White Matter Integrity and Cognitive Impairment in First-Episode Psychosis
Publication: American Journal of Psychiatry
Abstract
Objective
Impaired cognitive function has been identified as a core feature of schizophrenia. However, a significant proportion of patients do not show any cognitive deficits. The aim of this study was to assess if there were differences in white matter integrity between patients with and without cognitive impairment.
Method
A diffusion tensor imaging study and neurocognitive assessment were conducted in 49 patients with first-episode psychosis and 41 healthy comparison subjects. Subjects were assessed using the Continuous Performance Test, the Grooved Pegboard Test, the Rey Auditory Verbal Learning Test, and the Trail Making Test Part B. For each test, the patient sample was subdivided according to performance, with those scoring more than one standard deviation below the normative mean categorized as impaired. For each cognitive domain, white matter fractional anisotropy in deficit and nondeficit subgroups was compared using a voxel-based analysis. A nonparametric statistical method, controlling for multiple comparisons, was applied.
Results
Impairment on the Trail Making Test Part B was associated with reduced fractional anisotropy in the right/left anterior thalamic radiation and inferior fronto-occipital fasciculus, forceps minor, and left superior and inferior longitudinal fasciculi. Patients exhibiting Grooved Pegboard Test impairment showed reduced fractional anisotropy in the forceps minor, inferior fronto-occipital fasciculus, anterior thalamic radiation, and corticospinal and corticopontine tracts. Impaired performance on the Rey Auditory Verbal Learning Test and Continuous Performance Test was not associated with significant differences in fractional anisotropy.
Conclusion
Deficits in executive and motor functioning in patients with first-episode psychosis are associated with reductions in white matter integrity in the major fasciculi that connect the frontal and temporal cortices as well as in pathways connecting cortical and subcortical regions. Their presence at the onset of illness, in minimally medicated patients, indicates that these findings are not attributable to effects of chronic illness or its treatment.
Impaired cognitive function has been identified as a core feature of schizophrenia (1). Functional MRI studies have consistently provided evidence of disturbed cognitive function in patients with schizophrenia relative to comparison subjects (2–4), but the pathological processes underlying such impaired cognitive function are not known. Disruptions of white matter networks have been proposed as a potential mechanism for cognitive dysfunction in schizophrenia and in other neurological pathologies such as dementia (5), multiple sclerosis (6) and traumatic brain injury (7).
However, not all patients with schizophrenia show a cognitive impairment. Within the diagnostic category of schizophrenia in DSM-IV there may be a wide range in cognitive performance, from relatively intact cognition to severe deficits. Whether this corresponds to different pathophysiological processes is unclear. In a recent study, Wexler et al. (8) found that relative to healthy comparison subjects, neuropsychologically impaired patients had significantly smaller white matter volumes in several regions, whereas patients with no cognitive deficit (within 0.5 SD of healthy comparison subjects) did not differ from healthy subjects. The authors suggest that white matter pathology may play a primary role in the cognitive deficits, and that the differences observed between the two subgroups might correspond to differences in the disease process.
Diffusion tensor imaging has been proven to be a useful technique for detecting structural abnormalities in the white matter of individuals with cognitive impairment (9–13). In schizophrenia, only a few studies have investigated the association between white matter connections and cognitive performance (14–20). The findings to date have been inconclusive, perhaps due to the wide heterogeneity in the study populations and in the imaging methodologies used.
In the present study, using a voxel-based analysis method, we investigated white matter integrity—in patients with and without cognitive deficits—in four domains known to be impaired in schizophrenia: executive function, verbal memory, attention, and motor dexterity. We hypothesized that patients with cognitive impairments would have significantly lower fractional anisotropy than patients without cognitive deficits. More specifically, we predicted that 1) patients with impaired executive functioning would have lower fractional anisotropy in white matter areas containing tracts connecting the prefrontal cortex to temporal and parietal cortices; 2) patients with impaired verbal memory would show lower fractional anisotropy in fronto-temporal pathways (particularly the uncinate fasiculus); 3) patients with impaired attention would demonstrate reduced fractional anisotropy in frontostriatal tracts; and 4) patients with impaired motor functioning would have lower fractional anisotropy in white matter comprising fasciculi linking the inferior frontal and anterior cingulate cortex with subcortical nuclei. To minimize the potential effects of chronic illness and medication exposure, we conducted the study in minimally treated first-episode psychotic patients.
Method
Subjects
Patients were recruited from a consecutive sample of subjects included in the first-episode psychosis program of Cantabria (PAFIP) (21), Spain. Patients referred to the program were selected if they met the following criteria: 1) age 15–60 years, 2) living in the catchment area, 3) experiencing their first episode of psychosis, 4) no prior treatment with antipsychotic medication or, if previously treated, a total lifetime exposure less than 6 weeks, and 5) meeting DSM-IV criteria for brief psychotic disorder, schizophreniform disorder, schizophrenia, or schizoaffective disorder. Patients were excluded for any of the following reasons: 1) meeting DSM-IV criteria for drug dependence, 2) meeting DSM-IV criteria for mental retardation, or 3) having a history of neurological disease or head injury. Diagnosis per DSM-IV criteria was confirmed by an experienced psychiatrist 6 months after the initial contact. After the patients provided written informed consent, they were randomly assigned to receive aripiprazole (10–30 mg/day), quetiapine (200–600 mg/day), or ziprasidone (40–160 mg/day).
All the patients included in the first-episode psychosis program from February 2006 to June 2008 were invited to participate in the diffusion tensor imaging study. The protocol was approved by the Marques de Valdecilla University Hospital review board and was performed in accordance with international ethical standards. The scans were conducted as soon as the patients could tolerate the procedure following the initiation of treatment. The average time between initiation of medication and MRI scanning was 35.5 days (SD=30, maximum=161 days); the median was 25.5 days.
Healthy comparison subjects were recruited from the community through advertisements. They had no past or present psychiatric, neurological, or general medical illness, including substance abuse or significant loss of consciousness, as determined by using an abbreviated version of the Comprehensive Assessment of Symptoms and History (22). Healthy subjects were matched to the patients by age, sex, years of education, and laterality index. All participants provided informed consent after the study procedures were fully explained to them.
Cognitive Tasks
Cognitive functioning was evaluated 12 weeks after recruitment. To minimize type I error from our neurocognitive battery (23), we selected the three tasks with the worst patient performance compared with healthy volunteers (highest mean z scores) along with an additional task to evaluate verbal memory. The tasks were 1) the degraded stimulus portion of the Continuous Performance Test to measure attention; 2) time for dominant hand portion of the Grooved Pegboard Test to evaluate motor dexterity; 3) long delay recall from the Rey Auditory Verbal Learning Test to measure verbal memory; and d) total time on the Trail Making Test Part B to assess executive functioning.
Statistical Analysis
Each raw cognitive score was standardized to a z score (with a mean of 0 and a SD of 1) using the values of the healthy comparison group as reference. Patients were then classified as impaired on each cognitive function if performance was more than 1 SD below the normative mean. Pearson's chi-square for categorical data and Student's t tests for continuous variables were used to compare demographic data between cognitively impaired and unimpaired groups.
Image Acquisition
All MRI scans were obtained at Marques de Valdecilla University Hospital using a 1.5T General Electric SIGNA System (GE Medical Systems, Milwaukee). Diffusion-weighted imaging data were acquired with a single-shot echo planar imaging sequence aligned with the anterior-posterior commissure plane. Images were collected with diffusion sensitizing gradients applied along 25 nonparallel directions (b=1000 s/mm2), along with one image without diffusion weighting (b=0). Twenty-seven contiguous axial slices were acquired with a slice thickness of 5 mm and no gap. The acquisition parameters were as follows: echo time=91 msec; repetition time=8000 msec; field of view=30 cm; number of excitations=1; matrix size=128×128.
Additionally, high resolution structural images were acquired for anatomical review. Three-dimensional T1 weighted images, using a spoiled gradient echo sequence, were obtained in the coronal plane with the following parameters: echo time=5 msec; repetition time=24 msec; number of excitations=2; excitation flip angle=45º; field of view=26×19.5 cm; slice thickness=1.5 cm; and matrix size=256×192. Two-dimensional T2 and proton density weighted sequences were acquired as follows: 3.0-mm-thick coronal slices; repetition time=3000 msec; echo time=36 msec (for proton density) and 96 msec (for T2); number of excitations=1; field of view=26×26; and matrix=256×192.
The total scan time was 40 minutes. All scans were reviewed, and any scan with significant artifacts was repeated or discarded.
Image Processing
The diffusion-weighted images were first corrected for any eddy current-induced distortion and then masked with a modification of the brain extraction tool (BET) in the Functional Software Library (FSL) package (Oxford University, Centre for Functional MRI of the Brain, Oxford, U.K.; see Jones et al. [24]). The diffusion tensor was then calculated at each brain voxel and fractional anisotropy maps were constructed using in-house software (25).
A voxel-based method similar to voxel-based morphometry was used to analyze the diffusion tensor imaging images. Registration was performed using SPM2 (Statistical Parametric Mapping, Wellcome Department of Imaging Neuroscience, London, U.K.). A two-stage registration was used, in a manner similar to an "optimized voxel-based morphometry" analysis (26). First, the mean T2 weighted (non-diffusion-weighted, b=0) images from each subject were registered to the standard echo-planar imaging template provided by SPM2. The derived mapping parameters for each subject were applied to the coregistered fractional anisotropy images. The normalized fractional anisotropy images of all subjects were then averaged and smoothed to create a new, study-specific template. Finally, the fractional anisotropy images were reregistered to the study template.
The registered fractional anisotropy images were also segmented using SPM's default a priori tissue probability information. These segmented images were thresholded at a value of 0.10 to provide a binary mask of white matter that included all white matter voxels and excluded the voxels that were clearly gray matter or CSF. A 4-mm full-width at half-maximum smoothing filter was used to improve signal-to-noise ratio. The aforementioned mask was used for the smoothed images to restrict subsequent analyses to white matter.
Image Analysis
Between-group comparisons were performed using X-Brain Activation Mapping (XBAM), a program developed at the Institute of Psychiatry, London (27). XBAM uses nonparametric, permutation-based statistics, which are more suitable for this type of data than parametric approaches, since the residuals of fit to the general linear model may not show the Gaussian behavior required by parametric statistics.
To assess differences between groups, an analysis of variance (ANOVA) was conducted at each intracerebral voxel in standard space. Initially, a liberal statistical threshold (p≤0.05) was set to detect voxels putatively demonstrating differences between groups. Only those voxels at which all subjects contributed data were considered further, which, along with the aforementioned masking procedure, restricts the analysis to core white matter regions, reducing the search volume and thus the number of comparisons.
The program then searches for clusters of contiguous (10-connected) significant voxels in the observed data. For each cluster, it calculates the sum of all suprathreshold voxel statistics within that cluster—termed the cluster "mass." To assess the statistical significance at the cluster level, rather than set a single a priori threshold, we calculated, for a range of p values, the number of clusters that would be expected by chance alone. We then set the statistical threshold for cluster significance for this analysis at a p value such that the expected number of false positive clusters by chance alone would be less than one.
Four separate analyses were conducted, with fractional anisotropy values being compared between patients with and without deficit in each cognitive domain. The four comparisons conducted are all reported at a cluster-level significance threshold of p≤0.0025.
Results
Forty-nine right-handed first-episode psychotic patients and 41 healthy comparison subjects were included in the analyses. Healthy subjects were matched to the overall patient group for laterality, age, sex, and years of education. The findings from the direct diffusion tensor voxel-based morphometry comparison have been reported elsewhere (28). Table 1 shows the mean raw scores on the cognitive tasks for patients relative to the comparison subjects. For each cognitive domain, the patient group was divided into two subgroups according to the adjusted z scores. The mean z scores of deficit and nondeficit cognitive subtypes are also shown in Table 1. No significant differences were found in age (the most potential confounding factor for which there is some evidence of influence on fractional anisotropy values), sex, or duration of untreated psychosis between patients with and without deficit in any of the four cognitive domains (see table S1 in the data supplement that accompanies the online version of this article). A difference (at the upper limit for statistical significance [p=0.050]) was found in years of education between the patients showing deficits on the executive function task and those showing no deficits. A significant difference was also found in years of education between the patients with deficit and no deficit in the motor domain (p=0.027). To our knowledge, there is no evidence of the influence of education on fractional anisotropy.
Table 2 shows the four significant clusters identified in the analyses, the size of the clusters, the mean fractional anisotropy, the coordinates of the voxel of maximum significance, and the approximate white matter tracts include in these clusters, identified using the Talairach (30), Mori et al. (31), JHU and ICBM_DTI_8 atlases (32), all integrated into the FSL package http://www.fmrib.ox.ac.uk/fsl/fslview).
Group comparisons of fractional anisotropy revealed statistically significant lower values in one cluster in the group of patients with impaired motor skills compared with patients without deficit (Figure 1). This cluster was located in the left frontal white matter. This area mainly corresponds to the forceps minor, the inferior fronto-occipital fasciculus, and the anterior thalamic radiation, although inclusion of fibers of the uncinate, corticopontine, and corticospinal tracts cannot be excluded.
Figure 1. White Matter Regions in First-Episode Psychosis Patients in Which Fractional Anisotropy Was Significantly Lower Among Those Exhibiting a Motor Dexterity Task Deficit (N=27) Relative to Those With No Deficit (N=22)a
aDeficit classified as score > 1 SD below the normative mean.
In patients exhibiting executive function impairment, three clusters yielded lower fractional anisotropy values compared with patients without deficit (Figure 2). Cluster 1 (green) and cluster 2 (blue) include frontal white matter areas corresponding to parts of the left and right anterior thalamic radiation, forceps minor, and inferior fronto-occipital fasciculus. Cluster 3 (red) is located in the left temporal white matter including fibers from the superior longitudinal fasciculus, inferior longitudinal fasciculus, and inferior fronto-occipital fasciculus.
Figure 2. White Matter Regions in First-Episode Psychosis Patients in Which Fractional Anisotropy Was Significantly Lower Among Those Exhibiting Executive Function Impairment (N=30) Relative to Those With No Impairment (N=18)a
aDeficit classified as score > 1 SD below the normative mean. Cluster 1=green; cluster 2=blue; cluster 3=red.
For each individual, patients and comparison subjects, we extracted the mean fractional anisotropy values of these four significant clusters (one of the motor domain comparison and three of the executive domain comparison) identified by the voxel-based morphometry analysis. Figure 3 demonstrates the overall pattern of fractional anisotropy differences within these regions over all the subject groups.
Figure 3. Fractional Anisotropy Within Significant Clusters Identified in the Voxel-Based Morphometry Analysis in Healthy Subjects and First-Episode Psychosis Patients With Versus Without Cognitive Deficitsa
aDeficit classified as score > 1 SD below the normative mean.
Voxel-wise diffusion tensor imaging analysis did not show any regions where the values of fractional anisotropy were significantly different when we compared first-episode psychotic patients with poor performance in the attention task or in the verbal memory task with patients who had similar scores to healthy volunteers. There were no clusters in which fractional anisotropy was significantly lower in patients with normal performance compared with patients with poor performance in any of the four cognitive domains.
Discussion
Our data suggest that deficits in executive and motor functioning in first-episode psychotic patients are associated with reductions in white matter integrity in the major fasciculi that connect the frontal and temporal cortices as well as in pathways connecting cortical and subcortical regions. Since the patients had received a mean total of only 35.5 days of antipsychotic treatment, the findings are unlikely to be an effect of medication exposure. No significant correlation was found between years of education and the mean fractional anisotropy values in any of the significant clusters identified for each domain, so it is also unlikely that the results were due to group differences in education.
Fractional anisotropy reductions have been previously associated with cognitive impairments in the context of other disorders. Marenco et al. reported abnormalities in fractional anisotropy in individuals with Williams' syndrome (9), a genetic disorder with severe visuospatial cognitive deficit. DeLisi et al. (10) have also reported decreased fractional anisotropy values in Klinefelter's syndrome patients with specific cognitive deficit in executive functioning. Recently, correlations have been reported between fractional anisotropy values and motor skills (pegboard test) in type 1 diabetes mellitus (11), between fractional anisotropy values in superior longitudinal fasciculus and processing speed in healthy and brain injury groups (33), and between fractional anisotropy values in the external capsule and inferior-middle superior fasciculus and IQ in children of very low birth weight (13). Previous studies in schizophrenia have found an association between working memory impairment and reduced fractional anisotropy in the superior longitudinal fasciculus (14), declarative-episodic verbal memory deficit and lower fractional anisotropy in the uncinate fasciculus (19), and attentional deficits and lower fractional anisotropy in the cingulum (34). Collectively, these studies provide evidence that diffusion tensor imaging appears to be a useful tool for assessing the anatomical white matter substrate of neurocognitive dysfunction. However, reduced fractional anisotropy is a nonspecific feature that can result from multiple pathological processes, and the precise relationship between particular cognitive functions and specific white matter tracts is still not well understood.
Our study revealed differences in white matter integrity between patients with and without cognitive deficits. These results are consistent with the findings reported by Skranes et al. (13), in which adolescents of very low birth weight with low Grooved Pegboard Test performance (one standard deviation below that of healthy subjects) had significantly lower values of fractional anisotropy in several white matter tracts, relative to adolescents of very low birth weight and normal test performance. Our findings are also consistent with those reported by Kraus et al. (7), in which patients with severe traumatic brain injury showed more regions with significantly lower values of fractional anisotropy than in the group with mild traumatic brain injury. Finally, our results agree with recent data reported by Wexler et al. (8) in schizophrenia patients, in which the neuropsychologically impaired group (only) had significantly lower white matter volume than healthy subjects. Our findings support the hypothesis proposed by Wexler et al. that these different cognitive phenotypes may correspond with differences in the disease processes.
We failed to find the expected changes in fractional anisotropy in patients with verbal memory and sustained attention deficits. In the case of verbal memory domain, one of the reasons may be the lack of substantial differences in the score between groups. Of the impaired verbal memory group, less than 20% had a z score below two standard deviations. Despite the huge differences in performance between impaired and nonimpaired sustained attention groups, the voxel-wise analysis of this domain also showed no differences in fractional anisotropy. One possible explanation may be that because of the large impairments, the subgroup of patients classified as deficit-free was necessarily small, and thus the analysis may have been underpowered to assess this hypothesis. Another possibility is that attention deficits during the first episode of psychosis might be attributable (at least in part) to transient factors like acute symptoms or medication, and therefore subject to variation over time, and less likely to be related to structural abnormalities.
There are several limitations to our study that should be considered. Voxel-wise analysis involves multiple comparisons, and therefore has the potential risk of both type I and type II error. We sought to reduce these risks by analyzing only the white matter structure (to reduce comparisons at the voxel level, thus improving sensitivity), and by adopting a p value at the cluster level at which we expect less than one false positive over the imaging volume. Another potential limitation may be the error associated with normalization and registration processes. Finally, the voxel-based approach does not allow us to identify precisely which white matter tracts were affected. Future tractography studies should improve the specificity by localizing the fractional anisotropy variations to specific tracts.
The main strength of this study is its relatively large sample size of first-episode patients minimally exposed to antipsychotics. To our knowledge, this is the first study that demonstrates differences in white matter integrity in two different populations of psychotic patients—those with and without impaired cognition.
In conclusion, these findings suggest that moderate to severe deficits in executive and motor functioning are linked to structural deficits in white matter at very early stages of the illness. It should be noted that it was the cognitive impairment itself, and not the illness per se, that was the factor related to abnormalities in white matter structure. Future studies are needed to replicate these findings and to localize the specific tracts affected in different cognitive deficits.
Supplementary Material
File (ajp_167_04_451_01.pdf)
- View/Download
- 33.01 KB
References
1.
Keefe RS: Should cognitive impairment be included in the diagnostic criteria for schizophrenia? World Psychiatry 2008; 7:22–28
2.
Liu Y, Liang M, Zhou Y, He Y, Hao Y, Song M, Yu C, Liu H, Liu Z, Jiang T: Disrupted small-world networks in schizophrenia. Brain 2008; 131:945–961
3.
Meyer-Lindenberg AS, Olsen RK, Kohn PD, Brown T, Egan MF, Weinberger DR, Berman KF: Regionally specific disturbance of dorsolateral prefrontal-hippocampal functional connectivity in schizophrenia. Arch Gen Psychiatry 2005; 62:379–386
4.
Schlosser R, Gesierich T, Kaufmann B, Vucurevic G, Hunsche S, Gawehn J, Stoeter P: Altered effective connectivity during working memory performance in schizophrenia: a study with fMRI and structural equation modeling. Neuroimage 2003; 19:751–763
5.
Kavcic V, Ni H, Zhu T, Zhong J, Duffy CJ: White matter integrity linked to functional impairments in aging and early Alzheimer's disease. Alzheimers Dement 2008; 4:381–389
6.
Audoin B, Guye M, Reuter F, Au Duong MV, Confort-Gouny S, Malikova I, Soulier E, Viout P, Cherif AA, Cozzone PJ, Pelletier J, Ranjeva JP: Structure of WM bundles constituting the working memory system in early multiple sclerosis: a quantitative DTI tractography study. Neuroimage 2007; 36:1324–1330
7.
Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 2007; 130:2508–2519
8.
Wexler BE, Zhu H, Bell MD, Nicholls SS, Fulbright RK, Gore JC, Colibazzi T, Amat J, Bansal R, Peterson BS: Neuropsychological near normality and brain structure abnormality in schizophrenia. Am J Psychiatry 2009; 166:189–195
9.
Marenco S, Siuta MA, Kippenhan JS, Grodofsky S, Chang WL, Kohn P, Mervis CB, Morris CA, Weinberger DR, Meyer-Lindenberg A, Pierpaoli C, Berman KF: Genetic contributions to white matter architecture revealed by diffusion tensor imaging in Williams syndrome. Proc Natl Acad Sci U S A 2007; 104:15117–15122
10.
DeLisi LE, Maurizio AM, Svetina C, Ardekani B, Szulc K, Nierenberg J, Leonard J, Harvey PD: Klinefelter's syndrome (XXY) as a genetic model for psychotic disorders. Am J Med Genet B Neuropsychiatr Genet 2005; 135B:15–23
11.
Kodl CT, Franc DT, Rao JP, Anderson FS, Thomas W, Mueller BA, Lim KO, Seaquist ER: Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes 2008; 57:3083–3090
12.
Della Nave R, Foresti S, Pratesi A, Ginestroni A, Inzitari M, Salvadori E, Giannelli M, Diciotti S, Inzitari D, Mascalchi M: Whole-brain histogram and voxel-based analyses of diffusion tensor imaging in patients with leukoaraiosis: correlation with motor and cognitive impairment. AJNR Am J Neuroradiol 2007; 28:1313–1319
13.
Skranes J, Vangberg TR, Kulseng S, Indredavik MS, Evensen KA, Martinussen M, Dale AM, Haraldseth O, Brubakk AM: Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. Brain 2007; 130:654–666
14.
Karlsgodt KH, van Erp TG, Poldrack RA, Bearden CE, Nuechterlein KH, Cannon TD: Diffusion tensor imaging of the superior longitudinal fasciculus and working memory in recent-onset schizophrenia. Biol Psychiatry 2008; 63:512–518
15.
Szeszko PR, Robinson DG, Ashtari M, Vogel J, Betensky J, Sevy S, Ardekani BA, Lencz T, Malhotra AK, McCormack J, Miller R, Lim KO, Gunduz-Bruce H, Kane JM, Bilder RM: Clinical and neuropsychological correlates of white matter abnormalities in recent onset schizophrenia. Neuropsychopharmacology 2008; 33:976–984
16.
Schlosser RG, Nenadic I, Wagner G, Gullmar D, von Consbruch K, Kohler S, Schultz CC, Koch K, Fitzek C, Matthews PM, Reichenbach JR, Sauer H: White matter abnormalities and brain activation in schizophrenia: a combined DTI and fMRI study. Schizophr Res 2007; 89:1–11
17.
Lim KO, Ardekani BA, Nierenberg J, Butler PD, Javitt DC, Hoptman MJ: Voxelwise correlational analyses of white matter integrity in multiple cognitive domains in schizophrenia. Am J Psychiatry 2006; 163:2008–2010
18.
Okugawa G, Nobuhara K, Minami T, Takase K, Sugimoto T, Saito Y, Yoshimura M, Kinoshita T: Neural disorganization in the superior cerebellar peduncle and cognitive abnormality in patients with schizophrenia: a diffusion tensor imaging study. Prog Neuropsychopharmacol Biol Psychiatry 2006; 30:1408–1412
19.
Nestor PG, Kubicki M, Gurrera RJ, Niznikiewicz M, Frumin M, McCarley RW, Shenton ME: Neuropsychological correlates of diffusion tensor imaging in schizophrenia. Neuropsychology 2004; 18:629–637
20.
Kubicki M, Westin C-F, Maier SE, Frumin M, Nestor PG, Salisbury DF, Kikinis R, Jolesz FA, McCarley RW, Shenton ME: Uncinate fasciculus findings in schizophrenia: a magnetic resonance diffusion tensor imaging study. Am J Psychiatry 2002; 159:813–820
21.
Crespo-Facorro B, Perez-Iglesias R, Ramirez-Bonilla M, Martinez-Garcia O, Llorca J, Vazquez-Barquero JL: A practical clinical trial comparing haloperidol, risperidone, and olanzapine for the acute treatment of first-episode nonaffective psychosis. J Clin Psychiatry 2006; 67:1511–1521
22.
Andreasen NC, Flaum M, Arndt S: The Comprehensive Assessment of Symptoms and History (CASH): an instrument for assessing diagnosis and psychopathology. Arch Gen Psychiatry 1992; 49:615–623
23.
Gonzalez-Blanch C, Crespo-Facorro B, Alvarez-Jimenez M, Rodriguez-Sanchez JM, Pelayo-Teran JM, Perez-Iglesias R, Vazquez-Barquero JL: Cognitive dimensions in first-episode schizophrenia spectrum disorders. J Psychiatr Res 2007; 41:968–977
24.
Jones DK, Griffin LD, Alexander DC, Catani M, Horsfield MA, Howard R, Williams SC: Spatial normalization and averaging of diffusion tensor MRI data sets. Neuroimage 2002; 17:592–617
25.
Basser PJ, Pierpaoli C: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996; 111:209–219
26.
Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS: A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001; 14:21–36
27.
Bullmore ET, Suckling J, Overmeyer S, Rabe-Hesketh S, Taylor E, Brammer MJ: Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Trans Med Imaging 1999; 18:32–42
28.
Perez-Iglesias R, Tordesillas-Gutierrez D, Barker GJ, McGuire PK, Roiz-Santianez R, Mata I, de Lucas EM, Quintana F, Vazquez-Barquero JL, Crespo-Facorro B: White matter defects in first episode psychosis patients: a voxelwise analysis of diffusion tensor imaging. Neuroimage 2010; 49:199–204
29.
Lancaster JL, Tordesillas-Gutierrez D, Martinez M, Salinas F, Evans A, Zilles K, Mazziotta JC, Fox PT: Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 2007; 28:1194–1205
30.
Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT: Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 2000; 10:120–131
31.
Mori S, Wakana S, van Zijl PCM, Nagae-Poetscher LM: MRI atlas of human white matter. Amsterdam, Elsevier, 2005
32.
Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S: Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 2007; 36:630–644
33.
Turken A, Whitfield-Gabrieli S, Bammer R, Baldo JV, Dronkers NF, Gabrieli JD: Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage 2008; 42:1032–1044
34.
Nestor PG, Kubicki M, Spencer KM, Niznikiewicz M, McCarley RW, Shenton ME: Attentional networks and cingulum bundle in chronic schizophrenia. Schizophr Res 2007; 90:308–315
Information & Authors
Information
Published In
History
Received: 22 May 2009
Accepted: 16 November 2009
Published online: 1 April 2010
Published in print: April 2010
Authors
Competing Interests
Dr. Iglesias reports receiving support from Eli Lilly to attend conferences. Dr. McGuire reports receiving honoraria for lectures and consultancy fees from Eli Lilly, AstraZeneca, and Janssen Cilag. Dr. Barker reports receiving honoraria from General Electric (GE) for participating in advisory panel meetings and teaching GE programming courses. Dr. Mata reports receiving honoraria for lectures and support to attend conferences from Janssen Cilag. Dr. Vazquez-Barquero reports receiving unrestricted grant support from AstraZeneca, Pfizer, and Bristol-Myers Squibb. Dr. Crespo-Facorro reports receiving honoraria for his participation as a speaker at educational events from Bristol-Myers Squibb and Johnson & Johnson and consultant fees from Pfizer. He has received support to attend conferences from Johnson & Johnson. The remaining authors report no financial relationships with commercial interests.
Funding Information
Supported in part by grants from the Instituto de Salud Carlos III (FIS CP07/00008), Fundacio Seny, Fundación Marqués de Valdecilla.
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBGet Access
Login options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).