https://www.lcneuro.org/january-2020 daily 0.75 2023-01-19 https://www.lcneuro.org/january-2020/blog-post-title-one-pnakx monthly 0.5 2023-01-19 https://www.lcneuro.org/january-2020/blog-post-title-two-mm5ma monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1599157246932-IRDGF46JDDZ6O2981U81/Nicotine+Figure+for+Website-01.jpg Instrumentation - Development of new inhaled nicotine delivery method, and associated pharmacokinetics, for neuroimaging studies of smoking Scanner-safe instrumentation provides highly-controlled administration of nicotine for addiction studies of ad libitum smoking. https://www.lcneuro.org/analytic daily 0.75 2025-08-29 https://www.lcneuro.org/analytic/fixfit monthly 0.5 2025-08-28 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/6fef7e51-b9b8-4d2b-bbbe-7e1ef78ff2f5/Fig1.png Analytic Tools - FixFit - Make it stand out A: A schematic representing the goodness of fit landscape of a model with two interacting parameters. These interactions cause multiple parameter combinations to fit experimental data equally (the redundant maxima in red). B: The same landscape but with the two interacting parameters first combined into a single latent variable. In contrast to the native parameters, numerical fitting over latent variables will converge to a unique solution. C: FixFit generates such unique latent representations using a neural network with an encoder, bottleneck layer, and decoder. https://www.lcneuro.org/analytic/networkstability monthly 0.5 2025-02-07 https://www.lcneuro.org/analytic/mcg2gp1tq8gzfavj34rqpt92ne9d2k monthly 0.5 2025-08-28 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1648604543644-YQ0T9N35KH4ZB6FHWJSC/ControlCircuits%2528LMP3%2529.jpg Analytic Tools - Trajectory drift as a measure of neural circuit dysregulation - Make it stand out Trajectory Drift as a Measure of Feedback Control Error, and Thus Circuit Dysregulation. (a) In a classic engineering control application, such as autopilot, a vehicle corrects for deviations from its desired trajectory through negative feedback. As such, the difference between the autopilot’s actual versus desired trajectories provides a measure of its control error or dysregulation. (b) As per the autopilot example, we use trajectory drift as control error to calculate dysregulation across fMRI-derived control circuits, and demonstrate its application for three psychiatric use cases: neuroticism, depression, and bipolar disorder. (c) Schematic of the pipeline for the discovery of circuit architecture and dynamics from human fMRI and simulated time series using Time-Varying Autoregressive Model with Exogenous Inputs (TVARX) and other state-space models. We use trajectory drift between predicted and actual trajectories to quantify circuit dysregulation across subjects with varying degrees of severity for psychiatric symptoms. https://www.lcneuro.org/analytic/novel-denoising-algorithms-for-resting-state-fmri monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598310898163-OF8DQPA0ZV2CU21D9XNT/Screen+Shot+2020-08-24+at+7.14.36+PM.png Analytic Tools - Novel Denoising Algorithms for Resting State fMRI https://www.lcneuro.org/analytic/blog-post-title-two-4llw5 monthly 0.5 2025-08-29 https://www.lcneuro.org/analytic/blog-post-title-three-a6bjf monthly 0.5 2025-08-29 https://www.lcneuro.org/data daily 0.75 2023-05-15 https://www.lcneuro.org/data/eeg monthly 0.5 2023-05-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/51731560-b5b8-44f1-8426-82d08b9c7621/driussi_methods.jpg Data - Protecting the Aging Brain EEG Dataset - Make it stand out Figure. Schematic of experimental design and methods. (A) Design of within-subject, time-locked targeted metabolic resting-state EEG (rsEEG) experiment. To confirm the neuronal origins of increased brain network stability and synchrony, N = 36 young (µ_age = 26.9 ± 11.2 years), healthy participants underwent four rsEEG scans separated over two days. Following an overnight fast, participants were scanned at baseline and again 30 minutes after consuming a weight-dosed (395 mg/kg) ketone ester or calorie-matched glucose bolus. The rsEEG scans were then repeated using the opposite (ketotic or glycolytic) condition on the second day. (B) An example Global Field Synchronization (GFS) spectrum computed using human rsEEG. The real and imaginary components of Fourier-decomposed rsEEG time-series are plotted on the complex plane for each frequency value. The spread of these points is quantified using principal component analysis. The more the signals are in phase or anti-phase, the greater the difference in magnitude between the first and second principal components of the scatter plot cloud, and the greater the synchrony value (can range from 0 to 1). The scatter points of individual electrodes are color-coded by location on the scalp for illustrative purposes. (C) Schematic characterization of brain network instability. To calculate brain network instability, non-overlapping sliding window correlations are calculated over the entire rsEEG time-series, with strong correlations defining networks. The stability of the networks is then defined as the degree to which these networks persist over time (in units of τ ). (D) Schematic of the Larter-Breakspear neural mass model. Microscale parameters, along with intra-/inter- region coupling (c) and subcortical excitatory inputs (I0) govern the dynamics of the model output: simulated excitatory post-synaptic potentials (EPSPs). The mean EPSPs are multiplied with an EEG lead field to generate simulated EEG time-series, which are used to determine the effects of model parameter variation on synchrony https://www.lcneuro.org/data/pagbdatasets monthly 0.5 2022-03-25 https://www.lcneuro.org/data/blog-post-title-two-nf99f monthly 0.5 2022-03-28 https://www.lcneuro.org/data/blog-post-title-three-t3h24 monthly 0.5 2022-03-28 https://www.lcneuro.org/clinical-applications daily 0.75 2023-08-08 https://www.lcneuro.org/clinical-applications/acute-administration-of-ketone-beta-hydroxybutyrate-downregulates-7t-proton-magnetic-resonance-spectroscopy-derived-levels-of-anterior-and-posterior-cingulate-gaba-and-glutamate-in-healthy-adults monthly 0.5 2023-07-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/97e1859c-c35a-4bd5-99fe-eac90c0c2f76/MRS_Fig1.png Protecting the Aging Brain - Acute administration of ketone beta-hydroxybutyrate downregulates 7T proton magnetic resonance spectroscopy-derived levels of anterior and posterior cingulate GABA and glutamate in healthy adults - Make it stand out Figure: Study design and magnetic resonance spectrocopy (MRS) representative voxel placement and sample spectra. Study design for (A) Experiment 1 and (D) Experiment 2. Positioning of the MRS voxel on the (B) anterior cingulate cortex in Experiment 1 and (E) posterior cingulate cortex in Experiment 2, on the axial, sagittal and coronal planes. Sample MRS spectra from (C) Experiment 1 and (F) Experiment 2. https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1364945a-3d2d-4e49-84fb-c0c596e75a02/MRS_Fig2.png Protecting the Aging Brain - Acute administration of ketone beta-hydroxybutyrate downregulates 7T proton magnetic resonance spectroscopy-derived levels of anterior and posterior cingulate GABA and glutamate in healthy adults - Make it stand out Fig. MRS levels of metabolites. Box and whisker diagrams representing posterior cingulate distribution of (G) GABA, (H) GABA/ tCr, (I) Glu, (J) Glu/tCr in Experiment 2. Results are given in arbitrary units (A.U.). https://www.lcneuro.org/clinical-applications/blog-post-title-two-58nr2 monthly 0.5 2023-02-28 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598297945407-3YO5FNWN93LMM8RPB21S/Mujica-Parodi+PNAS+2020+MRS+Fig.+1.jpg Protecting the Aging Brain - Magnetic Resonance Spectroscopy for Ketone Bodies Supplementary Material Figure 1: The region of interest for single voxel spectroscopy was obtained from the frontal lobe. The voxel (red box) was positioned in front of the corpus collosum and was centered on the anterior cingulate gyrus in the A) sagittal and C) axial planes, and on the lateral ventricle in the B) coronal plane. https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598298048731-HAPFI3G9FOVE3TO9NA08/Mujica-Parodi+PNAS+2020+MRS+Fig.+2.jpg Protecting the Aging Brain - Magnetic Resonance Spectroscopy for Ketone Bodies Supplementary Material Figure 2: Brain glucose and bHB pharmacokinetic timecourse, as measured with 1H Magnetic Resonance Spectroscopy. Average D-b-hydroxybutyrate (bHB /Cr+PCr) and glucose levels (Glc/Cr+PCr) in the brain (voxel centered on anterior cingulate gyrus) peak around 30 minutes following a ketone or glucose bolus, and continue at peak for at least 90 minutes post-bolus. The increased volatility of glucose (as compared to ketones) in the brain is clearly visible, and suggests that scans comparing the two fuels should occur between 30-40 minutes post-bolus, as per our study design. https://www.lcneuro.org/clinical-applications/blog-post-title-four-2l7j8 monthly 0.5 2023-02-28 https://www.lcneuro.org/clinical-applications/blog-post-title-three-4zmm7 monthly 0.5 2022-03-22 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1607556131773-8HLPD3CDPJHQY21ZM7Q6/flow_new.png Protecting the Aging Brain - A data-driven predictome for cognitive, psychiatric, medical, and lifestyle factors on the brain</a> Neuropredictome https://www.lcneuro.org/clinical-applications/blog-post-title-one-7pn4z monthly 0.5 2023-03-02 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598300883651-4NZTBW80N750BM5MZH4R/Screen+Shot+2020-08-20+at+12.54.19+PM.png Protecting the Aging Brain - Type 2 Diabetes Accelerates Brain Aging Type 2 Diabetes Mellitus (T2DM) is associated with strong gray matter atrophy, consistent across the whole brain. We useed structural MRI data from 940 adults with T2DM (62.4±7.5y) and 20062 control subjects (65.4±6.8y). We then utilized multivariable linear regression to address the eects of T2DM on gray matter volume (measured in voxel count) across distinct anatomical regions. After accounting for the eects of age and education, individuals with T2DM showed signicantly decreased gray matter volume in both cortical and sub-cortical regions. The strongest effects were seen in the cerebellum (T=-15.4, P=2e-51) and ventral striatum (T=-10.5, P=6e-24), both showing 5.7% decrease in gray matter volume. *P ≤ .05; **P ≤ .01; ***P ≤ .001. https://www.lcneuro.org/the-brain-under-constraint daily 0.75 2023-08-08 https://www.lcneuro.org/the-brain-under-constraint/early-path-dominance-as-a-principle-for-neurodevelopment monthly 0.5 2023-04-13 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/43bd4c45-d901-4a92-8afa-d22b25d68730/EPD_Fig4.png The Brain Under Constraint - Early Path Dominance as a Principle for Neurodevelopment - Make it stand out Figure: Early Path Dominance Model: Proposed sequence of topological growth in brain development. (A) New edges, marked in red, start off short and thin (less dense) and become longer and wider (more dense) with each growth step. Figure created with biorender.com. (B) Simulations of the model are consistent with theory for the same α parameter. Violin plots represent 1000 independent runs of a graph with 727 nodes, the same number as in the Talairach atlas. https://www.lcneuro.org/the-brain-under-constraint/applying-methods-in-statistical-physics-towards-understanding-the-brain monthly 0.5 2020-08-31 https://www.lcneuro.org/the-brain-under-constraint/blog-post-title-four-krkdl monthly 0.5 2020-08-26 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598391606141-5TN5MVLS60TDN2P25I0I/Mujica-Parodi+Multiscale+Schematic.png The Brain Under Constraint - Multiscale Modeling of the Brain as a Function of Metabolic Constraints Multiscale Modeling. Our computational neuroscience modeling of network connectivity under energy constraints integrates scales that range from neurons and astrocytes all the way up to emergent dynamics seen with fMRI, in predicting individual trajectories. https://www.lcneuro.org/the-brain-under-constraint/blog-post-title-three-9mf7y monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1633849757569-YD44EJ3FO4BS0HKVJ25Q/Artboard+1%402x.png The Brain Under Constraint - Metabolism Modulates Global Synchrony in the Aging Brain Shown is the probability distribution of synchrony (s) vs. Λ, viewed from the top down. Aging from younger (yellow) to older (red) induces a sharp reconfiguration in the distribution of synchrony. Here, the plotted triangles correspond to the Λ values (from Cam-CAN) centered at ages 25 (younger) and 81 (older). https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1633850048592-P57T74RTOBJNW0Y0L0KQ/Artboard+1%403x.png The Brain Under Constraint - Metabolism Modulates Global Synchrony in the Aging Brain - Make it stand out Λ controls the balance between segregated (low s) and integrated (high s) networks. (A and B) FC (averaged over all Cam-CAN subjects) during low (A) and high (B) synchrony, visualized using the BrainNet Viewer showing the top five percentages of connections. (A) Low synchrony (s = 0) reflects segregation (Seg). (B) High synchrony ( | s | > 1/2) reflects integration (Int). (C) The fraction of time each subject (each data point and the specific value of Λ) spends in integrated (P Int ; orange) and segregated (P Seg ; blue) networks. Λ < 0 corresponds to large P Seg and small P Int , while Λ > 0 corresponds to the opposite. The cross-over in C occurs at the critical point, Λ = 0. https://www.lcneuro.org/the-brain-under-constraint/blog-post-title-two-fk639 monthly 0.5 2020-08-26 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598405381020-Y2E93O1DD351PSZ2N2LJ/Gruber+Game+Theory+2020.png The Brain Under Constraint - Using Game Theory to Model Resting State Network Dynamics https://www.lcneuro.org/the-brain-under-constraint/blog-post-title-one-j2z5h monthly 0.5 2020-08-31 https://www.lcneuro.org/circuit-discovery daily 0.75 2023-08-08 https://www.lcneuro.org/circuit-discovery/ion-gradient-driven-bifurcations-of-a-multi-scale-neuronal-model monthly 0.5 2023-04-11 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/c717323f-693b-4579-aca9-bae82120ff9f/lb-fig4.jpg Circuit Discovery - Ion gradient-driven bifurcations of a multi-scale neuronal model - Make it stand out Figure: Flip bifurcation in the potassium ion gradient lengthens refractory period. A. Family of local limit cycle continuations originating at the potassium gradient Hopf point (K ≈ −1.102). Unlike the sodium and calcium gradients, there is no torus (Neimark–Sacker) bifurcation between the Hopf point and the flip (period-doubling) bifurcation, which here occurs at K ≈ −0.61 (shown in red and labeled PD). B. Mean excitatory voltage waveforms under three conditions: baseline reversal potential (K = −0.7), depolarized just over the torus bifurcation (K = −0.59), and significantly hyperpolarized (K = −1.0). C. Attractors plotted in − phase space at each of the three K values listed in B (see legend in B for colors). Crossing the flip bifurcation into the physiologically relevant space below (yellow to blue) only shrinks the refractory period and induces more chaotic activity (increased filling of phase space by the trajectories). Moving further from the flip bifurcation (i.e., the gradient is more hyperpolarized, shown as blue to orange) increases the chaotic activity, but because there is no torus bifurcation the orbits do not collapse to a basin as seen in sodium and calcium. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) https://www.lcneuro.org/circuit-discovery/quantifying-control-circuit-regulation-in-the-human-brain monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1618244880223-AFWGPUBL2PY6U00RME5N/Kumar+2021+Circuit+Discovery+Schematic+%28FOR+WEB%29-01.png Circuit Discovery - Quantifying control circuit regulation in the human brain. Overview of the proposed framework for quantifying control circuit regulation in the human brain. a. The first step involves obtaining the circuit architecture for selected nodes by establishing inputs to each node via causal inference, thus forming a directed network. b. The second step identifies circuit functionality using system identification to obtain a linear state-space model for each node. The directed network representation allows identifying each node’s transfer function as a multi-input single-output (MISO) system. For example, the MISO model for node y uses inputs from nodes u1 and u2. c. The final step involves probing the system using simulated inputs to quantify sensitivity to perturbations and measuring how well regulated or dysregulated the circuit is (control error). Systems with a low damping ratio are prone to stronger oscillatory behavior and thus more sensitive to perturbations than systems with high damping. From: Kumar R, Strey HH, Mujica-Parodi LR. Quantifying control circuit regulation in the human brain. https://www.lcneuro.org/circuit-discovery/blog-post-title-four-9tnb4 monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598390525057-J3WU2QSVEAAFD67RXQ6I/FSN+2016+Figure+Integrated+Brain+and+Simulations-01.png Circuit Discovery - From Anxious to Reckless: A Control Systems Approach Unifies Prefrontal-Limbic Regulation Across the Spectrum of Threat Detection Figure: Regulation of the expanded prefrontal-limbic circuit in response to ambiguous threat tracks the spectrum of anxious to reckless. (a) Control circuit derived from power spectrum scale invariance and dynamic causal modeling (currently being tested with EEG); Simulation outputs generate (b) amgydala response amplitudes and (c) IFG dynamics consistent with multiple independent fMRI experiments totaling 226 adult participants. https://www.lcneuro.org/circuit-discovery/blog-post-title-three-n7xy7 monthly 0.5 2020-12-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598408047608-91C80PWNC2CZHRGIPA1J/Mujica-Parodi+Cybersecurity+Task.png Circuit Discovery - Individualized prediction of human pattern detection under conditions of “unknown unknowns” Cybersecurity Game: In real life, feedback is ambiguous. Here, individuals learn complex patterns that indicate threat. The task was developed in conjunction with intelligence analysts as a model for real-world detection of cybersecurity threats. https://www.lcneuro.org/circuit-discovery/blog-post-title-two-bafhl monthly 0.5 2020-12-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1607982135685-MJHW63CC6A26QO8YAOFI/AdobeStock_219361859.jpeg Circuit Discovery - Fight or Flight in the Faraday Cage https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1608002037911-2H21F1204SASSVSC8RT0/Ambiguous+Stimuli.jpg Circuit Discovery - Fight or Flight in the Faraday Cage After first conditioning fear to a target stimulus, we then measure how the Information Loop processes stimuli that are safe but perceptually similar to cues signaling threat. https://www.lcneuro.org/circuit-discovery/blog-post-title-one-t8zja monthly 0.5 2020-12-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598385642783-A2IPBD37DUTRHVSCNCCE/Mujica-Parodi+IJNP+2020+3+Types+of+Models.jpg Circuit Discovery - Making Sense of Computational Psychiatry Figure 1. Three “models”: Freudian iceberg model of the human mind, modern neuropsychiatric control circuit model, and dynamical systems model of a control circuit. (A) An example of the typical conceptual models historically grounding psychiatry, Freud’s theories assume that behavior reflects unconscious influences provided by components of the mind, including drives (“id”). (B) Schematic of the cortico-basal ganglia-thalamo-cortical (CBGTC) loop, integrating data from multiple neuroimaging (MR-PET) modalities. Crucially, the CBGTC loop can be treated either as a heuristic (as per the Freudian iceberg mind) in which it functions more as a map than a true model—meaning that it cannot be operationalized to make predictions over time—with new inputs, or it can be treated as a graphical representation of a dynamical system of differential equations as per (Frank, 2005) and the PID control circuit shown in C. (C) The dynamical system model is distinguished by the fact that, when presented with time-series inputs, it produces simulation outputs. These output trajectories can be compared with new data to rigorously assess the model’s validity. Images adapted from (Maia and Frank, 2011) and Arturo Urquizo (CC-BY 3.0). https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598385503156-0O6G9N8II1F8ZTD4VHDR/Mujica-Parodi+IJNP+2020+Control+Circuits.jpg Circuit Discovery - Making Sense of Computational Psychiatry Figure 2. Circuit discovery. To illustrate how transfer function structure changes with different circuit topologies, we show 3 transfer functions, each of which corresponds to a different kind of “motif,” with series (A), parallel (B), and feedback (C) connections. By using pairs of inputs (u) and outputs (y) to obtain their transfer function (system identification), we can systematically infer circuit topology. A hybrid version uses machine learning to pattern-match data dynamics to the canonical dynamics produced by each motif. This type of data-driven reverse engineering has been used successfully across science and engineering domains, including systems biology (Bongard and Lipson, 2007; Luo et al., 2010; Brunton et al., 2016). https://www.lcneuro.org/optimizing-signal daily 0.75 2023-11-16 https://www.lcneuro.org/optimizing-signal/blog-post-title-four-4rdya monthly 0.5 2022-04-18 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1608048362716-CARQPDZE1JOEWJYVD1SQ/braindancer-logo.jpg Optimizing Signal - BrainDancer(TM) https://www.lcneuro.org/optimizing-signal/blog-post-title-three-p8ksn monthly 0.5 2020-08-24 https://www.lcneuro.org/optimizing-signal/blog-post-title-two-xd9s4 monthly 0.5 2020-12-15 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598386406736-ETJPUNF4VFAOA1C71IBO/Strey+HH+PRE+2020+OU+Processes.png Optimizing Signal - Improving De-noising Methods for Stochastic Differential Equations Comparison of probability distributions of A and τ determined by least-square fit and maximum likelihood analysis. We plotted the estimated probability distributions from the samples using Gaussian kernel density estimation (KDE implementation in SciPy: scipy.stats.gaussian_ked). The least-square estimates for τ are significantly wider than the Bayesian estimate. (a) Histogram of A; (b) histogram of decay time τ. https://www.lcneuro.org/optimizing-signal/blog-post-title-one-wrwbw monthly 0.5 2020-12-22 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1598310404847-ACCU5VOHTYV2NRJVEVM1/Screen+Shot+2020-08-24+at+7.02.20+PM.png Optimizing Signal - Ground-truth resting-state signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics https://www.lcneuro.org/tasks daily 0.75 2023-11-16 https://www.lcneuro.org/tasks/blog-post-title-one-m4l75 monthly 0.5 2020-08-24 https://www.lcneuro.org/tasks/blog-post-title-three-sb2zd monthly 0.5 2020-08-20 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1597958671068-GILPLJWQ4GHQ38KXNDWN/oxy_fig_inner.png Tasks - Oxytocin Task https://www.lcneuro.org/tasks/fh8ku6zuac5gl62ynew1fx7j18792j monthly 0.5 2020-08-21 https://www.lcneuro.org/contact daily 0.75 2023-12-27 https://images.squarespace-cdn.com/content/v1/5ce435d6bea1ed00016ba287/1558964652247-5MWXNMFZVOPGE4TY6U2Q/MHeiderich_ReflexionenZwei-05-copy.jpg Contact https://www.lcneuro.org/mujicaparodi daily 0.75 2025-10-09 https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/51621f50-3ead-4f17-a48d-1710908bd743/Mujica-Parodi+2+-+National+Press+Club.jpg Mujica-Parodi https://www.lcneuro.org/outreach daily 0.75 2025-08-29 https://images.squarespace-cdn.com/content/v1/5ce435d6bea1ed00016ba287/1558964652247-5MWXNMFZVOPGE4TY6U2Q/MHeiderich_ReflexionenZwei-05-copy.jpg Outreach https://images.squarespace-cdn.com/content/v1/5eff4f855abd632e5dd98b69/1595555691661-OY9EZAZ0M7XS3E7LMC9Y/All+of+Sigma.jpg Outreach - K-12 STEM Education Part of our mission is nurturing future scientists, whether in developing an elementary school engineering curriculum or teaching at SigmaCamp, a science and math camp for ages 12-16. 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