Manish Saggar is an Instructor in Psychiatry department at the Stanford University School of Medicine. He is working with Dr. Allan Reiss in the Center for Interdisciplinary Brain Sciences Research (CIBSR). His research focuses on the intersections of cognitive science, neuroimaging, and computational modeling. The goal of his research is to develop novel experimental designs and computational analyses to better understand typical and atypical brain functioning. He is currently working on several projects including (a) finding the neural correlates of creativity and its enhancement across lifespan; (b) developing multi-person neuroimaging paradigms to assess the neural correlates of social interaction; and (c) constructing methods to characterize and model dynamics of brain’s intrinsic activity. Manish is also part of Stanford’s teaching team, where he is actively involved in teaching design thinking principles and their relation to mental health.

Manish received his Ph.D. in Computer Science from the University of Texas at Austin. As part of his doctoral work, he developed a computational model of brain processes underlying meditation training. He holds a Masters in Computer Science from the University of Texas at Austin and a Bachelors degree in Information Technology from the Indian Institute of Information Technology, Allahabad (India).

Honors & Awards

  • NARSAD Young Investigator Grant, Brain & Behavior Research Foundation (2016-2018)
  • NIH Career Development Award (K99/R00), National Institute of Mental Health (2015-2020)
  • Child Health Research Institute (CHRI) Postdoctoral Grant, Lucile Packard Foundation for Children’s Health (LPFCH) (2013-2014)
  • Seed-grant Award, Stanford’s Center for Cognitive and Neurobiological Imaging (CNI). (2012-2013)
  • Francisco J. Varela Memorial Grant Award, Mind and Life Institute (2006-2011)
  • Merit Scholarship, Indian Institute of Information Technology, Allahabad (IIIT-A), India (2001-2005)

Professional Education

  • Doctor of Philosophy, University of Texas at Austin, Computer Science (2011)
  • Master of Science, University of Texas at Austin, Computer Science (2009)
  • Bachelors in Technology, Indian Institute of Information Technology, Information Technology (2005)

Research & Scholarship

Current Research and Scholarly Interests

My research focuses on the intersections of cognitive science, neuroimaging, and computational modeling. The goal of my research is to develop novel experimental designs and computational analyses to better understand typical and atypical brain functioning. I am currently working on several projects including (a) finding the neural correlates of creativity and its enhancement across lifespan; (b) developing multi-person neuroimaging paradigms to assess the neural correlates of social interaction; and (c) constructing methods to characterize and model the dynamics of brain’s intrinsic activity.


All Publications

  • Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training NEUROIMAGE Saggar, M., Zanesco, A. P., King, B. G., Bridwell, D. A., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Wallace, B. A., Saron, C. D., Miikkulainen, R. 2015; 114: 88-104


    Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

    View details for DOI 10.1016/j.neuroimage.2015.03.073

    View details for Web of Science ID 000355002900007

    View details for PubMedID 25862265

  • Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity SCIENTIFIC REPORTS Saggar, M., Quintin, E., Kienitz, E., Bott, N. T., Sun, Z., Hong, W., Chien, Y., Liu, N., Dougherty, R. F., Royalty, A., Hawthorne, G., Reiss, A. L. 2015; 5


    A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural correlates of spontaneous improvisation and figural creativity in healthy adults. Participants were engaged in the word-guessing game of Pictionary(TM), using an MR-safe drawing tablet and no explicit instructions to be "creative". Using the primary contrast of drawing a given word versus drawing a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation in the cingulate and left prefrontal cortices negatively influenced task performance. Further, using parametric fMRI analysis, increasing subjective difficulty ratings for drawing the word engaged higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations has a facilitative effect on spontaneous improvisation and figural creativity.

    View details for DOI 10.1038/srep10894

    View details for Web of Science ID 000355548100001

    View details for PubMedID 26018874

  • Estimating individual contribution from group-based structural correlation networks. NeuroImage Saggar, M., Hosseini, S. M., Bruno, J. L., Quintin, E. M., Raman, M. M., Kesler, S. R., Reiss, A. L. 2015; 120: 274-284


    Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.

    View details for DOI 10.1016/j.neuroimage.2015.07.006

    View details for PubMedID 26162553

  • Examining the neural correlates of emergent equivalence relations in fragile X syndrome. Psychiatry research Klabunde, M., Saggar, M., Hustyi, K. M., Kelley, R. G., Reiss, A. L., Hall, S. S. 2015


    The neural mechanisms underlying the formation of stimulus equivalence relations are poorly understood, particularly in individuals with specific learning impairments. As part of a larger study, we used functional magnetic resonance imaging (fMRI) while participants with fragile X syndrome (FXS), and age- and IQ-matched controls with intellectual disability, were required to form new equivalence relations in the scanner. Following intensive training on matching fractions to pie charts (A=B relations) and pie charts to decimals (B=C relations) outside the scanner over a 2-day period, participants were tested on the trained (A=B, B=C) relations, as well as emergent symmetry (i.e., B=A and C=B) and transitivity/equivalence (i.e., A=C and C=A) relations inside the scanner. Eight participants with FXS (6 female, 2 male) and 10 controls, aged 10-23 years, were able to obtain at least 66.7% correct on the trained relations in the scanner and were included in the fMRI analyses. Across both groups, results showed that the emergence of symmetry relations was correlated with increased brain activation in the left inferior parietal lobule, left postcentral gyrus, and left insula, broadly supporting previous investigations of stimulus equivalence research in neurotypical populations. On the test of emergent transitivity/equivalence relations, activation was significantly greater in individuals with FXS compared with controls in the right middle temporal gyrus, left superior frontal gyrus and left precuneus. These data indicate that neural execution was significantly different in individuals with FXS than in age- and IQ-matched controls during stimulus equivalence formation. Further research concerning how gene-brain-behavior interactions may influence the emergence of stimulus equivalence in individuals with intellectual disabilities is needed.

    View details for DOI 10.1016/j.pscychresns.2015.06.009

    View details for PubMedID 26250852

  • Neural correlates of self-injurious behavior in Prader-Willi syndrome. Human brain mapping Klabunde, M., Saggar, M., Hustyi, K. M., Hammond, J. L., Reiss, A. L., Hall, S. S. 2015


    Individuals with Prader-Willi syndrome (PWS), a genetic disorder caused by mutations to the q11-13 region on chromosome 15, commonly show severe skin-picking behaviors that can cause open wounds and sores on the body. To our knowledge, however, no studies have examined the potential neural mechanisms underlying these behaviors. Seventeen individuals with PWS, aged 6-25 years, who showed severe skin-picking behaviors, were recruited and scanned on a 3T scanner. We used functional magnetic resonance imaging (fMRI) while episodes of skin picking were recorded on an MRI-safe video camera. Three participants displayed skin picking continuously throughout the scan, three participants did not display skin picking, and the data for one participant evidenced significant B0 inhomogeneity that could not be corrected. The data for the remaining 10 participants (six male, four female) who displayed a sufficient number of picking and nonpicking episodes were subjected to fMRI analysis. Results showed that regions involved in interoceptive, motor, attention, and somatosensory processing were activated during episodes of skin-picking behavior compared with nonpicking episodes. Scores obtained on the Self-Injury Trauma scale were significantly negatively correlated with mean activation within the right insula and left precentral gyrus. These data indicate that itch and pain processes appear to underlie skin-picking behaviors in PWS, suggesting that interoceptive disturbance may contribute to the severity and maintenance of abnormal skin-picking behaviors in PWS. Implications for treatments are discussed. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

    View details for DOI 10.1002/hbm.22903

    View details for PubMedID 26173182

  • Early signs of anomalous neural functional connectivity in healthy offspring of parents with bipolar disorder BIPOLAR DISORDERS Singh, M. K., Chang, K. D., Kelley, R. G., Saggar, M., Reiss, A. L., Gotlib, I. H. 2014; 16 (7): 678-689

    View details for DOI 10.1111/bdi.12221

    View details for Web of Science ID 000344373100002

  • Targeted intervention to increase creative capacity and performance: A randomized controlled pilot study THINKING SKILLS AND CREATIVITY Kienitz, E., Quintin, E., Saggar, M., Bott, N. T., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 57-66
  • Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility NEUROIMAGE Saggar, M., Shelly, E. W., Lepage, J., Hoeft, F., Reiss, A. L. 2014; 84: 648-656


    Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom.

    View details for DOI 10.1016/j.neuroimage.2013.09.046

    View details for Web of Science ID 000328868600059

    View details for PubMedID 24084068

  • Creativity training enhances goal-directed attention and information processing THINKING SKILLS AND CREATIVITY Bott, N., Quintin, E., Saggar, M., Kienitz, E., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 120-128
  • Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity FRONTIERS IN HUMAN NEUROSCIENCE Saggar, M., King, B. G., Zanesco, A. P., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Bridwell, D. A., Shaver, P. R., Rosenberg, E. L., Sahdra, B. K., Ferrer, E., Tang, A. C., Mangun, G. R., Wallace, B. A., Miikkulainen, R., Saron, C. D. 2012; 6


    The capacity to focus one's attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention (FA) meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG) data were collected during 6 min of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool (SMART), was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency (IAF) decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.

    View details for DOI 10.3389/fnhum.2012.00256

    View details for Web of Science ID 000309107100001

    View details for PubMedID 22973218

  • Behavioral, neuroimaging, and computational evidence for perceptual caching in repetition priming BRAIN RESEARCH Saggar, M., Miikkulainen, R., Schnyer, D. M. 2010; 1315: 75-91


    Repetition priming (RP) is a form of learning, whereby classification or identification performance is improved with item repetition. Various theories have been proposed to understand the basis of RP, including alterations in the representation of an object and associative stimulus-response bindings. There remain several aspects of RP that are still poorly understood, and it is unclear whether previous theories only apply to well-established object representations. This paper integrates behavioral, neuroimaging, and computational modeling experiments in a new RP study using novel objects. Behavioral and neuroimaging results were inconsistent with existing theories of RP, thus a new perceptual memory-based caching mechanism is formalized using computational modeling. The model instantiates a viable neural mechanism that not only accounts for the pattern seen in this experiment but also provides a plausible explanation for previous results that demonstrated residual priming after associative linkages were disrupted. Altogether, the current work helps advance our understanding of how brain utilizes repetition for faster information processing.

    View details for DOI 10.1016/j.brainres.2009.11.074

    View details for Web of Science ID 000275131300009

    View details for PubMedID 20005215

  • Memory Processes in Perceptual Decision Making Proceedings of the 30th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Miikkulainen R., Schnyer D. M. 2008
  • System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks International Joint Conference on Neural Networks Saggar M, Mericli, T., Andoni, S., Miikkulainen, R. 2007: 2239 -2244
  • A computational model of the motivation-learning interface Proceedings of the 29th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Markman A.B., Maddox W.T., Miikkulainen R. 2007
  • Autonomous Learning of Stable Quadruped Locomotion RoboCup 2006: Robot Soccer World Cup X, Lecture Notes in Computer Science Saggar M, DSilva T, Kohl N, Stone P 2007; 4434/2007: 98-109
  • Optimization of association rule mining using improved genetic algorithms IEEE International Conference on Systems, Man and Cybernetics Saggar M, Agrawal, A.K. , Lad, A. 2004; 4434/2007: 3725 - 3729

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