Alex Leow

Digital phenotyping, which is defined as quantifying someone’s behaviour with digital devices, provides unprecedented opportunities for understanding human mental health but is hampered by high levels of inter-individual variability. Here, we propose a new method to address this, parsing inter-individual variability by decomposing the digital phenotype dynamics into latent trajectories and using each individual’s trajectory membership as a moderator when modelling psychopathology over the same timeframe. We applied our method in the context of mood symptom exacerbation across the menstrual cycle, where symptom severity and timing are inconsistent between individuals. Using the BiAffect platform to collect smartphone typing dynamics, we found stable trajectories in smartphone movement rate: one group of participants showed substantial movement rate fluctuations across the menstrual cycle, whilst the others did not. Participants with movement fluctuations displayed increased fluctuations across the cycle in prospective anhedonia and depression ratings, but not in anxiety, irritability, and suicidal ideation.

Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing multimodal approaches struggle to align these heterogeneous signals. We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting to jointly model structural MRI and single nucleotide polymorphisms (SNPs) variations. By representing each anatomically parcellated brain region as a visual token and encoding SNP profiles as structured text, the framework enables cross-modal attention that links regional atrophy patterns to underlying genetic factors. Applied to the ADNI cohort, R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition (NC), subjective memory concerns (SMC), mild cognitive impairment (MCI), and AD. Beyond predictive accuracy, the model yields biologically meaningful explanations by identifying stage-specific brain regions and gene signatures, as well as coherent ROI-Gene association patterns across the disease continuum. Attention-based attribution revealed genes consistently enriched for established GWAS-supported AD risk loci, including APOE, BIN1, CLU, and RBFOX1. Stage-resolved neuroanatomical signatures identified shared vulnerability hubs across disease stages alongside stage-specific patterns: striatal involvement in subjective decline, frontotemporal engagement during prodromal impairment, and consolidated multimodal network disruption in AD. These results demonstrate that interpretable multimodal AI can synthesize imaging and genetics to reveal mechanistic insights, providing a foundation for clinically deployable tools that enable earlier risk stratification and inform precision therapeutic strategies in Alzheimer's disease.

Background: Depression, a prevalent mental health condition, affects millions worldwide. Many face significant barriers to accessing effective therapy. Metaverse has become a promising platform for addressing these challenges. Its 3D virtual environments allow users to engage through customizable avatars, supporting the elicitation of therapy-related emotions, an essential component of therapeutic interventions. Objective: This pilot study investigates the potential of the Metaverse to elicit therapy-related emotional responses in individuals experiencing depression. The objectives are to assess emotional arousal, mental workload (MWL), and affective engagement during an emotional elicitation task in a Metaverse environment and compare them with those observed in a similar real-world setting. Methods: We conducted a between-subjects experiment involving 28 participants: 14 had self-reported depression symptoms; 14 had minimal or no depression. Participants were assigned to a real-world environment or a Metaverse environment. Second Life (SL), a persistent 3D virtual world that utilizes human-like avatars for navigation, was the Metaverse environment. A desktop computer represented the real-world setting. Participants watched emotion-eliciting video clips to evoke amusement, anger, disgust, fear, and sadness. They reported their emotional arousal using the 16-item self-report inventory, MWL using the NASA Task Load Index, and emotional engagement using the Positive and Negative Affect Schedule. Results: Regardless of the environment, participants with self-reported depression symptoms reported significantly higher amusement ( F _1,24 =4.882, P =.04; self-reported depression: mean 5.00, SD 2.481; minimal or no depression: mean 3.07, SD 1.940), anger ( F _1,24 =7.067, P =.01; self-reported depression: mean 4.79, SD 2.190; minimal or no depression: mean 2.71, SD 1.899), sadness ( F _1,24 =4.353, P =.048; self-reported depression: mean 5.21, SD 2.293; minimal or no depression: mean 3.50, SD 1.912), and perceived performance ( F _1,24 =4.939, P =.04; self-reported depression: mean 91.07, SD 90.302; minimal or no depression: mean 31.07, SD 36.278) than those with minimal or no depression. Participants with minimal or no depression reported lower perceived effort in the SL environment than in the real-world setting (SL: mean 40.71, SD 38.016; real world: mean 158.571, SD 120.411; P =.02), indicating Metaverse’s potential to alleviate cognitive stress during emotionally eliciting tasks. There were no significant differences in perceived MWL or emotional engagement between the real-world and Metaverse settings. Conclusions: Individuals with self-reported depression symptoms exhibited heightened emotional responses across environmental settings, indicating the Metaverse may potentially elicit emotional arousal comparable to that in traditional settings. Participants with minimal or no depression symptoms reported lower perceived effort in the Metaverse, suggesting that virtual environments may reduce cognitive strain during emotionally evocative tasks. The lack of significant differences in emotional arousal, overall MWL, or emotional engagement across environments tentatively suggests that the Metaverse may deliver emotional experiences like those of real-world contexts. Collectively, this provides preliminary evidence that screen-based Metaverse platforms can elicit emotional responses like those commonly engaged during therapeutic depression interventions.

Diurnal rhythms are an integral feature of psychopathology but difficult to measure at scale. Smartphones are ubiquitous and therefore uniquely positioned to measure such rhythms non-invasively and continuously. Here, we propose a digital phenotyping framework to quantify diurnal rhythms. We use it to predict sleep duration from smartphone typing dynamics and analyse rhythm phase during time zone transitions with a clinical outpatient sample and a year-long longitudinal data set.

Neuronal hyperexcitation affects memory and neural processing across the Alzheimer’s disease (AD) cognitive continuum. Levetiracetam, an antiepileptic, shows promise in improving cognitive impairment by restoring the neural excitation/inhibition balance in AD patients. We previously identified a hyper-excitable phenotype in cognitively unimpaired female APOE-ε4 carriers relative to male counterparts cross-sectionally. This sex difference lacks longitudinal validation; however, clarifying the vulnerability of female ε4-carriers could better inform antiepileptic treatment efficacy. Here, we investigated this sex-by-ε4 interaction using a longitudinal design. We used resting-state fMRI and diffusion tensor imaging collected longitudinally from 106 participants who were cognitively unimpaired for at least one scan event but may have been assessed to have clinical dementia ratings corresponding to early mild cognitive impairment over time. By including scan events where participants transitioned to mild cognitive impairment, we modeled the trajectory of the whole-brain excitation-inhibition ratio throughout the preclinical cognitively healthy continuum and extended to early impairment. A linear mixed model revealed a significant three-way interaction among sex, ε4-status, and time, with female ε4-carriers showing a significant hyper-excitable trajectory. These findings suggest a possible pathway for preventative therapy targeting preclinical hyperexcitation in female ε4-carriers.

As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases.

Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. Methods: Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants’ compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. Results: We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). Conclusions: This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms.

Mental health disorders, such as depression and anxiety, are highly prevalent during pregnancy and postpartum and can have severe consequences on maternal morbidity and mortality, and on infant neurodevelopment. Yet the neurobiological mechanisms of these disorders are underexplored. Critically absent are longitudinal studies of mental health and brain alterations across the entire perinatal period using electroencephalography (EEG). In this study, a racially, ethnically, and socioeconomically diverse perinatal cohort (n=61, 45% Non-Hispanic Black, 32% Hispanic, 65% public insurance) was followed bimonthly from early pregnancy through postpartum. At each visit, participants completed an 8-minute closed-eye resting state EEG recording and self-reported validated questionnaires to assess depression and anxiety symptoms. Linear regression models showed EEG-measured relative delta waves were negatively associated with both depression and anxiety severity in the first trimester and postpartum. Alpha waves were positively associated with elevated symptoms of anxiety at postpartum. Early pregnancy alpha and delta waves were also associated with late third trimester pregnancy and postpartum depression and anxiety. This study is the first to report that specific brain waves are associated with mood and anxiety disorders during perinatal periods characterized by large reproductive hormonal fluctuations. This work is an initial step to understanding how changes in dynamic brain activity during the perinatal period may be used as possible unbiased markers of perinatal mood and anxiety disorders.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.e., incorrect No-Go trials) with respect to diurnal and potential learning effects. Mixed-effects models showed diurnal effects in reaction time but not accuracy, with a significant effect of hour on reaction time in the clinical sample and the open-science sample. Moreover, subjects improved on their response inhibition but not reaction time. Additionally, significant interactions emerged between depression symptom severity and time-of-day in both samples, supporting that repeated administration of our GNG task can yield mood-dependent circadian rhythm-aware biomarkers of neurocognitive function.

Background: Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model–based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health–related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system—namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)—by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories.