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Schizophr Bull. Jul 2010; 36(4): 713–722.
Published online Nov 5, 2008. doi:  10.1093/schbul/sbn145
PMCID: PMC2894601
Low-Frequency BOLD Fluctuations Demonstrate Altered Thalamocortical Connectivity in Schizophrenia
Robert C. Welsh,13 Ashley C. Chen,4 and Stephan F. Taylor3
2Department of Radiology, University of Michigan Ann Arbor, MI, 48109 USA
3Department of Psychiatry, University of Michigan, Ann Arbor, MI, 48109 USA
4Neuroscience Program, University of Michigan, Ann Arbor, MI, 48109 USA
1To whom correspondence should be addressed; Department of Radiology, Kresge III, Room 3315, 200 Zina Pitcher Place, Ann Arbor, MI 48109-0553, USA; tel: 734-647-6781, fax: 734-765-8541, e-mail: rcwelsh/at/med.umich.edu.
The thalamus plays a central and dynamic role in information transmission and processing in the brain. Multiple studies reveal increasing association between schizophrenia and dysfunction of the thalamus, in particular the medial dorsal nucleus (MDN), and its projection targets. The medial dorsal thalamic connections to the prefrontal cortex are of particular interest, and explicit in vivo evidence of this connection in healthy humans is sparse. Additionally, recent neuroimaging evidence has demonstrated disconnection among a variety of cortical regions in schizophrenia, though the MDN thalamic prefrontal cortex network has not been extensively probed in schizophrenia. To this end, we have examined thalamo-anterior cingulate cortex connectivity using detection of low-frequency blood oxygen level dependence fluctuations (LFBF) during a resting-state paradigm. Eleven schizophrenic patients and 12 healthy control participants were enrolled in a study of brain thalamocortical connectivity. Resting-state data were collected, and seed-based connectivity analysis was performed to identify the thalamocortical network. First, we have shown there is MDN thalamocortical connectivity in healthy controls, thus demonstrating that LFBF analysis is a manner to probe the thalamocortical network. Additionally, we have found there is statistically significantly reduced thalamocortical connectivity in schizophrenics compared with matched healthy controls. We did not observe any significant difference in motor networks between groups. We have shown that the thalamocortical network is observable using resting-state connectivity in healthy controls and that this network is altered in schizophrenia. These data support a disruption model of the thalamocortical network and are consistent with a disconnection hypothesis of schizophrenia.
Keywords: schizophrenia, thalamus, connectivity, fcMRI, resting state, cingulate
Considerable evidence has implicated thalamic nuclei in the pathophysiology of schizophrenia.1 Widespread disturbances in information processing and the failure of associative mental processes have been attributed to thalamic dysfunction.2,3 Functional imaging studies have revealed reduced activity in the thalamus,46 and structural imaging studies have identified volume reductions,79 although not all studies have reliably found reduced volume of the thalamus.10 With better resolution in neuroimaging techniques, more recent work has focused on the medial dorsal nucleus (MDN).1115 The MDN is one of the few brain regions where reductions in neurons have been observed in postmortem specimens1619 (though not inclusively20), and neurochemical studies have identified altered expression of metabotropic and ionotropic glutamate receptors in the MDN.1,21,22 Anatomically, the medial dorsal (MD) thalamus is the primary nucleus exchanging excitatory projections with the prefrontal cortex, the cortex responsible for carrying out the executive and emotion functions typically disrupted in schizophrenia.23 Thus, significant evidence implicates the thalamus in schizophrenia, but relatively few studies have addressed functional relationships of the thalamus with other brain structures in schizophrenia.
Once thought of as simple relay structures, it is now clear that thalamic nuclei, through thalamocortical connections, are key nodes in the establishment of oscillatory dynamics that integrate brain function.2426 Dysregulated glutamatergic projections from the thalamus to the frontal cortex have been proposed by several groups as a component of N-methyl-D-asparate receptor hypofunction in schizophrenia.1,27,28 To address this hypothesis, it is necessary to measure thalamocortical connectivity of the MDN with prefrontal regions. Examining the MDN, Mitleman et al.29 reported a “metabolic disconnection” between the MDN and widespread frontotemporal cortical regions, but the correlation reported between subjects did not measure the dynamic thalamocortical activity that occurs at the level of individual brains.
The recent discovery of low-frequency fluctuations in the blood oxygenation level dependence (BOLD) (LFBF) signal, usually identified during passive, resting states,30,31 provides a tool to analyse thalamocortical dynamics. Schizophrenia has been usefully conceptualized as a failure of integration, manifest as impaired connections between brain regions,32,33 and a growing number of studies have begun to demonstrate impaired connectivity across the brain.3437 This approach was employed by Whalley et al.,34 who examined within-subject correlations of seed voxels in the thalamus of healthy subjects and subjects at risk for psychosis. They found only negative correlations of the thalamus with prefrontal cortex and only in the high-risk subjects and no correlations, positive or negative, in the healthy controls. This result raises the question about the presence of LFBF thalamocortical connectivity in control subjects. Given that the MDN is a small structure and the resolution of functional magnetic resonance imaging (fMRI) is poor, we address 2 experimental questions: Do LFBF oscillations connect the MDN to its target regions in frontal cortex?, and Is this connectivity impaired in schizophrenia? In the study described below, we present results demonstrating thalamocortical connectivity with LFBF and direct evidence for impairment of this connectivity in schizophrenic patients.
Participants
From a university-staffed community mental health center, stable, medicated outpatients were recruited with diagnostic and statistical manual of mental disorder (DSM-IV) schizophrenia or schizoaffective disorder38 established by a Structured Clinical Interview for Diagnosis.39 All patients were without active depression, alcohol/substance abuse/dependence (>6 months without abuse/dependence), and significant medical illness that could affect cerebral function (eg, diabetes mellitus, hypertension). Subject assessment included clinical ratings by an experienced clinician (S.F.T.) on the Brief Psychiatric Rating Scale.40
Healthy controls were recruited by use of community advertisements. They were not taking medication, were without any Axis I psychiatric disorders (Structured Clinical Interview for Diagnosis, nonpatient version39), and had no psychosis in first-degree relatives. All participants completed a written consent form as approved by the Institutional Review Board of the University of Michigan Medical School.
We collected resting-state fMRI (fcMRI) data from patient subjects participating in a functional imaging study of emotional processing and from healthy subjects participating in this same study or another study examining performance monitoring (findings reported elsewhere). Fourteen patient subjects were evaluated to match the age range of the control subjects, and 3 were excluded due to excessive motion (see “fMRI Preprocessing” section) leaving 11 patients (8 schizophrenic, 3 schizoaffective). Of the healthy subjects, 13 were evaluated and 1 was excluded due to excessive motion, leaving 12 subjects for analysis. Demographic details are listed in table 1.
Table 1.
Table 1.
Demographics and Clinical Characteristics of Subjects
fcMRI Acquisition
All imaging was performed on a GE 3T Excite 2 (General Electric, Milwaukee, Wisconsin) at the University of Michigan Functional MRI Laboratory. For each participant studied, we acquired medium- and spoiled gradient recall (spgr) high-resolution anatomic images (T1-overlay, T1-spgr) and time series data (T2*). Time series data were acquired with a reverse-spiral k-space acquisition. Forty-slice (3 mm slice thickness) volumes were acquired every 2 s echo time, (TE = 30 ms, 642 matrix, field of view, (FOV = 220 mm), for a total of 180 volumes. To achieve thermal equilibrium of magnetization for the time series data, an initial 4 volumes (40 slices) were excited but not recorded. To facilitate physiological corrections, cardiac and respiratory cycles were recorded with MRI vendor supplied pulse-oximeter and respiratory belt. Medium-resolution anatomic images (T1-overlay) were acquired in same slice location (3 mm slice thickness) as the T2* volumes but at higher in-plane resolution (2562 matrix, FOV = 220 mm). High-resolution images (T1-spgr) had 1.5 mm slice thickness (2562 matrix, FOV = 220 mm).
Resting State Task
Participants were scanned for 6 min while they rested, eyes open, viewing a fixation crosshair to elicit resting-state metabolism.41
fcMRI Preprocessing
Several preprocessing steps were taken to reduce potential sources of noise and artifact. fMRI data were reconstructed off-line using custom code written in C.42 During task execution typical of fMRI experiments, cardiac cycle and respiration give rise to unexplained spatially temporally correlated variance and typically contribute to residual noise terms resulting in lowered statistical significance.43 Given there is no overt task in resting-state functional connectivity analysis,30 it is crucial to remove these systematic BOLD variations arising from cardiac and respiratory sources, which can either mask true functional connectivity signals or even give rise to what appears as a functional network.4446 Therefore, physiological correction of time series data was done and performed in the image domain.47 Slice timing and motion detection were done using the “slicetimer” and the “mcflirt” routines of the FSL fMRI analysis package (http://www.fmrib.ox.ac.uk/fsl/slicetimer/index.html).48 For the connectivity analysis, we used nonrealigned images, and we required that any motion exhibited be minimal (<0.4 mm translation and <0.1 degrees rotation)46 thereby avoiding motion-induced spatial-temporal correlations. For each participant's data set, the T1-overlay volume was coregistered, using Statistical Parameter Mapping, version 2 (SPM2; Wellcome Trust Center for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm49), to the time series data, followed by coregistration of the T1-spgr image to the coregistered T1-overlay volume. SPM2 was used to spatially normalize the coregistered T1-spgr to the Montreal Neurological Institute (MNI) template. The resulting normalization matrix was then applied to the slice-time–corrected, physiologically corrected, time series data. These normalized T2* times-series data were subsequently spatially smoothed with a 5-mm Gaussian kernel. The resulting T2* images had isotropic voxels, 3 mm on a side. Both, the thalamus connectivity and motor connectivity were then derived from these processed time series data.
Cross-Correlation Analysis
Each T2* volume from the physiologically and slice-time–corrected time series was intensity normalized (global normalization). The resulting time series was then detrended to remove slow drift and mean centered. Additionally, we sequentially regressed out nuisance temporal fluctuations in the MR signal by sampling the bulk white matter and cerebral spinal fluid.41 It has been observed that functional connectivity defined with low-frequency BOLD oscillations is in the 0.01- to 0.10-Hz band44,46; thus, the time course for each voxel was band-passed filtered in this range.
We performed a cross-correlation analysis for bihemispheric motor connectivity. To identify the motor strip, we analysed data from another T2* time series, with identical imaging parameters, when subjects performed a task (judging emotional faces or responding to a letter target) requiring a button press. Using the general linear model implemented in SPM2 with a 2-level analysis, the first level determined individual motor activation by estimating regressors for the button press, employing the times of the individual responses. The resulting contrast image for each subject was smoothed with a 5-mm Gaussian kernel and entered into the second-level one-sample Student t test. We pooled all subjects in a single group for determination of average location of left motor cortex subserving right finger button push. With this average activation, we defined a spherical region of interest (ROI) with a 5-mm radius for correlation analysis for motor connectivity (–42, –24, 54 mm, MNI frame).
For determination of thalamocortical connectivity, a seed region in the MDN was determined by the use of anatomic atlases.50,51 Left and right 2 × 2 × 2 voxel seed ROIs were defined in MNI space (coordinates: ±7.5, –13.5, 7.5 mm). For each participants’ data, we extracted the spatially averaged time course from this region.
For both correlation analyses, we extracted mean time courses from each region. Correlation coefficients were calculated between these average time courses and all other voxels of the brain resulting in a 3-dimensional correlation coefficient image (r image) for motor connectivity and an r image for thalamocortical connectivity. These r images were then transformed to z scores using a Fisher r-to-Z transformation.52 The resulting z images were then used in 1-sample and 2-sample Student t tests as implemented in SPM2, using a voxel-level statistical threshold of P ≤ .0025 (uncorrected) and a cluster extent of k ≥10 voxels.
To investigate sensitivity to placement of MD thalamus seed ROI, we translated the original 2 × 2 × 2 voxel ROI into 8 different locations, with a single voxel overlap with the original ROI. With these translated ROIs, we recalculated the group analyses as described above. These 8 locations (per side) were also queried for gray-matter density differences. The coregistered, spatially normalized, high spatial resolution, spgr images were segmented using routines in SPM2,53 and gray-matter likelihood values for the ROIS were extracted from the gray-matter segment images.
To determine connectivity from the MD thalamus to areas that are known to receive projections from the thalamus, we have used the Wake Forest University Pick Atlas add-on for SPM2 (WFU_PickAtlas, http://www.fmri.wfubmc.edu/cms/software) to define ROI's for Brodmann areas: 6, 8, 10, 11, 24, 32, 44, 45, and 46.
Thalamocortical Connectivity
The functional connectivity analysis for the right and left MDN thalamus revealed correlations with frontal cortical targets, including dorsal anterior cingulate cortex (ACC), rostral ACC, and dorsolateral PFC. Furthermore, correlations were also noted with the caudate nucleus, as well as the contralateral thalamus. Examination of the correlation strength revealed a clear dip when crossing the midline between thalami, suggesting that finding was not an artifact of excessive image smoothing. The schizophrenia group shows a lack of connectivity to frontal regions, though, as in the healthy controls, there is connectivity to the contralateral thalamus. Results for the healthy controls and the schizophrenic patients are enumerated in tables 25, respectively (cluster-level significance of P < .05, corrected). Subpeaks are only listed as well if they have a peak voxel false-discovery rate (FDR)54 significance of PFDR < .05 (FDR corrected). The connectivity maps by group are shown in figure 1.
Table 2.
Table 2.
Healthy Controls Activation Clusters, Right-Sided Seed in MDN Thalamus
Table 5.
Table 5.
Schizophrenic Participants’ Activation Clusters, Left-Sided Seed in MDN Thalamus
Fig. 1.
Fig. 1.
Thalamocortical Connectivity During Rest. All voxels thresholded at P ≤.0025 (uncorrected), cluster extend k ≥10 voxels. Images are shown in neurological convention. (A) Correlations to seed placed in left MDN of thalamus for healthy controls. (more ...)
Table 3.
Table 3.
Healthy Controls Activation Clusters, Left-Sided Seed in MDN Thalamus
Table 4.
Table 4.
Schizophrenic Participants’ Activation Clusters, Right-Sided Seed in MDN Thalamus
A group comparison using a 2-sample Student t test revealed that healthy controls statistically have greater MDN-thalamocortical connectivity than schizophrenic patients. This greater connectivity is present for both the left and right MDN seeds, as shown in figure 2 and summarized in table 6 (cluster-level significance of P < .05, corrected). Because the groups slightly differed in mean age and significantly differed in education, we examined the effects of these demographic variables on thalamocortical connectivity. There was no correlation between connectivity and education, but there was a correlation between age and thalamocortical connectivity in the healthy control group. However, entering age in an ANCOVA analysis between groups did not affect the group difference in thalamocortical connectivity.
Fig. 2.
Fig. 2.
Greater Medial Dorsal Thalamocortical Connectivity in Healthy Controls Compared With Schizophrenic Patients During Rest. All voxels thresholded at P≤.0025 (uncorrected), cluster extent k≥10 voxels. (A) Correlations to seed placed in left (more ...)
Table 6.
Table 6.
Significant Cluster Difference Between Healthy Control and Schizophrenic Patients
M1 Connectivity
To assess connectivity in a brain area not strongly implicated in schizophrenic pathophysiology, we used the motor activation defined ROI to demonstrate motor network connectivity during rest in both the healthy controls and patient populations. Shown in figure 3 are surface renderings of the one-sample t test results using a statistical threshold of P ≤.0025 (uncorrected) and a cluster extent of k ≥10 voxels. Though there is some qualitative difference supplementary motor area (SMA) connectivity, with the schizophrenic patients demonstrating weaker connectivity to M1, we found no statistically significant difference in motor connectivity between healthy controls and schizophrenic patients.
Fig. 3.
Fig. 3.
Motor Cortex Connectivity During Rest. All voxels thresholded at P ≤.0025 (uncorrected), cluster extent k ≥10 voxels. (A) Left hemisphere for healthy controls. (B) Right hemisphere for healthy controls. (C) Left hemisphere for schizophrenic (more ...)
Connectivity Sensitivity to ROI Placement
To examine the robustness of the correlation between MD thalamus and cortical areas, and the sensitivity of our signal to small shifts in the placement of the seed ROI, we performed 2 analyses. In this analysis, we systematically moved the location of the thalamic seed ROI, so that its center of mass was along 3-dimensional diagonals with a displacement of one voxel side in each direction, that is, a total a diagonal displacement distance of equation n1 voxels. We counted the voxels above threshold in 10 atlas-defined Brodmann regions of the frontal cortex, for each of the 8 displacements. As figure 4 shows, when the seed ROI in the left thalamus was translated in a dorsal direction, the correlation shifted some between frontal regions, but remained largely the same. Notably, correlations appeared principally in Brodmann areas 8, 9, 10, and 32. However, with ventral translation, most of the above-threshold voxels dropped out. Shifting the position of the seed ROI in the schizophrenic subjects did not improve the signal, with virtually no above-threshold voxels in the regions of interest.
Fig. 4.
Fig. 4.
Voxel Counts Present in Brodman Areas When Varying Left (A) and Right (B) MDN Seed Placement in Healthy Controls. Columns are organized as follows: dorsal values first, then original MDN seed location, and then ventral regions. +xindicates toward the (more ...)
Because reduced gray matter has been reported by some in the MD thalamus of schizophrenic patients,9 we measured the gray matter volumes at the seed ROI and in each of the 8 displacements. Figure 5 shows that at the primary seed ROI on both sides, the patients had nominally, but nonsignificantly, greater gray matter intensity than the healthy subjects. Throughout the range of translated seed ROI locations, gray matter density between groups tracked. For the ventral, medial displacements, there was a significant reduction in gray matter intensity for both groups, suggesting that these placements may have included some partial volume effects with the third ventricular space.
Fig. 5.
Fig. 5.
Gray Matter Likelihood for Region of Interest Placement for Left and Right Seeds, Respectively. Student t test (unequal variance) for seed voxel gray matter difference between controls and patients: left MDN thalamus P = .198; right MDN (more ...)
With the use of resting-state induced low-frequency BOLD oscillations, we have probed the thalamocortical network and have shown connectivity between the MDN of the thalamus and the prefrontal cortex in healthy controls, validating the use of fcMRI as a tool to examine this network. Using this tool, we have also presented evidence for reduced thalamocortical connectivity in schizophrenia. In addition to these primary results, we found connectivity between the MDN and striatum, which was reduced in the patients. These results have important implications for understanding disturbed network functions in schizophrenia.
Our functional results in healthy subjects parallel the known projections of the MDN to frontal cortex and subcortical structures, with some important exceptions. The MDN contains 3 principal subdivisions (magnocellular, parvocellular, and densocellular nuclei), distinguished by connectivity, myeloarchitecture, and cytoarchitecture.23,55,56 Tract-tracing studies in nonhuman primates have been recently supplemented by MRI tractography in living humans,57,58 also consistent with our functional results. Specifically, the dorsal-most, densocellular region of the MDN has the strongest projections to medial cortex and the striatum,55 where we found functional connectivity. When we displaced our seed ROI in a ventral direction, almost all connectivity disappeared, suggesting that the seed was on the dorsal aspect of the MDN. We also noted connectivity to the lateral cortex, consistent with coverage of the parvocellular nuclei. Although the ROI probably covered the magnocellular nuclei, with projections to orbitofrontal and ventrolateral cortex, we found relatively little connectivity in those regions. This may reflect poor sensitivity of the ROI, which was approximately 15% of the volume of the MDN, to pick up a signal in some subnuclei. Alternatively, different subnuclei of the MDN may exhibit different functional connectivity with cortex. Although these questions await further research, the correspondence of the functional signal with the known thalamocortical projections validates the use of fcMRI as a tool to probe this circuit in schizophrenia.
Because the MDN is a relatively small structure, difficult to localize on an MRI image, and because investigations have reported a reduced size of the MDN,9,11,13 it was important to rule out the possibility of a smaller structure causing reduced connectivity. At the MDN seed ROI, we did not observe gray matter differences between our 2 groups, suggesting that volumetric differences could not account for connectivity differences. It is also not likely that a failure to place the seed ROI in the correct location caused a reduced signal. By moving the seed ROI, we were able to probe the adjacent regions of our central seed location and determine connectivity as a function of displacement. For all of the dorsal displacements, the thalamocortical connectivity signal proved quite robust in the healthy subjects. In none of the subsequently displaced ROIs did we observe an increase in thalamocortical connectivity in schizophrenics. Thus, these analyses show that these methodological issues are unlikely to confound our results.
As mentioned in the “Introduction” section, growing evidence implicates the thalamus in schizophrenia,13 and these results provide the first evidence of functional thalamocortical disconnection in chronic schizophrenic patients, to our knowledge. While task-related functional MRI experiments can explicitly probe activity of the thalamus,15 resting-state connectivity probes the relationship of the thalamus with other brain regions. Under the assumption of quiescent communication between nodes along a healthy connection pathway, resting-state fcMRI demonstrates correlations in temporal fluctuations in the BOLD signal between the nodes. This loss of correlation in temporal fluctuations of the BOLD signal can arise either from aberrant functionality in the thalamus, the ACC,59,60 or degradation of the connection between the 2. Thus, while this disconnection is consistent with a dysregulation of excitatory glutamatergic projections from the thalamus to the cortex,1,27,28 other possibilities will require further exploration.
In addition to reduced thalamocortical connectivity to prefrontal cortex, we also found reduced connectivity to subcortical structures—specifically, bilateral caudate nuclei. This finding is not surprising, given the connectivity between MDN and striatum and the cortio-striatal-pallidal-thalamic loop circuits that organize basal ganglia function.24,61 The observation of impaired connectivity of the MDN with the caudate nucleus shows the functional deficit in patients is not confined to thalamocortical circuits, but affects integration in other regions.
Because brain activity was measured during the resting state, when the subjects had no overt engagement in any task, one needs to consider how variability in mental state might affect our results. Although the origin of low-frequency BOLD fluctuations—oscillatory activity in the frequency range of 0.01–0.1 Hz—remains obscure, they appear to reflect neuronal activity that binds together neural nodes, defining functionally meaningful networks.62,63 Connectivity for the M1 seed ROI was not significantly different between the groups, suggesting that reduced connectivity was not a generalized phenomenon of the schizophrenic subjects. While the sensitivity of thalamocortical connectivity to behavior has not been examined, even if it were shown to reflect changes in behavioral state, the demonstration of a difference between patients and healthy subjects would still be meaningful because the subjects received identical instructions about the scan.
Several considerations need to be kept in mind when interpreting our results. The sample size of 11 patients is relatively small, although the positive result in a small sample demonstrates a relatively strong effect. The patients in this study were chronic, with a long duration of illness, raising a question about whether or not these results generalize to a younger, less chronic population. Also the effect of psychotropic medication, which all of the patients were taking, on LFBFs have not yet been sufficiently studied. Of potential relevance for the question of medication, it is notable that LFBFs have been reported to be unaffected by general anesthesia.64
In conclusion, we have conducted the first study, to our knowledge, examining thalamocortical connectivity with LFBF, demonstrated the predicted prefrontal connectivity in healthy controls and the absence of this connectivity in schizophrenic patients. The results provide preliminary evidence demonstrating impairment in the oscillatory dynamics of a neurocircuit believed to be of key importance for schizophrenic pathophysiology.
Funding
National Institutes of Health (R01-MH64148) and the Mind Over Matter Foundation.
Acknowledgments
This work has been submitted in abstract form at the Society for Neuroscience, Washington, DC, 2006, and Biological Psychiatry, Washington, DC, 2008.
1. Clinton SM, Meador-Woodruff JH. Thalamic dysfunction in schizophrenia: neurochemical, neuropathological, and in vivo imaging abnormalities. Schizophr Res. 2004;69:237–253. [PubMed]
2. Jones EG. Cortical development and thalamic pathology in schizophrenia. Schizophr Bull. 1997;23:483–501. [PubMed]
3. Andreasen NC, Paradiso S, O'Leary DS. “Cognitive dysmetria” as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull. 1998;24:203–218. [PubMed]
4. Laurens KR, Kiehl KA, Ngan ET, Liddle PF. Attention orienting dysfunction during salient novel stimulus processing in schizophrenia. Schizophr Res. 2005;75:159–171. [PubMed]
5. Crespo-Facorro B, Paradiso S, Andreasen NC, et al. Recalling word lists reveals “cognitive dysmetria” in schizophrenia: a positron emission tomography study [see comments] Am J Psychiatry. 1999;156:386–392. [PubMed]
6. Buchsbaum MS, Someya T, Teng CY, et al. PET and MRI of the thalamus in never-medicated patients with schizophrenia. Am J Psychiatry. 1996;153:191–199. [PubMed]
7. Andreasen NC, Arndt S, Swayze V, II, et al. Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. Science. 1994;266:294–298. [PubMed]
8. Csernansky JG, Schindler MK, Splinter NR, et al. Abnormalities of thalamic volume and shape in schizophrenia. Am J Psychiatry. 2004;161:896–902. [PubMed]
9. Konick LC, Friedman L. Meta-analysis of thalamic size in schizophrenia. Biol Psychiatry. 2001;49:28–38. [PubMed]
10. Deicken RF, Eliaz Y, Chosiad L, Feiwell R, Rogers L. Magnetic resonance imaging of the thalamus in male patients with schizophrenia. Schizophr Res. 2002;58:135–144. [PubMed]
11. Kim JJ, Kim DJ, Kim TG, et al. Volumetric abnormalities in connectivity-based subregions of the thalamus in patients with chronic schizophrenia. Schizophr Res. 2007;97:226–235. [PubMed]
12. Hazlett EA, Buchsbaum MS, Kemether E, et al. Abnormal glucose metabolism in the mediodorsal nucleus of the thalamus in schizophrenia. Am J Psychiatry. 2004;161:305–314. [PubMed]
13. Gilbert AR, Rosenberg DR, Harenski K, Spencer S, Sweeney JA, Keshavan MS. Thalamic volumes in patients with first-episode schizophrenia. Am J Psychiatry. 2001;158:618–624. [PubMed]
14. Hazlett EA, Buchsbaum MS, Byne W, et al. Three-dimensional analysis with MRI and PET of the size, shape, and function of the thalamus in the schizophrenia spectrum. Am J Psychiatry. 1999;156:1190–1199. [PubMed]
15. Andrews J, Wang L, Csernansky JG, Gado MH, Barch DM. Abnormalities of thalamic activation and cognition in schizophrenia. Am J Psychiatry. 2006;163:463–469. [PubMed]
16. Pakkenberg B. Total nerve cell number in neocortex in chronic schizphrenics and conrols estimated using optical disectors. Biol Psychiatry. 1993;34:768–772. [PubMed]
17. Young KA, Manaye KF, Liang C, Hicks PB, German DC. Reduced number of mediodorsal and anterior thalamic neurons in schizophrenia. Biol Psychiatry. 2000;47:944–953. [PubMed]
18. Popken GJ, Bunney WE, Jr, Potkin SG, Jones EG. Subnucleus-specific loss of neurons in medial thalamus of schizophrenics. Proc Natl Acad Sci USA. 2000;97:9276–9280. [PubMed]
19. Byne W, Buchsbaum MS, Mattiace LA, et al. Postmortem assessment of thalamic nuclear volumes in subjects with schizophrenia. Am J Psychiatry. 2002;159:59–65. [PubMed]
20. Danos P, Schmidt A, Baumann B, et al. Volume and neuron number of the mediodorsal thalamic nucleus in schizophrenia: a replication study. Psychiatry Res. 2005;140:281–289. [PubMed]
21. Ibrahim HM, Hogg AJ, Jr, Healy DJ, Haroutunian V, Davis KL, Meador-Woodruff JH. Ionotropic glutamate receptor binding and subunit mRNA expression in thalamic nuclei in schizophrenia. Am J Psychiatry. 2000;157:1811–1823. [PubMed]
22. Watis L, Chen SH, Chua HC, Chong SA, Sim K. Glutamatergic abnormalities of the thalamus in schizophrenia: a systematic review. J Neural Transm. 2008;115:493–511. [PubMed]
23. Goldman-Rakic PS, Porrino LJ. The primate mediodorsal (MD) nucleus and its projection to the frontal lobe. J Comp Neurol. 1985;242:535–560. [PubMed]
24. Haber S, McFarland NR. The place of the thalamus in frontal cortical-basal ganglia circuits. Neuroscientist. 2001;7:315–324. [PubMed]
25. Jones EG. The thalamic matrix and thalamocortical synchrony. Trends Neurosci. 2001;24:595–601. [PubMed]
26. Steriade M. Grouping of brain rhythms in corticothalamic systems. Neuroscience. 2006;137:1087–1106. [PubMed]
27. Olney JW, Farber NB. NMDA receptor hypofunction and schizophrenia. Biol Psychiatry. 1995;37:667.
28. Stone JM, Morrison PD, Pilowsky LS. Glutamate and dopamine dysregulation in schizophrenia–a synthesis and selective review. J Psychopharmacol. 2007;21:440–452. [PubMed]
29. Mitelman SA, Byne W, Kemether EM, Hazlett EA, Buchsbaum MS. Metabolic disconnection between the mediodorsal nucleus of the thalamus and cortical Brodmann's areas of the left hemisphere in schizophrenia. Am J Psychiatry. 2005;162:1733–1735. [PubMed]
30. Biswal BB, Van Kylen J, Hyde JS. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed. 1997;10:165–170. [PubMed]
31. Lowe MJ, Mock BJ, Sorenson JA. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage. 1998;7:119–132. [PubMed]
32. Tononi G, Edelman GM. Schizophrenia and the mechanisms of conscious integration. Brain Res Brain Res Rev. 2000;31:391–400. [PubMed]
33. Friston KJ, Frith CD. Schizophrenia: a disconnection syndrome? Clin Neurosci. 1995;3:89–97. [PubMed]
34. Whalley HC, Simonotto E, Marshall I, et al. Functional disconnectivity in subjects at high genetic risk of schizophrenia. Brain. 2005;128:2097–2108. [PubMed]
35. Meyer-Lindenberg A, Poline JB, Kohn PD, et al. Evidence for abnormal cortical functional connectivity during working memory in schizophrenia. Am J Psychiatry. 2001;158:1809–1817. [PubMed]
36. Fletcher P, McKenna PJ, Friston KJ, Frith CD, Dolan RJ. Abnormal cingulate modulation of fronto-temporal connectivity in schizophrenia. Neuroimage. 1999;9:337–342. [PubMed]
37. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD. Aberrant “default mode” functional connectivity in schizophrenia. Am J Psychiatry. 2007;164:450–457. [PubMed]
38. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Washington, DC: American Psychiatric Association; 1994.
39. First MB, Spitzer RL, Gibbon M, Williams JB. Structured Clinical Interview for DSM-IV Axis I Disorders (SCID), Clinician Version: User's Guide. Washington, DC: American Psychiatric Press; 1996.
40. Overall JE, Gorham DR. Brief psychiatric rating scale. Psychol Rep. 1962;10:799–812.
41. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA. 2005;102:9673–9678. [PubMed]
42. Noll DC, Meyer CH, Pauly JM, Nishimura DG, Macovski A. A homogeneity correction method for magnetic resonance imaging with time-varying gradients. IEEE Trans Med Imaging. 1991;10:629–637. [PubMed]
43. Shmueli K, van Gelderen P, de Zwart JA, et al. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. Neuroimage. 2007;38:306–320. [PMC free article] [PubMed]
44. Cordes D, Haughton VM, Arfanakis K, et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol. 2001;22:1326–1333. [PubMed]
45. Lund TE. fcMRI—mapping functional connectivity or correlating cardiac-induced noise? Magn Reson Med. 2001;46:628–629. [PubMed]
46. Peltier SJ, Polk TA, Noll DC. Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm. Hum Brain Mapp. 2003;20:220–226. [PubMed]
47. Pfeuffer J, Van de Moortele PF, Ugurbil K, Hu X, Glover GH. Correction of physiologically induced global off-resonance effects in dynamic echo-planar and spiral functional imaging. Magn Reson Med. 2002;47:344–353. [PubMed]
48. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–841. [PubMed]
49. Ashburner J, Andersson JL, Friston KJ. High-dimensional image registration using symmetric priors. Neuroimage. 1999;9:619–628. [PubMed]
50. Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart, Germany: Thieme-Verlag; 1988.
51. Netter FH. Atlas of Human Anatomy, Professional Edition. Philadelphia, PA: WB Saunders; 2006.
52. Kanji GK. 100 Statistical Tests. London: Sage Publications; 1993.
53. Ashburner J, Hutton C, Frackowiak R, Johnsrude I, Price C, Friston K. Identifying global anatomical differences: deformation-based morphometry. Hum Brain Mapp. 1998;6:348–357. [PubMed]
54. Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage. 2002;15:870–878. [PubMed]
55. Ray JP, Price JL. The organization of projections from the mediodorsal nucleus of the thalamus to orbital and medial prefrontal cortex in macaque monkeys. J Comp Neurol. 1993;337:1–31. [PubMed]
56. Barbas H, Pandya D. (1991). In: Levin HS, Eisenberg HM, and Benton AL, eds. Frontal Lobe Function and Dysfunction. Oxford University Press, New York, 36–58.
57. Behrens TE, Jenkinson M, Robson MD, Smith SM, Johansen-Berg H. A consistent relationship between local white matter architecture and functional specialisation in medial frontal cortex. Neuroimage. 2006;30:220–227. [PubMed]
58. Behrens TE, Johansen-Berg H, Woolrich MW, et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci. 2003;6:750–757. [PubMed]
59. Tamminga CA, Vogel M, Gao X, Lahti AC, Holcomb HH. The limbic cortex in schizophrenia: focus on the anterior cingulate. Brain Res Rev. 2000;31:364–370. [PubMed]
60. Benes FM. Model generation and testing to probe neural circuitry in the cingulate cortex of postmortem schizophrenic brain. Schizophr Bull. 1998;24:219–230. [PubMed]
61. Alexander GE, Crutcher MD, DeLong MR. Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Prog Brain Res. 1990;85:119–146. [PubMed]
62. Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–2356. [PMC free article] [PubMed]
63. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA. 2003;100:253–258. [PubMed]
64. Vincent JL, Patel GH, Fox MD, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007;447:83–86. [PubMed]
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