Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121022
Shanshan Zhen , Mario Martinez-Saito , Rongjun Yu
The ability to infer a speaker's utterance within a particular context for the intended meaning is central to communication. Yet, little is known about the underlying neurocomputational mechanisms of pragmatic inference, let alone relevant differences among individuals. Here, using a reference game combined with model-based functional magnetic resonance imaging (fMRI), we showed that an individual-level pragmatic inference model was a better predictor of listeners’ performance than a population-level model. Our fMRI results showed that Bayesian posterior probability was positively correlated with activity in the ventromedial prefrontal cortex (vmPFC) and ventral striatum and negatively correlated with activity in dorsomedial PFC, anterior insula (AI), and inferior frontal gyrus (IFG). Importantly, individual differences in higher-order reasoning were correlated with stronger activation in IFG and AI and positively modulated the vmPFC functional connectivity with AI. Our findings provide a preliminary neurocomputational account of how the brain represents Bayesian belief inferences and the neural basis of heterogeneity in such reasoning.
{"title":"Beyond what was said: Neural computations underlying pragmatic reasoning in referential communication","authors":"Shanshan Zhen , Mario Martinez-Saito , Rongjun Yu","doi":"10.1016/j.neuroimage.2025.121022","DOIUrl":"10.1016/j.neuroimage.2025.121022","url":null,"abstract":"<div><div>The ability to infer a speaker's utterance within a particular context for the intended meaning is central to communication. Yet, little is known about the underlying neurocomputational mechanisms of pragmatic inference, let alone relevant differences among individuals. Here, using a reference game combined with model-based functional magnetic resonance imaging (fMRI), we showed that an individual-level pragmatic inference model was a better predictor of listeners’ performance than a population-level model. Our fMRI results showed that Bayesian posterior probability was positively correlated with activity in the ventromedial prefrontal cortex (vmPFC) and ventral striatum and negatively correlated with activity in dorsomedial PFC, anterior insula (AI), and inferior frontal gyrus (IFG). Importantly, individual differences in higher-order reasoning were correlated with stronger activation in IFG and AI and positively modulated the vmPFC functional connectivity with AI. Our findings provide a preliminary neurocomputational account of how the brain represents Bayesian belief inferences and the neural basis of heterogeneity in such reasoning.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121022"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971708","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121020
Yuanjun Kong , Xuye Yuan , Yiqing Hu , Bingkun Li , Dongwei Li , Jialiang Guo , Meirong Sun , Yan Song
Understanding the developmental trajectories of the auditory and visual systems is crucial to elucidate cognitive maturation and its associated relationships, which are essential for effectively navigating dynamic environments. Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. However, the evolution of these processes and their relationship over time remains unclear. In this study, we recorded electroencephalography signals from 118 participants (42 adults and 76 typically developing children) during separate visual localization and auditory-embedded fixation tasks. Further, we employed both ERP analysis and multivariate pattern machine learning to investigate developmental patterns. ERP amplitude and decoding accuracy provided convergent evidence underlying a linear developmental trajectory for visual selective attention and an inverted U-shaped trajectory for auditory change detection from childhood to adulthood. Importantly, our findings confirmed the established association of an N2 pc-MMN in adults using a larger sample size, and further identified a positive correlation between decoding accuracy for visual target location and decoding accuracy for auditory stimulus type specifically in adults. However, both visual-auditory correlation effects were absent in children. Our study provides neurophysiological insights into the distinct developmental trajectories of visual selective attention and auditory change detection. It highlights that the close relationship between individual differences in the two processes emerges alongside their respective maturation and does not become evident until adulthood.
{"title":"Development of the relationship between visual selective attention and auditory change detection","authors":"Yuanjun Kong , Xuye Yuan , Yiqing Hu , Bingkun Li , Dongwei Li , Jialiang Guo , Meirong Sun , Yan Song","doi":"10.1016/j.neuroimage.2025.121020","DOIUrl":"10.1016/j.neuroimage.2025.121020","url":null,"abstract":"<div><div>Understanding the developmental trajectories of the auditory and visual systems is crucial to elucidate cognitive maturation and its associated relationships, which are essential for effectively navigating dynamic environments. Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. However, the evolution of these processes and their relationship over time remains unclear. In this study, we recorded electroencephalography signals from 118 participants (42 adults and 76 typically developing children) during separate visual localization and auditory-embedded fixation tasks. Further, we employed both ERP analysis and multivariate pattern machine learning to investigate developmental patterns. ERP amplitude and decoding accuracy provided convergent evidence underlying a linear developmental trajectory for visual selective attention and an inverted U-shaped trajectory for auditory change detection from childhood to adulthood. Importantly, our findings confirmed the established association of an N2 pc-MMN in adults using a larger sample size, and further identified a positive correlation between decoding accuracy for visual target location and decoding accuracy for auditory stimulus type specifically in adults. However, both visual-auditory correlation effects were absent in children. Our study provides neurophysiological insights into the distinct developmental trajectories of visual selective attention and auditory change detection. It highlights that the close relationship between individual differences in the two processes emerges alongside their respective maturation and does not become evident until adulthood.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121020"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971714","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121045
Paul J. Weiser , Georg Langs , Wolfgang Bogner , Stanislav Motyka , Bernhard Strasser , Polina Golland , Nalini Singh , Jorg Dietrich , Erik Uhlmann , Tracy Batchelor , Daniel Cahill , Malte Hoffmann , Antoine Klauser , Ovidiu C. Andronesi
Introduction:
Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.
Methods:
Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm isotropic resolution with acquisition times between 4:11–9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics.
Results:
Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial–spectral quality and metabolite quantification with 12%–45% (P0.05) higher signal-to-noise and 8%–50% (P0.05) smaller Cramer–Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data.
Conclusion:
Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.
{"title":"Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging","authors":"Paul J. Weiser , Georg Langs , Wolfgang Bogner , Stanislav Motyka , Bernhard Strasser , Polina Golland , Nalini Singh , Jorg Dietrich , Erik Uhlmann , Tracy Batchelor , Daniel Cahill , Malte Hoffmann , Antoine Klauser , Ovidiu C. Andronesi","doi":"10.1016/j.neuroimage.2025.121045","DOIUrl":"10.1016/j.neuroimage.2025.121045","url":null,"abstract":"<div><h3>Introduction:</h3><div>Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.</div></div><div><h3>Methods:</h3><div>Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span> isotropic resolution with acquisition times between 4:11–9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics.</div></div><div><h3>Results:</h3><div>Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial–spectral quality and metabolite quantification with 12%–45% (P<span><math><mo><</mo></math></span>0.05) higher signal-to-noise and 8%–50% (P<span><math><mo><</mo></math></span>0.05) smaller Cramer–Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data.</div></div><div><h3>Conclusion:</h3><div>Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"309 ","pages":"Article 121045"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080696","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121006
Xi Deng , Meiru Bu , Jiali Liang , Yihao Sun , Liyan Li , Heishu Zheng , Zisan Zeng , Muliang Jiang , Bihong T. Chen
Background
The aim of this study was to establish an iron overload rat model to simulate the elevated iron levels in patients with thalassemia and to investigate the potential association between hippocampal iron deposition and cognition.
Methods
Two groups of iron overloaded rats and one group of control rats were used for this study. The Morris water maze (MWM) was used to test spatial reference memory indicated by escape latency time and number of MWM platform crossings. The magnetic susceptibility value of the hippocampal tissue, a measure of iron deposition, was assessed by quantitative susceptibility mapping (QSM) and was correlated with spatial reference memory performance. The iron content in hippocampal tissue sections of the rats were assessed using diaminobenzidine (DAB)-enhanced Perl's Prussian blue (PPB) staining.
Results
The rat groups with iron overload including the Group H and Group L had higher hippocampal magnetic susceptibility values than the control rat group, i.e., Group D. In addition, the iron overloaded groups had longer MWM escape latency than the control group, and reduced number of MWM platform crossings. There was a positive correlation between the mean escape latency and the mean hippocampal magnetic susceptibility value, a negative correlation between the number of platform crossings and the mean hippocampal magnetic susceptibility value, and a negative correlation between the number of platform crossings and the latent escape time in Group H and Group L.
Conclusion
This rat model simulating iron overload in thalassemia showed hippocampal iron overload being associated with impairment of spatial reference memory. QSM could be used to quantify brain iron overload in vivo, highlighting its potential clinical application for assessing cognitive impairment in patients with thalassemia.
背景：本研究的目的是建立铁超载大鼠模型来模拟地中海贫血患者铁水平升高，并探讨海马铁沉积与认知之间的潜在关联。方法：采用两组铁超载大鼠和一组对照大鼠进行研究。采用Morris水迷宫（Morris water maze， MWM）测试空间参考记忆，以逃避潜伏期和穿越Morris水迷宫平台的次数为指标。通过定量敏感性制图（QSM）评估海马组织的磁化率值，作为铁沉积的测量指标，并与空间参考记忆性能相关。采用二氨基联苯胺（DAB）增强Perl’s Prussian blue （PPB）染色法测定大鼠海马组织切片铁含量。结果：铁超载组（H组和L组）海马磁化率值高于对照组（d组），铁超载组的MWM逃逸潜伏期比对照组长，MWM平台穿越次数减少。H组和l组小鼠平均逃避潜伏期与海马平均磁化率值呈正相关，穿越平台次数与海马平均磁化率值呈负相关，穿越平台次数与潜伏逃避时间呈负相关。这个模拟地中海贫血铁超载的大鼠模型显示海马铁超载与空间参考记忆障碍有关。QSM可用于量化体内脑铁超载，强调其在评估地中海贫血患者认知功能障碍方面的潜在临床应用。
{"title":"Relationship between cognitive impairment and hippocampal iron overload: A quantitative susceptibility mapping study of a rat model","authors":"Xi Deng , Meiru Bu , Jiali Liang , Yihao Sun , Liyan Li , Heishu Zheng , Zisan Zeng , Muliang Jiang , Bihong T. Chen","doi":"10.1016/j.neuroimage.2025.121006","DOIUrl":"10.1016/j.neuroimage.2025.121006","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to establish an iron overload rat model to simulate the elevated iron levels in patients with thalassemia and to investigate the potential association between hippocampal iron deposition and cognition.</div></div><div><h3>Methods</h3><div>Two groups of iron overloaded rats and one group of control rats were used for this study. The Morris water maze (MWM) was used to test spatial reference memory indicated by escape latency time and number of MWM platform crossings. The magnetic susceptibility value of the hippocampal tissue, a measure of iron deposition, was assessed by quantitative susceptibility mapping (QSM) and was correlated with spatial reference memory performance. The iron content in hippocampal tissue sections of the rats were assessed using diaminobenzidine (DAB)-enhanced Perl's Prussian blue (PPB) staining.</div></div><div><h3>Results</h3><div>The rat groups with iron overload including the Group H and Group L had higher hippocampal magnetic susceptibility values than the control rat group, i.e., Group D. In addition, the iron overloaded groups had longer MWM escape latency than the control group, and reduced number of MWM platform crossings. There was a positive correlation between the mean escape latency and the mean hippocampal magnetic susceptibility value, a negative correlation between the number of platform crossings and the mean hippocampal magnetic susceptibility value, and a negative correlation between the number of platform crossings and the latent escape time in Group H and Group L.</div></div><div><h3>Conclusion</h3><div>This rat model simulating iron overload in thalassemia showed hippocampal iron overload being associated with impairment of spatial reference memory. QSM could be used to quantify brain iron overload <em>in vivo</em>, highlighting its potential clinical application for assessing cognitive impairment in patients with thalassemia.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121006"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952274","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121023
Rong Yao, Langhua Shi, Yan Niu, HaiFang Li, Xing Fan, Bin Wang
The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.
{"title":"Driving brain state transitions via Adaptive Local Energy Control Model","authors":"Rong Yao, Langhua Shi, Yan Niu, HaiFang Li, Xing Fan, Bin Wang","doi":"10.1016/j.neuroimage.2025.121023","DOIUrl":"10.1016/j.neuroimage.2025.121023","url":null,"abstract":"<div><div>The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121023"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971717","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121071
Yueye Zhao , Jianyi Liu , Xue'er Ma , Zi-Gang Huang , Jingjing Zhao
Previous research has shown that the thalamus is crucial in reading, with its function depending largely on its connections with the cortex. However, the relationship between the lateralization of thalamocortical connections and reading has not been well-explored. This study investigates the microstructure and its lateralization differences in thalamocortical white matter fiber tracts in individuals with varying reading abilities and explores their relationship with reading skills and early reading performances. The study involved 26 Mandarin-speaking adults with a history of reading difficulties and 35 typically developing Mandarin-speaking adults. Severity of reading difficulties were accessed via the Chinese Adult Reading History Questionnaire (C-ARHQ) self-reported by participants. Reading-related abilities including reading accuracy, phonological awareness, and rapid automatized naming were assessed. Neuroimaging data, including T1-weighted and diffusion-weighted images, were collected. Thalamocortical white matter fiber tracts were reconstructed using the constrained spherical deconvolution (CSD) model and grouped into six regions based on connections with bilateral brain areas. The Neurite Orientation Dispersion and Density Imaging (NODDI) model was employed to evaluate the microstructural properties of these tracts, calculating lateralization indices for the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (VISO). Results revealed that individuals with reading difficulties had significantly lower NDI values in the left and right frontal-thalamic and occipital-thalamic fiber tracts compared to good readers. Additionally, greater rightward lateralization of frontal-thalamic white matter fiber tracts was linked to poorer early reading performance in those with reading difficulties. Our study reveals atypical thalamocortical white matter connections in adults with a history of reading difficulties, and the lateralization of these connections is influenced by severity of early reading difficulties.
{"title":"Microstructural lateralization of thalamocortical connections in individuals with a history of reading difficulties","authors":"Yueye Zhao , Jianyi Liu , Xue'er Ma , Zi-Gang Huang , Jingjing Zhao","doi":"10.1016/j.neuroimage.2025.121071","DOIUrl":"10.1016/j.neuroimage.2025.121071","url":null,"abstract":"<div><div>Previous research has shown that the thalamus is crucial in reading, with its function depending largely on its connections with the cortex. However, the relationship between the lateralization of thalamocortical connections and reading has not been well-explored. This study investigates the microstructure and its lateralization differences in thalamocortical white matter fiber tracts in individuals with varying reading abilities and explores their relationship with reading skills and early reading performances. The study involved 26 Mandarin-speaking adults with a history of reading difficulties and 35 typically developing Mandarin-speaking adults. Severity of reading difficulties were accessed via the Chinese Adult Reading History Questionnaire (C-ARHQ) self-reported by participants. Reading-related abilities including reading accuracy, phonological awareness, and rapid automatized naming were assessed. Neuroimaging data, including T1-weighted and diffusion-weighted images, were collected. Thalamocortical white matter fiber tracts were reconstructed using the constrained spherical deconvolution (CSD) model and grouped into six regions based on connections with bilateral brain areas. The Neurite Orientation Dispersion and Density Imaging (NODDI) model was employed to evaluate the microstructural properties of these tracts, calculating lateralization indices for the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (VISO). Results revealed that individuals with reading difficulties had significantly lower NDI values in the left and right frontal-thalamic and occipital-thalamic fiber tracts compared to good readers. Additionally, greater rightward lateralization of frontal-thalamic white matter fiber tracts was linked to poorer early reading performance in those with reading difficulties. Our study reveals atypical thalamocortical white matter connections in adults with a history of reading difficulties, and the lateralization of these connections is influenced by severity of early reading difficulties.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121071"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080698","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2024.120990
Karla Ivankovic , Alessandro Principe , Justo Montoya-Gálvez , Linus Manubens-Gil , Riccardo Zucca , Pablo Villoslada , Mara Dierssen , Rodrigo Rocamora
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers. Without relying on specific connectivity variables, we focused on a general hypothesis that the EN undergoes the greatest magnitude of connectivity change during seizure generation, compared to other brain networks. To test this hypothesis, we developed a novel method for quantifying connectivity change between network states and applied it to identify surgical resection areas.
A network state was represented by random snapshots of connectivity within a defined time interval of an intracranial EEG recording. A binary classifier was applied to classify two network states. The classifier generalization performance estimated by cross-validation was employed as a continuous measure of connectivity change. The algorithm generated a network by iteratively adding nodes until the connectivity change magnitude decreased. The resulting network was compared to the surgical resection, and the overlap score was used to predict post-surgical outcomes. The framework was evaluated in a consecutive cohort of 21 patients with a post-surgical follow-up of minimum 3 years.
The best overlap between connectivity change networks and resections was obtained at the transition from pre-seizure to seizure (surgical outcome prediction ROC-AUC=90.3 %). However, all patients except one were correctly classified when considering the most informative time intervals. Time intervals proportional to seizure length were more informative than the almost universally used fixed intervals.
This study demonstrates that connectivity can be successfully classified with a machine learning analysis and provide information for distinguishing a separate epileptogenic functional network. In summary, the connectivity change analysis could accurately identify epileptogenic networks validated by surgery outcome classification. Connectivity change magnitude at seizure transition could potentially serve as an EN biomarker. The tool provided by this study may aid surgical decision-making.
{"title":"A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction","authors":"Karla Ivankovic , Alessandro Principe , Justo Montoya-Gálvez , Linus Manubens-Gil , Riccardo Zucca , Pablo Villoslada , Mara Dierssen , Rodrigo Rocamora","doi":"10.1016/j.neuroimage.2024.120990","DOIUrl":"10.1016/j.neuroimage.2024.120990","url":null,"abstract":"<div><div>The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers. Without relying on specific connectivity variables, we focused on a general hypothesis that the EN undergoes the greatest magnitude of connectivity change during seizure generation, compared to other brain networks. To test this hypothesis, we developed a novel method for quantifying connectivity change between network states and applied it to identify surgical resection areas.</div><div>A network state was represented by random snapshots of connectivity within a defined time interval of an intracranial EEG recording. A binary classifier was applied to classify two network states. The classifier generalization performance estimated by cross-validation was employed as a continuous measure of connectivity change. The algorithm generated a network by iteratively adding nodes until the connectivity change magnitude decreased. The resulting network was compared to the surgical resection, and the overlap score was used to predict post-surgical outcomes. The framework was evaluated in a consecutive cohort of 21 patients with a post-surgical follow-up of minimum 3 years.</div><div>The best overlap between connectivity change networks and resections was obtained at the transition from pre-seizure to seizure (surgical outcome prediction ROC-AUC=90.3 %). However, all patients except one were correctly classified when considering the most informative time intervals. Time intervals proportional to seizure length were more informative than the almost universally used fixed intervals.</div><div>This study demonstrates that connectivity can be successfully classified with a machine learning analysis and provide information for distinguishing a separate epileptogenic functional network. In summary, the connectivity change analysis could accurately identify epileptogenic networks validated by surgery outcome classification. Connectivity change magnitude at seizure transition could potentially serve as an EN biomarker. The tool provided by this study may aid surgical decision-making.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120990"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903201","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121003
Yajing Xu, Shan Yang, Cong Cao
Background
Although epigenomic and environment interactions (Epigenome × Environment; Epi × E) might constitute a novel mechanism underlying reward processing, direct evidence is still scarce. We conducted the first longitudinal study to investigate the extent to which DNA methylation of a stress-related gene—NR3C1—interacts with childhood maltreatment in association with young adult reward responsiveness (RR) and the downstream risk of depressive (anhedonia dimension in particular) and anxiety symptoms.
Method
A total of 192 Chinese university students aged 18∼25 (Mage = 21.08 ± 1.91 years; 59.4% females) were followed in two waves. Reward positivity (RewP) and its time‒frequency components were elicited via a classic monetary reward task. Cytosine methylation in the promoter exon 1F of NR3C1 (NR3C1-1F) was sequenced via buccal cells. Childhood maltreatment, self-reported RR and depressive and anxiety symptoms were assessed via questionnaires.
Results
NR3C1-1F methylation significantly interacted with childhood maltreatment on RewP but not the delta and theta components or self-reported RR. The severity and exposure number of childhood maltreatment were negatively associated with RewP among individuals with heightened NR3C1-1F methylation but positively associated with RewP among individuals with blunted NR3C1-1F methylation, demonstrating a “goodness-of-fit” interaction. This interaction was specifically linked with anhedonia dimension but not with total scores of depressive or anxiety symptoms.
Conclusions
The current findings provide preliminary evidence for an Epi × E interaction underlying reward processing, highlight cross-level analyses of electrophysiological signals and advance knowledge of the biological foundation of stress-induced reward function and relevant symptoms. However, caution should be paid to the generalizability of these findings in high-risk clinical samples given the high-functioning characteristic of the present sample.
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Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2024.120991
Fulong Wang , Fuzhi Cao , Yujie Ma , Ruochen Zhao , Ruonan Wang , Nan An , Min Xiang , Dawei Wang , Xiaolin Ning
Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is an novel non-invasive functional imaging technique that features more flexible sensor configurations and wearability; however, this also increases the requirement for environmental noise suppression. Subspace projection algorithms are widely used in MEG to suppress noise. However, in OPM-MEG systems with a limited number of channels, subspace projection methods that rely on spatial oversampling exhibit reduced performance. The homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model; however, it cannot address complex non-homogeneous noise. The spatiotemporal extended homogeneous field correction (teHFC) method uses multiple orthogonal projections to suppress disturbances. However, the signal and noise subspace are not completely orthogonal, limiting enhancement in the capabilities of the teHFC. Therefore, we propose an extended homogeneous field correction method based on oblique projection (opHFC), which overcomes the issue of non-orthogonality between the signal and noise subspace, enhancing the ability to suppress complex interferences. The opHFC constructs an oblique projection operator that divides the signals into internal and external components, eliminating complex interferences through temporal extension. We compared the opHFC with four benchmark methods by simulations and auditory and somatosensory evoked OPM-MEG experiments. The results demonstrate that opHFC provides superior noise suppression with minimal distortion, enhancing the signal quality at the sensor and source levels. Our method offers a novel approach to reducing interference in OPM-MEG systems, expanding their application scenarios, and providing high-quality signals for scientific research and clinical applications based on OPM-MEG.
{"title":"Extended homogeneous field correction method based on oblique projection in OPM-MEG","authors":"Fulong Wang , Fuzhi Cao , Yujie Ma , Ruochen Zhao , Ruonan Wang , Nan An , Min Xiang , Dawei Wang , Xiaolin Ning","doi":"10.1016/j.neuroimage.2024.120991","DOIUrl":"10.1016/j.neuroimage.2024.120991","url":null,"abstract":"<div><div>Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is an novel non-invasive functional imaging technique that features more flexible sensor configurations and wearability; however, this also increases the requirement for environmental noise suppression. Subspace projection algorithms are widely used in MEG to suppress noise. However, in OPM-MEG systems with a limited number of channels, subspace projection methods that rely on spatial oversampling exhibit reduced performance. The homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model; however, it cannot address complex non-homogeneous noise. The spatiotemporal extended homogeneous field correction (teHFC) method uses multiple orthogonal projections to suppress disturbances. However, the signal and noise subspace are not completely orthogonal, limiting enhancement in the capabilities of the teHFC. Therefore, we propose an extended homogeneous field correction method based on oblique projection (opHFC), which overcomes the issue of non-orthogonality between the signal and noise subspace, enhancing the ability to suppress complex interferences. The opHFC constructs an oblique projection operator that divides the signals into internal and external components, eliminating complex interferences through temporal extension. We compared the opHFC with four benchmark methods by simulations and auditory and somatosensory evoked OPM-MEG experiments. The results demonstrate that opHFC provides superior noise suppression with minimal distortion, enhancing the signal quality at the sensor and source levels. Our method offers a novel approach to reducing interference in OPM-MEG systems, expanding their application scenarios, and providing high-quality signals for scientific research and clinical applications based on OPM-MEG.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 120991"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932164","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}
Pub Date : 2025-02-01DOI: 10.1016/j.neuroimage.2025.121008
Chun-Wei Hsu , Chu-Chung Huang , Chih-Chin Heather Hsu , Yanchao Bi , Ovid Jyh-Lang Tzeng , Ching-Po Lin
In recent decades, converging evidence has reached a consensus that human speech production is carried out by large-scale hierarchical network comprising both language-selective and domain-general systems. However, it remains unclear how these systems interact during speech production and the specific contributions of their component regions. By utilizing a series of meta-analytic approaches based on various language tasks, we dissociated four major systems in this study: domain-general, high-level language, motor-perception, and speech-control systems. Using meta-analytic connectivity modeling, we found that while the domain-general system is coactivated with high-level language regions and speech-control networks, only the speech-control network at the ventral precentral gyrus is coactivated with other systems during different speech-related tasks, including motor perception. In summary, this study revisits the previously proposed language models using meta-analytic approaches and highlights the contribution of the speech-control network to the process of speech production independent of articulatory motor.
{"title":"Revisiting human language and speech production network: A meta-analytic connectivity modeling study","authors":"Chun-Wei Hsu , Chu-Chung Huang , Chih-Chin Heather Hsu , Yanchao Bi , Ovid Jyh-Lang Tzeng , Ching-Po Lin","doi":"10.1016/j.neuroimage.2025.121008","DOIUrl":"10.1016/j.neuroimage.2025.121008","url":null,"abstract":"<div><div>In recent decades, converging evidence has reached a consensus that human speech production is carried out by large-scale hierarchical network comprising both language-selective and domain-general systems. However, it remains unclear how these systems interact during speech production and the specific contributions of their component regions. By utilizing a series of meta-analytic approaches based on various language tasks, we dissociated four major systems in this study: domain-general, high-level language, motor-perception, and speech-control systems. Using meta-analytic connectivity modeling, we found that while the domain-general system is coactivated with high-level language regions and speech-control networks, only the speech-control network at the ventral precentral gyrus is coactivated with other systems during different speech-related tasks, including motor perception. In summary, this study revisits the previously proposed language models using meta-analytic approaches and highlights the contribution of the speech-control network to the process of speech production independent of articulatory motor.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"306 ","pages":"Article 121008"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952276","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}
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