Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia

Abstract

This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB) composite score (T = 50 ± 10) and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls (n = 39) had greater cortical thickness than both cognitively normal (n = 17) and below-normal range (n = 49) patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls (n = 24) or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment.

Global brain volume reductions in a sub-chronic phencyclidine animal model for schizophrenia and their relationship to recognition memory

“…In the white matter tracts, of these animals, in which both reduced volume and microstructural changes (increased fractional anisotropy) are detectable by MRI, they provide evidence for decreased myelin basic protein immunoreactivity suggestive of demyelination (Wu et al, 2016), as reported previously in this model (Xiu et al, 2014;Xiu et al, 2015). As already indicated, such future studies should also include testing for relationships between not only brain structure, but also function, (Dawson et al, 2010) and other cognitive domains that are impaired in the scPCP model, particularly given that human studies suggest complex relationships between brain anatomy and cognitive performance across different tasks and domains, including studies in patients with schizophrenia (Brandt et al, 2015;Heinrichs et al, 2017;Jirsaraie et al, 2018;Karnik-Henry et al, 2012;Massey et al, 2017,).…”

“…Whilst meta-analyses provide evidence for significant associations between frontal or temporal cortical thinning and positive and negative symptoms, respectively (Walton et al, 2017; Walton et al, 2018), there is evidence both for and against relationships between brain structure and cognitive performance in patients with schizophrenia. This most likely reflects heterogeneity in diagnosis, illness stage and the specific cognitive domains under investigation (Dempster et al, 2017; Heinrichs et al, 2017; Jirsaraie et al, 2018; Karnik-Henry et al, 2012; Massey et al, 2017). As such, efforts to improve our understanding of these associations, have the potential to help uncover the neuronal substrates of impaired cognition and so facilitate the discovery of novel therapeutic targets.…”

Mapping cortical surface features in treatment resistant schizophrenia with in vivo structural MRI

“…The genesis of these cortical decreases can only be speculated from this patient population as data was collected at a singular time point and thus future longitudinal studies should be required to follow up participants at a number of time points and begin participation in the study when patients are medication-free. In addition this chronic population could have been exposed to earlier comorbidities such as depression or mood disorders and secondary illnesses such as substance abuse and prolonged stress, which may have impacted the underlying brain pathology (Heinrichs et al, 2017;Konopaske et al, 2007). Recent studies have suggested that stress could contribute to an increased rate of cortical thinning, which should be considered when discussing patients with chronic schizophrenia (Harrison, 1999;Konopaske et al, 2007).…”

“…Neuroimaging studies using high resolution MRI (Magnetic Resonance Imaging) demonstrate widespread differences in patients with schizophrenia in cortical grey matter volume (CV) (Anderson et al, 2015;Molina et al, 2010Molina et al, , 2008Molina et al, , 2005Mouchlianitis et al, 2016;Zugman et al, 2013) cortical thickness (CT) (Heinrichs et al, 2017;Kuperberg et al, 2003;Narr et al, 2005;Nesvåg et al, 2008;Plitman et al, 2016;Rimol et al, 2010;Smiley et al, 2012;Sugihara et al, 2017) and surface area (SA) (Kong et al, 2015;van Haren et al, 2012;Xiao et al, 2015) compared to healthy controls (HC) (Maller et al, 2012;Quarantelli et al, 2014;Selemon and Goldman-Rakic, 1999). CV reductions in patients with schizophrenia are one of the most common findings in the condition, beginning with global grey matter reductions and associated increases in ventricular volume (Emami et al, 2016;Harvey et al, 1993;Honea et al, 2005;Kong et al, 2015;Shergill et al, 2001).…”

Cognitive reserve attenuates age-related cognitive decline in the context of putatively accelerated brain ageing in schizophrenia-spectrum disorders

“…This is inconsistent with the evidence of progressive age-related deterioration in brain regions known to support these functions (Hulshoff Pol et al ., 2002; Cropley et al ., 2017). In addition, there appears to be a subgroup of patients with significant brain structural and functional deficits who have normal levels of fluid cognition despite being of an equivalent age to controls or schizophrenia patients with compromised cognition, which further complicates interpretation of pertinent findings (Heinrichs et al ., 2017; Lewandowski et al ., 2019; Van Rheenen et al ., 2018).…”