Inference on the Macroscopic Dynamics of Spiking Neurons.

The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying the interactions of neurons. Estimating parameters of this class of generative model allows one to predict the system's dynamics and responses under changing inputs and, indeed, changing parameters. We first assume a set of known state-space equations and address the problem of inferring the lumped parameters from observed time series. Crucially, we consider this problem in the setting of bistability, random fluctuations in system dynamics, and partial observations, in which some states are hidden. To identify the most efficient estimation or inversion scheme in this particular system identification, we benchmark against state-of-the-art optimization and Bayesian estimation algorithms, highlighting their strengths and weaknesses. Additionally, we explore how well the statistical relationships between parameters are maintained across different scales. We found that deep neural density estimators outperform other algorithms in the inversion scheme, despite potentially resulting in overestimated uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. We then eschew the mean-field approximation and employ deep neural ODEs on spiking neurons, illustrating prediction of system dynamics and vector fields from microscopic states. Overall, this study affords an opportunity to predict brain dynamics and responses to various perturbations or pharmacological interventions using deep neural networks.

Long-term hypothermic storage of oocytes of the European common frog Rana temporaria at various pressure regimes in gas mixtures based on oxygen, carbon monoxide, and nitrous oxide.

In recent years, the challenge of preserving amphibian biodiversity has increasingly been addressed through technologies for the short-term storage of unfertilized spawn at low positive temperatures. Previously the possibility of using a 6.5 atm gaseous mixture of carbon monoxide and oxygen for prolonged hypothermic preservation of unfertilized oocytes for more than 4 days was shown. This study aimed to investigate the viability of oocytes R. temporaria preserved under conditions of hypothermia at 2.5, 3 and 6.5 excess atm pressure in the various gas mixture compositions (CO, N2O, O2) and pure oxygen. The use of pressure up to 3 excess atmospheres was significantly beneficial compared to 6.5 atm at the 7 days storage period. The results indicate that oxygen pressure is a critical factor in maintaining oocyte viability. Admixing CO or N2O to oxygen reduced variability in the results but did not significantly affect the measured indicators (fertilization, hatching) in the experimental groups. The composition CO + O2 (0.5/3.5 ratio, 3 excess atm) reliably extended the shelf life of viable oocytes, indistinguishable from native controls by fertilization and hatching rates, to 4 days. After 7 days, oocytes exhibited fertilization and hatching rates that were 79 % and 48 % compared to native control. Reducing the pressure of the preserving gas mixture to 3 atm, as utilized in this study, simplifies the practical implementation of gas preservation technology for maintaining endangered amphibian species during breeding in laboratory conditions.

Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic algorithm capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.

Virtual brain twins: from basic neuroscience to clinical use.

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

DUSP3 modulates IRES-dependent translation of mRNAs through dephosphorylation of the HNRNPC protein in cells under genotoxic stimulus.

BACKGROUND INFORMATION: The dual-specificity phosphatase 3 (DUSP3) regulates cell cycle progression, proliferation, senescence, and DNA repair pathways under genotoxic stress. This phosphatase interacts with HNRNPC protein suggesting an involvement in the regulation of HNRNPC-ribonucleoprotein complex stability. In this work, we investigate the impact of DUSP3 depletion on functions of HNRNPC aiming to suggest new roles for this enzyme. RESULTS: The DUSP3 knockdown results in the tyrosine hyperphosphorylation state of HNRNPC increasing its RNA binding ability. HNRNPC is present in the cytoplasm where it interacts with IRES trans-acting factors (ITAF) complex, which recruits the 40S ribosome on mRNA during protein synthesis, thus facilitating the translation of mRNAs containing IRES sequence in response to specific stimuli. In accordance with that, we found that DUSP3 is present in the 40S, monosomes and polysomes interacting with HNRNPC, just like other previously identified DUSP3 substrates/interacting partners such as PABP and NCL proteins. By downregulating DUSP3, Tyr-phosphorylated HNRNPC preferentially binds to IRES-containing mRNAs within ITAF complexes preferentially in synchronized or stressed cells, as evidenced by the higher levels of proteins such as c-MYC and XIAP, but not their mRNAs such as measured by qPCR. Under DUSP3 absence, this increased phosphorylated-HNRNPC/RNA interaction reduces HNRNPC-p53 binding in presence of RNAs releasing p53 for specialized cellular responses. Similarly, to HNRNPC, PABP physically interacts with DUSP3 in an RNA-dependent manner. CONCLUSIONS AND SIGNIFICANCE: Overall, DUSP3 can modulate cellular responses to genotoxic stimuli at the translational level by maintaining the stability of HNRNPC-ITAF complexes and regulating the intensity and specificity of RNA interactions with RRM-domain proteins.

Optimization of ictal aborting stimulation using the dynamotype taxonomy.

Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster's duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.

Low-dimensional organization of global brain states of reduced consciousness.

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators.

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.

White-matter degradation and dynamical compensation support age-related functional alterations in human brain.

Structural connectivity of the brain at different ages is analyzed using diffusion-weighted magnetic resonance imaging (MRI) data. The largest decrease of streamlines is found in frontal regions and for long inter-hemispheric links. The average length of the tracts also decreases, but the clustering is unaffected. From functional MRI we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of functional connectivity (FC) links captured by metaconnectivity. They indicate more stable dFC, but wider range and variance of MC, whereas static features of FC did not show any significant differences with age. We implement individual connectivity in whole-brain models and test several hypotheses for the mechanisms of operation among underlying neural system. We demonstrate that age-related functional fingerprints are only supported if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity and (ii) decrease of propagation velocity due to the loss of myelination. We also show that with these 2 conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best, and produces the largest variability at slow time-scales.

Investigation of Behavioural Pain Scale, Critical Care Pain Observation Tool, nociceptive flexion reflex and pupillary dilatation reflex as predictors of behavioural reactions to nociceptive procedures in critically ill patients unable to self-report pain.

BACKGROUND: Procedural pain is a common burden in critical care treatment and the prediction of nociceptive reactions remains challenging. Thus, we investigated the Behavioural Pain Scale (BPS), the Critical Pain Observational Tool (CPOT), the nociceptive flexion reflex (NFR), the pupillary dilation reflex (PDR) and the Richmond Agitation-Sedation Scale (RASS) as predictors of behavioural reactions to nociceptive procedures. METHODS: In this monocentric, prospective, observational study, we analysed data of 128 critically ill adults unable to self-report pain to investigate the predictability of behavioural reactions to two procedures: endotracheal suctioning and turning. Next to routine clinical data, CPOT, BPS, PDR, NFR, RASS, propofol and sufentanil doses were recorded before the procedures. RESULTS: For endotracheal suctioning, NFR, BPS, CPOT and RASS showed predictive performances significantly better than chance, but none of them performed significantly better than the sufentanil dose rate. For turning, BPS, CPOT and RASS showed predictive performances significantly better than chance, but only the RASS performed significantly better than the propofol dose rate. CONCLUSIONS: Behavioural reactions to both investigated clinical procedures can be predicted by observational scales or nociceptive reflexes. For endotracheal suctioning, none of the predictors performed superior to using the sufentanil dose rate as a predictor. As using sufentanil as a predictor requires no extra effort in contrast to the other predictors, none of the here investigated tools seem advisable for predicting behavioural reactions to endotracheal suctioning. For patient turning, the RASS predicts reactions better than any other tool. SIGNIFICANCE: In this observational study, we demonstrate that behavioural reactions to potentially nociceptive procedures in critical care treatment can be predicted by observational scales and nociceptive reflexes. However, for endotracheal suctioning, none of the predictors is superior to using the opioid dose rate as a predictor. For patient turning, the RASS predicts reactions better than any other parameters.

Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions.

Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes "critical" phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian's leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.

Towards an efficient validation of dynamical whole-brain models.

Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model's capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

Coding variants identified in patients with diabetes alter PICK1 BAR domain function in insulin granule biogenesis.

Bin/amphiphysin/Rvs (BAR) domains are positively charged crescent-shaped modules that mediate curvature of negatively charged lipid membranes during remodeling processes. The BAR domain proteins PICK1, ICA69, and the arfaptins have recently been demonstrated to coordinate the budding and formation of immature secretory granules (ISGs) at the trans-Golgi network. Here, we identify 4 coding variants in the PICK1 gene from a whole-exome screening of Danish patients with diabetes that each involve a change in positively charged residues in the PICK1 BAR domain. All 4 coding variants failed to rescue insulin content in INS-1E cells upon knock down of endogenous PICK1. Moreover, 2 variants showed dominant-negative properties. In vitro assays addressing BAR domain function suggested that the coding variants compromised BAR domain function but increased the capacity to cause fission of liposomes. Live confocal microscopy and super-resolution microscopy further revealed that PICK1 resides transiently on ISGs before egress via vesicular budding events. Interestingly, this egress of PICK1 was accelerated in the coding variants. We propose that PICK1 assists in or complements the removal of excess membrane and generic membrane trafficking proteins, and possibly also insulin, from ISGs during the maturation process; and that the coding variants may cause premature budding, possibly explaining their dominant-negative function.

Time Limiting Boundaries of Reversible Clinical Death in Rats Subjected to Ultra-Deep Hypothermia.

BACKGROUND: It is well known that body temperature maintenance between 20 and 35°C prevents hypoxic damage. However, data regarding the ideal duration and permissible temperature boundaries for ultra-deep hypothermia below 20°C are rather fragmentary. The aim of the present study was to determine the time limits of reversible clinical death in rats subjected to ultra-deep hypothermia at 1-8°C. RESULTS: Rat survival rates were directly dependent on the duration of clinical death. If clinical death did not exceed 35 min, animal viability could be restored. Extending the duration of clinical death longer than 45 min led to rat death, and cardiac functioning in these animals was not recovered. The rewarming rate and the lowest temperature of hypothermia experienced did not directly influence survival rates. CONCLUSIONS: In a rat model, reversible ultra-deep hypothermia as low as 1-8°C could be achieved without the application of hypercapnia or pharmacological support. The survival of animals was dependent on the duration of clinical death, which should not exceed 35 min.

Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics.

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

Normalizing the brain connectome for communication through synchronization.

Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N = 100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel, here-proposed, generalized framework of structural connectivity.

High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways.

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100,000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.

Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference.

Focal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient's brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus.

Molecular Brushes with a Polyimide Backbone and Poly(ε-Caprolactone) Side Chains by the Combination of ATRP, ROP, and CuAAC.

An approach to the synthesis of the novel molecular brushes with a polyimide (PI) backbone and poly(ε-caprolactone) (PCL) side chains was developed. To obtain such copolymers, a combination of various synthesis methods was used, including polycondensation, atom transfer radical polymerization (ATRP), ring opening polymerization (ROP), and Cu (I)-catalyzed azide-alkyne Huisgen cycloaddition (CuAAC). ATRP of 2-hydroxyethyl methacrylate (HEMA) on PI macroinitiator followed by ROP of ε-caprolactone (CL) provided a "brush on brush" structure PI-g-(PHEMA-g-PCL). For the synthesis of PI-g-PCL two synthetic routes combining ROP and CuAAC were compared: (1) polymer-analogous transformations of a multicenter PI macroinitiator with an initiating hydroxyl group separated from the main chain by a triazole ring followed by ROP of CL, or (2) a separate synthesis of macromonomers with the desirable functional groups (polyimide with azide groups and PCL with terminal alkyne groups), followed by a click reaction. Results showed that the first approach allows to obtain graft copolymers with a PI backbone and relatively short PCL side chains. While the implementation of the second approach leads to a more significant increase in the molecular weight, but unreacted linear PCL remains in the system. Obtained macroinitiators and copolymers were characterized using 1H NMR and IR spectroscopy, their molecular weight characteristics were determined by SEC with triple detection. TGA and DSC were used to determine their thermal properties. X-ray scattering data showed that the introduction of a polyimide block into the polycaprolactone matrix did not change the degree of crystallinity of PCL.

Identifying dominant-negative actions of a dopamine transporter variant in patients with parkinsonism and neuropsychiatric disease.

Dysfunctional dopaminergic neurotransmission is central to movement disorders and mental diseases. The dopamine transporter (DAT) regulates extracellular dopamine levels, but the genetic and mechanistic link between DAT function and dopamine-related pathologies is not clear. Particularly, the pathophysiological significance of monoallelic missense mutations in DAT is unknown. Here, we use clinical information, neuroimaging, and large-scale exome-sequencing data to uncover the occurrence and phenotypic spectrum of a DAT coding variant, DAT-K619N, which localizes to the critical C-terminal PSD-95/Discs-large/ZO-1 homology-binding motif of human DAT (hDAT). We identified the rare but recurrent hDAT-K619N variant in exome-sequenced samples of patients with neuropsychiatric diseases and a patient with early-onset neurodegenerative parkinsonism and comorbid neuropsychiatric disease. In cell cultures, hDAT-K619N displayed reduced uptake capacity, decreased surface expression, and accelerated turnover. Unilateral expression in mouse nigrostriatal neurons revealed differential effects of hDAT-K619N and hDAT-WT on dopamine-directed behaviors, and hDAT-K619N expression in Drosophila led to impairments in dopamine transmission with accompanying hyperlocomotion and age-dependent disturbances of the negative geotactic response. Moreover, cellular studies and viral expression of hDAT-K619N in mice demonstrated a dominant-negative effect of the hDAT-K619N mutant. Summarized, our results suggest that hDAT-K619N can effectuate dopamine dysfunction of pathological relevance in a dominant-negative manner.