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Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)

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., (© 2022. The Author(s).)

Sex differences in symptoms and executive control across schizophrenia spectrum disorders (SSD) are consistently reported. Similarly, these findings of sex differences are also observed in schizotypy, that is, schizophrenia-like features occurring in healthy individuals in the absence of a clinical diagnosis. This study aimed to examine the relationships between performance on three major domains of executive control: performance monitoring, response inhibition, and cognitive set-shifting, and schizotypy factor scores in both SSD patients and healthy controls (HCs), and whether sex moderated any relationships observed. A total of 111 (67 males and 44 females) patients with SSD and 258 (129 males and 129 females) HCs were included in this study. Schizotypal personality traits (in both SSD and HC) was assessed using the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE). Executive control performance was assessed using seven tasks. Stepwise linear regressions revealed that performance on cognitive set-shifting tasks was significantly associated with the introvertive anhedonia, cognitive disorganisation, and unusual experiences subscales of the O-LIFE. When sex was examined as a moderator, it was not a significant moderator of any of the relationships between cognitive set-shifting tasks and schizotypy factors. The results suggest that independent of sex, cognitive set-shifting ability is associated to an increased vulnerability to schizotypal personality traits, although performance monitoring and response inhibition did not., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The nature of the relationship between cognition and brain morphology in schizophrenia-spectrum disorders (SSD) and bipolar disorder (BD) is uncertain. This review aimed to address this, by providing a comprehensive systematic investigation of links between several cognitive domains and brain volume, cortical thickness, and cortical surface area in SSD and BD patients across early and established illness stages. An initial search of PubMed and Scopus databases resulted in 1486 articles, of which 124 met inclusion criteria and were reviewed in detail. The majority of studies focused on SSD, while those of BD were scarce. Replicated evidence for specific regions associated with indices of cognition was minimal, however for several cognitive domains, the frontal and temporal regions were broadly implicated across both recent-onset and established SSD, and to a lesser extent BD. Collectively, the findings of this review emphasize the significance of both frontal and temporal regions for some domains of cognition in SSD, while highlighting the need for future BD-related studies on this topic., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Cognitive heterogeneity in schizophrenia spectrum disorders (SSD) and bipolar disorder (BD) has been explored using clustering analyses. However, the resulting subgroups have not been cognitively validated beyond measures used as clustering variables themselves. We compared the emergent cross-diagnostic subgroups of SSD and BD patients on measures used to classify them, and also across a range of alternative cognitive measures assessing some of the same constructs., Method: Domain scores from the Matrics Consensus Cognitive Battery were used in a cross-diagnostic clustering analysis of 86 patients with SSD (n = 45) and BD (n = 41). The emergent subgroups were then compared to each other and healthy controls (n = 76) on these and alternative measures of these domains, as well as on premorbid IQ, global cognition and a proxy of cognitive decline., Results: A three-cluster solution was most appropriate, with subgroups labelled as Globally Impaired, Selectively Impaired, and Superior/Near-Normal relative to controls. With the exception of processing speed performance, the subgroups were generally differentiated on the cognitive domain scores used as clustering variables. Differences in cognitive performance among these subgroups were not always statistically significant when compared on the alternative cognitive measures. There was evidence of global cognitive impairment and putative cognitive decline in the two cognitively impaired subgroups., Limitations: For clustering analysis, sample size was relatively small., Conclusions: The overall pattern of findings tentatively suggest that emergent cross-diagnostic cognitive subgroups are not artefacts of the measures used to define them, but may represent the outcome of different cognitive trajectories., Competing Interests: Declaration of Competing Interest Over the last four years, Prof Pantelis has been on advisory boards for AstraZeneca, Janssen-Cilag, Lundbeck, and Servier; and he has received honoraria for talks presented at educational meetings organized by AstraZeneca, Eli Lilly, Janssen-Cilag, Lundbeck, Pfizer, and Shire. The other authors report no financial relationships with commercial interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objectives: Schizophrenia is a debilitating psychiatric illness associated with positive and negative symptoms as well as significant impairments in cognition. Current antipsychotic medications do not alleviate these cognitive deficits, and more effective therapeutic options are required. Increased oxidative stress and altered antioxidant levels, including glutathione (GSH) have been observed both in individuals with cognitive impairment and in people with schizophrenia. A GSH precursor, the antioxidant N -acetylcysteine (NAC) has been investigated as a novel treatment for the cognitive symptoms of schizophrenia, and recent research suggests that NAC may be a promising adjunctive treatment option. However, the current literature lacks integration as to why NAC may effectively improve cognition in schizophrenia. The present theoretical synthesis aimed to address this gap by examining the processes by which NAC may improve cognitive function in schizophrenia. Methods: The schizophrenia literature was reviewed in three key domains: cognitive impairment, the relationship between oxidative stress and cognition, and the efficacy of NAC as a novel treatment. This led to a theoretical analysis of the neurobiological processes by which NAC may improve cognition in schizophrenia. Results: This theoretical review concluded that improved cognition may result from a combination of factors, including decreased oxidative stress, neuroprotection of cognitive networks and an increase in glutamatergic modulation of the N -methyl-d-aspartate receptor system. Whilst a number of mechanisms by which NAC may improve cognition and symptoms in schizophrenia have been proposed, there is still limited understanding of the specific metabolic pathways involved and how they interrelate and modify specific symptomology. Discussion: Exploration of how NAC treatment may act to improve cognitive function could guide clinical trials by investigation of the specific neurotransmitter systems and processes involved, allowing for targeted neurological outcome measures. Future research would benefit from the investigation of both in vivo cortical GSH concentration and peripheral plasma GSH in a population of individuals with chronic schizophrenia.

We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits in the stop-signal paradigm. The analysis was applied to stop-signal data reported by Badcock et al. (Psychological Medicine 32: 87-297, 2002) and Hughes et al. (Biological Psychology 89: 220-231, 2012), where schizophrenia patients and control participants made rapid choice responses, but on some trials were signalled to stop their ongoing response. Previous research has assumed an inhibitory deficit in schizophrenia, because estimates of the mean time taken to react to the stop signal are longer in patients than controls. We showed that these longer estimates are partly due to failing to react to the stop signal ("trigger failures") and partly due to a slower initiation of inhibition, implicating a failure of attention rather than a deficit in the inhibitory process itself. Correlations between the probability of trigger failures and event-related potentials reported by Hughes et al. are interpreted as supporting the attentional account of inhibitory deficits. Our results, and those of Matzke et al. (2016), who report that controls also display a substantial although lower trigger-failure rate, indicate that attentional factors need to be taken into account when interpreting results from the stop-signal paradigm.

Inhibitory control deficits are well documented in schizophrenia, supported by impairment in an established measure of response inhibition, the stop-signal reaction time (SSRT). We investigated the neural basis of this impairment by comparing schizophrenia patients and controls matched for age, sex and education on behavioural, functional magnetic resonance imaging (fMRI) and event-related potential (ERP) indices of stop-signal task performance. Compared to controls, patients exhibited slower SSRT and reduced right inferior frontal gyrus (rIFG) activation, but rIFG activation correlated with SSRT in both groups. Go stimulus and stop-signal ERP components (N1/P3) were smaller in patients, but the peak latencies of stop-signal N1 and P3 were also delayed in patients, indicating impairment early in stop-signal processing. Additionally, response-locked lateralised readiness potentials indicated response preparation was prolonged in patients. An inability to engage rIFG may predicate slowed inhibition in patients, however multiple spatiotemporal irregularities in the networks underpinning stop-signal task performance may contribute to this deficit., (Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.)

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Background While advances in the field of functional magnetic resonance imaging (fMRI) provide new opportunities to study brain networks underlying the experience of hallucinations in psychosis, there are methodological challenges unique to symptom-capture studies. Study Design We extracted brain networks activated during hallucination-capture for schizophrenia patients when fMRI data collected from two sites was merged (combined N = 27). A multidimensional analysis technique was applied, which would allow separation of brain networks involved in the hallucinatory experience itself from those involved in the motor response of indicating the beginning and end of the perceived hallucinatory experience. To avoid reverse inference when attributing a function (e.g. a hallucination) to anatomical regions, it was required that longer hallucinatory experiences produce extended brain responses relative to shorter. Study Results For radio-speech sound files, an auditory perception brain network emerged, and displayed speech-duration-dependent hemodynamic responses (HDRs). However, in the hallucination-capture blocks, no network showed hallucination-duration-dependent HDRs, but a retrieved network that was anatomically classified as motor response emerged. Conclusions During symptom capture of hallucinations during fMRI, no HDR showed duration dependence, but a brain network anatomically matching the motor response network was retrieved. Previous reports on brain networks detected by fMRI during hallucination capture are reviewed in this context; namely, that the brain networks interpreted as involved in hallucinations may in fact be involved only in the motor response indicating the onset of the hallucination. [ABSTRACT FROM AUTHOR]

• External speech processing deficits are seen in individuals with AVH. • We consolidate the functional neuroimaging literature of these deficits. • Altered function and coupling of the temporal auditory regions were evident. • Some evidence of further limbic, default mode, salience and subcortical involvement. • We provide in-depth recommendations for future study designs. It has been documented that individuals who hear auditory verbal hallucinations (AVH) exhibit diminished capabilities in processing external speech. While functional neuroimaging studies have attempted to characterise the cortical regions and networks facilitating these deficits in a bid to understand AVH, considerable methodological heterogeneity has prevented a consensus being reached. The current systematic review investigated the neurobiological underpinnings of external speech processing deficits in voice-hearers in 38 studies published between January 1990 to June 2020. AVH-specific deviations in the activity and lateralisation of the temporal auditory regions were apparent when processing speech sounds, words and sentences. During active or affective listening tasks, functional connectivity changes arose within the language, limbic and default mode networks. However, poor study quality and lack of replicable results plague the field. A detailed list of recommendations has been provided to improve the quality of future research on this topic. [ABSTRACT FROM AUTHOR]

The nature of the relationship between cognition and brain morphology in schizophrenia-spectrum disorders (SSD) and bipolar disorder (BD) is uncertain. This review aimed to address this, by providing a comprehensive systematic investigation of links between several cognitive domains and brain volume, cortical thickness, and cortical surface area in SSD and BD patients across early and established illness stages. An initial search of PubMed and Scopus databases resulted in 1486 articles, of which 124 met inclusion criteria and were reviewed in detail. The majority of studies focused on SSD, while those of BD were scarce. Replicated evidence for specific regions associated with indices of cognition was minimal, however for several cognitive domains, the frontal and temporal regions were broadly implicated across both recent-onset and established SSD, and to a lesser extent BD. Collectively, the findings of this review emphasize the significance of both frontal and temporal regions for some domains of cognition in SSD, while highlighting the need for future BD-related studies on this topic. [ABSTRACT FROM AUTHOR]

Note ' /= ' is indicative that there is a significant difference in brain morphology between (sub)groups; ' = ' is indicative that there is no significant difference in brain morphology between (sub)groups. Correction to: Neuropsychology Review https://doi.org/10.1007/s11065-021-09533-0 The original version of this article unfortunately contained a mistake in Fig. Correction to: Brain Morphological Characteristics of Cognitive Subgroups of Schizophrenia-spectrum Disorders and Bipolar Disorder: a Systematic Review with Narrative Synthesis. [Extracted from the article]

Background: Schizophrenia spectrum disorders (SSD) are often characterised by a plateau or decline in cognitive abilities early in the prodrome. The cause of developmental alteration remains unknown, and investigation of genetic involvement in cognitive function in these disorders may assist the understanding of the underlying neurobiological mechanisms involved. Variation at two single nucleotide polymorphisms (SNPs) of the catechol-O-methyltransferase (COMT) gene have previously shown an influence on COMT protein levels and cognition; rs4680 and rs4818. Here we investigate the influence of the nonsynonymous “Val/Met” SNP rs4680 and a second functional SNP, rs4818, on tasks of cognitive flexibility and attention. Methods: The sample comprised 48 healthy controls (HC; age = 31.95 ± 12.80; 25 males, 23 females), and 43 with a diagnosis of SSD (age = 41.64 ± 10.36; 26 males, 17 females). Measures of cognitive flexibility and attention included the Wisconsin Card Sorting Test (WCST), Continuous Performance Test-Identical Pairs version (CPT-IP), Trail Making Test (TMT), and the D-KEFS Colour Word Interference Test (CWIT). Due to small cohort sizes, in our preliminary analyses we chose to compare people who should be most severely affected because of inheriting COMT haplotypes associated with poor cognitive functioning (GG rs4818 / GG rs4680: G-G haplotype) to those with haplotypes associated with better cognitive functioning (CC rs4818 / AA rs4680: C-A haplotype). Multivariate analysis of variance factors included COMT haplotype, diagnosis (HC and SSD), and gender, with Bonferroni correction for multiple comparisons; age was included as a covariate. Analyses were also conducted based on a non-functional SNP of the COMT gene; rs165599, as a negative control. Results: SSD exhibited reduced cognitive performance compared to HC; F(4, 75) = 8.810, p < .001. Investigation of C-A haplotype revealed an interaction with diagnosis on cognitive performance; F(8, 154) = 2.075, p = .041; SSD had reduced performance compared to HC for the WCST, CPT-IP, and TMT in C-A haplotypes (all p < .05). COMT haplotype also interacted with gender on cognitive performance (C-A haplotype; F(8, 154) = 2.315, p = .023, G-G haplotype; F(8, 154) = 2.706, p = .008). Males who were C-A non-carriers and /or G-G haplotype (high COMT activity groups) performed better on CPT-IP (both p < .05) and worse on CWIT (both p < .05) compared to females. Control SNP rs165599 revealed no main effects or significant interactions (all p > .05). Discussion: The role of the COMT gene in the cognitive abilities of SSD remains contentious as gene expression does not differ from a healthy population. This preliminary analysis revealed an interaction between diagnosis and COMT haplotype, however, this only reached statistical significance for the C-A haplotype, where SSD with C-A haplotype and C-A non-carriers had reduced performance compared to HC on most tasks except TMT. The different effects found across the tasks, which probed various elements of cognitive flexibility and attention, supports a nuanced role of COMT in cognitive function. Further, high COMT activity was beneficial for males on CPT-IP but not CWIT compared to females. Gender interaction remains a significant consideration in studies of the COMT gene, likely involving the catechol-estrogens which are substrates of COMT. As expected there was no significant results with control SNP rs165599, indicating that findings were due to the influence of SNPs rs4680 and rs4818 on COMT activity. [ABSTRACT FROM AUTHOR]

Background Although nosologically distinct, there is overlap in the disease presentation of individuals with schizophrenia (Sz) and bipolar disorder (BD). Both disorders are characterized by abnormalities in cognitive performance, and in brain volume, cortical thickness and surface area. It is possible that these abnormalities are related, however the nature of the brain structure-cognition relationship in these disorders is inconclusive. This review aims to elucidate the association between measures of brain morphology and cognitive impairment in Sz and BD. Methods Science Direct, PubMed, NCBI, and Web of Science databases were searched for papers investigating cognition and brain morphology in Sz and BD. Results Studies indicate that grey matter volume is generally positively associated with cognitive performance. Evidence suggests common positive relationships between premorbid IQ and volume of the frontal lobe, current IQ and the temporal lobe, and verbal memory/learning and hippocampal and amygdala volume. Positive thickness-cognition relationships included non-specific executive functioning and the frontal region, attention and the temporal region, and verbal learning and memory and the temporal region, specifically the right superior gyrus. Only a handful of studies have investigated surface area with regards to cognitive impairment in Sz and BD and observed positive relationships between premorbid IQ and the frontal region, and current IQ and the temporal region. Discussion Limitations of the literature stem from methodological and sampling issues, and from the disproportionate number of schizophrenia to bipolar disorder studies. There is a need for more novel approaches to uncover the nature of brain morphology-cognition relationships in BD and Sz. [ABSTRACT FROM AUTHOR]