Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

Genotype-dependent functional role of the anterior and posterior paraventricular thalamus in pavlovian conditioned approach

RATIONALE

The specific location of deviations from normative models of brain function varies considerably across individuals with the same diagnoses. However, as pathological processes are distributed across interconnected systems, this heterogeneity of individual brain deviations may also reveal similarities and differences between disorders. The paraventricular nucleus of the thalamus (PVT) is a potential switcher to various behavioral responses where functionally distinct cell types exist across its antero-posterior axis.

OBJECTIVES

This study aimed to test the hypothesis that genotype-dependent differences in the anterior and posterior PVT subregions (aPVT and pPVT) are involved in the Sign-tracking (ST) behavior expressed by C57BL/6J (C57) and DBA/2J (DBA) inbred mice.

METHODS

Based on previous findings, male mice of the two strains were tested at ten weeks of age. The density of c-Fos immunoreactivity along the antero-posterior axis of PVT was assessed following the expression of ST behavior. Selective excitotoxic lesions of the aPVT or the pPVT by the NMDA infusion were performed prior to development of ST behavior. Finally, the distribution of neuronal populations expressing the Drd2 and Gal genes (D2R + and Gal +) was measured by in situ hybridization (ISH).

RESULTS

The involvement of PVT subregions in ST behavior is strain-specific, as aPVT is crucial for ST acquisition in DBA mice while pPVT is crucial for C57 mice. Despite similar antero-posterior distribution of D2R + and Gal + neurons, density of D2R + neurons differentiate aPVT in C57 and DBA mice.

CONCLUSIONS

These genotype-dependent results offer valuable insights into the nuanced organization of brain networks and individual variability in behavioral responses.

Sample size matters when estimating test-retest reliability of behaviour

Intraclass correlation coefficients (ICCs) are a commonly used metric in test-retest reliability research to assess a measure's ability to quantify systematic between-subject differences. However, estimates of between-subject differences are also influenced by factors including within-subject variability, random errors, and measurement bias. Here, we use data collected from a large online sample (N = 150) to (1) quantify test-retest reliability of behavioural and computational measures of reversal learning using ICCs, and (2) use our dataset as the basis for a simulation study investigating the effects of sample size on variance component estimation and the association between estimates of variance components and ICC measures. In line with previously published work, we find reliable behavioural and computational measures of reversal learning, a commonly used assay of behavioural flexibility. Reliable estimates of between-subject, within-subject (across-session), and error variance components for behavioural and computational measures (with ± .05 precision and 80% confidence) required sample sizes ranging from 10 to over 300 (behavioural median N: between-subject = 167, within-subject = 34, error = 103; computational median N: between-subject = 68, within-subject = 20, error = 45). These sample sizes exceed those often used in reliability studies, suggesting that sample sizes larger than are commonly used for reliability studies (circa 30) are required to robustly estimate reliability of task performance measures. Additionally, we found that ICC estimates showed highly positive and highly negative correlations with between-subject and error variance components, respectively, as might be expected, which remained relatively stable across sample sizes. However, ICC estimates were weakly or not correlated with within-subject variance, providing evidence for the importance of variance decomposition for reliability studies.

Optimizing working memory assessment: development of shortened versions of complex spans, updating, and binding tasks

Given the lengthy administration of most working memory (WM) tasks, some researchers have developed reduced versions of these tests. However, they have focused primarily on complex spans. Recent studies suggested that estimating working memory capacity (WMC) using multiple tasks from different paradigms enhances measurement accuracy by isolating WMC variation from task- and paradigm-specific influences. Considering this, we evaluated whether complex spans, updating, and binding tasks could be shortened while maintaining robust psychometric properties. Participants completed full-length versions of tests from these paradigms, which were then segmented into early, intermediate, and later trial blocks. The shortened WM tasks were based on the early trial blocks. They accounted for most of the variance in a set of factor scores derived from the full-length versions of the WM tests (R2 = 0.90). Additionally, the shortened and full-length versions presented a similar ability to predict fluid intelligence (Gf). The shortened tasks reduced administration time by 35%, saving around 30 min. To help researchers select the most suitable combination of shortened and/or full-length tasks, we calculated the Gf and WMC variance predicted by every possible task combination and the respective administration time. We believe that the shortened WM tasks will be highly valuable to researchers, as they provide reliable and valid WMC estimates in a time-efficient manner. We also examined whether using tests from different paradigms provides better WMC estimates than employing collections of tasks from the same class. Our results confirmed this hypothesis, highlighting the importance of diverse task selection to accurately assess WMC.

Variation in brain aging: A review and perspective on the utility of individualized approaches to the study of functional networks in aging

Healthy aging is associated with cognitive decline across multiple domains, including executive function, memory, and attention. These cognitive changes can often influence an individual's ability to function and quality of life. However, the degree to which individuals experience cognitive decline, as well as the trajectory of these changes, exhibits wide variability across people. These cognitive abilities are thought to depend on the coordinated activity of large-scale networks. Like behavioral effects, large variation can be seen in brain structure and function with aging, including in large-scale functional networks. However, tracking this variation requires methods that reliably measure individual brain networks and their changes over time. Here, we review the literature on age-related cognitive decline and on age-related differences in brain structure and function. We focus particularly on functional networks and the individual variation that exists in these measures. We propose that novel individual-centered fMRI approaches can shed new light on patterns of inter- and intra-individual variability in aging. These approaches may be instrumental in understanding the neural bases of cognitive decline.

Trait state occasion (TSO) modeling of event-related potentials (ERPs)

Brain-based markers of psychopathology reflect risk factors for future mental illness or indicators of current disease states. One solution to differentiating trait-like risk factors from indicators of disease states is trait-state-occasion (TSO) modeling, a novel structural equation model that uses repeated observations to parse variance due to stable factors (i.e., trait) from that due to momentary changes (i.e., state). To date, TSO models have largely been applied to self-report data, with only a handful of studies applying TSO models to psychophysiological markers. Importantly, these psychophysiological studies have only applied TSO models to resting-state activity, making this the first study to model psychophysiological responses to stimuli in this way. This study conducted a "proof-of-concept" to examine trait- and state-variance in event-related potential (ERP) responses (specifically, startle-elicited N1 and P3 ERPs) to unpredictable threat in 83 adults across three time-points. TSO models were applied for the following condition contrasts: unpredictable shock>no shock and unpredictable shock>predictable shock. TSO models fit well for the N1 and P3 for both condition contrasts. In comparison to responses to no shock and predictable shock, respectively, the N1 and P3 to unpredictable threat showed substantial trait variance (N1 = 66 % & 84 %, P3 = 69 % & 71 %), less state residual variance (N1 = 32 % & 15 %, P3 = 28 % & 25 %) variance, and little autoregressive variance (N1 = 3 % & 2 %, P3 = 4 % & 6 %). Longitudinal modeling of task-based brain data can elucidate novel findings regarding the relative contribution of trait-/state-factors of biomarkers reflecting responses to stimuli.

Group-to-individual generalizability and individual-level inferences in cognitive neuroscience

Much of cognitive neuroscience research is focused on group-averages and interindividual brain-behavior associations. However, many theories core to the goal of cognitive neuroscience, such as hypothesized neural mechanisms for a behavior, are inherently based on intraindividual processes. To accommodate this mismatch between study design and theory, research frequently relies on an implicit assumption that group-level, between-person inferences extend to individual-level, within-person processes. The assumption of group-to-individual generalizability, formally referred to as ergodicity, requires that a process be both homogenous within a population and stationary within individuals over time. Our goal in this review is to assess this assumption and provide an accessible introduction to idiographic science (study of the individual) for the cognitive neuroscientist, ultimately laying a foundation for increased focus on the study of intraindividual processes. We first review the history of idiographic science in psychology to connect this longstanding literature with recent individual-level research goals in cognitive neuroscience. We then consider two requirements of group-to-individual generalizability, pattern homogeneity and stationarity, and suggest that most processes in cognitive neuroscience do not meet these assumptions. Consequently, interindividual findings are inappropriate for the intraindividual inferences that many theories are based on. To address this challenge, we suggest precision imaging as an ideal path forward for intraindividual study and present a research framework for complementary interindividual and intraindividual study.

Functional brain connectivity changes associated with day-to-day fluctuations in affective states

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models' generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

Reduced hippocampal volume unmasks distinct impacts of cumulative adverse childhood events (ACEs) on psychotic-like experiences in late childhood and early adolescence

Stress is associated with increased vulnerability to psychosis, yet the mechanisms that contribute to these effects are poorly understood. Substantial literature has linked reduced hippocampal volume to both psychosis risk and early life stress. However, less work has explored the direct and indirect effects of stress on psychosis through the hippocampus in preclinical samples- when vulnerability for psychosis is accumulating. The current paper leverages the Adolescent Brain Cognitive Development (ABCD) Study sample to examine whether objective psychosocial stressors, specifically adverse childhood experiences (ACE), are linked to vulnerability for psychosis, measured by psychotic-like experiences (PLE) severity, in late childhood and early adolescence, both directly and indirectly through the deleterious effects of stress on the hippocampus. Baseline data from 11,728 individuals included previously examined and validated items to assess ACE exposure, hippocampal volume, and PLE severity - a developmentally appropriate metric of risk for psychosis. Objective psychosocial stress exposure in childhood was associated with elevated PLE severity during the transition from childhood to adolescence. Hippocampal volume was significantly reduced in individuals with greater PLE severity and greater childhood stress exposure compared to peers with low symptoms or low stress exposure. These findings are consistent with a hippocampal vulnerability model of psychosis risk. Stress exposure may cumulatively impact hippocampal volume and may also reflect a direct pathway of psychosis risk. Objective psychosocial stress should be considered as a treatment target that may impact neurodevelopment and psychosis risk.

Quality over quantity: powering neuroimaging samples in psychiatry

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Dynamic Real-Time Biosensing Enabled Biorhythm Tracking for Psychiatric Disorders

This review article explores the transformative potential of dynamic, real-time biosensing in biorhythm tracking for psychiatric disorders. Psychiatric diseases, characterized by a complex, heterogeneous, and multifactorial pathophysiology, pose challenges in both diagnosis and treatment. Common denominators in the pathophysiology of psychiatric diseases include disruptions in the stress response, sleep-wake cycle, energy metabolism, and immune response: all of these are characterized by a strong biorhythmic regulation (e.g., circadian), leading to dynamic changes in the levels of biomarkers involved. Technological and practical limitations have hindered the analysis of such dynamic processes to date. The integration of biosensors marks a paradigm shift in psychiatric research. These advanced technologies enable multiplex, non-invasive, and near-continuous analysis of biorhythmic biomarkers in real time, overcoming the constraints of conventional approaches. Focusing on the regulation of the stress response, sleep/wake cycle, energy metabolism, and immune response, biosensing allows for a deeper understanding of the heterogeneous and multifactorial pathophysiology of psychiatric diseases. The potential applications of nanobiosensing in biorhythm tracking, however, extend beyond observation. Continuous monitoring of biomarkers can provide a foundation for personalized medicine in Psychiatry, and allow for the transition from syndromal diagnostic entities to pathophysiology-based psychiatric diagnoses. This evolution promises enhanced disease tracking, early relapse prediction, and tailored disease management and treatment strategies. As non-invasive biosensing continues to advance, its integration into biorhythm tracking holds promise not only to unravel the intricate etiology of psychiatric disorders but also for ushering in a new era of precision medicine, ultimately improving the outcomes and quality of life for individuals grappling with these challenging conditions.

Psychiatric neuroimaging designs for individualised, cohort, and population studies

Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.

Toward actionable neural markers of depression risk?

The search for neural markers of depression remains challenging. Despite progress, neuroimaging results have generally not yielded actionable findings that could transform how we understand and treat this disorder. However, in a recent study, Lynch and colleagues identified enlargement of the frontrostriatal salience network as a reproducible, trait-like marker of depression.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Towards personalized precision functional mapping in infancy

The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Accurate Machine Learning Prediction in Psychiatry Needs the Right Kind of Information

This Viewpoint discusses the type and amount of data needed for machine learning models to accurately predict diagnoses and treatment outcomes at the individual patient level.

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.