Kanha Batra

All Publications

• Predicting Future Development of Stress-Induced Anhedonia From Cortical Dynamics and Facial Expression. bioRxiv : the preprint server for biology Coley, A. A., Batra, K., Delahanty, J. M., Keyes, L. R., Pamintuan, R., Ramot, A., Hagemann, J., Lee, C. R., Liu, V., Adivikolanu, H., Cressy, J., Jia, C., Massa, F., LeDuke, D., Gabir, M., Durubeh, B., Linderhof, L., Patel, R., Wichmann, R., Li, H., Fischer, K. B., Pereira, T., Tye, K. M. 2024

  Abstract

  The current state of mental health treatment for individuals diagnosed with major depressive disorder leaves billions of individuals with first-line therapies that are ineffective or burdened with undesirable side effects. One major obstacle is that distinct pathologies may currently be diagnosed as the same disease and prescribed the same treatments. The key to developing antidepressants with ubiquitous efficacy is to first identify a strategy to differentiate between heterogeneous conditions. Major depression is characterized by hallmark features such as anhedonia and a loss of motivation1,2, and it has been recognized that even among inbred mice raised under identical housing conditions, we observe heterogeneity in their susceptibility and resilience to stress3. Anhedonia, a condition identified in multiple neuropsychiatric disorders, is described as the inability to experience pleasure and is linked to anomalous medial prefrontal cortex (mPFC) activity4. The mPFC is responsible for higher order functions5-8, such as valence encoding; however, it remains unknown how mPFC valence-specific neuronal population activity is affected during anhedonic conditions. To test this, we implemented the unpredictable chronic mild stress (CMS) protocol9-11 in mice and examined hedonic behaviors following stress and ketamine treatment. We used unsupervised clustering to delineate individual variability in hedonic behavior in response to stress. We then performed in vivo 2-photon calcium imaging to longitudinally track mPFC valence-specific neuronal population dynamics during a Pavlovian discrimination task. Chronic mild stress mice exhibited a blunted effect in the ratio of mPFC neural population responses to rewards relative to punishments after stress that rebounds following ketamine treatment. Also, a linear classifier revealed that we can decode susceptibility to chronic mild stress based on mPFC valence-encoding properties prior to stress-exposure and behavioral expression of susceptibility. Lastly, we utilized markerless pose tracking computer vision tools to predict whether a mouse would become resilient or susceptible based on facial expressions during a Pavlovian discrimination task. These results indicate that mPFC valence encoding properties and behavior are predictive of anhedonic states. Altogether, these experiments point to the need for increased granularity in the measurement of both behavior and neural activity, as these factors can predict the predisposition to stress-induced anhedonia.

  View details for DOI 10.1101/2024.12.18.629202

  View details for PubMedID 39764017

• Social Isolation Recruits Amygdala-Cortical Circuitry to Escalate Alcohol Drinking Patel, R., Patarino, M., Kim, K., Pamintuan, R., Taschbach, F., Li, H., Lee, C., van Hoek, A., Castro, R., Cazares, C., Miranda, R., Jia, C., Delahanty, J., Batra, K., Keyes, L., Libster, A., Wichmann, R., Pereira, T. D., Benna, M., Tye, K. SPRINGERNATURE. 2023: 486-487

  View details for Web of Science ID 001126640300485

• Thalamus sends information about arousal but not valence to the amygdala. Psychopharmacology Leppla, C. A., Keyes, L. R., Glober, G., Matthews, G. A., Batra, K., Jay, M., Feng, Y., Chen, H. S., Mills, F., Delahanty, J., Olson, J. M., Nieh, E. H., Namburi, P., Wildes, C., Wichmann, R., Beyeler, A., Kimchi, E. Y., Tye, K. M. 2023; 240 (3): 477-499

  Abstract

  The basolateral amygdala (BLA) and medial geniculate nucleus of the thalamus (MGN) have both been shown to be necessary for the formation of associative learning. While the role that the BLA plays in this process has long been emphasized, the MGN has been less well-studied and surrounded by debate regarding whether the relay of sensory information is active or passive.We seek to understand the role the MGN has within the thalamoamgydala circuit in the formation of associative learning.Here, we use optogenetics and in vivo electrophysiological recordings to dissect the MGN-BLA circuit and explore the specific subpopulations for evidence of learning and synthesis of information that could impact downstream BLA encoding. We employ various machine learning techniques to investigate function within neural subpopulations. We introduce a novel method to investigate tonic changes across trial-by-trial structure, which offers an alternative approach to traditional trial-averaging techniques.We find that the MGN appears to encode arousal but not valence, unlike the BLA which encodes for both. We find that the MGN and the BLA appear to react differently to expected and unexpected outcomes; the BLA biased responses toward reward prediction error and the MGN focused on anticipated punishment. We uncover evidence of tonic changes by visualizing changes across trials during inter-trial intervals (baseline epochs) for a subset of cells.We conclude that the MGN-BLA projector population acts as both filter and transferer of information by relaying information about the salience of cues to the amygdala, but these signals are not valence-specified.

  View details for DOI 10.1007/s00213-022-06284-5

  View details for PubMedID 36522481

  View details for PubMedCentralID PMC9928937

• Neurotensin orchestrates valence assignment in the amygdala. Nature Li, H., Namburi, P., Olson, J. M., Borio, M., Lemieux, M. E., Beyeler, A., Calhoon, G. G., Hitora-Imamura, N., Coley, A. A., Libster, A., Bal, A., Jin, X., Wang, H., Jia, C., Choudhury, S. R., Shi, X., Felix-Ortiz, A. C., de la Fuente, V., Barth, V. P., King, H. O., Izadmehr, E. M., Revanna, J. S., Batra, K., Fischer, K. B., Keyes, L. R., Padilla-Coreano, N., Siciliano, C. A., McCullough, K. M., Wichmann, R., Ressler, K. J., Fiete, I. R., Zhang, F., Li, Y., Tye, K. M. 2022; 608 (7923): 586-592

  Abstract

  The ability to associate temporally segregated information and assign positive or negative valence to environmental cues is paramount for survival. Studies have shown that different projections from the basolateral amygdala (BLA) are potentiated following reward or punishment learning1-7. However, we do not yet understand how valence-specific information is routed to the BLA neurons with the appropriate downstream projections, nor do we understand how to reconcile the sub-second timescales of synaptic plasticity8-11 with the longer timescales separating the predictive cues from their outcomes. Here we demonstrate that neurotensin (NT)-expressing neurons in the paraventricular nucleus of the thalamus (PVT) projecting to the BLA (PVT-BLA:NT) mediate valence assignment by exerting NT concentration-dependent modulation in BLA during associative learning. We found that optogenetic activation of the PVT-BLA:NT projection promotes reward learning, whereas PVT-BLA projection-specific knockout of the NT gene (Nts) augments punishment learning. Using genetically encoded calcium and NT sensors, we further revealed that both calcium dynamics within the PVT-BLA:NT projection and NT concentrations in the BLA are enhanced after reward learning and reduced after punishment learning. Finally, we showed that CRISPR-mediated knockout of the Nts gene in the PVT-BLA pathway blunts BLA neural dynamics and attenuates the preference for active behavioural strategies to reward and punishment predictive cues. In sum, we have identified NT as a neuropeptide that signals valence in the BLA, and showed that NT is a critical neuromodulator that orchestrates positive and negative valence assignment in amygdala neurons by extending valence-specific plasticity to behaviourally relevant timescales.

  View details for DOI 10.1038/s41586-022-04964-y

  View details for PubMedID 35859170

  View details for PubMedCentralID PMC9583860

• Cortical ensembles orchestrate social competition through hypothalamic outputs. Nature Padilla-Coreano, N., Batra, K., Patarino, M., Chen, Z., Rock, R. R., Zhang, R., Hausmann, S. B., Weddington, J. C., Patel, R., Zhang, Y. E., Fang, H. S., Mishra, S., LeDuke, D. O., Revanna, J., Li, H., Borio, M., Pamintuan, R., Bal, A., Keyes, L. R., Libster, A., Wichmann, R., Mills, F., Taschbach, F. H., Matthews, G. A., Curley, J. P., Fiete, I. R., Lu, C., Tye, K. M. 2022; 603 (7902): 667-671

  Abstract

  Most social species self-organize into dominance hierarchies1,2, which decreases aggression and conserves energy3,4, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank5-9 and guides behaviour on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents7,8 and humans10,11. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known. We developed a social competition assay in which mice compete for rewards, as well as a computer vision tool (AlphaTracker) to track multiple, unmarked animals. A hidden Markov model combined with generalized linear models was able to decode social competition behaviour from mPFC ensemble activity. Population dynamics in the mPFC predicted social rank and competitive success. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus promote dominance behaviour during reward competition. Thus, we reveal a cortico-hypothalamic circuit by which the mPFC exerts top-down modulation of social dominance.

  View details for DOI 10.1038/s41586-022-04507-5

  View details for PubMedID 35296862

  View details for PubMedCentralID PMC9576144

• Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone. Scientific reports Digma, L. A., Feng, C. H., Conlin, C. C., Rodríguez-Soto, A. E., Zhong, A. Y., Hussain, T. S., Lui, A. J., Batra, K., Simon, A. B., Karunamuni, R., Kuperman, J., Rakow-Penner, R., Hahn, M. E., Dale, A. M., Seibert, T. M. 2022; 12 (1): 265

  Abstract

  Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10-20). On average (± SD), participants' largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0-7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10-12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

  View details for DOI 10.1038/s41598-021-04467-2

  View details for PubMedID 34997164

  View details for PubMedCentralID PMC8741963

• Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging. Journal of magnetic resonance imaging : JMRI Feng, C. H., Conlin, C. C., Batra, K., Rodríguez-Soto, A. E., Karunamuni, R., Simon, A., Kuperman, J., Rakow-Penner, R., Hahn, M. E., Dale, A. M., Seibert, T. M. 2021; 54 (3): 975-984

  Abstract

  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.To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Retrospective.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.A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.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.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.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).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.3 TECHNICAL EFFICACY: Stage 2.

  View details for DOI 10.1002/jmri.27623

  View details for PubMedID 33786915

  View details for PubMedCentralID PMC8363567