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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Biol Psychiatry. Author manuscript; available in PMC 2010 April 15.
Published in final edited form as:
PMCID: PMC2855119
NIHMSID: NIHMS81964

Progressive Deformation of Deep Brain Nuclei and Hippocampal-Amygdala Formation in Schizophrenia

Abstract

Background

Progressive decreases in cortical gray matter volume have been reported in individuals with schizophrenia. However, studies of progressive change in deep brain nuclei and hippocampal-amygdala formation have not yielded consistent findings.

Methods

Two high-resolution, T1-weighted magnetic resonance images were collected two years apart in 56 schizophrenia and 62 control subjects. Large-deformation high-dimensional brain mapping was used to generate surfaces for deep brain nuclei and hippocampal-amygdala formation at baseline and follow-up. Repeated-measures ANOVA was used to test for longitudinal changes in volume and shape.

Results

The pattern of progressive changes in the deep brain nuclei and hippocampal-amygdala formation in subjects with schizophrenia and controls was variable. Of the structures that receive direct projections from the cortex, the thalamus, caudate nucleus, nucleus accumbens and hippocampus showed changes specific to subjects with schizophrenia, and changes in the amygdala and putamen were similar in both groups. While different at baseline, no progressive change was observed in the globus pallidus, which does not receive direct projections from the cortex.

Conclusions

These findings suggest that the disease process of schizophrenia is associated with progressive effects on brain structure, and that brain structures that receive direct, excitatory connections from the cortex may be more likely to show progressive changes, as compared to brain structures that receive indirect, inhibitory connections from the cortex. These findings are also somewhat consistent with the hypothesis that overactivity of excitatory pathways in the brain may contribute to the neural degeneration that occurs in at least a subgroup of individuals with schizophrenia.

Keywords: Amygdala, Hippocampus, Basal Ganglia, Thalamus, Longitudinal Change, Brain Mapping

INTRODUCTION

Post-mortem studies of individuals with schizophrenia suggest gray matter volume loss across a range of brain structures (13). Similarly, in vivomagnetic resonance (MR) imaging studies provide evidence of widespread gray matter change in individuals with schizophrenia (49), including some who are early in their course of illness (1012).

Recent longitudinal MR studies offer evidence of progressive gray matter loss (1214) in the cortex. However, the observed pattern of time-dependent changes in deep brain nuclei and hippocampal-amygdala formation has not been consistent (1416), and this inconsistency may be due to the fact that the same method of image acquisition and analysis has not been used to survey a variety of relevant structures within the same subjects. The presence or absence of time-dependent changes in these structures may depend on the nature of their connections with the cerebral cortex. The thalamus and striatum all have direct, excitatory connections with the cortex, while the globus pallidus receives inhibitory signals from the striatum and limbic structures, and in turn, passes inhibitory signals to the thalamus. Information from different cortical areas, including the prefrontal, frontal, motor, and sensory areas, is first received by the striatum via excitatory projections, and then passed on to the thalamus via inhibitory projections from the globus pallidus. Finally, the thalamus projects to the cortex, completing a cortico-basal ganglia-thalamo-cortical loop (17). The hippocampus and the amygdala also have reciprocal excitatory connections with the medial prefrontal cortex (18, 19), and project to the mediodorsal nucleus of the thalamus.

In this study, we characterized the patterns of change in shape and volume in the deep brain nuclei and hippocampal-amygdala formation in schizophrenia. Based on the potential for overactive excitatory projections to damage neurons and their processes (i.e, excitotoxicity) (20, 21), we hypothesized that structures with direct, excitatory connections with the cortex (thalamus, striatum, hippocampus and amygdala) would show time-dependent changes, while structures with indirect, inhibitory connections with the cortex (globus pallidus) would not. Since brain structural changes may be accompanied by changes in the cognitive functions supported by these structures, we also examined the correlation between these changes.

METHODS

Participants

The subjects in the present study were selected from groups of schizophrenia (n=139, M/F=90/49, age=35.0±13.0 years) and healthy comparison subjects (n=136, M/F=73/63, age=33.3±14.1 years) that were enrolled into an ongoing study of brain structure and schizophrenia. From these subjects, 56 schizophrenia and 62 healthy comparison subjects returned for follow-up and were included in the present study. All individuals gave written informed consent for participation after the risks and benefits were explained. Subject characteristics are summarized in Table 1.

Table 1
Subject Characteristics: mean (SD [range]), t-test or chi-square statistics are shown where appropriate.

Individual diagnosis was determined by consensus between a research psychiatrist who conducted a semi-structured interview and a research assistant who conducted the Structured Clinical Interview for the DSM-IV (SCID-IV) (22), using criteria from the DSM-IV (23). The research assistant had Master’s-level degree in a mental health discipline and considerable clinical experience. No individual had an unstable medical or neurological disorder, or head injury with loss of consciousness, nor did any meet DSM-IV criteria for substance abuse or dependence for one month prior to participation. The symptoms of the individuals with schizophrenia had remained unchanged for at least two weeks (24) prior to the baseline and follow-up assessments.

During the two-year study period, 39 subjects with schizophrenia were treated with atypical antipsychotic drugs alone, 13 with typical antipsychotic drugs alone or a combination of both, 7 did not provide treatment information. Treatment with adjunctive medications, including mood stabilizers and anti-depressants, was common.

The severity of psychopathology in the individuals with schizophrenia was assessed at both time points using the Scale for the Assessment of Positive Symptoms (SAPS) (25) and Scale for the Assessment of Negative Symptoms (SANS) (26). Baseline psychopathology data was used as reference to compute Z-scores for each psychopathology item. Selected clusters of psychopathological item Z-scores were averaged to form composite scores for three clinical domains – positive symptoms, negative symptoms, and thought disorganization (27).

The subject assessment also included neuropsychological tests on tasks relevant to cognition in schizophrenia (2833). Z-scores were first computed using data from a larger population of research subjects (164 schizophrenia, 161 comparison subjects). The Z-scores were then grouped to form composite scores for four cognitive domains (27):

Working Memory

Wechsler Adult Intelligence Scale – Third Edition (WAIS-III (34)) digit span (total forward and backwards), WAIS-III spatial span (total forward and backwards), WAIS-III letter-number sequencing, and the CPT-IP (overall d-prime) (35).

Episodic Memory

Wechsler Memory Scale – Third Edition (WMS-III) logical memory and WMS-III family pictures.

Executive Function

Verbal fluency (phonological for letter ‘S’ plus categorical for animals), Trails B (scored time to completion), WAIS-III matrix reasoning, and Wisconsin Card Sorting Test perserverative errors.

Crystallized Intelligence

Standard scores from the vocabulary subtest of WAIS-III.

Image Collection

All MR scans were collected on a Magnetom 1.5-Tesla Siemens scanner with a standard head coil using a turbo-FLASH sequence (TR=20ms, TE=5.4ms, flip angle=30°, 180 slices, FOV=256mm, matrix=256×256, time=13.5min) that acquired 1mm3 isotropic whole-head images (36). Signed 16-bit images were compressed to unsigned 8-bit images by linear interpolation of voxel intensities.

To control for varying brain sizes we computed an atlas scaling factor (ASF) for each individual at baseline (37). The ASF is the reciprocal of the determinant of the alignment matrix to Talairach atlas space and it represents the degree of volume expansion or contraction required for alignment. The ASF approximated brain plus ventricular volume and exhibited group difference at baseline (F=4.3,df=1,111,p=0.04), therefore it was used as a covariate in statistical analyses.

Surface Mapping

At baseline, the surfaces of the deep brain nuclei and hippocampal-amygdala formation were transferred from a template scan (from a subject otherwise not included in this study) by applying Large-Deformation High-Dimensional Brain Mapping (HDBM-LD) to each scan (9, 38). We have previously established the validity and reliability of HDBM-LD for mapping the hippocampus (39), thalamus (40), and basal ganglia (41). For the amygdala, we compared HDBM-LD-generated segmentations to expert manual segmentations in scans of 10 randomly selected subjects. The average (SD) overlap of voxels was 74.3% (11.3)%, volume ICC was 0.82, L1-error was 0.29 (0.06), and mean surface-to-surface distance was 0.33 (0.07) voxels. These error measures were comparable to the accuracy measures we obtained in other structures, and for detailed explanations of these measures see Wang et al (41).

To map the surfaces at follow-up, baseline and follow-up scans were first registered using a nine-parameter affine transformation (42) to adjust for changes in head position and scanner-drift (43). Next, HDBM-LD was used in neuroanatomical regions immediately surrounding the structures of interest, at twice the native-scan resolution. In the literature, whole-brain mapping has been used to characterize whole-brain changes over time (44, 45), while mapping of subregions has been used for serial mapping of specific structures (46).

Note that during the mapping procedure, the only manual interaction occurred during the mapping at baseline: prior to diffeomorphic transformations, anatomic landmarks were placed by expert raters who were blinded to the group status of the scan being landmarked. Detailed landmarking procedures can be found in our previous publications (9, 38, 39, 41).

Statistical Analysis

The left and right volumes of each structure at baseline and follow-up were computed as the volumes enclosed by the transformation-derived surfaces. There were no missing values in the data. These volumes were entered into a repeated-measures analysis of variance (RM-ANOVA) model, with diagnostic group as the main effect, and hemisphere and time as repeated factors. The main group and time effects and group-by-time interaction were examined. Baseline group effect was also examined in a similar RM-ANOVA.

To quantitate surface shape, we first applied principal components analysis (PCA) to the baseline right-hand-side surfaces for dimensionality reduction. The first 15 principal components (PC) accounted for >80% of total variance in the right-hemisphere surface in all structures (ranging 82.9%–97.7%). There were no missing values in the data. For each structure, the 15 PC scores were entered into a RM-ANOVA with diagnostic group as the main effect, and hemisphere and time as repeated factors. The 15 PCs were modeled in a general linear model (GLM) as repeated factors of identity type. The PC-by-group term was then reported as the main group effect across all PCs. Modeling the PCs in this doubly repeated-measures analysis was equivalent to an RM-MANOVA with a customized transformation matrix where within each PC the follow-up score was subtracted from the baseline score. Main effects of group and time, and group-by-time interaction were examined. Baseline group effect was also examined in a similar RM-ANOVA. Finally, for each subject and structure at each time point, a canonical score (8) was computed based on the left-right average PC scores , and was used to correlate with other measures (below).

Correlation with Cognition and Psychopathology

We calculated the residualized change for all cognition and psychopathology variables by regressing the baseline variables onto the follow-up variables, and then subtracting the predicted follow-up value from the observed follow-up value. The relationship between changes in measures of cognition, psychopathology and measures of change in brain structure were then examined by calculating the correlations between the residualized measures (47) using Spearman’s rho. Significance was not adjusted for multiple comparisons because these relationships were examined on an exploratory basis.

RESULTS

Participants

Comparing with the subjects who returned for follow-up, subjects who did not return were younger (30.8±13.5 years, p=0.027) and had a shorter duration of illness (10.5±9.9 years, p=0.013) at baseline, but they did not differ in psychopathology as assessed using the total scores from SAPS (p=0.23) and SANS (p=0.65).

For the schizophrenia subjects, a repeated-measures general linear model with time as a repeated factor on clinical symptom domain scores showed no time effect for positive symptoms (F=0.67,df=1,54,p=0.42) or thought disorganization (F=1.8,df=1,54,p=0.18). However there was a time effect for negative symptoms (F=5.2,df=1,54,p=0.027). Over the period of study, negative symptoms improved from 0.35 at baseline to 0.11 at follow-up.

As expected, there were significant group effects at baseline for all four cognitive domain scores (p<0.0001, see Table 2). Further, a repeated-measures general linear model with time as a repeated factor showed no time effect for working memory (F=0.08,df=1,51,p=0.77), episodic memory (F=0.14,df=1,51,p=0.70) or crystallized IQ (F=0.14,df=1,51,p=0.70). However, there was a significant time effect for executive function (F=5.0,df=1,51,p=0.029); i.e., the domain scores changed from −0.56 at baseline to −0.40 at follow-up, reflecting mild improvement.

Table 2
Means (SD) z-scores of psychopathology and cognitive measures at baseline and follow-up

Thalamus

For volume, baseline group effect trended toward significance, with schizophrenia subjects having smaller left and right volumes (see Table 3 and Table 4). Longitudinal analysis showed a trend toward significant main group effect, a significant time effect and a trend toward significant group-by-time interaction, with the schizophrenia subjects showing a more rapid two-year reduction as compared to the comparison subjects (see Table 3 and Table 4). The hemisphere effect was significant in both the baseline (R>L,F=18.2,df=1,116,p<.0001) and longitudinal models (F=21.6,df=1,115,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=0,df=1,115,p=0.95), or hemisphere-by-group-by-time interaction (F=0.11,df=1,115,p=0.74).

Table 3
Mean (SD) of volumes at baseline and follow-up: thalamus, hippocampus, amygdala, nucleus accumbens, caudate nucleus, globus pallidus and putamen
Table 4
Statistical comparisons of each of the following structures: thalamus, hippocampus, amygdala, nucleus accumbens, caudate nucleus, globus pallidus and putamen, with no covariates

For surface shape, baseline group effect was significant. Longitudinal analysis showed significant group effect, time effect, and group-by-time interaction. The hemisphere effect was significant in both the baseline (F=12.8,df=15,102,p<.0001) and longitudinal models (F=12.5,df=15,101,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=1.2,df=15,102,p=0.30), or hemisphere-by-group-by-time interaction (F=1.3,df=15,101,p=0.21).

Hippocampus

For volume, baseline group effect was not significant. Longitudinal analysis showed a significant time effect without a significant group effect and group-by-time interaction. The hemisphere effect was significant in both the baseline (R>L,F=519,df=1,114,p<.0001) and longitudinal models (F=476,df=1,113,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=0.24,df=1,114),p=0.62), or hemisphere-by-group-by-time interaction (F=0.51,df=1,113,p=0.47).

For surface shape, baseline group effect not significant. Longitudinal analysis showed significant time effect and group-by-time interaction without significant group effect. The hemisphere effect was significant in both the baseline (F=417,df=15,100,p<.0001) and longitudinal models (F=411,df=15,99,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=1.0,df=15,100,p=0.42), or hemisphere-by-group-by-time interaction (F=0.46,df=15,99,p=0.95).

Amygdala

For volume, baseline group effect was significance, with schizophrenia subjects having smaller volumes on both the left and right sides. Longitudinal analysis showed a significant group effect and time effect with no group-by-time interaction. The hemisphere effect was significant in both the baseline (R>L,F=33,df=1,114,p<.0001) and longitudinal models F=32, df=1,113, p<.0001). Further analysis showed significant hemisphere-by-group interaction at baseline (reduced asymmetry for schizophrenia, F=7.1,df=1,114,p=0.009), but no hemisphere-by-group-by-time interaction (F=1.12,df=1,113,p=0.29).

For surface shape, baseline group effect was significant (. Longitudinal analysis showed significant group effect and time effect with no group-by-time interaction. The hemisphere effect was significant in both the baseline (F=295,df=15,100,p<.0001) and longitudinal models (F=285, df=15,99, p<.0001). Further analysis showed trend toward significant hemisphere-by-group interaction at baseline (F=1.7,df=15,100,p=0.067), but no hemisphere-by-group-by-time interaction (F=0.79,df=15,99,p=0.68).

Caudate Nucleus

For volume, baseline group effect was not significant. Longitudinal analysis showed a trend toward significance main group effect, no significant time effect and a significant group-by-time interaction, with the schizophrenia subjects showing a more rapid two-year reduction as compared to the comparison subjects. The hemisphere effect was significant in both the baseline (R<L,F=9.2,df=1,116,p=0.003) and longitudinal models (F=11, df=1,115, p=0.0015). Further analysis showed no hemisphere-by-group interaction at baseline (F=1.5,df=1,116,p=0.21), nor hemisphere-bygroup-by-time interaction (F=0.2,df=1,115,p=0.66).

For surface shape, baseline group effect was significant. Longitudinal analysis showed significant group effect, time effect, and group-by-time interaction. The hemisphere effect was significant in both the baseline (F=14,df=15,102,p<.0001) and longitudinal models (F=14,df=15,101,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=1.2,df=15,102,p=0.29), nor hemisphere-by-group-by-time interaction (F=0.62,df=15,101,p=0.85).

Putamen

For volume, baseline group effect was not significant. Longitudinal analysis showed no significant group effect, time effect or group-by-time interaction. The hemisphere effect was significant in both the baseline (R<L,F=21,df=1,116,p<.0001) and longitudinal models (F=22,df=1,115,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=0.01, df=1,116, p=0.92), nor hemisphere-by-group-by-time interaction (F=0.52, df=1,115, p=0.47).

For surface shape, baseline group effect was. Longitudinal analysis showed significant group effect and time effect, without group-by-time interaction. The hemisphere effect was significant in both the baseline (F=6.0,df=15,102,p<.0001) and longitudinal models (F=6.0,df=15,101,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=1.5,df=15,102,p=0.11), nor hemisphere-by-group-by-time interaction (F=0.84,df=15,101,p=0.63).

Nucleus Accumbens

For volume, baseline group effect was not significant. Longitudinal analysis showed no significant group effect, time effect or group-by-time interaction. The hemisphere effect was not significant in either the baseline (F=0.02,df=1,116,p=0.89) or longitudinal model (F=0.02,df=1,115,p=0.89).

For surface shape, baseline group effect trended toward significance. Longitudinal analysis showed significant time effect and group-by-time interaction without significant group effect. The hemisphere effect was significant in both the baseline (F=12,df=15,102,p<.0001) and longitudinal models (F=12,df=15,101,p<.0001). Further analysis showed a significant hemisphere-by-group interaction at baseline (F=1.9,df=15,102,p=0.033), but no hemisphere-by-group-by-time interaction (F=0.81,df=15,101,p=0.67).

Globus Pallidus

For volume, baseline group effect was not significant. Longitudinal analysis showed no significant main group effect, time effect or group-by-time interaction. The hemisphere effect was not significant in either the baseline or longitudinal models. Further analysis showed no hemisphere-by-group interaction at baseline, nor hemisphere-by-group-by-time interaction.

For surface shape, baseline group effect was significant. Longitudinal analysis showed significant group effect with a significant time effect or group-by-time interaction. The hemisphere effect was significant in both the baseline (F=8.1,df=15,102,p<.0001) and longitudinal models (F=9.1,df=15,101,p<.0001). Further analysis showed no hemisphere-by-group interaction at baseline (F=0.78,df=15,102,p=0.70), nor hemisphere-by-group-by-time interaction (F=0.36,df=15,101,p=0.99).

Covariate

Statistical comparisons using baseline ASF as a covariate are reported in Table 5. Group and time effects for the thalamus volume, time effect for the thalamus surface shape became non-significant. Time effect for the hippocampus volume and surface shape became non-significant. Time effect for the amygdala volume and surface shape became non-significant. Time effect for the caudate nucleus surface shape became non-significant. Time effect for the putamen surface shape became non-significant. Baseline group effect and time effect for the nucleus accumbens surface shape became non-significant. Group effect for the globus pallidus surface shape became non-significant, however the time effect for its shape became significant. All other effects remained unchanged.

Table 5
Statistical comparisons of each of the following structures: thalamus, hippocampus, amygdala, nucleus accumbens, caudate nucleus, globus pallidus and putamen, with atlas scaling factor at baseline as a covariate

Correlation with Changes in Psychopathology and Cognition in Schizophrenia Subjects

There was a correlation between the residualized change in positive symptoms and the residualized change in thalamus shape (canonical score; r=0.32,p=0.019). As the shape of the thalamic surface became progressively more abnormal (i.e., more disparate from the surface shape of the healthy comparison subjects), positive symptoms became more severe. A similar inverse correlation at the trend level was found between the residualized change in positive symptoms and thalamic volume (r=−0.25,p=0.066). As the thalamic volume became progressively smaller, positive symptoms became more severe. These correlations remained significant after partialling out baseline ASF (canonical score: r=0.34,p=0.017; volume: r=−0.28,p=0.051). No other correlations between changes in psychopathology and brain structure were significant.

There was a correlation between the residualized change in episodic memory and the residualized change in caudate nucleus shape (r=0.39,p=0.0039). Worsened episodic memory performance was associated with progressively more abnormal shape of the caudate nucleus surface (i.e., more disparate from the surface shape of the healthy comparison subjects). This correlation remained significant after partialling out baseline ASF (r=0.38,p=0.0084). There was a correlation between the residualized change in crystallized IQ and the residualized change in nucleus accumbens shape (r=−0.45, p=0.0008) and globus pallidus shape (r=−0.30, p=0.030). Lowered crystallized IQ was associated with progressively more abnormal shape of the nucleus accumbens and globus pallidus surfaces (i.e., more disparate from the surface shape of the healthy comparison subjects). These correlations remained significant after partialling out baseline ASF (Na: r=−0.49, p=0.0004; Pl: r=−0.26, p=0.078). There were no other correlations between changes in cognition and brain structure.

Effect of Antipsychotic Treatment during the Study Period

Since some evidence suggest that antipsychotic medications may affect cortical gray matter (48, 49), we assessed the impact of the type of antipsychotic drug treatment on changes in brain structure in the schizophrenia subjects by examining the effects of time and time-by-drug treatment type (atypical vs. typical antipsychotics) interaction on measures of structure volume and shape. We found that the thalamic shape showed a significant time-by-treatment type interaction (F=2.01,df=15,36,p=0.043). In individuals with schizophrenia treated with atypical antipsychotics during the 2-year follow-up period, the inward deviation of the thalamic surface in the superior and lateral regions was deeper relative to subjects treated with typical antipsychotics during that same time period (not shown). There were no other time effects or time-by-treatment interactions for other brain structures.

DISCUSSION

In this study, we found that progressive change in the deep brain nuclei and hippocampal-amygdala formation in subjects with schizophrenia was of modest magnitude and extent: As predicted, the structure of the thalamus, caudate, hippocampus and nucleus accumbens showed disease-specific progressive (shape) changes, while the globus pallidus did not. However, putamen and amygdala showed progressive changes that were similar in the groups of schizophrenia and comparison subjects. When adjusted for ASF, only thalamus, caudate and hippocampus showed group differences in the rate of shape change.

In several structures, we found that changes in brain structural shape showed more substantial group differences while volume changes were small and not wide spread. That abnormality of shape change could be revealed without significant volume reductions was consistent with our previous studies of brain structure in schizophrenia (7, 50, 51). This pattern of findings suggests that schizophrenia is associated with progressive changes in particular subregions , rather than the entire extent of a given structure. However, because the observed progressive change is not a widespread phenomenon in subjects with schizophrenia, but rather of modest magnitude and limited to only a few structures, interpretation of finding should be made with caution. Figure 1 depicts the differential pattern of progressive change in the thalamus and caudate surfaces for the schizophrenia and comparison subjects. The thalamic surface regions approximating the ventrolateral nucleus and the central part of the caudate nucleus are marked in red and white circles, respectively. Regions where the most clear group differences occurred included the ventrolateral (as well as ventroanterior) nuclei of the thalamus, which receive their inputs from the dorsolateral striatum via the direct and indirect pathways through the globus pallidus, and interact with motor and premotor areas of the brain (5256). Even before the advent of antipsychotic medications, individuals with schizophrenia have been observed to develop abnormal involuntary movements (e.g., tardive dyskinesia). Moreover, the ventrolateral and ventroanterior nuclei of the thalamus also interact with the association, sensory and the dorsolateral prefrontal cortices (5759). Notably, other groups have reported progressive dysfunction in these regions of the cerebral cortex in subjects with schizophrenia (5, 13, 6064).

Figure 1
Caudate and Thalamus Surface Deformations in Individuals with Schizophrenia and Comparison Subjects

The above pattern of findings provide limited support for our overall hypothesis that brain structures that receive direct, excitatory connections from the cortex are more likely to show progressive changes, as compared to brain structures that receive indirect, inhibitory connections from the cortex. It is also somewhat consistent with the hypothesis that overactivity of excitatory pathways in the brain may contribute to the neural degeneration that occurs in at least a subgroup of patients with schizophrenia (20, 21), although our results indicate that all such brain regions are not equally affected. Detailed post-mortem studies of these structures, as well as clinical trials of neuroprotective drugs that target the process of excitotoxicity, may be helpful in further testing of this hypothesis.

It should be noted that the relationship between neurotoxicity and gray matter volumetric reductions is unclear, since possibilities other than neuronal loss, including neuropil, water content and synaptic pruning, may also account for reductions in gray matter volume as detected by imaging studies. Also, many factors could have contributed to the divergent pattern of time-dependent changes as well as the unaccounted variance observed in this study, and they include intrinsic features of these structures (i.e. plasticity), medication related changes, genetic factors and sensitivity to stress. For example, caudate nucleus may be more sensitive to D2 blockade than hippocampus (65), and hippocampus may be more vulnerable to stress than the other structures (66, 67).

There were several important limitations to this study. First, the sample was one of convenience. The subjects with schizophrenia had a wide variation in duration of illness, and were not followed under “controlled” conditions. Those who returned for follow-up may have been more severely affected compared with those who did not return, resulting in the selective retention of schizophrenia subjects with a more progressive component to their illness. However, due to the limited sample size, we would not able to test the hypothesis that neuroanatomical progression may be a feature of a particular subgroup of schizophrenia subjects. Thus, it is possible that larger progressive changes, or progressive changes in a larger number of deep brain nuclei and hippocampal-amygdala formation, might occur as an early feature of the disease process. Second, a significant shape difference could be consistent with either a highly localized volume loss or a change in the conformation of the structure. At present, our methods cannot distinguish between these two possibilities. The changes reported by this study could also be caused by factors other than the underlying disease (schizophrenia), such as antipsychotic medication use and the often marginalized and un-healthy life style of the patient group. Also, the treatment conditions during the time interval were variable between subjects. Thus, similar or different patterns of changes in neuroanatomical structures might be observed in untreated patients or in patients treated with different types of drugs. The presence of a significant time-by-treatment interaction effect on the thalamus shape (but not volume) suggests that progressive changes in particular subregions of the thalamus may also be associated with the type of antipsychotic drug treatment received by the schizophrenia subject. We did not have data on which subject had a history of substance abuse or dependence prior to the month preceding study enrollment; hence we cannot address the question of a possible relationship between substance use in schizophrenia and our measures of brain pathology and progression. Another limitation is that the MR images were collected on a relatively low-field (1.5-Tesla) scanner. As with any longitudinal study, the methods used for data collection are often not “cutting edge” by the time the data from all time points have been collected. Higher-field (e.g., 3-Tesla) scanners may produce better contrast-to-noise ratio which may better reveal changes and differences in the changes.

To our knowledge, this study would be the first to apply the same methods for assessing progressive changes in structure across a wide variety of deep brain nuclei and hippocampal-amygdala formation in subjects with schizophrenia, and thus, our results may be particularly valuable for addressing the question of whether progressive changes are characteristic of a particular subset of these structures.

Supplementary Material

ACKNOWLEDGEMENTS

Funding for this study was provided by NIMH grants R01-MH056584, the Conte Center for the Neuroscience of Mental Disorders at Washington University School of Medicine (P20-MH071616), and P41-RR15241.

Footnotes

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FINANCIAL DISCLOSURES

The authors reported no biomedical financial interests or potential conflicts of interest.

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