Which of the following drugs is considered as a typical antipsychotic drug?

INTRODUCTION

The exact mechanism by which typical and atypical antipsychotics exert their different therapeutic effects is still unclear [Meltzer et al, 1999]. The introduction of antipsychotic drugs in the early 1950s revolutionized the treatment of psychoses, and schizophrenia in particular. These drugs [typical antipsychotics, for example, haloperidol and chlorpromazine] act on the dopaminergic system, blocking the dopamine type 2 [D2] receptors in mesolimbic areas [Carlsson, 1978]. Owing to this D2 blockade, they also induce a number of side effects, among which extrapyramidal symptoms are the most prominent [Dazzan and Murray, 2002; Dazzan et al, 2004]. Recently, a new generation of antipsychotic drugs, the atypical antipsychotics, have become available [for example, clozapine, risperidone, olanzapine, and quetiapine]. Atypical antipsychotics have lower affinity and occupancy for the dopaminergic receptors, and a high degree of occupancy of the serotoninergic receptors 5-HT2A [Meltzer et al, 1989]. Compared to typical antipsychotics, atypicals induce fewer extra-pyramidal side effects, but the exact neurobiological substrate of this difference is still unknown.

One way to better understand the mechanisms underlying the different side effects of typical and atypical antipsychotics is to use neuroimaging to investigate brain changes associated with a specific class of antipsychotic drug. Until now, structural magnetic resonance imaging [MRI] studies of antipsychotics effects have been restricted to the evaluation of a few selected subcortical regions. The most consistent finding has been that typical antipsychotics are associated with an enlargement of the basal ganglia, and that this enlargement does not occur, or can be reversed, with atypical antipsychotics [Chakos et al, 1994; Corson et al, 1999]. Different effects of typicals and atypicals have also been described at cortical level in functional imaging studies. Specifically, typical antipsychotic agents seem to significantly reduce relative blood flow in the frontal cortex, while atypicals may be associated with a smaller blood flow decrease in this area [Honey et al, 1999; Miller et al, 2001].

One limitation of the existing studies on effects of antipsychotics on brain structure is that they have often investigated patients treated with antipsychotics for many years, with different types of antipsychotic drugs, and at a variety of doses. This makes it difficult to disentangle which brain changes are due to a specific class of antipsychotics, and which are due to the illness and its progression [Dazzan and Murray, 1999]. Therefore, these questions can be better addressed by investigating subjects at the initial stages of psychosis, when treatment would have occurred for only a short time, and the dose taken is easier to establish. They therefore represent an ideal sample to investigate the differential, short-term, effects of antipsychotics on brain structure.

In the present exploratory study, we investigated the relationship between brain structure and antipsychotic treatment in an epidemiologically based sample of patients at the first psychotic episode, both antipsychotic-treated and antipsychotic-free. Using an epidemiologically based sample limits the potential bias of recruiting subjects selected because of their treatment. We collected a complete medication history for each subject, and used high-resolution MRI and voxel-based methods of image analysis. Voxel-based analysis has the major advantage of allowing the evaluation of the entire brain rather than of a few preselected regions; furthermore, it is automated, which means that it does not require the identification of anatomical boundaries nor manual tracing of the regions of interest [Dazzan et al, 2004]. This study aimed: [1] to investigate whether antipsychotics have a measurable effect on brain anatomy; and if so, [2] to investigate if typical and atypical antipsychotics affect brain anatomy differently.

PATIENTS AND METHODS

Subjects were recruited as part of a large epidemiological study [ÆSOP: aetiology and ethnicity in schizophrenia and other psychoses], carried out in three English cities, which investigated the higher rates of schizophrenia in the African-Caribbean population in the United Kingdom [Dazzan et al, 2004]. Ethical approval for the study was granted by the Ethical Committee of the Institute of Psychiatry, and the participants gave written informed consent, in accordance with the Declaration of Helsinki.

As part of the South London arm of this study, we approached subjects aged 16–65, who consecutively presented for the first time to the local psychiatric services for a functional psychotic illness [ICD10 F 10–19, excluding coding F 1 x.0 for acute intoxication; F 20–29 and F 30–39, psychotic codings] [World Health Organisation, 1992], over a 3-year period. Exclusion criteria were: [a] a history of head trauma resulting in loss of consciousness for over 1 h; [b] the presence of a disease of the central nervous system; [c] moderate or severe learning disabilities as defined by ICD-10 [World Health Organisation, 1992]; [d] poor fluency in English language; [e] transient psychotic symptoms resulting from acute intoxication as defined by ICD-10 [World Health Organisation, 1992], following the administration of alcohol or other psychoactive substance.

A total of 281 patients met the inclusion criteria and were invited to participate in the overall London arm of the ÆSOP study: 90 refused to take part in the investigation. Of the remaining 191 who participated, 115 patients consented to have an MRI scan. These 115 patients were on average 6 years younger [mean age 27.9±8.4 years vs 33.7±12.3 years, t=3.5, P=0.001] and had a higher proportion of white British subjects [36 vs 18%, χ2=6.95, P=0.008]. They were comparable to the total sample in terms of gender. Ten patients terminated the scanning session before full image acquisition had been achieved and a further 15 scans were excluded from the analysis [13 due to subject motion, one because of congenital hydrocephalus, and one because of the presence of a subarachnoid cyst].

Clinical Measures

We interviewed patients using the WHO Schedules for Clinical Assessment in Neuropsychiatry [SCAN] [World Health Organisation, 1994]. We made a diagnosis according to ICD-10 criteria [World Health Organisation, 1992] by consensus in meetings with senior clinicians [RM or JL] from the Institute of Psychiatry, in which all clinical information was presented. A total symptomatology score was obtained by summing the SCAN's individual symptom item scores as per Wing and Sturt [1978] procedure for the Present State Examination [PSE] [Wing et al, 1974; Wing and Sturt, 1978]. This was an appropriate model to adopt as the SCAN incorporates the 10th edition of the PSE. Duration of illness [DOI] was operationalized as the time in weeks between the onset of psychotic symptoms and the MRI scan date. We used both the patients' medical notes and the information obtained from the SCAN interviews to establish the onset.

The premorbid IQ was estimated by the National Adult Reading Test [NART] [Nelson and Willison, 1991]. We assessed handedness according to the Annett Hand Preference Questionnaire [Annett, 1970].

Pharmacological Treatment

From clinical notes, we completed a medication record for each patient. We calculated the total duration of antipsychotic exposure in days and the daily antipsychotic dose at the time of MRI scan, converted into chlorpromazine equivalents for typical antipsychotics [Bazire, 1998; Bezchlibnyk-Butler and Jeffries, 2000; Taylor et al, 1999]. We also recorded information on treatment with anticholinergic drugs, antidepressants, and mood stabilizers. Therapeutic interventions [type of medication and length of treatment] were decided by the responsible clinical team, based on clinical presentation, and were not influenced by participation to the study. For the purposes of the main study, we obtained one MRI scan as soon as possible after first presentation to the services, independently on length of antipsychotic treatment. Depending on their current treatment, subjects were divided into three groups: [1] typical antipsychotics; [2] atypical antipsychotics; [3] drug-free. On the basis of existing literature on antipsychotic washout, we considered ‘drug-free’ those subjects who had not taken any antipsychotic in the 3 weeks prior to the MRI scan [Farde et al, 1986; Miller et al, 1997a, 1997b, 2001]. We considered subjects as being on treatment with typical antipsychotics if they had been taking one typical antipsychotic only for at least 2 weeks prior to MRI and had not taken more than one dose of an atypical antipsychotic during this time. The same criteria were used for the subjects on atypicals. Therefore, the allocation of subjects to each of these three groups was nonrandomized, but based on the medication prescribed by the in-charge clinician at the time of MRI scan. According to the existing literature on brain changes following administration of antipsychotics [Chakos et al, 1994; Cohen et al, 2003; Christensen et al, 2004; Grunder et al, 2003; Honey et al, 1999; Huang et al, 1999; Miller et al, 1997a, 1997b, 2001; Wotanis et al, 2003], the mean length of treatment of 8 weeks of our sample would be sufficient to observe brain changes in association with antipsychotic use.

Structural MRI

Image acquisition

Scans were acquired with a GE Signa 1.5-T system [GE Medical Systems, Milwaukee], at the Maudsley Hospital, London. Contiguous, interleaved proton-density- and T2-weighted images, each 3-mm thick, were acquired in the coronal plane, to provide whole brain coverage. A repetition time [TR] of 4000 ms and effective echo times [TE] of 20 and 85 ms were used with an 8-echo train length. The matrix size was 256 × 192, collected from a rectangular field-of-view of 22 × 16.5 cm2, giving an in-plane resolution of 0.859 mm in both directions. The total acquisition time was 10 min and 12 s.

Image processing

The methods used for segmentation and registration of each fast spin echo data set have been described in detail elsewhere [Bullmore et al, 1999; Suckling et al, 1999a]. Briefly, extra-cerebral tissues were initially removed, using an automated algorithm. Manual editing of the skull-stripped images was necessary only to remove brainstem and cerebellum from the cerebral hemispheres and diencephalon. The probability of each intracerebral voxel belonging to each of four possible tissue classes [grey matter, white matter, cerebrospinal fluid [CSF], or dura/vasculature] was then estimated with a modified fuzzy clustering algorithm [Suckling et al, 1999b]. This type of segmentation assigns, to each voxel, a value in the range 0–1 assuming to indicate the fraction of the voxel comprised by each tissue type [for example, a grey matter value of 0.7, means that 70% of the tissue represented by that voxel is grey matter; therefore, the value indicates the proportion of the voxel occupied by grey matter].

A template image in the standard space of Talairach and Tournoux [1988] was constructed using the AFNI program from proton-density images acquired from six healthy subjects [Dazzan et al, 2004]. Maps of tissue distribution were then registered onto the template by registering each proton density image using a nine-parameter affine registration, minimizing the grey-level difference between images. This registration aligns all the images together, and scales them to the same gross dimensions. The derived mapping was then applied to the corresponding tissue maps.

Between-group differences in grey matter volume were estimated by fitting an analysis of covariance [ANCOVA] model at each intracerebral voxel in standard space covarying for age at scan and total grey matter volume [an estimate automatically provided by the program]. Covarying for global grey matter may not be preferable to covarying for intracranial volume in a classical comparison of patients with schizophrenia vs healthy controls, because there is often a reduction in cortical volume in patients with schizophrenia. In that case, covarying for this variable may therefore remove effects of interest. However, in the present study [consisting only of patient groups] there were no differences in total grey matter volume across the groups. Hence, we consider grey matter volume an appropriate covariate.

Permutation testing was used to assess statistical significance, and regional relationships were tested at the level of voxel clusters [Bullmore et al, 1999; Sigmundsson et al, 2001]. Given that structural brain changes are likely to extend over a number of contiguous voxels, test statistics which incorporate spatial information, such as 3D cluster mass [the sum of suprathreshold voxel statistics], are generally more powerful than other test statistics, which are informed only by data at a single voxel. For each analysis, we calculate the critical value in the null distribution of cluster mass at which