Indian Journal of Psychological Medicine

: 2008  |  Volume : 30  |  Issue : 2  |  Page : 83--89

Structural cerebellar abnormalities in antipsychotic-naive schizophrenia: Evidence for cognitive dysmetria

Rashmi Arasappa1, Naren P Rao1, G Venkatasubramanian1, PN Jayakumar2, BN Gangadhar1,  
1 Department of Psychiatry, NIMHANS, Bangalore-560 029, India
2 Department of Neuroimaging and Interventional Radiology, NIMHANS, Bangalore-560 029, India

Correspondence Address:
Rashmi Arasappa
Door No. 325, 2nd F Cross, 3rd Stage, 3rd Block, Basaveshwara Nagar, Bangalore-560 029


Background: źDQ╗Cognitive dysmetriaźDQ╗ has been proposed as a unifying model to explain the pathogenesis of schizophrenia. Most of the previous studies examining structural cerebellar abnormalities in schizophrenia were confounded by various factors like antipsychotic treatment, comorbid alcohol dependence, and low-resolution imaging procedure with manual morphometric analysis. In this study, we describe the first report of structural cerebellar abnormalities in antipsychotic-naive schizophrenia patients using high-resolution imaging (3.0 Tesla MRI). Aim: This study was aimed at examining the structural cerebellar abnormalities in antipsychotic-naive schizophrenia patients and its correlation with psychopathology. Methods: Brain imaging of 20 antipsychotic-naive schizophrenia patients and their age, gender, and years of education matched 20 healthy controls was done using 3.0 Tesla MRI machine. Image analysis was done using the optimized Voxel Based Morphometry (VBM), an automated unbiased technique. Results: Antipsychotic-naive schizophrenia patients had significant cerebellar gray matter volume deficits compared to healthy controls. The Scale for the Assessment of Negative Symptoms (SANS) score in patients had negative correlation with cerebellar gray matter volume. Conclusion: Structural cerebellar abnormalities and their negative correlation with negative syndrome in antipsychotic-naive schizophrenia patients support the cognitive dysmetria.

How to cite this article:
Arasappa R, Rao NP, Venkatasubramanian G, Jayakumar P N, Gangadhar B N. Structural cerebellar abnormalities in antipsychotic-naive schizophrenia: Evidence for cognitive dysmetria.Indian J Psychol Med 2008;30:83-89

How to cite this URL:
Arasappa R, Rao NP, Venkatasubramanian G, Jayakumar P N, Gangadhar B N. Structural cerebellar abnormalities in antipsychotic-naive schizophrenia: Evidence for cognitive dysmetria. Indian J Psychol Med [serial online] 2008 [cited 2020 May 28 ];30:83-89
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Full Text


Cerebellum, well known for its motor coordination, has been suggested in recent times to have an important role in cognition. [1] The motor coordination is brought about by the feedback between sensory motor cortex and the cerebellum, mediated through thalamus. Similar circuit (where the feedback is between cerebellum and prefrontal cortex, mediated through thalamus - the corticocerebellar thalamic cortical circuit (CCTCC)) has been implicated in the function of monitoring and coordinating the smooth execution of mental activity, that is, synchrony. Disruption in this circuit has been proposed as to cause cognitive dysmetria leading on to disordered cognition and symptoms of schizophrenia. [2]

Some of the earlier studies in patients with schizophrenia, using structural and functional imaging methods, have shown abnormalities in the cerebellum. CT scan study [3] showed degeneration of cerebellar vermis and enlargement of third ventricle and on magnetic resonance imaging (MRI) small cerebellar volume, smaller vermian and its subregions volume, and reversed cerebellar asymmetry in males were seen. [4],[5],[6] In addition, a proton magnetic resonance spectroscopy (MRS) study [7] showed lower N-acetylaspartate in cerebellar cortex and vermis in schizophrenia patients. Similar abnormalities have been noted in functional MRI (fMRI) study. [8] On positron emission tomography (PET) scan, decreased blood flow was seen in right anterior cingulate, right thalamus, and bilateral cerebellum during novel memory task. [9] Another study [10] showed absence of normal activation of prefrontal thalamic circuit in schizophrenia patients.

However, there are few studies with conflicting results - In one study, there was no difference between patients and controls in cerebellar vermis and height of fourth ventricle. [11] In another study, no abnormality in posterior fossa structures size was found in patients with schizophrenia. [12]

These discrepancies in findings could potentially be due to some of the methodological factors like selection of patients and image acquisition.

One of the important confounding factors is effect of drugs on brain volume, and only a few studies have been conducted on drug-naive patients. [13],[5] Effect of comorbid conditions like alcohol dependence syndrome (ADS) can confound the results due to the effect of alcohol on cerebellum. [14] However, all these studies used MRI machine of strength 1.5 Tesla (T) or below, which has a low signal-noise ratio (SNR). As images acquired with higher SNR gives more accurate spatial representation and better structural depiction like contrast between white and gray matter on magnetization prepared rapid acquisition gradient echo (MPRAGE) sequences, [15] it is preferred to acquire images using a machine with higher SNR like 3.0 T. There will be a high SNR in 3.0 T, double that of 1.5 T. In addition, high SNR also acquires images more rapidly thereby decreasing the time spent by the patient inside the MRI gantry which is necessary in uncooperative patients. Some of the studies analyzed images utilizing procedure with significant manual involvement.

And, to avoid the biases, it is preferable to use fully automated whole-brain measurement technique namely Voxel Based Morphometry (VBM). [16]

Thus, in the current study, we addressed these methodological issues:

We acquired images using 3 Tesla MRI scanner. The image analysis was done using fully automated analysis technique VBM. This provides an unbiased means of identifying regions of structural abnormalities in schizophrenia. [17] The methodology was updated and optimized [18] to reduce errors due to systematic differences in head shape, variations in segmentation, inconsistent brain stripping, and errors introduced by spatial normalization.We included only drug-naive schizophrenia patients. We excluded patients with comorbid ADS, thereby removing the confounding effect of alcohol on cerebellar volume.

In this study, we examined cerebellar gray matter volume differences between antipsychotic-naive schizophrenia patients ( n = 20) and age, gender, and education matched healthy controls ( n = 20). We hypothesized that cerebellar gray matter volume would be reduced in schizophrenia patients compared to matched healthy controls. Also, we predicted that the cerebellar gray matter volume deficit will have a negative correlation with the positive and negative syndrome (i.e., more severe the psychopathology more deficient will be cerebellar gray matter volume).

 Materials and Methods


The study sample consisted of 20 drug-naive schizophrenia patients and 20 healthy controls. Patients meeting the DSM-IV [19] diagnostic criteria for schizophrenia were recruited from the Out Patient Department of Psychiatry in National Institute of Mental Health And Neurosciences, Bangalore, India. The diagnosis was established by applying MINI [20] and also it was confirmed by another experienced psychiatrist through clinical interview. The duration of illness in months was 37.4 ▒ 32.86. The patients were not exposed to any kind of psychotropics before or at the time of assessments. They had neither history of medical illness nor comorbid psychiatric illness including substance dependence.

The psychopathology was assessed by Scale for the assessment of positive symptoms (SAPS) [21] and Scale for the assessment of negative symptoms (SANS). [22] The presenting author examined the inter-rater reliability for SAPS and SANS rating scale with two other trained residents. The intraclass correlation coefficients of >0.9 were achieved indicating good inter-rater reliability.

Age, sex, and education matched healthy controls with values as shown in [Table 1], who volunteered for the participation in the study, were recruited from the hospital staff and their friends. The psychiatric diagnosis in them, were ruled out by applying MINI. [20] They had neither history of medical illness nor substance dependence. There was no family history of psychiatric illness including ADS in their first-degree relatives. All 40 subjects were right handed. Written informed consent was taken from all subjects before assessment. The study was approved by the NIMHANS ethics committee.

Scanning protocol

MRI was done with 3.0 Tesla scanner. T 1 weighted three-dimensional Magnetization Prepared Rapid Acquisition Gradient Echo sequence was performed (TR = 8.1 msec, TE = 3.7 msec, nutation angle = 8 degree, FOV = 256 mm, slice thickness = 1 mm without interslice gap, NEX = 1, matrix = 256*256) yielding 165 sagittal slices.

Image processing

The image processing used is that designed by Venkatasubramanian et al . [23] The procedure is as follows: The Optimized VBM protocol was implemented within Matlab (Mathworks, Natick, Mass) through Statistical Parametric Mapping 2 software (SPM2), [24],[25] SPM2 uses an updated segmentation model with improved bias correction component that can segment brain abnormalities better than previous versions (Wellcome Department of Imaging Neuroscience, London;

Preprocessing of structural data followed a number of defined stages: [18]

Creation of customized gray and white matter templates.Segmentation and extraction of a brain image. Normalization of gray/white matter images. Segmentation and extraction of normalized whole brain images. Modulation and smoothing.

Creation of customized templates

Customized templates used in this study have been created from Indian subjects. [23] The procedure for preparing this customized templates is as follows: each structural MRI was normalized to the standard statistical parametric mapping T 1 template; segmented into CSF, gray matter, and white matter compartments; gray and white matter templates were created by averaging the 57 subjects' (30 schizophrenia patients and 27 healthy controls) smoothed normalized gray/white matter images. These images were then smoothed (8-mm full width at half maximum isotropic Gaussian kernel) and averaged to create gray and white matter templates in stereotactic space. The customized templates were created from the Indian subjects in order to avoid any potential bias for spatial normalization. [18]

Segmentation and extraction of a brain image

Segmentation and extraction is a fully automated procedure to remove scalp tissue, skull, and dural venous sinus voxels. The statistical parametric mapping segmentation employs a mixture model cluster analysis (after correcting for non uniformity in image intensity) to identify voxel intensities that match particular tissue types combined with a priori probabilistic knowledge of the spatial distribution of tissues. [26] Initially, the original structural MRI is segmented into gray and white matter images. This is followed by various automated procedures involving erosion followed by conditional dilatation that would result in removal of unconnected nonbrain voxels from the segmented images. These series of operations would yield extracted gray and white matter partitions in native space. [18]

Normalization of gray/white matter images

Extracted gray and white matter images were spatially normalized to match the customized gray and white matter templates. Spatial normalization is an image-processing step, more specifically, an image registration method. Human brains differ in size and shape, and one goal of spatial normalization is to deform human brain scans to match a template brain scan. After spatial normalization, a specific location in one subject's brain scan corresponds to the same location in another subject's brain scan. The steps involved in the spatial normalization are: 1) specification/estimation of warp-field, and 2) application of warp-field with resampling. Such normalization typically involves not only translation and rotation, but also scaling and nonlinear warping of the brain surface to match a standard template. In a study involving multiple subjects, spatial normalization is performed to ensure the correspondence and hence the uniformity of brain regional localizations. This would facilitate comparison and various other statistical analyses. The normalization parameters were then reapplied to the original structural images to maximize optimal segmentation of fully normalized images, and these normalized images were resliced to a final voxel size of 1 mm 3 and segmented into gray/white matter and CSF/non-CSF partitions.

Segmentation and extraction of normalized whole brain images

The optimally normalized whole brain structural images, which are now in stereotactic space (based on Montreal Neurological Institute (MNI) template) [27] , are then segmented into gray and white matter, CSF, and non-CSF partitions and subject to a second extraction of normalized segmented gray/white matter images. The brain extraction step is repeated at this stage because some nonbrain voxels from scalp, skull, or venous sinuses in the optimally normalized whole brain images could still remain outside the brain margins on segmented gray/white matter images. [26]

Modulation and smoothing

As a result of nonlinear spatial normalization, the volumes of certain brain regions may grow, whereas others may shrink. In order to preserve the volume of a particular tissue (gray or white matter or CSF) within a voxel, a further processing step is incorporated. This involves multiplying (or modulating) voxel values in the segmented images by the Jacobian determinants derived from the spatial normalization step. In effect, an analysis of modulated data tests for regional differences in the absolute amount (volume) of gray matter. [16]

The modulated gray matter images are then smoothed. Images are often smoothed (similar to the 'blur' effect used in some image-editing software) by which voxels are averaged with their neighbors, typically using a Gaussian filter with a 12-mm Full-Width Half Maximum [FWHM] kernel, to make the data less noisy. The process of smoothing conditioned the residuals to conform more closely to the Gaussian random field model underlying the statistical process used for adjusting ' P ' values, [28],[29] Thus, the optimized VBM preprocessing yielded normalized, segmented, modulated, and smoothed images (gray matter, white matter, and CSF images) with a voxel size of 1 mm 3 . [16],[18],[30]

 Statistical Analysis

Statistical analyses of clinical variable

The statistical analysis was performed using the Statistical Package for Social Sciences-13.0. The clinical data were analyzed using the independent samples t -test.

 Statistical Parametric Mapping: Optimized Voxel Based Morphometry Analyses

Group comparison for cerebellar gray matter volume differences

Group comparisons for cerebellar gray matter volume differences (using a mask for cerebellum) were performed using two-sample t -test within the framework of general linear model in SPM2. Statistical parametric maps were constructed to test for cerebellar gray matter volume differences between patients and controls. These were automatically analyzed by the SPM software on a voxel-by-voxel basis. Significance corrections for multiple comparisons were done using false discovery rate (FDR) correction ( P [31] using the small volume correction function for a sphere of 10-mm radius. FDR is a new approach to the multiple comparisons problem. Instead of controlling the chance of any false positives (as in Bonferroni or random field methods), FDR controls the expected proportion of false positives among supra-threshold voxels. A FDR threshold is determined from the observed P -value distribution and hence is adaptive to the observations in a specific dataset.

Correlation between SANS score and cerebellar gray matter volume

Statistical parametric maps were also examined for correlation between cerebellar gray matter volumes and SANS. This analysis examined for correlates in specific a priori region, that is, cerebellum rather than an exploratory whole brain analysis. Since the analysis was preselected gray matter voxels in cerebellum, significance was inferred with uncorrected P values.


Schizophrenia patients did not differ from the healthy controls in their age, gender, and years of education with values as shown in [Table 1]. The patients had a score of 28.4 ▒ 14.55 in SAPS and 66.4 ▒ 32.05 in SANS rating scales. The optimized VBM analysis showed cerebellar gray matter volume deficits in antipsychotic-naive schizophrenia patients compared to their matched healthy controls, with the P values as shown in [Table 2]. The SANS score had significant negative correlation with cerebellar gray matter volume with the P values as shown in [Table 3].


To the best of our knowledge, this is the first study to examine structural cerebellar abnormalities in antipsychotic-naive schizophrenia patients using 3.0 Tesla MRI machine. In this study we used fully automated whole brain measurement technique, optimized VBM, which provides an unbiased means of identifying regions of structural abnormalities. The cerebellar gray matter volume was reduced in antipsychotic-naive schizophrenia patients in comparison to age, gender, and education matched healthy controls as shown in [Figure 1]. In addition, there was a significant negative correlation between SANS score and cerebellar gray matter volume in patients as shown in [Figure 2]. That is, higher the SANS score lesser was the cerebellar gray matter.

Cerebellar gray matter volume deficits

Our findings are in accord with findings of earlier studies showing smaller vermis in both medicated schizophrenia patients [32],[4],[33] and drug-naive schizophrenia patients. [13],[5] Reduced cerebellar volume was seen in earlier studies in Indian population. [34] Similarly, negative syndrome score as assessed by SANS had negative correlation with cerebellar gray matter volume as seen in previous studies. [35]

Cortico cerebellar thalamic cortical circuit

Cerebellum is connected to cerebral cortex (both sensory motor and nonmotor cortex) by corticocerebellar thalamic cortical circuit (CCTCC). Fibers arising from the cerebral cortex descend through internal capsule and pontine nuclei crossing midline to reach middle cerebellar peduncle of the opposite side. These fibers then reach the cerebellar cortex and project to cerebellar nuclei. Impulses from cerebellum (mainly dentate nuclei) are relayed into thalamus and thalamocortical fibers carry them to cerebral cortex, thereby completing the circuit. [1],[2]

The inhibitory purkinje cells and the excitatory granule cells work together and modulate the activity of the cerebral cortex. The unique structure and connectivity of the Purkinje cells permits them to discriminate and recognize specific input conditions, such as variation in spatial locations or in patterns of auditory input. Purkinje cells [gama amino butyric acid (GABA)ergic neurons] inhibit deep cerebellar nuclei and determine what information is returned to cerebral cortex from cerebellum. [36] Thus, cerebellum functions as coordinator of mental processes in CCTCC, an important circuitry for cognitive function.

Cognitive dysmetria in schizophrenia

Dsymetria derived from Greek word means bad (dys), measure or moderation (metron). Cognitive dysmetria results in difficulty in coordinating processing, prioritizing, retrieval, and expression of information.

It has been proposed that diverse symptoms of schizophrenia like hallucinations, delusions, disorganization, negative symptoms have cognitive dysmetria as the basic deficit. [37] That is, disruption in the CCTCC results in cognitive dysmetria which in turn leads to impairment of smooth coordination of mental activities ultimately giving rise to symptoms of schizophrenia. [2] The pattern and error detection task, a function of cerebellum, is flawed in patients with schizophrenia. [36] In addition, decreased size and density of purkinje cells is seen in patients with schizophrenia, [38],[39] which further supports above view. Thus, our finding that patients with schizophrenia have reduced cerebellar gray volume supports the proposed cognitive dysmetria in schizophrenia.

Our findings also support unitary model of schizophrenia proposed by Bleuler; [40] symptoms of schizophrenia are not primary, but rather, secondary to fundamental problem in cognitive process. Thus, dysfunction in cerebellum, an important organ in cognitive processing, has potential role in the pathogenesis of schizophrenia as seen in our study.

Methodological issues

Strengths of study include, first time evaluation of cerebellar structural abnormalities in drug-naive schizophrenia, using 3.0 Tesla MRI.

Other methodological advantages are:

Unbiased image analysis using optimized VBM technique.Inclusion of age, sex, and education matched controls. Good inter-rater reliability for SANS and SAPS ratings. A homogeneous population with exclusion of substance dependence in subjects and controls and their first-degree relatives, an important confounding factor.


Patients with schizophrenia have cerebellar structural abnormalities in the form of reduced gray matter volume in comparison with age, sex, and education matched healthy controls. Cerebellar abnormalities had significant correlation with negative syndrome, an important clinical indicator. The findings give support to the presence of cognitive dysmetria in patients with schizophrenia. Future functional imaging studies with improvised methodology are needed to further examine the structural abnormality seen in our study. Also, prospective studies are required to examine whether these deficits are progressive.


This study was supported by ICMR funded project titled Clinical Correlates of Structural Cerebellar Abnormalities in Antipsychotic-naοve Schizophrenia - ICMR/GV/039 .


1Schmahmann JD, Pandya DN. Prefrontal cortex projections to the basilar pons in rhesus monkey: Implications for the cerebellar contribution to higher function. Neurosci Lett 1995;199:175-8.
2Andreasen NC, Nopoulos P, O'Leary DS, Miller DD, Wassink T, Flaum M. Defining the phenotype of schizophrenia: Cognitive dysmetria and its neural mechanisms. Biol Psychiatry 1999;46:908-20.
3Sandyk R, Kay SR, Merriam AE. Atrophy of the cerebellar vermis: Relevance to the symptoms of schizophrenia. Int J Neurosci 1991;57:205-12.
4Okugawa G, Sedvall GC, Agartz I. Smaller cerebellar vermis but not hemisphere volumes in patients with chronic schizophrenia. Am J Psychiatry 2003;160:1614-7.
5Ho BC, Mola C, Andreasen NC. Cerebellar dysfunction in neuroleptic naive schizophrenia patients: Clinical, cognitive and neuroanatomic correlates of cerebellar neurologic signs. Biol Psychiatry 2004;55:1146-53.
6Szeszko PR, Gunning-Dixon F, Ashtari M, Snyder PJ, Lieberman JA, Bilder RM. Reversed cerebellar asymmetry in men with first-episode schizophrenia. Biol Psychiatry 2003;53:450-9.
7Ende G, Hubrich P, Walter S, Weber-Fahr W, Kammerer N, Braus DF, et al . Further evidence for altered cerebellar neuronal integrity in schizophrenia. Am J Psychiatry 2005;162:790-2.
8Mendrek A, Laurens KR, Kiehl KA, Ngan ET, Stip E, Liddle PF. Changes in distributed neural circuitry function in patients with first-episode schizophrenia. Br J Psychiatry 2004;185:205-14.
9Crespo-Facorro B, Paradiso S, Andreasen NC, O'Leary DS, Watkins GL, Boles Ponto LL, et al . Recalling word lists reveals "cognitive dysmetria" in schizophrenia: a positron emission tomography study. Am J Psychiatry 1999;156:386-92.
10Andreasen NC, O'Leary DS, Cizadlo T, Arndt S, Rezai K, Ponto LL, et al . Schizophrenia and cognitive dysmetria: A positron-emission tomography study of dysfunctional prefrontal-thalamic-cerebellar circuitry. Proc Natl Acad Sci U S A 1996;93:9985-90.
11Mathew RJ, Partain CL. Midsagittal sections of the cerebellar vermis and fourth ventricle obtained with magnetic resonance imaging of schizophrenic patients. Am J Psychiatry 1985;142:970-1.
12Aylward EH, Reiss A, Barta PE, Tien A, Han W, Lee J, et al . Magnetic resonance imaging measurement of posterior fossa structures in schizophrenia. Am J Psychiatry 1994;151:1448-52.
13Ichimiya T, Okubo Y, Suhara T, Sudo Y. Reduced volume of the cerebellar vermis in neuroleptic-naive schizophrenia. Biol Psychiatry 2001;49:20-7.
14Joyal CC, Pennanen C, Tiihonen E, Laakso MP, Tiihonen J, Aronen HJ. MRI volumetry of the vermis and the cerebellar hemispheres in men with schizophrenia. Psychiatry Res 2004;131:115-24.
15Willinek WA, Kuhl CK. 3.0 T neuroimaging: technical considerations and clinical applications. Neuroimaging Clin N Am 2006;16:217-28, ix.
16Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000;11:805-21.
17Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: A meta-analysis of voxel-based morphometry studies. Am J Psychiatry 2005;162:2233-45.
18Good 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.
19Diagnostic and Statistical Manual of Mental Disorders, Text Revision. 4th ed. American Psychiatric Association; 1994.
20Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al . The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998;59:22-33;quiz 34-57.
21Andreasen NC. The Scale for the Assessment of Positive Symptoms (SAPS). Iowa City, IA: University of Iowa; 1984.
22Andreasen NC. The Scale for the Assessment of Negative Symptoms (SANS). Iowa City, IA: University of Iowa; 1983.
23Venkatasubramanian G, Jayakumar PN, Gangadhar BN, Keshavan MS. Neuroanatomical correlates of neurological soft signs in antipsychotic-naοve schizophrenia. Psychiatry Res 2008;164:215-22.
24Friston K, Ashburner J, Frith CD, Poline J-B, Heather JD, RSJ F. Spatial registration and normalization of images. Hum Brain Mapping 1995a;2:165-89.
25Friston K, Holmes AP, Worsley K, Poline J-B, Frith CD, RSJ. F. Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapping 1995b 2:189-210.
26Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Hum Brain Mapping 1999;7:254-66.
27Evans AC, Collins DL, Mills SR. 3D statistical neuroanatomical models from 305 MRI volumes. IEEE Nucl Sci Symp Med Imaging Conf Proc 1993;108:1877-8.
28Ashburner J, Neelin P, Collins DL, Evans A, Friston K. Incorporating prior knowledge into image registration. Neuroimage 1997;6:344-52.
29Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC. A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapping 1996;4:58-73.
30Ananth H, Popescu I, Critchley HD, Good CD, Frackowiak RS, Dolan RJ. Cortical and subcortical gray matter abnormalities in schizophrenia determined through structural magnetic resonance imaging with optimized volumetric voxel-based morphometry. Am J Psychiatry 2002;159:1497-505.
31Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002;15:870-8.
32Nopoulos PC, Ceilley JW, Gailis EA, Andreasen NC. An MRI study of cerebellar vermis morphology in patients with schizophrenia: Evidence in support of the cognitive dysmetria concept. Biol Psychiatry 1999;46:703-11.
33Loeber RT, Cintron CM, Yurgelun-Todd DA. Morphometry of individual cerebellar lobules in schizophrenia. Am J Psychiatry 2001;158:952-4.
34Venkatasubramanian G, Gangadhar BN, Jayakumar PN, Janakiramaiah N, Keshavan M. Striato-cerebellar abnormalities in never-treated schizophrenia: Evidence for neurodevelopmental etiopathogenesis. German J Psychiatry 2003;6:1-7.
35Wassink TH, Andreasen NC, Nopoulos P, Flaum M. Cerebellar morphology as a predictor of symptom and psychosocial outcome in schizophrenia. Biol Psychiatry 1999;45:41-8.
36Andreasen NC, Pierson R. The role of the cerebellum in schizophrenia. Biol Psychiatry 2008;64:81-8.
37Andreasen NC, Paradiso S, O'Leary DS. "Cognitive dysmetria" as an integrative theory of schizophrenia: A dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull 1998;24:203-18.
38Tran KD, Smutzer GS, Doty RL, Arnold SE. Reduced Purkinje cell size in the cerebellar vermis of elderly patients with schizophrenia. Am J Psychiatry 1998;155:1288-90.
39Weinberger DR, Kleinman JE, Luchins DJ, Bigelow LB, Wyatt RJ. Cerebellar pathology in schizophrenia: a controlled postmortem study. Am J Psychiatry 1980;137:359-61.
40Bleuler E. Dementia Praecox or the Group of Schizophrenias [Zinkin J, trans]. New York: International Universities Press; 1950.