Psychiatric Neuroimaging Research Articles

Psychiatric Neuroimaging at a Crossroads: Insights from Psychiatric Genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is well-equipped to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide “eras” of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and other omics approaches. The evolution of psychiatric genetic research offers valuable lessons that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

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.

Continuous Contributions to Psychiatric Neuroimaging Through the ENIGMA Consortium

Continuous Contributions to Psychiatric Neuroimaging Through the ENIGMA Consortium

Attention-deficit hyperactivity disorder symptoms and brain morphology: Addressing potential selection bias with inverse probability weighting.

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9-11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI.

Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses. Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses. Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise. Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

Clinical Utility of Psychiatric Neuroimaging: Exploring Psychoradiology and Use of Machine Learning in Psychoradiology

Clinical Utility of Psychiatric Neuroimaging: Exploring Psychoradiology and Use of Machine Learning in Psychoradiology

Tailoring Psychiatric Neuroimaging to Translational Goals

This Viewpoint discusses tailoring psychiatric neuroimaging paradigms toward clear translational and practical end goals in an effort to focus resources, improve reproducibility, and accelerate neuroimaging’s clinical utility.

Applying Functional Imaging to Clinical Practice: Are We Making Progress Toward Its Promise?

Applying Functional Imaging to Clinical Practice: Are We Making Progress Toward Its Promise?

Contemporary Approaches Toward Neuromodulationof Fear Extinction and Its Underlying Neural Circuits.

Neuroscience and neuroimaging research have now identified brain nodes that are involved in the acquisition, storage, and expression of conditioned fear and its extinction. These brain regions include the ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), amygdala, insular cortex, and hippocampus. Psychiatric neuroimaging research shows that functional dysregulation of these brain regions might contribute to the etiology and symptomatology of various psychopathologies, including anxiety disorders and post traumatic stress disorder (PTSD) (Barad et al. Biol Psychiatry 60:322-328, 2006; Greco and Liberzon Neuropsychopharmacology 41:320-334, 2015; Milad et al. Biol Psychiatry 62:1191-1194, 2007a, Biol Psychiatry 62:446-454, b; Maren and Quirk Nat Rev Neurosci 5:844-852, 2004; Milad and Quirk Annu Rev Psychol 63:129, 2012; Phelps et al. Neuron 43:897-905, 2004; Shin and Liberzon Neuropsychopharmacology 35:169-191, 2009). Combined, these findings indicate that targeting the activation of these nodes and modulating their functional interactions might offer an opportunity to further our understanding of how fear and threat responses are formed and regulated in the human brain, which could lead to enhancing the efficacy of current treatments or creating novel treatments for PTSD and other psychiatric disorders (Marin et al. Depress Anxiety 31:269-278, 2014; Milad et al. Behav Res Ther 62:17-23, 2014). Device-based neuromodulation techniques provide a promising means for directly changing or regulating activity in the fear extinction network by targeting functionally connected brain regions via stimulation patterns (Raij et al. Biol Psychiatry 84:129-137, 2018; Marković et al. Front Hum Neurosci 15:138, 2021). In the past ten years, notable advancements in the precision, safety, comfort, accessibility, and control of administration have been made to the established device-based neuromodulation techniques to improve their efficacy. In this chapter we discuss ten years of progress surrounding device-based neuromodulation techniques-Electroconvulsive Therapy (ECT), Transcranial Magnetic Stimulation (TMS), Magnetic Seizure Therapy (MST), Transcranial Focused Ultrasound (TUS), Deep Brain Stimulation (DBS), Vagus Nerve Stimulation (VNS), and Transcranial Electrical Stimulation (tES)-as research and clinical tools for enhancing fear extinction and treating PTSD symptoms. Additionally, we consider the emerging research, current limitations, and possible future directions for these techniques.

NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.

For better translational outcomes, researchers and clinicians alike demand novel tools to distill complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper—designed specifically for neuroimaging data—that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic “ongoing” cognition. Looking forward, we hope our framework will help researchers push the boundaries of psychiatric neuroimaging toward generating insights at the single-participant level across consortium-size datasets.

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities.

The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

Narrative Devices: Neurotechnologies, Information, and Self-Constitution

This article provides a conceptual and normative framework through which we may understand the potentially ethically significant roles that information generated by neurotechnologies about our brains and minds may play in our construction of our identities. Neuroethics debates currently focus disproportionately on the ways that third parties may (ab)use these kinds of information. These debates occlude interests we may have in whether and how we ourselves encounter information about our own brains and minds. This gap is not yet adequately addressed by most allusions in the literature to potential identity impacts. These lack the requisite conceptual or normative foundations to explain why we should be concerned about such effects or how they might be addressed. This article seeks to fill this gap by presenting a normative account of identity as constituted by embodied self-narratives. It proposes that information generated by neurotechnologies can play significant content-supplying and interpretive roles in our construction of our self-narratives. It argues, to the extent that these roles support and detract from the coherence and inhabitability of these narratives, access to information about our brains and minds engages non-trivial identity-related interests. These claims are illustrated using examples drawn from empirical literature reporting reactions to information generated by implantable predictive BCIs and psychiatric neuroimaging. The article concludes by highlighting ways in which information generated by neurotechnologies might be governed so as to protect information subjects’ interests in developing and inhabiting their own identities.

Narrative Devices: Neurotechnologies, Information, and Self-constitution

This article provides a conceptual and normative framework through which we may understand the potentially ethically significant roles that information generated by neurotechnologies about our brains and minds may play in our construction of our identities. Neuroethics debates currently focus disproportionately on the ways that third parties may (ab)use these kinds of information and, in particular, putative threats to ‘neuro-privacy’. These debates occlude interests we may have in whether and how we ourselves encounter information about our own brains and minds. This gap is not yet adequately addressed by most allusions in the literature to potential identity impacts. These lack the requisite conceptual or normative foundations to explain why we should be concerned about such effects or how they might be addressed. This article addresses this gap by presenting a normative account of identity as constituted by embodied self-narratives. It proposes that information generated by neurotechnologies can play significant content-supplying and interpretive roles in our construction of our self-narratives. It argues, to the extent that these roles support and detract from the coherence and inhabitability of our self-narratives, access to information about our brains and minds engages non-trivial identity-related interests. These claims are illustrated using examples drawn from empirical literature reporting reactions to information generated by implantable predictive BCIs and psychiatric neuroimaging. The article concludes by highlighting ways in which information generated by neurotechnologies should be governed so as to protect information subjects’ interests in developing and inhabiting their own identities.

Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology.

To accurately detect, track progression of, and develop novel treatments for mental illnesses, a diagnostic framework is needed that is grounded in biological features. Here, we present the case for utilizing personalized neuroimaging, computational modeling, standardized computing, and ecologically valid neuroimaging to anchor psychiatric nosology in biology.

Advances in neurodegenerative and psychiatric imaging: introductory editorial.

Advances in neurodegenerative and psychiatric imaging: introductory editorial.

A Reckoning and Research Agenda for Neuroimaging in Psychiatry.

Human neuroimaging has been a core component of both research in psychiatry and conceptual models of the brain circuit-level mechanisms underlying psychopathology. Despite landmark neuroimaging research over the past 25 years, we still lack the level of precision and insight needed for bringing neuroimaging tools into clinical care contexts. This brief review examines historical research trends in psychiatric neuroimaging, as well as the basic assumptions underlying current efforts, in order to understand factors that have limited the impact of neuroimaging efforts thus far. These factors include the pitfalls of case-control designs, confounders inherent in associational research approaches, and the challenges in embracing fully data-driven analyses. Several critical gaps emerge, the addressing of which could provide the critical new insights that have long been sought from neuroimaging. These include transitioning from group-to individual-level analyses (and through this to intervention studies carried out robustly at the level of individual patients), building "big data" from a longitudinal perspective and not only a cross-sectional one, a greater focus on identifying causal mechanisms, and the development of tools such as electroencephalography in addition to the dominant MRI methods to aid translation to real-world clinical care. Despite the still-unrealized potential of psychiatric neuroimaging, there is now much to be excited about as previous learnings are converted into fundamentally new directions. Indeed, we may now be at an inflection point for neuroimaging if the typical study designs are left in the past and the field systematically and thoughtfully embraces the challenges that the past 25 years of research have now made apparent.

Progress in psychoradiology, the clinical application of psychiatric neuroimaging.

Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.

T152. Optimized Volume Censoring in Simultaneous Multi-Slice (Multiband) Resting-State fMRI

Simultaneous multi-slice (multiband) fMRI is a novel acceleration technique that allows for dramatically improved spatiotemporal resolution. However, BOLD signal artifacts due to motion remain a critical confound in studies of functional connectivity that must be addressed to produce reliable and reproducible findings in psychiatric neuroimaging. While volume censoring of high-motion volumes (scrubbing) is an effective technique, empirically-validated bases for determining optimal censoring thresholds are unclear, particularly in multiband fMRI.

Cerebellar-Prefrontal Network Connectivity and Negative Symptoms in Schizophrenia.

The interpretability of results in psychiatric neuroimaging is significantly limited by an overreliance on correlational relationships. Purely correlational studies cannot alone determine whether behavior-imaging relationships are causal to illness, functionally compensatory processes, or purely epiphenomena. Negative symptoms (e.g., anhedonia, amotivation, and expressive deficits) are refractory to current medications and are among the foremost causes of disability in schizophrenia. The authors used a two-step approach in identifying and then empirically testing a brain network model of schizophrenia symptoms. In the first cohort (N=44), a data-driven resting-state functional connectivity analysis was used to identify a network with connectivity that corresponds to negative symptom severity. In the second cohort (N=11), this network connectivity was modulated with 5 days of twice-daily transcranial magnetic stimulation (TMS) to the cerebellar midline. A breakdown of connectivity in a specific dorsolateral prefrontal cortex-to-cerebellum network directly corresponded to negative symptom severity. Restoration of network connectivity with TMS corresponded to amelioration of negative symptoms, showing a statistically significant strong relationship of negative symptom change in response to functional connectivity change. These results demonstrate that a connectivity breakdown between the cerebellum and the right dorsolateral prefrontal cortex is associated with negative symptom severity and that correction of this breakdown ameliorates negative symptom severity, supporting a novel network hypothesis for medication-refractory negative symptoms and suggesting that network manipulation may establish causal relationships between network markers and clinical phenomena.

Theoretical Approaches of Machine Learning to Schizophrenia

Machine learning techniques have been successfully used to analyze neuroimaging data in the context of disease diagnosis in recent years. In this study, we present an overview of contemporary support vector machine-based methods developed and used in psychiatric neuroimaging for schizophrenia research. We focus in particular on our group's algorithms, which have been used to categorize schizophrenia patients and healthy controls, and compare their accuracy findings to those of other recently published studies. First, we'll go over some basic pattern recognition and machine learning terms. Then, for each study, we describe and discuss it independently, emphasizing the key characteristics that distinguish each approach. Finally, conclusions are reached as a result of comparing the data obtained using the various methodologies presented to determine how beneficial automatic categorization systems are in understanding the molecular underpinnings of schizophrenia. The primary implications of applying these approaches in clinical practice are then discussed.