The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis.

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.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum.

BACKGROUND: 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. METHOD: Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). RESULTS: 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. CONCLUSIONS: 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.

Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis.

BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

Evolution of neuropsychological dysfunction during the course of schizophrenia and bipolar disorder.

BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.