Brain structure characteristics in intellectually superior schizophrenia

Brain Morphological Characteristics of Cognitive Subgroups of Schizophrenia-Spectrum Disorders and Bipolar Disorder: A Systematic Review with Narrative Synthesis

Despite a growing body of research, there is yet to be a cohesive synthesis of studies examining differences in brain morphology according to patterns of cognitive function among both schizophrenia-spectrum disorder (SSD) and bipolar disorder (BD) individuals. We aimed to provide a systematic overview of the morphological differences-inclusive of grey and white matter volume, cortical thickness, and cortical surface area-between cognitive subgroups of these disorders and healthy controls, and between cognitive subgroups themselves. An initial search of PubMed and Scopus databases resulted in 1486 articles of which 20 met inclusion criteria and were reviewed in detail. The findings of this review do not provide strong evidence that cognitive subgroups of SSD or BD map to unique patterns of brain morphology. There is preliminary evidence to suggest that reductions in cortical thickness may be more strongly associated with cognitive impairment, whilst volumetric deficits may be largely tied to the presence of disease.

The neurobiological characterization of distinct cognitive subtypes in early-phase schizophrenia-spectrum disorders

INTRODUCTION

Cognitive deficits are present in some, but not all patients with schizophrenia-spectrum disorders (SSD). We and others have demonstrated three cognitive clusters: cognitively intact patients, patients with deficits in a few domains and those with global cognitive deficits. This study aimed to identify cognitive subtypes of early-phase SSD with matched controls as a reference group, and evaluated cognitive subgroups regarding clinical and brain volumetric measures.

METHODS

Eighty-six early-phase SSD patients were included. Hierarchical cluster analysis was conducted using global performance on the Brief Assessment of Cognition in Schizophrenia (BACS). Cognitive subgroups were subsequently related to clinical and brain volumetric measures (cortical, subcortical and cortical thickness) using ANCOVA.

RESULTS

Three distinct cognitive clusters emerged: relative to controls we found one cluster of patients with preserved cognition (n = 25), one moderately impaired cluster (n = 38) and one severely impaired cluster (n = 23). Cognitive subgroups were characterized by differences in volume of the left postcentral gyrus, left middle caudal frontal gyrus and left insula, while differences in cortical thickness were predominantly found in fronto-parietal regions. No differences were demonstrated in subcortical brain volume.

DISCUSSION

Current results replicate the existence of three distinct cognitive subgroups including one relatively large group with preserved cognitive function. Cognitive subgroups were characterized by differences in cortical regional brain volume and cortical thickness, suggesting associations with cortical, but not subcortical development and cognitive functioning such as attention, executive functions and speed of processing.

Characterising Demographic, Clinical and Functional Features of Cognitive Subgroups in Schizophrenia Spectrum Disorders: A Systematic Review

Considerable cognitive heterogeneity is present within the schizophrenia spectrum disorder (SSD) population. Several subgroups characterised by more homogenous cognitive profiles have been identified. It is not yet clear however, whether these subgroups represent different points along a continuum of cognitive symptom severity, or whether they reflect unique profiles of the disorder. One way to determine this is by comparing subgroups on their non-cognitive characteristics. The aim of the present review was to systematically summarise our current understanding of the non-cognitive features of the cognitive subgroups of schizophrenia spectrum disorder (SSD). Thirty-five relevant studies were identified from January 1980 to March 2020. Cognitive subgroups were consistently compared on age, sex, education, age of illness onset, illness duration, positive, negative and disorganised symptoms, depression and psychosocial functioning. It was revealed that subgroups were consistently distinguished by education, negative symptom severity and degree of functional impairment; with subgroups characterised by worse cognitive functioning performing/rated worse on these characteristics. The lack of consistent subgroup differences for the majority of the non-cognitive characteristics provides partial support for the notion that cognitive subgrouping in SSD is not simply reflecting a rehash of previously identified clinical subtypes. However, as subgroups were consistently distinguished by three characteristics known to be associated with cognition, our understanding of the extent to which the cognitive subgrouping approach is representing separate subtypes versus subdivisions along a continuum of symptom severity is still not definitive.

Brain morphology does not clearly map to cognition in individuals on the bipolar-schizophrenia-spectrum: a cross-diagnostic study of cognitive subgroups

BACKGROUND

Characterisation of brain morphological features common to cognitively similar individuals with bipolar disorder (BD) and schizophrenia spectrum disorders (SSD) may be key to understanding their shared neurobiological deficits. In the current study we examined whether three previously characterised cross-diagnostic cognitive subgroups differed among themselves and in comparison to healthy controls across measures of brain morphology.

METHOD

T1-weighted structural magnetic resonance imaging scans were obtained for 143 individuals; 65 healthy controls and 78 patients (SSD, n = 40; BD I, n = 38) classified into three cross-diagnostic cognitive subgroups: Globally Impaired (n = 24), Selectively Impaired (n = 32), and Superior/Near-Normal (n = 22). Cognitive subgroups were compared to each other and healthy controls on three separate analyses investigating (1) global, (2) regional, and (3) vertex-wise comparisons of brain volume, thickness, and surface area.

RESULTS

No significant subgroup differences were evident in global measures of brain morphology. In region of interest analyses, the Selectively Impaired subgroup had greater right accumbens volume than those Superior/Near-Normal subgroup and healthy controls, and the Superior/Near-Normal subgroup had reduced volume of the left entorhinal region compared to all other groups. In vertex-wise comparisons, the Globally Impaired subgroup had greater right precentral volume than the Selectively Impaired subgroup, and thicker cortex in the postcentral region relative to the Superior/Near-Normal subgroup.

LIMITATIONS

Exploration of medication effects was limited in our data.

CONCLUSIONS

Although some differences were evident in this sample, generally cross-diagnostic cognitive subgroups of individuals with SSD and BD did not appear to be clearly distinguished by patterns in brain morphology.

The relationship between physical activity, clinical and cognitive characteristics and BDNF mRNA levels in patients with severe mental disorders

Background: Here we aimed to clarify the association of physical activity with cognitive function and current mood in severe mental disorders in the most extensive sample to date. Secondly, we aimed to investigate the relationship between physical activity and BDNF mRNA levels.Methods: Three hundred and six patients with a DSM-IV schizophrenia (SZ) or bipolar disorder (BD) spectrum diagnosis were included. Clinical characteristics were assessed using the Structured Clinical Interview for DSM-IV. Depressive symptomatology was measured using the Inventory of Depressive Symptoms (IDS-C) and the Calgary Depression Scale for Schizophrenia (CDSS). All patients underwent neuropsychological assessment. Physical activity was measured as hours spent on any regular physical activity (≥ or ˂90 min) per week. BDNF mRNA was measured in plasma using standardised procedures.Results: Patients with ≥90 min of physical activity per week had fewer depressive symptoms (P ˂0.001, Cohen's d = 0.48) and performed significantly better on working memory (P ˂ 0.001, d = 0.44) and executive functioning tasks (P ˂ 0.001, d = 0.50) compared to the ˂90-min group. BDNF mRNA was positively associated with physical activity (P = 0.046) and cognitive functioning (P = 0.037).Conclusions: Our study suggests a positive association between self-reported physical activity, cognitive function, mood and BDNF mRNA levels in severe mental disorders.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Premorbid IQ subgroups in first episode non affective psychosis patients: Long-term sex differences in function and neurocognition

BACKGROUND

Low IQ has been associated with schizophrenia, even to the point of being posited as a possible causal factor for psychosis. However, individuals with normal and high IQ also develop psychotic illnesses. The aim of this study was to characterize premorbid IQ subgroups at first episode of psychosis (FEP).

METHODS

The study sample comes from a large epidemiological, 3-year longitudinal, intervention program on psychosis containing individuals living in a catchment area in Spain. Estimated premorbid IQ (epIQ) scores were used to build low (<90), normal (90-110) and high (>110) epIQ subgroups in samples of FEP patients (N=292) and healthy controls (N=199). The epIQ subgroups were compared in sociodemographic, neuropsychological, clinical and premorbid characteristics. Long-term functional and cognitive outcome, with a focus on sex differences, were also explored.

RESULTS

Low-epIQ was more frequently found in FEP patients (28.8%) than in healthy controls (14.6%). Low-epIQ patients were more likely to have worse premorbid adjustment, belong to low socioeconomic status families, have less years of education, and to be single, unemployed, and younger. They presented more severe impairments in processing speed, executive and global cognitive function. Female patients with low-epIQ showed better baseline function and more stable outcome than males.

CONCLUSIONS

Our results indicate that low premorbid IQ is a morbid manifestation, easily detected in a subgroup of FEP patients that predicts poorer outcome particularly in males. This perspective provides important information for the tailoring of subgroup-specific early intervention programs for psychosis.