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\begin{document}

\title{Working Memory and Sensory Memory in Subclinical High Schizotypy: An
Avenue for Understanding Schizophrenia?}



\author[1]{Sarah Haigh}%
\author[1]{Marian Berryhill}%
\author[1]{Alexandrea Kilgore-Gomez}%
\author[2]{Michael Dodd}%
\affil[1]{University of Nevada Reno}%
\affil[2]{University of Nebraska-Lincoln}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\selectlanguage{english}
\begin{abstract}
The search for robust, reliable biomarkers of schizophrenia remains a
high priority in psychiatry. Biomarkers are valuable because they can
reveal the underlying mechanisms of symptoms and monitor treatment
progress, and may predict future risk of developing schizophrenia.
Despite the existence of various promising biomarkers that relate to
symptoms across the schizophrenia-spectrum, and despite published
recommendations encouraging multivariate metrics, they are rarely
investigated simultaneously within the same individuals. In those with
schizophrenia, the magnitude of purported biomarkers is complicated by
comorbid diagnoses, medications, and other treatments. Here, we argue
three points. First, we reiterate the importance of assessing multiple
biomarkers simultaneously. Second, we argue that investigating
biomarkers in those with schizophrenia-related traits (schizotypy) in
the general population can accelerate progress in understanding the
mechanisms of schizophrenia. We focus on biomarkers of sensory and
working memory in schizophrenia and their smaller effects in individuals
with nonclinical schizotypy. Third, we note irregularities across
research domains leading to the current situation in which there is a
preponderance of data on auditory sensory memory and visual working
memory, but markedly less in visual (iconic) memory and auditory working
memory, particularly when focusing on schizotypy where data are either
scarce or inconsistent. Together, this review highlights opportunities
for researchers without access to clinical populations to address gaps
in knowledge. We conclude by highlighting the theory that early sensory
memory deficits contribute negatively to working memory and vice versa.
This presents a mechanistic perspective where biomarkers may interact
with one another and impact schizophrenia-related symptoms.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Schizophrenia and schizophrenia spectrum disorders (SSD) remain a
prominent public health risk. There are persistent gaps in knowledge
surrounding the biological mechanisms of symptoms affecting cognition,
behavior, and emotion. To track the emergence and progression of
symptoms, there is a need for reliable biomarkers of schizophrenia and
SSD. \emph{These biomarkers can reveal underlying mechanisms as well as
complementing existing clinical interviewing methods. Biomarkers may
also be used} to predict conversion from high-risk to diagnosis
(N\selectlanguage{ngerman}äätänen et al., 2016). Currently there is no gold standard biomarker
that reliably tracks symptom severity (Lieberman et al., 2019), although
there is convergence regarding risk factors and cognitive deficits
(Mohn-Haugen et al., 2022). This has led to many calls for multivariate
biomarker metrics to more comprehensively assess an individual's risk
level (e.g., (Cannon et al., 2016; Price et al., 2006; Seidman, Shapiro,
et al., 2016). \emph{Although this opinion is not controversial, there
remains a tendency to focus on single biomarkers and their predictive
power. This may be due to the simplicity of understanding how a single
biomarker reflects underlying processing schizophrenia and SSD, and to
the added technical difficulties and financial challenges involved when
measuring multiple biomarkers.} Here, \emph{we support the call
fo}\textbf{r} a multivariate biomarker, and we extend this view by
suggesting that to accelerate our understanding of the neural mechanisms
associated with symptoms, it would be valuable to test across the entire
SSD, including neurotypical individuals with \emph{high subclinical
schizotypy} symptomatology \emph{in the general population} . This
suggestion \emph{has the added benefit of increasing the pool of
researchers available to investigate questions of clinical relevance,
but who do not have access to clinical populations.}

Here, we define \emph{schizotypy} as the
\emph{subclinical}characteristics associated with schizophrenia that are
prevalent within the general population. Schizotypy in this context is
not a diagnosis as the schizophrenia-related traits are not severe
enough to warrant intervention. It is unknown if participants with high
schizotypy traits will develop schizophrenia and/or psychosis in the
future, as many of these studies are cross-sectional in design \emph{and
recruit from the general population} . However, one value of focusing on
schizotypy is that participants with psychiatric or neurological
diagnosis can be removed. This reduces the likelihood of comorbid
conditions and medications that may confound the findings. Therefore, it
would be expeditious to include \emph{a larger swath of the spectrum to
include} the larger \emph{subclinical} population, and to combine
discrete biomarkers that track symptom severity.

Existing biomarkers include: genetic polymorphisms (Ettinger et al.,
2014), oculomotor abnormalities (reviewed in: (Levy et al., 2010)), and
atypical neural responses detected using EEG and fMRI. Unfortunately,
many of these biomarkers were identified in isolation rather than in
tandem with other biomarkers associated with a particular sensory or
cognitive domain. This is despite success when taking a multifactorial
approach to monitoring multiple neural (Kent et al., 2004; Price et al.,
2006; Ranlund et al., 2018; Taylor et al., 2017) and behavioral
(Seidman, Shapiro, et al., 2016) measures to help distinguish between
schizophrenia and other psychosis-related conditions such as bipolar
disorder. \emph{Due to the success seen in those diagnosed with
schizophrenia, we recommend the same multifactorial approach for the
subclinical schizotypy population.} For the current review, we revisit
EEG biomarkers from several related literatures: abnormal
\emph{sensory}memory (SM) and \emph{working} memory (WM), in both
auditory and visual modalities. These topics have well-developed
literatures with a substantial foundation in the clinical schizophrenia
and SSD populations. We focused on sensory and working memory
specifically, to explore how these biomarkers may interact due to
downstream deficits impacting later processing. We discuss the use of
sensory and working memory measures as biomarkers of symptom severity
from a cross-sectional perspective - how do these markers reflect on
their internal processes at this moment? We conclude by discussing how
\emph{some of the biomarkers we discuss may} be promising as predictive
biomarkers of the onset of psychosis and schizophrenia, and briefly
summarize some of the literature up to this point.

\textbf{Scope of Review: Auditory \emph{and} Visual, Sensory
\emph{and}Working Memory}

Perturbed bottom-up functioning impairs upstream cognition (Javitt,
2009; Javitt \& Sweet, 2015). If \emph{sensory} encoding is impaired
then the information derived from the impoverished signal will produce
further problems. An immediate consequence of impaired SM is reduced
information available for WM. In addition, WM deficits in schizophrenia
could negatively impact SM too: If WM is inefficient, then manipulating
incoming sensory information will also be impaired. Improvements
anywhere along this loop could have cascading consequences (Figure 1). A
first step is to characterize the relationships between SM and WM
biomarkers and how they are impacted along SSD.

\textbf{{[}Insert Figure 1 here{]}}

Because psychiatric populations are difficult to access, recruit and
retain in research studies we must leverage all data points including
findings from the subclinical population. We subscribe to the view that
the general population falls along a spectrum of schizotypal symptoms
(theoretical review in (Grant et al., 2018); see Figure 2 for
illustration). People with more symptoms, but within neurotypical range,
are referred to as having `high schizotypy'. First degree relatives of
those with schizophrenia typically have high schizotypy ratings, and are
considered to be at high clinical risk (Kalmady et al., 2020; Le et al.,
2020). As noted, studies in first degree relatives and in neurotypical
individuals with high schizotypy scores avoid complications and
confounds such as medication type, dosage, duration, and mental health
comorbidities. An important challenge in \emph{subclinical} populations
is that any biomarker would be weaker. We began our review by searching
specific sensory and working memory terms in PubMed and counting the
number of studies identified; see Table 1. We then summarize the reports
of abnormal auditory and visual SM in SSD and turn to WM before
discussing whether empirically derived biomarkers can serve as
indicators of clinical remediation.

\textbf{{[}Insert Figure 2 here{]}}

\textbf{{[}Insert Table 1 here{]}}

\section*{Sensory Memory in Schizophrenia Spectrum
Disorders}

{\label{sensory-memory-in-schizophrenia-spectrum-disorders}}

Sensory memory (SM) refers to the window over which the neural response
to a sensory stimulus remains active after the stimulus is withdrawn.
The duration of the memory trace is brief (e.g., (Sperling, 1960) and
considered to be preattentive (Naatanen et al., 1982). Measures of SM
are appealing as a clinical biomarker because they are unaffected by
attentional focus - which is compromised in schizophrenia. The SM trace
is an input for predictive coding and very sensitive to changes in
sensory stimuli (Winkler, 2007; Winkler et al., 2009). It is also
extremely adaptive to the current environment (May \& Tiitinen, 2010;
May \& Tiitinen, 2007). Abnormal SM in those with
schizophrenia\emph{may} be domain general (Neuhaus et al., 2013;
Saint-Amour et al., 2007). To date the most research has been conducted
in audition and vision, although sensory memory in the somatosensory and
olfactory domains is reduced in schizophrenia (Somatosensory: (Ludwig et
al., 2016) Haigh et al., 2016; Olfactory: (Moberg et al., 2003; Wu et
al., 1993)) and can be tracked using similar methods to vision and
audition (Restuccia et al., 2009). Despite the numerous reports of
oculomotor dysfunction in schizophrenia and SSD, relatively little has
been reported in visual SM. We will now discuss each in turn.

\subsection*{Auditory Sensory Memory}

{\label{auditory-sensory-memory}}

Poor auditory SM impairs the encoding of simple parameters of sounds.
Neural measures of \emph{auditory} SM are a clinically useful biomarker.
They are minimally affected by attention and can be measured before the
participant is aware of the percept, thereby isolating early sensory
processing from upstream cognitive processes.

In people with schizophrenia there are numerous examples of deficits in
auditory SM, including impaired pitch discrimination (distinguishing
between tones of different pitches; (McLachlan et al., 2013), tone-pair
matching (Rabinowicz et al., 2000), sound localization (Donde et al.,
2020; Donde, Martinez, et al., 2019), discrimination of intensity,
duration and sequences (Schnakenberg Martin et al., 2018) and auditory
scene segregation (Ramage et al., 2012; Weintraub et al., 2012).
Moreover, there is evidence that mismatch negativity amplitude is lower
in those with schizophrenia, reflective of a general auditory sensory
memory deficit (Catts et al., 1995), lower educational achievement
before the onset of schizophrenia, worse cognitive symptoms, poorer
global function, and longer duration of illness (Friedman et al., 2012).
There is some question over whether these abnormal auditory responses
are due to sensory \emph{memory} or if they are due to problems encoding
the information in the first place. While it is difficult to disentangle
how poor early encoding impacts the ability to encode short-term memory
traces, there is evidence that both individuals with chronic
schizophrenia and individuals at their first-episode of psychosis show
deficits in encoding deviants in complex patterns (Haigh et al., 2016;
Haigh, Coffman, et al., 2017; Salisbury et al., 2018), suggesting that
memory impairments persist after encoding. However, other aspects of
audition are preserved, including the time-course of auditory SM (March
et al., 1999) and the ability to categorize syllables (Dale et al.,
2010; Haigh et al., 2019). Categorization can also show the expected
shifts when participants are habituated to one syllable (for example,
adapting to one syllable category makes it easier to detect a syllable
in another category) (Haigh et al., 2019) suggesting residual plasticity
in the auditory system. Finally, more closely associated with specific
symptoms of schizophrenia, auditory hallucinators reporting phenomenon
of speech and voices exhibit worse auditory SM (McCleery et al., 2018),
highlighting the link between auditory processing and symptomology.

There is evidence of similar, but smaller, SM abnormalities in
schizotypy that are similar to those reported in schizophrenia. However,
the number of studies focusing on schizotypy are relatively small and
often identify potential biomarkers in isolation. For example, sensory
cortices send a `corollary discharge' indicating a self-generated
response compared to external in origin (Sperry, 1950). Auditory
corollary discharges are produced in the superior temporal gyri
(Creutzfeldt et al., 1989; Muller-Preuss \& Ploog, 1981) and are
manifest by reduced auditory N1 ERP responses to
\emph{self-generated}speech compared to the N1 response to externally
generated speech (Ford \& Mathalon, 2005). Individuals with
schizophrenia show equivalent N1 amplitudes for self-generated and
externally generated speech, suggesting they cannot differentiate
between conditions. Impaired corollary discharge could reflect a
confusion between self-generated auditory hallucinations and externally
generated speech (Ford \& Mathalon, 2005). In schizophrenia there is a
reliable reduction in N1 amplitude (Salisbury, 2010; Shelley et al.,
1999); reviewed in: (Rosburg et al., 2008) that correlate with the
perceptual deficit (Javitt et al., 2000). Similarly, individuals with
high schizotypy show equally large N1 responses to external and
self-generated speech, whereas neurotypical participants with low
schizotypy show N1 suppression to self-generated speech (Oestreich et
al., 2016). Those high in schizotypy show reduced precision in auditory
sensory memory despite normal rates of acquisition and decay (Bates,
2005).

\emph{I\textbf{n addition, reductions in well-characterized
cognitive-related ERPs in combination with sensory ERPs to simple
auditory stimuli have shown some utility. For example, reductions in the
P3 complex (Javitt et al., 1995; Pritchard et al., 1985), are evident
early in the disease course (Devrim-Ucok et al., 2016; Salisbury et al.,
2020), as are reductions}} in steady-state potentials (Light et al.,
2006), and in the slow-wave potentials associated with auditory scene
segmentation (Coffman et al., 2018; Coffman et al., 2017) that correlate
with hypoactive midcingulate cortex (Coffman et al., 2018). Reduced
amplitude of P50, mismatch negativity, P3, and antisaccades (making eye
movements in the opposite direction to a cue) are reported in
individuals with schizophrenia, and significant reductions in P50 and P3
in relatives, compared to controls. However, combining all four measures
in a multivariate analysis permitted the model to accurately (80\%)
classify group assignment (Price et al., 2006). In short, throughout
early auditory sensory processing there are atypical ERPs in the
schizophrenia population. Further investigation in high schizotypy
individuals to expressly investigate auditory SM would be valuable to
understand the boundary conditions between intact and impaired function
in SSD.

\subsection*{Auditory Mismatch
Negativity}

{\label{auditory-mismatch-negativity}}

The most robust neural measure of SM is the \textbf{mismatch negativity
(MMN).} In neurotypical individuals, fronto-central electrodes record
larger (more negative) responses to an infrequent deviant stimulus than
to the standard stimulus at around 100-200ms after stimulus-onset
(Naatanen, 1985). The MMN is calculated by subtracting the EEG response
to the standard tone from response to the deviant tone producing a
difference waveform (Näätänen, 1985). In individuals with chronic
schizophrenia, the MMN is \emph{smaller} (Cohen's d: 0.99) (Javitt \&
Sweet, 2015; Umbricht \& Krljes, 2005), and magnetic activity in the
planum temporale (secondary auditory cortex) is reduced during auditory
SM tasks that generated a smaller (magnetic) MMN (Kircher et al., 2004).
Reduced MMN generalizes across stimuli (Erickson et al., 2016; Umbricht
\& Krljes, 2005) and includes responses to pattern deviants based on
grouping (Haigh et al., 2016), pitch (Haigh, Matteis, et al., 2017),
rules (Haigh et al., 2019), and phonemes (Fisher et al., 2019).

In SSD the degree of MMN reduction correlates with symptom severity
(Fisher et al., 2019; Light \& Braff, 2005). Greater reductions are
apparent in chronic schizophrenia compared to individuals at high-risk
(Shin et al., 2012). Local and global change detection have both been
found to be poor in schizophrenia suggesting evidence of abnormal
predictive coding that cannot be due to abnormal adaptation to simple
stimulus parameters (Kirihara et al., 2020). Together, these data
highlight a pervasive deficit in preconscious auditory SM that occurs
early in the auditory processing stream (Escera \& Malmierca, 2014). MMN
is sensitive to the balance between excitatory and inhibitory
neurotransmitters in the auditory cortex (Holliday et al., 2018). There
is some debate regarding neurotransmitter systems' roles in the auditory
MMN. Some evidence indicates a restored MMN with nicotinic agonists,
implicating acetylcholinergic modulation (Baldeweg et al., 2006) but see
(Knott et al., 2014), whereas other studies implicate acetylcholinergic
(Kantrowitz et al., 2018) or serotonergic influence (Sehatpour et al.,
2022).

In SSD more broadly, there is some evidence of reduced MMN. For example,
MMN to pitch oddballs successfully discriminates between neurotypicals
and people diagnosed with schizotypal personality disorder (Niznikiewicz
et al., 2009). Furthermore, combining MMN responses to multiple
deviants: duration, gap or silence instead of a tone, or location of
tone, lead to identification accuracy up to 80.5\% when using
multivariate machine learning techniques. The combined MMN accuracy
correlated with global assessment of functioning, highlighting auditory
MMN as a biomarker of symptoms in chronic schizophrenia (Taylor et al.,
2017). MMN deficits are evident in early course schizophrenia (within
2-years of the first episode of psychosis; (Salisbury et al., 2007)).
There are inconsistent findings in those within 6-months of initial
psychosis (Erickson et al., 2016; Haigh, Coffman, et al., 2017), and in
those at-risk of developing schizophrenia (compare (Shin et al., 2012)
to (Hsieh et al., 2019)), or who are unaffected first-degree relatives
(Magno et al., 2008). Several accounts address why MMN deficits are
greater in those with advanced disease. One theory is that MMN is tied
to symptom severity (Kantrowitz et al., 2018), as MMN correlates with
negative symptoms in chronic (\textgreater{}5 years) patients (Catts et
al., 1995). \emph{Of relevance, the MMN correlates with negative
symptoms in first-episode patients, and with} measures of social
functioning (Murphy et al., 2020). Certainly, individuals with chronic
schizophrenia have the most severe symptoms and neurological
consequences. For example, MMN correlates with reduced grey matter
volume in primary auditory cortex and both MMN deficits and the great
matter volume reductions increase with the duration of time past
first-episode (Salisbury et al., 2020; Salisbury et al., 2007). A recent
meta-analysis indicated that MMN shows progressively greater
abnormality, with first degree relatives showing the smallest deficit,
followed by individuals at high-risk of developing schizophrenia, and
finally recently diagnosed (early-stage schizophrenia), compared to
those with chronic schizophrenia (Erickson et al., 2016); Figure 3).

\textbf{{[}Insert Figure 3 here{]}}

A few inconsistent MMN findings are worth mentioning. First, some people
`recover' without repeated psychotic episodes, and they do not exhibit
an abnormal MMN (Kim et al., 2020). This highlights a key gap - few
longitudinal studies have been conducted to ascertain how biomarkers
change over time or with vacillating symptoms. Second, a small study in
the subclinical population reported that high scorers on the
suspiciousness subscale had \emph{larger} MMN amplitudes than low
scorers (Broyd et al., 2016). Thus, the current literature presents
competing perspectives: 1) that increased symptom load reliably predicts
abnormal MMN (e.g., (Claridge \& Beech, 1995), but 2) in the subclinical
population more subtle patterns of preserved and impaired MMN emerge.
Clearly, more research is needed to address these gaps in our
understanding.

\subsection*{Oculomotor and Low-Level Visual
Deficits}

{\label{oculomotor-and-low-level-visual-deficits}}

Despite the wealth of data cataloging impaired auditory SM in
schizophrenia and SSD, there is relatively little in the visual SM
domain. This is surprising given the prevalence of oculomotor deficits
in schizophrenia, suggesting that the visual system is affected. Before
describing the studies that have reported on visual SM, we note research
pinpointing oculomotor deficits in SSD. We address this issue first
because oculomotor deficits lead to atypical sampling of the environment
and these contribute to abnormal visual sensation. Oculomotor responses
are disrupted along SSD with atypical performance in saccadic tasks
(e.g., double-step, adaptation, anti-saccade) and smooth pursuit
(O'Driscoll \& Callahan, 2008; Thakkar \& Rolfs, 2019; Wolf et al.,
2021). A proposed mechanism is that abnormal corollary discharge (e.g.,
(Feinberg, 1978) disrupts knowledge about eye position, resulting in
inaccurate eye movements with more interruptions during visual tracking.
Findings in nonhuman primates and lesion studies implicate impaired
signaling in the mediodorsal thalamus, which serves as an intermediary
between subcortical eye movement control areas (e.g., superior
colliculus) and frontal eye fields (Thakkar \& Rolfs, 2019).
Inactivating this pathway creates a mismatch between where an organism
believes their eyes are directed and where they are actually looking and
thus alters visual perception (Cavanaugh et al., 2016). In those with
schizophrenia the abnormal visual scan patterns of faces and social
settings contribute to aberrant social interactions (Patel et al.,
2020). Oculomotor deficits contribute to higher order cognitive deficits
because those with schizophrenia sample and perceive the environment
abnormally.

Approximately \textasciitilde{}60\% of individuals with schizophrenia
have distortions in visual perception (Phillipson \& Harris, 1985) and
\textgreater{}33\% experience visual hallucinations (Silverstein \& Lai,
2021). The range of perceptual deficits is broad and includes worse
performance in assessments of contrast sensitivity (Harper et al.,
2020), detection of contour (Keane et al., 2014), color (Fernandes et
al., 2019), biological motion (Okruszek \& Pilecka, 2017) but see
(Keane, Peng, et al., 2018), faces (McCleery et al., 2015), and stronger
afterimages (Thakkar et al., 2021). The extent of afterimage deficits is
clinically relevant as they predict illness severity (Keane, Cruz, et
al., 2018).

Similarly, accounts for visual deficits in schizophrenia spectrum
disorders identify abnormalities in the anatomy of the retina (reviewed
in reviewed in (Bernardin et al., 2017; Silverstein et al., 2020), as
well as atypical network-level connectivity, both hypo- and hyper-
connectivity, including cortical - medial temporal lobe abnormalities
(reviewed in: (Silverstein \& Lai, 2021). Together, the consistent
oculomotor and low-level visual deficits suggest that processing from as
early as the retina is abnormal in schizophrenia.

\subsection*{Visual Sensory Memory Biomarkers of Symptom
Severity}

{\label{visual-sensory-memory-biomarkers-of-symptom-severity}}

Given the range of oculomotor and low-level visual deficits identified
in SSD, it is surprising how little work has been conducted in iconic
memory. Several studies investigating iconic memory decay in those with
schizophrenia have reported no differences in schizophrenia (Hahn et
al., 2011; Knight et al., 1978). We know of no research evaluating
iconic SM in \emph{schizotypy in the general population} .

Similarly, it is noteworthy that in comparison to the high volume of
research on the auditory MMN, there is little research assessing the
visual MMN in schizophrenia and in SSD. However, atypical visual MMN are
documented and confirm that abnormal responses are not isolated to the
auditory system. For example, the visual MMN is significantly reduced in
individuals with schizophrenia in an oddball task using letters as
stimuli (Neuhaus et al., 2013), visual motion direction (Urban et al.,
2008) or horizontal compared to vertical grating patterns as oddballs
(Farkas et al., 2015), with a similarly large effect size in group
differences. The same pattern of reduced MMN amplitude has been
characterized in studies using emotional faces (Csukly et al., 2013; She
et al., 2017; Yin et al., 2018), in which the MMN reduction correlates
with behavioral performance on an emotion recognition task (Csukly et
al., 2013). One recent finding reports that individuals with
schizophrenia showed normal visual MMN elicited by fearful faces but
diminished amplitude MMNs elicited by neutral faces (Vogel et al.,
2018). A new study has now identified larger visual MMN to happy
compared to sad and neutral faces and found that MMN amplitude to the
happy faces correlated with schizotypy scores, and interestingly, with
measures of autism characteristics, highlighting the difficulty with MMN
and specificity of diagnosis (Ford et al., 2022).

Another ERP that is linked to sensory memory is the P1. Visual P1
responses were significantly smaller in schizophrenia in response to low
(but not high) spatial frequency gratings that was not related to visual
acuity (Farkas et al., 2015). Although the P1 amplitudes did not
correlate with symptoms, they were related to cognitive performance, as
well as education level, duration of illness, and measures of global
functioning (Farkas et al., 2015). Visual MMN has also been elicited in
response to changes in the direction of moving dots and is significantly
reduced in schizophrenia (Urban et al., 2008). Visual MMN reduction was
again associated with global functioning, general symptoms, and
medication dosage (Urban et al., 2008), highlighting the parallels
between visual and auditory MMN. Despite these similarities across the
modalities, we found no findings describing the visual MMN in other SSD
or high schizotypy participants.

In summary, despite notable visual and oculomotor involvement in SSD,
and a wealth of data demonstrating \emph{auditory} SM deficits, little
attention has been paid to \emph{visual} SM specifically and no evidence
of atypical iconic memory in SSD. Expanding visual SM research in the
SSD population is needed to complement the auditory SM work and to
deepen our understanding of visual SM contributions to other aspects of
cognition.

\section*{Working Memory Deficits in Schizophrenia Spectrum
Disorders}

{\label{working-memory-deficits-in-schizophrenia-spectrum-disorders}}

Working memory (WM) deficits are a canonical deficit in people with
schizophrenia (for reviews, (Forbes et al., 2009; Lett et al., 2014;
Reichenberg, 2010), those with high schizotypy (Siddi et al., 2017), and
those at high-risk of psychosis (Millman et al., 2022). WM impairments
are broad and include verbal (reviewed in (Seabury \& Cannon, 2020),
visuospatial, and executive functioning aspects of WM (Barch \& Ceaser,
2012; Forbes et al., 2009). It is worth noting that poor WM performance
in people with schizophrenia is associated with a variety of neural
differences including reduced gray matter volume (Du et al., 2022;
Kochunov et al., 2022), abnormal (hypo- and hyper-) connectivity
patterns (Ding et al., 2019; Du et al., 2022; Fryer et al., 2015;
Hashimoto et al., 2010; Schutte et al., 2021; Seabury \& Cannon, 2020;
Unschuld et al., 2014), hypoactive clusters (Seabury \& Cannon, 2020),
and abnormal oscillations (Reilly et al., 2018). At a lower level, one
account of the WM deficit in schizophrenia suggests that
PFC\emph{inhibition} is disrupted, leading to impaired WM (Meiron et
al., 2022). PFC inhibition is thought to be driven by abnormal
interneuron function, which in turn reduces gamma oscillation amplitude
(Reilly et al., 2018); see also (Toader et al., 2020). A challenge in
specifying underlying neural mechanisms of the WM deficit is that there
is variability across samples with different patterns emerging in
distinguishable subgroups (Rodriguez et al., 2019). Advances in
neuroimaging provide important future paths for identifying biomarkers
of working memory that reflect schizophrenia symptoms, and for producing
therapies to mitigate symptoms. In the paragraphs below, we specify the
findings specific to auditory and visual WM as a parallel to the
sections on auditory and visual SM above.

\subsection*{Auditory Working Memory}

{\label{auditory-working-memory}}

Auditory WM is impaired across SSD. Deficits are known in schizophrenia,
their first-degree relatives (Seidman et al., 2012; Seidman,
Pousada-Casal, et al., 2016), and in the high-risk population (Higuchi
et al., 2013; Rutschmann et al., 1980), as is verbal WM (Seabury \&
Cannon, 2020). Importantly, this deficit is apparent even in
early-course schizophrenia (Gonzalez-Blanch et al., 2006) and is
associated with abnormal neural responses (Leicht et al., 2015;
Papageorgiou et al., 2001). Importantly, auditory WM performance
predicts the presence of auditory hallucinations (Gisselgard et al.,
2014; Jenkins et al., 2018). Therefore, similar to auditory SM, WM
deficits may be related to \emph{symptoms} rather than onset of
psychosis.

Potential WM biomarkers are associated with altered neural function and
patterns of neural connectivity. The neural correlates of WM deficits
include the PFC (for a review, see (Hashimoto et al., 2010), and
relevant sensory cortices (e.g., (Javitt \& Sweet, 2015; Menon et al.,
2001; Stevens et al., 1998). However, when investigating the neural
correlates associated with abnormal WM functioning in schizophrenia,
there is inconsistency. Several studies reported PFC\emph{hyperactivity}
, whereas others reported the opposite (for a review see (Leitman et
al., 2005). Other brain areas implicated in WM retrieval include
parietal regions (for a review, see (Funahashi, 2017; Hamilton et al.,
2018; Olson \& Berryhill, 2009). Parietal function is also abnormal in
schizophrenia particularly in those who hallucinate (Hashimoto et al.,
2010; Wible et al., 2009) and hypoactive temporal lobe activity (Wible
et al., 2009) correlates with schizophrenia symptom severity (Menon et
al., 2001). More recent analyses investigating altered connectivity
extend these regions-of-interest findings by identifying network
dysfunction. For example, increased dorsolateral and medial PFC
connectivity in those with schizophrenia and their nonclinical
first-degree relatives, compared to controls (Unschuld et al., 2014),
and within left superior frontal gyrus (Ding et al., 2019). Several
distinct patterns of hyper- and hypo- connectivity are associated with
deficits in verbal WM in people with schizophrenia -- including altered
salience network (SN), default mode network (DMN), and frontoparietal
network (FPN) interactions (Rodriguez et al., 2019). Together, there is
a network-level deficit in schizophrenia that impacts auditory WM.

Some evidence suggests auditory WM deficits are associated with other
auditory-related symptoms such as auditory hallucinations (Geng et al.,
2020; Gisselgard et al., 2014; Jenkins et al., 2018), and auditory
processing deficits (Moschopoulos et al., 2021). For example, deficits
in MMN to pitch deviants were related to deficits in prosody processing
(non-verbal communication of emotion) in individuals with schizophrenia
(Leitman et al., 2005) and P50 suppression in the auditory ERP
correlated with WM performance (Hamilton et al., 2018). Network
connectivity also identifies altered DMN activity associated with
auditory hallucinations (Geng et al., 2020). Finally, there is an
intriguing observation that the visual word form area engages during
auditory perception and WM in people with schizophrenia (Herman et al.,
2020), suggests that broadly altered structure and function underlies
auditory WM function.

Worse WM performance is implicated in schizotypy (Park \& McTigue, 1997;
Siddi et al., 2017), particularly in those who displayed more positive
and negative symptoms (Schmidt-Hansen \& Honey, 2009), and was
associated with worse communication abilities (Kerns \& Becker, 2008).
It has also been suggested that poorer working memory performance may be
a biomarker for increased risk of developing psychosis due to the large
effect sizes shown in schizotypy (Siddi et al., 2017). However, there is
debate as to the robustness of the WM deficit (Lenzenweger \& Gold,
2000) and whether the effect is due to impaired attentional ability,
with some evidence indicating dissociable deficits (Marsh et al., 2017).
In summary, abnormalities in auditory WM in schizophrenia are seen in
diminished form in individuals with high schizotypy. This supports the
idea that traits associated with schizophrenia are on a spectrum.
Notably, compared to the auditory SM research, there is remarkably
little research conducted on biomarkers of auditory WM deficits across
the SSD.

\subsection*{Visual Working Memory in Schizophrenia Spectrum
Disorders}

{\label{visual-working-memory-in-schizophrenia-spectrum-disorders}}

A broad literature investigates visual WM deficits in people with
schizophrenia and related disorders. Deficits in object, spatial and
verbal WM are well-documented in review articles (e.g., (Barch, 2005;
Haenschel \& Linden, 2011; Piskulic et al., 2007). Briefly, across
measures of accuracy, reaction time, and confidence, individuals with
schizophrenia broadly perform less accurately, more slowly, and less
confidently than neurotypicals. Impaired WM extends to the at-risk and
schizotypy populations. In the at-risk population, two recent
meta-analyses point to WM performance as reflecting symptom severity.
Overall cognition, attention, processing speed, and WM were found to be
significantly worse in those who were high-risk compared to healthy
controls using the MATRICS Consensus Cognitive Battery (MCCB; (Zheng et
al., 2018). Including both visual and auditory measures of working
memory and symptoms using the Schedules for Clinical Assessment in
Neuropsychiatry was able to distinguish between individuals diagnosed
with schizophrenia, their unaffected first-degree relatives, and
non-clinical individuals (Kent et al., 2004). Importantly, neural
accounts for the visual WM impairment note the contributing factor of
abnormal SM and atypical prefrontal function (e.g., (Haenschel \&
Linden, 2011).

A few biomarkers are associated with visual WM in SSD. There is a lower
amplitude P100 (P1) responses over electrode site Oz in individuals with
schizophrenia (Haenschel et al., 2009), and in those with high
schizotypy (Koychev et al., 2010) to a visual delayed discrimination
task, highlighting the contribution of early sensory processing on later
cognition. Others report that schizophrenia participants have abnormal
theta power during n-back performance, and reduced P100 in response to
TMS pulses (Hoy et al., 2021). Importantly, atypical gamma oscillations
can be observed in first-episode psychosis (Missonnier et al., 2020).
Additional effects likely arise from gamma-band oscillatory activity
during WM tasks that fails to increase with increased WM load in those
with schizophrenia (Basar-Eroglu et al., 2007; Cho et al., 2006). A
second biomarker of encoding-related WM deficit is the N2pc, which is
also reduced in those with schizophrenia (Mayer et al., 2020). Broadly
atypical frontal lobe activations in at risk youth (van Gool et al.,
2022) and connectivity patterns are known across schizophrenia spectrum
disorders (Briend et al., 2020; Schmidt et al., 2014), and at risk
samples (Schutte et al., 2021).

\section*{What Does This Mean for Biomarkers of Schizophrenia-Spectrum
Disorders?}

{\label{what-does-this-mean-for-biomarkers-of-schizophrenia-spectrum-disorders}}

In surveying the SSD literature, a patchy pattern emerges: biomarkers
associated with \emph{auditory} SM (particularly the MMN)
and\emph{visual} WM in individuals with schizophrenia have been examined
in detail and appear to be related to symptom severity. In contrast, the
complement, visual SM and auditory WM, receive less attention. Progress
is also hampered because there is little work conducted in SSD apart
from those with a schizophrenia diagnosis. Diagnosed individuals often
have comorbid diagnoses and require medications which can obscure the
relationship between symptoms and biomarkers. To gain a foothold in the
untapped areas (see Table 1), we argue that filling in the missing cells
(visual SM, auditory WM) across SSD will accelerate the pace of data
collection and expedite our ability to distinguish between competing
theoretical perspectives. A final deficiency is that apart from the
auditory MMN, there is little consistency in methodology, making studies
difficult to generalize across. The standardization of auditory MMN
procedures may be one of the reasons why the auditory MMN is one of the
more compelling biomarkers of schizophrenia.

Our goal is to make clear that countering these oversights may improve
the identification and characterization of useful biomarkers to better
assess and mitigate SSD. For many researchers, including ourselves,
gaining access to substantial clinical populations is improbable.
Testing special populations, especially those with psychosis, impose
ethical and practical difficulties with regards to care and consent. In
contrast, evaluating subclinical populations provides a useful option in
investigating SSD. It is also a conservative approach because those with
high schizotypy are expected to show smaller, more modest effects than
those with psychiatric diagnoses. As noted, perceptually, the auditory
MMN is the best studied biomarker associated with schizophrenia and
abnormalities persist across SSD, including in the subclinical
population. In visual WM, the range of analyses is more varied without
commitment to a particular biomarker of SSD. The absence of clear
biomarkers associated with the complementary areas of visual SM and
auditory WM make for a puzzling circumstance. \emph{Assuming a similar
pattern of sensory and working memory abnormalities in schizotypy to
those found in diagnosed schizophrenia, focusing on schizotypy can help
identify causal mechanisms that are linked to symptom severity.} In
short, we encourage ERP research in auditory and visual SM, and auditory
and visual WM, preferably in the same clinical individuals as much as
possible, and across SSD and subclinical populations.

\subsection*{How Could Biomarkers of Symptom Severity Improve Clinical
Outcomes?}

{\label{how-could-biomarkers-of-symptom-severity-improve-clinical-outcomes}}

One goal of documenting behavioral deficits and associated biomarkers of
symptom severity in schizotypy is to improve clinical
outcomes,\emph{potentially by identifying appropriate treatments related
to the underlying mechanisms} . Biomarkers could also document a
patient's progress, such as improvements of deterioration over time, and
potentially identify the mechanism of improvement. For example, there is
growing evidence that sensory training methods can improve cognition in
schizophrenia. Auditory training methods that require learning to
discriminate between different frequency modulated sweeps improve
auditory processing speed (Biagianti et al., 2016) and executive
functioning (Dale et al., 2016). Extending this observation,
multisensory (visual and audio) training on three simultaneity judgment
tasks reduced the temporal binding window in a neurotypical group, with
effects lasting seven days. The multisensory versions had bigger effects
than the unisensory versions. Training duration had little effect on
improvements in the temporal binding window (Zerr et al., 2019). A
review of sensory-targeted cognitive training methods found consistent
improvements in multiple areas of cognition (Donde, Mondino, et al.,
2019), including WM (Hubacher et al., 2013; Lawlor-Savage \& Goghari,
2014). If the early auditory signal is improved, this would benefit
later auditory processing. However, benefits were short-lived and
time-intensive. One theorized mechanism of sensory training is that it
induces neural plasticity, providing a window to normalize sensory and
cognitive functioning. Indeed, Donde et al. (2019) reviewed biomarkers
of schizophrenia that \emph{normalized} after sensory training. They
highlighted that training imposes \emph{no known side effects} and it
may provide a more holistic approach to reducing schizophrenia
symptomatology. Others note that ignoring sensory issues creates an
informational ``bottleneck'' that hampers patients' ability to improve
cognitive functioning (Genevsky et al., 2010). Training in processing
speed showed greater improvements across cognitive abilities compared to
narrow effects after WM training (Cassetta et al., 2019). Improving
low-level sensory processing benefits upstream processing, but more work
is essential to determine which approach best alleviates symptoms.

\emph{Focusing on schizotypy may allow for some of the mechanisms to be
disentangled through more detailed investigation without clinical
complications such as medications and contact time in the clinic.}

Other treatments, including noninvasive stimulation approaches (e.g.,
transcranial direct current stimulation: tDCS; transcranial magnetic
stimulation: TMS), report improvements in sensory and cognitive markers,
and reduce symptoms (reviewed in (Berryhill \& Martin, 2018). For
example, anodal tDCS improved pitch MMN amplitude and 2-back WM
performance in people with schizophrenia (Impey et al., 2017). A
separate study reported improved auditory P50 ERP response and reduced
auditory hallucinations after anodal tDCS (Kim et al., 2018). In
treatment resistant individuals with schizophrenia, a TMS protocol
(intermittent theta burst stimulation) targeting left PFC improved
visual WM ability and reduced symptoms (Wang et al., 2022). However, a
recent meta-analysis evaluating findings from TMS and tDCS approaches
reports no collective benefit across 22 studies (Sloan et al., 2021).
Early-stage neurostimulation findings show promise, in terms of behavior
and normalized biomarkers. However, small sample sizes, brief protocols,
and a variable clinical population contribute to heterogeneous outcomes
and a need for optimization. \emph{It is also unclear how stimulation
methods would impact a less symptomatic population, particularly if the
biomarkers in schizotypy are less pronounced than they are in diagnosed
individuals.}

\subsection*{Can Biomarkers be Used to Predict Schizophrenia
Onset?}

{\label{can-biomarkers-be-used-to-predict-schizophrenia-onset}}

When assessing biomarkers of symptom severity, there is a burgeoning
question \emph{as to whether some of the biomarkers already discussed
here} can be used to predict the onset of schizophrenia.

\emph{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 \emph{sensitive} to predict onset of psychosis \emph{and}be
\emph{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). \emph{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 \emph{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 \emph{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).

\section*{Concluding Thoughts}

{\label{concluding-thoughts}}

To address the public health challenge associated with schizophrenia, we
should accelerate our understanding of SSD through identification of
useful biomarkers of SSD. This includes conducting research in
overlooked sensory modalities (e.g., chemical senses, somatosensation).
We need to be more cognizant of gaps in our understanding of sensory
processing and their contributions to later cognitive processing. One
way to do this is to measure responses from different parts of the
sensory processing stream simultaneously. Advancements in technology,
reduction in the price of certain electrophysiology equipment, and
improvements in methodology have made this approach a reality without
having to build equipment from scratch.

SM and WM deficits in those with high schizotypy and individuals with
diagnosed schizophrenia, highlight that schizophrenia really is a
spectrum of symptoms. Focusing on schizotypy requires a larger sample
size is needed to detect the smaller effects (compared to schizophrenia)
but offers a straightforward method for assessing relationships between
symptom severity and functioning along the schizophrenia spectrum.

\emph{Due to the lack of studies focusing on multisensory and
multidomain functioning in schizotypy, we are forced to assume that the
benefits of multivariate biomarkers will be as helpful in characterizing
those with high schizotypy as it should be in the diagnosed population.}

The deficits in SM and WM along the schizophrenia spectrum offer insight
into how these previously disparate abnormalities interact and accrue
downstream effects. Furthermore, they highlight the value of
investigating the use of early-stage sensory interventions for
treatment. There is already some evidence of \emph{training} in SM or WM
to alleviate deficits. The effectiveness of sensory training on symptoms
has implications for other conditions associated with sensory
abnormalities. Assessing biomarkers along the sensory pathway and
treating early sensory symptoms is being used in autism (Dinstein et
al., 2015; Haigh, 2018; Whitaker et al., 2016)) and in bipolar
depression (for a review, see (Sit \& Haigh, 2019)). However, it will be
important to demonstrate whether these mechanisms are (1) causal, and
(2) sensory modality specific.

Comparing SM and WM has the added benefit of identifying biomarkers and
neural mechanisms that are unique to schizophrenia symptomatology. For
example, schizophrenia is indistinguishable from autism on
neuropsychological tests, including WM (Eack et al., 2013). Whereas the
schizophrenia population differs from autism in their sensory responses,
namely that individuals with schizophrenia typically under-respond
whereas individuals with autism tend to over-respond (Haigh et al.,
2016; Haigh et al., 2022). Identifying multiple biomarkers associated
with schizophrenia and SSD more broadly can help with diagnostic
specificity with greater accuracy than single markers.

To improve outcomes associated with schizophrenia, new approaches are
needed. We advocate for collecting multiple biomarkers from available,
subclinical populations that can collectively monitor symptoms\emph{and
reveal the mechanisms underlying the symptoms} . Alternatively, they may
identify treatment successes. With a more complete mechanistic
understanding of schizophrenia, such a suite of biomarkers could
identify appropriate courses of treatment that will improve long-term
outcomes.

\section*{Acknowledgments}

{\label{acknowledgments}}

This material is based upon work supported by NIMH R15MH122935 to SH, MB
and MD. The content is solely the responsibility of the authors and does
not represent the official views of any funding agency.

\textbf{Conflicts of interest statement:} the authors have no conflicts
of interest to declare.

\textbf{Author contributions:} SMH and MEB contributed equally to the
manuscript. AKG assisted with the literature search and summary. MD
contributed to the writing of the manuscript. All authors approved the
final submission.

\textbf{Graphical Abstract:}

\textbf{Graphical text:} Degraded processing of the incoming sensory
signal can impact the ability to manipulate sensory information in
working memory and, similarly, poor working memory can degrade the
processing on incoming sensory signal. This interaction is being
explored in individuals with schizophrenia and along the schizophrenia
spectrum.

\textbf{Figure Legends:}

\emph{\textbf{Figure 1.} Model of the relationship between poor sensory
memory and poor working memory: low quality incoming signal (sensory
memory) can impact the ability to manipulate sensory information in
working memory. Similarly, poor ability to manipulate information can
degrade the processing of the incoming signal.}

\emph{\textbf{Figure 2.} Schizophrenia-related symptoms across the
spectrum, organized in order of symptom severity.}

\emph{\textbf{Figure 3.} Illustration of differences in mismatch
negativity where individuals with schizophrenia show the smallest MMN
(grey), followed by high schizotypy / subclinical individuals (grey,
dark line), and low schizotypy showing the largest MMN (black).}

\textbf{Table Legend:}

\emph{\textbf{Table 1.} Number of papers identified when search terms
were entered into PubMed*. Auditory and visual modalities were added to
search terms for schizophrenia and schizotypy separately. The percent of
total search terms that focused on auditory or visual processing are
shown. Owing to the mismatch negativity dominating the sensory memory
literature, mismatch negativity (MMN) was searched for separately.
Highlighted cells show the modality dominance: over twice the number of
papers are published in one modality over the other.}







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