Biology subjects

Dombrovski, A. Y.

Publications and source records attributed to Dombrovski, A. Y..

5 recordsLinked to original sources

bioRxiv2025

Prefrontal and Subcortical Value Representation during Explore-Exploit Decision-Making and Suicide Attempts

BackgroundThis study aims to understand learning and decision-making in suicidal behavior by investigating temporal dynamics of reward value encoding in individuals with late-life depression and a history of suicide attempts. MethodsIn a retrospective case-control study, 134 older adults (33 with depression and history of suicide attempts, 29 with depression and suicidal ideation but no past attempts, 32 with depression and no suicidal ideation/past attempts, and 40 psychiatrically healthy controls) completed an explore-exploit decision-making task during functional MRI (fMRI). Multilevel models of deconvolved fMRI time series, time-locked to trial events, interrogated whether temporal patterns of reward value encoding was associated with suicidal behavior. ResultsSuicidal behavior in general was associated with blunted ventral PFC (vPFC) value signals, but profiles varied as a function of attempt lethality. Specifically, low-lethality suicide attempts and excessive behavioral shifts were associated with abolished phasic responses to value updates in the default network vPFC and its connected regions, including the striatum, amygdala, and hippocampus. Additionally, an unexpected pattern of sustained negative value responses was observed in vPFC and striatal control subregions, and hippocampus of high-lethality suicide attempters. ConclusionsDiverging patterns of decision-related responses may reflect different paths toward suicidal behavior. Impaired value updating in individuals with low-lethality suicide attempts suggests a failure to integrate recent outcomes alongside prior experience, potentially relating to over-reactivity to stressors and a lower threshold for suicide attempts. In contrast, increased control network responses to difficult choices in high-lethality attempters may underlie cognitive constriction and consideration of a narrow set of potential solutions.

neuroscience↗

bioRxiv2025

Prefrontal default-mode network interactions with posterior hippocampus during exploration

Hippocampal maps and ventral prefrontal cortex (vPFC) value and goal representations support foraging in continuous spaces. How might hippocampal-vPFC interactions control the balance between behavioral exploration and exploitation? Using fMRI and reinforcement learning modeling, we investigated vPFC and hippocampal responses as humans explored and exploited a continuous one-dimensional space, with out-of-session and out-of-sample replication. The spatial distribution of rewards, or value landscape, modulated activity in the hippocampus and default network vPFC subregions, but not in ventrolateral prefrontal control subregions or medial orbitofrontal limbic subregions. While prefrontal default network and hippocampus displayed higher activity in less complex, easy-to-exploit value landscapes, vPFC-hippocampal connectivity increased in uncertain landscapes requiring exploration. Further, synchronization between prefrontal default network and posterior hippocampus scaled with behavioral exploration. Considered alongside electrophysiological studies, our findings suggest that locations to be explored are identified through coordinated activity binding prefrontal default network value representations to posterior hippocampal maps. Significance StatementThe ventral prefrontal cortex represents goals and values, while the hippocampus contains maps of physical and abstract spaces. In recent years, neuroscientists have sought to understand how hippocampal-prefrontal interactions help us to solve complex problems such as deciding whether to exploit known rewards or explore in search of better alternatives. Using functional magnetic resonance imaging and reinforcement learning modeling, we examine these interactions as humans explore and exploit an environment with a complex distribution of rewards or value landscape. We describe how this value landscape modulates activity and connectivity between prefrontal cortex and hippocampus, controlling the balance between exploration and exploitation.

neuroscience↗

bioRxiv2023

Reinforcement-based option competition in human dorsal stream during exploration/exploitation of a continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on maps in the dorsal stream that guide visual search, locomotion, and grasp. For example, an animal swinging from one tree limb to the next uses rapidly evolving sensorimotor representations to decide when to harvest a reward. We show that such exploration/exploitation depends on dynamic maps of competing option values in the human dorsal stream. Using a reinforcement learning (RL) model capable of rapid learning and efficient exploration and exploitation, we show that preferred options are selectively maintained on the map while the values of spatiotemporally distant alternatives are compressed. Consistent with biophysical models of cortical option competition, dorsal stream BOLD signal increased and posterior cortical {beta}1/ oscillations desynchronized as the number of potentially valuable options grew, matching predictions of information-compressing RL rather than traditional RL that caches long-term values. BOLD and {beta}1/ responses were correlated and predicted the successful transition from exploration to exploitation. These option competition dynamics were observed across parietal and frontal dorsal stream regions, but not in the occipito-temporal MT+ sensitive to the average reward rate. Our results also illustrate that models diverging predictions about information dynamics can help to adjudicate between them based on population activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/541828v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@155b3c8org.highwire.dtl.DTLVardef@18e28c5org.highwire.dtl.DTLVardef@16d7c1org.highwire.dtl.DTLVardef@1aed684_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

The Dark Side of Mentalizing: Learning Signals in the Default Network During Social Exchanges Support Cooperation and Exploitation

The evolution of human social cognitive capacities such as mentalizing was associated with the expansion of frontoparietal cortical networks, particularly the default network. Mentalizing supports prosocial behaviors, but recent evidence indicates it may also serve a darker side of human social behavior. Using a computational reinforcement learning model of decision-making on a social exchange task, we examined how individuals optimized their approach to social interactions based on a counterparts behavior and prior reputation. We found that learning signals encoded in the default network scaled with reciprocal cooperation and were stronger in individuals who were more exploitative and manipulative, but weaker in those who were more callous and less empathic. These learning signals, which help to update predictions about others behavior, accounted for associations between exploitativeness, callousness, and social reciprocity. Separately, we found that callousness, but not exploitativeness, was associated with a behavioral insensitivity to prior reputation effects. While the entire default network was involved in reciprocal cooperation, sensitivity to reputation was selectively related to the activity of the medial temporal subsystem. Overall, our findings suggest that the emergence of social cognitive capacities associated with the expansion of the default network likely enabled humans to not only cooperate effectively with others, but to exploit and manipulate others as well. SignificanceTo navigate complex social lives, humans must learn from their interactions with others and adjust their own behavior accordingly. Here, we show that humans learn to predict the behavior of social counterparts by integrating reputational information with both observed and counterfactual feedback acquired during social experience. We find that superior learning during social interactions is related to empathy and compassion and associated with activity of the brains default network. Paradoxically however, learning signals in the default network are also associated with manipulativeness and exploitativeness, suggesting that the ability to anticipate others behavior can serve both the light and dark sides of human social behavior.

neuroscience↗

bioRxiv2020

Encoding of reinforcement along the hippocampal long axis and the transition from exploration to exploitation

When making decisions, should one exploit known good options or explore potentially better alternatives? Exploration of spatially unstructured options depends on the neocortex, striatum, and amygdala. In natural environments, however, better options often cluster together, forming structured value distributions. The hippocampus binds reward information into allocentric cognitive maps to support navigation and foraging in such spaces. Using a reinforcement learning task with a spatially structured reward function, we show that human posterior hippocampus (PH) invigorates exploration while anterior hippocampus (AH) supports the transition to exploitation. These dynamics depend on differential reinforcement representations in the PH and AH. Whereas local reward prediction error signals are early and phasic in the PH tail, global value maximum signals are delayed and sustained in the AH body. AH compresses reinforcement information across episodes, updating the location and prominence of the value maximum and displaying goal cell-like ramping activity when navigating toward it.

neuroscience↗