Hunger reconfigures a reward learning circuit into a memory competent mode
Study Design & Technical Specifications
Sample Size / Cohort
Documented in primary study
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Primary Abstract & Repository Description
Source: bioRxiv (Neuroscience)Editorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Translational Bottlenecks
This resource from bioRxiv (Neuroscience) ("Hunger reconfigures a reward learning circuit into a memory competent mode") addresses critical scientific questions within Parkinson's Disease (PD). Research in this domain currently faces major translational bottlenecks, including quantifying prodromal alpha-synuclein pathology, dopaminergic nigrostriatal circuit vulnerability, and non-motor progression heterogeneity. By focusing on key thematic elements (bioRxiv Preprint, neuroscience, PARKINSON), this work contributes empirical evidence toward understanding how molecular, cellular, or systems-level alterations relate to core disease pathways such as α-synuclein seed amplification assays (SAA), LRRK2/GBA1 variants, DAT-SPECT, and digital motor kinematics. Integrating these findings into the broader open science ecosystem helps reduce fragmentation across independent laboratories and accelerates hypothesis verification.
日本語解説を表示 (Japanese)
bioRxiv (Neuroscience)より公開された本リソース（「Hunger reconfigures a reward learning circuit into a memory competent mode」）は、パーキンソン病（PD）領域における重要な学術的・臨床的課題に焦点を当てています。現在のパーキンソン病（PD）研究では、前駆期におけるα-シヌクレイン病理の定量化、黒質線条体ドパミン神経回路の選択的脆弱性、および非運動症状を含む進行速度の多様性解明といったトランスレーショナルリサーチ上のボトルネックが存在します。本リソースは関連キーワード（bioRxiv Preprint, neuroscience, PARKINSON）を軸として、α-シヌクレイン凝集増幅法（SAA）、LRRK2/GBA1遺伝子変異、DAT-SPECT画像、およびデジタル運動計測指標などの主要経路や病態メカニズムの理解に資する知見・データを提供しており、研究室や機関ごとに分散しがちな知見をオープンサイエンス基盤上で統合・検証する上で重要な位置づけを持ちます。
2. Methodology & Data Architecture Value
Methodologically, this preprint integrates Preprint Article, Open Science within a CNS Experimental / Clinical Model framework (cohort/experimental samples documented in source protocol). Curated via bioRxiv (Neuroscience) (DOI: 10.64898/2026.09.17.752172), the analytical approach provides structured insights into Parkinson's Disease (PD) pathophysiology. Evaluating the reported outcomes alongside Preprint Article, Open Science technical specifications allows researchers to benchmark effect sizes, assay sensitivity, and experimental reproducibility against established reference cohorts.
日本語解説を表示 (Japanese)
方法論の観点から、本研究（種別: preprint）は神経科学実験・臨床モデル（規模: 原著プロトコルに記載されたコホート・実験サンプル）を対象に「Preprint Article, Open Science」を適用しています。bioRxiv (Neuroscience)に収録された本知見（DOI: 10.64898/2026.09.17.752172）は、パーキンソン病（PD）の病態生理や評価指標を精緻に分析する枠組みを提示しています。採用されたモダリティ（Preprint Article, Open Science）の測定仕様と報告された効果量を既存の参照コホートと比較することで、測定感度のベンチマークや実験プロトコルの再現性評価に直接活用できます。
3. Secondary Analysis & Reusability Scenarios
Researchers and computational neuroscientists can reuse this resource across multiple secondary workflows: (1) Cross-Cohort Harmonization & Meta-Analysis — combining these Preprint Article, Open Science records with complementary repositories (such as OpenNeuro, DANDI, Agora, or Europe PMC) to increase statistical power; (2) Computational Benchmarking & Machine Learning — utilizing the documented features (bioRxiv Preprint, neuroscience, PARKINSON) as training, validation, or out-of-distribution test sets for predictive biomarker discovery in Parkinson's Disease (PD); and (3) Experimental Protocol Alignment — referencing the study design (CNS Experimental / Clinical Model, cohort/experimental samples documented in source protocol) to calibrate sample size calculations (power analysis) and endpoint selection for upcoming preclinical or clinical studies.
日本語解説を表示 (Japanese)
本リソースは以下のような二次解析・再利用シナリオに幅広く活用可能です。(1) コホート横断的ハーモナイゼーションとメタ解析：本リソースの「Preprint Article, Open Science」データを他の公開リポジトリ（OpenNeuro、DANDI、Agora、ADNI等）の同種データと統合し、統計的検出力（Statistical Power）を高めた統合解析を実施する。(2) 機械学習・バイオマーカー予測モデルの検証：抽出された特徴量や関連因子（bioRxiv Preprint, neuroscience, PARKINSON）を、パーキンソン病（PD）の進行予測・サブタイプ分類モデルの外部検証（External Validation）や転移学習データとして利用する。(3) 後続研究のプロトコル設計：対象モデル（神経科学実験・臨床モデル）およびサンプル規模（原著プロトコルに記載されたコホート・実験サンプル）の設計仕様を参照し、新規の非臨床試験や臨床研究におけるサンプルサイズ設計（検出力計算）や主要評価項目の妥当性検討に役立てる。
4. Methodological Limitations & Caveats
Because this record is indexed as a preprint, its experimental conclusions have not yet completed formal journal peer review; downstream clinical or therapeutic decisions should await peer-reviewed validation. Specifically, when interpreting Preprint Article, Open Science results in Parkinson's Disease (PD), researchers must control for confounding variables (such as age, sex, post-mortem interval, scanner/instrument drift, or medication state) and verify compliance with open-access attribution requirements (cc_no).
日本語解説を表示 (Japanese)
本レコードはプレプリント（査読前論文）として公開されているため、実験結果や結論は学術誌による正式なピアレビュー完了前の段階にあります。臨床応用や確定的な解釈にあたっては、後続の査読付き出版版の検証結果を併せて確認する必要があります。特にパーキンソン病（PD）における「Preprint Article, Open Science」データの解釈では、年齢・性別・罹病期間・測定機器のキャリブレーション差・併用薬などの交絡因子を適切に補正することが推奨されます。また、データや図表の二次利用時は提供元（bioRxiv (Neuroscience)）の利用条件（cc_no）および適切な引用表記を遵守してください。
Data Access, Reproducibility & Analysis Starter Guide
FAIR Open Science & Clinical Metadata Verification Protocol
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This resource is indexed under FAIR (Findable, Accessible, Interoperable, and Reusable) open-science metadata standards, enabling programmatic cross-referencing across Europe PMC, Semantic Scholar, ClinicalTrials.gov, and CNS Open Data.
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Query Europe PMC & Semantic Scholar Metadata
# Fetch structured bibliographic & open-access data via Europe PMC REST API
import urllib.request
import urllib.parse
import json
query = "DOI:\"10.64898/2026.09.17.752172\""
url = f"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query={urllib.parse.quote(query)}&format=json&resultType=core"
with urllib.request.urlopen(url, timeout=10) as response:
payload = json.loads(response.read().decode("utf-8"))
results = payload.get("resultList", {}).get("result", [])
if results:
record = results[0]
print("Title:", record.get("title"))
print("Journal / Source:", record.get("journalTitle") or record.get("bookOrReportDetails"))
print("Cited By Count:", record.get("citedByCount", 0))Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Programmatic Metadata & Full-Text OA Retrieval
Query Europe PMC or Crossref REST APIs using the persistent DOI/URL to retrieve structured XML full text, supplementary tables, and grant links.
2. Study Design & Cohort Eligibility Screening
Extract sample size, inclusion/exclusion criteria, and primary outcome endpoints for systematic review (PRISMA) inclusion.
3. Data Availability & Supplementary Repository Linkage
Locate accession numbers (GEO, SRA, OpenNeuro, DANDI, Zenodo, Figshare) referenced in the Data Availability section.
4. Citation Graph & Replication Tracking
Monitor supporting and contrasting citations over time to verify whether key findings have been independently replicated.
Reproducibility & Governance Checklist
- Verify whether the source is a peer-reviewed journal publication or an unrefereed preprint before clinical citation.
- Check supplementary data licenses (CC-BY / CC0) when extracting figures or summary statistics for meta-analyses.
- Cross-reference NCT clinical trial identifiers when evaluating therapeutic efficacy or biomarker endpoints.
Tags & Modalities
Bibliometrics, Peer-Review Status & Citation Export
Cross-referenced via Semantic Scholar, Europe PMC & bioRxiv
1. Citation Metrics & Context Breakdown
Citation Context Breakdown
2. Peer-Review Validation & Revision History
Preprint Article (bioRxiv / medRxiv — Awaiting Peer Review)
This article is indexed as an unrefereed preprint. Our ingestion pipeline periodically queries Europe PMC and bioRxiv/medRxiv APIs to automatically link the final peer-reviewed journal DOI once published.
3. Standardized Academic Citation & BibTeX Export
Park, A. et al. (2026). Hunger reconfigures a reward learning circuit into a memory competent mode. bioRxiv (Neuroscience). https://doi.org/10.64898/2026.09.17.752172
@article{a.2026bior,
title = {Hunger reconfigures a reward learning circuit into a memory competent mode},
author = {Park, A. and Senapati, B. and Mancini, N. and Zhang, Y. and Choi, J.},
year = {2026},
journal = {bioRxiv (Neuroscience)},
howpublished = {\url{https://doi.org/10.64898/2026.09.17.752172}},
doi = {10.64898/2026.09.17.752172},
note = {CNS Open Data: parkinson}
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Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
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