How do we know our social rank? Most social species, from insects to humans, self-organize into social dominance hierarchies (1-4). The establishment of social ranks serves to decrease aggression, conserve energy, and maximize survival for the entire group (5-8). Despite dominance behaviors being critical for successful interactions and ultimately, survival, we have only begun to learn how the brain represents social rank (9-12) and guides behavior based on this representation. The medial prefrontal cortex (mPFC) has been implicated in the expression of social dominance in rodents (10,11), and in social rank learning in humans (13,14). Yet precisely how the mPFC encodes rank and which circuits mediate this computation is not known. We developed a trial-based social competition assay in which mice compete for rewards, as well as a computer vision tool to track multiple, unmarked animals. With the development of a deep learning computer vision tool (AlphaTracker) and wireless electrophysiology recording devices, we have established a novel platform to facilitate quantitative examination of how the brain gives rise to social behaviors. We describe nine behavioral states during social competition that were accurately decoded from mPFC ensemble activity using a hidden Markov model combined with generalized linear models (HMM-GLM). Population dynamics in the mPFC were predictive of social rank and competitive success. This population-level rank representation translated into differences in the individual cell responses to task-relevant events across ranks. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus contribute to the prediction of social rank and promote dominance behavior during the reward competition. Thus, we reveal a cortico-hypothalamic circuit by which mPFC exerts topdown modulation of social dominance. Main TextThe medial prefrontal cortex (mPFC) is best known for its role in working memory, decision-making, reward learning and goal-oriented behavior [15][16][17][18][19] . Theories about mPFC function emphasize that it integrates sensory and limbic information to exibly guide behavior based on task rules 20,21 . mPFC circuitry has also been broadly implicated in social cognition [22][23][24] , social behaviors 25,26 , social

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Background Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four‐compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4‐C1, yielded greatest tumor conspicuity. Purpose To evaluate the slowest diffusion compartment of a four‐compartment spectrum imaging model (RSI4‐C1) as a quantitative voxel‐level classifier of PCa. Study Type Retrospective. Subjects Forty‐six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty‐three men had benign prostates, and the other 23 men had PCa. Field Strength/Sequence A 3 T, multishell diffusion‐weighted and axial T2‐weighted sequences. Assessment High‐confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4‐C1 and conventional ADC. Classifier images were also generated. Statistical Tests Voxel‐level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient‐level case resampling. RSI4‐C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false‐positive rate for a sensitivity of 90% (FPR90). Statistical significance was assessed using bootstrap difference with two‐sided α = 0.05. Results RSI4‐C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951–0.991] vs. 0.922 [0.878–0.948]) and lower FPR90 (0.032 [0.009–0.082] vs. 0.201 [0.132–0.290]). These improvements were statistically significant (P < 0.05). Data Conclusion RSI4‐C1 yielded a quantitative, voxel‐level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false‐positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. Evidence Level 3 Technical Efficacy Stage 2