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Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored. Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently. Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Psychiatric disorders have clear heritable risk. Several large-scale genome-wide association studies have revealed a strong association between susceptibility for psychiatric disorders, including bipolar disease, schizophrenia and major depression, and a haplotype located in an intronic region of the L-type voltage-gated calcium channel (VGCC) subunit gene CACNA1C (peak associated SNP rs1006737), making it one of the most replicable and consistent associations in psychiatric genetics. In the current study, we used induced human neurons to reveal a functional phenotype associated with this psychiatric risk variant. We generated induced human neurons, or iN cells, from more than 20 individuals harboring homozygous risk genotypes, heterozygous or homozygous non-risk genotypes at the rs1006737 locus. Using these iNs, we performed electrophysiology and quantitative PCR experiments that demonstrated increased L-type VGCC current density as well as increased mRNA expression of CACNA1C in iNs homozygous for the risk genotype, compared with non-risk genotypes. These studies demonstrate that the risk genotype at rs1006737 is associated with significant functional alterations in human iNs, and may direct future efforts at developing novel therapeutics for the treatment of psychiatric disease.

Background Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. Methods Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. Results We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right ( p  = 0.03), but not left ( p  = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p  = .00023; p  = .00023) originating in selective PFC subregions. Conclusions In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.

Converging evidence suggests bioenergetic abnormalities in bipolar disorder (BD). In the brain, phosphocreatine (PCr) acts a reservoir of high-energy phosphate (HEP) bonds, and creatine kinases (CK) catalyze the transfer of HEP from adenosine triphosphate (ATP) to PCr and from PCr back to ATP, at times of increased need. This study examined the activity of this mechanism in BD by measuring the levels of HEP molecules during a stimulus paradigm that increased local energy demand. Twenty-three patients diagnosed with BD-I and 22 healthy controls (HC) were included. Levels of phosphorus metabolites were measured at baseline and during visual stimulation in the occipital lobe using 31 P magnetic resonance spectroscopy at 4T. Changes in metabolite levels showed different patterns between the groups. During stimulation, HC had significant reductions in PCr but not in ATP, as expected. In contrast, BD patients had significant reductions in ATP but not in PCr. In addition, PCr/ATP ratio was lower at baseline in patients, and there was a higher change in this measure during stimulation. This pattern suggests a disease-related failure to replenish ATP from PCr through CK enzyme catalysis during tissue activation. Further studies measuring the CK flux in BD are required to confirm and extend this finding.

Cognitive dysfunction is a core feature of psychotic disorders; however, substantial variability exists both within and between subjects in terms of cognitive domains of dysfunction, and a clear 'profile' of cognitive strengths and weaknesses characteristic of any diagnosis or psychosis as a whole has not emerged. Cluster analysis provides an opportunity to group individuals using a data-driven approach rather than predetermined grouping criteria. While several studies have identified meaningful cognitive clusters in schizophrenia, no study to date has examined cognition in a cross-diagnostic sample of patients with psychotic disorders using a cluster approach. We aimed to examine cognitive variables in a sample of 167 patients with psychosis using cluster methods. Subjects with schizophrenia (n = 41), schizo-affective disorder (n = 53) or bipolar disorder with psychosis (n = 73) were assessed using a battery of cognitive and clinical measures. Cognitive data were analysed using Ward's method, followed by a K-means cluster approach. Clusters were then compared on diagnosis and measures of clinical symptoms, demographic variables and community functioning. A four-cluster solution was selected, including a 'neuropsychologically normal' cluster, a globally and significantly impaired cluster, and two clusters of mixed cognitive profiles. Clusters differed on several clinical variables; diagnoses were distributed amongst all clusters, although not evenly. Identification of groups of patients who share similar neurocognitive profiles may help pinpoint relevant neural abnormalities underlying these traits. Such groupings may also hasten the development of individualized treatment approaches, including cognitive remediation tailored to patients' specific cognitive profiles.

Neurocognitive dysfunction in schizophrenia (SZ), bipolar (BD) and related disorders represents a core feature of these illnesses, possibly a marker of underlying pathophysiology. Substantial overlap in domains of neuropsychological deficits has been reported among these disorders after illness onset. However, it is unclear whether deficits follow the same longitudinal pre- and post-morbid course across diagnoses. We examine evidence for neurocognitive dysfunction as a core feature of all idiopathic psychotic illnesses, and trace its evolution from pre-morbid and prodromal states through the emergence of overt psychosis and into chronic illness in patients with SZ, BD and related disorders. Articles reporting on neuropsychological functioning in patients with SZ, BD and related disorders before and after illness onset were reviewed. Given the vast literature on these topics and the present focus on cross-diagnostic comparisons, priority was given to primary data papers that assessed cross-diagnostic samples and recent meta-analyses. Patients with SZ exhibit dysfunction preceding the onset of illness, which becomes more pronounced in the prodrome and early years following diagnosis, then settles into a stable pattern. Patients with BD generally exhibit typical cognitive development pre-morbidly, but demonstrate deficits by first episode that are amplified with worsening symptoms and exacerbations. Neuropsychological deficits represent a core feature of SZ and BD; however, their onset and progression differ between diagnostic groups. A lifetime perspective on the evolution of neurocognitive deficits in SZ and BD reveals distinct patterns, and may provide a useful guide to the examination of the pathophysiological processes underpinning these functions across disorders.

This corrects the article DOI: 10.1038/mp.2017.42.

Correction to: Molecular Psychiatry advance online publication, 18 November 2014; doi:10.1038/mp.2014.143 Following publication of this paper, the authors noticed that cell line GM02036 was missing from Supplementary Table 1. The correct version of the Supplementary Information can be found linked to this paper.

Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.