In this study, neuropsychologically defined schizophrenia subtypes were identified through unsupervised clustering processes similar to those used in previous studies (Allen et al., 1998
; Bell et al., 2010
; Hill et al., 2002
). The rationale for the current study was to take into account a broader spectrum of neuropsychological performance to define impairment and “normality,” as it was believed these measures in aggregate best related to global evaluation of underlying neuroanatomic abnormalities in cortical thickness. Ultimately it was determined that a 2-group clustering solution was the best fit for the data; however, this is not to imply that only two cognitive schizophrenia subtypes exist. Indeed, previous studies have found upwards of four to five subtypes, depending on neuropsychological variables and methods used (Goldstein et al., 1998a
; Heinrichs & Awad, 1993
; Horan & Goldstein, 2003
). The intent of this study was to effectively identify NPNN subjects, without particular regard to the separation and degrees of impairment within NPI participants. It is believed that this 2-cluster method was fairly robust as NPNN classification remained highly intact (93- 100% overlap) across various algorithms and when forcing a different number of solutions. Further support for the method is that our resultant cognitive subtypes had neuropsychological and demographic profiles remarkably similar to those found in previous studies (Allen et al., 1998
; Goldstein et al., 1998a
; Hill et al., 2002
; Horan & Goldstein, 2003
; Palmer et al., 1997
; Seaton et al., 1999
Regarding the primary cortical thickness analysis, the NPI group displayed a somewhat atypical pattern of strong posterior changes, but one that has been noted in other work (Narr et al., 2005
). With the exception of partial involvement in the superior and inferior frontal gyrus and anterior cingulate, there was a general lack of characteristic frontal lobe thinning, which, while surprising, is not without precedent (Narr et al., 2005
; Wiegand et al., 2004
). The absence of significant thinning in the NPNN – COM and NPNN – NPI vertex-wise analyses was unexpected as initially it was hypothesized an attenuated pattern would emerge that matched their cognitive profile. However, effect-size and uncorrected contrast maps for these comparisons suggest trend differences, indicating subtle characteristics unique to this group may exist, reinforcing the notion that “near-normal” status is not synonymous with “unaffected.” Indeed, follow-up ROI analyses revealed comparable thinning in NPNN and NPI from COM in frontal areas, but significantly differing for each other in temporal, occipital and parietal regions. This suggests that despite having a neurobiological illness, a subset of schizophrenia patients with “near-normal” cognitive functioning may also have relatively “near-normal” cortical gray matter thickness, which is intermediate between healthy and severely impaired states (). That the NPNN group demonstrates few neurobiological differences from COM subjects, yet significantly differs from them on most cognitive measures is interesting in that it highlights certain aspects of the illness still affect this group. While these individuals may reside in the lower cognitive band of “normality,” there appears to exist some neurobiological compensatory mechanism preventing them from functioning similar to their NPI counterparts.
The finding also has implications for the influence of cortical thickness on the expression of various clinical aspects of the illness. Consistent with our results, it is common for impaired cluster groups to experience more negative symptoms compared to their less impaired counterparts, but show similar positive and disorganized symptomatology (Kremen et al., 2000
; Palmer et al., 1997
). Hitherto, these findings have yet to be associated with anatomical results; hence future work would focus on both the structural and functional aspects of cognitive subtypes in an effort to clarify the influence of these factors. For example, if cortical gray matter is not contributing to the clinical expression of the schizophrenic condition in NPNN subjects, then investigations of localized changes in other regions, such as subcortical nuclei (Wang et al., 2008
) and white matter pathways (Voineskos et al., 2010
), or unified networks of these structures, could provide alternative explanations.
Several possibilities for this “near-normal” phenomenon in schizophrenia are postulated: first, NPNN subjects may be experiencing a different form of the disease, with separate underlying neurobiological processes mild in their expression of schizophrenia-like structural and functional deficits, similar to that proposed by Murray and associates (1987
); second, NPNN may represents an early transitory state, where individuals eventually progress to, and remain neuropsychologically impaired as disease effects persist. This second explanation seems less likely given that, with the exception of peri-onset changes, neuropsychological deficits appear relatively stable over the course of the illness (Albus et al., 2002
; Heaton et al., 2001
; Hill et al., 2004
). Indeed, subjects in our study were more chronic, suggesting neuropsychological profiles less amenable to progressive deterioration. One final theory is that the observed differences in cluster groups may be explained by current concepts of brain and cognitive reserve (Satz et al., 2011
; Stern, 2002
). It is possible that a latent factor related to the separation of schizophrenia participants into subgroups is one associated with cognitive reserve, where NPNN subjects are those who experience greater advantages in variables underlying this concept.
The literature on illness severity and duration suggests that the degree of cognitive performance is not related to these variables (Holthausen et al., 2002
). Our analysis of DOI favors the model of separate disease entities, but also indicates that it wields some influence in NPNN cluster groups. Only for NPNN subjects were correlations found between DOI and select neuropsychological measures related to executive functioning, whereas no relationships were found in NPI subjects; however, these relationships were attenuated when corrected for multiple comparisons. This is similar to Goldstein and colleagues (1998b
), who also found low correlations between age, as a proxy for illness duration, and neuropsychological performance in an impaired cognitive cluster, but significant correlations in a near-normal cognitive cluster. Also, in their study on the effect of age on cortical thickness, Kubota and coworkers (2011
) found that illness duration did not influence changes in regional thickness for their schizophrenia group. Despite the attenuation of some region-specific effects when controlling for DOI, our results were similar in that differences in cortical thickness between NPI and NPNN subjects still remained. Overall, our result argues against a general effect of disease severity on the neuroanatomical differences between subgroups, but still leaves room for further investigation of factors contributing to neuropsychological heterogeneity.
In conclusion, our study adds insight into the cortical signature of neuropsychologically near-normal schizophrenia. Future work would include examining white matter and functional measures, accompanied by longitudinal change estimates. Increased characterization of this population will continue to elucidate the relationship between cognitive and neurobiological features in schizophrenia, and assist in exploration of disease heterogeneity.