia Patients
Nathan Swanson
1 The Mind Research Network, Albuquerque, New Mexico 87106
Tom Eichele
1 The Mind Research Network, Albuquerque, New Mexico 87106
3 Dept. of Biological and Medical Psychology, University of Bergen, Bergen, Norway
Godfrey Pearlson
5 Olin Neuropsychiatry Research Center, Hartford, CT, 06106
6 Dept. of Psychiatry, Yale University, New Haven, CT, 06519
Kent Kiehl
1 The Mind Research Network, Albuquerque, New Mexico 87106
4 Dept. of Psychology, University of New Mexico, Albuquerque, New Mexico, 87131
Vince D Calhoun
1 The Mind Research Network, Albuquerque, New Mexico 87106
2 Dept. of ECE, University of New Mexico, Albuquerque, New Mexico, 87131
5 Olin Neuropsychiatry Research Center, Hartford, CT, 06106
6 Dept. of Psychiatry, Yale University, New Haven, CT, 06519
Abstract
We investigate lateral differences in the intrinsic fluctuations comprising the default mode network (DMN) for healthy controls (HC) and patients with schizophrenia (SZ), both during rest and during an auditory oddball (AOD) task. Our motivation for this study comes from multiple prior hypotheses of disturbed hemispheric asymmetry in schizophrenia and more recently observed lateral abnormalities in the DMN for SZ during AOD. We hypothesized that significant lateral differences would be found in HC during both rest and AOD, and SZ would show differences from HC due to hemispheric dysfunction. Our study examined 28 healthy controls and 28 SZ outpatients. The scans were conducted on a Siemens Allegra 3T dedicated head scanner. There were numerous cross-group lateral fluctuations that were found in both AOD and rest. During the resting state, within-group results showed the largest functional asymmetries in the inferior parietal lobule for HC whereas functional asymmetries were seen in posterior cingulate gyrus for SZ. Comparing asymmetries between groups, in resting state and/or performing AOD, areas showing significant differences between group (HC > SZ) included inferior parietal lobule and posterior cingulate. Our results support the hypothesis that schizophrenia is characterized by abnormal hemispheric asymmetry. Secondly, the number of similarities in cross-group AOD and rest data suggests that neurological disruptions in SZ that may cause evoked symptoms are also detectable in SZ during resting conditions. Furthermore, the results suggest a reduction in activity in language-related areas for SZ compared to HC during rest.
INTRODUCTION
The human brain‘s default mode network (DMN) has been the focus of considerable study in recent years (Bai, et al. 2008; Buckner, et al. 2008; Raichle, et al. 2001; Zhou, et al. 2007). The DMN has been identified as consisting of regions including ventral medial prefrontal cortex, posterior cingulate/retrosplenial cortex, inferior parietal lobule, and dorsal medial prefrontal cortex (Buckner, et al. 2008). This network consistently shows signal decreases during task performance. While the DMN is primarily identified during rest, it also manifests with systematic task/or event-related deactivations that are related to behavioral variability (Eichele, et al. 2008; Singh and Fawcett 2008; Weissman, et al. 2006). While resting state activity has long been known in neurophysiology, the first imaging evidence of spatially organized brain activity at rest originated with Ingvar (Ingvar 1979). DMN activity was not fully revealed until higher resolution positron emission tomography yielded images consistently showing DMN activity (Mezrow, et al. 1995). However, the nature of the DMN remained relatively unexplored until Raichle’s seminal work (Raichle, et al. 2001) This opened a broad range of related topics – from the standpoint of understanding the intrinsic nature of the DMN, the interaction between intrinsic and evoked activity (Fox, et al. 2006; Fox, et al. 2005), the relationship between electrophysiologic and hemodynamic activity (Eichele, et al. 2005; Laufs, et al. 2003; Mantini, et al. 2007b; Scheeringa, et al. 2008), and associations with various clinical states (Bai, et al. 2008; Calhoun, et al. 2008; Garrity, et al. 2007; Greicius, et al. 2004; Kim, et al. 2009).
Since then, the DMN has been examined in more detail using functional magnetic resonance imaging (fMRI) using a variety of approaches, including seed-based correlation (Vincent, et al. 2006) and independent component analysis (ICA) (Calhoun, et al. 2001; Calhoun, et al. 2008) as well as concurrent EEG-fMRI (Eichele, et al. 2005; Laufs, et al. 2003; Mantini, et al. 2007a; Scheeringa, et al. 2008). While the majority of fMRI studies look at evoked responses, the DMN, by contrast, has larger blood-oxygen-level dependent (BOLD) signals when a subject is resting quietly (Calhoun, et al. 2008; Kim, et al. 2009; Raichle, et al. 2001). Although a number of studies have examined brain activity and functional connectivity during resting state in schizophrenia, not specific to the DMN (Calhoun, et al. 2008; Liang, et al. 2006; Liu, et al. 2006; Liu, et al. 2008), there has been little work on the laterality of functional brain networks in healthy controls and even less so in patients with schizophrenia. Recent work suggests that laterality of intrinsic functional brain networks is an important consideration (Liu, et al. 2009). The focus of the current study is to compare the lateral differences between DMN regions in the left and right hemispheres of the brain for both HC and SZ groups. The human brain has marked structural asymmetries, especially in regions that play a significant role in language (Barta, et al. 1990; Frederikse, et al. 2000). Many functional regional networks, such as those mediating motor, speech, working memory and attentional functions are organized in a lateralized fashion as well, where intrinsic activity is consistently stronger in one hemispheric region than in its contralateral homologue (Tommasi 2009).
Disturbances in hemispheric lateralization are widely reported in schizophrenia, including electrophysiological (Gruzelier, et al. 1999), structural (Schlaepfer, et al. 1994), and functional (Pearlson, et al. 1996; Ross and Pearlson 1996) domains. There has been a long-standing hypothesis linking schizophrenia with a lateral dysfunction between the two hemispheres. For example, it has been empirically verified that SZ patients are slower than HC in responding to stimuli in a way that requires cross-hemispheric communication in the brain (Barnett, et al. 2005). Many functions disturbed in schizophrenia, such as language, are among the most asymmetrically distributed in the human cortex. This had lead some researchers, of whom Crow is one of the best-known, to hypothesize that disturbed cerebral asymmetry is fundamental to the etiology of the disorder (Crow 2008). Although these disturbances are clearly observed in behavioral tests, little is known about whether the neurological disruptions that cause these disturbances are present while at rest as well. Indeed, a characterization of lateralization patterns in intrinsic and evoked brain activity in SZ may help give a more comprehensive understanding of SZ.
As the DMN spans both brain hemispheres, it is natural to inquire whether significant laterality effects exist for HC and/or SZ during relaxed resting and task performance, and whether significant between-group laterality differences are detectable. Previous studies report SZ-related cognitive dysfunction in domains linked to the DMN (Bluhm, et al. 2007; Garrity, et al. 2007; Harrison, et al. 2007; Zhou, et al. 2007). Although there have been some reports of lateral differences of individual regions within the DMN (Stevens, et al. 2005; Williamson 2007; Wimber, et al. 2008), to our knowledge no study to date has specifically examined the lateral differences of the DMN as a whole, nor discussed lateral asymmetry findings within this context.
In this study, we examined laterality differences in BOLD signals acquired during two fMRI paradigms, during an auditory oddball (AOD) task and during rest, within hemodynamic responses from the DMN in 28 HC and 28 SZ patients. Just over two-thirds of the data set(s) were used in a previous study (Calhoun, et al. 2008) using different analysis methods. We focus on examining the voxel-wise lateral differences (Stevens, et al. 2005) of independent component maps that reflect the DMN in these groups.
The AOD task was selected as means to observe the degree to which the lateral differences were modulated in the presence of a task (Calhoun, et al. 2008). In this case, the DMN shows the reduced overall activity in response to the task stimuli. This allows us to clearly see the similarities of the lateral fluctuations across task and rest in the DMN. We hypothesized that HC would show a L>R DMN lateral bias when contrasted to SZ, due to SZ-related impairments in higher-order cognitive function that are localized to the left hemisphere, including language, during rest (Cohen, et al. 2000; S. Baron-Cohen 2000). We also hypothesized that there would be significantly decreased lateral differences in the DMN in schizophrenia, supporting the hypothesis that schizophrenia patients have cross-hemispheric disruptions (Andreasen, et al. 2008) that interfere with cognitive activity. Finally, we conjectured that there would be similar neurological abnormalities between SZ and HC observed across task due to fundamental disruptions present in SZ both during task and while at rest.
METHODS
Participants
Participants consisted of 28 (5 females) healthy controls and 28 (5 females) chronic schizophrenia outpatients, all of whom gave written, informed, IRB-approved consent at Hartford Hospital and were compensated for participation. Schizophrenia was diagnosed according to the DSM-IV TR criteria on the basis of a structured clinical interview (SCID) (First MB 1995) administered by a research nurse and by review of the medical records. All of the patients were chronic schizophrenia patients (PANSS: positive score 16±7; negative score 16±5.) and all of them were on medications (including abilify(6), ambient(3), sonata(3), ativan(3), clozaril(2), cogentin(7), depakote(4), desyrel(3), klonopin(3), prolixin(2), risperdal(8), seroquel(6), zyprexa(2)). Exclusion criteria included auditory or visual impairment, mental retardation (full scale IQ < 70), traumatic brain injury with loss of consciousness greater than 15 min, and presence or history of any CNS neurological illness. Participants were also excluded if they met criteria for alcohol or drug dependence within the past 6 months or showed a positive urine toxicology screen (screening was for cocaine, opioids including methadone, cannabis, amphetamine, barbiturates, PCP, propoxyphene, and benzodiazepines) on the day of scanning. Patients were slightly older than controls (SZ: mean age=36.8, range 19–59; HC: mean age=28.9, range 18–57). All but four patients and one control were right handed. All participants were able to perform the AOD task successfully during practice prior to the scanning session. Healthy participants were free of any DSM-IV TR Axis I disorder or psychotropic medication.
Experimental Design
All participants were scanned during both an auditory oddball task and while at rest. The two scans were randomly ordered. The AOD consists of detecting an infrequent sound within a series of regular and different sounds. The task consisted of two runs of auditory stimuli presented to each participant by a computer stimulus presentation system via insert earphones embedded within 30-dB sound attenuating MR compatible headphones. The standard stimulus was a 500-Hz tone, the target stimulus was a 1,000-Hz tone, and the novel stimuli consisted of non-repeating random digital noises (e.g., tone sweeps, whistles). The target and novel stimuli each occurred with a probability of 0.10; the standard stimuli occurred with a probability of 0.80. The stimulus duration was 200 ms with a 1000, 1500, or 2000 ms inter-stimulus interval randomly chosen with equal probability. All stimuli were presented at 80 dB above the standard threshold of hearing. All participants reported that they could hear the stimuli and discriminate them from the background scanner noise. Prior to entry into the scanning room, each participant performed a practice block of 10 trials to ensure understanding of the instructions. The participants were instructed to respond as quickly and accurately as possible with their right index finger every time they heard the target stimulus and not to respond to the non-target stimuli or the novel stimuli. An MRI compatible fiber-optic response device (Lightwave Medical, Vancouver, BC) was used to acquire behavioral responses for both tasks. The stimulus paradigm data acquisition techniques and previously found stimulus-related activation are described more fully elsewhere (Kiehl, et al. 2005). Participants also performed a 5-min resting state scan and were instructed to rest quietly without falling asleep with their eyes open without fixation.
Image Acquisition
Scans were acquired at the Olin Neuropsychiatry Research Center at the Institute of Living/Hartford Hospital on a Siemens Allegra 3T dedicated head scanner equipped with 40 mT/m gradients and a standard quadrature head coil. The functional scans were acquired transaxially using gradient-echo echo-planar-imaging with the following parameters: repeat time (TR) 1.50 s, echo time (TE) 27 ms, field of view 24 cm, acquisition matrix 64×64, flip angle 70°, voxel size 3.75×3.75×4 mm3, slice thickness 4 mm, gap 1 mm, 29 slices, ascending acquisition. Six “dummy” scans were acquired at the beginning to allow for longitudinal equilibrium, after which the paradigm was automatically triggered to start by the scanner. The AOD consisted of two 8-min runs and the resting state scan consisted of one 5-min run.
Preprocessing
Data were preprocessed using the SPM5 (http://www.fil.ion.ucl.ac.uk/spm/software/spm5/) software package. Data were motion corrected using INRIalign—a motion correction algorithm unbiased by local signal changes (Freire, et al. 2002), spatially normalized into the standard Montreal Neurological Institute space using a symmetric template (Stevens, et al. 2005), and spatially smoothed with a 10×10×10 mm3 full width at half-maximum Gaussian kernel. For reporting in tabular form, coordinates were converted to the standard space of Talairach and Tournoux (Talairach and Tournoux 1988). Following spatial normalization, the data (originally acquired at 3.75×3.75×4 mm3) were resliced to 3×3×3 mm3, resulting in 53×63×46 voxels. No temporal filtering of the data was performed. Group spatial ICA (Calhoun, et al. 2009) was used to decompose all the data into components using the GIFT software (http://icatb.sourceforge.net/) as follows. Dimension estimation, to determine the number of components, was performed using the minimum description length criteria, modified to account for spatial correlation (Li, et al. 2007). Using this approach, the auditory oddball and the resting data were both estimated to have 19 components. A more complete explanation of the ICA algorithm and its theoretical constructs, as well as choosing the appropriate number of components, can be found elsewhere (Bell and Sejnowski 1995; Calhoun, et al. 2001; Stevens, et al. 2007). Once the estimate of the number of components was determined, we applied ICA to the data using group ICA (Calhoun, et al. 2009) as follows. Data from all subjects were concatenated and this aggregate data set reduced to 19 temporal dimensions using PCA, followed by an independent component estimation using the infomax algorithm (Bell and Sejnowski 1995). It is important to note that group ICA is performed on all the subjects at once, and significant between-group differences are determined by a second level analysis of the ICA results.
Creation of Spatial Maps, T-tests, and Comparisons
For each participant, spatial maps were then reconstructed and converted to Z values, hence the intensities of the image provide a relative strength of the degree to which the component contributes to the data (Beckmann, et al. 2005). For each data set each of the 19 components were manually inspected to identify the default component. The default mode component was identified by spatially correlating all components with a default mode mask generated by WFU Pickatlas developed at Wake Forest Pharmaceuticals University (http://www.fmri.wfubmc.edu/) (Maldjian, et al. 2003). This mask contained the posterior parietal cortex (Brodmann’s area 7), the frontal pole (Brodmann’s area 10), and the occipitoparietal junction (Brodmann’s area 39), as well as the posterior cingulate and precuneus (Raichle, et al. 2001). This template was smoothed with a 3-mm3 Gaussian kernel. The component that (spatially) correlated most significantly with the template was selected as the default mode component. For each subject, default mode components from each run of the task were then converted to z values and averaged to produce one default mode component. For the DMN component, we also tested for within group differences for AOD and the resting state and did not find any significant difference (p<.05, FDR corrected).
A voxel-wise laterality map was created for each of the respective images by subtracting the image from itself after flipping in the left/right direction (Stevens, et al. 2005). Only in-brain voxels were included in the analysis. The lateral maps were computed using the LUI software (http://mialab.mrn.org/software/). Next, a voxelwise one-sample t-test was computed for the lateralized images for each group, both for auditory and rest (Calhoun, et al. 2001). Note that the voxel-wise laterality map avoids the problem of a laterality index (which requires voxel counting, and thus is sensitive to the threshold). 2-sample t-tests were also calculated across group. All results were tested using a significance threshold of p < 0.05, false discovery rate corrected (Genovese, et al. 2002). Figure 1 gives an illustration of our data processing pipeline. Both the correlated (positive) and anti-correlated (negative) DMN regions (Fox, et al. 2005) were included in our results (see Figure 2 for details). Note that the hemispheres show R>L on the left side of the brain and L>R on the right side of the brain for in-group results. The cross-group maps show (R-L) HC > (R-L) SZ on the left side of the images and (R-L) SZ > (R-L) HC on the right side of the images. These images can also be symmetrically interpreted as (L-R) SZ > (L-R) HC on the left side and (L-R) HC > (L-R) SZ on the right side. We compared the SZ data to patient symptom profiles and behavioral results, but found no significant results to report (p < .05, false discovery rate corrected).
The data analysis pipeline of our results. Preprocessing including motion correction, spatially normalization and spatial smooth were computed using SPM5. GIFT was used to implement ICA. Default mode component was identified by spatially correlating all components with a default mode mask. Finally, the lateral maps were computed using the LUI software.
Control, patient, and control versus patient laterality maps for default mode network including both correlated (positive) and anti-correlated (negative) DMN regions (Fox, et al. 2005) at rest and during an auditory oddball task (p < .05 FDR corrected).
RESULTS
We present the significant lateralized findings from our 1-sample and 2-sample t-tests, both for rest and AOD. Figure 2 illustrates the maps of the significant lateral differences of the DMN, both in-group and cross-group, for rest and AOD. The SZ patients had a success average of 98.3% and an average total reaction time of 464ms for performing the AOD task, while HC had a 99.8% success rate average, with an average total reaction time of 394ms. Two-sample T-tests indicated the group differences were significant (for success rate, t=2.465, p=0.017; for reaction time, t=− 3.049, p=0.004), both patients and controls were able to perform the task well. See Table 1 for a full listing of significant in-group lateral differences and their coordinates in the Montreal Neurologic Institute (MNI) standard space. See Table 2 for the respective cross-group lateral differences.
Table I
The in-group MNI coordinates and anatomic labels of significant lateral differences1.
| Region (Positive) | Brodmann | Vol cm3 | Max Activation (R>L/L>R) | Region (Negative) | Brodmann | Vol cm3 | Max Activation (R>L/L>R) |
|---|---|---|---|---|---|---|---|
| Rest Healthy Control | Rest Healthy | ||||||
| Inferior Parietal Lobule | 39 7 40 | 0.2/1.7 | 3.5(53,− 42,24)/8.7(− 36,- | Middle Frontal Gyrus | 6 | 1.0/0.0 | 5.1(36,6,52)/na |
| Middle Temporal Gyrus | 39 | 0.0/1.9 | na/6.6(− 39,− 66,23) | Middle Temporal | 37 | 0.3/0.0 | 4.4(39,− 69,20)/na |
| Posterior Cingulate | 23 29 30 31 | 1.0/1.0 | 5.6(6,− 43,21)/4.6(− 12,- | Medial Frontal Gyrus | 0.1/0.0 | 4.4(9,20,46)/na | |
| Superior Temporal Gyrus | 22 39 13 | 1.2/0.1 | 5.5(59,− 51,19)/5.3(− 36,- | Fusiform Gyrus | 37 | 0.4/0.0 | 4.3(33,− 59,− 7)/na |
| Cingulate Gyrus | 23 31 24 | 3.1/0.0 | 5.2(3,− 22,31)/na | Parahippocampal Gyrus | 37 | 0.2/0.0 | 4.1(33,− 38,− 8)/na |
| Cuneus | 18 30 | 0.0/1.0 | na/5.0(− 12,− 69,20) | Precuneus | 19 | 0.1/0.0 | 4.1(33,− 83,37)/na |
| Supramarginal Gyrus | 40 | 0.7/0.1 | 4.9(59,− 51,22)/3.7(− 36,- | Middle Occipital Gyrus | 18 19 | 0.0/0.6 | na/4.0(− 21,− 96,8) |
| Medial Frontal Gyrus | 10 11 | 0.0/0.4 | na/4.5(− 9,43,− 10) | Inferior Temporal Gyrus | 37 20 | 0.2/0.0 | 3.9(53,− 50,− 13)/na |
| Inferior Temporal Gyrus | 20 | 0.1/0.0 | 4.0(59,− 7,− 22)/na | Lingual Gyrus | 0.0/0.3 | na/3.8(− 15,− 87,− 1) | |
| Fusiform Gyrus | 20 | 0.1/0.0 | 4.0(59,− 7,− 25)/na | Cuneus | 18 19 | 0.0/0.4 | na/3.7(− 21,− 96,5) |
| Anterior Cingulate | 32 | 0.0/0.3 | na/3.9(− 6,40,− 10) | Superior Frontal Gyrus | 10 9 | 0.4/0.0 | 3.5(6,20,49)/na |
| Paracentral Lobule | 31 6 5 | 0.2/0.0 | 3.7(3,− 21,43)/na | Inferior Frontal Gyrus | 0.1/0.0 | 3.5(18,22,− 16)/na | |
| Cerebellum | 0.0/0.0 | 3.2(15,− 38,− 8)/na | |||||
| Caudate | Caudate Tail | 0.0/0.0 | 3.2(33,− 32,2)/na | ||||
| Inferior Parietal Lobule | 0.1/0.0 | 3.2(42,− 44,55)/na | |||||
| Postcentral Gyrus | 2 | 0.0/0.0 | 3.1(50,− 27,40)/na | ||||
| Rest Schizophrenic | Rest Schizophrenic | ||||||
| Posterior Cingulate | 23 30 29 | 0.9/0.0 | 5.6(6,− 43,21)/na | Superior Frontal Gyrus | 6 | 1.6/0.0 | 7.4(6,20,52)/na |
| Cingulate Gyrus | 23 24 31 | 1.2/0.0 | 5.4(3,− 16,31)/na | Medial Frontal Gyrus | 32 | 0.2/0.0 | 4.5(9,20,46)/na |
| Supramarginal Gyrus | 40 | 0.4/0.0 | 4.5(56,− 45,24)/na | Middle Frontal Gyrus | 6 | 0.2/0.0 | 4.5(33,8,49)/na |
| Inferior Parietal Lobule | 40 | 0.2/0.0 | 4.3(56,− 42,27)/na | Parahippocampal Gyrus | 37 | 0.0/0.1 | 3.6(30,− 44,− 10)/4.3(− 24,− 49,5) |
| Superior Frontal Gyrus | 8 | 0.0/0.1 | na/3.8(− 24,25,43) | Fusiform Gyrus | 37 | 0.3/0.0 | 4.3(50,− 53,− 15)/na |
| Middle Frontal Gyrus | 8 | 0.0/0.0 | na/3.5(− 21,28,40) | Inferior Temporal Gyrus | 20 | 0.1/0.0 | 3.8(50,− 56,− 12)/na |
| Cingulate Gyrus | 0.0/0.1 | na/3.8(− 18,13,30) | |||||
| Lingual Gyrus | 0.0/0.1 | na/3.7(− 24,− 52,0) | |||||
| Inferior Frontal Gyrus | 0.0/0.0 | 3.6(50,23,− 6)/na | |||||
| AOD Healthy Control | AOD Healthy Control | ||||||
| Inferior Parietal Lobule | 40 39 7 | 0.9/0.6 | 6.4(50,− 39,27)/4.4(− 36,- | Middle Frontal Gyrus | 46 10 9 6 | 0.0/4.4 | na/6.8(− 42,36,18) |
| Supramarginal Gyrus | 40 | 2.8/0.1 | 5.6(50,− 42,30)/3.7(− 39,- | Inferior Frontal Gyrus | 46 47 6 44 9 13 | 0.0/3.6 | na/5.7(− 42,33,12) |
| Cingulate Gyrus | 24 23 31 | 4.7/0.0 | 5.5(6,− 7,28)/na | Fusiform Gyrus | 37 | 0.8/0.0 | 5.0(36,− 44,− 8)/na |
| Superior Temporal Gyrus | 22 13 39 | 2.2/0.1 | 5.1(59,− 54,19)/4.6(− 39,- | Parahippocampal Gyrus | 19 37 | 0.8/0.0 | 4.9(36,− 44,− 5)/na |
| Middle Temporal Gyrus | 39 21 | 0.1/1.8 | 4.0(56,− 4,− 20)/4.9(− 39,- | Inferior Parietal Lobule | 40 | 0.0/1.4 | na/4.8(− 53,− 41,52) |
| Anterior Cingulate | 32 | 0.0/0.2 | na/4.5(− 6,40,− 10) | Medial Frontal Gyrus | 6 | 0.0/0.3 | na/4.8(− 21,6,52) |
| Medial Frontal Gyrus | 11 10 9 6 | 0.6/0.9 | 4.1(6,56,14)/4.5(− 6,40,− 12) | Insula | 47 13 | 0.0/1.5 | na/4.6(− 33,17,− 1) |
| Posterior Cingulate | 23 30 29 31 | 1.2/1.0 | 4.4(3,− 40,24)/3.7(− 15,− 61,9) | Superior Frontal Gyrus | 9 6 | 0.0/0.4 | na/4.3(− 42,37,31) |
| Inferior Temporal Gyrus | 21 | 0.2/0.0 | 4.2(59,− 7,− 17)/na | Lingual Gyrus | 18 19 | 0.5/0.0 | 4.3(21,− 70,1)/na |
| Cuneus | 18 30 | 0.0/0.4 | na/3.8(− 12,− 61,6) | Middle Temporal Gyrus | 37 | 0.5/0.0 | 3.9(56,− 50,− 10)/na |
| Paracentral Lobule | 31 6 | 0.5/0.0 | 3.6(3,− 21,43)/na | Postcentral Gyrus | 40 | 0.0/0.1 | na/3.8(− 56,− 35,49) |
| Lingual Gyrus | 18 | 0.0/0.1 | na/2.9(− 12,− 55,3) | Precentral Gyrus | 6 44 | 0.0/0.4 | na/3.7(− 45,1,28) |
| Fusiform Gyrus | 0.0/0.0 | 2.8(56,− 4,− 23)/na | Middle Occipital Gyrus | 0.4/0.0 | 3.7(24,− 73,4)/na | ||
| Cingulate Gyrus | 24 | 0.0/0.1 | na/3.6(− 18,− 1,47) | ||||
| Inferior Temporal Gyrus | 20 | 0.1/0.0 | 3.3(56,− 50,− 13)/na | ||||
| AOD Schizophrenic | AOD Schizophrenic | ||||||
| Cingulate Gyrus | 23 24 31 | 1.4/0.0 | 6.0(3,− 19,31)/na | Insula | 13 47 | 0.0/2.8 | na/5.4(− 39,3,0) |
| Precuneus | 19 39 7 31 23 | 2.7/3.0 | 4.5(12,− 65,45)/5.3(− 39,- | Middle Frontal Gyrus | 46 9 10 | 0.0/4.7 | na/5.1(− 45,33,26) |
| Inferior Temporal Gyrus | 20 21 | 0.4/0.0 | 5.1(59,− 7,− 22)/na | Superior Occipital | 19 | 0.3/0.0 | 4.2(42,− 80,26)/na |
| Fusiform Gyrus | 20 | 0.2/0.0 | 5.0(59,− 7,− 25)/na | Superior Temporal | 38 22 | 0.1/0.2 | 3.7(50,17,− 11)/4.1(− 39,2,− 13) |
| Inferior Parietal Lobule | 39 40 7 | 0.3/0.5 | 3.8(59,− 51,38)/4.7(− 42,- | Superior Frontal Gyrus | 9 10 | 0.0/0.8 | na/4.1(− 45,37,31) |
| Superior Temporal Gyrus | 22 39 13 | 0.8/0.1 | 4.6(62,− 51,19)/4.2(− 42,- | Inferior Frontal Gyrus | 47 46 45 44 | 0.2/1.3 | 3.8(50,20,− 9)/3.9(− 50,36,12) |
| Supramarginal Gyrus | 40 | 1.6/0.1 | 4.6(59,− 51,33)/3.2(− 42,- | Precentral Gyrus | 44 6 | 0.0/0.8 | 3.5(24,− 15,48)/3.6(− 50,6,8) |
| Middle Temporal Gyrus | 39 21 | 0.0/1.3 | 3.9(56,− 4,− 20)/4.4(− 42,- | Inferior Temporal Gyrus | 37 | 0.0/0.2 | na/3.6(− 45,− 70,1) |
| Posterior Cingulate | 30 23 29 31 | 0.6/0.9 | 3.9(9,− 46,22)/3.9(− 3,− 58,8) | Middle Occipital Gyrus | 0.0/0.5 | na/3.4(− 45,− 73,1) | |
| Cuneus | 30 18 | 0.0/0.2 | na/3.4(− 6,− 61,9) | Precuneus | 19 | 0.2/0.0 | 3.4(27,− 80,34)/na |
| Middle Frontal Gyrus | 0.0/0.1 | na/3.1(− 36,19,38) | Middle Temporal Gyrus | 19 | 0.1/0.0 | 3.4(42,− 80,23)/na | |
| Lingual Gyrus | 18 | 0.0/0.0 | na/3.1(− 9,− 58,3) | Cerebellum | 0.1/0.0 | 3.2(15,− 41,− 8)/na | |
| Cuneus | 19 | 0.2/0.0 | 3.1(27,− 83,35)/na | ||||
| Inferior Occipital Gyrus | 0.0/0.0 | na/3.0(− 45,− 76,− 1) | |||||
| Parahippocampal Gyrus | 37 | 0.0/0.0 | 3.0(27,− 44,− 8)/na | ||||
| Postcentral Gyrus | 0.0/0.0 | na/2.9(− 65,− 5,17) | |||||
Table II
The cross-group MNI coordinates and anatomic labels of significant lateral differences.
| Region (Positive) | Brodmann Area | Vol cm3 | Max Activation (R>L/L>R) | Region (Negative) | Brodmann Area | Vol cm3 | Max Activation (R>L/L>R) |
|---|---|---|---|---|---|---|---|
| Rest HC > SZ | Rest HC > SZ | ||||||
| Inferior Parietal Lobule | 7 40 39 | 0.0/0.8 | na/3.1(− 39,− 65,47) | Lingual Gyrus | 19 | 0.2/0.0 | 3.5(24,− 52,0)/na |
| Cuneus | 18 | 0.0/0.4 | na/2.5(− 3,− 72,17) | Superior Frontal Gyrus | 6 8 9 | 0.1/1.0 | 2.2(33,51,28)/3.4(− 6,20,54) |
| Cingulate Gyrus | 31 | 0.0/0.1 | na/2.5(− 12,− 45,41) | Parahippocampal Gyrus | 19 | 0.2/0.0 | 3.3(24,− 49,5)/na |
| Middle Frontal Gyrus | 0.1/0.0 | 2.4(39,19,38)/na | Middle Occipital Gyrus | 19 18 | 0.0/0.2 | 2.5(27,− 58,3)/3.0(− 33,− 92,16) | |
| Paracentral Lobule | 5 | 0.1/0.0 | 2.4(3,− 32,54)/2.3(− 9,− 41,49) | Superior Temporal Gyrus | 38 | 0.0/0.2 | na/3.0(− 48,17,− 13) |
| Posterior Cingulate | 31 | 0.0/0.2 | na/2.3(− 12,− 58,14) | Cingulate Gyrus | 24 32 | 0.0/0.1 | na/2.7(− 12,2,47) |
| Medial Frontal Gyrus | 6 | 0.0/0.0 | na/2.2(− 3,− 21,51) | Medial Frontal Gyrus | 6 | 0.0/0.2 | na/2.6(− 12,2,50) |
| Anterior Cingulate | 32 | 0.0/0.0 | 2.1(3,36,23)/na | Inferior Frontal Gyrus | 10 | 0.1/0.1 | 2.2(53,36,12)/2.5(− 45,20,− 11) |
| Superior Temporal Gyrus | 22 | 0.0/0.0 | 2.1(62,− 49,16)/na | Middle Frontal Gyrus | 0.0/0.2 | 2.3(30,48,34)/2.4(− 48,46,− 5) | |
| Fusiform Gyrus | 0.0/0.0 | 2.2(33,− 62,− 7)/na | |||||
| Postcentral Gyrus | 1 | 0.0/0.0 | 2.2(56,− 15,45)/na | ||||
| Inferior Occipital Gyrus | 0.0/0.0 | 2.1(30,− 79,− 6)/na | |||||
| AOD HC > SZ | AOD HC > SZ | ||||||
| Cingulate Gyrus | 0.1/0.0 | 3.1(12,− 19,26)/na | Middle Occipital Gyrus | 19 | 2.1/0.0 | 4.0(30,− 70,3)/na | |
| Middle Frontal Gyrus | 8 | 0.3/0.0 | 3.0(36,22,38)/na | Lingual Gyrus | 19 18 | 1.0/0.0 | 4.0(24,− 70,1)/na |
| Medial Frontal Gyrus | 10 11 | 0.0/0.3 | na/3.0(− 9,40,− 10) | Inferior Parietal Lobule | 40 | 0.0/0.8 | na/3.5(− 56,− 33,40) |
| Anterior Cingulate | 32 | 0.0/0.1 | na/2.9(− 6,40,−10) | Inferior Occipital Gyrus | 18 19 | 0.4/0.0 | 3.4(33,−73,−4)/na |
| Precentral Gyrus | 9 | 0.1/0.0 | 2.9(36,22,35)/na | Middle Temporal Gyrus | 0.2/0.0 | 3.4(39,−49,8)/na | |
| Fusiform Gyrus | 20 | 0.0/0.0 | na/2.9(−59,−7,−25) | Inferior Temporal Gyrus | 0.2/0.0 | 3.2(45,−73,−1)/na | |
| Cuneus | 19 | 0.0/0.4 | na/3.1(−24,−86,29) | ||||
| Inferior Frontal Gyrus | 47 | 0.0/0.4 | na/3.1(−53,17,−6) | ||||
| Superior Temporal Gyrus | 38 22 | 0.1/0.3 | 2.9(39,2,−13)/3.1(−53,14,−6) | ||||
| Fusiform Gyrus | 37 | 0.3/0.0 | 3.1(36,−44,−8)/na | ||||
| Parahippocampal Gyrus | 19 | 0.1/0.0 | 2.8(36,−44,−5)/na | ||||
| Insula | 0.1/0.0 | 2.8(39,3,3)/na | |||||
| Postcentral Gyrus | 40 | 0.0/0.1 | na/2.7(−56,−35,49) | ||||
Rest
For HC, there were significant (L>R) activity differences in inferior parietal lobule (IPL), cuneus and middle temporal gyrus. The IPL had a particularly marked difference (see top-left map of Figure 2). For (R>L), the superior temporal gyrus and the middle frontal gyrus were also significantly asymmetric. For SZ significant (R>L) differences included posterior cingulate, cingulate gyrus and superior frontal gyrus. For HC > SZ (L>R), inferior parietal and superior frontal gyri showed lateral differences between groups.
AOD
In HC, there were (L>R) differences in middle and inferior frontal gyri, as well as supramarginal gyrus for R>L. For SZ (L>R), insula and middle frontal gyrus showed lateral differences. For R>L, the cingulate gyrus were significantly asymmetric. For HC > SZ (L>R), the inferior parietal lobule showed asymmetry and for (R>L), middle occipital gyrus and lingual gyrus had significant lateral differences.
DISCUSSION
We explored lateral differences of the DMN in HC and SZ during rest and while performing an AOD task. The goals of this work were (1) test our hypothesis for any L>R lateral bias in HC, as well as test for significant lateral differences between groups (2) to use these lateral differences to explore the nature of schizophrenia during rest and a defined task. The AOD task was selected as means to observe the degree to which the lateral differences were modulated in the presence of a task (Calhoun, et al. 2008). This allows us to clearly see the similarities of the lateral differences across task and rest in the DMN. Our results are consistent with the position that at least some of the neurological disruptions that may cause symptoms of SZ observed during task are also present during rest, due to the similar lateral differences in various regions during task and rest. This is consistent with the notion of “resting-state”-related symptoms of SZ, which has been previously suggested. In this work we attempted to further establish this notion by identifying similar neurological abnormalities across task and rest, which are found by examining the lateral hemispheric differences. This may help us better understand the effects of these rest-state disruptions by comparing them to similar disruptions observed during the task. We discuss several of these differences between HC and SZ in non task-evoked activity, and we suggest that these differences are possibly related to internal language processing and level of wakefulness.
Our results showed a significant L>R bias in the DMN of HC, particularly marked in inferior parietal lobule (IPL) at rest. The IPL is normally asymmetric and this asymmetry is disturbed in schizophrenia (Frederikse, et al. 2000). The left IPL has been shown to be involved in language formulation, and our finding is consistent with our prediction and suggests that HC, more than SZ, may be involved in higher-order cognitive function during the resting scans, including language (Cohen, et al. 2000; S. Baron-Cohen 2000). SZ subjects, on the other hand, showed significantly less of this difference. These same characteristics are present during AOD performance as well, suggesting that similar task-related impairment persists during rest. While we cannot conclusively determine a specific impairment association within the scope of this study, this idea deserves further investigation. However, in the current study, the observed lateral abnormality similarities across AOD and rest suggest parallel similarities in causation. Lateralization differences during rest may stem from intrinsic structural/functional brains differences, which then affect diverse states,.
These results also suggest utility as possible SZ biomarkers at rest, as well as during task, at the group level. More speculatively, the fact that SZ displayed similar, but overall significantly less asymmetry compared to HC may reflect a compensatory need for SZ to draw on the right hemisphere secondarily to compensate for deficits in left hemisphere in the DMN regions, consistent with a hypothesis that SZ may be associated with dysfunctional hemisphere connectivity (Andreasen, et al. 2008). Previous research has shown that SZ have increased functional network connectivity (inter-dependencies between resting networks) than HC including in the default mode network (Demirci, et al. 2009; Jafri, et al. 2008). This may explain the reduced lateral differences in SZ, as more functional network connectivity would diminish cortical specificity.
The DMN is also thought to be correlated with theory of mind (TOM) (Spreng, et al. 2008). TOM refers to the ability to infer the thoughts or intentions of other people. A R>L hemisphere intrinsic fluctuation bias has been shown in SZ compared to HC (Andreasen, et al. 2008) in tasks invoking TOM. Our results are consistent with this finding, in showing that HC have a significantly greater left-hemisphere bias than SZ. This may suggest that as with language, there may be significantly less TOM activity in SZ, as suggested by (Bora, et al. 2009). Both impaired language and TOM activity may suggest overall reduced internal thought generation or inner dialog during rest for SZ or disruption of these processes by hallucinations.
BOLD fluctuation patterns similar to ours comparing HC to SZ were reported by (Olbrich, et al. 2008), who compared different stages of sleep. Furthermore, the right (but not left) posterior cingulate significantly deactivates while under sedation, which also resembles our results comparing HC > SZ. We verified that this particular lateral difference was due to the right posterior cingulate’s hemodynamic response being reduced in SZ. This might suggest that SZ are less attentive to their surroundings during AOD, and particularly during rest – being in a more “restful” or “sedated” state than HC. This may be explained by the antipsychotic medications taken by all of our SZ patients. A similar conclusion was made by (Calhoun, et al. 2008).
The IPL and posterior cingulate asymmetry differences suggest that there may be specific impairments in SZ that manifest during rest as interior language formulation and wakefulness abnormalities. Further research in resting state/DMN differences should be carried out to explore this. In particular, the notion of differing levels of wakefulness and internal dialog may illuminate the mechanism of certain SZ symptoms. For example, visual hallucinations in SZ may be allied to dreams, or to “daydreaming”, but appear more real because of the impaired wakefulness-related network. Similarly, the auditory hallucinations in SZ may stem from disruptions in processes that normally produce internal dialog (Ditman and Kuperberg 2005).
While these inferences regarding language and wakefulness are speculative, they are supported in part both by prior research and the currently observed lateral findings. While the intrinsic effects of such neurological differences at rest are hard to evaluate, the large number of DMN regions in the present study showing similar cross-group lateral differences both during task and rest conditions suggests that the neurological disruptions associated with task-evoked symptoms in SZ also affect patients during rest. Clearly, further attempts to clarify these across-task similarities should be made. In particular, a better understanding of why these cross-group lateral differences are found both during task and rest may help clarify underlying disease mechanisms. Furthermore, understanding how SZ symptoms are associated with DMN disruption would help provide a better understanding of DMN function. Finally, studying the lateral fluctuations of the DMN will give better understanding of hemispheric connectivity in both SZ and of the DMN.
Some limitations of the current study are that we do not have information about illness duration for patients, and that patients were also medicated, so we cannot rule out medication effects as an explanation for group differences. In addition, we only examined the laterality of the DMN network. Recent work suggests the presence of laterality difference in multiple resting state networks and further work needs to be done to evaluate more general laterality differences in patients versus controls (Liu, et al. 2009).
CONCLUSION
We compared lateral asymmetries between HC and SZ in the DMN, both during rest and during performance of an AOD task. We found numerous lateralized group differences that showed similar patterns in both the AOD task and during rest, suggesting that the neurological disruptions that provoke task-evoked abnormalities in SZ may also affect patients during rest. In particular, we found significant lateral differences in the L>R IPL for HC and that this pattern differs significantly from SZ, both during rest and during the AOD task. Asymmetries between HC and SZ are also evident in the posterior cingulate (L>R) and other areas. Our results support the theory that SZ is influenced by hemispheric dysfunction. Numerous other asymmetry differences were detected that deserve further investigation, which we leave for future work.
Acknowledgments
We would like to thank Prof. Kenneth Hugdahl from the University of Bergen for helpful discussion. This work was supported by NIH grant 7 RO1 EB000840 (PI: Calhoun) and NIMH R01 MH072681 (PI: Kiehl). Tom Eichele was supported by a grant from the L. Meltzer foundation (801616).
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