Human brain state dynamics are highly reproducible and associated with neural and behavioral features.

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Unveiling the Structure in Mental Disorder Presentations.

Importance: DSM criteria are polythetic, allowing for heterogeneity of symptoms among individuals with the same disorder. In empirical research, most combinations were not found or only rarely found, prompting criticism of this heterogeneity. Objective: To elaborate how symptom-based definitions and assessments contribute to a distinct probability pattern for the occurrence of symptom combinations. Design, Setting, and Participants: This cross-sectional study involved a theoretical argument, simulation, and secondary data analysis of 4 preexisting datasets, each consisting of symptoms from 1 of the following syndromes: posttraumatic stress disorder, depression, schizophrenia, and anxiety. Data were obtained from various sources, including the National Institute of Mental Health Data Archive and Department of Veteran Affairs. A total of 155 474 participants were included (individual studies were 3930 to 63 742 individuals in size). Data were analyzed between July 2021 and January 2024. Exposure: For each participant, the presence or absence of each assessed symptom and their combination was determined. The number of all combinations and their individual frequencies were assessed. Main Outcome and Measure: Probability or frequency of unique symptom combinations and their distribution. Results: Among the 155 474 participants, the mean (SD) age was 47.5 (14.8) years; 33 933 (21.8%) self-identified as female and 121 541 (78.2%) as male. Because of the interrelation between symptoms, some symptom combinations were significantly more likely than others. The distribution of the combinations' probability was heavily skewed with most combinations having a very low probability. Across all 4 empirical samples, the 1% most common combinations were prevalent in a total of 33.1% to 78.6% of the corresponding sample. At the same time, many combinations (ranging from 41.7% to 99.8%) were reported by less than 1% of the sample. Conclusions and Relevance: This study found that within-disorder symptom heterogeneity followed a specific pattern consisting of few prevalent, prototypical combinations and numerous combinations with a very low probability of occurrence. Future discussions about the revision of diagnostic criteria should take this specific pattern into account by focusing not only on the absolute number of symptom combinations but also on their individual and cumulative probabilities. Findings from clinical populations using common diagnostic criteria may have limited generalizability to the large group of individuals with a low-probability symptom combination.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks.

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.

Ketamine induces multiple individually distinct whole-brain functional connectivity signatures.

Background: Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine's molecular mechanisms connect to its neural and behavioral effects. Methods: We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets. Results: We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine's data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. Conclusions: These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry. Funding: This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016-0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 - 2056) (FXV). Clinical trial number: NCT03842800.

Ketamine is a widely used anesthetic as well as a popular illegal recreational drug. Recently, it has also gained attention as a potential treatment for depression, particularly in cases that don't respond to conventional therapies. However, individuals can vary in their response to ketamine. For example, the drug can alter some people's perception, such as seeing objects as larger or small than they are, while other individuals are unaffected. Although a single dose of ketamine was shown to improve depression symptoms in approximately 65% of patients, the treatment does not work for a significant portion of patients. Understanding why ketamine does not work for everyone could help to identify which patients would benefit most from the treatment. Previous studies investigating ketamine as a treatment for depression have typically included a group of individuals given ketamine and a group given a placebo drug. Assuming people respond similarly to ketamine, the responses in each group were averaged and compared to one another. However, this averaging of results may have masked any individual differences in response to ketamine. As a result, Moujaes et al. set out to investigate whether individuals show differences in brain activity and behavior in response to ketamine. Moujaes et al. monitored the brain activity and behavior of 40 healthy individuals that were first given a placebo drug and then ketamine. The results showed that brain activity and behavior varied significantly between individuals after ketamine administration. Genetic analysis revealed that different gene expression patterns paired with differences in ketamine response in individuals ­ an effect that was hidden when the results were averaged. Ketamine also caused greater differences in brain activity and behavior between individuals than other drugs, such as psychedelics, suggesting ketamine generates a particularly complex response in people. In the future, extending these findings in healthy individuals to those with depression will be crucial for determining whether differences in response to ketamine align with how effective ketamine treatment is for an individual.

On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations.

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.

Static and dynamic fMRI-derived functional connectomes represent largely similar information.

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Human brain state dynamics reflect individual neuro-phenotypes.

Neural activity and behavior manifest state and trait dynamics, as well as variation within and between individuals. However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks.

Spatial locations can be encoded and maintained in working memory using high-precision, fine-grained representations that are cognitively demanding, or coarse and less demanding categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six fMRI studies, resulting in a sample of 153 (77 women, 25 ± 6 years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activation, with fine-grained representations requiring greater engagement of attentional and control brain systems, while categorical representations were associated with decreased inhibition of the default network. These findings may indicate a greater need for ongoing maintenance and protection against interference for fine-grained compared to categorical representations.

The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques.

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit systematic differences in the extent of their anatomical connections within the thalamus. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to perform brain-wide probabilistic tractography for 828 healthy adults from the Human Connectome Project. We then developed a framework to quantify the spatial extent of each cortical area's anatomical connections within the thalamus. Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy. Our results revealed two distinct corticothalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, associated with fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, associated with slow, feed-back information flow. These findings were consistent across human subjects and were also observed in macaques, indicating cross-species generalizability. Overall, our study demonstrates that sensorimotor and association cortical areas exhibit differences in the spatial extent of their anatomical connections within the thalamus, which may support functionally-distinct cortico-thalamic information flow.

Illness Phase as a Key Assessment and Intervention Window for Psychosis.

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

Functional brain networks reflect spatial and temporal autocorrelation.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

QuNex-An integrative platform for reproducible neuroimaging analytics.

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

Human generalization of internal representations through prototype learning with goal-directed attention.

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that discriminate between alternatives, or by distance measurements against prototypes and individual exemplars. Each provide advantages and drawbacks for generalization. We therefore developed theoretical models that leverage both discriminative and distance components to form internal representations via action-reward feedback. We then developed three latent-state learning tasks to test how humans use goal-oriented discrimination attention and prototypes/exemplar representations. The majority of participants attended to both goal-relevant discriminative features and the covariance of features within a prototype. A minority of participants relied only on the discriminative feature. Behaviour of all participants could be captured by parameterizing a model combining prototype representations with goal-oriented discriminative attention.

Static and dynamic functional connectomes represent largely similar information.

Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Toward Mapping Neurobehavioral Heterogeneity of Psychedelic Neurobiology in Humans.

Precision psychiatry aims to identify markers of interindividual variability that allow for predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical research and development for psychiatric drugs, multiple studies now point to the potential of psychedelics as a promising, fast-acting, and long-lasting treatment for some psychiatric symptoms. Yet, given the highly psychoactive nature of these substances, a precision medicine approach is essential to map the neural signals related to clinical efficacy to identify patients who can maximally benefit from this treatment. Recent studies have shown that bridging the gap between pharmacology, systems-level neural response in humans, and individual experience is possible for psychedelic substances, therefore paving the way for a precision neuropsychiatric therapeutic development. Specifically, it has been shown that the integration of brain-wide positron emission tomography or transcriptomic data, i.e., receptor distribution for the serotonin 2A receptor, with computational neuroimaging methods can simulate the effect of psychedelics on the human brain. These novel computational psychiatry approaches allow for modeling interindividual differences in neural as well as subjective effects of psychedelic substances. Collectively, this review provides a deep dive into psychedelic pharmaconeuroimaging studies with a core focus on how recent computational psychiatry advances in biophysically based circuit modeling can be leveraged to predict individual responses. Finally, we emphasize the importance of human pharmacological neuroimaging for the continued precision therapeutic development of psychedelics.

Multitask representations in the human cortex transform along a sensory-to-motor hierarchy.

Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory-association-motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition.

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns.

Importance: Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective: To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design: Double blind randomized controlled trial. Setting: Multicenter. Participants: Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions: Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures: To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results: 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance: Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Geometry of neural computation unifies working memory and planning.

Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.

Reward and loss incentives improve spatial working memory by shaping trial-by-trial posterior frontoparietal signals.

Integrating motivational signals with cognition is critical for goal-directed activities. The mechanisms that link neural changes with motivated working memory continue to be understood. Here, we tested how externally cued and non-cued (internally represented) reward and loss impact spatial working memory precision and neural circuits in human subjects using fMRI. We translated the classic delayed-response spatial working memory paradigm from non-human primate studies to take advantage of a continuous numeric measure of working memory precision, and the wealth of translational neuroscience yielded by these studies. Our results demonstrated that both cued and non-cued reward and loss improved spatial working memory precision. Visual association regions of the posterior prefrontal and parietal cortices, specifically the precentral sulcus (PCS) and intraparietal sulcus (IPS), had increased BOLD signal during incentivized spatial working memory. A subset of these regions had trial-by-trial increases in BOLD signal that were associated with better working memory precision, suggesting that these regions may be critical for linking neural signals with motivated working memory. In contrast, regions straddling executive networks, including areas in the dorsolateral prefrontal cortex, anterior parietal cortex and cerebellum displayed decreased BOLD signal during incentivized working memory. While reward and loss similarly impacted working memory processes, they dissociated during feedback when money won or avoided in loss was given based on working memory performance. During feedback, the trial-by-trial amount and valence of reward/loss received was dissociated amongst regions such as the ventral striatum, habenula and periaqueductal gray. Overall, this work suggests motivated spatial working memory is supported by complex sensory processes, and that the IPS and PCS in the posterior frontoparietal cortices may be key regions for integrating motivational signals with spatial working memory precision.