This assumes that biomarkers related to symptom severity
worsen in some way immediately before the onset of schizophrenia.
However, even if this is not the case, it could be that a different
combination of symptom-related biomarkers is helpful in predicting
conversion.
Ideally a biomarker could be used to identify single individuals as
approaching schizophrenia with enough confidence to justify beginning
treatment before an official diagnosis. Although biomarkers need to have
a high degree of predictive accuracy, there is also the issue of
specificity. Individuals with bipolar disorder may experience psychosis,
but unlike schizophrenia, it is not necessary for diagnosis. This
overlap in psychosis means that a successful biomarker needs to be
sufficiently sensitive to predict onset of psychosis andbe specific to SSD (Insel et al., 2011).
As previously discussed, biomarkers of sensory memory, particularly the
auditory MMN, have been singled out for being reliably smaller in
chronic schizophrenia compared to neurotypical individuals (Naatanen et
al., 2015; Umbricht & Krljes, 2005). A recent
large-sample study found typical MMN in unaffected siblings of people
with schizophrenia, leading them to propose that reduced MMN may be
closely linked with the onset of psychosis (Donaldson et al., 2021).However, efforts to date have found that the auditory MMN is only
slightly smaller in early course schizophrenia (Lho et al., 2020;
Salisbury et al., 2007) and is not reliably reduced in individuals at
their first-episode of schizophrenia (Erickson et al., 2016; Haigh,
Coffman, et al., 2017), or in first-degree unaffected relatives (Magno
et al., 2008). Furthermore, longitudinal studies following individuals
who are at-risk of developing schizophrenia have yet to detect robust
reductions in auditory MMN that provide any confidence of impending
conversion to psychosis (Atkinson et al., 2017; Erickson et al., 2016;
Koshiyama et al., 2017).
However, the emphasis on a single biomarker to predict psychosis
onset may be the issue. In the at-risk population, two recent
meta-analyses point to WM performance as a predictor of developing
psychosis. For instance, re-evaluating findings relying on the MATRICS
Consensus Cognitive Battery identified the factors of overall cognition,
attention, processing speed, and WM as significantly predictive
of developing psychosis (Zheng et al., 2018). This is broadly consistent
with an earlier meta-analysis that selected WM and visual learning to
identify individuals likely to develop psychosis (De Herdt et al.,
2013). In individuals with schizotypy, meta-analysis again pointed to a
general deficit across verbal and visuospatial WM as a significant
predictor of developing psychosis (Siddi et al., 2017). Multifactorial
analyses of multiple biomarkers have improved specificity of predicting
who is at-risk of developing schizophrenia later in life (Seidman,
Shapiro, et al., 2016) and with improvements in machine learning, more
sophisticated tools are available to detect even subtle patterns of
functional abnormalities in SSD (Tai et al., 2019).