Pub Date : 2024-05-06DOI: 10.1101/2024.05.04.24306582
Yusuf Akhtar, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Rachel Kogan, Mostafa Al-noury, Mahdie Hosseini, Leihui Tong, Samarth Mannikeri, Dewey Odhner, Joseph M. Mcdonough, Carina Lott, Abigail Clark, Patrick J. Cahill, Jason B. Anari, Drew A. Torigian
Purpose Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient’s deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces.
{"title":"Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI","authors":"Yusuf Akhtar, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Rachel Kogan, Mostafa Al-noury, Mahdie Hosseini, Leihui Tong, Samarth Mannikeri, Dewey Odhner, Joseph M. Mcdonough, Carina Lott, Abigail Clark, Patrick J. Cahill, Jason B. Anari, Drew A. Torigian","doi":"10.1101/2024.05.04.24306582","DOIUrl":"https://doi.org/10.1101/2024.05.04.24306582","url":null,"abstract":"<strong>Purpose</strong> Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient’s deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"181 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.1101/2024.05.03.24306067
Andrzej Liebert, Hannes Schreiter, Lorenz A Kapsner, Jessica Eberle, Chris Ehring, Dominique Hadler, Luise Brock, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt
Background Virtual contrast-enhanced (vCE) imaging techniques are an emerging topic of research in breast MRI.
背景 虚拟对比度增强（vCE）成像技术是乳腺磁共振成像的一个新兴研究课题。
{"title":"Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks","authors":"Andrzej Liebert, Hannes Schreiter, Lorenz A Kapsner, Jessica Eberle, Chris Ehring, Dominique Hadler, Luise Brock, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt","doi":"10.1101/2024.05.03.24306067","DOIUrl":"https://doi.org/10.1101/2024.05.03.24306067","url":null,"abstract":"<strong>Background</strong> Virtual contrast-enhanced (vCE) imaging techniques are an emerging topic of research in breast MRI.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-05DOI: 10.1101/2024.05.03.24306816
Ines Horvat-Menih, Mary McLean, Maria Jesus Zamora-Morales, Marta Wylot, Joshua Kaggie, Alixander S Khan, Andrew B Gill, Joao Duarte, Matthew J Locke, Iosif A Mendichovszky, Hao Li, Andrew N Priest, Anne Y Warren, Sarah J Welsh, James O Jones, James N Armitage, Thomas J Mitchell, Grant D Stewart, Ferdia A Gallagher
Introduction Localised renal masses are an increasing burden on healthcare due to the rising number of cases. However, conventional imaging cannot reliably distinguish between benign and malignant renal masses, and renal mass biopsies are unable to characterise the entirety of the tumour due to sampling error, which may lead to delayed treatment or overtreatment. There is an unmet clinical need to develop novel imaging techniques to characterise renal masses more accurately. Renal tumours demonstrate characteristic metabolic reprogramming, and novel MRI methods have the potential to detect these metabolic perturbations which may therefore aid accurate characterisation. Here we present our study protocol for the Investigation of the differential biology of Benign and Malignant renal masses using advanced magnetic resonance imaging techniques (IBM-Renal).
{"title":"Investigation of the differential biology between benign and malignant renal masses using advanced magnetic resonance imaging techniques (IBM-Renal): a multi-arm, non-randomised feasibility study","authors":"Ines Horvat-Menih, Mary McLean, Maria Jesus Zamora-Morales, Marta Wylot, Joshua Kaggie, Alixander S Khan, Andrew B Gill, Joao Duarte, Matthew J Locke, Iosif A Mendichovszky, Hao Li, Andrew N Priest, Anne Y Warren, Sarah J Welsh, James O Jones, James N Armitage, Thomas J Mitchell, Grant D Stewart, Ferdia A Gallagher","doi":"10.1101/2024.05.03.24306816","DOIUrl":"https://doi.org/10.1101/2024.05.03.24306816","url":null,"abstract":"<strong>Introduction</strong> Localised renal masses are an increasing burden on healthcare due to the rising number of cases. However, conventional imaging cannot reliably distinguish between benign and malignant renal masses, and renal mass biopsies are unable to characterise the entirety of the tumour due to sampling error, which may lead to delayed treatment or overtreatment. There is an unmet clinical need to develop novel imaging techniques to characterise renal masses more accurately. Renal tumours demonstrate characteristic metabolic reprogramming, and novel MRI methods have the potential to detect these metabolic perturbations which may therefore aid accurate characterisation. Here we present our study protocol for the Investigation of the differential biology of Benign and Malignant renal masses using advanced magnetic resonance imaging techniques (IBM-Renal).","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"335 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1101/2024.05.02.24306551
Mahdie Hosseini, Jayaram K. Udupa, You Hao, Yubing Tong, Caiyun Wu, Yusuf Akhtar, Mostafa Al-Noury, Shiva Shaghaghi, Joseph M. McDonough, David M. Biko, Samantha Gogel, Oscar H. Mayer, Patrick J. Cahill, Drew A. Torigian, Jason B. Anari
Purpose Thoracic insufficiency syndrome (TIS) affects ventilatory function due to spinal and thoracic deformities limiting lung space and diaphragmatic motion. Corrective orthopedic surgery can be used to help normalize skeletal anatomy, restoring lung space and diaphragmatic motion. This study employs free-breathing dynamic MRI (dMRI) and quantifies the 3D motion of each hemi-diaphragm surface in normal and TIS patients, and evaluates effects of surgical intervention.
{"title":"Assessment of 3D hemi-diaphragmatic motion via free-breathing dynamic MRI in pediatric thoracic insufficiency syndrome","authors":"Mahdie Hosseini, Jayaram K. Udupa, You Hao, Yubing Tong, Caiyun Wu, Yusuf Akhtar, Mostafa Al-Noury, Shiva Shaghaghi, Joseph M. McDonough, David M. Biko, Samantha Gogel, Oscar H. Mayer, Patrick J. Cahill, Drew A. Torigian, Jason B. Anari","doi":"10.1101/2024.05.02.24306551","DOIUrl":"https://doi.org/10.1101/2024.05.02.24306551","url":null,"abstract":"<strong>Purpose</strong> Thoracic insufficiency syndrome (TIS) affects ventilatory function due to spinal and thoracic deformities limiting lung space and diaphragmatic motion. Corrective orthopedic surgery can be used to help normalize skeletal anatomy, restoring lung space and diaphragmatic motion. This study employs free-breathing dynamic MRI (dMRI) and quantifies the 3D motion of each hemi-diaphragm surface in normal and TIS patients, and evaluates effects of surgical intervention.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"284 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background Large language models (LLMs) like GPT-4 demonstrate promising capabilities in medical image analysis, but their practical utility is hindered by substantial misdiagnosis rates ranging from 30-50%.
{"title":"Prompt Engineering Strategies Improve the Diagnostic Accuracy of GPT-4 Turbo in Neuroradiology Cases","authors":"Akihiko Wada, Toshiaki Akashi, George Shih, Akifumi Hagiwara, Mitsuo Nishizawa, Yayoi Hayakawa, Junko Kikuta, Keigo Shimoji, Katsuhiro Sano, Koji Kamagata, Atsushi Nakanishi, Shigeki Aoki","doi":"10.1101/2024.04.29.24306583","DOIUrl":"https://doi.org/10.1101/2024.04.29.24306583","url":null,"abstract":"<strong>Background</strong> Large language models (LLMs) like GPT-4 demonstrate promising capabilities in medical image analysis, but their practical utility is hindered by substantial misdiagnosis rates ranging from 30-50%.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1101/2024.04.30.24306508
Dongju Kang, Hyunji Jung, Kyoungjune Pak
Purpose COVID-19, caused by the SARS-CoV-2 virus, has significantly altered modern society and lifestyles. We investigated its impact on brain glucose metabolism by meta-analyzing existing studies that utilized 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans of the brain.
{"title":"Altered Brain Glucose Metabolism in COVID-19 disease: An activation likelihood estimation Meta-analysis","authors":"Dongju Kang, Hyunji Jung, Kyoungjune Pak","doi":"10.1101/2024.04.30.24306508","DOIUrl":"https://doi.org/10.1101/2024.04.30.24306508","url":null,"abstract":"<strong>Purpose</strong> COVID-19, caused by the SARS-CoV-2 virus, has significantly altered modern society and lifestyles. We investigated its impact on brain glucose metabolism by meta-analyzing existing studies that utilized 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans of the brain.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1101/2024.04.30.24306619
Alexandra F. Bonthrone, Manuel Blesa Cábez, A. David Edwards, Jo V. Hajnal, Serena J. Counsell, James P. Boardman
Large diffusion-weighted brain MRI (dMRI) studies in neonates are crucial for developmental neuroscience. Our aim was to investigate the utility of ComBat, and empirical Bayes tool for multisite harmonization, for removing site effects from white matter (WM) dMRI measures in healthy infants born 37-42+6 weeks from the Theirworld Edinburgh Birth Cohort (n=86) and Developing Human Connectome Project (n=287).
{"title":"Harmonizing multisite neonatal diffusion-weighted brain MRI data for developmental neuroscience","authors":"Alexandra F. Bonthrone, Manuel Blesa Cábez, A. David Edwards, Jo V. Hajnal, Serena J. Counsell, James P. Boardman","doi":"10.1101/2024.04.30.24306619","DOIUrl":"https://doi.org/10.1101/2024.04.30.24306619","url":null,"abstract":"Large diffusion-weighted brain MRI (dMRI) studies in neonates are crucial for developmental neuroscience. Our aim was to investigate the utility of ComBat, and empirical Bayes tool for multisite harmonization, for removing site effects from white matter (WM) dMRI measures in healthy infants born 37-42+6 weeks from the Theirworld Edinburgh Birth Cohort (n=86) and Developing Human Connectome Project (n=287).","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1101/2024.04.28.24305337
Paulina Šiurytė, Sebastian Weingärtner
Even though Magnetic Resonance Imaging (MRI) exams are performed up to 16 times per every 100 inhabitants each year, patient comfort and acceptance rates are strongly compromised by exposure to loud acoustic noise. Here we present a system for acoustic noise cancellation using anti-noise derived from predicted scanner sounds. In this approach, termed predictive noise canceling (PNC), the acoustic fingerprint of an MRI system is obtained during a 60 s calibration, and used to predict anti-noise for arbitrary scan procedures. PNC achieves acoustic noise attenuation of up to 13 dB across a wide range of clinical MRI sequences, with spectral noise peak reduction of up to 96.76 % occurring between 0.6 and 1.2 kHz. These results suggest that predicted scanner noise can achieve substantial in-bore noise cancellation with the prospect of providing a cheap and scanner-independent solution for improved patient comfort.
{"title":"Lowering The Acoustic Noise Burden in MRI with Predictive Noise Canceling","authors":"Paulina Šiurytė, Sebastian Weingärtner","doi":"10.1101/2024.04.28.24305337","DOIUrl":"https://doi.org/10.1101/2024.04.28.24305337","url":null,"abstract":"Even though Magnetic Resonance Imaging (MRI) exams are performed up to 16 times per every 100 inhabitants each year, patient comfort and acceptance rates are strongly compromised by exposure to loud acoustic noise. Here we present a system for acoustic noise cancellation using anti-noise derived from predicted scanner sounds. In this approach, termed predictive noise canceling (PNC), the acoustic fingerprint of an MRI system is obtained during a 60 s calibration, and used to predict anti-noise for arbitrary scan procedures. PNC achieves acoustic noise attenuation of up to 13 dB across a wide range of clinical MRI sequences, with spectral noise peak reduction of up to 96.76 % occurring between 0.6 and 1.2 kHz. These results suggest that predicted scanner noise can achieve substantial in-bore noise cancellation with the prospect of providing a cheap and scanner-independent solution for improved patient comfort.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1101/2024.04.26.24306153
Zoe Calulo Rivera, Felipe González-Seguel, Arimitsu Horikawa-Strakovsky, Catherine Granger, Aarti Sarwal, Sanjay Dhar, George Ntoumenopoulos, Jin Chen, V. K. Cody Bumgardner, Selina M. Parry, Kirby P. Mayer, Yuan Wen
Introduction/Aims Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.
{"title":"MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography","authors":"Zoe Calulo Rivera, Felipe González-Seguel, Arimitsu Horikawa-Strakovsky, Catherine Granger, Aarti Sarwal, Sanjay Dhar, George Ntoumenopoulos, Jin Chen, V. K. Cody Bumgardner, Selina M. Parry, Kirby P. Mayer, Yuan Wen","doi":"10.1101/2024.04.26.24306153","DOIUrl":"https://doi.org/10.1101/2024.04.26.24306153","url":null,"abstract":"<strong>Introduction/Aims</strong> Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1101/2024.04.26.24305395
Amir Ebneabbasi, Mortaza Afshani, Arman Seyed-Ahmadi, Varun Warrier, Richard A.I. Bethlehem, Timothy Rittman
Neuroimaging could accurately reflect human behaviour in health and disease, but the mechanism by which image-derived phenotypes correspond to neurotransmitter systems remains uncertain. Prior studies have explored spatial correlations between neuroimaging phenotypes and positron emission tomography radiotracers. However, the influence of neurotransmitters goes beyond the receptors/transporters, influencing a wider array of intracellular components as pivotal parts of neurotransmitter pathways. Here, we used unsupervised learning to understand how the brain maps of healthy function (i.e., magnetoencephalography frequency-specific power) and abnormal structure (i.e., disorder-specific cortical thickness) are closely anchored to underlying neurotransmitter pathways assessed by gene expression data. To do this, we used large-scale datasets of the Human Connectome Project (HCP), Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and Allen Human Brain Atlas (AHBA). We considered spatial and random gene null models to mitigate false positives. We replicate our analyses using different gene stability thresholds. This analytic approach paves the way for personalised medicine and advanced biomarkers.
{"title":"How Do Neurotransmitter Pathways Contribute to Neuroimaging Phenotypes?","authors":"Amir Ebneabbasi, Mortaza Afshani, Arman Seyed-Ahmadi, Varun Warrier, Richard A.I. Bethlehem, Timothy Rittman","doi":"10.1101/2024.04.26.24305395","DOIUrl":"https://doi.org/10.1101/2024.04.26.24305395","url":null,"abstract":"Neuroimaging could accurately reflect human behaviour in health and disease, but the mechanism by which image-derived phenotypes correspond to neurotransmitter systems remains uncertain. Prior studies have explored spatial correlations between neuroimaging phenotypes and positron emission tomography radiotracers. However, the influence of neurotransmitters goes beyond the receptors/transporters, influencing a wider array of intracellular components as pivotal parts of neurotransmitter pathways. Here, we used unsupervised learning to understand how the brain maps of healthy function (i.e., magnetoencephalography frequency-specific power) and abnormal structure (i.e., disorder-specific cortical thickness) are closely anchored to underlying neurotransmitter pathways assessed by gene expression data. To do this, we used large-scale datasets of the Human Connectome Project (HCP), Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and Allen Human Brain Atlas (AHBA). We considered spatial and random gene null models to mitigate false positives. We replicate our analyses using different gene stability thresholds. This analytic approach paves the way for personalised medicine and advanced biomarkers.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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