Pub Date : 2026-01-22DOI: 10.1016/j.neuroimage.2026.121749
Peter Van Schuerbeek , Manon Roose , Alina Monica Ionescu , Maarten Naeyaert , Hubert Raeymaekers
Task based fMRI data suffers from scanner and physiologic noise. Consequently, finding the task based BOLD responses out of the noise is challenging. To improve the power to detect the BOLD responses, multi-echo (ME) fMRI combined with ICA based denoising (MEICA) and single-echo (SE) fMRI at high temporal resolution (<1 s) have been introduced. Both techniques have been found to give better activation maps than a traditional fMRI experiment at low temporal resolution (1.5–3 s).
In this study, we introduced a new U-shaped convolutional neural network DUNE to denoise ME-fMRI data as an alternative to MEICA in 2 ME-fMRI experiments. The resulting activation maps found after denoising with DUNE were compared with those found after denoising with MEICA and similar SE-fMRI experiments at high temporal resolution.
Our results revealed that DUNE was successful in reducing the noise while preserving the BOLD effects of interest comparable to MEICA and SE-fMRI. This latter result showed the potential of using a U-shaped convolutional neural network DUNE to denoise ME-fMRI data.
{"title":"The evaluation of DUNE: a U-Net-based neural network to denoise multi-echo fMRI data","authors":"Peter Van Schuerbeek , Manon Roose , Alina Monica Ionescu , Maarten Naeyaert , Hubert Raeymaekers","doi":"10.1016/j.neuroimage.2026.121749","DOIUrl":"10.1016/j.neuroimage.2026.121749","url":null,"abstract":"<div><div>Task based fMRI data suffers from scanner and physiologic noise. Consequently, finding the task based BOLD responses out of the noise is challenging. To improve the power to detect the BOLD responses, multi-echo (ME) fMRI combined with ICA based denoising (MEICA) and single-echo (SE) fMRI at high temporal resolution (<1 s) have been introduced. Both techniques have been found to give better activation maps than a traditional fMRI experiment at low temporal resolution (1.5–3 s).</div><div>In this study, we introduced a new U-shaped convolutional neural network DUNE to denoise ME-fMRI data as an alternative to MEICA in 2 ME-fMRI experiments. The resulting activation maps found after denoising with DUNE were compared with those found after denoising with MEICA and similar SE-fMRI experiments at high temporal resolution.</div><div>Our results revealed that DUNE was successful in reducing the noise while preserving the BOLD effects of interest comparable to MEICA and SE-fMRI. This latter result showed the potential of using a U-shaped convolutional neural network DUNE to denoise ME-fMRI data.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121749"},"PeriodicalIF":4.5,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.neuroimage.2026.121747
Jace A. Willis , Christopher E. Wright , Ruoqian Zhu , Yilan Ruan , Joshua Stallings , Amada M. Abrego , Takfarinas Medani , Promit Moitra , Arjun Ramakrishnan , Charles E. Schroeder , Anand A. Joshi , Nitin Tandon , Richard M. Leahy , John C. Mosher , John P. Seymour
Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants.
{"title":"Optimizing electrode placement and information capacity for local field potentials in cortex","authors":"Jace A. Willis , Christopher E. Wright , Ruoqian Zhu , Yilan Ruan , Joshua Stallings , Amada M. Abrego , Takfarinas Medani , Promit Moitra , Arjun Ramakrishnan , Charles E. Schroeder , Anand A. Joshi , Nitin Tandon , Richard M. Leahy , John C. Mosher , John P. Seymour","doi":"10.1016/j.neuroimage.2026.121747","DOIUrl":"10.1016/j.neuroimage.2026.121747","url":null,"abstract":"<div><div>Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121747"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.neuroimage.2026.121746
Meiqi Wu , Menglin Liang , Chenhui Mao , Liling Dong , Qi Ge , Yuying Li , Jingnan Wang , Chao Ren , Haiqiong Zhang , Zhenghai Huang , Haiqun Xing , Xueqian Yang , Yuan Wang , Runze Wu , Feng Feng , Mengchao Cui , Jing Gao , Li Huo
[18F]Florbetazine ([18F]FBZ) is a novel Aβ tracer with imaging characteristics similar to [11C]PiB. This study aimed to establish Centiloid conversion equations for [18F]FBZ and to evaluate its quantification precision relative to [11C]PiB across different image-processing pipelines and effective image resolutions (EIRs).
Methods
Paired [11C]PiB and [18F]FBZ PET scans were acquired in 53 participants. Centiloid conversion equations for [18F]FBZ standardized uptake value ratio (SUVR), derived from both the standard SPM pipeline and a FreeSurfer pipeline, were calculated following the Level-2 analysis proposed by Klunk et al. The variance ratio of Centiloids derived from [18F]FBZ SUVR to those derived from standard [11C]PiB SUVR in YCs was computed to compare quantification precision. Additionally, the linear relationships between [18F]FBZ and [11C]PiB SUVR were evaluated under different EIRs.
Results
The Centiloid conversion equation for [18F]FBZ SUVR using the standard SPM pipeline was: Centiloid=98.6 × [18F]FBZ SUVRstd–99.8 (variance ratio=0.92). For the FreeSurfer pipeline, the conversion was: Centiloid=110.1 × [18F]FBZ SUVRfs–108.1 (variance ratio=0.55). Robust linear correlations between [11C]PiB and [18F]FBZ SUVR were observed across EIRs with the SPM pipeline, whereas regression parameters varied across EIRs with the FreeSurfer pipeline.
Conclusion
[18F]Florbetazine SUVR can be reliably converted to Centiloid units. Compared with [11C]PiB, [18F]FBZ demonstrated equal or improved quantification precision, supporting its broader use in clinical and research Aβ imaging.
{"title":"Standardized quantification of [18F]Florbetazine amyloid PET with the Centiloid scale","authors":"Meiqi Wu , Menglin Liang , Chenhui Mao , Liling Dong , Qi Ge , Yuying Li , Jingnan Wang , Chao Ren , Haiqiong Zhang , Zhenghai Huang , Haiqun Xing , Xueqian Yang , Yuan Wang , Runze Wu , Feng Feng , Mengchao Cui , Jing Gao , Li Huo","doi":"10.1016/j.neuroimage.2026.121746","DOIUrl":"10.1016/j.neuroimage.2026.121746","url":null,"abstract":"<div><div>[<sup>18</sup>F]Florbetazine ([<sup>18</sup>F]FBZ) is a novel Aβ tracer with imaging characteristics similar to [<sup>11</sup>C]PiB. This study aimed to establish Centiloid conversion equations for [<sup>18</sup>F]FBZ and to evaluate its quantification precision relative to [<sup>11</sup>C]PiB across different image-processing pipelines and effective image resolutions (EIRs).</div></div><div><h3>Methods</h3><div>Paired [<sup>11</sup>C]PiB and [<sup>18</sup>F]FBZ PET scans were acquired in 53 participants. Centiloid conversion equations for [<sup>18</sup>F]FBZ standardized uptake value ratio (SUVR), derived from both the standard SPM pipeline and a FreeSurfer pipeline, were calculated following the Level-2 analysis proposed by Klunk et al. The variance ratio of Centiloids derived from [<sup>18</sup>F]FBZ SUVR to those derived from standard [<sup>11</sup>C]PiB SUVR in YCs was computed to compare quantification precision. Additionally, the linear relationships between [<sup>18</sup>F]FBZ and [<sup>11</sup>C]PiB SUVR were evaluated under different EIRs.</div></div><div><h3>Results</h3><div>The Centiloid conversion equation for [<sup>18</sup>F]FBZ SUVR using the standard SPM pipeline was: <em>Centiloid=98.6 × [<sup>18</sup>F]FBZ SUVR<sub>std</sub>–99.8</em> (variance ratio=0.92). For the FreeSurfer pipeline, the conversion was: <em>Centiloid=110.1 × [<sup>18</sup>F]FBZ SUVR<sub>fs</sub>–108.1</em> (variance ratio=0.55). Robust linear correlations between [<sup>11</sup>C]PiB and [<sup>18</sup>F]FBZ SUVR were observed across EIRs with the SPM pipeline, whereas regression parameters varied across EIRs with the FreeSurfer pipeline.</div></div><div><h3>Conclusion</h3><div>[<sup>18</sup>F]Florbetazine SUVR can be reliably converted to Centiloid units. Compared with [<sup>11</sup>C]PiB, [<sup>18</sup>F]FBZ demonstrated equal or improved quantification precision, supporting its broader use in clinical and research Aβ imaging.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121746"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.neuroimage.2026.121745
Yidan Song , Yanlin Wu , Yiping Zhou , Yanmei Wang , Panpan Zhang , Xifu Zheng
As a core feature of social anxiety disorder (SAD), the neural mechanisms through which fear of positive evaluation (FPE) influences the processing of social feedback remain unclear. This study employed the Social Judgment Paradigm (SJP) to compare neural activity in individuals with high and low FPE when they received expected or unexpected social acceptance or rejection feedback. Behavioral results indicated that the low FPE group exhibited a positive expectancy bias, whereas the high FPE group showed no such tendency. Neuroelectrophysiological findings revealed that unexpected feedback elicited larger feedback-related negativity (FRN) and stronger theta oscillations compared to expected feedback. More importantly, high FPE individuals demonstrated abnormal patterns in the late stages of social feedback processing: increased cognitive conflict in response to expected acceptance feedback (enhanced P3 amplitude and theta activity), blunted reactivity to unexpected acceptance feedback (reduced theta activity), and hypersensitivity to unexpected rejection feedback (enhanced P3 amplitude and theta activity). The findings suggest that high FPE individuals exhibit a bidirectional differentiation in processing social evaluative information, which may lead to reduced positive emotional experiences and impaired emotion regulation. Despite the limitations of using a non-clinical sample, our results reveal FPE-specific neural characteristics and their role in abnormal social feedback processing. Notably, the P3 and theta oscillations may serve as potential physiological markers for identifying social anxiety individuals primarily characterized by fear of positive evaluation, thereby providing direction for developing more targeted therapeutic interventions in the future.
{"title":"Fear of positive evaluation linked to aberrant neural processing of social acceptance and rejection: Evidence from ERPs and neural oscillations","authors":"Yidan Song , Yanlin Wu , Yiping Zhou , Yanmei Wang , Panpan Zhang , Xifu Zheng","doi":"10.1016/j.neuroimage.2026.121745","DOIUrl":"10.1016/j.neuroimage.2026.121745","url":null,"abstract":"<div><div>As a core feature of social anxiety disorder (SAD), the neural mechanisms through which fear of positive evaluation (FPE) influences the processing of social feedback remain unclear. This study employed the Social Judgment Paradigm (SJP) to compare neural activity in individuals with high and low FPE when they received expected or unexpected social acceptance or rejection feedback. Behavioral results indicated that the low FPE group exhibited a positive expectancy bias, whereas the high FPE group showed no such tendency. Neuroelectrophysiological findings revealed that unexpected feedback elicited larger feedback-related negativity (FRN) and stronger theta oscillations compared to expected feedback. More importantly, high FPE individuals demonstrated abnormal patterns in the late stages of social feedback processing: increased cognitive conflict in response to expected acceptance feedback (enhanced P3 amplitude and theta activity), blunted reactivity to unexpected acceptance feedback (reduced theta activity), and hypersensitivity to unexpected rejection feedback (enhanced P3 amplitude and theta activity). The findings suggest that high FPE individuals exhibit a bidirectional differentiation in processing social evaluative information, which may lead to reduced positive emotional experiences and impaired emotion regulation. Despite the limitations of using a non-clinical sample, our results reveal FPE-specific neural characteristics and their role in abnormal social feedback processing. Notably, the P3 and theta oscillations may serve as potential physiological markers for identifying social anxiety individuals primarily characterized by fear of positive evaluation, thereby providing direction for developing more targeted therapeutic interventions in the future.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121745"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.neuroimage.2026.121743
Zi-Jian Feng , Ziyu Wei , Liquan Hong , Hongli Fang , Yu Han , Peifeng Yang , Dongsheng Lv , Yu-Feng Zang
Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimer’s disease–related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95 % CI: 0.0–12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.
个性化重复经颅磁刺激（rTMS）越来越依赖于静息状态功能磁共振成像（fMRI）来选择刺激位点，然而大多数管道依赖于用户定义的阈值和图谱掩模，这可以改变个性化的目标。我们提出了一种基于分水岭的方法，在图形用户界面中实现，该方法对功能图像进行阈值无关的分割，以支持rTMS目标定位。作为概念验证，我们重点研究了默认模式网络中与阿尔茨海默病相关的回路，将后扣带皮层（PCC）指定为深部有效区，将下顶叶（IPL）指定为浅表刺激目标。在21名健康参与者的队列中，与传统的基于阈值的面罩约束峰策略进行定量比较，发现PCC峰的一致性很高，但IPL目标的中位空间位移为6.0 mm （95% CI: 0.0-12.7 mm）。定性的例子进一步说明分水岭分割减少了邻近功能簇的偏差、图谱边界的截断以及多个局部峰之间的模糊性。通过将目标定义与用户选择的阈值解耦，并将方法打包到一个可访问的工具箱中，该框架为个性化fmri引导的rTMS提供了一个通用的工具。
{"title":"A watershed algorithm GUI for personalized fMRI-guided rTMS target","authors":"Zi-Jian Feng , Ziyu Wei , Liquan Hong , Hongli Fang , Yu Han , Peifeng Yang , Dongsheng Lv , Yu-Feng Zang","doi":"10.1016/j.neuroimage.2026.121743","DOIUrl":"10.1016/j.neuroimage.2026.121743","url":null,"abstract":"<div><div>Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimer’s disease–related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95 % CI: 0.0–12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121743"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.neuroimage.2026.121738
Jia Li , Rong Wang , Jianze Wu , Qian Xiao , Yuan Zhong
Pediatric bipolar disorder (PBD) is characterized by disrupted cognitive control, particularly in response inhibition under emotional interference. However, the neural underpinnings of these deficits, particularly how these impairments vary across emotional valence and whether they reflect trait markers or state alterations, remain unclear. While traditional univariate fMRI analyses reveal broad activation differences, they lack sensitivity to fine-grained neural patterns. This study aims to examine the neural representations of emotional response inhibition in PBD under valence-dependent interference using representational similarity analysis(RSA). We included manic (n = 15) and euthymic (n = 18) PBD patients, along with matched healthy controls (n = 17). Participants completed an emotional Go/NoGo task with happy, sad, and neutral faces during fMRI. Six contrast conditions were modeled to assess trait- and state-related effects. Whole-brain searchlight RSA (8 mm radius) was used to identify regions showing group differences in neural representational patterns. Results showed that emotional response inhibition engaged distributed neural systems, with distinct patterns across valence conditions. Compared to controls, PBD patients exhibited trait-related representational differences during happy inhibition, sad inhibition, and sad-specific inhibition, involving regions such as the precentral gyrus, middle frontal gyrus, and inferior parietal lobule. Manic patients showed state-related reductions in neural representations during sad-specific inhibition within frontal areas compared to euthymic patients. These findings indicate that emotional response inhibition deficits in PBD arise from both trait- and state-dependent abnormalities in neural representations. The study highlights the value of multivariate fMRI in uncovering clinically relevant biomarkers and provides a novel framework for developing phase-specific interventions.
{"title":"Neural representations of emotional response inhibition reveal trait and state biomarkers in pediatric bipolar disorder","authors":"Jia Li , Rong Wang , Jianze Wu , Qian Xiao , Yuan Zhong","doi":"10.1016/j.neuroimage.2026.121738","DOIUrl":"10.1016/j.neuroimage.2026.121738","url":null,"abstract":"<div><div>Pediatric bipolar disorder (PBD) is characterized by disrupted cognitive control, particularly in response inhibition under emotional interference. However, the neural underpinnings of these deficits, particularly how these impairments vary across emotional valence and whether they reflect trait markers or state alterations, remain unclear. While traditional univariate fMRI analyses reveal broad activation differences, they lack sensitivity to fine-grained neural patterns. This study aims to examine the neural representations of emotional response inhibition in PBD under valence-dependent interference using representational similarity analysis(RSA). We included manic (<em>n</em> = 15) and euthymic (<em>n</em> = 18) PBD patients, along with matched healthy controls (<em>n</em> = 17). Participants completed an emotional Go/NoGo task with happy, sad, and neutral faces during fMRI. Six contrast conditions were modeled to assess trait- and state-related effects. Whole-brain searchlight RSA (8 mm radius) was used to identify regions showing group differences in neural representational patterns. Results showed that emotional response inhibition engaged distributed neural systems, with distinct patterns across valence conditions. Compared to controls, PBD patients exhibited trait-related representational differences during happy inhibition, sad inhibition, and sad-specific inhibition, involving regions such as the precentral gyrus, middle frontal gyrus, and inferior parietal lobule. Manic patients showed state-related reductions in neural representations during sad-specific inhibition within frontal areas compared to euthymic patients. These findings indicate that emotional response inhibition deficits in PBD arise from both trait- and state-dependent abnormalities in neural representations. The study highlights the value of multivariate fMRI in uncovering clinically relevant biomarkers and provides a novel framework for developing phase-specific interventions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121738"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.neuroimage.2026.121735
Qi Li , Mei Du , Jing Xiao , Ting Li , Kesong Hu , Song Tu , Xun Liu , Lingxiao Wang , Weine Dai
Addiction disorders, encompassing non-substance and substance addiction, are a prevalent and devastating class of mental illnesses. The dual-system theory of addiction posits that abnormal processing in the brain's reward and control systems underlies addictive behaviors. However, challenges in previous neuroimaging studies on addiction, including small sample sizes, subjective narrative reviews, and inconsistent findings, have limited the comprehensive clarification of the neural mechanisms underlying addiction. This meta-analysis addresses these limitations by integrating neuroimaging data from multiple studies. Here, the random-effects activation likelihood estimation (ALE) method was applied to systematically synthesize data to elucidate the neural mechanisms of reward processing and cognitive control in non-substance and substance addiction. The study revealed that individuals with non-substance addiction presented increased dorsal anterior cingulate cortex (dACC) and caudate activation during cue reactivity, enhanced putamen and globus pallidus responses to non-specific rewards, and no significant group differences in cognitive control tasks. In contrast, substance addiction was found to be characterized by heightened activation in the rostral anterior cingulate cortex (rACC), ventromedial prefrontal cortex (vmPFC) and putamen during cue reactivity, accompanied by reduced responses of the nucleus accumbens (NAc) to non-specific rewards and reduced activation in the inferior parietal lobule (IPL) during cognitive-control tasks. These findings not only reveal a potential "motivation-control equilibrium" mechanism in non-substance addiction but also support the dual-system framework for substance addiction, providing neurobiological targets for precision interventions.
{"title":"Divergent neural mechanisms of reward processing and cognitive control in non-substance and substance addiction: A meta-analytic perspective","authors":"Qi Li , Mei Du , Jing Xiao , Ting Li , Kesong Hu , Song Tu , Xun Liu , Lingxiao Wang , Weine Dai","doi":"10.1016/j.neuroimage.2026.121735","DOIUrl":"10.1016/j.neuroimage.2026.121735","url":null,"abstract":"<div><div>Addiction disorders, encompassing non-substance and substance addiction, are a prevalent and devastating class of mental illnesses. The dual-system theory of addiction posits that abnormal processing in the brain's reward and control systems underlies addictive behaviors. However, challenges in previous neuroimaging studies on addiction, including small sample sizes, subjective narrative reviews, and inconsistent findings, have limited the comprehensive clarification of the neural mechanisms underlying addiction. This meta-analysis addresses these limitations by integrating neuroimaging data from multiple studies. Here, the random-effects activation likelihood estimation (ALE) method was applied to systematically synthesize data to elucidate the neural mechanisms of reward processing and cognitive control in non-substance and substance addiction. The study revealed that individuals with non-substance addiction presented increased dorsal anterior cingulate cortex (dACC) and caudate activation during cue reactivity, enhanced putamen and globus pallidus responses to non-specific rewards, and no significant group differences in cognitive control tasks. In contrast, substance addiction was found to be characterized by heightened activation in the rostral anterior cingulate cortex (rACC), ventromedial prefrontal cortex (vmPFC) and putamen during cue reactivity, accompanied by reduced responses of the nucleus accumbens (NAc) to non-specific rewards and reduced activation in the inferior parietal lobule (IPL) during cognitive-control tasks. These findings not only reveal a potential \"motivation-control equilibrium\" mechanism in non-substance addiction but also support the dual-system framework for substance addiction, providing neurobiological targets for precision interventions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121735"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.neuroimage.2026.121740
Andrés Perissinotti , Arnau Farré-Melero , Francisco J. López-González , María del Carmen Mallón-Araujo , Julia Cortés , Xavier Setoain , Andrea Fritsch , Katherine Quintero , Angela Esteban , Silvia Morbelli , Matteo Bauckneht , Alberto Miceli , Aida Niñerola-Baizán , Pablo Aguiar , Jesús Silva-Rodríguez
Purpose
Quantitative analysis of [18F]FDG-PET images is expected to improve the localization of foci in non-lesional epilepsy. However, the lack of reliable gold standards has prevented a comprehensive evaluation of the potential improvements derived from this approach. Here, we aimed at evaluating these improvements using a novel dataset of realistic simulated studies.
Methods
125 realistic simulated [18F]FDG-PET studies were generated (100 with synthetic hypometabolic foci (HF) with different levels of identification complexity and 25 controls). Eight nuclear physicians performed visual rating (VR) and were given the chance to modify their assessment after reviewing quantitative results (QR). Physicians reported the presence/absence of HF, HF location, and diagnostic confidence (DC) before/after QR. Success Rate (SR) of physician’s assessments was analyzed, as well as inter-rater agreement and changes in DC.
Results
In 31.3% of the assessments, physicians changed their interpretation after QR, with SR increasing from 16.3% to 61.0% in these cases. Overall SR improved from 49.5% in VR to 63.5% in QR, mostly on pathologic cases (relative improvement: +34.0%). Improvement was found at each level of HF identification complexity and was higher for challenging cases (relative improvement: +71.8%). Inter-rater agreement also improved significantly (0.273 vs. 0.475, p < 0.001). QR also significantly increased DC ("High" confidence of 8.1% on VR vs. 38.5% on QR, p < 0.001).
Conclusion
Quantitative analysis significantly improved diagnostic accuracy, confidence and inter-rater agreement, especially in challenging cases. Furthermore, this work introduces a novel methodological approach using simulated MRI-negative epilepsy [18F]FDG-PET images for realistic quantification research studies.
{"title":"Added value of quantitative [18F]FDG-PET analysis in MRI-negative epilepsy: A simulation-based study using realistic ground-truths","authors":"Andrés Perissinotti , Arnau Farré-Melero , Francisco J. López-González , María del Carmen Mallón-Araujo , Julia Cortés , Xavier Setoain , Andrea Fritsch , Katherine Quintero , Angela Esteban , Silvia Morbelli , Matteo Bauckneht , Alberto Miceli , Aida Niñerola-Baizán , Pablo Aguiar , Jesús Silva-Rodríguez","doi":"10.1016/j.neuroimage.2026.121740","DOIUrl":"10.1016/j.neuroimage.2026.121740","url":null,"abstract":"<div><h3>Purpose</h3><div>Quantitative analysis of [<sup>18</sup>F]FDG-PET images is expected to improve the localization of foci in non-lesional epilepsy. However, the lack of reliable gold standards has prevented a comprehensive evaluation of the potential improvements derived from this approach. Here, we aimed at evaluating these improvements using a novel dataset of realistic simulated studies.</div></div><div><h3>Methods</h3><div>125 realistic simulated [<sup>18</sup>F]FDG-PET studies were generated (100 with synthetic hypometabolic foci (HF) with different levels of identification complexity and 25 controls). Eight nuclear physicians performed visual rating (VR) and were given the chance to modify their assessment after reviewing quantitative results (QR). Physicians reported the presence/absence of HF, HF location, and diagnostic confidence (DC) before/after QR. Success Rate (SR) of physician’s assessments was analyzed, as well as inter-rater agreement and changes in DC.</div></div><div><h3>Results</h3><div>In 31.3% of the assessments, physicians changed their interpretation after QR, with SR increasing from 16.3% to 61.0% in these cases. Overall SR improved from 49.5% in VR to 63.5% in QR, mostly on pathologic cases (relative improvement: +34.0%). Improvement was found at each level of HF identification complexity and was higher for challenging cases (relative improvement: +71.8%). Inter-rater agreement also improved significantly (0.273 vs. 0.475, <em>p</em> < 0.001). QR also significantly increased DC (\"High\" confidence of 8.1% on VR vs. 38.5% on QR, <em>p</em> < 0.001).</div></div><div><h3>Conclusion</h3><div>Quantitative analysis significantly improved diagnostic accuracy, confidence and inter-rater agreement, especially in challenging cases. Furthermore, this work introduces a novel methodological approach using simulated MRI-negative epilepsy [<sup>18</sup>F]FDG-PET images for realistic quantification research studies.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121740"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.neuroimage.2026.121739
Jianhui Lv , Shalli Rani , Keqin Li , Ning Liu
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting language networks and speech production pathways in the brain. Patients demonstrate symptoms including aphasia, reduced syntactic complexity, and diminished verbal fluency that reflects underlying neural pathology in language-related cortical areas. Current detection methods rely on resource-intensive neuroimaging, invasive biomarker sampling, and extensive neuropsychological testing, creating substantial barriers to early diagnosis. While researchers have explored using acoustic features, paralinguistic markers, and text-based features for AD detection, existing approaches face fundamental limitations: traditional acoustic methods fail to capture semantic-cognitive content, text transcription is labor-intensive, and automatic speech recognition quality suffers due to pronunciation variations and cognitive impairments in elderly populations. This paper introduces cognitive acoustic symbolic transformation for ALzheimer’s (COASTAL), a neurobiologically-inspired framework that models hierarchical speech processing pathways. COASTAL transforms acoustic patterns into discrete symbolic elements through a specialized transformation module before applying contextual analysis that mirrors prefrontal-temporal language networks. Evaluated on the ADReSSo corpus, COASTAL achieved 70.42% accuracy, outperforming established baselines by 5.63%. Integration with complementary self-supervised approaches through hierarchical fusion improved performance to 77.46%. Analysis revealed that preserving fine-grained temporal features through shallower transformation architecture significantly enhanced diagnostic accuracy, aligning with neuropsychological evidence that subtle timing patterns in speech provide sensitive markers of cognitive decline.
{"title":"Neural-linguistic analysis for Alzheimer’s detection: A deep learning approach informed by cognitive neuroscience","authors":"Jianhui Lv , Shalli Rani , Keqin Li , Ning Liu","doi":"10.1016/j.neuroimage.2026.121739","DOIUrl":"10.1016/j.neuroimage.2026.121739","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting language networks and speech production pathways in the brain. Patients demonstrate symptoms including aphasia, reduced syntactic complexity, and diminished verbal fluency that reflects underlying neural pathology in language-related cortical areas. Current detection methods rely on resource-intensive neuroimaging, invasive biomarker sampling, and extensive neuropsychological testing, creating substantial barriers to early diagnosis. While researchers have explored using acoustic features, paralinguistic markers, and text-based features for AD detection, existing approaches face fundamental limitations: traditional acoustic methods fail to capture semantic-cognitive content, text transcription is labor-intensive, and automatic speech recognition quality suffers due to pronunciation variations and cognitive impairments in elderly populations. This paper introduces cognitive acoustic symbolic transformation for ALzheimer’s (COASTAL), a neurobiologically-inspired framework that models hierarchical speech processing pathways. COASTAL transforms acoustic patterns into discrete symbolic elements through a specialized transformation module before applying contextual analysis that mirrors prefrontal-temporal language networks. Evaluated on the ADReSSo corpus, COASTAL achieved 70.42% accuracy, outperforming established baselines by 5.63%. Integration with complementary self-supervised approaches through hierarchical fusion improved performance to 77.46%. Analysis revealed that preserving fine-grained temporal features through shallower transformation architecture significantly enhanced diagnostic accuracy, aligning with neuropsychological evidence that subtle timing patterns in speech provide sensitive markers of cognitive decline.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121739"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding how brain tissue properties change with age is crucial for identifying early markers of neurodegenerative disease. However, the biophysical alterations and their molecular bases remain poorly understood. Quantitative MRI (qMRI) offers non-invasive insight into brain tissue properties. In this study, we employed three qMRI metrics—quantitative susceptibility mapping (QSM), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*)—to investigate age-related brain changes across the adult lifespan. Applying linear and nonlinear modeling, we observed distinct patterns of cross-sectional age-related biophysical alterations (early, late, and inverted-U patterns) in the human brain. Predictive modeling identified subcortical and thalamic regions as key contributors to age estimation. Integrating transcriptomic data revealed that these imaging-derived patterns spatially co-localize with gene expression signatures enriched in neurodevelopmental and neurodegenerative pathways. Our study advances current understanding by integrating multimodal qMRI age-related patterns and transcriptomics, uncovering distinct aging patterns, candidate age-sensitive imaging features that warrant further validation, and their potential molecular underpinnings.
{"title":"Multimodal quantitative MRI reveals age-related biophysical alterations in the human brain across the adult lifespan","authors":"Xiang Chen , Zhiyuan Yuan , Jie Zhang , Xiao-Yong Zhang","doi":"10.1016/j.neuroimage.2026.121742","DOIUrl":"10.1016/j.neuroimage.2026.121742","url":null,"abstract":"<div><div>Understanding how brain tissue properties change with age is crucial for identifying early markers of neurodegenerative disease. However, the biophysical alterations and their molecular bases remain poorly understood. Quantitative MRI (qMRI) offers non-invasive insight into brain tissue properties. In this study, we employed three qMRI metrics—quantitative susceptibility mapping (QSM), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*)—to investigate age-related brain changes across the adult lifespan. Applying linear and nonlinear modeling, we observed distinct patterns of cross-sectional age-related biophysical alterations (early, late, and inverted-U patterns) in the human brain. Predictive modeling identified subcortical and thalamic regions as key contributors to age estimation. Integrating transcriptomic data revealed that these imaging-derived patterns spatially co-localize with gene expression signatures enriched in neurodevelopmental and neurodegenerative pathways. Our study advances current understanding by integrating multimodal qMRI age-related patterns and transcriptomics, uncovering distinct aging patterns, candidate age-sensitive imaging features that warrant further validation, and their potential molecular underpinnings.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121742"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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