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White matter structure in young adults with familial risk for psychosis – The Oulu Brain and Mind Study
Psychiatry Research: Neuroimaging 233 (2015) 388–393
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Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns
White matter structure in young adults with familial risk for psychosis – The Oulu Brain and Mind Study Jenni Koivukangas a,b,c,n, Lassi Björnholm a,b, Osmo Tervonen b, Jouko Miettunen a,d,e, Tanja Nordström c,d,e, Vesa Kiviniemi b, Pirjo Mäki a,d, Erika Jääskeläinen a,d,e, Sari Mukkala a,d, Irma Moilanen c,f,g, Jennifer H. Barnett h, Peter B. Jones h, Juha Nikkinen b, Juha Veijola a,c,d a
Department of Psychiatry, Institute of Clinical Medicine, University of Oulu, Oulu, Finland Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland Thule Doctoral Programme, University of Oulu, Oulu, Finland d Department of Psychiatry, Oulu University Hospital, Oulu, Finland e Institute of Health Sciences, University of Oulu, Oulu, Finland f Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland g PEDEGO Research Center, University of Oulu, Oulu, Finland h Department of Psychiatry, University of Cambridge, Cambridge, UK b c
art ic l e i nf o
a b s t r a c t
Article history: Received 20 October 2014 Received in revised form 21 January 2015 Accepted 27 June 2015 Available online 2 July 2015
According to the disconnectivity model, disruptions in neural connectivity play an essential role in the pathology of schizophrenia. The aim of this study was to determine whether these abnormalities are present in young adults with familial risk (FR) for psychosis in the general population based sample. We used diffusion tensor imaging (DTI) and tract-based spatial statistics to compare whole-brain fractional anisotropy, mean diffusivity, and axial and radial diffusion in 47 (17 males) FR subjects to 51 controls (17 males). All the participants were aged between 20 and 25 years and were members of the Northern Finland Birth Cohort 1986 (Oulu Brain and Mind Study). Region of interest analyses were conducted for 12 tracts. Separately, we analysed whole-brain FA for the subgroup with FR for schizophrenia (n ¼13) compared with 13 gender-matched controls. Contrary to our expectations there were no differences in any of the DTI measures between FR and control groups. This suggests that white matter abnormalities may not be a genetic feature for risk of psychosis and preceding the onset of a psychotic disorder. Our findings do not support the theory of disconnectivity as a primary sign of psychosis in young adults with FR for the illness. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: DTI TBSS Birth cohort Risk for psychosis
1. Introduction Friston and Frith (1995) suggested that dysfunctional connectivity plays an important role in the pathology of schizophrenia. Diffusion tensor imaging (DTI) studies have found decreased white matter (WM) integrity in the frontal and temporal lobes in schizophrenia (Ellison-Wright and Bullmore, 2009). DTI is a magnetic resonance imaging (MRI) method based on water diffusion in tissues (Basser et al., 1994). The nature of diffusion can be described using indices, such as mean diffusivity (MD) which describes the strength of diffusion, and fractional anisotropy (FA) which describes the asymmetry of diffusion (Basser and Pierpaoli, n Correspondence to: Department of Psychiatry, P.O. Box 5000, 90014 University of Oulu, Finland. Fax: þ 358 85 375 661. E-mail address: jenni.koivukangas@oulu.fi (J. Koivukangas).
http://dx.doi.org/10.1016/j.pscychresns.2015.06.015 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.
1996). FA represents a ratio between diffusion along the fibre axis (L1), and diffusion perpendicular to axis (L2–L3), thus alterations may arise from diffusivity change in either of these (Le Bihan et al., 2001). People with a first-degree relative with schizophrenia have an elevated risk of the illness (Gottesman, 1991). Comparison between individuals at risk for psychosis and control subjects has methodological advantages. Both of these groups are free from antipsychotic medications and illness related factors, which may affect the WM integrity (Samartzis et al., 2014). Recent studies report reduced WM connectivity in several brain regions in subjects with a family member with schizophrenia when compared to healthy controls (Peters and Karlsgodt, 2015). Alterations in WM structure in family risk (FR) subjects has been found in regions including medial frontal regions (Camchong et al., 2009), corpus callosum (Knöchel et al., 2012), anterior limb of
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internal capsule (Muñoz Maniega et al., 2008), cingulate and angular area (Hoptman et al., 2008), prefrontal cortex, and hippocampus (Hao et al., 2009), and in healthy siblings of patients with childhood-onset schizophrenia in cuneus (Moran et al., 2015). In addition to decreased connectivity, increased FA has been found (Hoptman et al., 2008; Boos et al., 2013), and one study found no WM differences (Domen et al., 2013). In a recent systematic review Chiapponi et al. (2013) reviewed age-related structural WM trajectories in patients with schizophrenia. They concluded that results are variable and different brain areas change with different trajectories. In the present study we evaluated young adults with FR for psychosis in a general population sample. Subjects were in their early adulthood, at the age of high risk for developing schizophrenia (Delisi, 1992; Häfner et al., 1994; Giedd et al., 2008). We hypothesised that subjects with FR for psychosis in the general population would show abnormal WM integrity, and that subjects with FR for schizophrenia would show lower FA than subjects with a parent with a non-schizophrenia psychosis or a control group.
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2.2. Invitation The study procedure, details of subject selection and the field study conducted between 2007 and 2010 have been described by Veijola et al. (2013). The Finnish Hospital Discharge Register (FHDR) was used to detect hospital-treated psychiatric diagnoses in parents between 1972 and 2005 (ICD-8, and ICD-9 codes 295-299 and ICD-10 codes F20-33, except non-psychotic mood disorders). In the invitation phase a history of any psychotic episodes according to the FHDR until the end of 2008, or having the right to reimbursable antipsychotic medication before the end of 2005 according to the registers of the Social Insurance Institute (SII) of Finland, were exclusion criteria. The SII register includes information about the right to reimbursable medication due to psychotic disorders. According to FHDR 272 of the cohort members had a parent with a psychotic disorder, of whom one had died, five were living abroad, and for four subjects no address was available. Altogether 262 FR subjects were invited into the study (Fig. 1). A control group was formed by inviting a random sample of cohort members using the following exclusion criteria: a history of any psychotic episodes, having symptomatic risk for psychosis or having a parent with psychotic illness or A-type personality disorder. Symptomatic risk for psychosis was defined as having attenuated psychosis-like experiences and some degree of functional impairment in the educational, social or health domain. After exclusions the number of potential controls was 8763 and of this group, 193 (2.2%) individuals were randomly selected to form a control group. One of the subjects had died and no address was available for another. An invitation letter was therefore sent to 191 individuals.
2. Methods 2.3. Field study and variables used in the study 2.1. Oulu brain and mind study Oulu Brain and Mind study members are a subsample of the Northern Finland Birth Cohort 1986 (NFBC 1986). NFBC 1986 comprises children (n¼ 9432) with an expected date of birth between 1st July 1985 and 30th June 1986 (Järvelin et al., 1993). The cohort members were born in the two northernmost provinces of Finland: Oulu and Lapland. Data collection was started prospectively before birth and has continued since. The Ethics committee of the Northern Ostrobothnia Hospital District in Finland has approved the study.
A sub study of the NFBC 1986, the Oulu Brain and Mind Study (Veijola et al., 2013) took place between 2007 and 2010 when the participants were 20–25 years old. The protocol allowed two thirds of the participants to be scanned with DTI. A cognitive test battery and background questionnaires were also completed. The Structured Interview for Prodromal Syndromes (SIPS) (McGlashan et al., 2001) was conducted by trained interviewers and used to determine possible lifetime psychotic episodes, previous prodromal syndromes and current prodromal symptoms. Participants also gave a urine sample to assess drug use.
Fig. 1. Flow chart of the study sample in the Oulu Brain and Mind Study.
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Handedness was defined by asking which hand the subjects preferred to use when writing. Educational level was based on a question about basic education and categorised into three classes: less than nine school years, comprehensive school, and matriculation. During the field study participants were asked if they drank too much alcohol (not true, somewhat or sometimes true, very true or often true) and about current psychiatric medication. IQ was estimated from the Matrix Reasoning and Vocabulary tests of the WAISIII (Wechsler Adult Intelligence Scale III Edition, Finnish version) (Wechsler, 1997). Matrix Reasoning assesses perception of details, spatial perception and analogical reasoning. Vocabulary assesses the ability to express word meanings and word knowledge (Mukkala et al., 2011). Global Assessment of Functioning, GAF (American Psychiatric Association, 1994) was evaluated during the psychiatric interview. GAF is a numeric scale (0 through 100) used to rate occupational, social, and psychological functioning.
means using a randomiser tool (5000 permutations) with threshold-free cluster enhancement (tfce) correction method. To study only the presumably more representative voxels – considering minimal partial-volume-effect, registration errors and tract-construction artefacts – the skeleton was further limited with an FA threshold of 0.5. An additional analysis of corpus callosum, cingulum, fornix, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and uncinate fasciculus was also conducted. The tract selection was based on findings in extant literature from DTI studies with FR subjects, and main tracts connecting the hemispheres and fronto-temporal and fronto-parietal regions. The ROIs were formed by masking the original FA skeleton (thresholded at 0.3), one structure at a time, according to two atlases included in FSL, namely the JHU ICBM-DTI81 White-Matter Labels and JHU White-Matter Tractography Atlas (lower probability threshold 25%). 2.7. Statistical analysis
2.4. Final study groups Fifty-three of the invited FR subjects and 55 of the controls participated and were DTI scanned. The exclusion criteria for both groups were: history of head trauma with loss of consciousness for 30 min or more, severe neurological illness, or a history of psychosis. One participant in the control group had a history of head trauma with about 30 min of unconsciousness and was excluded. According to SIPS interview, one participant in the FR and one in the control group had a history of psychotic episodes and were excluded. In addition, five FR and two control participants had to be excluded due to a low-quality scan. The final groups consisted of 47 FR and 51 control participants (Fig. 1). Written informed consent was obtained from all participants. In the FR group 13 paticipants had a parent with schizophrenia and 34 with another psychotic disorder (schizoaffective disorder, n¼ 2; schizophreniform disorder, n ¼2; delusional disorder, n ¼4; psychotic depressive disorder, n¼ 6; psychotic bipolar disorder, n ¼8; other psychotic disorder, n¼ 12). According to the FHDR, none of the participants in our final sample had both parents with psychosis or a parent with solely a diagnosis of an A-type personality disorder. Seven participants in the FR group and two in the control group had a current prodromal syndrome according to the SIPS interview. One participant in the control group had a positive urine drug test for opioids and the urine test was missing for one control. These tests were negative in the FR group. None of the participants in the FR group reported having psychiatric medication while two control participant used psychiatric medication.
We compared the differences in FA, MD and L1–L3 maps between FR for psychosis and control groups. In addition, we randomly selected 13 gender-matched control subjects to compare with the 13 subjects who had a parent with schizophrenia. A p-value o 0.05 was considered statistically significant in all of the analyses. When comparing demographic and clinical data between study groups IBM SPSS statistics, Version 20 (1989, 2011 SPSS Inc., an IBM Company) was used for statistical analyses. Chi square test was used for categorical variables and independent-samples t-test for continuous variables. 2.8. Attrition analysis Forty-seven subjects with FR out of 272 (17%) invited FR subjects participated in the study. The corresponding figures in the control group were 51 out of 193 (26%) control subjects. According to FHDR, 30% of the non-participating FR subjects and 28% of the participants had a parent with schizophrenia (Pearson Chi-Square Test p ¼0.727). In the FR group 8.0% of the non-participants and 6.4% of the participants had been treated in hospital between the years 2001 and 2005 due to a psychiatric disorder. In controls 2.8% (n¼ 4) of the non-participating and none of the participating subjects had been treated in hospital due to a non-psychotic psychiatric disorder (Fisher’s Exact Test p ¼0.575).
2.5. Imaging protocol
3. Results All MRI scans were obtained at the Oulu University Hospital using a GE Signa 1.5 T system. Diffusion-weighted imaging data were acquired with single-shot echo planar imaging. Parameters for the DTI were as follows: TR (time of repetition) 8700 ms, optimised TE (echo time) by the scanner (approximately 90–100 ms), NEX (number of excitations) ¼ 1, and FOV (field of view) 24 24 cm2. Slice thickness was 3.0 mm, and matrix size 128 128, with a resulting voxel size of 1.875 1.875 3.0 mm3. The data were reconstructed into a 256 256 matrix (inplane resolution 0.9375 0.9375 mm2). The diffusion gradients were applied along 40 nonparallel directions (b¼ 1000 s/mm2) and one without diffusion weighting (b¼ 0). 2.6. Processing of the DTI data Data pre-processing was conducted with FSL 5.01 software package (Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library; see www. fmrib.ox.ac.uk/fsl). The diffusion-weighted images were registered to the non-diffusion-weighted image by affine transformations to minimise distortions due to eddy currents and to reduce simple head motion via eddy current correction. Images were checked visually by one of the authors (LB), and subjects with image distortions and anatomical changes were removed blind to group. Voxelwise statistical analysis of the DTI data was carried out using TBSS TractBased Spatial Statistics (Smith et al., 2006), a part of the FSL (Smith et al., 2004). First, non-diffusion images were brain-extracted using the BET Brain Extraction Tool (Smith, 2002), after which the results were used to create brain-only diffusion images. FA images were then created by fitting a tensor model to the raw diffusion data using FDT (FMRIB’s Diffusion Toolbox). Images from all individuals were aligned to an FMRIB58 FA standard-space image using a nonlinear registration tool FNIRT (FMRIB technical reports TR07JA1 & TR07JA2 from www.fmrib.ox.ac.uk/ana lysis/techrep). FA maps were then averaged to produce a group mean image, which was fed into non-maximum-suppression to find the maximum FA value in the tract perpendicular direction. The resulting FA skeleton represents the centres of all major tracts common to the group. The FA map was thresholded at 0.3. Aligned FA data from each subject FA data were projected onto this skeleton by searching for the maximum local FA value and the resulting data were fed into voxelwise grouplevel analysis. Skeletonised FA was tested voxelwise for differences between the study group
The demographic characteristics and clinical data of the groups are presented in Tables 1 and 2. Of the 98 participants, 34 (35%) were male. The mean age of the whole sample was 22.3 (SD 0.8) years. The groups were similar with respect to gender, age, handedness and educational level. We found no significant difference between the groups in intelligence, GAF or alcohol use. In TBSS analyses no significant differences between participants with FR for psychosis and the control group were found in FA, MD or L1–L3. There were no differences in these measures when comparing a subgroup of participants with FR for schizophrenia to a control group. Mean FA values were 0.664 (SD 0.0194) for participants with FR for psychosis and 0.663 (SD 0.0188) for controls. Mean FA values by group are shown in Fig. 2. Analysis of the FA skeleton of 0.3 or the more strongly limited skeleton of 0.5 threshold resulted in no difference between the groups. In the ROI analysis with 12 separate ROIs no significant differences were found between the FR and control group.
4. Discussion 4.1. Main findings In this study we evaluated a group of young adults with FR for psychosis in a general population. We originally assumed that they would have lower FA than controls in the same regions that have been identified in patients with schizophrenia. We did not find any differences between the groups.
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4.2. Comparison to earlier studies In the literature there is evidence suggesting that FA is decreased in subjects with a family member with schizophrenia (Hoptman et al., 2008; Muñoz Maniega et al., 2008; Camchong et al., 2009; Hao et al., 2009; Knöchel et al., 2012; Moran et al., 2015), but the results are conflicting. Domen et al. (2013) studied patients with psychotic disorder (schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, and psychotic disorder not otherwise specified), siblings of patients and healthy controls. They found that mean FA values were generally lower in siblings than in controls, but differences were not extensive or statistically significant when using the TBSS method. Increased FA has also been shown in FR subjects in right middle frontal gyri, bilateral pontine tegmental WM, left subgenual anterior cingulate WM (Hoptman et al., 2008) and in arcuate fasciculus bilaterally (Boos et al., 2013) compared to healthy control subjects. Muñoz Maniega et al. (2008) evaluated 22 individuals (mean age 30, SD7 3) at high genetic risk for developing schizophrenia in addition to subjects with schizophrenia and healthy controls. They recruited subjects with two or more affected first- or second-degree relatives. In voxel-based analysis (VBA) they did not find any difference between high genetic risk and control groups, but using ROI analysis they found decreased FA in ALIC (anterior limb of internal capsules) in the high-risk group. Individuals having one parent with psychosis has a lower risk for developing psychosis compared to subjects with both parents affected (Gottesman et al., 2010). In our study, all the subjects had only one parent with a psychotic illness. In the literature regarding WM structure in FR subjects, the age range is usually wide. Subjects' age is an important factor when examining brain structure. The general trend is that average FA rises through childhood and adolescence, being highest at the age of around 33 years, after which it declines due to aging processes. MD follows a similar but inverse trajectory, declining until the age of around 38 years and increasing after that (Hasan et al., 2007). Non-psychotic siblings of patients with childhood-onset schizophrenia show slower WM development growth rates in the parietal lobes (Gogtay et al., 2012) and grey matter deficits (Gogtay et al., 2007) during childhood when compared to controls, but these changes seem to normalise with age. In a recent systematic review Chiapponi et al. (2013) reviewed age-related structural white matter trajectories in patients with schizophrenia. They concluded that results are variable and different brain areas change with different trajectories; in some regions changes appear at the time of illness onset while in other brain areas they are present in earlier stages of life. Some changes stabilise after the acute phase and some impairments continue to worsen; for Table 1 Demographics in the familial risk for psychosis (FR) and Control groups in the Oulu Brain and Mind Study. Variable
FR group (n¼ 47)
Control group (n¼ 51)
Statistical testing
p-value
Age, years [M (SD)] Gender, male [n (%)] Handedness, right [n (%)] Education level [n (%)] o 9 school years Comprehensive (9–11 school years) Matriculation (4 11 school years)
22.3 (0.8) 17 (36) 44 (94)
22.2 (0.7) 17 (33) 50 (98)
t¼ 1.0 χ2 ¼ 0.087 χ2 ¼ 1.2 χ2 ¼ 2.6
0.336 0.768 0.269 0.273
1 (2) 19 (40)
0 (0) 15 (29)
27 (57)
36 (71)
M ¼mean, SD ¼ standard deviation
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Table 2 Clinical data in the familial risk for psychosis (FR) and Control groups in the Oulu Brain and Mind Study. Variable
FR group (n¼ 47)a
Control group (n¼ 51)b
Statistical testing
p-value
Estimated IQ [M (SD)] GAF [M (SD)] Alcohol use: I drink too much alcohol [n (%)] Not true Somewhat or sometimes true Very true or often true
111.4 (22.1) 82.3 (5.9)
108.2 (22.6) 83.4 (8.1)
t¼ 0.70 t¼ 0.75 χ2 ¼0.53
0.488 0.453 0.769
31 (71) 11 (25)
37 (74) 12 (24)
2 (5)
1 (2)
Estimated IQ ¼intelligence quotient estimated by two subtests from the Weschsler intelligence Scale III, Finnish version. M¼ mean, SD ¼standard deviation, GAF¼ Global Assessment of Functioning. a b
Data missing for the family risk for psychosis group: three for alcohol use. Data missing for the control group: one for alcohol use.
example the uncinate fasciculus is affected around the onset of schizophrenia, but is not affected early in life. In an analysis of resting state fMRI in this same cohort, FR participants showed lower levels of activity in the posterior cingulate cortex (Jukuri et al., 2013). However, the neurocognitive profile of FR participants was not significantly different from that of control subjects (Mukkala et al., 2011). Putting these findings together with our present finding of no disruptions in WM tract integrity, it may be concluded that familial risk does not work through connectivity in white matter, but connectivity through in certain grey matter networks. These results are similar to those reported by Yaakub et al. (2013) in a sample with clinical risk of psychosis: working memory was preserved but alteration in brain activity was found. 4.3. Strengths and limitations The main strength of the study is the unique general population-based birth-cohort setting. Participants had the same ethnic background and they were young adults of the same age. To detect parental psychosis we used the FHDR which has been shown to have high reliability in detecting psychoses (Perälä et al., 2007). On the other hand, parental diagnoses were only register-based and parents did not take part in any clinical examination or interviews. For the analyses, we used TBSS, a method which does not need smoothing, and thus increases the sensitivity and interpretability of the results when compared to VBA (Smith et al., 2006). Some limitations should be considered in our study. The participation rate was relatively low. However, the attrition analysis showed no differences between participants and non-participants in the FR group with respect to hospitalisation due to psychiatric disorder. The number of participants having a FR for psychosis was low, limiting the generalisability of the study results. This applies especially to the sub group of subjects who had a parent with schizophrenia, which is why the further analyses with subgroups may not be conclusive. The FR group was heterogeneous with respect to the psychotic diagnosis of the parent. The negative result in our group comparison may reflect a relative lack of power: because of data variance and a limited study sample, the effect of group difference is easily lost after correction for multiple comparisons. To confirm the results, we analysed the data further with a higher FA skeleton threshold. This was done to limit the data studied to the tracts with minimum inter-subject variability and the least partial-volume-effect. The method also alleviates the problem of multiple comparisons by reducing the
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Fig. 2. Box plot of the mean fractional anisotropy in three groups of subjects: parent with schizophrenia, parent with non-schizophrenia psychosis and control groups.
studied volume, thus amplifying the group differences found at these locations. Indeed, the most statistically significant voxels appear in these prominent tracts across the analysis. Finally, the ROI analysis of the 12 tract volumes was conducted in order to reveal the effect of these interesting tracts on the group comparison in the presence of a limited voxel number. The imaging protocol was not optimal to detect small differences between groups. The slices were relatively thick (3.0 mm) and at the time, MRI with 1.5 T magnetic field was used instead of the more accurate 3.0 T field. This may have caused type II error in the results.
5. Conclusions Contrary to our hypothesis, we did not find differences in WM structure between individuals with a FR of psychosis and controls. Our results suggest that WM abnormalities are not a genetic feature of risk for psychosis. Our findings do not support the theory of structural disconnection as a primary sign of psychosis in young adults.
Nikkinen analysed the DTI data. Tanja Nordström and Jenni Koivukangas conducted statistical analyses. Jenni Koivukangas, Juha Veijola, Lassi Björnholm and Juha Nikkinen drafted the first version of the manuscript. All authors have drafted the manuscript and approved the final version.
Acknowledgements We wish to thank all the participants, field study researchers and the staff in Ward 73 in the University Hospital of Oulu, Finland. This work was supported by grants from the Academy of Finland (Grant codes #124257, 212828, 214273), the Sigrid Jusélius Foundation, the Yrjö Jahnsson Foundation, Thule Institute, The Signe and Ane Gyllenberg Foundation, Finland, UK Medical Research Council and the NARSAD: Brain and Behaviour Research Fund.
References Conflict of interest All the authors declared no conflicts of interest.
Contributors Vesa Kiviniemi, Juha Nikkinen, Jouko Miettunen, Pirjo Mäki, Erika Jääskeläinen, Sari Mukkala, Irma Moilanen, Jennifer H Barnett, Peter B Jones, Osmo Tervonen and Juha Veijola contributed to the study design. Juha Veijola, Pirjo Mäki, Vesa Kiviniemi, Juha Nikkinen, Erika Jääskeläinen, Jenni Koivukangas and Sari Mukkala participated in collection of the data. Lassi Björnholm and Juha
American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth ed. Washington, DC. Basser, P.J., Mattiello, J., LeBihan, D., 1994. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66 (1), 259–267. Basser, P.J., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 111, 209–219. Boos, H.B., Mandl, R.C., van Haren, N.E., Cahn, W., van Baal, G.C., Kahn, R.S., Hulshoff Pol, H.E., 2013. Tract-based diffusion tensor imaging in patients with schizophrenia and their non-psychotic siblings. Eur. Neuropsychopharmacol. 23 (4), 295–304. Camchong, J., Lim, K.O., Sponheim, S.R., Macdonald, A.W., 2009. Frontal white matter integrity as an endophenotype for schizophrenia: diffusion tensor imaging in monozygotic twins and patients’ nonpsychotic relatives. Front. Hum. Neuroscience 3, 35. Chiapponi, C., Piras, F., Fagioli, S., Piras, F., Caltagirone, C., Spalletta, G., 2013. Agerelated brain trajectories in schizophrenia: a systematic review of structural
J. Koivukangas et al. / Psychiatry Research: Neuroimaging 233 (2015) 388–393
MRI studies. Psychiatry Res. 214 (2), 83–93. Delisi, L., 1992. The significance of age of onset for schizophrenia. Schizophr. Bull. 18 (2), 209–215. Domen, P.A., Michielse, S., Gronenschild, E., Habets, P., Roebroeck, A., Schruers, K., van Os, J., Marcelis, M., for G.R.O.U.P, 2013. Microstructural white matter alterations in psychotic disorder: a family-based diffusion tensor imaging study. Schizophr. Res. 146 (1–3), 291–300. Ellison-Wright, I., Bullmore, E., 2009. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr. Res. 108 (1–3), 3–10. Friston, K.J., Frith, C.D., 1995. Schizophrenia: a disconnection syndrome? J. Clin. Neurosci. 3 (2), 89–97. Giedd, J.N., Keshavan, M., Paus, T., 2008. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9 (12), 947–957. Gogtay, N., Greenstein, D., Lenane, M., Clasen, L., Sharp, W., Gochman, P., Butler, P., Evans, A., Rapoport, J., 2007. Cortical brain development in nonpsychotic siblings of patients with childhood-onset schizophrenia. Arch. Gen. Psychiatry 64 (7), 772–780. Gogtay, N., Hua, X., Stidd, R., Boyle, C.P., Lee, S., Weisinger, B., Chavez, A., Giedd, J.N., Clasen, L., Toga, A.W., Rapoport, J.L., Thompson, P.M., 2012. Delayed white matter growth trajectory in young nonpsychotic siblings of patients with childhood-onset schizophrenia. Arch. Gen. Psychiatry 69 (9), 875–884. Gottesman, I.I., 1991. Schizophrenia Genesis. The Origins of Madness. W. H. Freeman and Company, New York, NY. Gottesman, I.I., Laursen, T.M., Bertelsen, A., Mortensen, P.B., 2010. Severe mental disorders in offspring with 2 psychiatrically ill parents. Arch. Gen. Psychiatry 67 (3), 252–257. Hao, Y., Yan, Q., Liu, H., Xu, L., Xue, Z., Song, X., Kaneko, Y., Jiang, T., Liu, Z., Shan, B., 2009. Schizophrenia patients and their healthy siblings share disruption of white matter integrity in the left prefrontal cortex and the hippocampus but not the anterior cingulate cortex. Schizophr. Res. 114 (1–3), 128–135. Hasan, K.M., Sankar, A., Halphen, C., Kramer, L.A., Brandt, M.E., Juranek, J., Cirino, P. T., Fletcher, J.M., Papanicolaou, A.C., Ewing-Cobbs, L., 2007. Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. Neuroreport 18 (16), 1735–1739. Hoptman, M.J., Nierenberg, J., Bertisch, H.C., Catalano, D., Ardekani, B.A., Branch, C. A., Delisi, L.E., 2008. A DTI study of white matter microstructure in individuals at high genetic risk for schizophrenia. Schizophr. Res. 106 (2–3), 115–124. Häfner, H., Maurer, K., Löffler, W., Fätkenheuer, B., an der Heiden, W., RiecherRössler, A., Behrens, S., Gattaz, W.F., 1994. The epidemiology of early schizophrenia. Influence of age and gender on onset and early course. Br. J. Psychiatry 23 (23), 29–38. Jukuri, T., Kiviniemi, V., Nikkinen, J., Miettunen, J., Mäki, P., Jääskelainen, E., Mukkala, S., Koivukangas, J., Nordström, T., Taanila, A., Moilanen, I., Heinimaa, M., Barnett, J.H., Jones, P.B., Murray, G.K., Veijola, J., 2013. Default mode network in young people with familial risk for psychosis—the Oulu Brain and Mind study. Schizophr. Res. 143 (2–3), 239–245. Järvelin, M.R., Hartikainen-Sorri, A.L., Rantakallio, P., 1993. Labour induction policy in hospitals of different levels of specialisation. Br. J. Obstet. Gynaecol. 100 (4), 310–315. Knöchel, C., Oertel-Knöchel, V., Schönmeyer, R., Rotarska-Jagiela, A., van de Ven, V., Prvulovic, D., Haenschel, C., Uhlhaas, P., Pantel, J., Hampel, H., Linden, D.E., 2012. Interhemispheric hypoconnectivity in schizophrenia: fiber integrity and volume differences of the corpus callosum in patients and unaffected relatives. Neuroimage 59 (2), 926–934.
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Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabriat, H., 2001. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13 (4), 534–546. McGlashan, T.H., Miller, T.J., Woods, S.W., Rosen, J.L., Hoffman, R.E., Davidson, L., 2001. SIPS, Structured Interview for Prodromal Syndromes-Version for Present Prodromal Syndromes. Version 3.0 PRIME Research Clinic. Yale School of Medicine, New Haven, CT. Moran, M.E., Luscher, Z.I., McAdams, H., Hsu, J.T., Greenstein, D., Clasen, L., Ludovici, K., Lloyd, J., Rapoport, J., Mori, S., Gogtay, N., 2015. Comparing fractional anisotropy in patients with childhood-onset schizophrenia, their healthy siblings, and normal volunteers through DTI. Schizophr. Bull. 41 (1), 66–73. Mukkala, S., Ilonen, T., Nordström, T., Miettunen, J., Loukkola, J., Barnett, J.H., Murray, G.K., Jääskelainen, E., Mäki, P., Taanila, A., Moilanen, I., Jones, P.B., Heinimaa, M., Veijola, J., 2011. Different vulnerability indicators for psychosis and their neuropsychological characteristics in the Northern Finland 1986 Birth Cohort. J. Clin. Exp. Neuropsychol. 33 (4), 385–394. Muñoz Maniega, S., Lymer, G.K., Bastin, M.E., Marjoram, D., Job, D.E., Moorhead, T. W., Owens, D.G., Johnstone, E.C., McIntosh, A.M., Lawrie, S.M., 2008. A diffusion tensor MRI study of white matter integrity in subjects at high genetic risk of schizophrenia. Schizophr. Res. 106 (2–3), 132–139. Perälä, J., Suvisaari, J., Saarni, S.I., Kuoppasalmi, K., Isometsä, E., Pirkola, S., Partonen, T., Tuulio-Henriksson, A., Hintikka, J., Kieseppä, T., Härkänen, T., Koskinen, S., Lönnqvist, J., 2007. Lifetime prevalence of psychotic and bipolar I disorders in a general population. Arch. Gen. Psychiatry 64 (1), 19–28. Peters, B.D., Karlsgodt, K.H., 2015. White matter development in the early stages of psychosis. Schizophr. Res. 161 (1), 61–69. Samartzis, L., Dima, D., Fusar-Poli, P., Kyriakopoulos, M., 2014. White matter alterations in early stages of schizophrenia: a systematic review of diffusion tensor imaging studies. J. Neuroimaging 24 (2), 101–110. Smith, S.M., 2002. Fast robust automated brain extraction. Hum. Brain Mapp. 17 (3), 143–155. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., JohansenBerg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M., 2004. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl 1), S208–S219. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C. E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31 (4), 1487–1505. Veijola, J., Maki, P., Jääskelainen, E., Koivukangas, J., Moilanen, I., Taanila, A., Nordström, T., Hurtig, T., Kiviniemi, V., Mukkala, S., Heinimaa, M., Lindholm, P., Jones, P.B., Barnett, J.H., Murray, G.K., Miettunen, J., 2013. Young people at risk for psychosis: case finding and sample characteristics of the Oulu Brain and Mind Study. Early Interv. Psychiatry 7 (2), 146–154. Wechsler, D., 1997. Wechsler Adult Intelligence Scale–Third Edition. Manual, San Antonio, Tx, The Psychological Corporation. Yaakub, S.N., Dorairaj, K., Poh, J.S., Asplund, C.L., Krishnan, R., Lee, J., Keefe, R.S., Adcock, R.A., Wood, S.J., Chee, M.W., 2013. Preserved working memory and altered brain activation in persons at risk for psychosis. Am. J. Psychiatry 170 (11), 1297–1307.
Contents lists available at ScienceDirect
Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns
White matter structure in young adults with familial risk for psychosis – The Oulu Brain and Mind Study Jenni Koivukangas a,b,c,n, Lassi Björnholm a,b, Osmo Tervonen b, Jouko Miettunen a,d,e, Tanja Nordström c,d,e, Vesa Kiviniemi b, Pirjo Mäki a,d, Erika Jääskeläinen a,d,e, Sari Mukkala a,d, Irma Moilanen c,f,g, Jennifer H. Barnett h, Peter B. Jones h, Juha Nikkinen b, Juha Veijola a,c,d a
Department of Psychiatry, Institute of Clinical Medicine, University of Oulu, Oulu, Finland Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland Thule Doctoral Programme, University of Oulu, Oulu, Finland d Department of Psychiatry, Oulu University Hospital, Oulu, Finland e Institute of Health Sciences, University of Oulu, Oulu, Finland f Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland g PEDEGO Research Center, University of Oulu, Oulu, Finland h Department of Psychiatry, University of Cambridge, Cambridge, UK b c
art ic l e i nf o
a b s t r a c t
Article history: Received 20 October 2014 Received in revised form 21 January 2015 Accepted 27 June 2015 Available online 2 July 2015
According to the disconnectivity model, disruptions in neural connectivity play an essential role in the pathology of schizophrenia. The aim of this study was to determine whether these abnormalities are present in young adults with familial risk (FR) for psychosis in the general population based sample. We used diffusion tensor imaging (DTI) and tract-based spatial statistics to compare whole-brain fractional anisotropy, mean diffusivity, and axial and radial diffusion in 47 (17 males) FR subjects to 51 controls (17 males). All the participants were aged between 20 and 25 years and were members of the Northern Finland Birth Cohort 1986 (Oulu Brain and Mind Study). Region of interest analyses were conducted for 12 tracts. Separately, we analysed whole-brain FA for the subgroup with FR for schizophrenia (n ¼13) compared with 13 gender-matched controls. Contrary to our expectations there were no differences in any of the DTI measures between FR and control groups. This suggests that white matter abnormalities may not be a genetic feature for risk of psychosis and preceding the onset of a psychotic disorder. Our findings do not support the theory of disconnectivity as a primary sign of psychosis in young adults with FR for the illness. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: DTI TBSS Birth cohort Risk for psychosis
1. Introduction Friston and Frith (1995) suggested that dysfunctional connectivity plays an important role in the pathology of schizophrenia. Diffusion tensor imaging (DTI) studies have found decreased white matter (WM) integrity in the frontal and temporal lobes in schizophrenia (Ellison-Wright and Bullmore, 2009). DTI is a magnetic resonance imaging (MRI) method based on water diffusion in tissues (Basser et al., 1994). The nature of diffusion can be described using indices, such as mean diffusivity (MD) which describes the strength of diffusion, and fractional anisotropy (FA) which describes the asymmetry of diffusion (Basser and Pierpaoli, n Correspondence to: Department of Psychiatry, P.O. Box 5000, 90014 University of Oulu, Finland. Fax: þ 358 85 375 661. E-mail address: jenni.koivukangas@oulu.fi (J. Koivukangas).
http://dx.doi.org/10.1016/j.pscychresns.2015.06.015 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.
1996). FA represents a ratio between diffusion along the fibre axis (L1), and diffusion perpendicular to axis (L2–L3), thus alterations may arise from diffusivity change in either of these (Le Bihan et al., 2001). People with a first-degree relative with schizophrenia have an elevated risk of the illness (Gottesman, 1991). Comparison between individuals at risk for psychosis and control subjects has methodological advantages. Both of these groups are free from antipsychotic medications and illness related factors, which may affect the WM integrity (Samartzis et al., 2014). Recent studies report reduced WM connectivity in several brain regions in subjects with a family member with schizophrenia when compared to healthy controls (Peters and Karlsgodt, 2015). Alterations in WM structure in family risk (FR) subjects has been found in regions including medial frontal regions (Camchong et al., 2009), corpus callosum (Knöchel et al., 2012), anterior limb of
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internal capsule (Muñoz Maniega et al., 2008), cingulate and angular area (Hoptman et al., 2008), prefrontal cortex, and hippocampus (Hao et al., 2009), and in healthy siblings of patients with childhood-onset schizophrenia in cuneus (Moran et al., 2015). In addition to decreased connectivity, increased FA has been found (Hoptman et al., 2008; Boos et al., 2013), and one study found no WM differences (Domen et al., 2013). In a recent systematic review Chiapponi et al. (2013) reviewed age-related structural WM trajectories in patients with schizophrenia. They concluded that results are variable and different brain areas change with different trajectories. In the present study we evaluated young adults with FR for psychosis in a general population sample. Subjects were in their early adulthood, at the age of high risk for developing schizophrenia (Delisi, 1992; Häfner et al., 1994; Giedd et al., 2008). We hypothesised that subjects with FR for psychosis in the general population would show abnormal WM integrity, and that subjects with FR for schizophrenia would show lower FA than subjects with a parent with a non-schizophrenia psychosis or a control group.
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2.2. Invitation The study procedure, details of subject selection and the field study conducted between 2007 and 2010 have been described by Veijola et al. (2013). The Finnish Hospital Discharge Register (FHDR) was used to detect hospital-treated psychiatric diagnoses in parents between 1972 and 2005 (ICD-8, and ICD-9 codes 295-299 and ICD-10 codes F20-33, except non-psychotic mood disorders). In the invitation phase a history of any psychotic episodes according to the FHDR until the end of 2008, or having the right to reimbursable antipsychotic medication before the end of 2005 according to the registers of the Social Insurance Institute (SII) of Finland, were exclusion criteria. The SII register includes information about the right to reimbursable medication due to psychotic disorders. According to FHDR 272 of the cohort members had a parent with a psychotic disorder, of whom one had died, five were living abroad, and for four subjects no address was available. Altogether 262 FR subjects were invited into the study (Fig. 1). A control group was formed by inviting a random sample of cohort members using the following exclusion criteria: a history of any psychotic episodes, having symptomatic risk for psychosis or having a parent with psychotic illness or A-type personality disorder. Symptomatic risk for psychosis was defined as having attenuated psychosis-like experiences and some degree of functional impairment in the educational, social or health domain. After exclusions the number of potential controls was 8763 and of this group, 193 (2.2%) individuals were randomly selected to form a control group. One of the subjects had died and no address was available for another. An invitation letter was therefore sent to 191 individuals.
2. Methods 2.3. Field study and variables used in the study 2.1. Oulu brain and mind study Oulu Brain and Mind study members are a subsample of the Northern Finland Birth Cohort 1986 (NFBC 1986). NFBC 1986 comprises children (n¼ 9432) with an expected date of birth between 1st July 1985 and 30th June 1986 (Järvelin et al., 1993). The cohort members were born in the two northernmost provinces of Finland: Oulu and Lapland. Data collection was started prospectively before birth and has continued since. The Ethics committee of the Northern Ostrobothnia Hospital District in Finland has approved the study.
A sub study of the NFBC 1986, the Oulu Brain and Mind Study (Veijola et al., 2013) took place between 2007 and 2010 when the participants were 20–25 years old. The protocol allowed two thirds of the participants to be scanned with DTI. A cognitive test battery and background questionnaires were also completed. The Structured Interview for Prodromal Syndromes (SIPS) (McGlashan et al., 2001) was conducted by trained interviewers and used to determine possible lifetime psychotic episodes, previous prodromal syndromes and current prodromal symptoms. Participants also gave a urine sample to assess drug use.
Fig. 1. Flow chart of the study sample in the Oulu Brain and Mind Study.
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Handedness was defined by asking which hand the subjects preferred to use when writing. Educational level was based on a question about basic education and categorised into three classes: less than nine school years, comprehensive school, and matriculation. During the field study participants were asked if they drank too much alcohol (not true, somewhat or sometimes true, very true or often true) and about current psychiatric medication. IQ was estimated from the Matrix Reasoning and Vocabulary tests of the WAISIII (Wechsler Adult Intelligence Scale III Edition, Finnish version) (Wechsler, 1997). Matrix Reasoning assesses perception of details, spatial perception and analogical reasoning. Vocabulary assesses the ability to express word meanings and word knowledge (Mukkala et al., 2011). Global Assessment of Functioning, GAF (American Psychiatric Association, 1994) was evaluated during the psychiatric interview. GAF is a numeric scale (0 through 100) used to rate occupational, social, and psychological functioning.
means using a randomiser tool (5000 permutations) with threshold-free cluster enhancement (tfce) correction method. To study only the presumably more representative voxels – considering minimal partial-volume-effect, registration errors and tract-construction artefacts – the skeleton was further limited with an FA threshold of 0.5. An additional analysis of corpus callosum, cingulum, fornix, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and uncinate fasciculus was also conducted. The tract selection was based on findings in extant literature from DTI studies with FR subjects, and main tracts connecting the hemispheres and fronto-temporal and fronto-parietal regions. The ROIs were formed by masking the original FA skeleton (thresholded at 0.3), one structure at a time, according to two atlases included in FSL, namely the JHU ICBM-DTI81 White-Matter Labels and JHU White-Matter Tractography Atlas (lower probability threshold 25%). 2.7. Statistical analysis
2.4. Final study groups Fifty-three of the invited FR subjects and 55 of the controls participated and were DTI scanned. The exclusion criteria for both groups were: history of head trauma with loss of consciousness for 30 min or more, severe neurological illness, or a history of psychosis. One participant in the control group had a history of head trauma with about 30 min of unconsciousness and was excluded. According to SIPS interview, one participant in the FR and one in the control group had a history of psychotic episodes and were excluded. In addition, five FR and two control participants had to be excluded due to a low-quality scan. The final groups consisted of 47 FR and 51 control participants (Fig. 1). Written informed consent was obtained from all participants. In the FR group 13 paticipants had a parent with schizophrenia and 34 with another psychotic disorder (schizoaffective disorder, n¼ 2; schizophreniform disorder, n ¼2; delusional disorder, n ¼4; psychotic depressive disorder, n¼ 6; psychotic bipolar disorder, n ¼8; other psychotic disorder, n¼ 12). According to the FHDR, none of the participants in our final sample had both parents with psychosis or a parent with solely a diagnosis of an A-type personality disorder. Seven participants in the FR group and two in the control group had a current prodromal syndrome according to the SIPS interview. One participant in the control group had a positive urine drug test for opioids and the urine test was missing for one control. These tests were negative in the FR group. None of the participants in the FR group reported having psychiatric medication while two control participant used psychiatric medication.
We compared the differences in FA, MD and L1–L3 maps between FR for psychosis and control groups. In addition, we randomly selected 13 gender-matched control subjects to compare with the 13 subjects who had a parent with schizophrenia. A p-value o 0.05 was considered statistically significant in all of the analyses. When comparing demographic and clinical data between study groups IBM SPSS statistics, Version 20 (1989, 2011 SPSS Inc., an IBM Company) was used for statistical analyses. Chi square test was used for categorical variables and independent-samples t-test for continuous variables. 2.8. Attrition analysis Forty-seven subjects with FR out of 272 (17%) invited FR subjects participated in the study. The corresponding figures in the control group were 51 out of 193 (26%) control subjects. According to FHDR, 30% of the non-participating FR subjects and 28% of the participants had a parent with schizophrenia (Pearson Chi-Square Test p ¼0.727). In the FR group 8.0% of the non-participants and 6.4% of the participants had been treated in hospital between the years 2001 and 2005 due to a psychiatric disorder. In controls 2.8% (n¼ 4) of the non-participating and none of the participating subjects had been treated in hospital due to a non-psychotic psychiatric disorder (Fisher’s Exact Test p ¼0.575).
2.5. Imaging protocol
3. Results All MRI scans were obtained at the Oulu University Hospital using a GE Signa 1.5 T system. Diffusion-weighted imaging data were acquired with single-shot echo planar imaging. Parameters for the DTI were as follows: TR (time of repetition) 8700 ms, optimised TE (echo time) by the scanner (approximately 90–100 ms), NEX (number of excitations) ¼ 1, and FOV (field of view) 24 24 cm2. Slice thickness was 3.0 mm, and matrix size 128 128, with a resulting voxel size of 1.875 1.875 3.0 mm3. The data were reconstructed into a 256 256 matrix (inplane resolution 0.9375 0.9375 mm2). The diffusion gradients were applied along 40 nonparallel directions (b¼ 1000 s/mm2) and one without diffusion weighting (b¼ 0). 2.6. Processing of the DTI data Data pre-processing was conducted with FSL 5.01 software package (Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library; see www. fmrib.ox.ac.uk/fsl). The diffusion-weighted images were registered to the non-diffusion-weighted image by affine transformations to minimise distortions due to eddy currents and to reduce simple head motion via eddy current correction. Images were checked visually by one of the authors (LB), and subjects with image distortions and anatomical changes were removed blind to group. Voxelwise statistical analysis of the DTI data was carried out using TBSS TractBased Spatial Statistics (Smith et al., 2006), a part of the FSL (Smith et al., 2004). First, non-diffusion images were brain-extracted using the BET Brain Extraction Tool (Smith, 2002), after which the results were used to create brain-only diffusion images. FA images were then created by fitting a tensor model to the raw diffusion data using FDT (FMRIB’s Diffusion Toolbox). Images from all individuals were aligned to an FMRIB58 FA standard-space image using a nonlinear registration tool FNIRT (FMRIB technical reports TR07JA1 & TR07JA2 from www.fmrib.ox.ac.uk/ana lysis/techrep). FA maps were then averaged to produce a group mean image, which was fed into non-maximum-suppression to find the maximum FA value in the tract perpendicular direction. The resulting FA skeleton represents the centres of all major tracts common to the group. The FA map was thresholded at 0.3. Aligned FA data from each subject FA data were projected onto this skeleton by searching for the maximum local FA value and the resulting data were fed into voxelwise grouplevel analysis. Skeletonised FA was tested voxelwise for differences between the study group
The demographic characteristics and clinical data of the groups are presented in Tables 1 and 2. Of the 98 participants, 34 (35%) were male. The mean age of the whole sample was 22.3 (SD 0.8) years. The groups were similar with respect to gender, age, handedness and educational level. We found no significant difference between the groups in intelligence, GAF or alcohol use. In TBSS analyses no significant differences between participants with FR for psychosis and the control group were found in FA, MD or L1–L3. There were no differences in these measures when comparing a subgroup of participants with FR for schizophrenia to a control group. Mean FA values were 0.664 (SD 0.0194) for participants with FR for psychosis and 0.663 (SD 0.0188) for controls. Mean FA values by group are shown in Fig. 2. Analysis of the FA skeleton of 0.3 or the more strongly limited skeleton of 0.5 threshold resulted in no difference between the groups. In the ROI analysis with 12 separate ROIs no significant differences were found between the FR and control group.
4. Discussion 4.1. Main findings In this study we evaluated a group of young adults with FR for psychosis in a general population. We originally assumed that they would have lower FA than controls in the same regions that have been identified in patients with schizophrenia. We did not find any differences between the groups.
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4.2. Comparison to earlier studies In the literature there is evidence suggesting that FA is decreased in subjects with a family member with schizophrenia (Hoptman et al., 2008; Muñoz Maniega et al., 2008; Camchong et al., 2009; Hao et al., 2009; Knöchel et al., 2012; Moran et al., 2015), but the results are conflicting. Domen et al. (2013) studied patients with psychotic disorder (schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, and psychotic disorder not otherwise specified), siblings of patients and healthy controls. They found that mean FA values were generally lower in siblings than in controls, but differences were not extensive or statistically significant when using the TBSS method. Increased FA has also been shown in FR subjects in right middle frontal gyri, bilateral pontine tegmental WM, left subgenual anterior cingulate WM (Hoptman et al., 2008) and in arcuate fasciculus bilaterally (Boos et al., 2013) compared to healthy control subjects. Muñoz Maniega et al. (2008) evaluated 22 individuals (mean age 30, SD7 3) at high genetic risk for developing schizophrenia in addition to subjects with schizophrenia and healthy controls. They recruited subjects with two or more affected first- or second-degree relatives. In voxel-based analysis (VBA) they did not find any difference between high genetic risk and control groups, but using ROI analysis they found decreased FA in ALIC (anterior limb of internal capsules) in the high-risk group. Individuals having one parent with psychosis has a lower risk for developing psychosis compared to subjects with both parents affected (Gottesman et al., 2010). In our study, all the subjects had only one parent with a psychotic illness. In the literature regarding WM structure in FR subjects, the age range is usually wide. Subjects' age is an important factor when examining brain structure. The general trend is that average FA rises through childhood and adolescence, being highest at the age of around 33 years, after which it declines due to aging processes. MD follows a similar but inverse trajectory, declining until the age of around 38 years and increasing after that (Hasan et al., 2007). Non-psychotic siblings of patients with childhood-onset schizophrenia show slower WM development growth rates in the parietal lobes (Gogtay et al., 2012) and grey matter deficits (Gogtay et al., 2007) during childhood when compared to controls, but these changes seem to normalise with age. In a recent systematic review Chiapponi et al. (2013) reviewed age-related structural white matter trajectories in patients with schizophrenia. They concluded that results are variable and different brain areas change with different trajectories; in some regions changes appear at the time of illness onset while in other brain areas they are present in earlier stages of life. Some changes stabilise after the acute phase and some impairments continue to worsen; for Table 1 Demographics in the familial risk for psychosis (FR) and Control groups in the Oulu Brain and Mind Study. Variable
FR group (n¼ 47)
Control group (n¼ 51)
Statistical testing
p-value
Age, years [M (SD)] Gender, male [n (%)] Handedness, right [n (%)] Education level [n (%)] o 9 school years Comprehensive (9–11 school years) Matriculation (4 11 school years)
22.3 (0.8) 17 (36) 44 (94)
22.2 (0.7) 17 (33) 50 (98)
t¼ 1.0 χ2 ¼ 0.087 χ2 ¼ 1.2 χ2 ¼ 2.6
0.336 0.768 0.269 0.273
1 (2) 19 (40)
0 (0) 15 (29)
27 (57)
36 (71)
M ¼mean, SD ¼ standard deviation
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Table 2 Clinical data in the familial risk for psychosis (FR) and Control groups in the Oulu Brain and Mind Study. Variable
FR group (n¼ 47)a
Control group (n¼ 51)b
Statistical testing
p-value
Estimated IQ [M (SD)] GAF [M (SD)] Alcohol use: I drink too much alcohol [n (%)] Not true Somewhat or sometimes true Very true or often true
111.4 (22.1) 82.3 (5.9)
108.2 (22.6) 83.4 (8.1)
t¼ 0.70 t¼ 0.75 χ2 ¼0.53
0.488 0.453 0.769
31 (71) 11 (25)
37 (74) 12 (24)
2 (5)
1 (2)
Estimated IQ ¼intelligence quotient estimated by two subtests from the Weschsler intelligence Scale III, Finnish version. M¼ mean, SD ¼standard deviation, GAF¼ Global Assessment of Functioning. a b
Data missing for the family risk for psychosis group: three for alcohol use. Data missing for the control group: one for alcohol use.
example the uncinate fasciculus is affected around the onset of schizophrenia, but is not affected early in life. In an analysis of resting state fMRI in this same cohort, FR participants showed lower levels of activity in the posterior cingulate cortex (Jukuri et al., 2013). However, the neurocognitive profile of FR participants was not significantly different from that of control subjects (Mukkala et al., 2011). Putting these findings together with our present finding of no disruptions in WM tract integrity, it may be concluded that familial risk does not work through connectivity in white matter, but connectivity through in certain grey matter networks. These results are similar to those reported by Yaakub et al. (2013) in a sample with clinical risk of psychosis: working memory was preserved but alteration in brain activity was found. 4.3. Strengths and limitations The main strength of the study is the unique general population-based birth-cohort setting. Participants had the same ethnic background and they were young adults of the same age. To detect parental psychosis we used the FHDR which has been shown to have high reliability in detecting psychoses (Perälä et al., 2007). On the other hand, parental diagnoses were only register-based and parents did not take part in any clinical examination or interviews. For the analyses, we used TBSS, a method which does not need smoothing, and thus increases the sensitivity and interpretability of the results when compared to VBA (Smith et al., 2006). Some limitations should be considered in our study. The participation rate was relatively low. However, the attrition analysis showed no differences between participants and non-participants in the FR group with respect to hospitalisation due to psychiatric disorder. The number of participants having a FR for psychosis was low, limiting the generalisability of the study results. This applies especially to the sub group of subjects who had a parent with schizophrenia, which is why the further analyses with subgroups may not be conclusive. The FR group was heterogeneous with respect to the psychotic diagnosis of the parent. The negative result in our group comparison may reflect a relative lack of power: because of data variance and a limited study sample, the effect of group difference is easily lost after correction for multiple comparisons. To confirm the results, we analysed the data further with a higher FA skeleton threshold. This was done to limit the data studied to the tracts with minimum inter-subject variability and the least partial-volume-effect. The method also alleviates the problem of multiple comparisons by reducing the
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Fig. 2. Box plot of the mean fractional anisotropy in three groups of subjects: parent with schizophrenia, parent with non-schizophrenia psychosis and control groups.
studied volume, thus amplifying the group differences found at these locations. Indeed, the most statistically significant voxels appear in these prominent tracts across the analysis. Finally, the ROI analysis of the 12 tract volumes was conducted in order to reveal the effect of these interesting tracts on the group comparison in the presence of a limited voxel number. The imaging protocol was not optimal to detect small differences between groups. The slices were relatively thick (3.0 mm) and at the time, MRI with 1.5 T magnetic field was used instead of the more accurate 3.0 T field. This may have caused type II error in the results.
5. Conclusions Contrary to our hypothesis, we did not find differences in WM structure between individuals with a FR of psychosis and controls. Our results suggest that WM abnormalities are not a genetic feature of risk for psychosis. Our findings do not support the theory of structural disconnection as a primary sign of psychosis in young adults.
Nikkinen analysed the DTI data. Tanja Nordström and Jenni Koivukangas conducted statistical analyses. Jenni Koivukangas, Juha Veijola, Lassi Björnholm and Juha Nikkinen drafted the first version of the manuscript. All authors have drafted the manuscript and approved the final version.
Acknowledgements We wish to thank all the participants, field study researchers and the staff in Ward 73 in the University Hospital of Oulu, Finland. This work was supported by grants from the Academy of Finland (Grant codes #124257, 212828, 214273), the Sigrid Jusélius Foundation, the Yrjö Jahnsson Foundation, Thule Institute, The Signe and Ane Gyllenberg Foundation, Finland, UK Medical Research Council and the NARSAD: Brain and Behaviour Research Fund.
References Conflict of interest All the authors declared no conflicts of interest.
Contributors Vesa Kiviniemi, Juha Nikkinen, Jouko Miettunen, Pirjo Mäki, Erika Jääskeläinen, Sari Mukkala, Irma Moilanen, Jennifer H Barnett, Peter B Jones, Osmo Tervonen and Juha Veijola contributed to the study design. Juha Veijola, Pirjo Mäki, Vesa Kiviniemi, Juha Nikkinen, Erika Jääskeläinen, Jenni Koivukangas and Sari Mukkala participated in collection of the data. Lassi Björnholm and Juha
American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth ed. Washington, DC. Basser, P.J., Mattiello, J., LeBihan, D., 1994. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66 (1), 259–267. Basser, P.J., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 111, 209–219. Boos, H.B., Mandl, R.C., van Haren, N.E., Cahn, W., van Baal, G.C., Kahn, R.S., Hulshoff Pol, H.E., 2013. Tract-based diffusion tensor imaging in patients with schizophrenia and their non-psychotic siblings. Eur. Neuropsychopharmacol. 23 (4), 295–304. Camchong, J., Lim, K.O., Sponheim, S.R., Macdonald, A.W., 2009. Frontal white matter integrity as an endophenotype for schizophrenia: diffusion tensor imaging in monozygotic twins and patients’ nonpsychotic relatives. Front. Hum. Neuroscience 3, 35. Chiapponi, C., Piras, F., Fagioli, S., Piras, F., Caltagirone, C., Spalletta, G., 2013. Agerelated brain trajectories in schizophrenia: a systematic review of structural
J. Koivukangas et al. / Psychiatry Research: Neuroimaging 233 (2015) 388–393
MRI studies. Psychiatry Res. 214 (2), 83–93. Delisi, L., 1992. The significance of age of onset for schizophrenia. Schizophr. Bull. 18 (2), 209–215. Domen, P.A., Michielse, S., Gronenschild, E., Habets, P., Roebroeck, A., Schruers, K., van Os, J., Marcelis, M., for G.R.O.U.P, 2013. Microstructural white matter alterations in psychotic disorder: a family-based diffusion tensor imaging study. Schizophr. Res. 146 (1–3), 291–300. Ellison-Wright, I., Bullmore, E., 2009. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr. Res. 108 (1–3), 3–10. Friston, K.J., Frith, C.D., 1995. Schizophrenia: a disconnection syndrome? J. Clin. Neurosci. 3 (2), 89–97. Giedd, J.N., Keshavan, M., Paus, T., 2008. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9 (12), 947–957. Gogtay, N., Greenstein, D., Lenane, M., Clasen, L., Sharp, W., Gochman, P., Butler, P., Evans, A., Rapoport, J., 2007. Cortical brain development in nonpsychotic siblings of patients with childhood-onset schizophrenia. Arch. Gen. Psychiatry 64 (7), 772–780. Gogtay, N., Hua, X., Stidd, R., Boyle, C.P., Lee, S., Weisinger, B., Chavez, A., Giedd, J.N., Clasen, L., Toga, A.W., Rapoport, J.L., Thompson, P.M., 2012. Delayed white matter growth trajectory in young nonpsychotic siblings of patients with childhood-onset schizophrenia. Arch. Gen. Psychiatry 69 (9), 875–884. Gottesman, I.I., 1991. Schizophrenia Genesis. The Origins of Madness. W. H. Freeman and Company, New York, NY. Gottesman, I.I., Laursen, T.M., Bertelsen, A., Mortensen, P.B., 2010. Severe mental disorders in offspring with 2 psychiatrically ill parents. Arch. Gen. Psychiatry 67 (3), 252–257. Hao, Y., Yan, Q., Liu, H., Xu, L., Xue, Z., Song, X., Kaneko, Y., Jiang, T., Liu, Z., Shan, B., 2009. Schizophrenia patients and their healthy siblings share disruption of white matter integrity in the left prefrontal cortex and the hippocampus but not the anterior cingulate cortex. Schizophr. Res. 114 (1–3), 128–135. Hasan, K.M., Sankar, A., Halphen, C., Kramer, L.A., Brandt, M.E., Juranek, J., Cirino, P. T., Fletcher, J.M., Papanicolaou, A.C., Ewing-Cobbs, L., 2007. Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. Neuroreport 18 (16), 1735–1739. Hoptman, M.J., Nierenberg, J., Bertisch, H.C., Catalano, D., Ardekani, B.A., Branch, C. A., Delisi, L.E., 2008. A DTI study of white matter microstructure in individuals at high genetic risk for schizophrenia. Schizophr. Res. 106 (2–3), 115–124. Häfner, H., Maurer, K., Löffler, W., Fätkenheuer, B., an der Heiden, W., RiecherRössler, A., Behrens, S., Gattaz, W.F., 1994. The epidemiology of early schizophrenia. Influence of age and gender on onset and early course. Br. J. Psychiatry 23 (23), 29–38. Jukuri, T., Kiviniemi, V., Nikkinen, J., Miettunen, J., Mäki, P., Jääskelainen, E., Mukkala, S., Koivukangas, J., Nordström, T., Taanila, A., Moilanen, I., Heinimaa, M., Barnett, J.H., Jones, P.B., Murray, G.K., Veijola, J., 2013. Default mode network in young people with familial risk for psychosis—the Oulu Brain and Mind study. Schizophr. Res. 143 (2–3), 239–245. Järvelin, M.R., Hartikainen-Sorri, A.L., Rantakallio, P., 1993. Labour induction policy in hospitals of different levels of specialisation. Br. J. Obstet. Gynaecol. 100 (4), 310–315. Knöchel, C., Oertel-Knöchel, V., Schönmeyer, R., Rotarska-Jagiela, A., van de Ven, V., Prvulovic, D., Haenschel, C., Uhlhaas, P., Pantel, J., Hampel, H., Linden, D.E., 2012. Interhemispheric hypoconnectivity in schizophrenia: fiber integrity and volume differences of the corpus callosum in patients and unaffected relatives. Neuroimage 59 (2), 926–934.
393
Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabriat, H., 2001. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13 (4), 534–546. McGlashan, T.H., Miller, T.J., Woods, S.W., Rosen, J.L., Hoffman, R.E., Davidson, L., 2001. SIPS, Structured Interview for Prodromal Syndromes-Version for Present Prodromal Syndromes. Version 3.0 PRIME Research Clinic. Yale School of Medicine, New Haven, CT. Moran, M.E., Luscher, Z.I., McAdams, H., Hsu, J.T., Greenstein, D., Clasen, L., Ludovici, K., Lloyd, J., Rapoport, J., Mori, S., Gogtay, N., 2015. Comparing fractional anisotropy in patients with childhood-onset schizophrenia, their healthy siblings, and normal volunteers through DTI. Schizophr. Bull. 41 (1), 66–73. Mukkala, S., Ilonen, T., Nordström, T., Miettunen, J., Loukkola, J., Barnett, J.H., Murray, G.K., Jääskelainen, E., Mäki, P., Taanila, A., Moilanen, I., Jones, P.B., Heinimaa, M., Veijola, J., 2011. Different vulnerability indicators for psychosis and their neuropsychological characteristics in the Northern Finland 1986 Birth Cohort. J. Clin. Exp. Neuropsychol. 33 (4), 385–394. Muñoz Maniega, S., Lymer, G.K., Bastin, M.E., Marjoram, D., Job, D.E., Moorhead, T. W., Owens, D.G., Johnstone, E.C., McIntosh, A.M., Lawrie, S.M., 2008. A diffusion tensor MRI study of white matter integrity in subjects at high genetic risk of schizophrenia. Schizophr. Res. 106 (2–3), 132–139. Perälä, J., Suvisaari, J., Saarni, S.I., Kuoppasalmi, K., Isometsä, E., Pirkola, S., Partonen, T., Tuulio-Henriksson, A., Hintikka, J., Kieseppä, T., Härkänen, T., Koskinen, S., Lönnqvist, J., 2007. Lifetime prevalence of psychotic and bipolar I disorders in a general population. Arch. Gen. Psychiatry 64 (1), 19–28. Peters, B.D., Karlsgodt, K.H., 2015. White matter development in the early stages of psychosis. Schizophr. Res. 161 (1), 61–69. Samartzis, L., Dima, D., Fusar-Poli, P., Kyriakopoulos, M., 2014. White matter alterations in early stages of schizophrenia: a systematic review of diffusion tensor imaging studies. J. Neuroimaging 24 (2), 101–110. Smith, S.M., 2002. Fast robust automated brain extraction. Hum. Brain Mapp. 17 (3), 143–155. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., JohansenBerg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M., 2004. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl 1), S208–S219. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C. E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31 (4), 1487–1505. Veijola, J., Maki, P., Jääskelainen, E., Koivukangas, J., Moilanen, I., Taanila, A., Nordström, T., Hurtig, T., Kiviniemi, V., Mukkala, S., Heinimaa, M., Lindholm, P., Jones, P.B., Barnett, J.H., Murray, G.K., Miettunen, J., 2013. Young people at risk for psychosis: case finding and sample characteristics of the Oulu Brain and Mind Study. Early Interv. Psychiatry 7 (2), 146–154. Wechsler, D., 1997. Wechsler Adult Intelligence Scale–Third Edition. Manual, San Antonio, Tx, The Psychological Corporation. Yaakub, S.N., Dorairaj, K., Poh, J.S., Asplund, C.L., Krishnan, R., Lee, J., Keefe, R.S., Adcock, R.A., Wood, S.J., Chee, M.W., 2013. Preserved working memory and altered brain activation in persons at risk for psychosis. Am. J. Psychiatry 170 (11), 1297–1307.