Dorsolateral prefrontal cortex volume in patients with deficit or nondeficit schizophrenia

Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269

Contents lists available at SciVerse ScienceDirect

Progress in Neuro-Psychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp

Dorsolateral prefrontal cortex volume in patients with deficit or nondeficit schizophrenia Umberto Volpe a,⁎, Armida Mucci a, Mario Quarantelli b, Silvana Galderisi a, Mario Maj a a b

Department of Psychiatry, University of Naples SUN, Largo Madonna delle Grazie, 80138 Naples, Italy Biostructure and Bioimaging Institute, CNR and Diagnostic Imaging, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy

a r t i c l e

i n f o

Article history: Received 20 December 2011 Received in revised form 30 January 2012 Accepted 5 February 2012 Available online 10 February 2012 Keywords: Deficit schizophrenia Dorsolateral prefrontal cortex MRI Negative symptoms

a b s t r a c t Deficit schizophrenia (DS) represents a promising putative clinical subtype of schizophrenia and is characterized by the presence of primary and enduring negative symptoms. Previous studies have often reported a reduced amount of gray matter within prefrontal and temporal cortices in schizophrenia subjects with prevailing negative symptoms; however, the evidence concerning brain structural abnormalities in patients with DS remains controversial. The aim of the present study was to investigate whether patients with DS differed from those with nondeficit schizophrenia (NDS) with respect to the volume of the dorsolateral prefrontal cortex (DLPFC) and hippocampus, two brain areas considered as key regions in the pathogenesis of schizophrenia. In the present study a 3D-T1w MR imaging procedure and an extensive clinical assessment was carried out in 18 patients with schizophrenia, (10 DS and 8 NDS). 3D MPRAGE images were preprocessed with SPM software and two regions of interest (hippocampus and DLPFC) were manually traced to obtain their gray matter volumes. We found a significant reduction of DLPFC in the entire schizophrenia group, with respect to healthy subjects. Although the subgroup of patients with DS had a more severe clinical picture and more impaired social functioning, the DLPFC volume reduction was greater in NDS than in DS patients. In conclusion, according to our structural neuroimaging findings, DS patients, although characterized by a more severe clinical picture and a worse outcome, show less neurobiological abnormalities. © 2012 Elsevier Inc. All rights reserved.

1. Introduction Current diagnostic criteria of schizophrenia have been widely criticized (Maj, 1998; Tandon et al., 2008). To solve the clinical puzzle of the heterogeneity of this complex syndrome, putative diagnostic subtypes have been proposed. Deficit schizophrenia (DS) is currently regarded as one of the most promising schizophrenia subtypes (Galderisi and Maj, 2009; Kirkpatrick and Galderisi, 2008). Although DS is a relatively rare clinical condition [its prevalence is less than 30% in clinical samples and below 20% in population studies; (Kirkpatrick et al., 2006)], it represents a stable clinical subtype of schizophrenia, characterized by the presence of primary and enduring negative symptoms (Carpenter et al., 1988), associated with a greater impairment of neurocognitive abilities (Buchanan et al., 1994; Cascella et al., 2008; Galderisi et al., 2002), social cognition (Cohen et al., 2007), and fronto-parietal functioning (Delamillieure et al., 2004), with a poorer response to treatment and with a worse outcome.

⁎ Corresponding author. Tel.: + 39 081 5666512; fax: + 39 081 5666523. E-mail address: [email protected] (U. Volpe). 0278-5846/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.pnpbp.2012.02.003

Previous studies, investigating brain structural correlates of schizophrenia, have often reported an enlargement of brain lateral ventricles, an involvement of medial temporal structures (among those, especially hippocampus) and of frontal lobes (most notably prefrontal cortices) (for a recent review on the topic see, Shenton et al., 2010). However, previous evidence suggested that specific sub-regions of the frontal (namely, the dorsolateral prefrontal cortex or DLPFC) and temporal (in particular, the hippocampus) lobes might represent promising “candidate areas” to explain some keyaspects of the schizophrenia clinical picture (Meyer-Lindenberg et al., 2005). Previous studies (Besson et al., 1987; Chua et al., 1997; Ho et al., 2003; Mathalon et al., 2001; Pearlson et al., 1984; Roth et al., 2004; Williams et al., 1985) showed that schizophrenia patients with a high load of negative symptoms tend to have a reduced amount of gray matter within the prefrontal cortices, although the data remained controversial (Pfefferbaum and Zipursky, 1991; Wible et al., 2001, 1995; Williamson et al., 1991). Classical studies on hippocampus in schizophrenia have posited its involvement in spatial and episodic memory as well as in regulating affective states (Phillips et al., 2003), suggesting that volumetric changes of this brain region might be related to flat affect and anhedonia; however, studies specifically dealing with this issue did not

U. Volpe et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269

report any association between hippocampus structural abnormalities and a specific symptom profile (Szeszko et al., 2003; Wang et al., 2008). In the above studies, however, no comparison was made between subjects with primary negative symptoms (DS) and those with secondary negative symptoms, so that no inference concerning the possible role of structural abnormalities in prefrontal cortices and hippocampus in the pathogenesis of primary and enduring negative symptoms can be drawn. Few studies investigated structural prefrontal and hippocampal abnormalities in patients with DS. Buchanan et al. (1993) assessed prefrontal volumes in subjects with DS or nondeficit schizophrenia (NDS) and reported reduced prefrontal volumes in NDS patients only; hippocampal gray matter resulted to be reduced in both DS and NDS subgroups. Other studies (Gur et al., 1994; Mozley et al., 1994; Sigmundsson et al., 2001; Turetsky et al., 1995) also reported medial temporal and prefrontal structural abnormalities in patients with DS. Previous studies by our group (Galderisi et al., 2008; Quarantelli et al., 2002) confirmed a loss of grey matter within frontal and temporal structures in subjects with schizophrenia, with respect to healthy controls, while no structural difference was detected in frontal lobe cortices between DS and NDS patients. More recently, also studies using standardized voxel-based approaches (Cascella et al., 2010; Koutsouleris et al., 2008) reported that patients with prominent negative symptoms had a significant gray matter loss in superior temporal and frontal cortices, without showing a specific involvement of DLPFC or hippocampus. However, voxel-based automated structural imaging methods, although overcoming the possible bias of an a priori hypothesis, may not be sufficiently sensitive to detect small volume differences (Klauschen et al., 2009). The present study aimed to investigate whether patients with DS differ from those with NDS with respect to the volume of the DLPFC and hippocampus, two brain areas considered as key-regions in the pathogenesis of schizophrenia. To this aim, the gray matter volumes of the hippocampus and of the DLPFC were assessed in healthy subjects and in patients with a standardized diagnosis of DS or NDS, by using a three-dimensional high resolution T1-weighted set of MR images. Correlations between such morphometric measures and relevant clinical characteristics of the two patient subgroups were also explored.

2. Methods 2.1. Subjects Subjects were recruited as part of a larger national multicenter study (Galderisi et al., 2008). However, MRI high-resolution data were available only for patients recruited among those attending the outpatient unit of the Department of Psychiatry of the University of Naples SUN. For inclusion in the study, patients had to meet the following inclusion criteria: a) a DSM-IV diagnosis of schizophrenia, confirmed by the Structured Clinical Interview for DSM-IV (SCID-I); b) an age between 16 and 55 years; c) no previous history of severe mental retardation, alcoholism and drug dependence or abuse in the last 12 months; d) no previous electroconvulsive therapy and stabilized pharmacological treatment therapy within the last 3 months; e) no significant changes in the clinical state or in drug treatment during the preceding 3 months; f) right handedness, as revealed by the Edinburgh questionnaire (Oldfield, 1971); and g) willingness to participate in the study procedures, expressed by providing written informed consent after complete description of the study. Patients meeting these criteria were then classified as having either DS or NDS after being interviewed with the Schedule for the Deficit Syndrome (SDS; Kirkpatrick et al., 1989). All subjects diagnosed as DS according to the SDS were enrolled in the study.

265

For each recruited patient with DS, a patient with NDS and a healthy control comparable for age (± 3 years), were recruited among those attending the outpatient unit and among personnel and students of the Department of Psychiatry of University of Naples SUN, respectively. Thirty one patients with DS (25 males, 9 females, age range 20–51 years), 31 patients with NDS (26 males, 6 females, age range 19–54 years) and 31 healthy controls (21 males and 10 females, age range 18–50 years) were initially enrolled. Due to different reasons (insufficient cooperation during the MRI acquisition procedures; removal of the consent to participate in the MRI procedures; data format incompatibility during analytical procedures; exclusion of image sets with detectable motion artefacts), not all subjects completed the MR imaging protocol. In the end, high resolution MR data were available for 10 DS (9 males, 1 female), 8 NDS (7 males, 1 female) and 8 healthy (7 males, 1 female) subjects. The demographic, clinical and global volumetric characteristics of the subjects included in our experimental sample are summarized in Table 1. No significant differences were found, in terms of clinical and socio/demographical data, with respect to our larger sample described in the multicenter study (Galderisi et al., 2002). The study was approved by the local ethical committee of the University of Naples SUN and was carried out in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. 2.2. Psychopathological evaluation Psychopathological evaluation was carried out by means of the Extended Brief Psychiatric Rating Scale (EBPRS, ver. 4.0; Ventura et al., 1993); the Scale for the Assessment of Negative Symptoms Table 1 Clinical, MR volumetric and demographic characteristics of the experimental sample.

Age (years) Age of onset (years) Duration of illness (years) Antipsychotic drug dose (chlorpromazine equivalents.) MRI volumes ICV GM WM CSF EBPRS (factors) Thought disturbance Activation Anxiety Depression Hostility Anergia SANS and SAPS Positive dimension Negative dimension Disorganization SCOS Admissions Work functioning Interpersonal relationships Symptoms Total

DS patients (N = 10)

NDS patients (N = 8)

Healthy controls (N = 8)

mean

SD

mean

SD

mean

SD

35.8 22.4 13.2

7.4 3.7 7.9

34.2 21.6 13.1

8.1 4.2 6.5

33.0

7.5

403.67

292.55 574.80*

308.11

1302.60 655.15 489.31 158.14

126.59 52.78 45.31 73.50

1316.34 641.48 525.97 148.89

125.65 74.18 44.83 47.85

1374.75 727.76 510.90 136.09

107.10 45.73 62.63 43.89

6.79 4.41 6.03 1.28 4.53 10.06*

2.74 1.50 2.08 0.60 1.58 3.23

7.68 4.61 5.64 1.00 5.35 8.23

3.25 1.67 2.43 0.20 2.23 2.89

3.15 12.50** 2.38

2.72 3.22 1.91

4.26 10.39 2.90

3.00 3.30 1.87

3.79 0.74 1.12**

0.41 0.90 1.43

3.60 1.13 2.10

0.93 1.22 1.54

1.74 7.38*

1.02 2.13

1.87 8.70

1.10 2.74

ICV = Intracranial volume (all MR volumes are expressed as cc); GM = gray matter; WM = white matter; CSF: cerebrospinal fluid. EBPRS = Extended Brief Psychiatric Rating Scale; SANS = Scale for the Assessment of Negative Symptoms; SAPS = Scale for the Assessment of Positive Symptoms; SCOS = Strauss–Carpenter Outcome Scale. Significant differences between DS and NDS patients are shown in italics; *p b 0.05; **p b 0.01.

266

U. Volpe et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269

(SANS; Andreasen, 1984a) and the Scale for the Assessment of Positive Symptoms (SAPS; Andreasen, 1984b). For the evaluation of the social functioning, the scores for the domains “hospitalization,” “work,” “social relationships,” and “symptoms” of the Strauss–Carpenter Outcome Scale (SCOS; Strauss and Carpenter, 1972) were used. 2.3. Neuroradiological evaluation The imaging protocol consisted of a high-resolution magnetization prepared rapid acquisition gradient echo (MPRAGE) T1weighted magnetic resonance examination, which provided 128 coronal slices, covering the whole brain. The recording parameters were as follows: TR = 10 ms; TE= 4 ms; TI= 150 ms; matrix = 192 × 256; THK = 1.64 mm; FOV = 250 mm; NEX = 3; flip angle = 10 deg. The obtained MPRAGE images were pre-processed, normalized and segmented into grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF), using the standard algorithms implemented within the SPM5 software (developed by the Institute of Neurology, University College of London, UK and available at http://www.fil.ion. ucl.ac.uk/spm/software/spm5). Briefly, this software uses a “cluster algorithm” which identifies the voxel intensities belonging to a given cerebral tissue and integrates this information with an a priori knowledge concerning spatial distribution of such clusters in a normative data set (Ashburner and Friston, 1997). After the segmentation, for each subject, three sets of images were available corresponding respectively to gray matter (GM), white matter (WM) and cerebral-spinal fluid (CSF) maps: basically, they represented maps of statistical probability that each voxel belonged to a given cerebral tissue (with numerical probability ranging from 0— i.e., no probability to 1—highest probability). 2.4. Volumetric analysis of MR images Quantitative measures on gray matter segmented images were manually obtained from each scan, using an home-made software, written in MATLAB (ver. 6.0; The Mathworks Inc., MA, USA). The software allowed to manually delineate regions of interests (ROIs) on coronal slices and to select, as belonging to gray matter within those regions, only voxels which had a p ≥ 78% (i.e., intravoxel probability value > 200; range 0–255). Once voxels were manually selected, the software performed the computation of the milliliters of GM by multiplying the pixel volume by the number of pixels belonging to that region. ROIs included the dorso-lateral prefrontal cortex (DLPFC), as previously defined by Prasad et al. (2005), and the hippocampus, according to the criteria proposed by Shenton et al. (1992). Briefly, DLPFC had the superior frontal sulcus as superior limit; the superior border of Sylvian fissure as inferior limit; the most posterior coronal slice in which the genu of corpus callosum appeared as posterior limit; the tenth coronal slice preceding the posterior border as anterior limit; the lateral border of the brain as lateral border; the line connecting the most medial point of superior frontal sulcus and the horizontal branch of the Sylvian fissure as medial border. The hippocampus had the first coronal slice in which cerebral peduncles were present as anterior limit, and the last coronal slice in which the fibres of the crux fornicis as posterior limit appeared. In order to ensure the reliability of manual measures, five MR image sets, not included in the experimental data set, were analysed by two properly trained investigators, blind to the subjects' diagnosis. The intraclass correlation coefficient (ICC) resulted to be 0.91 for the DLPFCs and 0.82 for the hippocampi. The training procedure was supervised by a consultant neuroradiologist. Furthermore, a specialist in neuroradiology independently performed the volumetric measures on the same five MRI image sets and the MANOVA on the measures taken by the trained investigators and those taken by the neuroradiologist did not reveal statistically significant differences.

To take into account individual whole brain structural differences, all measures were normalized for individual total intracranial volume (ICV, calculated from segmented maps as the sum of GM, WM, and CSF). Thus, all measures used for further analyses are provided as fractional results (ICV%). 2.5. Statistical analyses All statistical analyses were performed by means of the software Statistica 6.0 (Statsoft Inc., 2000). Statistical significance of differences among the three groups for age and education was tested by means of one-way analysis of variance (ANOVA). ANOVA was used also to test the presence of eventual differences between the two schizophrenia groups for age of onset and duration of illness. Since it is well known that age and the use of antipsychotics drugs may exert significant influence over brain gray matter volumes, in order to exclude possible biases related to age distribution or to the use of different doses of antipsychotic drugs, all group comparisons of the brain regional volumes were planned to be carries out by a repeated measure multiple analysis of covariance (MANCOVA), with “age” and “mean antipsychotic drug dose” (calculated as chlorpromazine Eq.) as covariates and cerebral hemisphere as “within-group” factor. Differences between the two patients subgroups were further analyzed by means of MANCOVAs with “age” and “antipsychotic drug dosage” as covariates. Furthermore, since previous studies also reported that previous use of clozapine may exert a significant impact on GM volumes specifically in frontal regions, with respect to other antipsychotics (Molina et al., 2005a), and since 4 patients in the DS group and 3 patients in NDS group received clozapine, we also introduced the “use of clozapine” as further covariate in our MANCOVAs. When significant effects or interactions were present, follow-up ANCOVAs were used. Bonferroni correction was applied, when needed. Effect size of volumetric differences between the DS and NDS subgroups were evaluated using Cohen's d test. Correlations between volumetric measures and clinical variables (EBPRS scores, SANS scores, SAPS scores, SCOS scores, duration of illness and dosage of antipsychotic drug) were explored by means of Pearson's correlation test. Statistical significance level was set at p b 0.05 for all tests. 3. Results The sociodemographic and clinical data of our final sample are summarized in Table 1. The two patients groups were comparable for age of onset and duration of illness. None of them had any previous history of drug or alcohol abuse. All patients received antipsychotic drugs, but none of them was taking lithium. The average dose of antipsychotic drug (±SD), expressed in chlorpromazine equivalents, was 452.5 ± 320.9 (chlorpromazine equivalent doses were computed for oral antipsychotic medication using the method presented by Woods, 2003); the mean daily dose of antipsychotic drug was significantly higher in the NDS group, with respect to the DS group (see Table 1). DS patients also had significantly higher scores for the factor “Anergia” of the EBPRS (F1,63 = 5.77; p b 0.02) and for the SANS negative dimension (F1,63 = 6.81; p b 0.01). Finally, DS patients showed lower scores, with respect to NDS patients, for the “interpersonal relationships” item (F1,62 = 7.00; p b 0.01) and for the total score (F1,62 = 4.66; p b 0.03) of the SCOS, confirming the presence of a worse functioning in the DS group. The MANCOVA on total intracranial volumes as well as on gray matter, white matter and cerebrospinal fluid volumetric measures did not show significant differences among the three diagnostic subgroups (F6,38 = 1.46; p b 0.16), although on average deficit patients

U. Volpe et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269

had lower ICV values with respect to NDS and healthy subjects, but higher GM volumes with respect to NDS subjects (see Table 1). The repeated measures MANCOVA with the three diagnostic groups on GM volume measures revealed a significant effect of the “diagnosis” categorical factor (i.e., DS/NDS/HC) (F2,20 = 6.31; p b 0.007). It also showed a significant interaction between the factors “diagnosis” and “region of interest” (F2,20 = 6.52; p b 0.006); post-hoc analyses revealed that GM volume in DLPFC was reduced in schizophrenia subjects with respect to healthy controls: both DS and NDS patients had smaller DLPFC GM volumes, with respect to control subjects (DS patients: DLPFC right, F1,16 = 34.07; p b 0.00002; left, F1,16 = 8.58; p b 0.009; NDS patients: DLPFC right, F1,16 = 92.02; p b 0.0000001; left, F1,16 = 25.471; p b 0.0002, respectively). Furthermore, the NDS group had smaller DLPFC GM volumes than the DS one (DLPFC right, F1,16 = 2.83; p b 0.01; left, F1,16 = 3.57; p b 0.007). DLPFC volumetric differences among the three groups are depicted in Fig. 1. The effect size of the difference of the left DLPFC volumes among DS and NDS subjects was in the moderate-large range (d = 0.895), and that of the difference in the right DLPFC was very high (d = 12.761). Gray matter volumes in hippocampi were larger in healthy controls (ICV% mean ± SD; right: 0.35 ± 0.06; left: 0.33 ± 0.04) with respect to DS (right: 0.34 ± 0.05; left: 0.32 ± 0.05) and NDS (right: 0.31 ± 0.06; left: 0.32 ± 0.06) subjects; however, such differences did not reach statistical significance. No correlation between volumetric measures and clinical variables was significant. 4. Discussion According to our data, DS subjects showed more severe negative symptoms and greater impairment of social functioning, with respect to the NDS group. These differences were not the effect of earlier age of onset, longer illness duration, or drug dose. These results are in line with previous observations (Kirkpatrick et al., 2001) and also with the findings of our larger multi-centre study (Galderisi et al., 2002). As also reported in previous studies (Arango et al., 2008), no significant differences were found in total brain volumes between the two schizophrenia subgroups, but both deficit and nondeficit patients had lower GM values, with respect to healthy controls, although such difference was not statistically significant. The evaluation of group differences in brain regions of interest showed a reduction of GM in the dorsolateral prefrontal cortices of subjects with schizophrenia, with respect to healthy subjects; this abnormality was more pronounced in the NDS subgroup, with respect to the DS subjects. Our results are in line with a previous study: Buchanan et al. (1993) also investigated prefrontal and hippocampal

volumes in deficit schizophrenia and reported the presence of smaller prefrontal volumes in subjects with nondeficit forms of schizophrenia with respect to both patients with deficit schizophrenia and healthy controls, and smaller volumes of the amygdala/hippocampus complex in both schizophrenic subgroups, with respect to controls; Buchanan et al. demonstrated a significant difference between schizophrenia subgroups considering the entire prefrontal cortex (including both its dorsolateral and orbitofrontal portions), while our results are limited to the dorsolateral subcomponent of the prefrontal cortex. Furthermore, Buchanan et al. pointed out that the prefrontal differences between the two schizophrenic groups were “primarily due to smaller white matter volumes”, whereas in our analysis we included regional gray matter volumes only. Our data are not consistent with previous studies reporting an association between disrupted structural integrity of prefrontal cortex and negative symptoms of schizophrenia (Goldman-Rakic and Selemon, 1997). In our sample, subjects with primary and enduring negative symptoms showed a reduction of gray matter in prefrontal cortices with respect to healthy controls, but they did not represent the group in which this abnormality was more marked. The presence of less severe structural abnormalities does not rule out the possibility that in DS patients the DLPFC is more functionally impaired, as reported in other studies (Gonul et al., 2003; Heckers et al., 1999; Lahti et al., 2001). As a matter of fact, a previous multi-modal brain imaging study (Molina et al., 2005b) provides an experimental confirmation to this possibility: authors evaluated the structure (by means of MRI) and the function (by means of positron emission tomography [PET]) of DLPFC as well as event-related potentials [ERPs], as evoked by a classical auditory “oddball” paradigm, of healthy and schizophrenia subjects, providing evidence that a reduction of the P3 component of ERPs was significantly correlated to the reduction of metabolic activity of the prefrontal regions of the schizophrenia subjects, even in the absence of significant cerebral atrophy in this region. Furthermore, the evidence of both hyper- and hypofrontality in schizophrenia may be just the reflection of the structural (Davidson and Heinrichs, 2003) and functional (Manoach, 2003) heterogeneity intrinsic to the syndrome, if considered as a whole. The few available brain imaging evidences concerning the structural (Galderisi et al., 2008; Mozley et al., 1994; Quarantelli et al., 2002; Turetsky et al., 1995) and functional (Delamillieure et al., 2000; Lahti et al., 2001) abnormalities of PFC in DS still appear conflicting. The apparent discrepancy between these data may be related to a functional involvement of the prefrontal cortex in the pathophysiology of schizophrenia (Antonova et al., 2005; Pantelis et al., 2005). Clinical and course differences between deficit and nondeficit schizophrenia might be related to a “functional disconnection” between frontal and temporal

8%

8%

7%

7% HC

6%

DS

267

HC

6%

NDS

DS NDS

5%

5%

4%

4%

Fig. 1. DLPFC gray matter volumes, expressed as ICV%, in deficit schizophrenia (DS; N = 10), nondeficit schizophrenia (NDS; N = 8) and healthy subjects (HC; N = 8). Left DLPFC gray matter volume was reduced in both schizophrenic subgroups with respect to healthy controls (DS vs. HC p b 0.0001; NDS vs. HC p b 0.01); NDS patients showed significantly lower GM values of their right DLFPC with respect to that of HC (p b 0.001), whereas no significant differences were found between DS and healthy subjects. Furthermore, the NDS group had significantly smaller DLPFC GM volumes than the DS one (DLPFC right: F1,16 = 2.83; p b 0.01; left: F1,16 = 3.57; p b 0.007).

268

U. Volpe et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269

areas, which is not detected by gray matter volumetry, especially when explored in gross brain areas. A post-mortem study proved the presence of specific white matter characteristics in PFC of DS patients: Kirkpatrick et al. (1999) found an increased density of interstitial white matter cells in Brodmann area 46 and concluded that DS differ from NDS in terms of pathophysiological mechanisms. A recent study, using a multimodal imaging approach reported the presence of white matter alterations in deficit schizophrenia, confirming the disruption of the integrity of superior longitudinal fasciculus in DS subjects and concluding for an altered fronto-parietal network functioning in subjects affected by the syndrome (Rowland et al., 2009). These data seem to be in favor of the presence of a prefrontal disconnection in DS that was not the focus in our study. Our results did not show significant gray matter volume differences between HC, DS and NDS subjects in the hippocampi. This region has been repeatedly described as a key-region to understand the pathophysiology of schizophrenia (Gur et al., 2007; Steen et al., 2006); however, negative findings have previously been reported (Shenton et al., 2001). Our study has some limitations and our conclusions have to be regarded with caution. In fact, our final experimental sample was small, due to issues commonly reported in MRI studies of schizophrenia (such as lack of patients' cooperation, head motion artifacts, etc.), and these data should be replicated in a larger population of DS and NDS patients. Patients with NDS received a significantly higher dose of antipsychotic drugs, possibly reflecting differences in the clinical picture. In fact, clinicians might tend to use lower doses of antipsychotic drugs in the presence of more severe negative symptoms; moreover, although not significantly so, patients with DS had less positive symptoms than those with NDS. Studies in drug-free patients at their first episode of illness might further clarify the contribution of antipsychotic treatment to the presence and the degree of brain structural abnormalities in the two patient subgroups. In conclusion, our structural neuroimaging findings in subjects with schizophrenia indicate that DS patients, although characterized by a more severe clinical picture and a worse outcome, had a lower degree of neurobiological abnormalities (i.e., gray matter loss in DLPFC, independent of antipsychotic medication). The interpretation of such evidence should be tempered not only by the fact that we examined gray matter volumes in two discrete areas and in two relatively small subgroups of patients, but also by the recent literature evidence concerning structural brain imaging in autism showing that higher gray matter volumes are not necessarily expression of normal brain development, since also increased brain volume (particularly, in frontal areas) might be the expression of other neurobiological abnormalities, such as underconnectivity (Stigler et al., 2011). Acknowledgments Authors want to thank Eng. Antonio Maddaloni (Naples) for his help in developing the software for the analysis of the brain volumes of interest. References Andreasen NC. The Scale for the Assessment of Negative Symptoms (SANS). Iowa City, IA: The University of Iowa; 1984a. Andreasen NC. The Scale for the Assessment of Positive Symptoms (SAPS). Iowa City, IA: The University of Iowa; 1984b. Antonova E, Kumari V, Morris R, Halari R, Anilkumar A, Mehrotra R, et al. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol Psychiatry 2005;58:457–67. Arango C, McMahon RP, Lefkowitz DM, Pearlson G, Kirkpatrick B, Buchanan RW. Patterns of cranial, brain and sulcal CSF volumes in male and female deficit and nondeficit patients with schizophrenia. Psychiatry Res 2008;162(2):91-100. Ashburner J, Friston K. Multimodal image coregistration and partitioning—a unified framework. Neuroimage 1997;6:209–17. Besson JA, Corrigan FM, Cherryman GR, Smith FW. Nuclear magnetic resonance brain imaging in chronic schizophrenia. Br J Psychiatry 1987;150:161–3.

Buchanan RW, Breier A, Kirkpatrick B, Elkashef A, Munson RC, Gellad F, et al. Structural abnormalities in deficit and nondeficit schizophrenia. Am J Psychiatry 1993;150:59–65. Buchanan RW, Strauss ME, Kirkpatrick B, Holstein C, Breier A, Carpenter Jr WT. Neuropsychological impairments in deficit vs. nondeficit forms of schizophrenia. Arch Gen Psychiatry 1994;51(10):804–11. Carpenter Jr WT, Heinrichs DW, Wagman AM. Deficit and nondeficit forms of schizophrenia: the concept. Am J Psychiatry 1988;145:578–83. Cascella NG, Testa SM, Meyer SM, Rao VA, Diaz-Asper CM, Pearlson GD, et al. Neuropsychological impairment in deficit vs. non-deficit schizophrenia. J Psychiatr Res 2008;42:930–7. Cascella NG, Fieldstone SC, Rao VA, Pearlson GD, Sawa A, Schretlen DJ. Gray-matter abnormalities in deficit schizophrenia. Schizophr Res 2010;120:63–70. Chua SE, Wright IC, Poline JB, Liddle PF, Murray RM, Frackowiak RS, et al. Grey matter correlates of syndromes in schizophrenia. A semi-automated analysis of structural magnetic resonance images. Br J Psychiatry 1997;170:406–10. Cohen AS, Saperstein AM, Gold JM, Kirkpatrick B, Carpenter Jr WT, Buchanan RW. Neuropsychology of the deficit syndrome: new data and meta-analysis of findings to date. Schizophr Bull 2007;33:1201–12. Davidson LL, Heinrichs RW. Quantification of frontal and temporal lobe brain-imaging findings in schizophrenia: a meta-analysis. Psychiatry Res 2003;122:69–87. Delamillieure P, Fernandez J, Constans JM, Brazo P, Benali K, Abadie P, et al. Proton magnetic resonance spectroscopy of the medial prefrontal cortex in patients with deficit schizophrenia: preliminary report. Am J Psychiatry 2000;157(4):641–3. Delamillieure P, Constans JM, Fernandez J, Brazo P, Dollfus S. Relationship between performance on the Stroop test and N-acetylaspartate in the medial prefrontal cortex in deficit and nondeficit schizophrenia: preliminary results. Psychiatry Res 2004;132:87–9. Galderisi S, Maj M. Deficit schizophrenia: an overview of clinical. Biological and treatment aspects. Eur Psychiatry 2009;24:493–500. Galderisi S, Maj M, Mucci A, Cassano GB, Invernizzi G, Rossi A, et al. Historical, psychopathological, neurological, and neuropsychological aspects of deficit schizophrenia: a multicenter study. Am J Psychiatry 2002;159:983–90. Galderisi S, Quarantelli M, Volpe U, Mucci A, Cassano GB, Invernizzi G, et al. Patterns of structural MRI abnormalities in deficit and nondeficit schizophrenia. Schizophr Bull 2008;34:393–401. Goldman-Rakic PS, Selemon LD. Functional and anatomical aspects of prefrontal pathology in schizophrenia. Schizophr Bull 1997;23:437–58. Gonul AS, Kula M, Eşel E, Tutuş A, Sofuoglu S. A Tc-99m HMPAO SPECT study of regional cerebral blood flow in drug-free schizophrenic patients with deficit and nondeficit syndrome. Psychiatry Res 2003;123:199–205. Gur RE, Mozley PD, Shtasel DL, Cannon TD, Gallacher F, Turetsky B, et al. Clinical subtypes of schizophrenia: differences in brain and CSF volume. Am J Psychiatry 1994;151:343–50. Gur RE, Keshavan MS, Lawrie SM. Deconstructing psychosis with human brain imaging. Schizophr Bull 2007;33:921–31. Heckers S, Goff D, Schacter DL, Savage CR, Fischman AJ, Alpert NM, et al. Functional imaging of memory retrieval in deficit vs nondeficit schizophrenia. Arch Gen Psychiatry 1999;56:1117–23. Ho BC, Andreasen NC, Nopoulos P, Arndt S, Magnotta V, Flaum M. Progressive structural brain abnormalities and their relationship to clinical outcome: a longitudinal magnetic resonance imaging study early in schizophrenia. Arch Gen Psychiatry 2003;60(6):585–94. Kirkpatrick B, Galderisi S. Deficit schizophrenia: an update. World Psychiatry 2008;7: 143–7. Kirkpatrick B, Buchanan RW, McKenney PD, Alphs LD, Carpenter Jr WT. The Schedule for the Deficit syndrome: an instrument for research in schizophrenia. Psychiatry Res 1989;30:119–23. Kirkpatrick B, Conley RC, Kakoyannis A, Reep RL, Roberts RC. The interstitial cells of the white matter in the inferior parietal cortex in schizophrenia: an unbiased cell-counting study. Synapse 1999;34:95-102. Kirkpatrick B, Buchanan RW, Ross DE, Carpenter Jr WT. A separate disease within the syndrome of schizophrenia. Arch Gen Psychiatry 2001;58:165–71. Kirkpatrick B, Fenton WS, Carpenter Jr WT, Marder SR. The NIMH-MATRICS consensus statement on negative symptoms. Schizophr Bull 2006;32:214–9. Klauschen F, Goldman A, Barra V, Meyer-Lindenberg A, Lundervold A. Evaluation of automated brain MR image segmentation and volumetry methods. Hum Brain Mapp 2009;30:1310–27. Koutsouleris N, Gaser C, Jäger M, Bottlender R, Frodl T, Holzinger S, et al. Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxelbased morphometric study. Neuroimage 2008;39:1600–12. Lahti AC, Holcomb HH, Medoff DR, Weiler MA, Tamminga CA, Carpenter Jr WT. Abnormal patterns of regional cerebral blood flow in schizophrenia with primary negative symptoms during an effortful auditory recognition task. Am J Psychiatry 2001;158:1797–808. Maj M. Critique of the DSM-IV operational criteria for schizophrenia. Br J Psychiatry 1998;172:458–60. Manoach DS. Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res 2003;60:285–98. Mathalon DH, Sullivan EV, Lim KO, Pfefferbaum A. Progressive brain volume changes and the clinical course of schizophrenia in men: a longitudinal magnetic resonance imaging study. Arch Gen Psychiatry 2001;58:148–57. Meyer-Lindenberg AS, Olsen RK, Kohn PD, Brown T, Egan MF, Weinberger DR, et al. Regionally specific disturbance of dorsolateral prefrontal-hippocampal functional connectivity in schizophrenia. Arch Gen Psychiatry 2005;62(4): 379–86.

U. Volpe et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 37 (2012) 264–269 Molina V, Reig S, Sanz J, Palomo T, Benito C, Sánchez J, et al. Increase in gray matter and decrease in white matter volumes in the cortex during treatment with atypical neuroleptics in schizophrenia. Schizophr Res 2005a;80:61–71. Molina V, Sanz J, Muñoz F, Casado P, Hinojosa JA, Sarramea F, et al. Dorsolateral prefrontal cortex contribution to abnormalities of the P300 component of the eventrelated potential in schizophrenia. Psychiatry Res 2005b;140:17–26. Mozley PD, Gur RE, Resnick SM, Shtasel DL, Richards J, Kohn M, et al. Magnetic resonance imaging in schizophrenia: relationship with clinical measures. Schizophr Res 1994;12:195–203. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971;9:97-113. Pantelis C, Yücel M, Wood SJ, Velakoulis D, Sun D, Berger G, et al. Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull 2005;31:672–96. Pearlson GD, Garbacz DJ, Breakey WR, Ahn HS, DePaulo JR. Lateral ventricular enlargement associated with persistent unemployment and negative symptoms in both schizophrenia and bipolar disorder. Psychiatry Res 1984;12:1–9. Pfefferbaum A, Zipursky RB. Neuroimaging studies of schizophrenia. Schizophr Res 1991;4:193–208. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biol Psychiatry 2003;54:504–14. Prasad KM, Sahni SD, Rohm BR, Keshavan MS. Dorsolateral prefrontal cortex morphology and short-term outcome in first-episode schizophrenia. Psychiatry Res 2005;140(2): 147–55. Quarantelli M, Larobina M, Volpe U, Amati G, Tedeschi E, Ciarmiello A, et al. Stereotaxybased regional brain volumetry applied to segmented MRI: validation and results in deficit and nondeficit schizophrenia. Neuroimage 2002;17:373–84. Roth RM, Flashman LA, Saykin AJ, McAllister TW, Vidaver R. Apathy in schizophrenia: reduced frontal lobe volume and neuropsychological deficits. Am J Psychiatry 2004;161:157–9. Rowland LM, Spieker EA, Francis A, Barker PB, Carpenter WT, Buchanan RW. White matter alterations in deficit schizophrenia. Neuropsychopharmacology 2009;34: 1514–22. Shenton ME, Kikinis R, Jolesz FA, Pollak SD, LeMay M, Wible CG, et al. Abnormalities of the left temporal lobe and thought disorder in schizophrenia. A quantitative magnetic resonance imaging study. N Engl J Med 1992;327(9):604–12. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res 2001;49:1-52. Shenton ME, Whitford TJ, Kubicki M. Structural neuroimaging in schizophrenia: from methods to insights to treatments. Dialogues Clin Neurosci 2010;12:317–32. Sigmundsson T, Suckling J, Maier M, Williams S, Bullmore E, Greenwood K, et al. Structural abnormalities in frontal. temporal. and limbic regions and interconnecting

269

white matter tracts in schizophrenic patients with prominent negative symptoms. Am J Psychiatry 2001;158:234–43. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry 2006;188:510–8. Stigler KA, McDonald BC, Anand A, Saykin AJ, McDougle CJ. Structural and functional magnetic resonance imaging of autism spectrum disorders. Brain Res 2011;1380: 146–61. Strauss JS, Carpenter Jr WT. The prediction of outcome in schizophrenia. I. Characteristics of outcome. Arch Gen Psychiatry 1972;27:739–46. Szeszko PR, Goldberg E, Gunduz-Bruce H, Ashtari M, Robinson D, Malhotra AK, et al. Smaller anterior hippocampal formation volume in antipsychotic-naive patients with first-episode schizophrenia. Am J Psychiatry 2003;160:2190–7. Tandon R, Keshavan MS, Nasrallah HA Schizophrenia. “Just the facts”: what we know in 2008 part 1: overview. Schizophr Res 2008;100:4-19. Turetsky B, Cowell PE, Gur RC, Grossman RI, Shtasel DL, Gur RE. Frontal and temporal lobe brain volumes in schizophrenia. Relationship to symptoms and clinical subtype. Arch Gen Psychiatry 1995;52:1061–70. Ventura F, Green MF, Shaner A, Liberman RP. Training and quality assurance with the Brief Psychiatric Rating Scale: "The drift busters". Int J Methods Psychiatr Res 1993;3:221–4. Wang L, Mamah D, Harms MP, Karnik M, Price JL, Gado MH, et al. Progressive deformation of deep brain nuclei and hippocampal–amygdala formation in schizophrenia. Biol Psychiatry 2008;64:1060–8. Wible CG, Shenton ME, Hokama H, Kikinis R, Jolesz FA, Metcalf D, et al. Prefrontal cortex and schizophrenia. A quantitative magnetic resonance imaging study. Arch Gen Psychiatry 1995;52:279–88. Wible CG, Anderson J, Shenton ME, Kricun A, Hirayasu Y, Tanaka S, et al. Prefrontal cortex, negative symptoms, and schizophrenia: an MRI study. Psychiatry Res 2001;108:65–78. Williams AO, Reveley MA, Kolakowska T, Ardern M, Mandelbrote BM. Schizophrenia with good and poor outcome. II: cerebral ventricular size and its clinical significance. Br J Psychiatry 1985;146:239–46. Williamson P, Pelz D, Merskey H, Morrison S, Conlon P. Correlation of negative symptoms in schizophrenia with frontal lobe parameters on magnetic resonance imaging. Br J Psychiatry 1991;159:130–4. Woods SW. Chlorpromazine equivalent doses for the newer atypical antipsychotics. J Clin Psychiatry 2003;64:663–7.