About

Shmuel Lissek is an Associate Professor of Psychology at the University of Minnesota's College of Liberal Arts. His research focuses on understanding what happens in the human brain and body during fear-related learning, memory, and decision making, with particular attention to aberrancies in these psychobiological processes associated with clinical anxiety. His work employs neuroimaging, psychophysiologic, pharmacologic, and behavioral assays of classical and operant fear-conditioning, utilizing a cross-species approach that integrates animal data to elucidate the neurobiology of fear and behavioral avoidance in humans. Lissek's specific areas of interest include the generalization of conditioned fear to stimuli resembling learned danger cues, which serves as a pathogenic marker for conditions such as PTSD, panic disorder, and generalized anxiety disorder. He investigates the neurobiology, psychophysiology, and pharmacologic modifiability of conditioned fear generalization, as well as the conditioning-dependent plasticity in neural representations of learned danger cues that may underlie the generalization process. Additionally, his research explores the psychobiologic substrates of classically conditioned fear as predictors of operant avoidance, contributing to a deeper understanding of anxiety disorders and their underlying mechanisms.

Research topics

• Mathematics
• Neuroscience
• Psychology
• Cognitive psychology
• Audiology
• Medicine

Selected publications

• Intolerance of uncertainty as a predictor of anxiety severity and trajectory during the COVID-19 pandemic

  UNC Libraries · 2026-02-04

  articleOpen access
  PublisherDOI

• Resting-State Functional Connectivity of the Amygdala and Hippocampus in PTSD: Results From the PGC-ENIGMA PTSD Working Group

  American Journal of Psychiatry · 2026-04-02 · 3 citations

  article

  OBJECTIVE: Studies investigating resting-state functional connectivity of the amygdala and hippocampus have produced inconsistent findings. The authors' objective was to conduct the largest systematic comparison of alterations in functional connectivity of the amygdala and hippocampus in individuals with posttraumatic stress disorder (PTSD) using a multicohort mega-analysis with uniform processing steps and parameters across all cohorts. METHODS: Resting-state functional MRI data from 1,017 PTSD patients and 1,702 control participants from 32 international sites were centrally preprocessed with HALFpipe and analyzed using the Image-Based Meta- and Mega-Analysis (IBMMA) package for neuroimaging processing. Group-level seed-based whole-brain analyses were completed for the right and left amygdala and hippocampus. Additional correlation analyses were conducted between PTSD norm-severity scores and resting-state functional connectivity (rs-FC). RESULTS: Compared to control participants, individuals with PTSD showed stronger rs-FC between the left amygdala seed and right hippocampus and amygdala and the left and right lingual gyri. Greater PTSD total norm-severity scores were significantly associated with rs-FC between the left amygdala and right hippocampus/amygdala and rs-FC between the right amygdala and left hippocampus/amygdala. CONCLUSIONS: Greater connectivity between subcortical threat centers involved in fear processing, memory, and extinction learning characterizes the resting state in PTSD. Future directions include investigating how different interventions, such as brain stimulation, neurofeedback, and psychotherapy, might modulate the aberrant neural networks in PTSD.

  PublisherDOI

• Disrupted Thalamocortical Coupling and Canonical Resting-State Network Integration in Posttraumatic Stress Disorder

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A whole-brain voxel-based analysis of structural abnormalities in PTSD: An ENIGMA-PGC study

  European Psychiatry · 2025-01-01 · 5 citations

  reviewOpen access

  Abstract Background Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group. Methods T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9). Results PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, p corrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, p corrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum ( p corrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients ( p corrected = .001). Conclusions PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

  PublisherOA PDFDOI

• Neural correlates of human fear conditioning and sources of variability in 2199 individuals

  2025-07-15

  preprintOpen access

  Pavlovian fear conditioning is a fundamental process in both health and disease. We investigated its neural correlates and sources of variability using harmonized functional magnetic resonance imaging data from 2,199 individuals across nine countries, including 1,888 healthy individuals and 311 with anxiety-related or depressive disorders. Using mega-analysis and normative modelling, we show that fear conditioning consistently engages brain regions within the "central autonomic–interoceptive" or "salience" network. Several task variables strongly modulate activity in these regions, contributing to variability in neural responses. Additionally, brain activation patterns differ between healthy individuals and those with anxiety-related or depressive disorders, with distinct profiles characterizing specific disorders such as post-traumatic stress disorder and obsessive-compulsive disorder. While the neural correlates of fear conditioning are highly generalizable at the population level, variability arises from differences in task design and clinical status, highlighting the importance of methodological diversity in capturing fear learning mechanisms.

  PublisherOA PDFDOI

• Data-Driven Approach to Dynamic Resting State Functional Connectivity in Post-Traumatic Stress Disorder: An ENIGMA-PGC PTSD Study

  Utrecht University Repository (Utrecht University) · 2025-08-01

  articleOpen access

  Using functional magnetic resonance imaging (fMRI), symptoms of posttraumatic stress disorder (PTSD) have been associated with aberrations in brain networks in the absence of a given cognitive demand or task, called resting-state networks. Prior work has focused on disruption in the static functional connectivity (FC) among specific regions constrained by a priori hypotheses. However, dynamic FC, an approach that examines brain network characteristics over time, may provide a more sensitive measure to understand the network properties underlying dysfunction in PTSD. Further, using a data-driven analytic approach may reveal the contribution of other larger network disturbances beyond those revealed by hypothesis-driven examinations of ROIs or canonical networks. Therefore, the current study used group independent components analysis (ICA) and graph theory principles to identify, characterize, and subsequently compare brain network dynamics and recurrent connectivity states in a large sample of trauma exposed individuals (N = 1035) with and without PTSD from the ENIGMA-PGC PTSD workgroup. Neither static FC nor dynamic FC results showed robust differences between groups. There were also no group differences in dwell time or number of transitions of recurrent connectivity states. This multi-cohort sample with heterogenous trauma types and demographic features offers a significantly larger scale approach than prior literature with smaller homogenous trauma cohorts. Heterogeneity of PTSD, especially within diffuse brain networks, may not be captured by evaluating only diagnostic groups, further work should be done to evaluate brain network dynamics with respect to specific symptom profiles and trauma types.

  Publisher

• Structural Covariance of Early Visual Cortex Is Negatively Associated With Posttraumatic Stress Disorder Symptoms: A Mega-Analysis From the ENIGMA PTSD Working Group

  Biological Psychiatry Cognitive Neuroscience and Neuroimaging · 2025-07-23

  articleOpen access
  PublisherOA PDFDOI

• Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

  NeuroImage · 2025-10-23 · 1 citations

  articleOpen access

  • IBMMA efficiently handles large-scale datasets with parallel processing. • Streamlines meta- and mega-analysis workflows through an automated pipeline. • Robustly handles missing voxel-data common in multi-site neuroimaging datasets. • Enables diverse statistical designs beyond the constraints of traditional software. The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large- n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

  PublisherDOI

• Data‐Driven Approach to Dynamic Resting State Functional Connectivity in Post‐Traumatic Stress Disorder: An <scp>ENIGMA</scp>‐<scp>PGC PTSD</scp> Study

  Human Brain Mapping · 2025-07-30 · 1 citations

  articleOpen access

  Using functional magnetic resonance imaging (fMRI), symptoms of posttraumatic stress disorder (PTSD) have been associated with aberrations in brain networks in the absence of a given cognitive demand or task, called resting-state networks. Prior work has focused on disruption in the static functional connectivity (FC) among specific regions constrained by a priori hypotheses. However, dynamic FC, an approach that examines brain network characteristics over time, may provide a more sensitive measure to understand the network properties underlying dysfunction in PTSD. Further, using a data-driven analytic approach may reveal the contribution of other larger network disturbances beyond those revealed by hypothesis-driven examinations of ROIs or canonical networks. Therefore, the current study used group independent components analysis (ICA) and graph theory principles to identify, characterize, and subsequently compare brain network dynamics and recurrent connectivity states in a large sample of trauma exposed individuals (N = 1035) with and without PTSD from the ENIGMA-PGC PTSD workgroup. Neither static FC nor dynamic FC results showed robust differences between groups. There were also no group differences in dwell time or number of transitions of recurrent connectivity states. This multi-cohort sample with heterogenous trauma types and demographic features offers a significantly larger scale approach than prior literature with smaller homogenous trauma cohorts. Heterogeneity of PTSD, especially within diffuse brain networks, may not be captured by evaluating only diagnostic groups, further work should be done to evaluate brain network dynamics with respect to specific symptom profiles and trauma types.

  PublisherOA PDFDOI

• Disrupted thalamocortical functional connectivity and canonical resting-state network integration in posttraumatic stress disorder

  NeuroImage Clinical · 2025-12-12 · 2 citations

  articleOpen access

  The thalamus exhibits widespread connectivity to the entire cortical mantle, yet distinct thalamic subregions possess unique connectivity profiles and functional roles. While the thalamus has been consistently implicated in posttraumatic stress disorder (PTSD), fine-grained investigations examining thalamic subregions and nuclei remain sparse. We examined how resting-state functional connectivity (RSFC) of thalamic nuclei with the cortex and large-scale brain networks may contribute to PTSD using high-resolution functional magnetic resonance imaging (fMRI) data from a multi-site dataset of PTSD cases and controls (n = 397). We show that the pulvinar nuclei exhibit weaker RSFC with sensorimotor and salience regions, while the medial geniculate nucleus (MGN) exhibits stronger RSFC with the sensorimotor cortex in PTSD. Greater PTSD severity correlated with weaker RSFC between both the pulvinar and mediodorsal thalamus and cortical sensory/motor regions in the frontal, parietal, and occipital lobes. We identified that the default mode network of PTSD participants had stronger RSFC with the mediodorsal thalamus, while the salience and somatosensory networks exhibited stronger RSFC with somatomotor thalamic nuclei. Fine-grained thalamic mapping is important for uncovering thalamocortical disruptions in PTSD. Thalamic RSFC shows a shift toward heightened subcortical sensory responsivity and diminished voluntary control and cognitive regulation in PTSD.

  PublisherDOI

Recent grants

• NIH Grant R00MH080130

  NIH · $742k · 2014

Frequent coauthors

• Christian Grillon

  National Institute of Mental Health

  87 shared

• Iris Lange

  European Graduate School of Neuroscience

  85 shared

• Jim van Os

  Maastricht University

  85 shared

• Koen Schruers

  84 shared

• Jindra Bakker

  Maastricht University

  83 shared

• Thérèse van Amelsvoort

  Maastricht University

  83 shared

• Stijn Michielse

  Maastricht University

  83 shared

• Liesbet Goossens

  Erasmus University Rotterdam

  83 shared

Labs

• ANGST LabPI