Nimhans Neuropsychological Battery Price

[Berenice Pretlow]

Cognitiveimpairment is a consistent feature of schizophrenia as conclusively proven by numerous original research studies and meta-analyses [1-4]. Cognitive deficits are seen not only in patients diagnosed as having schizophrenia [5], but also in the at-risk state/prodrome [6,7], and even in the unaffected first degree relatives of index patients with schizophrenia [8,9]; thus, they are considered endophenotypes of the disorder [10].

Cross-sectional studies of patients with neuroleptic-nave, recent-onset and chronic schizophrenia have shown comparable magnitude of diffuse cognitive impairment affecting multiple domains across the different phases of the illness [11,12]. However, there is evidence to suggest that the effect of illness chronicity on cognition may be different across various domains. Certain domains like working memory, executive functioning, and processing speed may show differentially severe impairment in patients with chronic schizophrenia [7,13-15]. A majority of the cross-sectional and longitudinal studies have examined the effect of chronicity over a period of 10 years or less [16]. The few longitudinal studies in adult-onset schizophrenia that have examined the effect of prolonged duration of illness (15 years or more) on cognitive functioning have shown discrepant results. Bonner-Jackson et al. [17] reported that processing speed and general knowledge test performance in patients remained stable at 20 years from baseline; Andreasen et al. [18] reported progressive decline in regional gray and white matter volumes that significantly correlated with the performance in learning, working memory, and problem solving tests at 18 years follow-up; Albus et al. [1] reported an overall stable cognitive performance in multiple domains with significant improvement in verbal intelligence over 15 years, although a subset of patients showed significant deterioration in visuomotor processing speed; Fett et al. [19] reported significant decline in all the cognitive domains except verbal fluency and verbal knowledge between assessments done 18 years apart.

The socio-demographic details of the study participants are summarized in Table 1. Clinical symptom ratings were done for all the patients using the Psychotic Symptom Rating Scales (PSYRATS) [33], the Scale for the Assess-ment of Negative Symptoms (SANS) [34], and the Scale for the Assessment of Positive Symptoms (SAPS) [35]. Medication associated abnormal movements were measured using the Abnormal Involuntary Movement Scale [36], the Simpson-Angus Scale [37], and the Barnes Akathisia Rating Scale [38] (Table 2). The socio-demographic details of the study participants are summarized in Table 1.

English, Hindi, and Malayalam versions of the GNA were administered according to the language preferences of the study participants. Bi-/multi-lingual mental health professional volunteers under the supervision of AL, VM, and JPJ ensured equivalence of the content of items and their intended meaning in the Hindi and Malayalam translations with those of the original English version (semantic and content equivalence).

The GNA battery was administered in single session, by a psychiatrist (VM). The tests were administered in the same sequence and as per the standard instructions for administration of the GNA. All the participants completed the full battery including all the tests. The average durations of the GNA sessions were 21.2 1.2 minutes in the healthy subjects and 25.8 2.3 minutes in the schizophrenia sample. The individual test items were scored and entered in a spreadsheet and visually double-checked for data entry errors before the analysis.

We converted the raw individual test scores to z-scores using the overall mean and pooled standard deviation of the three groups, and grouped the z-scores into the six cognitive domains (Table 3) by averaging the z-scores of the tests under each domain, so that all of them would be in the same scale for better interpretability of scores across the various cognitive domains. This approach is often followed in neuropsychological studies where the ranges of different test scores vary to make it convenient to interpret and compare the shape or pattern of cognitive profiles of groups [41,42]. Thus, the GNA test battery which had seven cognitive tests with 13 scorable items was reduced into six meaningful and comparable domains, which were then analyzed using R, version 4.0.5 (R Studio 1.3.1073) [43].

The MR images were acquired on a 3 Tesla Philips Ingenia CX scanner (Philips Healthcare, Best, Netherlands) using a 32-channel head coil (TR 6.5 ms, TE 2.9 ms, flip angle 9, 192 slices in sagittal orientation, voxel size 1mm isotropic). The T1-weighted images were checked for MR artefacts, motion, and structural abnormalities using a systematic quality check pipeline [44]. The scans were also reviewed independently by a neuroradiologist to rule out any gross morphological abnormality. After quality assurance, we set the origin of T1-weighted images approximately at the anterior commissure using acpcdetect v2.0 [45-47] ( ). The T1-weighted images were then segmented into gray matter, white matter and, cerebrospinal fluid tissue classes using the Com-putational Anatomy Toolbox [48] (CAT12, version 1727, -jena.github.io/cat/) with SPM12 (version 7771, ) running in the background on MATLAB R2016a (MathWorks; ). We used the modulated normalized gray matter images smoothed by a Gaussian kernel of 6 mm full width at half maximum for voxel-level correlation of gray matter with cognitive scores.

To examine the association of cognitive scores with gray matter volumes, we performed a voxel-level correlation with cognitive scores in the overall sample (n = 47) and included the total intracranial volume (TIV) and age as covariates of no interest in a general linear model framework, as implemented in CAT12. We employed non-parametric statistical inference using the Threshold Free Cluster Enhancement (TFCE) algorithm [55] implemented in the TFCE toolbox (version 210, -jena.de/tfce/). We used the default settings (Smith method; 5000 permutations, cluster size weighting of E = 0.5) and examined the results at a stringent family-wise error (FWE) rate threshold of FWER In the overall schizophrenia sample (recent-onset and chronic combined), we performed a multivariate GLM (MANCOVA) to evaluate the main effect of patient group on the cognitive scores (Table 8). To avoid multicol-linearity, age was not included in the model as there were significant between-group differences in age (Table 1 and Supplementary Fig. 2 [available online]). In such a situation where the covariate substantially differs between groups, estimation of covariate adjusted means would not be meaningful [57]. Age at onset of symptoms was added as a covariate in view of its link to cognitive impairment [58,59] and also to account for the potential bias of patients with earlier age at onset being overrepresented in the chronic schizophrenia group. The cumulative dose- years of antipsychotic exposure were substantially different between ROSZ and CHSZ; therefore, we included the average daily dose of antipsychotics (cumulative antipsychotic exposure divided by the total duration of treatment) as a covariate, to avoid multicollinearity. Our schizo-phrenia samples consisted of patients predominantly exposed to second generation antipsychotics (Supplementary Tables 1, 2; available online). Anticholinergic (trihexy-phenidyl) dose was not added in the model due to the negligible difference between ROSZ and CHSZ (Supplementary Table 1; available online).

Post-hoc univariate analyses were done to explore the pattern of univariate group effects, keeping in mind the possibility that multivariate main effect of group could have been subtle, and hence not detected in our small sample (Table 9).

In the overall sample (n = 47) we found significant positive correlation between perceptual comparison speed and gray matter volume of the left anterior-medial temporal lobe and adjacent regions, after adjusting for TIV and age (TFCE, p Verbal working memory (digit span backward) scores showed significant positive correlation with the right precentral gyrus, the left inferior frontal gyrus, and the left anterior lateral temporal lobe volumes (Fig. 4 and Table 11). Other cognitive domains did not show statistically significant correlation with the gray matter volumes after correction for multiple comparisons.

In this pilot study using GNA, we examined the patterns of neurocognitive impairment in recent onset and chronic schizophrenia, and studied the relationship between these deficits and brain morphometry to explore the link, if any, between the various cognitive domains and brain morphometry.

There is considerable evidence that negative symptoms have direct association with cognitive dysfunction [109, 110], while positive symptoms have a distracting effect on cognitive task performance [111]. Additionally, greater degree of positive symptoms warrants higher dose of medications like antipsychotics, anticholinergics, benzodiazepines etc. which can impede cognitive performance through anticholinergic effects [112,113], sedation, and psychomotor slowing due to extrapyramidal effects [114, 115]. In our study, we excluded patients who were on benzodiazepines. The ROSZ group had higher positive and negative symptom scores than CHSZ, and the difference was statistically significant for PSYRATS scores (Table 2). SAPS scores significantly contributed to reduction in cognitive scores in our schizophrenia sample (Table 8). Our sample consisted of patients who were cooperative and motivated for assessments and MR imaging, and therefore did not include patients with prominent negative symptoms. This unavoidable selection bias, along with the small sample size, might explain why we did not find significant effects of SANS ratings on cognitive per-formance.

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