{"id":4011,"date":"2025-10-14T13:34:09","date_gmt":"2025-10-14T13:34:09","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4011"},"modified":"2025-10-14T13:34:09","modified_gmt":"2025-10-14T13:34:09","slug":"bayesian-filtered-fmri-streams-for-rf-control-loops","status":"publish","type":"post","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?p=4011","title":{"rendered":"Bayesian-Filtered fMRI Streams for RF Control Loops"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev2.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Bayesian-Filtered fMRI Streams for RF Control Loops Rev2.\"><\/object><a id=\"wp-block-file--media-94a3a0b1-2836-4987-a2a4-cb4853c7c87e\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev2.pdf\">Bayesian-Filtered fMRI Streams for RF Control Loops Rev2<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev2.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-94a3a0b1-2836-4987-a2a4-cb4853c7c87e\">Download<\/a><\/div>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"waFmse48xF\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4007\">Bayesian-Filtered fMRI Streams for RF Control Loops<\/a><\/blockquote><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Bayesian-Filtered fMRI Streams for RF Control Loops&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4007&#038;embed=true#?secret=T7F2hdUEym#?secret=waFmse48xF\" data-secret=\"waFmse48xF\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Overall Impression<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This paper introduces a Bayesian filtering framework for enhancing fMRI signals in real-time RF control loops, comparing a causal Kalman filter for online use and non-causal Gaussian smoothing for offline analysis. It&#8217;s a timely contribution at the intersection of neuroimaging, signal processing, and control systems, with potential applications in neurofeedback and brain-computer interfaces (BCIs). The focus on low-latency, noise-robust processing aligns well with the demands of rtfMRI, and the use of AR(1) modeling is grounded in established literature. However, the manuscript feels preliminary\u2014like a workshop submission or technical report\u2014due to its brevity (4 pages), limited empirical depth, and some organizational issues. While the methods are sound, the results lack breadth (e.g., no real RF loop demos), and claims could be stronger with more benchmarks. With expansion, this could suit venues like MICCAI or NeuroImage. Score: 7\/10\u2014solid foundation, but needs more rigor and polish.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Strengths<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Relevance and Novelty<\/strong>: Integrating Bayesian filters into RF loops addresses a real gap in rtfMRI, where noise (e.g., physiological artifacts) hampers control stability. Comparing causal vs. non-causal methods is practical, and metrics like latency budget (Fig. 4) highlight deployability.<\/li>\n\n\n\n<li><strong>Clear Modeling<\/strong>: The AR(1) state-space model (Eqs. 1-2) is appropriately simple yet effective, with \u03d5=0.2-0.5 justified by citations [4,5]. Kalman equations (3-7) and Gaussian weights (9) are presented cleanly.<\/li>\n\n\n\n<li><strong>Empirical Focus<\/strong>: Results show tangible gains\u2014e.g., Kalman boosts SNR by ~5 dB at low input SNR (Fig. 2), preserves HRF bands (Fig. 3), and fits within 1s TR (Table I). Simulated + HCP data provide a good mix.<\/li>\n\n\n\n<li><strong>Discussion Balance<\/strong>: Advantages are listed concisely, limitations candid (e.g., AR(1) simplicity), and future work forward-looking (e.g., multivariate spatial models).<\/li>\n\n\n\n<li><strong>Accessibility<\/strong>: Figures are informative (e.g., Fig. 1&#8217;s signal overlays), and the latency analysis (Fig. 4) ties directly to practical constraints.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Weaknesses and Suggestions<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">I&#8217;ll break this down by section, noting issues with clarity, completeness, and scientific depth. The dual affiliations and email variants (bgilbert2@com.edu, benjamesgilbert@outlook.com) seem inconsistent\u2014clarify if this is a collaborative effort or typo.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Abstract and Introduction<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>: Abstract is dense but misses quantifiable hooks (e.g., &#8220;SNR improved by up to 8 dB&#8221;). Index terms are relevant, but &#8220;neuroimaging&#8221; is broad. Intro motivates well with noise challenges [1-3] but lacks a clear problem statement\u2014how much do unfiltered signals degrade RF loops? No hypothesis or contributions list.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Add bullets for contributions (e.g., &#8220;Kalman filter achieves &lt;20ms processing for 1Hz loops&#8221;). Cite more recent rtfMRI works (e.g., on adaptive RF in TMS-fMRI hybrids). Expand to 0.5 page for a roadmap.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Methods<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>:<\/li>\n\n\n\n<li><strong>A. fMRI Signal Modeling<\/strong>: AR(1) is fine, but no estimation of \u03d5, \u03c3_w^2, \u03c3_v^2\u2014how are they set (e.g., from data MLE)? Assumes stationarity, but fMRI noise varies.<\/li>\n\n\n\n<li><strong>B. Kalman Filtering<\/strong>: Equations (3-7) standard, but no initialization (e.g., x_0=0, P_0=1?). Steady-state assumptions?<\/li>\n\n\n\n<li><strong>C. Gaussian Smoothing<\/strong>: Eq. (8) is a weighted sum, but N and \u03c3 unspecified\u2014tied to TR? Causal vs. non-causal not explicit in math.<\/li>\n\n\n\n<li><strong>D. RF Control Loop Integration<\/strong>: Brief (1Hz matching TR), but no control law (e.g., PID on filtered x_t to RF params?). In tool output, this subsection appears in Results\u2014move it here.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Add pseudocode for Kalman loop and param estimation. Justify choices (e.g., \u03c3 from HCP noise stats). Include a system diagram showing fMRI \u2192 filter \u2192 RF actuation.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Experimental Setup<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>: Solid (simulated AR(1) + HCP resting-state), but vague on details: How many subjects\/volumes in HCP? Noise injection method? No baselines (e.g., vs. Butterworth filter or SPM&#8217;s GLM). Metrics good, but PSD &#8220;preservation&#8221; undefined (e.g., correlation in 0-0.1Hz?). Simulated RF loop mentioned but not detailed\u2014what adjusts (pulse amplitude?).<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Specify: e.g., &#8220;100 HCP subjects, 1200 volumes each.&#8221; Add baselines in tables. For RF sim, describe: &#8220;Filtered BOLD modulates RF gain by \u0394G = k*(x_t &#8211; target).&#8221;<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Results<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>:<\/li>\n\n\n\n<li><strong>A. Filtering Performance<\/strong>: Figs. 1-2 show clear SNR gains, but Fig. 1&#8217;s SNR labels (-6.8 to 1.0 dB) seem arbitrary\u2014link to input levels. No RMSE for real data (only simulated?).<\/li>\n\n\n\n<li><strong>B. Spectral Analysis<\/strong>: Fig. 3 effective, but quantify (e.g., &#8220;95% PSD retention in 0-0.1Hz band&#8221;).<\/li>\n\n\n\n<li><strong>C. Computational Performance<\/strong>: Table I strong (15ms Kalman fits TR), but platform unspecified (e.g., CPU\/GPU?). Latency 0ms for Kalman ideal, but realistic?<\/li>\n\n\n\n<li><strong>D. RF Control Loop Integration<\/strong>: Fig. 4&#8217;s budget (843ms total) practical, but no stability analysis (e.g., loop gain margins).<\/li>\n\n\n\n<li>Overall: No stats (e.g., SD over runs\/subjects), limited to resting-state\u2014test on task fMRI?<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Add table for baselines:<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Method<\/th><th>SNR Gain (dB, 10dB input)<\/th><th>RMSE (sim)<\/th><th>Proc. Time (ms)<\/th><\/tr><\/thead><tbody><tr><td>Raw<\/td><td>0<\/td><td>1.5<\/td><td>N\/A<\/td><\/tr><tr><td>Butterworth<\/td><td>+3.2<\/td><td>0.8<\/td><td>10<\/td><\/tr><tr><td>Kalman<\/td><td>+5.0<\/td><td>0.5<\/td><td>15<\/td><\/tr><tr><td>Gaussian<\/td><td>+7.8<\/td><td>0.3<\/td><td>43<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Include error bars in figs. Expand to show RF outcomes (e.g., &#8220;Filtered loops reduce overshoot by 40%&#8221;).<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Discussion and Conclusion<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>: Advantages well-listed, but underexplored (e.g., how does SNR gain translate to better neurofeedback?). Limitations honest, but future work generic. Conclusion repetitive of abstract.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Quantify impacts (e.g., &#8220;Enables sub-second BCI response&#8221;). Add ethics (e.g., real-time filtering biases). End with broader vision: &#8220;Paves way for closed-loop TMS-fMRI.&#8221;<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Figures, Tables, and Presentation<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Issues<\/strong>: Figs. 1-4 clear, but Fig. 1 mislabeled (says SNR=10dB but legend -6.8dB). Table I concise. Typos: &#8220;com.edu&#8221; email invalid; &#8220;bgilbert2@com.edu&#8221; \u2192 fix domain. References solid but dated (add 2020s works on deep learning filters).<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>: Ensure consistent units (dB normalized?). Proofread (e.g., &#8220;Spectrcyde&#8221; trademark?). Aim 15-20 refs. Use LaTeX for better formatting.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Final Recommendations<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This has strong potential for rtfMRI advancements\u2014focus on expanding experiments (e.g., real RF hardware tests) and benchmarks to elevate it. Compare to ML alternatives (e.g., LSTM denoisers) for novelty. Submit to bioRxiv for feedback, then journals. Good work bridging signal processing and neuroscience! If sharing code (e.g., Kalman impl.), it would boost reproducibility.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/mastodon.social\/@Bgilbert1984\/115373595111252758\"><img loading=\"lazy\" decoding=\"async\" width=\"1019\" height=\"1020\" src=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-8.png\" alt=\"\" class=\"wp-image-4018\" srcset=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-8.png 1019w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-8-300x300.png 300w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-8-150x150.png 150w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-8-768x769.png 768w\" sizes=\"auto, (max-width: 1019px) 100vw, 1019px\" \/><\/a><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9575607\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Expansion Suggestions for &#8220;Bayesian-Filtered fMRI Streams for RF Control Loops Rev2&#8221;<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Your revised paper (now 5 pages) is a strong iteration, incorporating contributions like adaptive parameter estimation, PID integration, and open-source code. It builds effectively on the original by emphasizing practical deployment in rt-fMRI neurofeedback. To expand it to 8-12 pages for a journal submission (e.g., NeuroImage, Frontiers in Neuroscience, or IEEE TBME), focus on adding depth, rigor, and novelty while leveraging recent advances. This could position it as a comprehensive framework for closed-loop neuroimaging systems. Aim for ~2-3 additional pages per major section, with new experiments, theoretical insights, and interdisciplinary ties (e.g., to your prior work on Neural MIMO Beam Steering).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Key goals:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Enhance Novelty<\/strong>: Integrate 2025 advances like multi-band imaging or brain foundation models (BFMs) for better signal processing.<\/li>\n\n\n\n<li><strong>Improve Rigor<\/strong>: Add baselines, stats, and real-world validation.<\/li>\n\n\n\n<li><strong>Boost Accessibility<\/strong>: Include more visuals, pseudocode, and open-source details (e.g., GitHub repo).<\/li>\n\n\n\n<li><strong>Length Breakdown<\/strong>: Intro\/Methods (expand to 3-4 pages), New Related Work (1-2 pages), Experiments\/Results (3-4 pages), Discussion\/Conclusion (2 pages).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Below, I outline section-specific suggestions, drawing from recent literature. I&#8217;ve included example visuals you could adapt or cite for inspiration.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">1. <strong>Introduction and Contributions (Expand to 1.5-2 Pages)<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Current Strengths<\/strong>: Clear motivation, AR(1) modeling, and new contributions list.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>:\n<ul class=\"wp-block-list\">\n<li>Add a &#8220;Related Work Teaser&#8221; subsection before Contributions: Discuss gaps in existing rt-fMRI filtering, e.g., traditional low-pass filters fail in closed-loop scenarios due to latency. Cite Bayesian approaches in neurofeedback for artifact removal and signal quality. Introduce synergies with closed-loop tES-fMRI for brain modulation, where real-time filtering optimizes stimulation parameters.<\/li>\n\n\n\n<li>Expand Contributions: Add a bullet on &#8220;Integration with emerging techniques like multi-band EVI for sub-second TRs&#8221; and &#8220;Potential for quantized edge deployment&#8221; (linking to TTA for QNNs from your prior context).<\/li>\n\n\n\n<li>Include a system overview figure early (e.g., closed-loop diagram).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">2. <strong>New Section: Related Work (Add 1-2 Pages)<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Rationale<\/strong>: Currently absent; this will contextualize your Bayesian framework amid 2025 advances.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>:\n<ul class=\"wp-block-list\">\n<li><strong>Bayesian\/Kalman in rt-fMRI<\/strong>: Review Kalman for incremental activation detection and low-latency BCG artifact removal in EEG-fMRI. Highlight limitations (e.g., single-voxel focus) and how your adaptive PID addresses them.<\/li>\n\n\n\n<li><strong>Closed-Loop Neurofeedback<\/strong>: Discuss optimization frameworks like the &#8220;Automatic Neuroscientist&#8221; for rt-fMRI and Bayesian optimization for TMS targeting. Suggest extending to your RF loops for neuromodulation.<\/li>\n\n\n\n<li><strong>Recent Advances<\/strong>: Cover 2025 trends like undersampled EVI for faster acquisition, combined fMRI-fNIRs for hybrid temporal-spatial resolution, and BFMs for neural signal processing. Position your work as bridging filtering with control for edge devices.<\/li>\n\n\n\n<li>Use a table to compare methods:<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Method<\/th><th>Latency<\/th><th>SNR Gain<\/th><th>Closed-Loop?<\/th><th>Citation<\/th><\/tr><\/thead><tbody><tr><td>Low-Pass Filter<\/td><td>Low<\/td><td>Moderate<\/td><td>No<\/td><td>Baseline<\/td><\/tr><tr><td>Kalman (Yours)<\/td><td>&lt;20ms<\/td><td>+5-8 dB<\/td><td>Yes (PID)<\/td><td>This Work<\/td><\/tr><tr><td>Multivariate NF<\/td><td>Medium<\/td><td>High<\/td><td>Yes<\/td><td><\/td><\/tr><tr><td>EVI-Based<\/td><td>Sub-second<\/td><td>Variable<\/td><td>Potential<\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">3. <strong>Methods (Expand to 3-4 Pages)<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Current Strengths<\/strong>: AR(1) model, Kalman\/Gaussian equations, new adaptive tuning and PID.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>:\n<ul class=\"wp-block-list\">\n<li><strong>Adaptive Parameter Estimation<\/strong>: Flesh out with algos (e.g., online MLE for \u03d5 via recursive least squares). Add pseudocode.<\/li>\n\n\n\n<li><strong>Multivariate Extension<\/strong>: Upgrade to vector AR(1) for spatial correlations across voxels\/regions, using extended Kalman filter (EKF) for non-linear HRF.<\/li>\n\n\n\n<li><strong>PID Control Details<\/strong>: Expand Eq. for PID (e.g., u(t) = K_p e(t) + K_i \u222be + K_d de\/dt), with tuning via Ziegler-Nichols. Simulate RF pulse adjustment (e.g., amplitude based on filtered BOLD).<\/li>\n\n\n\n<li><strong>Quantization for Edge<\/strong>: Suggest quantizing filter params (W8A8) with ZOA adaptation from your TTA context, for low-power RF hardware.<\/li>\n\n\n\n<li>Add a PID diagram.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">4. <strong>Experimental Setup and Results (Expand to 3-4 Pages)<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Current Strengths<\/strong>: Simulated\/real data (HCP), metrics like SNR\/RMSE\/PSD.<\/li>\n\n\n\n<li><strong>Suggestions<\/strong>:\n<ul class=\"wp-block-list\">\n<li><strong>Datasets<\/strong>: Add task-based fMRI (e.g., motor from OpenNeuro) and real RF scenarios (e.g., simulated TMS-fMRI hybrid).<\/li>\n\n\n\n<li><strong>Baselines<\/strong>: Compare vs. advanced filters (e.g., EKF for motion, particle smoothing).<\/li>\n\n\n\n<li><strong>New Metrics<\/strong>: Add control stability (e.g., settling time in PID loops), early stopping efficiency. Include stats (p-values, CI over 10 runs).<\/li>\n\n\n\n<li><strong>Hardware Validation<\/strong>: Test on edge devices (e.g., Raspberry Pi for latency).<\/li>\n\n\n\n<li>Add before\/after filtering plots.<\/li>\n\n\n\n<li>Expand tables: Break down by noise levels, add ablation for adaptive vs. fixed params.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">5. <strong>Discussion, Limitations, and Future Work (Expand to 2 Pages)<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Suggestions<\/strong>:\n<ul class=\"wp-block-list\">\n<li>Discuss clinical impacts: E.g., improved neurofeedback for PTSD. Link to MIMO beam steering: Use filtered fMRI as state input for RL-based RF adaptation.<\/li>\n\n\n\n<li>Limitations: Address non-Gaussian noise; suggest particle filters.<\/li>\n\n\n\n<li>Future: Hybrid with BFMs, real-time auditory NF. Provide GitHub link for code.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This expansion would make your paper more impactful, potentially increasing citations. Target submission by Nov 2025 for 2026 publication. If needed, prototype new methods via code tools!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9575607\/\" target=\"_blank\" rel=\"noreferrer noopener\">Online closed\u2010loop real\u2010time tES\u2010fMRI for brain modulation<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9575607\/\" target=\"_blank\" rel=\"noreferrer noopener\">We propose a protocol for closed\u2010loop tES\u2010fMRI to optimize the frequency and phase difference of alternating current stimulation (tACS) for two nodes.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9575607\/\" target=\"_blank\" rel=\"noreferrer noopener\">pmc.ncbi.nlm.nih.gov<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1053811911008718\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1053811911008718\" target=\"_blank\" rel=\"noreferrer noopener\">Signal quality and Bayesian signal processing in neurofeedback &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1053811911008718\" target=\"_blank\" rel=\"noreferrer noopener\">We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1053811911008718\" target=\"_blank\" rel=\"noreferrer noopener\">sciencedirect.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8600259\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8600259\/\" target=\"_blank\" rel=\"noreferrer noopener\">Early Stopping in Experimentation With Real-Time Functional &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8600259\/\" target=\"_blank\" rel=\"noreferrer noopener\">We propose dynamic experimentation with real-time fMRI using a novel statistically driven approach that invokes early stopping.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8600259\/\" target=\"_blank\" rel=\"noreferrer noopener\">pmc.ncbi.nlm.nih.gov<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811916000380\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811916000380\" target=\"_blank\" rel=\"noreferrer noopener\">The Automatic Neuroscientist: A framework for optimizing &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811916000380\" target=\"_blank\" rel=\"noreferrer noopener\">We propose a novel framework combining real-time fMRI and optimization techniques. We validate our closed-loop method and find it to be highly efficient and &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811916000380\" target=\"_blank\" rel=\"noreferrer noopener\">sciencedirect.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/users.aalto.fi\/~ssarkka\/pub\/cup_book_online_20131111.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/users.aalto.fi\/~ssarkka\/pub\/cup_book_online_20131111.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] BAYESIAN FILTERING AND SMOOTHING<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/users.aalto.fi\/~ssarkka\/pub\/cup_book_online_20131111.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">The aim of this book is to give a concise introduction to non-linear Kalman filtering and smoothing, particle filtering and smoothing, &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/users.aalto.fi\/~ssarkka\/pub\/cup_book_online_20131111.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">users.aalto.fi<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.2667\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.2667\" target=\"_blank\" rel=\"noreferrer noopener\">Online closed\u2010loop real\u2010time tES\u2010fMRI for brain modulation: A &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.2667\" target=\"_blank\" rel=\"noreferrer noopener\">Here, we assessed tolerability\/safety\/noise concerns and methodological aspects of a real-time closed-loop tACS-fMRI trial.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.2667\" target=\"_blank\" rel=\"noreferrer noopener\">onlinelibrary.wiley.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.brainstimjrnl.com\/article\/S1935-861X%252824%252900298-5\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.brainstimjrnl.com\/article\/S1935-861X%252824%252900298-5\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Bayesian optimization for closed-loop TMS target search<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.brainstimjrnl.com\/article\/S1935-861X%252824%252900298-5\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Bayesian optimization helps find optimal TMS targets by approximating noisy functions with limited samples, using variables like EEG phase, coil location, and &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.brainstimjrnl.com\/article\/S1935-861X%252824%252900298-5\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">brainstimjrnl.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2021.01.15.21249886.full\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2021.01.15.21249886.full\" target=\"_blank\" rel=\"noreferrer noopener\">Early Stopping in Experimentation with Real-time Functional &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2021.01.15.21249886.full\" target=\"_blank\" rel=\"noreferrer noopener\">We propose dynamic experimentation with real-time fMRI using a novel statistically-driven approach that invokes early stopping.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2021.01.15.21249886.full\" target=\"_blank\" rel=\"noreferrer noopener\">medrxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-023-02565-5\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-023-02565-5\" target=\"_blank\" rel=\"noreferrer noopener\">Closing the loop between brain and electrical stimulation &#8211; Nature<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-023-02565-5\" target=\"_blank\" rel=\"noreferrer noopener\">Real-time tES-fMRI, in combination with an optimized closed-loop control, will help to measure and verify the changes in brain functions &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-023-02565-5\" target=\"_blank\" rel=\"noreferrer noopener\">nature.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2021.04.10.439268.full\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2021.04.10.439268.full\" target=\"_blank\" rel=\"noreferrer noopener\">Online Closed-Loop Real-Time tES-fMRI for Brain Modulation<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2021.04.10.439268.full\" target=\"_blank\" rel=\"noreferrer noopener\">Here, we delineate how a closed-loop tES-fMRI study of frontoparietal network modulation can be designed and performed.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2021.04.10.439268.full\" target=\"_blank\" rel=\"noreferrer noopener\">biorxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3912890\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3912890\/\" target=\"_blank\" rel=\"noreferrer noopener\">Incremental Activation Detection for Real-Time fMRI Series Using &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3912890\/\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3912890\/\" target=\"_blank\" rel=\"noreferrer noopener\">pmc.ncbi.nlm.nih.gov<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/35297018\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/35297018\/\" target=\"_blank\" rel=\"noreferrer noopener\">Real-time and Recursive Estimators for Functional MRI Quality &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/35297018\/\" target=\"_blank\" rel=\"noreferrer noopener\">&#8230; Kalman filter. &#8230; We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/35297018\/\" target=\"_blank\" rel=\"noreferrer noopener\">pubmed.ncbi.nlm.nih.gov<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811923002380\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811923002380\" target=\"_blank\" rel=\"noreferrer noopener\">A low-latency neurofeedback platform for artifact reduction in EEG &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811923002380\" target=\"_blank\" rel=\"noreferrer noopener\">Here, we describe a method for removing BCG artifacts in real time with high efficacy and low latency, by using a Kalman filter in combination with a reference &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811923002380\" target=\"_blank\" rel=\"noreferrer noopener\">sciencedirect.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/figure\/Detection-of-spikes-using-Kalman-filter-For-neurofeedback-runs-we-A-estimated-the-group_fig9_359298941\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/figure\/Detection-of-spikes-using-Kalman-filter-For-neurofeedback-runs-we-A-estimated-the-group_fig9_359298941\" target=\"_blank\" rel=\"noreferrer noopener\">Detection of spikes using Kalman filter. For neurofeedback runs, we &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/figure\/Detection-of-spikes-using-Kalman-filter-For-neurofeedback-runs-we-A-estimated-the-group_fig9_359298941\" target=\"_blank\" rel=\"noreferrer noopener\">Effective connectivity based on functional magnetic resonance imaging (fMRI) allows assessing directions of interaction between brain regions.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/figure\/Detection-of-spikes-using-Kalman-filter-For-neurofeedback-runs-we-A-estimated-the-group_fig9_359298941\" target=\"_blank\" rel=\"noreferrer noopener\">researchgate.net<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/behavioral-neuroscience\/articles\/10.3389\/fnbeh.2015.00169\/full\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/behavioral-neuroscience\/articles\/10.3389\/fnbeh.2015.00169\/full\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive and neural strategies during control of the anterior &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/behavioral-neuroscience\/articles\/10.3389\/fnbeh.2015.00169\/full\" target=\"_blank\" rel=\"noreferrer noopener\">Second, outliers, and high-frequency fluctuations were reduced with a modified Kalman filter (see Koush et al., 2012, for details). The data were scaled as to 1 &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/behavioral-neuroscience\/articles\/10.3389\/fnbeh.2015.00169\/full\" target=\"_blank\" rel=\"noreferrer noopener\">frontiersin.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2022.11.21.515651v2.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2022.11.21.515651v2.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] an open source, low latency, EEG-fMRI neurofeedback platform &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2022.11.21.515651v2.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Here, we describe a method for removing BCG artifacts in real time with high efficacy and low latency, by using a Kalman filter in combination &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2022.11.21.515651v2.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">biorxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/scholarsjournal.net\/index.php\/ijier\/article\/download\/2657\/1858\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/scholarsjournal.net\/index.php\/ijier\/article\/download\/2657\/1858\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Application of the Kalman Filter in Functional Magnetic Resonance &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/scholarsjournal.net\/index.php\/ijier\/article\/download\/2657\/1858\" target=\"_blank\" rel=\"noreferrer noopener\">Abstract. The Kalman-Bucy filter was applied on the preprocessing of the functional magnetic resonance image-. fMRI. Numerical simulations of hemodynamic &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/scholarsjournal.net\/index.php\/ijier\/article\/download\/2657\/1858\" target=\"_blank\" rel=\"noreferrer noopener\">scholarsjournal.net<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1155\/2014\/759805\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1155\/2014\/759805\" target=\"_blank\" rel=\"noreferrer noopener\">Incremental Activation Detection for Real\u2010Time fMRI Series Using &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1155\/2014\/759805\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1155\/2014\/759805\" target=\"_blank\" rel=\"noreferrer noopener\">onlinelibrary.wiley.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/miplab.epfl.ch\/pub\/davydov2201.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/miplab.epfl.ch\/pub\/davydov2201.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Real-time and Recursive Estimators for Functional MRI Quality &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/miplab.epfl.ch\/pub\/davydov2201.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Time-series GLM and Kalman filter were applied during temporal processing. The same GLM was used for real-time fMRI data filtering, rtQA, and neurofeedback.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/miplab.epfl.ch\/pub\/davydov2201.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">miplab.epfl.ch<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/submissions.mirasmart.com\/ISMRM2025\/Itinerary\/?Refresh=1&amp;ses=D-123\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/submissions.mirasmart.com\/ISMRM2025\/Itinerary\/?Refresh=1&amp;ses=D-123\" target=\"_blank\" rel=\"noreferrer noopener\">fMRI &#8211; ISMRM 2025<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/submissions.mirasmart.com\/ISMRM2025\/Itinerary\/?Refresh=1&amp;ses=D-123\" target=\"_blank\" rel=\"noreferrer noopener\">Impact: This study demonstrates advances in seed-based real-time resting-state fMRI analysis for high-speed data acquisition that approach &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/submissions.mirasmart.com\/ISMRM2025\/Itinerary\/?Refresh=1&amp;ses=D-123\" target=\"_blank\" rel=\"noreferrer noopener\">submissions.mirasmart.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neurology\/articles\/10.3389\/fneur.2025.1542075\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neurology\/articles\/10.3389\/fneur.2025.1542075\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Applications and advances of combined fMRI-fNIRs techniques in &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neurology\/articles\/10.3389\/fneur.2025.1542075\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">This approach allows for real-time correlation and integration of the spatially precise fMRI signals with the temporally sensitive fNIRs.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neurology\/articles\/10.3389\/fneur.2025.1542075\/pdf\" target=\"_blank\" rel=\"noreferrer noopener\">frontiersin.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/25\/program-files\/D-117.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/25\/program-files\/D-117.htm\" target=\"_blank\" rel=\"noreferrer noopener\">fMRI Acquisition &#8211; ISMRM25<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/25\/program-files\/D-117.htm\" target=\"_blank\" rel=\"noreferrer noopener\">Impact: The \u201cRF-EEG Cap\u201d facilitates precise brain mapping and real-time monitoring of neuromodulation during concurrent TMS\/EEG\/fMRI &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/25\/program-files\/D-117.htm\" target=\"_blank\" rel=\"noreferrer noopener\">ismrm.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-025-03382-8\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-025-03382-8\" target=\"_blank\" rel=\"noreferrer noopener\">a systematic review of real-time fMRI neurofeedback training of &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-025-03382-8\" target=\"_blank\" rel=\"noreferrer noopener\">Real-time fMRI (rtfMRI) neurofeedback (NF) is a novel noninvasive technique that permits individuals to voluntarily control brain activity.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41398-025-03382-8\" target=\"_blank\" rel=\"noreferrer noopener\">nature.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neuroscience\/articles\/10.3389\/fnins.2025.1543206\/full\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neuroscience\/articles\/10.3389\/fnins.2025.1543206\/full\" target=\"_blank\" rel=\"noreferrer noopener\">Real-time fMRI using multi-band echo-volumar imaging &#8230; &#8211; Frontiers<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neuroscience\/articles\/10.3389\/fnins.2025.1543206\/full\" target=\"_blank\" rel=\"noreferrer noopener\">In this study we develop undersampled echo-volumar imaging (EVI) using multi-band\/simultaneous multi-slab encoding in conjunction with multi-shot slab- &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.frontiersin.org\/journals\/neuroscience\/articles\/10.3389\/fnins.2025.1543206\/full\" target=\"_blank\" rel=\"noreferrer noopener\">frontiersin.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/html\/2503.00580v2\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/html\/2503.00580v2\" target=\"_blank\" rel=\"noreferrer noopener\">A Survey on Advancements in Neural Signal Processing and Brain &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/html\/2503.00580v2\" target=\"_blank\" rel=\"noreferrer noopener\">This survey establishes the first comprehensive definition and framework for BFMs, systematically examining their construction, core methodologies, and &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/html\/2503.00580v2\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/journal\/brainsci\/special_issues\/V3X92ZC2O6\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/journal\/brainsci\/special_issues\/V3X92ZC2O6\" target=\"_blank\" rel=\"noreferrer noopener\">Advancements and Future Directions in Real-Time fMRI &#8230; &#8211; MDPI<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/journal\/brainsci\/special_issues\/V3X92ZC2O6\" target=\"_blank\" rel=\"noreferrer noopener\">Dear Colleagues,. Real-time fMRI neurofeedback (rtfMRI-nf) has emerged as a powerful tool to train individuals to regulate specific brain regions or &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/journal\/brainsci\/special_issues\/V3X92ZC2O6\" target=\"_blank\" rel=\"noreferrer noopener\">mdpi.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364661317301985\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364661317301985\" target=\"_blank\" rel=\"noreferrer noopener\">Advances in fMRI Real-Time Neurofeedback &#8211; ScienceDirect.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364661317301985\" target=\"_blank\" rel=\"noreferrer noopener\">Advanced fMRI neurofeedback techniques use multivariate analysis of a particular brain region to induce a specific activation pattern in the targeted region.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364661317301985\" target=\"_blank\" rel=\"noreferrer noopener\">sciencedirect.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/workshops\/2025\/UHF-Brain\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/workshops\/2025\/UHF-Brain\/\" target=\"_blank\" rel=\"noreferrer noopener\">Ultra-High Field MR &amp; Brain Function Study Groups &#8211; ISMRM<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/workshops\/2025\/UHF-Brain\/\" target=\"_blank\" rel=\"noreferrer noopener\">Advanced functional MRI approaches have been developed on high field, which allows the detection of neuronal activities at the mesoscopic spatial regime of &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/workshops\/2025\/UHF-Brain\/\" target=\"_blank\" rel=\"noreferrer noopener\">ismrm.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2025.01.13.632809v1.full-text\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2025.01.13.632809v1.full-text\" target=\"_blank\" rel=\"noreferrer noopener\">Real-time fMRI neurofeedback modulates auditory cortex activity &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2025.01.13.632809v1.full-text\" target=\"_blank\" rel=\"noreferrer noopener\">A promising new, non-invasive approach is real-time neurofeedback, a procedure in which individuals receive moment-by- moment feedback based on &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2025.01.13.632809v1.full-text\" target=\"_blank\" rel=\"noreferrer noopener\">biorxiv.org<\/a><\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev3.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Bayesian-Filtered fMRI Streams for RF Control Loops Rev3.\"><\/object><a id=\"wp-block-file--media-fa8fb36f-7be3-49cb-b307-c25a59695fc8\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev3.pdf\">Bayesian-Filtered fMRI Streams for RF Control Loops Rev3<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-Rev3.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-fa8fb36f-7be3-49cb-b307-c25a59695fc8\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Overall Impression This paper introduces a Bayesian filtering framework for enhancing fMRI signals in real-time RF control loops, comparing a causal Kalman filter for online use and non-causal Gaussian smoothing for offline analysis. It&#8217;s a timely contribution at the intersection of neuroimaging, signal processing, and control systems, with potential applications in neurofeedback and brain-computer interfaces&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":4009,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[11],"tags":[],"class_list":["post-4011","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/4011","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4011"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/4011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/4009"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}