Biology subjects

Verstynen, T.

Publications and source records attributed to Verstynen, T..

6 recordsLinked to original sources

bioRxiv2018

Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity. Author summaryCognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an "upwards mapping" approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping "up" the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

neuroscience↗

bioRxiv2018

Binding during sequence learning does not alter cortical representations of individual actions

As a movement sequence is learned, serially ordered actions get bound together into sets in order to reduce computational complexity during planning and execution. Here we examined how the binding of serial actions alters the cortical representations of individual movements. Across five weeks of practice, healthy human subjects learned either a complex 32-item sequence of finger movements (Trained group, N=9) or randomly ordered actions (Control group, N=9). After five weeks of training, responses during sequence production in the Trained group were correlated, consistent with being bound together under a common command. These behavioral changes, however, did not coincide with plasticity in the multivariate representations of individual finger movements, assessed using fMRI, at any level of the cortical motor hierarchy. This suggests that the representations of individual actions remain stable, even as the execution of those same actions become bound together in the context of producing a well learned sequence.

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bioRxiv2017

Abdominal adiposity negatively associates with the rate of long term sequential skill learning

Obesity is associated with functional and structural differences in the corticostriatal systems of the brain. These pathways are known to be critical for the acquisition of complex sensorimotor skills, such as the ability to learn a coordinated sequence of actions. Thus, individual differences in obesity should be associated with reduced efficiency of learning sequential skills. Here we measured long-term sequence learning across five days of training on the serial reaction time task in a cohort of neurologically healthy adults (N=30) with body types ranging from lean to obese. As expected, individuals with a greater degree of central adiposity, measured as central waist circumference, exhibited slower rates of learning, across all training days, than their leaner counterparts. This association between learning and central adiposity was restricted to response speeds, but not accuracy. These findings show that obesity is negatively associated with the efficiency of learning a long-term sequential skill, possibly due to previously observed associations between obesity and general basal ganglia function.

neuroscience↗

bioRxiv2017

Dual learning mechanisms drive adaptive inhibitory control

Goal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions intersect in the basal ganglia (BG), which has at least two targets of plasticity: a dopaminergic modulation of striatal pathways and cortical modulation of the subthalamic nucleus (STN). Dual learning mechanisms suggests that feedback signals have a multifaceted impact on BG-dependent decisions. Using a hybrid of accumulation-to-bound decision models and reinforcement learning, we modeled the performance of humans in a stop-signal task where participants (N=75) learned the prior distribution of the timing of a stop signal through trial-and-error feedback. Changes in the drift-rate of the action execution process were driven by errors in action timing, whereas adaptation in the boundary height served to increase caution following failed stops. These findings highlight two interactive learning mechanisms for adapting the control of goal-directed actions based on dissociable dimensions of feedback error.\n\nAuthor SummaryMany complex behavioral goals rely on ones ability to regulate the timing of action execution while also maintaining enough control to cancel actions in response to \"Stop\" cues in the environment. Here we examined how these two fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulator models of decision making. The synthesis of these two theoretical frameworks is motivated by previous work showing that reinforcement learning and control rely on overlapping circuitry in the basal ganglia. Leveraging knowledge about the interaction of learning and control signals in this network, we formulated a computational model in which performance feedback is used to modulate key mechanisms of the decision process to facilitate goal acquisition. Model-based analysis of behavioral data collected on an adaptive stop-signal task revealed two critical learning mechanisms: one that adjusts the accumulation rate of the \"Go\" signal to errors in action timing and another that exercises caution by raising the height of the execution boundary after a failed Stop trial. We show how these independent learning mechanisms interact over the course of learning, shedding light on the behavioral effects plasticity in different pathways of the basal ganglia.

neuroscience↗

bioRxiv2017

A Population-Based Atlas Of The Macroscale Structural Connectome In The Human Brain

A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N=842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles annotated by 80 anatomical labels. The trajectories were subsequently clustered and labeled by a team of experienced neuroanatomists in order to conform to prior neuroanatomical knowledge. A multi-level network topology was then described using whole-brain connectograms, with subdivisions of the association pathways showing small-worldness in intra-hemisphere connections, projection pathways showing hub structures at thalamus, putamen, and brainstem, and commissural pathways showing bridges connecting cerebral hemispheres to provide global efficiency. This atlas of the structural connectome provides representative organization of human brain white matter, complementary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectome studies.

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bioRxiv2017

Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N=841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.\n\nAuthor SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.

neuroscience↗