Human Brain Mapping最新文献

{"title":"Neuroanatomical Changes in the Stopping Network Across the Adult Lifespan Assessed With Quantitative and Diffusion MRI","authors":"Sarah A. Kemp,&nbsp;Pierre-Louis Bazin,&nbsp;Steven Miletić,&nbsp;Russell J. Boag,&nbsp;Max C. Keuken,&nbsp;Mark R. Hinder,&nbsp;Birte U. Forstmann","doi":"10.1002/hbm.70240","DOIUrl":"https://doi.org/10.1002/hbm.70240","url":null,"abstract":"<p>Response inhibition, the cancellation of planned movement, is essential for everyday motor control. Extensive fMRI and brain stimulation research provides evidence for the crucial role of a number of cortical and subcortical regions in response inhibition, including the subthalamic nucleus (STN), presupplementary motor area (preSMA) and the inferior frontal gyrus (IFG). Current models assume that these regions operate as a network, with action cancellation originating in the cortical areas and then executed rapidly via the subcortex. Response inhibition slows in older age, a change that has been attributed to deterioration or changes in the connectivity and integrity of this network. However, previous research has mainly used whole-brain approaches when investigating changes in structural connectivity across the lifespan or has used simpler measures to investigate structural ageing. Here, we used high-resolution quantitative and diffusion MRI to extensively examine the anatomical changes that occur in this network across the lifespan. We found age-related changes in iron concentration in these tracts, increases in the apparent diffusion coefficient and some evidence for a decrease in myelin content. Conversely, we found very little evidence for age-related anatomical changes in the regions themselves. We propose that some of the functional changes observed in these regions in older adult populations (e.g., increased BOLD recruitment) are a reflection of alterations to the connectivity between the regions rather than localised regional change.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction","authors":"Lingfei He,&nbsp;Siyu Wang,&nbsp;Cheng Chen,&nbsp;Yaping Wang,&nbsp;Qingcheng Fan,&nbsp;Congying Chu,&nbsp;Lingzhong Fan,&nbsp;Junhai Xu","doi":"10.1002/hbm.70239","DOIUrl":"https://doi.org/10.1002/hbm.70239","url":null,"abstract":"<p>Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which brain parts contribute significantly to the predictions. To tackle this challenge, we first trained a lightweight, fully CNN model for brain age estimation on a large sample data set (<i>N</i> = 3054, age range = [8,80 years]) and tested it on an independent data set (<i>N</i> = 555, age range = [8,80 years]). We then developed an interpretable scheme combining network occlusion sensitivity analysis (NOSA) with a fine-grained human brain atlas to uncover the learned invariance of the model. Our findings show that the dorsolateral, dorsomedial frontal cortex, anterior cingulate cortex, and thalamus had the highest contributions to age prediction across the lifespan. More interestingly, we observed that different regions showed divergent patterns in their predictions for specific age groups and that the bilateral hemispheres contributed differently to the predictions. Regions in the frontal lobe were essential predictors in both the developmental and aging stages, with the thalamus remaining relatively stable and saliently correlated with other regional changes throughout the lifespan. The lateral and medial temporal brain regions gradually became involved during the aging phase. At the network level, the frontoparietal and the default mode networks show an inverted U-shape contribution from the developmental to the aging stages. The framework could identify regional contributions to the brain age prediction model, which could help increase the model interpretability when serving as an aging biomarker.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating Development Trajectories of Brain Functional Abnormalities in Major Depressive Disorder Utilizing a Data-Driven Disease Progression Model","authors":"Yuhong Zheng,&nbsp;Peng Wang,&nbsp;Chi Yao,&nbsp;Jinghua Wang,&nbsp;Jinhui Wang,&nbsp;Shao-Wei Xue","doi":"10.1002/hbm.70249","DOIUrl":"https://doi.org/10.1002/hbm.70249","url":null,"abstract":"<p>Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data-driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross-sectional amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Caudolenticular Gray Matter Bridges in the Human Brain Striatum Through Diffusion Magnetic Resonance Imaging and Tractography","authors":"Graham Little,&nbsp;Charles Poirier,&nbsp;Arnaud Bore,&nbsp;Martin Parent,&nbsp;Laurent Petit,&nbsp;Maxime Descoteaux","doi":"10.1002/hbm.70245","DOIUrl":"https://doi.org/10.1002/hbm.70245","url":null,"abstract":"<p>In primates, the putamen and the caudate nucleus are connected by ~1 mm-thick caudolenticular gray matter bridges (CLGBs) interspersed between the white matter bundles of the internal capsule. Little is understood about the functional or microstructural properties of the CLGBs. In studies proposing high resolution diffusion magnetic resonance imaging (dMRI) techniques, CLGBs have been qualitatively identified as an example of superior imaging quality; however, the microstructural properties of these structures have yet to be examined. In this study, it is demonstrated for the first time that dMRI is sensitive to an organized anisotropic signal oriented in the direction parallel to the CLGBs, suggesting that dMRI could be a useful imaging method for probing the microstructure of the CLGBs. To demonstrate the anisotropic diffusion signal is coherently organized along the extent of the CLGBs and to enable a subsequent CLGB microstructural measurement, a customized tractography seeding and filtering method is proposed that utilizes the shape of the human striatum (putamen + caudate nucleus) to reconstruct the CLGBs in 3D. The proposed seeding strategy seeds tractography streamlines outward and normal to the surface of a 3D model of the striatum such that reconstructed streamlines are more likely to follow the diffusion signal peaks aligned parallel to the CLGBs. The method is applied to three different diffusion datasets, namely a high resolution 760 μm isotropic diffusion dataset acquired on a single subject, the test–retest cohort included as part of the human connectome project (<i>N</i> = 44) with diffusion data acquired at 1.25 mm isotropic, and a locally acquired “clinical” test–retest dataset acquired at 2.0 mm isotropic (<i>N</i> = 24). Reconstructed CLGBs directly overlap expected gray matter regions in the human brain for all three datasets. In addition, the method is shown to accurately reconstruct CLGBs repeatedly across multiple test–retest cohorts. The tractography CLGB reconstructions are then used to extract a quantitative measurement of microstructure from a local model of the diffusion signal along the CLGBs themselves. This is the first work to comprehensively study the CLGBs in vivo using dMRI and presents techniques suitable for future human neuroscience studies targeting these structures.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-Temporal Signatures of Cognitive Function After Pediatric Arterial Ischemic Stroke—A Pilot Study","authors":"Karl-Heinz Nenning,&nbsp;Florian Ph. S. Fischmeister,&nbsp;Astrid Novak,&nbsp;Rainer Seidl,&nbsp;Smadar Ovadia-Caro,&nbsp;Ting Xu,&nbsp;Gregor Kasprian,&nbsp;Lisa Bartha-Doering,&nbsp;Kathrin Kollndorfer","doi":"10.1002/hbm.70248","DOIUrl":"https://doi.org/10.1002/hbm.70248","url":null,"abstract":"<p>Childhood arterial ischemic stroke is a severe disorder that can cause lasting cognitive impairments, particularly in executive functions. Although early research assumed an improved outcome in childhood stroke patients compared to adults, more recent studies indicate similar rates of disabilities and cognitive impairment, with widespread brain network disruptions underlying these deficits. Here, we used resting-state fMRI to study alterations in functional brain dynamics and their association with cognitive outcome in children and adolescents after childhood stroke. We used co-activation pattern analysis to characterize five recurring brain states and their temporal properties in a cohort of 16 patients and 17 age-matched controls. We found that in pediatric stroke patients, a specific brain state characterizing the frontoparietal network was more prevalent and more frequently involved in state transitions. This was paralleled by lower occurrence rates of a brain state related to default mode network deactivation. Moreover, our analysis showed that these dynamics relate more to the extent to which the lesion impacts functional networks than to lesion size alone. Taken together, our findings suggest that disrupted brain dynamics following childhood stroke relate to cognitive performance and that the location of a focal lesion can have wide-ranging implications on brain state dynamics.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series","authors":"Varun Madan Mohan,&nbsp;Thomas F. Varley,&nbsp;Robin F. H. Cash,&nbsp;Caio Seguin,&nbsp;Andrew Zalesky","doi":"10.1002/hbm.70223","DOIUrl":"https://doi.org/10.1002/hbm.70223","url":null,"abstract":"<p>Tracking how activity or signal perturbations propagate in nervous systems is crucial to understanding interareal communication in the brain. Current analytical methodologies are not well suited to systematically infer interareal activity propagation from neural time series recordings. Here, we propose Event-marked Windowed Communication (EWC), a framework to infer activity propagation between neural elements by tracking the statistical consequence of spontaneous, endogenous regional perturbations. EWC tracks the downstream effect of these perturbations by subsampling the neural time series and quantifying statistical dependences using established functional connectivity measures. We test EWC on simulations of neural dynamics and demonstrate the retrieval of ground truth motifs of directional signaling, over a range of model configurations. We also show that EWC can capture activity propagation in a computationally efficient manner by benchmarking it against more advanced FC estimation methods such as transfer entropy. Lastly, we showcase the utility of EWC to infer whole-brain activity propagation maps from magnetoencephalography (MEG) recordings. Networks computed using EWC were compared to those inferred using transfer entropy and were found to be highly correlated (median <i>r</i> = 0.81 across subjects). Importantly, our framework is flexible and can be applied to activity time series captured by diverse functional neuroimaging modalities, opening new avenues for the study of neural communication.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline","authors":"Qinjie Li,&nbsp;Liang Cui,&nbsp;Yihui Guan,&nbsp;Yuehua Li,&nbsp;Fang Xie,&nbsp;Qihao Guo","doi":"10.1002/hbm.70238","DOIUrl":"https://doi.org/10.1002/hbm.70238","url":null,"abstract":"<p>There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four individuals with subjective cognitive decline (SCD) were enrolled in this study. All subjects completed neuropsychological assessments and underwent 18F-florbetapir PET, structural MRI, and functional MRI. A total of 315 features were extracted from the MRI, demographics, and neuropsychological scales and selected using the least absolute shrinkage and selection operator (LASSO). The logistic regression (LR) model, based on machine learning, was trained to classify SCD as either β-amyloid (Aβ) positive or negative. A nomogram was established using a multivariate LR model to predict the risk of Aβ+. The performance of the prediction model and nomogram was assessed with area under the curve (AUC) and calibration. The final model was based on the right rostral anterior cingulate thickness, the grey matter volume of the right inferior temporal, the ReHo of the left posterior cingulate gyrus and right superior temporal gyrus, as well as MoCA-B and AVLT-R. In the training set, the model achieved a good AUC of 0.78 for predicting Aβ+, with an accuracy of 0.72. The validation of the model also yielded a favorable discriminatory ability with an AUC of 0.88 and an accuracy of 0.83. We have established and validated a model based on cognitive, sMRI, and fMRI data that exhibits adequate discrimination. This model has the potential to predict amyloid status in the SCD group and provide a noninvasive, cost-effective way that might facilitate early screening, clinical diagnosis, and drug clinical trials.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Electrical Correlates of Subjective Happiness","authors":"Wataru Sato,&nbsp;Takanori Kochiyama,&nbsp;Shota Uono","doi":"10.1002/hbm.70224","DOIUrl":"https://doi.org/10.1002/hbm.70224","url":null,"abstract":"<p>Happiness is a subjective experience that can serve as the ultimate goal for humans. A recent study that employed resting-state functional magnetic resonance imaging (fMRI) reported that spontaneous fluctuation (fractional amplitude of low-frequency fluctuation: fALFF) in the precuneus is negatively associated with subjective happiness. However, little is known about the neural electrical correlates of subjective happiness, which can provide direct evidence of neural activity and insights regarding the underlying psychological, cellular, and neurotransmitter mechanisms. Therefore, we measured 400-channel whole-head magnetoencephalography (MEG) during resting state in participants whose subjective happiness was evaluated using questionnaires. We conducted source reconstruction analysis utilizing bandpass-filtered MEG data and analyzed the fALFF of the band-limited power time series as an index of spontaneous neural fluctuation. Gamma-band fALFF values in the right precuneus were negatively associated with subjective happiness scores (partial correlation coefficient = −0.56). These findings indicate that subjective happiness has a neural electrical correlate of reduced spontaneous fluctuation of gamma-band neuronal oscillations in the right precuneus, and that it could be mediated by a reduction in wandering, clinging self-consciousness through heightened <i>N</i>-methyl-<span>d</span>-aspartate-dependent gamma-aminobutyric acid-ergic parvalbumin inhibitory interneuron activity.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Connectivity Patterns Following Mild Traumatic Brain Injury and the Association With Longitudinal Cognitive Function","authors":"Goretti España-Irla,&nbsp;Emma M. Tinney,&nbsp;Meishan Ai,&nbsp;Mark Nwakamma,&nbsp;Timothy P. Morris","doi":"10.1002/hbm.70237","DOIUrl":"https://doi.org/10.1002/hbm.70237","url":null,"abstract":"&lt;p&gt;Functional magnetic resonance imaging (fMRI) has revealed subtle neuroplastic changes in brain networks following mild traumatic brain injury (mTBI), even when standard clinical imaging fails to detect abnormalities. However, prior findings have been inconsistent, in part due to methodological differences and high researcher degrees of freedom in region-based analyses, which often rely on predefined hypotheses and overlook complex, distributed connectivity patterns. Here, we apply an unbiased, data-driven multi-voxel pattern analysis (MVPA) to examine whole-brain functional connectivity differences in a large cohort of individuals with acute mTBI. Unlike conventional statistical approaches, MVPA enables a data-driven analysis of brain-wide connectivity patterns without requiring prior assumptions about the location or nature of abnormalities, allowing for the identification of the most informative features. This approach provides an exploratory characterization of whole-brain functional connectivity patterns and their relationship with cognitive recovery, offering new insights into the neural mechanisms underlying post-injury outcomes. A total of 265 adults (87 women) between 18 and 83 years old with Glasgow Coma Scale (GCS) scores of 13–15 were included in this analysis. Two replicate samples (&lt;i&gt;n&lt;/i&gt; = 165, &lt;i&gt;n&lt;/i&gt; = 155), with similar demographic characteristics, were also included. Data were collected as part of the prospective multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI). The goal of this study was to assess whole-brain functional connectivity patterns using fc-MVPA and post hoc seed-to-voxel analyses in a large, well-characterized sample to determine if changes in functional connectivity can differentiate subacute mTBI (within 2 weeks of injury) from a matched group of orthopedic control subjects (&lt;i&gt;n&lt;/i&gt; = 49). Additionally, we aimed to investigate whether these connectivity patterns were linked to cognitive performance at 2 weeks, 6 months, and 12 months post-injury to better understand cognitive trajectories and recovery over time in individuals with mTBI. Voxel-to-voxel functional connectivity across the entire connectome revealed significant differences between TBI and no TBI in the functional connectivity patterns of 8 clusters (&lt;i&gt;p&lt;/i&gt;-voxel &lt; 0.001, FEW cluster-level &lt;i&gt;p&lt;/i&gt; &lt; 0.05) (&lt;i&gt;k&lt;/i&gt; &gt; 40, Fmax = 15.36), including right occipital cortex, anterior cingulate gyrus, inferior and middle temporal gyrus, right thalamus, left cerebellum, and the bilateral frontal pole. These clusters belong mainly to the visual network (VIS), frontoparietal network (FPN), default mode network (DMN) and limbic network (LIM). Post hoc characterization of each significant cluster revealed by MVPA using seed-to-voxel analysis showed a mixed pattern of connectivity between relevant networks and subcortico-cortical connections. After connectivity characterization, visual-motor skills assessed with Trail M","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurometabolite Alterations Associated With Cannabis Use: A Proton Magnetic Resonance Spectroscopy Meta-Analysis","authors":"Anna E. Kirkland,&nbsp;Brittney D. Browning,&nbsp;ReJoyce Green,&nbsp;Samuel O. Agbeh,&nbsp;Lindsay M. Squeglia","doi":"10.1002/hbm.70236","DOIUrl":"https://doi.org/10.1002/hbm.70236","url":null,"abstract":"<p>Little is known about the neurometabolic effects of cannabis use. Using meta-analytic modeling of proton magnetic resonance spectroscopy (1H-MRS) studies, this study aimed to assess the differences in brain metabolite levels associated with cannabis use (PROSPERO: CRD42020209890) to inform treatment development for cannabis use disorder (CUD). Hedge's <i>g</i> with random-effects modeling was used, and heterogeneity and publication bias indices were assessed. A complete literature search was conducted, and 15 studies met the inclusion criteria (e.g., 1H-MRS, cannabis group compared to a control group, brain region-specific results, necessary data to complete modeling). There were 29 models across gray matter regions in the brain. All models had between 2 and 5 studies (<i>k</i>), indicating that results should be interpreted with caution due to the limited number of available studies. Compared to the control groups, the cannabis-using groups showed lower levels of GABA and N-acetylaspartate in the anterior cingulate cortex (<i>k</i> = 3); lower glutamate in the basal ganglia/striatum (<i>k</i> = 2); and lower glutamine and <i>myo</i>-inositol in the thalamus (<i>k</i> = 2; although the two effect sizes came from the same sample). This is the first meta-analysis to consolidate the extant 1H-MRS studies focused on the neurometabolic effects of cannabis. Despite the few studies available, the evidence suggests cannabis use may impact important neural processes, including glutamatergic and GABAergic functioning (glutamate, glutamine, and GABA), neural health (N-acetylaspartate), and glial functioning (<i>myo</i>-inositol). The findings should be interpreted with caution considering the small sample size; the inability to test the impact of demographic, substance use, and methodological factors; and the heterogeneity of studies. Understanding the neurobiological effects of cannabis may inspire novel pharmacotherapy and/or psychosocial interventions for CUD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}