Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

Radial Glial Lineage Progression and Differential Intermediate Progenitor Amplification Underlie Striatal Compartments and Circuit Organization.

The circuitry of the striatum is characterized by two organizational plans: the division into striosome and matrix compartments, thought to mediate evaluation and action, and the direct and indirect pathways, thought to promote or suppress behavior. The developmental origins of these organizations and their developmental relationships are unknown, leaving a conceptual gap in understanding the cortico-basal ganglia system. Through genetic fate mapping, we demonstrate that striosome-matrix compartmentalization arises from a lineage program embedded in lateral ganglionic eminence radial glial progenitors mediating neurogenesis through two distinct types of intermediate progenitors (IPs). The early phase of this program produces striosomal spiny projection neurons (SPNs) through fate-restricted apical IPs (aIPSs) with limited capacity; the late phase produces matrix SPNs through fate-restricted basal IPs (bIPMs) with expanded capacity. Notably, direct and indirect pathway SPNs arise within both aIPS and bIPM pools, suggesting that striosome-matrix architecture is the fundamental organizational plan of basal ganglia circuitry.

Chronic Stress Alters Striosome-Circuit Dynamics, Leading to Aberrant Decision-Making.

Effective evaluation of costs and benefits is a core survival capacity that in humans is considered as optimal, "rational" decision-making. This capacity is vulnerable in neuropsychiatric disorders and in the aftermath of chronic stress, in which aberrant choices and high-risk behaviors occur. We report that chronic stress exposure in rodents produces abnormal evaluation of costs and benefits resembling non-optimal decision-making in which choices of high-cost/high-reward options are sharply increased. Concomitantly, alterations in the task-related spike activity of medial prefrontal neurons correspond with increased activity of their striosome-predominant striatal projection neuron targets and with decreased and delayed striatal fast-firing interneuron activity. These effects of chronic stress on prefronto-striatal circuit dynamics could be blocked or be mimicked by selective optogenetic manipulation of these circuits. We suggest that altered excitation-inhibition dynamics of striosome-based circuit function could be an underlying mechanism by which chronic stress contributes to disorders characterized by aberrant decision-making under conflict. VIDEO ABSTRACT.

Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.

A Corticostriatal Path Targeting Striosomes Controls Decision-Making under Conflict.

A striking neurochemical form of compartmentalization has been found in the striatum of humans and other species, dividing it into striosomes and matrix. The function of this organization has been unclear, but the anatomical connections of striosomes indicate their relation to emotion-related brain regions, including the medial prefrontal cortex. We capitalized on this fact by combining pathway-specific optogenetics and electrophysiology in behaving rats to search for selective functions of striosomes. We demonstrate that a medial prefronto-striosomal circuit is selectively active in and causally necessary for cost-benefit decision-making under approach-avoidance conflict conditions known to evoke anxiety in humans. We show that this circuit has unique dynamic properties likely reflecting striatal interneuron function. These findings demonstrate that cognitive and emotion-related functions are, like sensory-motor processing, subject to encoding within compartmentally organized representations in the forebrain and suggest that striosome-targeting corticostriatal circuits can underlie neural processing of decisions fundamental for survival.

A multistage mathematical approach to automated clustering of high-dimensional noisy data.

A critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.

Shifting responsibly: the importance of striatal modularity to reinforcement learning in uncertain environments.

We propose here that the modular organization of the striatum reflects a context-sensitive modular learning architecture in which clustered striosome-matrisome domains participate in modular reinforcement learning (RL). Based on anatomical and physiological evidence, it has been suggested that the modular organization of the striatum could represent a learning architecture. There is not, however, a coherent view of how such a learning architecture could relate to the organization of striatal outputs into the direct and indirect pathways of the basal ganglia, nor a clear formulation of how such a modular architecture relates to the RL functions attributed to the striatum. Here, we hypothesize that striosome-matrisome modules not only learn to bias behavior toward specific actions, as in standard RL, but also learn to assess their own relevance to the environmental context and modulate their own learning and activity on this basis. We further hypothesize that the contextual relevance or "responsibility" of modules is determined by errors in predictions of environmental features and that such responsibility is assigned by striosomes and conveyed to matrisomes via local circuit interneurons. To examine these hypotheses and to identify the general requirements for realizing this architecture in the nervous system, we developed a simple modular RL model. We then constructed a network model of basal ganglia circuitry that includes these modules and the direct and indirect pathways. Based on simple assumptions, this model suggests that while the direct pathway may promote actions based on striatal action values, the indirect pathway may act as a gating network that facilitates or suppresses behavioral modules on the basis of striatal responsibility signals. Our modeling functionally unites the modular compartmental organization of the striatum with the direct-indirect pathway divisions of the basal ganglia, a step that we suggest will have important clinical implications.