Early Career Awardees

Kara Dempster

Imagine you are hearing voices and experiencing paranoid delusions. Now imagine you were told that there was treatment for those symptoms. You anticipate relief finally. Now imagine that the treatment the experts offer you does not work. You wonder if they have gotten the diagnosis wrong. This is the plight of an individual with treatment resistant schizophrenia.  

My name is Kara Dempster and I am an early career clinician researcher at Dalhousie University in Halifax Nova Scotia with an interest in elucidating the underlying neurobiological correlates of treatment resistant schizophrenia.  

A third of individuals with schizophrenia do not respond to first line antipsychotic treatments and meet criteria for treatment resistant schizophrenia, the majority being poor responders from their first episode of psychosis. The evidence for clozapine is unequivocal in this group. Despite this, it is vastly underutilized. In an ideal world, individuals could be offered clozapine from the onset of their illness, or even after a single failed antipsychotic trial. 

The issue of determining clozapine eligibility is of particular importance in early phase psychosis as the response to clozapine is more robust. In addition, the onset of psychosis occurs during a crucial developmental period. It is simply harder to get back on track developmentally if one has missed out on normative functional experiences due to severe mental illness at this critical time. 

It has been hypothesized that there may be two subtypes of schizophrenia; one characterized by good response to dopamine blocking treatments, and the later being resistant to this pharmacology. Several studies have found that elevated glutamate in early psychosis is associated with poorer response to treatment at various discrete intervals. To date, no one has examined the association of elevated glutamate in early phase psychosis, and eligibility for clozapine.  

In breast cancer, for example, each individual is not offered the same treatment. Treatment choices are based off the type of cancer, and other factors like hormone receptor status. Offering someone a medication that does not at all address  the underlying cause of their symptoms seems grossly inadequate in comparison. The ability to use brain based measures to predict who would benefit from clozapine treatment would allow us to offer appropriate individuals this disease modifying treatment earlier in the course of illness.  

In conclusion, our current process of offering all individuals with schizophrenia the same medication without any consideration of underlying neurobiology is antiquated and contributes to prolonged suffering and disability. Elevated glutamate may be one marker of treatment resistant schizophrenia. I invite you to come see my poster on Tuesday April 1. I will be presenting my work on “The Association of Anterior Cingulate Cortex Glutamate and Clozapine Eligibility in an Early Psychosis Sample”.  

Leda Kennedy

Hello, my name is Leda Kennedy and the focus of my research is to learn more about how
factors like cannabis use and early life adversity impact long-term outcomes for youth
identified as being at clinical high risk for psychosis.
So knowing more about these risk factors and how they relate to one another and how they
influence lifetime outcomes in this population will help us develop more precise early
interventions
for clinical high risk youth that are informed by lifetime evidence, meaning that these
interventions
may be more successful in the long term.
So a little background, youth at clinical high risk for psychosis are generally aged
between 12 and 30 and experience attenuated symptoms of psychosis, such as changes in
thinking, hallucinations, or difficulty completing daily tasks or being involved in their social
lives.
So these young people are considered at increased risk for developing chronic psychiatric
conditions,
with research showing that approximately 20 to 30% of CHR youth develop psychosis spectrum
conditions within the first two years following the onset of symptoms.
So despite significant attention placed towards studying these young people over time, we
actually
know very little about their clinical and functional outcomes beyond that typical two-year
window.
So essentially, what happens to these folks after this time period and what factors influence
whether symptoms continue to get worse, whether they remit, whether they improve, similarly
with functioning?
What factors impact whether someone's functioning, restores, comes back, how it was before
they
had symptoms?
What impacts whether functioning continues to decline?
So we also know that there are several important risk factors for CHR youth in the early course
of illness that do impact short-term outcomes.
So these could be factors like cannabis use, exposure to early life trauma, family history,
stress.
So we largely ask the question in our group, do these same risk factors influence outcomes
across the lifespan?
So why does this matter?
Why do we ask these questions?
So really we think identifying factors that do influence lifetime outcomes tells us about
interventions that might be important at the time of identification.
So when we deem that someone might be at clinical high risk for psychosis when symptoms first
start and interventions that might be important across the lifespan for CHR individuals, so
across different life stages.
So this information also gives us insight into naturalistic patterns of behavior over time
in this population.
So what are cannabis use patterns over the lifetime?
What are further exposure or compounded exposure to trauma over the lifetime in this
population?
And importantly, if we know what factors matter regarding long-term outcomes that are
detectable
at baseline, so immediately when we first identify someone as being a CHR for psychosis, we
can
intervene earlier and potentially with more success.
But importantly, it's been very difficult to study CHR youth for a long period of time
in our field.
So we're just starting now to have the opportunity and the availability to study these individuals
over the course of their life.
So, so far, and what's the focus of my poster presentation at this year's SIRS conference,
we found that cannabis use and exposure to early life adversity are associated with one
another in a large sample of CHR youth who are re-contacted and re-evaluated five to
20 years following initial identification as being a CHR.
Importantly, we also found that these factors like cannabis use and early life adversity
also appear to be associated with persistent symptoms across the lifetime compared to those
CHR youth without those risk factors.
So that signifies that cannabis use, early life adversity, and potentially the interaction
between the two are associated with poorer clinical outcomes across the lifespan in
the CHR sample.
So some important implications for the real world we think our questions might be able
to begin to address is that knowing more about the trajectory of CHR symptoms across
the lifetime helps us improve disease prediction and understanding of the overall course of
CHR.
It also highlights time periods in life beyond that traditional two-year window where interventions
may be important.
And most importantly, it allows us to improve the precision of those interventions.
So can we instead employ interventions that are targeted towards comorbid trauma and
substance
use or interventions that individually target some of these risk factors?
I want to thank you so much for your attention and I look forward to discussing my findings
in depth during poster session one.
I sincerely thank the SIRS Congress for this award and for the opportunity to share our
work with our SIRS colleagues.
I will see you all in Chicago.

Lingling Wang

Good day, everyone. I'm Lingling Wang, a post-doctor from shanghai normal university. I'm thrilled to be here at SIRS. MY work is mostly focused on the underlying mechanism of anhedonia symptoms. the topic that I’m most interested in is called adaptive coding, a phenomenon that I think everyone is experiencing everyday. For example, right after receiving the paycheck, let’s say it’s 10k,a 4k purchase would be a relatively affordable amount. However, as the various bills start to pile up, so the money available is only about 5k right now. the perception of the value of 4k changes. Suddenly, that 4k purchase seems like a luxury. This shift in behaviorr shows how our coding of the value adapts to the changing context. that is, adaptive coding, it show us how flexible our value coding system are by taking the surrounding context into consideration. Previous studies have found that aberrant adaptive coding pattern contributes to the anhedonia symptom across disease boundaries in both clinical participants with schizophrenia and depression.  However, whether these atypical signs of adaptive coding already exist among the subclinical population remain unclear. individuals with schizotypal trait, subthreshold depression, and autistic trait all show signs of anhedonia symptoms. So, investigation of adaptive coding pattern in these early stages of disease would offer more insights regarding the role of adaptive coding play on anhedonia symptoms. So in this study, what we did is to compare the behavioural and neural responses of adaptive coding in people with schizotypal trait, subthreshold depression and autistic trait. For details regarding this study can be seen during the poster session. Thank you all for your attention, and I'm eager to have in-depth discussions with you all later.  

Mariana Lopes

Slide 1
Hello, I'm Dr Marina Lopes from King's College London. I work on eating disorders and in the ENTER
study we collaborated with the psychosis group to apply interventions used on binge eating treatment
to address weight gain caused by antipsychotic medication.

Slide 2
As we know, weight gain due to antipsychotics can reduce the quality of life and even lead to patients
discontinuing their medication. This highlights the need for effective interventions to help manage
eating behaviours and food cravings in this group.

Slide 3
One novel approach is Approach Bias Modification Training (ABM). This is a computer-based
intervention designed to retrain automatic responses towards food. It works like a gym for the brain:
participants push away images of high-calorie foods using a joystick, training them to avoid
approaching high-calorie food. Studies have shown that approach bias modification training can
reduce cravings, shift attention away from food cues, and even lower binge eating symptoms. We are
also exploring Transcranial Direct Current Stimulation (tDCS). TDCS is a non-invasive brain stimulation
technique that targets the dorsolateral prefrontal cortex (DLPFC), a key region involved in self-
regulation and impulse control. TDCS has been shown to reduce food cravings and improve eating
behaviours in individuals with binge eating disorder and obesity. The stimulation is quick, mild and
well-tolerated.

Slide 4
In the treatment of binge eating disorder, we are exploring cognitive training and brain stimulation to
reduce food cravings and binge eating episodes. Approach Bias modification training is a form of
cognitive training. It works like a gym for the brain, helping individuals to avoid high calorie foods.
Transcranial direct current stimulation is a mild brain stimulation technique that targets the
dorsolateral prefrontal cortex, the DLPFC, a region associated with control of eating via mood, reward
valuation, attention and inhibitory control.
The ENTER study was a feasibility randomised controlled trial that combined cognitive training with
brain stimulation to help reduce food cravings and binge eating episode in people taking antipsychotic
medication. By integrating these approaches, we aim to be able to offer better support for individuals
on antipsychotic treatment and improve their long term physical and mental health outcomes. I look
forward to sharing our results with you during my poster session. Thank you.

Sun Meng

Hi, I am Sun Meng from China. Thank you for having me here! I have been focused on early screening and prevention of mental disorders in adolescents for 10 years. 

I would like to give a brief introduction of my research. 

Fist is why we do it. Compelling evidence suggests that mental disorders always develop from middle adolescence. So we think there should be some early signs in this period. And if we can detect them and take measures on them, maybe we could prevent them from converting to clinical outcomes. 

Scientists have found some experiences occurring in these individuals at high risk for psychosis and named them psychotic-like experiences (we call it PLE for short). Our study further verified the association of PLE with a series of mental disorders, such as mood disorders, anxiety disorders, substance use disorders and schizophrenia spectrum disorders. However, we still have little knowledge on these experiences. We don’t know the overall clinical manisfestation of individual with PLE, and we don’t know how PLE occur, develop and finally convert to mental disorders, which hinder us to do something to stop this process. 

To address these issues, in the past five years, we conducted a cohort study among Chinese college students who are exactly at the very risky age. We screened out individuals with PLE, made clinical evaluation, performed EEG examination, and followed up them for three years. Today we bring part of our new findings here. 

We found that PLE seem to be an undifferentiated state on the continuum of psychosis, characterized by multi-dimensional subclinical changes. Also, we found time-variant associations between PLE and other symptoms and adverse life events, as well as the interaction within PLE during its development. We believe these findings can help to better understand PLE and shed light on future early intervention for these individuals. 

Please see poster M1 and T34 for our detailed findings. Thank you! 

Moritz Haaf

Transcript for the 3-Minute SIRS Early Career Video 

Hello, my name is Moritz Haaf, and I’m researching the role of the neurotransmitter glutamate in schizophrenia. While a dysfunction of glutamatergic neurotransmission has been implicated in schizophrenia for three decades, no pharmacological treatments targeting the glutamate system are clinically available today. Finding markers to identify the group of people who might benefit from this type of therapy could help us address difficult-to-treat symptoms such as negative symptoms and cognitive challenges. 

In this context it is important to consider the essential role of glutamate for the delicate balance between neuronal excitation and inhibition. A central piece to the disbalance found in people with schizophrenia is a hypofunction of the NMDA receptor. Despite our understanding of these dysfunctions, current biomarkers fall short in capturing the complex impact on neural activity. Traditional EEG methods may overlook key dynamics hidden in the aperiodic, or non-oscillatory, aspects of the signal. To tackle this challenge, we modelled NMDA receptor hypofunction in healthy volunteers using the receptor antagonist ketamine. 

Each participant underwent separate sessions one with ketamine and one with a placebo during which we recorded resting-state EEGs. This allowed us to investigate the changes in neural dynamics associated with NMDA receptor hypofunction compared with placebo. On the one hand we were able to demonstrate changes in the background or aperiodic EEG activity that could explain previously reported changes in gamma activity. On the other hand, we used an approach to geometrically capture the alterations in neural dynamics, to leverage the benefit of EEG’s high temporal solution. 

Why does this matter? By linking alterations in the aperiodic slope and EEG complexity directly to NMDA receptor hypofunction, we open the possibility of using these measures as potential biomarkers for schizophrenia. This could be transformative, not only in improving diagnosis but also in tailoring targeted interventions using glutamatergic agents. By leveraging EEG's accessibility, especially in resource-limited settings, corresponding biomarkers could help us to improve outcomes for individuals affected by schizophrenia worldwide.  

I invite you to ask questions about our methodology, our results, and the implications of these findings at our poster. Thank you for your attention! 

Nadia Alam

Slide 1: Title Slide 

Hello everyone. My name is Nadia Alam, and I am a PhD researcher at Warwick Medical School. Today, I will be presenting our study on Digital Phenotyping and Serious Mental Disorders, Predicting Symptom Re-Emergence and Relapse Among Slum Residents in Dhaka, Bangladesh: A Machine Learning Study. This research explores how smartphone data and advanced AI applications can help bridge the gap in mental health care for vulnerable populations. 

Slide 2: Mental Health Crisis in LMIC Slums 

Mental and behavioural disorders account for 12% of the global disease burden, with over 70% of this impact affecting low- and middle-income countries. Nearly 80% of individuals affected by serious mental disorders live in LMICs, where access to treatment remains severely limited. Factors such as rapid urbanisation, extreme poverty, and frequent exposure to trauma exacerbate mental health issues in these settings​. One of the most vulnerable populations within LMICs is slum dwellers. In Dhaka’s Korail slum, which houses approximately 200,000 people, poor living conditions, overcrowding, and inadequate access to healthcare worsen the impact of mental illness​. The lack of formal healthcare infrastructure forces many residents to rely on informal providers, traditional healers, or NGOs for medical care, which often leads to delayed or inappropriate treatment for severe mental disorders​.  A particularly alarming challenge in slum communities is the high rate of relapse in psychotic disorders, such as schizophrenia and bipolar disorder. Relapse often results in hospitalisation, worsening social and economic outcomes, and increased stigma. However, traditional relapse monitoring relies on face-to-face clinical assessments, which are inaccessible, costly, and unsustainable in low resource settings​. Furthermore, Bangladesh has fewer than 0.5 psychiatrists per 100,000 people, making early intervention and continuous care practically impossible for most slum residents. Unfortunately, traditional clinical monitoring methods are expensive, require regular healthcare access, and are unsustainable for slum communities. This highlights the urgent need for scalable and accessible alternatives. 

Slide 3 Digital Phenotyping, LMICs and perceptions 

Digital phenotyping (DP) may solve this problem OR offers a solution, as it refers to the use of smartphone and wearable sensor data to monitor and predict health outcomes. It passively collects data such as movement patterns, screen activity, and communication behaviours, while also incorporating active data like self-reported questionnaires. Once this data is gathered, we can use advanced AI techniques, such as deep learning models to identify subtle behavioural shifts that may precede relapse. In high-income settings, ML models have successfully predicted schizophrenia relapse by analysing digital behaviour changes weeks before a clinical episode. However, these models are developed in contexts with continuous smartphone access and individualised device use. 

To understand how slum residents perceive digital phenotyping, we conducted eight focus group discussions in Korail, Dhaka. Our findings revealed several key insights: 

• Participants were cautious about sharing personal communication data such as the content of messages, but more comfortable with non-intrusive data, app usage patterns and location tracking. 

• Given that smartphones are often shared among family members, participants suggested a family-centered approach to ensure ethical implementation. 

• Many emphasised the importance of educational programs to build trust in DP technologies, indicating that awareness and engagement are critical for adoption. 

These findings highlight the potential of DP to bridge mental health care gaps while emphasising the need for culturally sensitive implementation strategies. 

Slide 4 

Building on the insights from our qualitative work, our next step is to conduct a longitudinal cohort study to assess the feasibility and effectiveness of digital phenotyping (DP) in predicting relapse among individuals with serious mental disorders (SMDs) in Dhaka’s Korail slum. This study will integrate passive smartphone data and active clinical assessments over six months to identify early warning signs of symptom deterioration. 

We will use a bespoke app developed in house, called DataDoc, which will be used to collect both active and passive data from participants: 

Passive data includes mobility patterns (GPS tracking), digital interactions (call/message frequency), phone usage (screen time, app activity), and sleep behaviors—all of which have been associated with changes in mental health status. 

Active data includes self-reported symptom scales, stress levels, and quality-of-life assessments collected via the app and in-person follow-ups. 

Machine learning algorithms will then analyse these data streams to detect subtle changes in behaviour that could indicate impending relapse. By leveraging predictive models trained on historical relapse cases, we aim to develop an early warning system that can alert healthcare providers, caregivers, or the individual themselves before a relapse occurs. 

This real-time, continuous monitoring approach could help mental healthcare in low-resource settings, offering a cost-effective and scalable alternative to traditional clinical monitoring. If successful, this could reduce hospitalisation rates, prevent severe symptom escalation, and improve long-term health outcomes for marginalised populations. 

Ultimately, this study will contribute to understanding how digital phenotyping can be adapted for LMIC contexts while addressing key challenges such as shared phone usage, digital literacy, and ethical considerations. 

Natasha Chilman

TRANSCRIPT 

Hello, my name is Dr Natasha Chilman, I’m from King’s College London, and I’m going to tell you about my SIRS talk which is taking place on Tuesday, where I will be presenting our study on inequities in mortality following a COVID-19 infection for people with severe mental illness. 

The COVID-19 pandemic highlighted health disparities faced by people with severe mental illness, which includes people schizophrenia, bipolar disorder, and other non-organic psychoses. Concerningly, international studies have shown that people with severe mental illness experienced excess mortality following a COVID-19 infection compared to people without severe mental illness. However, the problem with our current evidence-base is that most of these studies were conducted during the pandemic or in the very immediate aftermath, which means follow-ups tend to be limited and focused on early stages of the pandemic.  

This matters, because we now live in a post-COVID-19-pandemic era, where vaccines have been developed and rolled out, and there are no restrictions. However we do not know whether mortality inequities for people with severe mental illnesses stayed the same, got worse, or better after the public health emergency phase and after the vaccination roll-out. Investigating mortality trends throughout and beyond the pandemic is also critical to ensuring an equitable response to future public health crises. 

Our study aimed to fill this gap by investigating mortality for people with SMI across different stages of the COVID-19 pandemic and beyond. 

In my talk I will describe how we used an innovative whole-country data-linkage for our study. 

The British Heart Foundation Data Science Centre CVD-COVID-UK dataset includes all people registered in primary care services in England since November 2019, which includes over 58-million people. The primary care dataset is linked to multiple other population-level datasets, including hospital records, COVID-19 testing centres, COVID-19 vaccination records, and mortality records. The data is de-identified for access and analysis by approved researchers in a Secure Data Environment.  

We analysed the records of over 13-million people who had positive covid-19 test or diagnosis between January 2020 and June 2023, to investigate mortality for people with severe mental illness at a scale which has not been possible in England until now. 

Understanding inequities for people with severe mental illness throughout and since the COVID-19 pandemic is essential for our present and for our future. There are no spoilers here on our findings – but please join us on Tuesday to hear more about what we did and what we found! I look forward to meeting you. 

Perry Bok Man Leung

00:00 

Unknown speaker 

Hi everyone, I'm Perry. At the first Congress this year, I'm presenting my ongoing work titled Neutral Exposures to Psychotropics and the Risk of Developing Neurodevelopmental Disorders, which is a population-based retrospective birth cohort study in Hong Kong.  

00:15 

Unknown speaker 

You may ask what the importance of this work are. First, studies show that over 10% of pregnant women were exposed to psychotropics. A study from Netherland showed maternal and psychotic exposures had almost doubled in 2010s compared to 2000s.  

00:31 

Unknown speaker 

Although there are a lot of evidence on changes in pharmacokinetics of psychotropics in pregnant women, drug trials were rarely done due to ethical reasons. This study aimed to test whether psychotropic drugs or underlying severe mental illnesses could increase risk of neurodevelopmental disorders in offsprings.  

00:51 

Unknown speaker 

I used electronic health records in the public healthcare service in Hong Kong with over 360,000 singleton births who were born between 2004 and 2015 and followed them retrospectively until 2022. Birth episodes with prenatal exposure to psychotropics, including antipsychotics, antidepressants, anticonfulsants, and benzodiazepines were identified and classified into exposures of each drug class, such as typical and atypical antipsychotics,  

01:21 

Unknown speaker 

SSRI and SNRI, etc. Maternal family history of schizophrenia, major depression, and bipolar disorders are adjusted. My outcomes include eight common neurodevelopmental disorders, and I used some sophisticated study design, like negative control analysis and sibling smash designs, which can compare only siblings with different prenatal exposures to account for maternal family factors, like underlying health and maternal genetic influences.  

01:54 

Unknown speaker 

My results will have important clinical implications. First, assessing the safety of drug use is vital. Discontinuing the drugs may trigger relapses in schizophrenia and other severe mental illnesses, and switching to drugs that have evidence on safety could be a better option.  

02:12 

Unknown speaker 

Second, there are increased trends in the prenatal use of psychotropic drugs and incidence of neurodevelopmental disorders, making it important to study if there is any link between them. Lastly, identifying risk factors allows clinicians and healthcare workers to have informed choices.  

02:28 

Unknown speaker 

Proactive care at early stage can lead to better treatment outcomes. Thank you for listening. If you are interested in knowing the results, please come talk to me during the post-session on Sunday. I look forward to meeting all of you.  

02:41 

Unknown speaker 

Thank you. 

Pilar Torrecilla

Transcript 

Hello everyone, my name is Pilar Torrecilla and I am honored to present this talk as an early career awardee at the 2025 SIRS conference about predictive validity of psychotic like experiences in daily life 8 years later. 

Imagine being able to track the earliest signs of psychosis as they unfold in real-time, in everyday life. Psychosis is not an all-or-nothing phenomenon; it exists on a continuum, from subtle and transient psychotic-like experiences to full-blown psychotic disorders, such as schizophrenia. But how do we measure this dynamic process as it happens? 

Our research employs Experience Sampling Methodology (or ESM)— ESM is a real-time assessment technique that prompts individuals to report their symptoms and experiences throughout the day.  Unlike traditional assessments, ESM minimizes retrospective bias and allows an in-depth look at risk and resilience factors in real time and in real life, thus enhancing ecological validity.   

In our study, the Barcelona Longitudinal Investigation of Schizotypy (BLISS) study, we tracked college students for nearly eight years. We found that positive and negative schizotypy traits measured at time 1 were associated with psychotic manifestations in daily life 8 years later but also, that psychotic manifestations in daily life measured at time 2, predicted the presence of similar manifestations 6 years later. Finally, we found that some of these associations were moderated by high genetic susceptibility related to psychosis proneness. 

These findings validate the assessment of psychotic experiences in daily life and support the relevance of integrating ESM into research and clinical practice to a) improve early detection of psychosis risk, b) to develop personalized interventions and c) even to help us to further understand the influence of genetic susceptibility and environmental factors in psychosis.  

If you are interested in this research and would like to know more about it, please join us at the ‘At risk population and risk predictions’ oral session on Monday at 3.45pm. 

Thank you so much.