Neuroimage. Reports最新文献

{"title":"PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's disease","authors":"Yasunori Aoki ,&nbsp;Rei Takahashi ,&nbsp;Roberto D. Pascual-Marqui ,&nbsp;Masahiro Hata ,&nbsp;Shun Takahashi ,&nbsp;Ryouhei Ishii ,&nbsp;Masao Iwase ,&nbsp;Mariko Maenishi ,&nbsp;Young-Ok Kim ,&nbsp;Yuki Yamamoto ,&nbsp;Sakura Hikida ,&nbsp;Kana Maruyama ,&nbsp;Etsuro Mori ,&nbsp;Manabu Ikeda","doi":"10.1016/j.ynirp.2025.100317","DOIUrl":"10.1016/j.ynirp.2025.100317","url":null,"abstract":"<div><div>Alzheimer's disease (AD) —the most common form of dementia— begins with mild memory loss and gradually progresses, eventually resulting in a generalized loss of brain function. The pathological changes of AD in the brain cortex begin decades before the onset of symptoms. Improvements in lifestyle habits and disease-modifying treatments before the onset of the disease have been shown to help prevent or delay the onset of AD. However, diagnosis of AD is difficult at the early stage —or even at the prodromal stage [i.e., mild cognitive impairment due to AD (MCIAD)]— since normal aging and other types of dementia also involve memory impairment. Therefore, there is an urgent need to identify markers for the detection of AD at the early stage or pre-onset stage. In this study, we applied exact low-resolution brain electromagnetic tomography (eLORETA) as the source estimation method to electroencephalography (EEG) data. We obtained cortical electrical activity in 96 drug-free AD patients and 147 healthy subjects to train the final model, in addition to activity for 21 MCIAD patients and seven healthy subjects for the purpose of its evaluation. We then applied the low-code machine learning library of PyCaret, with three preprocessing steps (subject-wise normalization, age-difference correction, and log-transformation) to the eLORETA data of AD and healthy subjects. Of the many machine learning classification models used, the linear discriminant analysis (LDA) model showed the highest accuracy, identifying 10 AD patients and 15 healthy subjects with an accuracy of 100.0 %. The LDA model of eLORETA has high transparency and we visualized the discriminant function of the LDA final model using Viewer in eLORETA. Cortical electrical activities in the delta, theta and alpha frequency bands increased in the right dorsolateral prefrontal cortex (DLPFC) regions, as the degree of AD increased (Figs. 2–4). Cortical electrical activity in the beta frequency band decreased in the posterior cingulate cortex (PCC) regions, as the degree of AD increased (Fig. 5) Furthermore, the LDA final model correctly identified 21 MCIAD patients and seven healthy subjects with an accuracy of 96.4 %. Our findings indicate that the LDA final model of eLORETA had the capacity to detect physiological features of AD in EEG data, even before the onset of the disease. Overall, PyCaret with three preprocessing steps after eLORETA source estimation can create an accurate EEG classification model, which makes a significant contribution to the early detection of AD among the many individuals in the general population who remain undiagnosed.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926511","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}

{"title":"The impact of moral injury-related content on reasoning and its neural correlates: Data from the Canadian Armed Forces (CAF)","authors":"Oshin Vartanian ,&nbsp;Anthony Nazarov ,&nbsp;Timothy K. Lam ,&nbsp;Erin Collins ,&nbsp;Megan M. Thompson ,&nbsp;Shawn G. Rhind ,&nbsp;Stacey Silins ,&nbsp;Maria Shiu ,&nbsp;Elaine Maceda ,&nbsp;Kristen King ,&nbsp;Janani Vallikanthan ,&nbsp;Maitri Lad","doi":"10.1016/j.ynirp.2025.100310","DOIUrl":"10.1016/j.ynirp.2025.100310","url":null,"abstract":"<div><div>Recently, there has been growing interest in understanding the causes and consequences of <em>moral injury</em>—defined as the functionally impairing psychological, biological, spiritual, behavioural, and social impact of perpetrating, failing to prevent, bearing witness to, or being a victim of acts that transgress deeply-held moral beliefs and expectations. Neuroimaging studies have revealed that moral injury is associated with functional alterations in regions that underlie emotions, somatosensory processing, internally-oriented thoughts, and cognitive control. However, to date, no study has examined the impact of moral injury on how people reason, or its neural correlates. We hypothesized that content referencing moral injury themes would reduce reasoning accuracy, and engage structures associated with memory and/or emotion. We tested this hypothesis by administering structurally identical arguments that included neutral content or content referencing salient moral injury outcomes (e.g., shame, anger, trust violations) to Canadian Armed Forces members in the fMRI scanner. As predicted, relative to neutral content, reasoning accuracy was reduced on arguments with moral injury themes, particularly in participants who surpassed clinical thresholds on the <em>Moral Injury Outcome Scale</em> (≥31) and the Posttraumatic Stress Disorder Checklist for DSM-5 (≥33), suggesting that reductions in reasoning accuracy might be driven by elevated moral injury symptoms and psychological distress. Furthermore, reasoning on arguments with moral injury-related content engaged the right posterior parahippocampus (BA 19). Given this region's role in representing contextual associations in episodic memory, this suggests that content with moral injury themes might trigger contextual associations that interfere with the reasoning system.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738771","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}

{"title":"Neurobiological and psychological indicators of post-traumatic stress symptoms in children and adolescents treated for a posterior fossa brain tumour","authors":"Elizaveta Igoshina ,&nbsp;Iska Moxon-Emre ,&nbsp;Suzanne Laughlin ,&nbsp;Julie Tseng ,&nbsp;Donald J. Mabbott","doi":"10.1016/j.ynirp.2026.100318","DOIUrl":"10.1016/j.ynirp.2026.100318","url":null,"abstract":"<div><h3>Background</h3><div>Despite the established associations between paediatric brain tumour treatment, distress, and post-traumatic stress symptoms (PTSS), neither the treatment nor tumour pathology reliably account for symptom severity at the individual level. This study examined whether treatment-related changes in fronto-limbic white matter (WM), brain tissue thought to support emotion, is associated with additional variance in PTSS beyond the effect of treatment and endorsing having experienced a distressing event.</div></div><div><h3>Methods</h3><div>Thirty-six children and adolescents treated for a posterior fossa brain tumour and 17 typically developing children (TDC) completed a questionnaire assessing whether they had experienced a distressing event and measured PTSS and underwent diffusion tensor imaging. Diffusivity metrics of fronto-limbic tracts and tracts that do not support emotional functioning (control tracts) were obtained. A continuous measure of the physical, cognitive, and emotional strain of therapy (i.e., treatment burden), was estimated using the Neurological Predictor Scale. Partial Least Squares path modelling was used in an exploratory, theory-guided framework to examine statistical associations among treatment burden, distress, WM, and PTSS.</div></div><div><h3>Results</h3><div>Patients demonstrated greater radial diffusivity than TDC across fronto-limbic and control tracts. Fronto-limbic WM was associated with PTSS severity within the subclinical range and mediated the effects of treatment burden on PTSS. The control tract WM was not associated with PTSS. PTSS severity was also associated with endorsing a distressing event.</div></div><div><h3>Conclusions</h3><div>The findings support two indicators associated with dimensional variation in PTSS severity: an expected psychological indicator, through endorsing having experienced a distressing event, and a neurobiological indicator, via treatment-related changes to fronto-limbic WM. These results reflect correlational patterns in subclinical symptoms and should be interpreted as exploratory.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037879","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}

{"title":"Functional brain connectivity in patients with de novo Parkinson's disease","authors":"Eliseo Picchi ,&nbsp;Silvia Minosse ,&nbsp;Noemi Pucci ,&nbsp;Matteo Conti ,&nbsp;Davide Mascioli ,&nbsp;Alessandro Stefani ,&nbsp;Francesco Garaci ,&nbsp;Valentina Ferrazzoli ,&nbsp;Valerio Da Ros ,&nbsp;Tommaso Schirinzi ,&nbsp;Francesca Di Giuliano","doi":"10.1016/j.ynirp.2026.100327","DOIUrl":"10.1016/j.ynirp.2026.100327","url":null,"abstract":"<div><h3>Introduction</h3><div>This study aims to identify early brain network changes in <em>de novo</em> Parkinson's disease (PD) using resting state-functional Magnetic Resonance Imaging (rs-fMRI), graph-theoretical analysis, and a functional brain network disruption index (k), applied here for the first time to <em>de novo</em> PD.</div></div><div><h3>Materials and methods</h3><div>The study enrolled untreated <em>de novo</em> PD patients and age- and sex-matched healthy controls. PD patients underwent comprehensive clinical assessments (MDS-UPDRS III, H&amp;Y, MMSE, MoCA, NMSS). MRI data were acquired on a 3T system, including 3D T1-weighted MPRAGE and rs-fMRI. rs-fMRI data were pre-processed and analysed using graph theory.</div></div><div><h3>Results</h3><div>The study included 30 <em>de novo</em> PD patients and 30 healthy controls. While global network metrics did not differ significantly, local metrics revealed a reduced disruption index k in <em>de novo</em> PD patients. The disruption index k was negatively correlated with MMSE scores and demonstrated strong discriminatory power between PD patients and healthy controls based on clustering coefficient metrics. Significant differences in hub regions were found, as some disappeared in PD patients while others emerged compared to healthy controls.</div></div><div><h3>Conclusions</h3><div>This study provides evidence of widespread functional alterations in the local brain networks of <em>de novo</em> Parkinson's disease (PD) patients, suggesting early reorganization of brain connectivity. The disruption index (k) demonstrated the ability to detect early and subtle changes in functional brain networks in <em>de novo</em> Parkinson patients.</div></div><div><h3>Significance</h3><div>rs-fMRI can provide valuable insights into the early stages of PD pathophysiology helping to understand the complexity of PD.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100327"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215218","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}

{"title":"Volumetric brain analysis and associated retinal thinning in autosomal dominant optic atrophy patients","authors":"Punpath Pajareeyapong ,&nbsp;Sittaya Buathong ,&nbsp;Sasi Thammasarnsophon ,&nbsp;Kanchalika Sathianvichitr ,&nbsp;Natthapon Rattanathamsakul ,&nbsp;Akarawit Eiamsamarng ,&nbsp;Niphon Chirapapaisan ,&nbsp;Chanon Ngamsombat","doi":"10.1016/j.ynirp.2025.100314","DOIUrl":"10.1016/j.ynirp.2025.100314","url":null,"abstract":"<div><h3>Introduction</h3><div>Dominant optic atrophy (DOA) is an inherited mitochondrial disorder characterized by retinal thinning and progressive visual loss. When accompanied by additional neurological or systemic features, such as progressive external ophthalmoplegia, myopathy, or deafness, it is classified as DOA-plus (DOA+). Although central nervous system involvement has been associated with cortical and cerebellar atrophy, specific regional patterns remain unclear. This study aimed to investigate cortical lobe alterations in DOA+ patients and examine the association between retinal thinning and structural changes in the primary visual cortex (V1).</div></div><div><h3>Methods</h3><div>Seven DOA+ patients and seven age- and sex-matched healthy controls underwent 3T brain MRI, including 3D T1-weighted imaging, and optical coherence tomography (OCT). Cortical parameters including surface area, gray matter volume, and cortical thickness were quantified using automated whole-brain analysis. Comparisons between DOA+ patients and control groups were performed using independent <em>t</em>-tests, and associations between OCT metrics and V1 cortical measures were assessed with Spearman's rank correlation.</div></div><div><h3>Results</h3><div>DOA+ patients showed a trend toward atrophy in V1 and across all cortical lobes, with statistically significant differences observed only in V1 and occipital lobe (p &lt; 0.001). The occipital lobe demonstrated the greatest reduction in gray matter volume (25.1%, p &lt; 0.001). A positive correlation was observed between average RNFL thickness and average V1 thickness (ρ = 0.90, p = 0.037).</div></div><div><h3>Conclusion</h3><div>DOA+ patients showed significant atrophy in occipital lobe. An association between retinal thinning and average V1 thickness was observed. However, a definite causal relationship cannot be established. Further studies in larger, genetically diverse cohorts are needed to validate these findings.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926501","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}

{"title":"Early diagnosis of Alzheimer's disease based on brain morphological changes: A comprehensive approach combining voxel-based morphometry and deep learning","authors":"Mohammad Rezaei ,&nbsp;Shaghayegh Mohammadikhaveh ,&nbsp;Hadis Faraji ,&nbsp;Ramin Ardalani ,&nbsp;Mina Rezaei ,&nbsp;Alireza Shirazinodeh","doi":"10.1016/j.ynirp.2025.100315","DOIUrl":"10.1016/j.ynirp.2025.100315","url":null,"abstract":"<div><div>Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for example, CNNs learning mathematical patterns from statistical correlations in the data without regard to biological cues whatsoever, and merely apply filters such as max pooling, never grasping what the biological cues they are supposed to investigate are. This blind procedure can indeed be in technical language; however, it does not help to identify meaningful insights into neuroimaging, where interpretability is essential, and such inadequacies pose a grave challenge. In our research, rather than depending on the CNNs and FCNs only for the feature extractions, we have integrated biologically motivated features into voxel-based morphometry as well as deep learning. Our goal is to analyze T1-weighted MRI scans and T2-Flair images to investigate the characteristics of gray matter, white matter, cerebrospinal fluid, and white matter Hyperintensity in patients with mild cognitive impairment (MCI) who lie on the spectrum between normal aging and Alzheimer's disease (AD). So we extracted critical structural features such as white matter Hyperintensity, gray matter volume, white matter volume, cerebrospinal fluid (CSF) volume, and cortical thickness. These are biologically meaningful biomarkers that reflect the neurodegenerative alterations directly. To validate our method, after the detection of biological features, we have converted them into 3-bit, 4-bit, 8-bit, and 16-bit images. These images were used as inputs for both FCN and CNN models to investigate the early symptoms of AD from classified intracranial features.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926561","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}

{"title":"Using, misusing, and improving online machine learning-based meta-analysis of neuroimaging published data: A perspective on NeuroQuery","authors":"Yara Mahafza ,&nbsp;Irvine Mason ,&nbsp;Andre Telfer ,&nbsp;Argel Aguilar-Valles,&nbsp;Amedeo D'Angiulli","doi":"10.1016/j.ynirp.2025.100306","DOIUrl":"10.1016/j.ynirp.2025.100306","url":null,"abstract":"<div><div>Online, text-based meta-analysis tools for large databases represent a new digital advance for medical, health, and neuroscience research, among other fields. NeuroQuery is an instance of such a tool for neuroimaging research; it employs supervised machine learning to draw from over 13,000 publications and perform a meta-synthesis, generating predictive fMRI scans based on keyword combinations. Although NeuroQuery is a sophisticated tool, a lack of understanding of how it practically works and its limitations may lead to flawed results and conclusions, undermining its potential value. We review potential risks and limitations, including algorithm limitations, potential biases in the database, and user misinterpretation. Simulating the perspective of an end user, we present an example of unreliable but possible metanalysis results on autistic spectrum disorder (ASD). We then report an analysis of the underlying query from a sophisticated user perspective. Using the same examples, we illustrate possible improvements for the use of NeuroQuery and identify how this tool may be valuable in the context of emerging machine-learning meta-analytical approaches. Although a thorough understanding of NeuroQuery is helpful, we conclude that understanding its limitations plays a more critical role in ensuring validity and reliability of its use. While NeuroQuery is currently not appropriate for rigorous scientific analysis, it could be useful for hypothesis development, preliminary fMRI data mining, exploratory and supplemental analysis as well as literature survey.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100306"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694907","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}

{"title":"Altered structural networks and cognitive functioning in long-term survivors of pediatric brain tumors","authors":"Kristien Bullens ,&nbsp;Charlotte Sleurs ,&nbsp;Jeroen Blommaert ,&nbsp;Rebeca Alejandra Gavrila Laic ,&nbsp;Ahmed Radwan ,&nbsp;Laurien De Roeck ,&nbsp;Patrick Dupont ,&nbsp;Jurgen Lemiere ,&nbsp;Sandra Jacobs","doi":"10.1016/j.ynirp.2025.100307","DOIUrl":"10.1016/j.ynirp.2025.100307","url":null,"abstract":"<div><div>Pediatric brain tumor (PBT) survivors frequently exhibit long-term changes in brain network organization and cognition. This study investigated structural brain networks among PBT survivors, exploring the potential influence of radiotherapy during childhood.</div><div>Cognitive assessments and MRIs were acquired in 20 irradiated and 26 non-irradiated PBT survivors, and 47 healthy controls. Multi-shell diffusion-weighted MRIs were processed to perform tractography and construct weighted graphs. Whole-brain and local graph measures, including hub scores, were calculated. Group differences in cognitive performance and network measures were analyzed using ANOVA or Kruskal-Wallis. Additionally, the susceptibility of hub regions to reorganization and their relationship to cognition were explored.</div><div>PBT survivors showed poorer performance on the Peabody Picture Vocabulary Task, some Wechsler Adult Intelligence Scale subtests, and figure tapping task of the Amsterdam Neuropsychologic Tasks. Structural network analyses revealed higher whole-brain clustering coefficients in both survivor groups. Locally, clustering coefficients were higher in several regions, particularly in irradiated survivors. Although hub locations were largely preserved, their relative strength showed variability. Differences in local graph measures were more frequently significant in hub regions with higher hub scores. These findings indicate that survivors’ structural brain networks undergo reorganization following a PBT and its treatment, especially if survivors had received radiotherapy. Clustering coefficient emerged as the most prominently altered network measure, which was linked to cognitive performance, particularly in hub regions. These results highlight the potential role of structural brain networks to unravel the cause of long-term cognitive outcomes in PBT survivors.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738773","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}

{"title":"Resting-state fMRI analysis of functional connectivity and temporal dynamics differences between cocaine users and healthy controls","authors":"Sravani Varanasi ,&nbsp;Tianye Zhai ,&nbsp;Hong Gu ,&nbsp;Betty Jo Salmeron ,&nbsp;Yihong Yang ,&nbsp;Fow-Sen Choa","doi":"10.1016/j.ynirp.2025.100304","DOIUrl":"10.1016/j.ynirp.2025.100304","url":null,"abstract":"<div><div>Understanding alterations in functional connectivity among individuals with substance use disorder (SUD) is critical for elucidating the neural mechanisms underlying addiction. In this study, we applied Energy Landscape Analysis (ELA), an energy-based machine learning method, to examine whole-brain functional connectivity differences between SUD patients and healthy controls (HCs). A key methodological challenge in ELA lies in the selection of appropriate Regions of Interest (ROIs) from comprehensive brain atlases. To address this, we employed seed-based connectivity analysis to identify task-relevant ROIs, thereby overcoming the limitation of focusing on a restricted subset of regions. The dataset comprised 53 cocaine users (CUs) and 52 age- and sex-matched HCs, with functional MRI data preprocessed using the CONN toolbox. ROI-to-ROI seed-based connectivity was computed through first- and second-level analyses. ELA revealed that HCs exhibited stronger positive connectivity between cerebellar and visual regions, whereas CUs showed stronger positive connectivity between the cerebellum and the inferior temporal gyrus (temporooccipital part; toITG). Seven low-energy connectivity states were identified that differentiated the two groups. In these states, the cerebellum and toITG demonstrated antagonistic activation patterns, while the cerebellum and visual cortex co-activated in HCs. Temporal dynamics analyses further indicated that HCs visited these low-energy states more frequently, driven by shorter dwell times but higher transition rates. These findings suggest that cocaine addiction may reflect a weakening of adaptive, protective (“guardian”) connectivity patterns, rather than an increased propensity to remain in maladaptive connectivity states. Collectively, these results highlight key network-level distinctions between HCs and CUs and offer new insights into the neurobiological mechanisms of cocaine addiction.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100304"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791805","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}

{"title":"A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future","authors":"Neel Joshi ,&nbsp;Jade Guarnera ,&nbsp;Kh Tohidul Islam ,&nbsp;Zhaolin Chen ,&nbsp;Parisa Zakavi ,&nbsp;Hamed Akhlaghi ,&nbsp;Jean Lee ,&nbsp;Karen Caeyenberghs ,&nbsp;Juan F. Domínguez D","doi":"10.1016/j.ynirp.2026.100324","DOIUrl":"10.1016/j.ynirp.2026.100324","url":null,"abstract":"<div><div>Acquired Brain Injury (ABI) refers to any post-birth damage to the brain, commonly resulting from traumatic events (traumatic brain injury) or non-traumatic events (such as stroke, brain disease, or infection). In Australia, ABI places a substantial economic burden on society. Assessment using high-field (HF) MRI is critical after sustaining an ABI however its usage is impeded by scanner availability and stringent safety requirements. Portable ultra-low-field (ULF MRI) may address these challenges. This review summarises literature on portable ULF MRI use in ABI patients, and outlines its safety and efficacy in detecting structural abnormalities and assessing extent of damage. A systematic search using four databases yielded 20 studies: six studies in stroke, one in traumatic brain injury (TBI), three in brain tumours, three in multiple sclerosis, and eight in other neurological disorders. The findings suggest that portable ULF MRI has been used primarily in critical care and found it was safe for use in the presence of ferromagnetic material and with patients on mechanical aids (e.g. ventilator). Most studies also reported high sensitivity and specificity in detecting various lesions (e.g. ischemic stroke, TBI), particularly those that utilised deep learning to generate a synthetic scan. Strong volumetric correlations between portable ULF MRI and traditional neuroimaging were also reported. Portable ULF MRI usage in ABI patients is encouraging, however lack of diverse ABI samples and quantitative analyses limit its generalisability. Studies in a research context, carried out under controlled conditions, with larger samples (and across multiple centres) are necessary to ascertain portable ULF MRI's validity and reliability vis-à-vis conventional HF MRI.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100324"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146183779","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}