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Overview This project will develop and pilot NeuroSEED™, a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo. 2. Introduction Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED™ leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize: H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy. H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving. H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference. Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions. 3. Methods Participants/Sample We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies. Materials and Procedures Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout. Planned Analyses Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset. • H1: Craving classifier accuracy tested via k-fold cross-validation. • H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post. • H3: Mixed models will assess persistence of effects across days. Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing. Ethics and Data Management Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks. 4. Pilot Data (Feasibility) Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol. 5. Budget (Itemized, Current 2025 Pricing) Item Vendor / Justification Estimated Cost (USD) EEG/fNIRS Headset Rental (3 months) OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program) $1,500 tACS Device Rental Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories) $1,450 Participant Compensation $50 × 10 participants $500 Sensory Cue Modules Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set $300 Custom Software Development Signal pipeline scripting (EEG decoding + stimulation trigger integration) $650 Cloud Storage & Data Sharing Zenodo/OSF + AWS credits for backup $200 Total $4,600 Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs. Vendor Citations: OpenBCI Galea pricing: https://shop.openbci.com Neuroelectrics Starstim rental: https://www.neuroelectrics.com 6. References 1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010). 2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016). 3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011). 4. Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013). 5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020). 6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014). 7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019).5a:T1fe7,NeuroSEED™: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive 1. Overview This project will develop and pilot NeuroSEED™, a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo. 2. Introduction Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED™ leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize: H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy. H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving. H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference. Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions. 3. Methods Participants/Sample We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies. Materials and Procedures Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout. Planned Analyses Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset. • H1: Craving classifier accuracy tested via k-fold cross-validation. • H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post. • H3: Mixed models will assess persistence of effects across days. Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing. Ethics and Data Management Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks. 4. Pilot Data (Feasibility) Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol. 5. Budget (Itemized, Current 2025 Pricing) Item Vendor / Justification Estimated Cost (USD) EEG/fNIRS Headset Rental (3 months) OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program) $1,500 tACS Device Rental Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories) $1,450 Participant Compensation $50 × 10 participants $500 Sensory Cue Modules Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set $300 Custom Software Development Signal pipeline scripting (EEG decoding + stimulation trigger integration) $650 Cloud Storage & Data Sharing Zenodo/OSF + AWS credits for backup $200 Total $4,600 Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs. Vendor Citations: OpenBCI Galea pricing: https://shop.openbci.com Neuroelectrics Starstim rental: https://www.neuroelectrics.com 6. References 1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010). 2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016). 3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011). 4. Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013). 5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020). 6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014). 7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019).5b:T51cc,{"type":"doc","content":[{"type":"heading","attrs":{"id":"1bf12e4f-8d57-4e6e-ab52-08e7e028aa48","data-toc-id":"1bf12e4f-8d57-4e6e-ab52-08e7e028aa48","textAlign":null,"level":1},"content":[{"type":"text","text":"NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":": Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive"}]},{"type":"paragraph","attrs":{"id":"36166c89-1e2b-466a-8e05-b1682d8e2033","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"56eeb6cb-936a-4c31-917c-dadeb1e5215c","class":null,"textAlign":null},"content":[{"type":"text","text":"1. Overview"}]},{"type":"paragraph","attrs":{"id":"bf4d89f3-9efe-466a-9032-15e50af97fc8","class":null,"textAlign":null},"content":[{"type":"text","text":"This project will develop and pilot NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":", a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo."}]},{"type":"paragraph","attrs":{"id":"3d9699d2-ca01-42b5-8b70-f36241fc2177","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"b46d73d0-8e24-40fa-8023-d7fbd0c45fb6","class":null,"textAlign":null},"content":[{"type":"text","text":"2. Introduction"}]},{"type":"paragraph","attrs":{"id":"362ae574-052d-4ff6-8203-69f35b8922dd","class":null,"textAlign":null},"content":[{"type":"text","text":"Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":" leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize:"}]},{"type":"paragraph","attrs":{"id":"fa3095ac-010d-40c3-8ba2-47241038f457","class":null,"textAlign":null},"content":[{"type":"text","text":"H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy."}]},{"type":"paragraph","attrs":{"id":"68b7f3d2-8e15-4133-85f5-54ee0dbddece","class":null,"textAlign":null},"content":[{"type":"text","text":"H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving."}]},{"type":"paragraph","attrs":{"id":"d62395e6-054c-46fb-9cca-cb46952cda7c","class":null,"textAlign":null},"content":[{"type":"text","text":"H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference."}]},{"type":"paragraph","attrs":{"id":"22d5e4cf-c46a-48ab-871c-b33471a0a7c3","class":null,"textAlign":null},"content":[{"type":"text","text":"Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions."}]},{"type":"paragraph","attrs":{"id":"f7ce1d13-b367-4069-8330-18e0044de7f2","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"49fd2dbd-f6e9-467c-b1a5-8c262400e5e0","class":null,"textAlign":null},"content":[{"type":"text","text":"3. Methods"}]},{"type":"paragraph","attrs":{"id":"2b5f4251-dcd2-4e85-aa64-87b1f8ad5405","class":null,"textAlign":null},"content":[{"type":"text","text":"Participants/Sample"}]},{"type":"paragraph","attrs":{"id":"e195ce06-7c28-4b6a-bed2-149a240ad28f","class":null,"textAlign":null},"content":[{"type":"text","text":"We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies."}]},{"type":"paragraph","attrs":{"id":"ddedd79f-5bdf-462b-a034-ba0307f6717b","class":null,"textAlign":null},"content":[{"type":"text","text":"Materials and Procedures"}]},{"type":"paragraph","attrs":{"id":"d3620f19-b63e-400a-8bb2-d994290b7b95","class":null,"textAlign":null},"content":[{"type":"text","text":"Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout."}]},{"type":"paragraph","attrs":{"id":"555f95b8-f0a7-450f-86b2-6b2a190e7176","class":null,"textAlign":null},"content":[{"type":"text","text":"Planned Analyses"}]},{"type":"paragraph","attrs":{"id":"56788e7b-f028-4ee0-b6d3-89122ebc732e","class":null,"textAlign":null},"content":[{"type":"text","text":"Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset."}]},{"type":"paragraph","attrs":{"id":"d651a319-3cb1-41f0-85b2-3a87e0b35572","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH1: Craving classifier accuracy tested via k-fold cross-validation."}]},{"type":"paragraph","attrs":{"id":"677ab737-33b1-48a1-a417-33abfc45cab0","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post."}]},{"type":"paragraph","attrs":{"id":"cd9f0cd1-e253-4a78-88de-1890719060ac","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH3: Mixed models will assess persistence of effects across days."}]},{"type":"paragraph","attrs":{"id":"4669760b-42c9-4b58-8603-6e02aa9caca8","class":null,"textAlign":null},"content":[{"type":"text","text":"Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing."}]},{"type":"paragraph","attrs":{"id":"8873fa17-c79d-436d-9838-46c4f1e434e8","class":null,"textAlign":null},"content":[{"type":"text","text":"Ethics and Data Management"}]},{"type":"paragraph","attrs":{"id":"489e242a-06aa-4d0a-90ba-a1b5c5943039","class":null,"textAlign":null},"content":[{"type":"text","text":"Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks."}]},{"type":"paragraph","attrs":{"id":"06653eed-86a6-40e7-988d-55a5aeb5ec43","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"6633d728-94ee-47af-8ed4-16b4ddb739a4","class":null,"textAlign":null},"content":[{"type":"text","text":"4. Pilot Data (Feasibility)"}]},{"type":"paragraph","attrs":{"id":"92be0661-245d-440e-a4c2-ab74de381827","class":null,"textAlign":null},"content":[{"type":"text","text":"Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol."}]},{"type":"paragraph","attrs":{"id":"76805a62-0895-454c-b22f-a3344a7c49cd","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"b5854b02-f9b0-4c1e-b7cc-a84f523aa5ae","class":null,"textAlign":null},"content":[{"type":"text","text":"5. 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funds ($400) reserved for publication fees, bug fixes, or unexpected costs."}]}]},{"type":"quoteCaption"}]},{"type":"paragraph","attrs":{"id":"aa7f70f0-4c48-4e06-b0ca-195e72db7f6f","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Vendor Citations:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"f1c47a5b-df1b-4604-bd92-17d493ef5dde","class":null,"textAlign":null},"content":[{"type":"text","text":"OpenBCI Galea pricing: "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://shop.openbci.com","target":"_blank","rel":"noopener noreferrer nofollow","class":"link"}}],"text":"https://shop.openbci.com"}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"b01bdac8-22ce-47dc-aa0e-c6999f02f88e","class":null,"textAlign":null},"content":[{"type":"text","text":"Neuroelectrics Starstim rental: "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.neuroelectrics.com","target":"_blank","rel":"noopener noreferrer nofollow","class":"link"}}],"text":"https://www.neuroelectrics.com"}]}]}]},{"type":"paragraph","attrs":{"id":"46e88297-e527-48d4-a24b-4cd10f5c046c","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"0af6ef49-5dd7-4b64-a36b-104a31ad6deb","class":null,"textAlign":null},"content":[{"type":"text","text":"6. References"}]},{"type":"paragraph","attrs":{"id":"133183e0-1db3-408d-8bbe-cdb369488a20","class":null,"textAlign":null},"content":[{"type":"text","text":"\t1.\tVolkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010)."}]},{"type":"paragraph","attrs":{"id":"e1bc84cd-acf6-4f11-bd0d-3c4e3353d674","class":null,"textAlign":null},"content":[{"type":"text","text":"\t2.\tKoob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016)."}]},{"type":"paragraph","attrs":{"id":"a30fc126-709f-42ed-be25-57c8055bb711","class":null,"textAlign":null},"content":[{"type":"text","text":"\t3.\tSchiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011)."}]},{"type":"paragraph","attrs":{"id":"ec1ce1ce-1779-4ca7-b17b-d55d2cf651ff","class":null,"textAlign":null},"content":[{"type":"text","text":"\t4.\tHerrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013)."}]},{"type":"paragraph","attrs":{"id":"5b197f59-86fb-4435-94fa-2c2418ea8017","class":null,"textAlign":null},"content":[{"type":"text","text":"\t5.\tWierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020)."}]},{"type":"paragraph","attrs":{"id":"b625ace4-e78c-4c40-a7d1-903795812d98","class":null,"textAlign":null},"content":[{"type":"text","text":"\t6.\tArzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014)."}]},{"type":"paragraph","attrs":{"id":"9c61a46a-d438-407a-9001-c0e81597314f","class":null,"textAlign":null},"content":[{"type":"text","text":"\t7.\tReinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019)."}]}]}5c:T1fe7,NeuroSEED™: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive 1. Overview This project will develop and pilot NeuroSEED™, a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo. 2. Introduction Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED™ leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize: H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy. H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving. H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference. Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions. 3. Methods Participants/Sample We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies. Materials and Procedures Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout. Planned Analyses Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset. • H1: Craving classifier accuracy tested via k-fold cross-validation. • H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post. • H3: Mixed models will assess persistence of effects across days. Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing. Ethics and Data Management Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks. 4. Pilot Data (Feasibility) Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol. 5. Budget (Itemized, Current 2025 Pricing) Item Vendor / Justification Estimated Cost (USD) EEG/fNIRS Headset Rental (3 months) OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program) $1,500 tACS Device Rental Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories) $1,450 Participant Compensation $50 × 10 participants $500 Sensory Cue Modules Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set $300 Custom Software Development Signal pipeline scripting (EEG decoding + stimulation trigger integration) $650 Cloud Storage & Data Sharing Zenodo/OSF + AWS credits for backup $200 Total $4,600 Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs. Vendor Citations: OpenBCI Galea pricing: https://shop.openbci.com Neuroelectrics Starstim rental: https://www.neuroelectrics.com 6. References 1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010). 2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016). 3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011). 4. Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013). 5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020). 6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014). 7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019).5d:T51cc,{"type":"doc","content":[{"type":"heading","attrs":{"id":"1bf12e4f-8d57-4e6e-ab52-08e7e028aa48","data-toc-id":"1bf12e4f-8d57-4e6e-ab52-08e7e028aa48","textAlign":null,"level":1},"content":[{"type":"text","text":"NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":": Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive"}]},{"type":"paragraph","attrs":{"id":"36166c89-1e2b-466a-8e05-b1682d8e2033","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"56eeb6cb-936a-4c31-917c-dadeb1e5215c","class":null,"textAlign":null},"content":[{"type":"text","text":"1. Overview"}]},{"type":"paragraph","attrs":{"id":"bf4d89f3-9efe-466a-9032-15e50af97fc8","class":null,"textAlign":null},"content":[{"type":"text","text":"This project will develop and pilot NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":", a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo."}]},{"type":"paragraph","attrs":{"id":"3d9699d2-ca01-42b5-8b70-f36241fc2177","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"b46d73d0-8e24-40fa-8023-d7fbd0c45fb6","class":null,"textAlign":null},"content":[{"type":"text","text":"2. Introduction"}]},{"type":"paragraph","attrs":{"id":"362ae574-052d-4ff6-8203-69f35b8922dd","class":null,"textAlign":null},"content":[{"type":"text","text":"Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED"},{"type":"emoji","attrs":{"name":"tm"}},{"type":"text","text":" leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize:"}]},{"type":"paragraph","attrs":{"id":"fa3095ac-010d-40c3-8ba2-47241038f457","class":null,"textAlign":null},"content":[{"type":"text","text":"H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy."}]},{"type":"paragraph","attrs":{"id":"68b7f3d2-8e15-4133-85f5-54ee0dbddece","class":null,"textAlign":null},"content":[{"type":"text","text":"H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving."}]},{"type":"paragraph","attrs":{"id":"d62395e6-054c-46fb-9cca-cb46952cda7c","class":null,"textAlign":null},"content":[{"type":"text","text":"H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference."}]},{"type":"paragraph","attrs":{"id":"22d5e4cf-c46a-48ab-871c-b33471a0a7c3","class":null,"textAlign":null},"content":[{"type":"text","text":"Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions."}]},{"type":"paragraph","attrs":{"id":"f7ce1d13-b367-4069-8330-18e0044de7f2","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"49fd2dbd-f6e9-467c-b1a5-8c262400e5e0","class":null,"textAlign":null},"content":[{"type":"text","text":"3. Methods"}]},{"type":"paragraph","attrs":{"id":"2b5f4251-dcd2-4e85-aa64-87b1f8ad5405","class":null,"textAlign":null},"content":[{"type":"text","text":"Participants/Sample"}]},{"type":"paragraph","attrs":{"id":"e195ce06-7c28-4b6a-bed2-149a240ad28f","class":null,"textAlign":null},"content":[{"type":"text","text":"We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies."}]},{"type":"paragraph","attrs":{"id":"ddedd79f-5bdf-462b-a034-ba0307f6717b","class":null,"textAlign":null},"content":[{"type":"text","text":"Materials and Procedures"}]},{"type":"paragraph","attrs":{"id":"d3620f19-b63e-400a-8bb2-d994290b7b95","class":null,"textAlign":null},"content":[{"type":"text","text":"Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout."}]},{"type":"paragraph","attrs":{"id":"555f95b8-f0a7-450f-86b2-6b2a190e7176","class":null,"textAlign":null},"content":[{"type":"text","text":"Planned Analyses"}]},{"type":"paragraph","attrs":{"id":"56788e7b-f028-4ee0-b6d3-89122ebc732e","class":null,"textAlign":null},"content":[{"type":"text","text":"Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset."}]},{"type":"paragraph","attrs":{"id":"d651a319-3cb1-41f0-85b2-3a87e0b35572","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH1: Craving classifier accuracy tested via k-fold cross-validation."}]},{"type":"paragraph","attrs":{"id":"677ab737-33b1-48a1-a417-33abfc45cab0","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post."}]},{"type":"paragraph","attrs":{"id":"cd9f0cd1-e253-4a78-88de-1890719060ac","class":null,"textAlign":null},"content":[{"type":"text","text":"\t•\tH3: Mixed models will assess persistence of effects across days."}]},{"type":"paragraph","attrs":{"id":"4669760b-42c9-4b58-8603-6e02aa9caca8","class":null,"textAlign":null},"content":[{"type":"text","text":"Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing."}]},{"type":"paragraph","attrs":{"id":"8873fa17-c79d-436d-9838-46c4f1e434e8","class":null,"textAlign":null},"content":[{"type":"text","text":"Ethics and Data Management"}]},{"type":"paragraph","attrs":{"id":"489e242a-06aa-4d0a-90ba-a1b5c5943039","class":null,"textAlign":null},"content":[{"type":"text","text":"Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks."}]},{"type":"paragraph","attrs":{"id":"06653eed-86a6-40e7-988d-55a5aeb5ec43","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"6633d728-94ee-47af-8ed4-16b4ddb739a4","class":null,"textAlign":null},"content":[{"type":"text","text":"4. Pilot Data (Feasibility)"}]},{"type":"paragraph","attrs":{"id":"92be0661-245d-440e-a4c2-ab74de381827","class":null,"textAlign":null},"content":[{"type":"text","text":"Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol."}]},{"type":"paragraph","attrs":{"id":"76805a62-0895-454c-b22f-a3344a7c49cd","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"b5854b02-f9b0-4c1e-b7cc-a84f523aa5ae","class":null,"textAlign":null},"content":[{"type":"text","text":"5. 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Program)"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"564ab458-44b0-48dd-8d4b-441c18746121","class":null,"textAlign":null},"content":[{"type":"text","text":"$1,500"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"df4e5184-df9e-4741-bb52-1b183f9e0486","class":null,"textAlign":null},"content":[{"type":"text","text":"tACS Device Rental"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[322],"style":null},"content":[{"type":"paragraph","attrs":{"id":"d2912960-3f80-4744-9bff-dae9ea487f73","class":null,"textAlign":null},"content":[{"type":"text","text":"Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, 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set"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"517d2935-8fd6-40f5-9894-afab649c29ac","class":null,"textAlign":null},"content":[{"type":"text","text":"$300"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"c8774779-8321-471a-aad7-770bddd5b6eb","class":null,"textAlign":null},"content":[{"type":"text","text":"Custom Software Development"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[322],"style":null},"content":[{"type":"paragraph","attrs":{"id":"7704ecf7-3dbe-4dc3-8186-9fc20da51351","class":null,"textAlign":null},"content":[{"type":"text","text":"Signal pipeline scripting (EEG decoding + stimulation trigger integration)"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"f7983b5d-e42a-42b8-b3f3-4a7a600af217","class":null,"textAlign":null},"content":[{"type":"text","text":"$650"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"style":null},"content":[{"type":"paragraph","attrs":{"id":"668109a5-2e85-4a4f-bc42-3c2e1fcaae7c","class":null,"textAlign":null},"content":[{"type":"text","text":"Cloud Storage & Data Sharing"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[322],"style":null},"content":[{"type":"paragraph","attrs":{"id":"980ef389-2bd4-464c-be26-d0ddb221a20b","class":null,"textAlign":null},"content":[{"type":"text","text":"Zenodo/OSF + AWS credits for 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funds ($400) reserved for publication fees, bug fixes, or unexpected costs."}]}]},{"type":"quoteCaption"}]},{"type":"paragraph","attrs":{"id":"aa7f70f0-4c48-4e06-b0ca-195e72db7f6f","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Vendor Citations:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"f1c47a5b-df1b-4604-bd92-17d493ef5dde","class":null,"textAlign":null},"content":[{"type":"text","text":"OpenBCI Galea pricing: "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://shop.openbci.com","target":"_blank","rel":"noopener noreferrer nofollow","class":"link"}}],"text":"https://shop.openbci.com"}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"b01bdac8-22ce-47dc-aa0e-c6999f02f88e","class":null,"textAlign":null},"content":[{"type":"text","text":"Neuroelectrics Starstim rental: "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.neuroelectrics.com","target":"_blank","rel":"noopener noreferrer nofollow","class":"link"}}],"text":"https://www.neuroelectrics.com"}]}]}]},{"type":"paragraph","attrs":{"id":"46e88297-e527-48d4-a24b-4cd10f5c046c","class":null,"textAlign":null}},{"type":"paragraph","attrs":{"id":"0af6ef49-5dd7-4b64-a36b-104a31ad6deb","class":null,"textAlign":null},"content":[{"type":"text","text":"6. References"}]},{"type":"paragraph","attrs":{"id":"133183e0-1db3-408d-8bbe-cdb369488a20","class":null,"textAlign":null},"content":[{"type":"text","text":"\t1.\tVolkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010)."}]},{"type":"paragraph","attrs":{"id":"e1bc84cd-acf6-4f11-bd0d-3c4e3353d674","class":null,"textAlign":null},"content":[{"type":"text","text":"\t2.\tKoob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016)."}]},{"type":"paragraph","attrs":{"id":"a30fc126-709f-42ed-be25-57c8055bb711","class":null,"textAlign":null},"content":[{"type":"text","text":"\t3.\tSchiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011)."}]},{"type":"paragraph","attrs":{"id":"ec1ce1ce-1779-4ca7-b17b-d55d2cf651ff","class":null,"textAlign":null},"content":[{"type":"text","text":"\t4.\tHerrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013)."}]},{"type":"paragraph","attrs":{"id":"5b197f59-86fb-4435-94fa-2c2418ea8017","class":null,"textAlign":null},"content":[{"type":"text","text":"\t5.\tWierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020)."}]},{"type":"paragraph","attrs":{"id":"b625ace4-e78c-4c40-a7d1-903795812d98","class":null,"textAlign":null},"content":[{"type":"text","text":"\t6.\tArzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014)."}]},{"type":"paragraph","attrs":{"id":"9c61a46a-d438-407a-9001-c0e81597314f","class":null,"textAlign":null},"content":[{"type":"text","text":"\t7.\tReinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019)."}]}]}5e:T34fd,

NeuroSEED: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive

1. Overview

This project will develop and pilot NeuroSEED™, a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo.

2. Introduction

Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED™ leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize:

H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy.

H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving.

H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference.

Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions.

3. Methods

Participants/Sample

We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies.

Materials and Procedures

Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout.

Planned Analyses

Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset.

• H1: Craving classifier accuracy tested via k-fold cross-validation.

• H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post.

• H3: Mixed models will assess persistence of effects across days.

Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing.

Ethics and Data Management

Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks.

4. Pilot Data (Feasibility)

Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol.

5. Budget (Itemized, Current 2025 Pricing)

Item	Vendor / Justification	Estimated Cost (USD)
EEG/fNIRS Headset Rental (3 months)	OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program)	$1,500
tACS Device Rental	Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories)	$1,450
Participant Compensation	$50 × 10 participants	$500
Sensory Cue Modules	Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set	$300
Custom Software Development	Signal pipeline scripting (EEG decoding + stimulation trigger integration)	$650
Cloud Storage & Data Sharing	Zenodo/OSF + AWS credits for backup	$200
Total		$4,600

Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs.

Vendor Citations:

OpenBCI Galea pricing: https://shop.openbci.com
Neuroelectrics Starstim rental: https://www.neuroelectrics.com

6. References

1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010).

2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016).

3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011).

4. Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013).

5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020).

6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014).

7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019).

5f:T34fd,

NeuroSEED: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive

1. Overview

2. Introduction

H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy.

H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving.

H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference.

3. Methods

Participants/Sample

Materials and Procedures

Planned Analyses

Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset.

• H1: Craving classifier accuracy tested via k-fold cross-validation.

• H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post.

• H3: Mixed models will assess persistence of effects across days.

Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing.

Ethics and Data Management

4. Pilot Data (Feasibility)

5. Budget (Itemized, Current 2025 Pricing)

Item	Vendor / Justification	Estimated Cost (USD)
EEG/fNIRS Headset Rental (3 months)	OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program)	$1,500
tACS Device Rental	Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories)	$1,450
Participant Compensation	$50 × 10 participants	$500
Sensory Cue Modules	Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set	$300
Custom Software Development	Signal pipeline scripting (EEG decoding + stimulation trigger integration)	$650
Cloud Storage & Data Sharing	Zenodo/OSF + AWS credits for backup	$200
Total		$4,600

Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs.

Vendor Citations:

OpenBCI Galea pricing: https://shop.openbci.com
Neuroelectrics Starstim rental: https://www.neuroelectrics.com

6. References

1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010).

2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016).

3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011).

5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020).

6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014).

7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. (Nature Neuroscience, 2019).

60:T456,Although the proposal presents an innovative and scientifically motivated idea grounded in addiction neuroscience and memory reconsolidation, it suffers from critical methodological and feasibility shortcomings that preclude a higher rating. The sample size is inadequate even for a pilot (n=8-10), the statistical analysis plan lacks formal power analysis and multiple-comparisons correction, the timeline is unrealistic (6 months for full recruitment, intervention, and follow-up), and the investigator lacks demonstrated expertise in the core technical domains (EEG/fNIRS decoding, closed-loop stimulation, addiction neuroscience). The pilot data, while encouraging, derive from only 3 participants and do not include the intervention component. The proposal does not adequately address how generalization will be achieved or how contingency failures (e.g., classifier failure) will be managed within the compressed timeline. For a junior-researcher innovation grant, the ambition is appropriate, but the execution plan is underdeveloped and the team environment raises concerns about technical feasibility.61:T556,NeuroSEED proposes a novel closed-loop neurostimulation intervention combining EEG/fNIRS-based craving detection, targeted memory reactivation, and phase-locked tACS to disrupt addiction-related memory reconsolidation in nicotine-dependent individuals. The project is scientifically grounded in established addiction neuroscience and memory reconsolidation principles, with a clear hypothesis structure and reasonable pilot feasibility data (78% classifier accuracy on n=3). However, the proposal exhibits significant methodological and execution gaps: the sample size (8-10 participants) is extremely small even for a pilot, the statistical analysis plan lacks power analysis detail and multiple-comparisons correction, the timeline is compressed (6 months for recruitment, intervention, and follow-up), and the investigator's track record shows no apparent expertise in addiction neuroscience, EEG decoding, or closed-loop neuromodulation. The exact combination of methods (EEG/fNIRS-triggered TMR plus phase-locked tACS for nicotine craving) is highly novel and lacks published human validation. While the project aligns well with ResearchHub's junior-researcher innovation mandate and demonstrates open-science commitment, the execution risks are substantial, and the feasibility of achieving durable craving reduction within the stated timeline is questionable.62:T456,Although the proposal presents an innovative and scientifically motivated idea grounded in addiction neuroscience and memory reconsolidation, it suffers from critical methodological and feasibility shortcomings that preclude a higher rating. The sample size is inadequate even for a pilot (n=8-10), the statistical analysis plan lacks formal power analysis and multiple-comparisons correction, the timeline is unrealistic (6 months for full recruitment, intervention, and follow-up), and the investigator lacks demonstrated expertise in the core technical domains (EEG/fNIRS decoding, closed-loop stimulation, addiction neuroscience). The pilot data, while encouraging, derive from only 3 participants and do not include the intervention component. The proposal does not adequately address how generalization will be achieved or how contingency failures (e.g., classifier failure) will be managed within the compressed timeline. For a junior-researcher innovation grant, the ambition is appropriate, but the execution plan is underdeveloped and the team environment raises concerns about technical feasibility.63:T1fe4,NeuroSEED: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive 1. Overview This project will develop and pilot NeuroSEED™, a non-invasive closed-loop neural intervention that disrupts addiction-related memory traces by combining real-time craving detection with targeted memory reactivation (TMR) and temporally precise transcranial stimulation. Addiction is maintained by pathological reconsolidation of cue-reward memories. NeuroSEED aims to weaken or eliminate these circuits by reactivating craving memories and then interfering with their re-encoding through phase-locked tACS. The study will use an EEG/fNIRS-based wearable to detect endogenous craving states in individuals with nicotine dependence, triggering brief exposure to individualized craving cues, followed by theta- or gamma-frequency stimulation to interrupt reconsolidation. Ethics review is in progress under protocol ID NRG-8824, and stimulation will remain within FDA-compliant non-invasive safety thresholds. Institutional equipment and data facilities are secured. The timeline spans six months: (1) participant recruitment and classifier training (2 months), (2) closed-loop intervention sessions (3 months), and (3) follow-up, data analysis, and preprint submission (1 month). All data, code, and protocols will be preregistered on ResearchHub, and results will be released under CC-BY via GitHub and Zenodo. 2. Introduction Addiction is not merely behavioral—it is an entrenched loop of maladaptive memory encoding between environmental cues and subcortical reward pathways¹. These learned craving responses are repeatedly reconsolidated in the brain’s cortico-striatal network, particularly the ventral striatum, medial prefrontal cortex (mPFC), and insula². Recent work has demonstrated that these addiction-related memory traces can be weakened or disrupted if reactivated under specific neuromodulatory conditions³. NeuroSEED™ leverages this vulnerability by integrating real-time craving detection with Targeted Memory Reactivation (TMR) and time-locked non-invasive brain stimulation. Our central research question is whether disrupting craving-related memory reconsolidation can produce measurable and lasting reductions in addictive drive. We hypothesize: H1: Real-time craving states can be decoded from EEG/fNIRS signals with >80% classification accuracy. H2: TMR followed by phase-specific tACS (theta/gamma) will reduce EEG-based frontal-striatal coherence and subjective craving. H3: Intervention effects will persist beyond 72 hours post-session, suggesting durable reconsolidation interference. Impact Statement: If successful, NeuroSEED would establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication. This offers a radically new paradigm for relapse prevention and memory-based intervention in substance use and behavioral addictions. 3. Methods Participants/Sample We will recruit 8–10 adult participants (ages 18–45) with moderate nicotine dependence (Fagerström score ≥5), not currently undergoing cessation treatment. Exclusion criteria include psychiatric comorbidities, seizure history, or implanted electronic devices. Recruitment will occur through university mailing lists and community advertisements. This sample size allows preliminary within-subject comparisons with 80% power to detect medium effect sizes (d = 0.6–0.8) based on prior tACS craving disruption studies. Materials and Procedures Participants will complete a baseline craving task to collect EEG/fNIRS data during personalized cue exposure (e.g., images of smoking, scent modules, or voice prompts). These data will train a machine learning classifier (LDA or XGBoost) to detect craving states in real time. During the intervention session, participants wear an EEG+fNIRS headset (e.g., OpenBCI Galea or Kernel Flow), and when craving is detected, they are presented with a subliminal cue set designed to reactivate the addiction memory trace (visuals, sounds, olfactory triggers). Immediately afterward, theta- or gamma-band tACS is delivered via fronto-temporal electrodes for 90 seconds. Stimulation is phase-locked to peak craving epochs and remains within FDA-approved amplitude limits (≤2 mA). Subjective craving is assessed pre-, post-, and 24–72 hours post-intervention using VAS scales and QSU-Brief. HRV and pupil dilation are collected throughout. Planned Analyses Preprocessing includes EEG artifact rejection, fNIRS filtering (short channel regression), and time-locking to cue onset. • H1: Craving classifier accuracy tested via k-fold cross-validation. • H2: Paired t-tests and ANOVAs on frontal-striatal coherence (via PLV and Granger causality), and craving scores pre/post. • H3: Mixed models will assess persistence of effects across days. Contingency: IF craving classifier fails to generalize across sessions, THEN a fixed-cue stimulation protocol will be used for fallback testing. Ethics and Data Management Ethical approval is under review by the institutional board (NRG-8824). All participants will provide informed consent. Stimulation is non-invasive, brief, and within clinical parameters. De-identified EEG/fNIRS, behavioral, and physiological data will be uploaded to OpenNeuro and GitHub with full analysis scripts. Code will be documented with open-source Jupyter notebooks. 4. Pilot Data (Feasibility) Pilot feasibility data were collected from three nicotine-using participants during a passive cue-reactivity EEG task. Smoking-related imagery elicited a consistent increase in frontal theta power and right-insular beta activity relative to neutral cues. Using ~50 trials per participant, a linear discriminant analysis classifier achieved 78% mean classification accuracy (F1-score = 0.81) in distinguishing craving vs. neutral epochs based on 4 frontal electrodes. Although no stimulation or craving disruption was applied, this supports the feasibility of decoding craving states with wearable EEG. These results informed the choice of stimulation timing and window duration for the NeuroSEED protocol. 5. Budget (Itemized, Current 2025 Pricing) Item Vendor / Justification Estimated Cost (USD) EEG/fNIRS Headset Rental (3 months) OpenBCI Galea: EEG+PPG+EOG+EMG bundle (rental via OpenBCI Research Program) $1,500 tACS Device Rental Neuroelectrics Starstim Home Kit (includes stimulation electrodes, software, accessories) $1,450 Participant Compensation $50 × 10 participants $500 Sensory Cue Modules Scent diffusers ($80), auditory cue software (custom) + visual LCD cue set $300 Custom Software Development Signal pipeline scripting (EEG decoding + stimulation trigger integration) $650 Cloud Storage & Data Sharing Zenodo/OSF + AWS credits for backup $200 Total $4,600 Remaining funds ($400) reserved for publication fees, bug fixes, or unexpected costs. Vendor Citations: OpenBCI Galea pricing: https://shop.openbci.com Neuroelectrics Starstim rental: https://www.neuroelectrics.com 6. References 1. Volkow, N. D. The addicted human brain: insights from imaging studies. (National Institute on Drug Abuse, 2010). 2. Koob, G. F. & Volkow, N. D. Neurocircuitry of addiction. (Elsevier, 2016). 3. Schiller, D. & Phelps, E. A. Extinction learning and reconsolidation: mapping the temporal window. (Nature Neuroscience, 2011). 4. Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. (Frontiers in Human Neuroscience, 2013). 5. Wierenga, C. J. et al. Real-time EEG-based decoding of attentional states for closed-loop brain stimulation. (NeuroImage, 2020). 6. Arzi, A. et al. Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. (Journal of Neuroscience, 2014). 7. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. 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This is a critical omission for a quantitative study."},"metrics_defined":{"decision":"Partial","justification":"Primary metrics are defined: classifier accuracy (F1-score), frontal-striatal coherence (PLV and Granger causality), craving (VAS and QSU-Brief), and persistence (mixed-model estimates). However, effect-size metrics are not pre-specified, and secondary metrics (HRV, pupil dilation) lack clear operational definitions or thresholds."},"analysis_present":{"decision":"Yes","justification":"The proposal describes planned analyses for each hypothesis: k-fold cross-validation for classifier accuracy (H1), paired t-tests and ANOVAs for coherence and craving (H2), and mixed models for persistence (H3). 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Current pharmacological (nicotine replacement, varenicline, bupropion) and behavioral interventions have modest efficacy and high relapse rates. A non-invasive, memory-based intervention could address this unmet need."},"clinical_pathway":{"decision":"Partial","justification":"The proposal articulates a general clinical pathway (non-invasive, closed-loop system for addiction treatment) but lacks specific milestones or timelines. No mention of FDA approval pathway, clinical trial design, or commercialization strategy. The 72-hour follow-up window is too short to assess clinically meaningful outcomes (e.g., smoking abstinence, relapse rates). The proposal does not address how the intervention would be scaled or disseminated to clinical settings."},"milestones_defined":{"decision":"No","justification":"The proposal does not define specific clinical milestones (e.g., 'achieve 50% reduction in craving by month 3', 'demonstrate 6-month smoking abstinence in 50% of participants', 'FDA approval by year 2'). The only milestones mentioned are research-related (classifier training, intervention sessions, follow-up assessments), not clinical outcomes or regulatory pathways."}},"score":"Medium","rationale":"The proposal articulates a clear clinical pathway: if successful, NeuroSEED could establish the first non-invasive, closed-loop system for actively extinguishing addictive circuits without medication, offering a new paradigm for relapse prevention. Nicotine dependence is a significant unmet clinical need with limited effective treatments. 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This violates standard statistical rigor and makes the study underpowered for the stated aims.","Investigator lacks demonstrated expertise in core technical domains: No publications, collaborators, or institutional affiliations in addiction neuroscience, EEG/fNIRS decoding, or closed-loop neuromodulation are evident. For a highly technical proposal, this represents a critical execution risk.","Timeline is unrealistic and lacks contingency planning: Six months for full recruitment, intervention, and follow-up is compressed; no contingency time for recruitment delays, equipment failures, or classifier retraining. The proposal explicitly mentions a fallback protocol (fixed-cue stimulation) but does not explain how this would be implemented or analyzed within the timeline.","Multiple comparisons without correction strategy: The proposal plans to test frontal-striatal coherence (PLV and Granger causality), craving scores, HRV, and pupil dilation without specifying a multiple-comparisons correction (Bonferroni, FDR, etc.), increasing false-positive risk."],"overall_rating":"poor","major_strengths":["Novel and scientifically motivated approach: The integration of real-time EEG/fNIRS craving detection, targeted memory reactivation, and phase-locked tACS is innovative and grounded in established addiction neuroscience (cortico-striatal circuitry, memory reconsolidation principles) and recent work on non-invasive brain stimulation for craving reduction.","Clear hypothesis structure and pilot feasibility: Three well-articulated hypotheses (H1-H3) with measurable outcomes; pilot data from n=3 demonstrates 78% classifier accuracy for craving detection using frontal EEG, supporting the technical feasibility of the decoding component.","Strong open-science and reproducibility commitment: Preregistration on ResearchHub, data and code release via GitHub and Zenodo under CC-BY, open-source Jupyter notebooks, and planned preprint submission align well with contemporary best practices and ResearchHub's values.","Appropriate safety framing: Stimulation parameters (≤2 mA tACS) are within established non-invasive research thresholds, ethics review is in progress (NRG-8824), and informed consent procedures are described.","Reasonable budget and resource planning: Itemized budget ($4,600) with vendor justification and contingency reserves; institutional equipment and data facilities are secured."],"overall_summary":"$61","major_weaknesses":["Critically inadequate sample size and statistical power: n=8-10 is extremely small even for a pilot study. The proposal claims 80% power to detect medium effect sizes (d=0.6-0.8) but provides no formal power calculation, no justification for the sample size relative to the number of comparisons (frontal-striatal coherence, craving scores, HRV, pupil dilation), and no correction for multiple comparisons. Within-subject designs with n=8-10 are prone to noise and non-replicability.","Severe timeline compression and unrealistic feasibility: Six months total (2 months recruitment + 3 months intervention + 1 month analysis/preprint) is extremely tight for recruiting 8-10 nicotine-dependent participants, training classifiers, conducting multi-session interventions, and collecting 72-hour follow-up data. 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NeuroSEED: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive

NeuroSEED: Closed-Loop Cortico-Striatal Reprogramming for the Non-Invasive Erasure of Addictive Drive

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