Cortical thickness in neuropsychologically near-normal schizophrenia

Abstract

Schizophrenia is a severe psychiatric illness with widespread impairments of cognitive functioning; however, a certain percentage of subjects are known to perform in the normal range on neuropsychological measures. While the cognitive profiles of these individuals have been examined, there has been relatively little attention to the neuroanatomical characteristics of this important subgroup. The aims of this study were to statistically identify schizophrenia subjects with relatively normal cognition, examine their neuroanatomical characteristics relative to their more impaired counterparts using cortical thickness mapping, and to investigate relationships between these characteristics and demographic variables to better understand the nature of cognitive heterogeneity in schizophrenia. Clinical, neuropsychological, and MRI data were collected from schizophrenia (n = 79) and healthy subjects (n = 65). A series of clustering algorithms on neuropsychological scores was examined, and a 2-cluster solution that separated subjects into neuropsychologically near-normal (NPNN) and neuropsychologically impaired (NPI) groups was determined most appropriate. Surface-based cortical thickness mapping was utilized to examine differences in thinning among schizophrenia subtypes compared with the healthy participants. A widespread cortical thinning pattern characteristic of schizophrenia emerged in the NPI group, while NPNN subjects demonstrated very limited thinning relative to healthy comparison subjects. Analysis of illness duration indicated minimal effects on subtype classification and cortical thickness results. Findings suggest a strong link between cognitive impairment and cortical thinning in schizophrenia, where subjects with near-normal cognitive abilities also demonstrate near-normal cortical thickness patterns. While generally supportive of distinct etiological processes for cognitive subtypes, results provide direction for further examination of additional neuroanatomical differences.

Figure 1

Dendogram of SCZ clustering using Ward’s method

Figure 2

Mean (std error) neuropsychological z-scores for schizophrenia cluster (NPNN, NPI) and healthy comparison (COM) groups; VOCAB = WAIS-III Vocabulary, MR = WAIS-III Matrix Reasoning, DS = WMS-III Digit Span, SS = WMS-III Spatial Span, LNS = WMS-III Letter-Number Sequencing, LM = WMS-III Logical Memory, FP = WMS-III Family Pictures, FLUENCY = Lexical + Semantic fluency, TRAILS B = Trail Making Test Part B, WCST = perseverative errors on the Wisconsin Card Sorting Test

Figure 3

Cortical thickness difference maps between A) COM – NPI, B) COM – NPNN, and C) NPNN – NPI groups (p values are -log₁₀p)

Figure 4

Cohen’s d effect-size maps based on vertex-wise cortical thickness values between A) COM – NPI, B) COM – NPNN, and C) NPNN – NPI groups (maximum amplitude d = 0.5)

Figure 5

Mapped cortical thinning-based regions of interest (ROI) subdivided according to lobe; purple = frontal, pale green = temporal, blue = parietal, pale blue = occipital

Figure 6

Dot plots of mean cortical thickness values from lobe-specific ROIs for each group; * = differs from COM at p < 0.05, † = differs from NPNN at p < 0.05; based on post-hoc analyses