Using neuroimaging to help predict the onset of psychosis

Abstract

The aim of this review is to assess the potential for neuroimaging measures to facilitate prediction of the onset of psychosis. Research in this field has mainly involved people at 'ultra-high risk' (UHR) of psychosis, who have a very high risk of developing a psychotic disorder within a few years of presentation to mental health services.The review details the key findings and developments in this area to date, and examines the methodological and logistical challenges associated with making A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT2 predictions in an individual subject in a clinical setting. Key wordsPsychosis prediction; Ultra High-Risk of psychosis; machine learning; Support Vector Machines; multimodal neuroimaging; multicentre neuroimaging studies; graph analysis Psychosis prediction and the ultra high-risk statePsychosis describes a syndrome that includes symptoms such as hallucinations, delusions, disorganised thought, and catatonia Criteria for UHR status include attenuated psychotic symptoms and / or a brief limited intermittent psychotic episode and / or a genetically determined vulnerability, alongside deterioration in social and occupational functioning 7 8 . UHR status comes A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT3 with the caveat that those who meet UHR status are selectively those who have come into contact with clinical services. This review uses the term UHR throughout.The UHR population is strikingly heterogeneous in terms of clinical outcomes.Follow-up studies 9 10 suggest that 7 years after clinical presentation, approximately a third of UHR subjects will have developed a psychotic disorder, with most transitions occurring in the first 2 years 11. Most of those who do not develop a psychotic disorder will have persistent attenuated symptoms and / or have developed another mental health disorder, whilst 14% will have recovered (see figure 1).Clinical intervention in the UHR group may reduce the likelihood of the onset of a psychotic disorder 12. However, as most UHR subjects do not develop a psychotic disorder, providing preventative treatment to all of those at risk is clinically inefficient. Identifying biomarkers that could be used to stratify the UHR group according to clinical outcome would enable the selective delivery of preventative interventions to the subgroup that would benefit the most.It is difficult to predict clinical outcomes in an UHR subject on the basis of their clinical features at presentation. Although the clinical assessment at presentation has good diagnostic validity for ruling out a future psychotic disorder (meta analytical sensitivity of UHR assessment = 0.96) 13, it has only a modest ability to rule in a future psychotic disorder (meta-analytical specificity of UHR assessment = 0.47) 13.There is thus a need to find other forms of assessment that can improve the specificity of psychosis prediction. Fusar-Poli et al. (2015) 14 and based on data by Lin et al. (2011) 10 . A C C E P T E D M A N U S C R I P T ACCEPTED MANU...

Altered functional connectivity strength and its correlations with cognitive function in subjects with ultra‐high risk for psychosis at rest

“…As a key hub of the central executive network, the DLPFC plays a crucial role in working memory . Recently, cross‐sectional neuroimaging studies suggested that the prefrontal cortex was the site with the most robust abnormalities in UHR subjects and patients with schizophrenia . Reduced connectivity between the right inferior frontal gyrus and right posterior hippocampus was described in UHR subjects relative to healthy controls in a cross‐sectional study using delayed matching to sample task .…”

Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention

“…Furthermore, a series of MRI-based discrimination analyses by Koutsouleris et al [163][164][165] has suggested the feasibility of identifying individuals at the highest risk of developing psychosis using neuroanatomical patterns (especially cortical and subcortical volumes) at the single-subject level. Given the significant differences between the ARMS-P and -NP groups, also in the gross morphologic features (e.g., sulcogyral pattern), 20,118,119 functional neuroimaging data, 22,43 and event-related potentials (e.g., mismatch negativity), [166][167][168] it may be expected that the combination of potential biological predictive markers 169 in addition to clinical predictors, such as a high level of depression, cognitive impairments, poor functioning and negative symptoms, 170,171 enhances accuracy in the prediction of psychosis onset in the clinical setting. 172 However, it should be noted that the ARMS is a heterogeneous concept not only in the context of its outcome (i.e., later psychosis onset) but also for inclusion criteria.…”

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

“…Considering that clinical CHR instruments alone detect only about 47% of transitions after 3 years (14), efforts have been made to identify potential risk factors for psychosis in several symptomatological and biological readouts, or biomarkers, of the disorder (15) so that individualized prognostication may be enhanced. The presence of environmental adverse events (16), cognitive impairments (17), neuromorphological (18), and electrophysiological (19) and hematological (20) alterations, as well as resting-state (21) and task-related (22) neural activity and connectivity anomalies, has been consistently reported in people at risk for psychosis compared with healthy individuals. Some of these phenotypes have been associated with both disease course and transition to the overt disease (4).…”