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Published in final edited form as: Prog Neuropsychopharmacol Biol Psychiatry. 2012 Aug 15;40:187–192. doi: 10.1016/j.pnpbp.2012.08.003

Alterations in the Cerebral White Matter of Genetic High Risk Offspring of Patients with Schizophrenia Spectrum Disorder

Alan N Francis 1, Tejas S Bhojraj 1, Konasale M Prasad 2, Debra Montrose 2, Shaun M Eack 2, Rajaprabhakaran Rajarethinam 3, Ludger T van Elst 4, Matcheri S Keshavan 1,2
PMCID: PMC3635091  NIHMSID: NIHMS418938  PMID: 22910323

Abstract

Alterations in White Matter (WM) may be seen in young relatives at risk and may underlie vulnerability to Schizophrenia. We were interested in exploring which of the WM regions were altered in adolescent offspring at familial risk for Schizophrenia. We examined structural alterations in the offspring of subjects with Schizophrenia or Schizoaffective disorder (HR; n=65; 36 males) and healthy controls (HC; n=80: 37 males) matched for age and education. MRI images were collected using a GE 1.5T scanner at the University of Pittsburgh Medical Center. Image processing was done using FreeSurfer (MGH) by an experienced rater blind to clinical data. We used multivariate analysis of covariance, with intracranial volume (p> 0.05) and age as covariates. High Risk offspring had significant reductions in total WM, hemispheric WM and WM within left parietal and left cingulate cortices. Male offspring had more pronounced right hemisphere WM reductions than females.

Keywords: Schizophrenia, Genetic High-Risk, Offspring, FreeSurfer, MRI

1. Introduction

Schizophrenia represents a group of probably etiologically heterogeneous, severe mental disorders the neurobiological underpinnings of which are not yet fully understood (Keshavan et al, 2008a; MacDonald and Schulz, 2009). Schizophrenia has been thought to result from “disconnectivity” of white matter systems (Friston and Frith, 1995). It has been proposed that alterations in the white matter connectivity may give rise to the some of the symptoms found in schizophrenia. For example, Crow (1998) suggested that auditory hallucinations could arise from aberrant communication between language centers in the left and right temporal cortices. Hubl and colleagues reported alterations in lateral parts of the arcuate fasciculus in patients with hallucinations (Hubl et al. 2004). Aberrations in white matter connectivity between distributed systems could also lead to disorders of self-monitoring which in turn could lead to auditory hallucinations (Silbersweig and Stern, 1996, 1998).

Morphometric studies using ROI methodology have shown that in addition to the gray matter abnormalities, regional and global white matter volumes also have been compromised in schizophrenia (reviewed in Shenton et al., 2001). This is supported by several studies using different methodologies such as voxel based Morphometry [Giuliani et al, 2005; Meda et al., 2008), deformation based Morphometry (Davatzikos, 2005) and ADC (Apparent diffusion coefficient) based Morphometry (Ardekani et al., 2005). These various methods have shown, albeit with differing results, that white matter volumes in the brain are altered in schizophrenia. A recent review and meta-analysis (Olabi et al., 2011) of 928 patients and 867 controls examining 32 brain regions showed that patients’ annual WM volume reduction in several brain regions was −.32% in the frontal, −.32% in the parietal, and −.39% temporal lobes.

While there is growing evidence that schizophrenia subjects show changes in white matter (Kubicki et al., 2007), (Bloemen et al, 2010), there is very little known about whether these alterations are related to underlying liability to the illness. Family history remains one of the strongest etiologic factors in schizophrenia, with an estimated heritability of almost 80% (Mcgrath et al., 2008). The study of young relatives of patients with schizophrenia therefore offers a unique window into the premorbid liability to the illness (Keshavan et al, 2008b). The Preclinical (premorbid and prodromal) phase of the illness has been investigated in studies (Lawrie et al., 2001; Lymer et al., 2006; Lawrie et al., 2008) that have shown white matter alterations.

Although the evidence for white matter involvement is accumulating, it is unclear whether this involvement is limited to certain regions or whether they are widespread, and whether the familial susceptibility may have a role in the neurodevelopmental aspects of WM. To assess the potential role of WM in disrupted neurodevelopmental processes, we examined volumetric alterations in the white matter of familial HR subjects and Healthy controls. Few studies have looked at total WM volume, hemispheric WM volumes and regional WM volumes across both hemispheres in high risk subjects. These studies have not yielded consistent results (reviewed in Agnew-Blais & Seidman, in pressreviewed in Agnew-Blais & Seidman, 2012; Boos et al., 2007: meta-analysis).

We hypothesized that given their familial susceptibility for the illness, subjects at high risk for Schizophrenia would show volumetric WM alterations compared to age and sex matched healthy controls.

2. METHODS

2.1. Participants

The study was conducted at the Western Psychiatric Institute and Clinic, Pittsburgh. Sixty five racially diverse adolescents or young adult offspring (OS) of of schizophrenia probands and eighty healthy controls (HC) were recruited. The overall sample characteristics have been described in previous reports (Francis et al, 2011). Twenty eight OS subjects had one parent with Schizoaffective disorder (SZA) and thirty seven OS subjects has one parent with SZ. Of these, twenty three fathers had a diagnosis of SZ/SZA, while forty two mothers had a diagnosis of SZ/SZA. Offspring of parents with schizophrenia or schizoaffective disorder were recruited by approaching patients in the clinic and through advertisements. Healthy Controls were recruited through advertisements in the same community as OS subjects. Diagnostic assessments of HC and OS and parental diagnoses of schizophrenia or schizoaffective disorder used the structured clinical interviews for DSM-IV diagnoses (SCID)(First et al., 1995) and were confirmed using consensus meetings led by senior diagnosticians (M.S.K and D.M). Participants with an IQ < 80, lifetime evidence of a psychotic disorder, exposure to antipsychotic medications or anti-depressant medications, current or recent (within the previous month) substance use disorder, significant neurological or unstable medical conditions were excluded. All participants signed informed consent after the study was fully explained to them. For participants < 18 years of age, consent was provided by the parent or guardian, and the subjects provided informed assent. The study was approved by the University of Pittsburgh Institutional Review Board.

2.2. Image Acquisition

MRI scans were obtained on subjects using a GE 1.5T whole body scanner (GE Medical Systems, Milwaukee, Wisconsin). The detailed scanning protocol has been described in an earlier publication (Gilbert et al, 2001). The scans were T1 weighted images: three-dimensional spoiled gradient recalled (SPGR), acquired in a steady-state pulse sequence (124 coronal slices, 1.5 mm thickness, TE=5 msec, TR=25 msec, acquisition matrix=256×192, FOV=24 cm, flip angle 40°). Approximately 10% of the MRI scans with radio frequency inhomogeneity defects and motion artifacts were not included in the analysis.

2.3 Image Analysis

2.3.1 Semi-automated morphometric analysis using FreeSurfer

We used FreeSurfer (FS) 4.0.5 (64 bit version) (Massachusetts General Hospital, (Fischl et al, 2004; Desikan et al, 2006; Dale et al, 1999; Fischl et al, 1999; van der Kouwe et al, 2008;) running on Linux for morphometric analysis. FS, a semiautomated brain image morphometric software, has been used to study the brain morphology of several illnesses including schizophrenia (Fischl and Wald, 2007; Fischl et al, 2002;). FS has 3 automated stages (Fischl et al, 2004), each followed by manual image editing by an Image analyst (AF). Image processing included motion correction of volumetric T1-weighted images, removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Segonne et al., 2004), automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (Fischl et al., 2002, 2004b), intensity normalization, tessellation of the gray matter white matter boundary, automated topology correction (Fischl et al., 2001; Segonne et al., 2007), and surface deformation following intensity gradients to optimally place the gray/white and gray/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999). Detailed pictures of WM segmentation may be seen in Salat et al, (2009).

We chose FreeSurfer’s semi-automated morphometric analyses for specific reasons. Inter-subject variability and random error inherent in manual techniques is minimized by automated methods. A second reason is the economy of time for completion. By contrast, the possibility of systematic error in automated approaches can be detected and corrected by rigorous manual editing, such as that followed in this study. Each scan was carefully checked for errors of template registration, skull strip, segmentation and parcellation at each stage of the image processing. We performed Inter-Rater reliability between AF and NT (see acknowledgements) on the 33 WM regions of ten brains and obtained an average inter class correlation value = .99 (WM) and .96 (GM).

Our objective was to examine early WM volume alterations in the regions of the left and right hemispheres of non-psychotic High risk subjects and controls. Since two decades of research studies have consistently implicated regions within the frontal, temporal and parietal, and cingulate WM in the etiopathogenesis of SZ (reviewed in Shenton et al, 2001), we examined the WM volumes in these lobes. Although we did not include the other regions within the MANCOVA, we present ANCOVA analysis of the WM of all the other regions in Tables 1 and 2. The corpus callosum was not included since it was published elsewhere (Francis et al, 2011).

Table 1.

Demographic Information of Subject Populations

Variable Offspring (N = 65) Control (N = 80)
M (SD)/N (%) M (SD)/N (%) P b
Age 16.30 (3.40) 16.62 (3.65) 0.60
Male 38 (54%) 24 (38%) 0.08
Caucasian 34 (49%) 41 (65%) 0.08
Education (yrs) 9.72 (3.33) 10.18 (3.29) 0.43
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b

Fisher’s exact test or independent t-test, two-tailed, for significant differences between OS and HC participants.

Table 2.

LEFT HEMISPHERE – ALL Regional White Matter

LOBES GYRI Mean Volume in mm3 DF F P Partial Eta Squared
Controls Offspring
Mean SD Mean SD
Frontal Lateral Orbitofrontal 7162.1 1142.2 6652.9 937.6 145 7.48 0.007 0.05
Medial Orbitofrontal 3706.6 758.7 3388.2 676.7 145 7.16 0.008 0.05
Caudal Middle Frontal 6486.1 1180.1 6123.6 1214 145 3.86 0.05 0.02
Rostral Middle Frontal 12192.2 2297.2 11521.7 2044.2 145 3.18 0.07 0.02
Pars Orbitalis 862.5 261.7 809.7 222.9 145 1.21 .274 0.01
Pars Opercularis 3569.3 572.8 3405.9 666.2 145 1.64 .203 0.01
Pars Triangularis 3491.7 683.9 3314.5 666.7 145 1.38 .281 0.009
Superior Frontal 17649.3 2588.7 16731.9 2879 145 4.19 0.043 0.02
Frontal Pole 306.4 168.6 302.5 154.1 145 0.197 0.658 0.001
Temporal Middle temporal 5358.65 975.2 4931.02 1004.5 145 5.25 0.02 0.03
Inferior temporal 6138.25 1135.5 5565.35 1263.9 145 8.43 0.004 0.056
Planum Temporale 1413.43 192.8 1336.97 191.9 145 3.94 0.04 0.026
Superior Temporal 8026.61 1274 7599.6 1286 145 3.03 0.081 0.021
Entorhinal 463.05 192.2 401.9 154.4 145 3.19 0.07 0.02
Fusiform 5796.6 927 5365 931.6 145 6.55 0.012 0.04
Parahippocampal 1278 353.3 1177.9 244.3 145 3.034 0.084 0.02
Temporal Pole 568.2 247.4 541.9 173.4 145 0.498 0.491 0.003
Parietal* Supramarginal 8637.91 1357.6 7839.29 1338.2 145 13.99 0.001* 0.091
Superior parietal 12723.86 1936.6 11603.38 2027.4 145 13.68 0.001* 0.089
Angular 9511.95 1662.2 9050.17 1805.9 145 2.57 .111 0.018
Cuneus 2527.4 530.9 2414.7 651.7 145 .671 .414 0.004
Pericalcarine 3230.6 728.1 3006.3 767.2 145 2.146 0.145 0.015
Precuneus 8873.9 1557.8 8409.8 1493.8 145 3.40 0.067 0.02
Motor Somatosensory Post Central 7901.84 1371.4 7252.89 1160.2 145 9.36 0.003 0.063
Pre Central 13247.04 1892.5 12603.02 1874.8 145 2.83 0.09 0.094
Paracentral 3813 780 3697.1 722 145 0.28 0.6 0.001
Cingulate* Isthmus cingulate 2681.67 468.8 2519.37 422.4 145 4.27 0.03* 0.032
Posterior cingulate 3980.04 485.1 3689.91 505.5 145 11.37 0.001* 0.075
Caudal Anterior Cingulate 2176.74 372.3 2080.2 352.6 145 1.84 .177 0.013
Rostral Anterior Cingulate 1787.92 361.1 1762.22 335.6 145 .129 .720 0.0009
Occipital Lateral Occipital 10752.3 1943.1 9726.9 2233.3 145 7.51 0.007 0.05
Lingual 5208.7 1096 4948.7 1001.5 145 .867 .353 0.006
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*

-survived correction for multiple comparisons

2.4. Statistical Analysis

White matter volumes were normally distributed [Shapiro–Wilk’s test (W statistic, p>0.1)]. Multivariate analyses of covariance (MANCOVA) were used with Group and Sex as categorical predictors and Age and ICV as covariates to test differences in white matter volumes between offspring and healthy controls. We first compared total, left and right hemispheric WM across groups. We next performed several MANCOVA analyses on each of the lobes (right and left Frontal, Temporal, Parietal lobes, Motor-Somatosensory cortex and Cingulate cortices) as dependent variables. Tables 2 and 3 show the regions included in the MANCOVAS. Significant MANCOVAs were followed by univariate ANCOVAs to identify specific regional volumetric deficits. Univariate ANCOVAs were carried out on other regions in secondary analysis. Bonferroni corrections were used on the MANCOVAs and ANCOVA tests to control for multiple comparisons. We derived a Bonferroni corrected p value by dividing 0.05 by the number of MANCOVAs and the univariate tests within each MANCOVA. This alpha value was then used to determine whether each p value survived the correction or not.

Table 3.

RIGHT HEMISPHERE – ALL Regional White Matter

LOBES GYRI Mean Volume in mm3 DF F P Partial Eta Squared
Controls Offspring
Mean SD Mean SD
Frontal Lateral Orbitofrontal 6907.8 1055 6610.4 864.3 145 2.82 0.09 0.019
Medial Orbitofrontal 3953.2 677.9 3853.4 683.1 145 .67 0.41 0.004
Caudal Middle Frontal 5741.1 1019.5 5569.6 1060 145 0.74 .38 0.005
Rostral Middle Frontal 13049 2571.6 11893.8 2199.1 145 8.56 0.003 0.05
Pars Orbitalis 1026.8 281 965.9 229.6 145 1.42 0.234 0.01
Pars Opercularis 3493.7 644.2 3346.9 630.3 145 1.62 0.204 0.01
Pars Triangularis 3097.3 663.3 2823.2 587.2 145 7.41 0.007 0.05
Superior Frontal 18001.4 2894 17109.8 2787.8 145 3.6 0.05 0.025
Frontal Pole 421.4 176 397.1 181.5 145 .743 .390 0.005
Temporal Middle temporal 5972.9 1057.6 5367.5 964.4 145 13.75 0.0003 0.09
Inferior temporal 5662.8 1188 5123.5 1080.8 145 7.06 0.008 0.04
Planum Temporale 972.2 143.7 935.8 139.6 145 1.44 0.23 0.003
Superior Temporal 7228.4 1083 7030.9 1098.7 145 0.58 0.44 0.004
Entorhinal 551.4 219.6 532.2 174.1 145 .449 .504 0.003
Fusiform 5601.5 791.2 5215.5 1077.2 145 3.68 0.057 0.02
Parahippocampal 1347 232.6 1271.5 253.4 145 3.11 0.08 0.02
Temporal Pole 647.4 223.2 611 166.8 145 1.30 .256 0.009
Parietal Supramarginal 8401.2 1321.4 7778.6 1353.6 145 13.97 0.0002 0.06
Superior parietal 12076.1 1812.9 11444.5 2108.8 145 3.67 0.05 0.02
Angular 11891.7 2160.6 10638.9 2207.8 145 9.85 0.002 0.09
Cuneus 2553.8 630.5 2369.9 606.6 145 2.04 .155 0.014
Pericalcarine 3218 868.3 3071.8 865.2 145 .412 .522 0.002
Precuneus 8979.6 1561 8514.6 1603.6 145 2.93 0.089 0.02
Motor Somatosensory Post Central 7435.4 1106.9 7007.7 1228.2 145 4.87 0.02 0.03
Pre Central 13649.1 2194.3 13119.9 1999.6 145 0.84 0.361 0.006
Paracentral 4854.2 914.8 4731 794.5 145 .35 .554 0.002
Cingulate Isthmus cingulate 2584.3 405.9 2415.1 413 145 6.27 0.013 0.04
Posterior cingulate 3884.3 576.6 3762.4 475.8 145 1.28 0.259 0.009
Caudal Anterior Cingulate 2310.7 336 2258 425.9 145 .311 .578 0.002
Rostral Anterior Cingulate 1388.5 301.5 1372.2 344.4 145 .100 0.753 0.0007
Occipital Lateral Occipital 10355.3 1964.9 9692.2 1953.7 145 2.70 .102 0.01
Lingual 4938.7 1052.4 4508.4 956.1 145 3.93 0.049 0.02
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3. RESULTS

3.1. Total and Hemispheric White Matter

OS subjects had significantly reduced total white matter F (1, 145) = 15.54 p < 0.0001.

OS subjects had significantly less WM in the left hemisphere [HR - 204734.22 mm3 (SD) 27586] [HC - 218976.6 mm3 (SD) 27728.85] (F (1, 145) = 14.83 p < 0.0001] and right hemisphere [HR-206716.82 mm3 (SD) 28635.97] [HC - 221522.54 mm3 (SD) 27932.82] (F (1, 145) = 15.46 p < 0.0001].

3.2. Lobar effects

The MANCOVAs showed that left parietal [ F(6,142) = 3.42 p < 0.004], left motor-somatosensory [ F(3,142) = 3.3 p < 0.02] left cingulate [ F(4,141) = 3.44 p < 0.01] and right temporal[ F(8,141) = 2.5 p < 0.01], right parietal [ F(6,142) = 2.5 p< 0.02 ] had significantly reduced volume in High risk offspring compared to healthy controls. Only the left parietal cortex WM and left cingulate WM survived the correction for multiple MANCOVAs. Tables 2 and 3 shows the ANCOVA analysis for all the white matter regions in high risk subjects when compared to healthy control subjects.

3.3. Gender, Age and White Matter

There was a significant group X sex interaction in the right hemisphere WM volume F (1, 145) – 4.6408, p < 0.032. Although HR subjects had smaller RH WM volumes than their healthy counterparts, this appeared to be more pronounced in the males than females. There were no significant sex by group interaction in the left hemisphere WM. White matter correlated positively with age for all assessed regions but these correlations did not significantly differ between the groups.

4. Discussion

White matter constitutes the anatomical infrastructure for inter and intra hemispheric connectivity. In a sample of non-psychotic genetic high risk offspring and age matched healthy controls, our study showed that total WM volume in the right hemisphere was reduced by 6.68 % and the left hemisphere was reduced by 6.5 % in high risk subjects indicative of a developing vulnerability to the illness. In addition, male HR brains showed a greater reduction in the right hemisphere WM when compared to females. Our study showed that WM in the posterior regions of the brain namely left parietal cortex, and left cingulate cortex were significantly reduced in HR subjects. A study done on 22q11 deletion syndrome, which is a risk model for Schizophrenia, showed similar WM reductions in the parietal, temporal and occipital areas of the brain (da Silva Alves et al, 2011). It is possible that such a reduction of WM volume early in the preclinical phase may present as a decrease in the white matter fiber density in schizophrenia as shown in several DTI studies (Karlsgodt et al, 2009, Bloemen et al, 2010). In a separate study on the same sample, we showed that the splenium volume of the Corpus Callosum (Francis et al, 2011) was significantly reduced lending evidence that the inter-hemispheric connectivity between temporo-parietal regions could also be altered in this sample.

ROI based studies have shown that frontal WM is compromised in SZ (Brier et al., 1992; Buchanan et al,2004; Sanfilipo et al, 2000; Wible et al., 2001; Hulshoff-Pol 2002; Mathalon et al, 2003 and Buchsbaum et al, 2006). In a meta-analysis of 17 VBM studies of WM, Di et al, (2009) showed WM alterations in the right and medial frontal WM regions which was consistent with the ROI studies. In addition, they showed that the internal capsule WM is also altered. This is consistent with several DTI studies (Szeszko et al., 2005; Kubicki et al, 2005, 2007; Buchsbaum et al., 2006). In summary, WM alterations in the left hemisphere in HR subjects observed in this study lend evidence to the concept of a loss of connectivity between regions as a possible marker for susceptibility in schizophrenia. It is possible that such abnormalities may underlie a later misconnection syndrome as hypothesized by several investigators of white matter involvement in schizophrenia (Karlsgodt et al, 2009). More studies are warranted since genetic influences on WM volume and their relationship with cognitive abilities in genetic High risk subjects and healthy individuals are only being presently unraveled. While morphometric studies of WM such as the present study provide important clues to altered short and long range connectivity of WM fasciculi in the brain, Diffusion Tensor Imaging studies and fMRI connectivity studies are more likely to definitively inform these issues.

Fig 1.

Fig 1

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FreeSurfer White Matter Segmentation:

Fig 2.

Fig 2

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Highlights.

  • Our study compares WM volumes in Genetic High Risk against Control subjects.

  • WM volume in the R hemisphere was reduced by 6.68 % in high risk subjects.

  • Left hemisphere WM was also reduced by 6.5 %.

  • Male HR brains showed a greater reduction in the R hemisphere WM.

  • WM volumes of L parietal lobe, and L cingulate cortex were reduced in HR subjects.

Acknowledgments

Role of Funding Source

MH064023 (MSK)

MH045203 (MSK)

NARSAD Established Investigator award (MSK)

We are grateful to Diana Mermon MS for her assistance in clinical assessments and recruitment. Dr. Vaibhav Diwadkar helped with diagnostic ascertainments. Neeraj Tandon helped with the reliability analysis of FreeSurfer measurements.

Footnotes

Disclosure/Conflict of Interest Statement

This research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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