Pub Date : 2026-01-31DOI: 10.1016/j.ynirp.2026.100320
C.D. Richard , B. Porjesz , J.L. Meyers , A. Bingly , D.B. Chorlian , C. Kamarajan , G. Pandey , A. Anokhin , S. Brislin , W. Kuang , A.K. Pandey , S. Kinreich
Considerable evidence from functional neuroimaging and EEG coherence studies indicates that individuals afflicted with alcohol use disorder (AUD) manifest aberrant patterns of connectivity, particularly in frontal brain regions. Phase-amplitude coupling (PAC) is another form of functional connectivity, reflecting the association between the phase at one frequency and amplitude changes at a higher frequency. Significant PAC differences have been reported for other substance use disorders, but it has not yet been investigated in AUD. We compared frontomedial PAC strength during resting state, eyes closed, in adult participants with severe AUD and age-matched unaffected controls from the Collaborative Study on the Genetics of Alcoholism (COGA). Comodulograms of PAC estimates between phase frequencies (0.1–13 Hz) and amplitude frequencies (4–50 Hz) were calculated for all participants. PAC differences between AUD and unaffected groups were assessed at each phase-amplitude frequency pair in comodulograms to identify clusters of significant test results, reporting only those clusters satisfying all validation and significance testing steps. Severe AUD was associated with clusters of significantly greater PAC in alpha-gamma domains of both men and women. Candidate clusters were found in theta-gamma domains of both sexes, but were only significant greater in men with AUD. Significant PAC clusters were found in the delta-gamma domain of both sexes, though women with AUD showed significant decreases in contrast to greater PAC found in men with AUD. The significant PAC clusters identified in this exploratory study could provide new insights into the dysregulation of brain connectivity underlying AUD.
{"title":"Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state","authors":"C.D. Richard , B. Porjesz , J.L. Meyers , A. Bingly , D.B. Chorlian , C. Kamarajan , G. Pandey , A. Anokhin , S. Brislin , W. Kuang , A.K. Pandey , S. Kinreich","doi":"10.1016/j.ynirp.2026.100320","DOIUrl":"10.1016/j.ynirp.2026.100320","url":null,"abstract":"<div><div>Considerable evidence from functional neuroimaging and EEG coherence studies indicates that individuals afflicted with alcohol use disorder (AUD) manifest aberrant patterns of connectivity, particularly in frontal brain regions. Phase-amplitude coupling (PAC) is another form of functional connectivity, reflecting the association between the phase at one frequency and amplitude changes at a higher frequency. Significant PAC differences have been reported for other substance use disorders, but it has not yet been investigated in AUD. We compared frontomedial PAC strength during resting state, eyes closed, in adult participants with severe AUD and age-matched unaffected controls from the Collaborative Study on the Genetics of Alcoholism (COGA). Comodulograms of PAC estimates between phase frequencies (0.1–13 Hz) and amplitude frequencies (4–50 Hz) were calculated for all participants. PAC differences between AUD and unaffected groups were assessed at each phase-amplitude frequency pair in comodulograms to identify clusters of significant test results, reporting only those clusters satisfying all validation and significance testing steps. Severe AUD was associated with clusters of significantly greater PAC in alpha-gamma domains of both men and women. Candidate clusters were found in theta-gamma domains of both sexes, but were only significant greater in men with AUD. Significant PAC clusters were found in the delta-gamma domain of both sexes, though women with AUD showed significant decreases in contrast to greater PAC found in men with AUD. The significant PAC clusters identified in this exploratory study could provide new insights into the dysregulation of brain connectivity underlying AUD.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.ynirp.2026.100319
Sofie De Schrijver , Thomas Decramer , Peter Janssen
Neurons that are active during action execution and action observation (i.e. Action Observation/Execution Neurons, AOENs) are distributed across the brain in a network of parietal, motor, and prefrontal areas. In a previous study, we showed that most AOENs in ventral premotor area F5c, where they were discovered three decades ago, responded in a highly phasic way during the observation of a grasping action, did not require the perception of causality or a meaningful action, and even responded to static frames of the action videos. To assess whether these characteristics are shared with AOENs in other areas of the AOE network, we performed the first large-scale neural recordings during action execution and action observation in multiple frontal areas including dorsal premotor (PMd) area F2, primary motor (M1) cortex, ventral premotor area F5p, frontal eye field (FEF) and 45B. In all areas, AOENs displayed highly phasic responses during specific epochs of the action video and, in addition, strong responses to a moving object, similar to F5c. In addition, the population dynamics in PMv, PMd and M1 showed a shared representation between action execution and action observation, with an overlap that was as large as the overlap between action execution and passive viewing of translation movements. These results pose important constraints on the interpretation of action observation responses in frontal cortical areas.
{"title":"Action observation responses in macaque frontal cortex","authors":"Sofie De Schrijver , Thomas Decramer , Peter Janssen","doi":"10.1016/j.ynirp.2026.100319","DOIUrl":"10.1016/j.ynirp.2026.100319","url":null,"abstract":"<div><div>Neurons that are active during action execution and action observation (i.e. Action Observation/Execution Neurons, AOENs) are distributed across the brain in a network of parietal, motor, and prefrontal areas. In a previous study, we showed that most AOENs in ventral premotor area F5c, where they were discovered three decades ago, responded in a highly phasic way during the observation of a grasping action, did not require the perception of causality or a meaningful action, and even responded to static frames of the action videos. To assess whether these characteristics are shared with AOENs in other areas of the AOE network, we performed the first large-scale neural recordings during action execution and action observation in multiple frontal areas including dorsal premotor (PMd) area F2, primary motor (M1) cortex, ventral premotor area F5p, frontal eye field (FEF) and 45B. In all areas, AOENs displayed highly phasic responses during specific epochs of the action video and, in addition, strong responses to a moving object, similar to F5c. In addition, the population dynamics in PMv, PMd and M1 showed a shared representation between action execution and action observation, with an overlap that was as large as the overlap between action execution and passive viewing of translation movements. These results pose important constraints on the interpretation of action observation responses in frontal cortical areas.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.ynirp.2026.100323
Shoko Shimada , Toshiki Iwabuchi , Motofumi Sumiya , Koji Shimada , Shinichiro Takiguchi , Kai Makita , Akiko Yao , Takashi X. Fujisawa , Atsushi Senju , Akemi Tomoda
Adverse childhood experiences are a risk factor for attachment disorders. While several neuroimaging studies have shown changes in functional networks in children who have experienced institutional care, the results are inconsistent. Furthermore, no research has been conducted on how structured residential care, such as Japan's small-group family-style care, influences attachment-related symptoms and functional connectivity. This study compared attachment-related symptoms (reactive attachment disorder [RAD] and disinhibited social engagement disorder [DSED] symptoms) between youth aged 9–18 years raised in Japanese small-group residential care (RC; n = 31) and those raised in birth families but not in RC (NRC; n = 37). Group differences in resting-state functional connectivity were also analyzed using multivariate pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data. MVPA revealed group differences in whole-brain functional connectivity patterns from the right occipital pole and the left lingual gyrus (LLG). Functional connectivity between the LLG and the frontal medial cortex (FMC) was reduced in RC youth. LLG-FMC connectivity was positively correlated with RAD scores, while longer duration of stay in RC was negatively correlated with RAD symptoms. This study highlights caregiving environment's influence on attachment-related symptoms and functional connectivity, higher levels of RAD and DSED symptoms and reduced LLG-FMC functional connectivity in the RC group. However, this study further demonstrated not only the association between longer stays in family-like RC and the reduction of RAD symptoms but also changes in the connectivity. These findings suggest that stable, high-quality care may have the potential to mitigate adverse developmental outcomes.
不良的童年经历是依恋障碍的一个危险因素。虽然几项神经成像研究表明，经历过机构护理的儿童的功能网络发生了变化，但结果并不一致。此外，没有研究对结构化的住宿护理，如日本的小团体家庭式护理，如何影响依恋相关症状和功能连接进行过研究。本研究比较了日本小团体寄宿家庭（RC, n = 31）和非小团体寄宿家庭（NRC, n = 37）中9-18岁青少年的依恋相关症状（反应性依恋障碍[RAD]和去抑制性社会参与障碍[DSED]症状）。利用功能磁共振成像（fMRI）数据的多变量模式分析（MVPA）分析静息状态功能连通性的组间差异。MVPA显示了右枕极和左舌回（LLG）全脑功能连接模式的组差异。在RC青年中，LLG和额叶内侧皮层（FMC）之间的功能连通性降低。LLG-FMC连通性与RAD评分呈正相关，而RC停留时间较长与RAD症状负相关。本研究强调了护理环境对RC组依恋相关症状和功能连通性的影响，以及更高水平的RAD和DSED症状和降低的LLG-FMC功能连通性。然而，这项研究进一步证明，在家庭式RC中停留时间的延长不仅与RAD症状的减轻有关，而且还与连通性的改变有关。这些发现表明，稳定、高质量的护理可能有可能减轻不良的发育结果。
{"title":"Functional connectivity of youth in family-like residential care in Japan: Impact of reactive attachment disorder and disinhibited social engagement disorder symptoms","authors":"Shoko Shimada , Toshiki Iwabuchi , Motofumi Sumiya , Koji Shimada , Shinichiro Takiguchi , Kai Makita , Akiko Yao , Takashi X. Fujisawa , Atsushi Senju , Akemi Tomoda","doi":"10.1016/j.ynirp.2026.100323","DOIUrl":"10.1016/j.ynirp.2026.100323","url":null,"abstract":"<div><div>Adverse childhood experiences are a risk factor for attachment disorders. While several neuroimaging studies have shown changes in functional networks in children who have experienced institutional care, the results are inconsistent. Furthermore, no research has been conducted on how structured residential care, such as Japan's small-group family-style care, influences attachment-related symptoms and functional connectivity. This study compared attachment-related symptoms (reactive attachment disorder [RAD] and disinhibited social engagement disorder [DSED] symptoms) between youth aged 9–18 years raised in Japanese small-group residential care (RC; n = 31) and those raised in birth families but not in RC (NRC; n = 37). Group differences in resting-state functional connectivity were also analyzed using multivariate pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data. MVPA revealed group differences in whole-brain functional connectivity patterns from the right occipital pole and the left lingual gyrus (LLG). Functional connectivity between the LLG and the frontal medial cortex (FMC) was reduced in RC youth. LLG-FMC connectivity was positively correlated with RAD scores, while longer duration of stay in RC was negatively correlated with RAD symptoms. This study highlights caregiving environment's influence on attachment-related symptoms and functional connectivity, higher levels of RAD and DSED symptoms and reduced LLG-FMC functional connectivity in the RC group. However, this study further demonstrated not only the association between longer stays in family-like RC and the reduction of RAD symptoms but also changes in the connectivity. These findings suggest that stable, high-quality care may have the potential to mitigate adverse developmental outcomes.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.ynirp.2026.100322
Faith M. Wariri , Johanna C. Walker , Jillian Lee Wiggins , Jinbo Bi
Preadolescent irritability is a robust transdiagnostic neurodevelopmental predictor of later psychopathology, linked to altered reward processing—a common neurocognitive substrate across psychiatric disorders. However, its neurobiological mechanisms remain unclear. Deep learning (DL) excels in predicting neurodevelopmental vulnerabilities and detecting nonlinear brain-behavior relationships but often lacks explainability. Here, we integrate optimized prediction with explainability to characterize neural mechanisms of irritability using task-based fMRI from a large preadolescent sample (N = 1934; mean age = 9.95 years, 101 Persistently High Irritability [PHI], 1833 Persistently Low Irritability [PLI]). We trained three classifiers—artificial neural network (ANN), random forest (RF), XGBoost—to distinguish PHI from PLI using functional connectivity (FC) during reward anticipation. FC was assessed between four seeds (bilateral amygdala, ventral striatum) and 18 cortical/subcortical regions. Shapley additive explanations (SHAP) identified key connectivity predictors accounting for nonlinear effects. ANN (AUC = 0.73, p < .001) outperformed RF (AUC = 0.63, p = .03), XGBoost (AUC = 0.65, p = .01). SHAP revealed increased contralateral FC (e.g., right amygdala-left middle frontal gyrus) and decreased ipsilateral FC (e.g., left ventral striatum-left insula) generally predicted PHI, except amygdala connectivity, where higher ipsilateral FC predicted PHI. These findings highlight interplay between reward and emotion regulation circuits in persistent irritability, underscoring the potential of explainable DL to improve irritability prediction and enhance understanding of its neural mechanisms.
青春期前易怒是一种可靠的跨诊断神经发育预测因子，它与奖赏处理的改变有关，这是一种常见的神经认知基础，贯穿精神疾病。然而，其神经生物学机制尚不清楚。深度学习（DL）在预测神经发育脆弱性和检测非线性脑行为关系方面表现出色，但往往缺乏可解释性。在此，我们将优化预测与可解释性结合起来，利用基于任务的功能磁共振成像技术，从大量青春期前样本（N = 1934，平均年龄= 9.95岁，101例持续高易怒[PHI]， 1833例持续低易怒[PLI]）中描述烦躁的神经机制。我们训练了三个分类器——人工神经网络（ANN）、随机森林（RF）和xgboost——在奖励预期过程中使用功能连接（FC）来区分PHI和PLI。在四个种子（双侧杏仁核，腹侧纹状体）和18个皮质/皮质下区域之间评估FC。Shapley加性解释（SHAP）确定了考虑非线性效应的关键连通性预测因子。安(AUC = 0.73, p & lt;措施)优于射频(AUC = 0.63, p = 03), XGBoost (AUC = 0.65, p = . 01)。SHAP显示，对侧FC增加（如右侧杏仁核-左侧额叶中回），同侧FC减少（如左侧腹侧纹状体-左侧岛叶）通常预测PHI，但杏仁核连通性除外，同侧FC增加预测PHI。这些发现强调了持续性易怒的奖励和情绪调节回路之间的相互作用，强调了可解释的深度学习在改善易怒预测和加强对其神经机制的理解方面的潜力。
{"title":"SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability","authors":"Faith M. Wariri , Johanna C. Walker , Jillian Lee Wiggins , Jinbo Bi","doi":"10.1016/j.ynirp.2026.100322","DOIUrl":"10.1016/j.ynirp.2026.100322","url":null,"abstract":"<div><div>Preadolescent irritability is a robust transdiagnostic neurodevelopmental predictor of later psychopathology, linked to altered reward processing—a common neurocognitive substrate across psychiatric disorders. However, its neurobiological mechanisms remain unclear. Deep learning (DL) excels in predicting neurodevelopmental vulnerabilities and detecting nonlinear brain-behavior relationships but often lacks explainability. Here, we integrate optimized prediction with explainability to characterize neural mechanisms of irritability using task-based fMRI from a large preadolescent sample (N = 1934; mean age = 9.95 years, 101 Persistently High Irritability [PHI], 1833 Persistently Low Irritability [PLI]). We trained three classifiers—artificial neural network (ANN), random forest (RF), XGBoost—to distinguish PHI from PLI using functional connectivity (FC) during reward anticipation. FC was assessed between four seeds (bilateral amygdala, ventral striatum) and 18 cortical/subcortical regions. Shapley additive explanations (SHAP) identified key connectivity predictors accounting for nonlinear effects. ANN (AUC = 0.73, p < .001) outperformed RF (AUC = 0.63, p = .03), XGBoost (AUC = 0.65, p = .01). SHAP revealed increased contralateral FC (e.g., right amygdala-left middle frontal gyrus) and decreased ipsilateral FC (e.g., left ventral striatum-left insula) generally predicted PHI, except amygdala connectivity, where higher ipsilateral FC predicted PHI. These findings highlight interplay between reward and emotion regulation circuits in persistent irritability, underscoring the potential of explainable DL to improve irritability prediction and enhance understanding of its neural mechanisms.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ynirp.2026.100321
Diangang Fang , Wenxian Huang , Tong Mo , Xiaojing Lv , Guohua Liang , Binrang Yang , Hongwu Zeng
<div><h3>Objective</h3><div>SNAP-25, a synaptic vesicle docking protein, carries a polymorphism (rs3746544) in its 3′-UTR region that is associated with ADHD, yet its functional mechanism remains unknown. The purpose of this study is to evaluate the impact of synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphism (rs3746544) on spontaneous brain activity in children with attention deficit hyperactivity disorder (ADHD), employing the fractional amplitude of low-frequency fluctuation (fALFF) analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data, to explore its potential neurobiological mechanisms and neuroimaging biomarkers.</div></div><div><h3>Methods</h3><div>This study enrolled 56 boys with ADHD (aged 8–10 years) and 21 age-matched healthy boys as healthy controls (HCs). According to the SNAP-25 MnlI genotype, ADHD patients were divided into two groups: the TT homozygote group (TT group, n = 36) and the G-allele carrier group (TG group, n = 20). Rs-fMRI data were acquired and analyzed using fALFF to measure spontaneous brain activity.</div><div>One-sample <em>t</em>-tests were performed to calculate fALFF maps for each group, setting the threshold as a cluster greater than 20 voxels, with <em>P</em> < 0.01 after AlphaSim correction. Two-sample <em>t</em>-tests were performed to calculate the differences in fALFF values among the TT, TG, and HCs groups, with age as a covariate. A cluster of greater than 20 voxels, with <em>P</em> < 0.01 after AlphaSim correction, was considered to have statistically significant differences. Assessed the Working Memory Index (WMI) using the Wechsler Intelligence Scale for Children-IV (WISC-IV) in children with ADHD from the TT and TG groups.</div></div><div><h3>Results</h3><div>One-sample <em>t</em>-tests revealed that children with ADHD group (both TT and TG group) exhibited significantly lower fALFF values in the default mode network (DMN) and parieto-occipital cortex compared to HCs, while showing increased fALFF located in the posterior cerebellar lobe; Two-sample <em>t</em>-tests demonstrated that: (a) Compared to HCs, the ADHD group (both TT and TG group) showed widespread reductions of fALFF values across multiple brain regions, including the posterior cingulate cortex and precuneus. The TG group showed more pronounced decreases when compared with the TT group. (b) In comparison to the TG group, the TT group exhibited higher fALFF values in higher-order cognitive regions, such as the right superior frontal gyrus and left medial frontal gyrus, but lower fALFF values in the posterior cerebellar lobe and posterior cingulate cortex. The TT group had significantly higher WMI compared to the TG group (<em>t</em> = 2.098, <em>P</em> < 0.05).</div></div><div><h3>Conclusions</h3><div>The SNAP-25 gene MnlI polymorphism has an impact on spontaneous brain activity in children with ADHD, as measured by fALFF. This study reveals the potential mechanisms from the perspective
{"title":"Impact of SNAP-25 MnlI polymorphism on brain activity patterns in children with ADHD: Insights from fractional amplitude of low-frequency fluctuation analysis","authors":"Diangang Fang , Wenxian Huang , Tong Mo , Xiaojing Lv , Guohua Liang , Binrang Yang , Hongwu Zeng","doi":"10.1016/j.ynirp.2026.100321","DOIUrl":"10.1016/j.ynirp.2026.100321","url":null,"abstract":"<div><h3>Objective</h3><div>SNAP-25, a synaptic vesicle docking protein, carries a polymorphism (rs3746544) in its 3′-UTR region that is associated with ADHD, yet its functional mechanism remains unknown. The purpose of this study is to evaluate the impact of synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphism (rs3746544) on spontaneous brain activity in children with attention deficit hyperactivity disorder (ADHD), employing the fractional amplitude of low-frequency fluctuation (fALFF) analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data, to explore its potential neurobiological mechanisms and neuroimaging biomarkers.</div></div><div><h3>Methods</h3><div>This study enrolled 56 boys with ADHD (aged 8–10 years) and 21 age-matched healthy boys as healthy controls (HCs). According to the SNAP-25 MnlI genotype, ADHD patients were divided into two groups: the TT homozygote group (TT group, n = 36) and the G-allele carrier group (TG group, n = 20). Rs-fMRI data were acquired and analyzed using fALFF to measure spontaneous brain activity.</div><div>One-sample <em>t</em>-tests were performed to calculate fALFF maps for each group, setting the threshold as a cluster greater than 20 voxels, with <em>P</em> < 0.01 after AlphaSim correction. Two-sample <em>t</em>-tests were performed to calculate the differences in fALFF values among the TT, TG, and HCs groups, with age as a covariate. A cluster of greater than 20 voxels, with <em>P</em> < 0.01 after AlphaSim correction, was considered to have statistically significant differences. Assessed the Working Memory Index (WMI) using the Wechsler Intelligence Scale for Children-IV (WISC-IV) in children with ADHD from the TT and TG groups.</div></div><div><h3>Results</h3><div>One-sample <em>t</em>-tests revealed that children with ADHD group (both TT and TG group) exhibited significantly lower fALFF values in the default mode network (DMN) and parieto-occipital cortex compared to HCs, while showing increased fALFF located in the posterior cerebellar lobe; Two-sample <em>t</em>-tests demonstrated that: (a) Compared to HCs, the ADHD group (both TT and TG group) showed widespread reductions of fALFF values across multiple brain regions, including the posterior cingulate cortex and precuneus. The TG group showed more pronounced decreases when compared with the TT group. (b) In comparison to the TG group, the TT group exhibited higher fALFF values in higher-order cognitive regions, such as the right superior frontal gyrus and left medial frontal gyrus, but lower fALFF values in the posterior cerebellar lobe and posterior cingulate cortex. The TT group had significantly higher WMI compared to the TG group (<em>t</em> = 2.098, <em>P</em> < 0.05).</div></div><div><h3>Conclusions</h3><div>The SNAP-25 gene MnlI polymorphism has an impact on spontaneous brain activity in children with ADHD, as measured by fALFF. This study reveals the potential mechanisms from the perspective ","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ynirp.2026.100318
Elizaveta Igoshina , Iska Moxon-Emre , Suzanne Laughlin , Julie Tseng , Donald J. Mabbott
Background
Despite the established associations between paediatric brain tumour treatment, distress, and post-traumatic stress symptoms (PTSS), neither the treatment nor tumour pathology reliably account for symptom severity at the individual level. This study examined whether treatment-related changes in fronto-limbic white matter (WM), brain tissue thought to support emotion, is associated with additional variance in PTSS beyond the effect of treatment and endorsing having experienced a distressing event.
Methods
Thirty-six children and adolescents treated for a posterior fossa brain tumour and 17 typically developing children (TDC) completed a questionnaire assessing whether they had experienced a distressing event and measured PTSS and underwent diffusion tensor imaging. Diffusivity metrics of fronto-limbic tracts and tracts that do not support emotional functioning (control tracts) were obtained. A continuous measure of the physical, cognitive, and emotional strain of therapy (i.e., treatment burden), was estimated using the Neurological Predictor Scale. Partial Least Squares path modelling was used in an exploratory, theory-guided framework to examine statistical associations among treatment burden, distress, WM, and PTSS.
Results
Patients demonstrated greater radial diffusivity than TDC across fronto-limbic and control tracts. Fronto-limbic WM was associated with PTSS severity within the subclinical range and mediated the effects of treatment burden on PTSS. The control tract WM was not associated with PTSS. PTSS severity was also associated with endorsing a distressing event.
Conclusions
The findings support two indicators associated with dimensional variation in PTSS severity: an expected psychological indicator, through endorsing having experienced a distressing event, and a neurobiological indicator, via treatment-related changes to fronto-limbic WM. These results reflect correlational patterns in subclinical symptoms and should be interpreted as exploratory.
{"title":"Neurobiological and psychological indicators of post-traumatic stress symptoms in children and adolescents treated for a posterior fossa brain tumour","authors":"Elizaveta Igoshina , Iska Moxon-Emre , Suzanne Laughlin , Julie Tseng , Donald J. Mabbott","doi":"10.1016/j.ynirp.2026.100318","DOIUrl":"10.1016/j.ynirp.2026.100318","url":null,"abstract":"<div><h3>Background</h3><div>Despite the established associations between paediatric brain tumour treatment, distress, and post-traumatic stress symptoms (PTSS), neither the treatment nor tumour pathology reliably account for symptom severity at the individual level. This study examined whether treatment-related changes in fronto-limbic white matter (WM), brain tissue thought to support emotion, is associated with additional variance in PTSS beyond the effect of treatment and endorsing having experienced a distressing event.</div></div><div><h3>Methods</h3><div>Thirty-six children and adolescents treated for a posterior fossa brain tumour and 17 typically developing children (TDC) completed a questionnaire assessing whether they had experienced a distressing event and measured PTSS and underwent diffusion tensor imaging. Diffusivity metrics of fronto-limbic tracts and tracts that do not support emotional functioning (control tracts) were obtained. A continuous measure of the physical, cognitive, and emotional strain of therapy (i.e., treatment burden), was estimated using the Neurological Predictor Scale. Partial Least Squares path modelling was used in an exploratory, theory-guided framework to examine statistical associations among treatment burden, distress, WM, and PTSS.</div></div><div><h3>Results</h3><div>Patients demonstrated greater radial diffusivity than TDC across fronto-limbic and control tracts. Fronto-limbic WM was associated with PTSS severity within the subclinical range and mediated the effects of treatment burden on PTSS. The control tract WM was not associated with PTSS. PTSS severity was also associated with endorsing a distressing event.</div></div><div><h3>Conclusions</h3><div>The findings support two indicators associated with dimensional variation in PTSS severity: an expected psychological indicator, through endorsing having experienced a distressing event, and a neurobiological indicator, via treatment-related changes to fronto-limbic WM. These results reflect correlational patterns in subclinical symptoms and should be interpreted as exploratory.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1016/j.ynirp.2025.100317
Yasunori Aoki , Rei Takahashi , Roberto D. Pascual-Marqui , Masahiro Hata , Shun Takahashi , Ryouhei Ishii , Masao Iwase , Mariko Maenishi , Young-Ok Kim , Yuki Yamamoto , Sakura Hikida , Kana Maruyama , Etsuro Mori , Manabu Ikeda
Alzheimer's disease (AD) —the most common form of dementia— begins with mild memory loss and gradually progresses, eventually resulting in a generalized loss of brain function. The pathological changes of AD in the brain cortex begin decades before the onset of symptoms. Improvements in lifestyle habits and disease-modifying treatments before the onset of the disease have been shown to help prevent or delay the onset of AD. However, diagnosis of AD is difficult at the early stage —or even at the prodromal stage [i.e., mild cognitive impairment due to AD (MCIAD)]— since normal aging and other types of dementia also involve memory impairment. Therefore, there is an urgent need to identify markers for the detection of AD at the early stage or pre-onset stage. In this study, we applied exact low-resolution brain electromagnetic tomography (eLORETA) as the source estimation method to electroencephalography (EEG) data. We obtained cortical electrical activity in 96 drug-free AD patients and 147 healthy subjects to train the final model, in addition to activity for 21 MCIAD patients and seven healthy subjects for the purpose of its evaluation. We then applied the low-code machine learning library of PyCaret, with three preprocessing steps (subject-wise normalization, age-difference correction, and log-transformation) to the eLORETA data of AD and healthy subjects. Of the many machine learning classification models used, the linear discriminant analysis (LDA) model showed the highest accuracy, identifying 10 AD patients and 15 healthy subjects with an accuracy of 100.0 %. The LDA model of eLORETA has high transparency and we visualized the discriminant function of the LDA final model using Viewer in eLORETA. Cortical electrical activities in the delta, theta and alpha frequency bands increased in the right dorsolateral prefrontal cortex (DLPFC) regions, as the degree of AD increased (Figs. 2–4). Cortical electrical activity in the beta frequency band decreased in the posterior cingulate cortex (PCC) regions, as the degree of AD increased (Fig. 5) Furthermore, the LDA final model correctly identified 21 MCIAD patients and seven healthy subjects with an accuracy of 96.4 %. Our findings indicate that the LDA final model of eLORETA had the capacity to detect physiological features of AD in EEG data, even before the onset of the disease. Overall, PyCaret with three preprocessing steps after eLORETA source estimation can create an accurate EEG classification model, which makes a significant contribution to the early detection of AD among the many individuals in the general population who remain undiagnosed.
{"title":"PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's disease","authors":"Yasunori Aoki , Rei Takahashi , Roberto D. Pascual-Marqui , Masahiro Hata , Shun Takahashi , Ryouhei Ishii , Masao Iwase , Mariko Maenishi , Young-Ok Kim , Yuki Yamamoto , Sakura Hikida , Kana Maruyama , Etsuro Mori , Manabu Ikeda","doi":"10.1016/j.ynirp.2025.100317","DOIUrl":"10.1016/j.ynirp.2025.100317","url":null,"abstract":"<div><div>Alzheimer's disease (AD) —the most common form of dementia— begins with mild memory loss and gradually progresses, eventually resulting in a generalized loss of brain function. The pathological changes of AD in the brain cortex begin decades before the onset of symptoms. Improvements in lifestyle habits and disease-modifying treatments before the onset of the disease have been shown to help prevent or delay the onset of AD. However, diagnosis of AD is difficult at the early stage —or even at the prodromal stage [i.e., mild cognitive impairment due to AD (MCIAD)]— since normal aging and other types of dementia also involve memory impairment. Therefore, there is an urgent need to identify markers for the detection of AD at the early stage or pre-onset stage. In this study, we applied exact low-resolution brain electromagnetic tomography (eLORETA) as the source estimation method to electroencephalography (EEG) data. We obtained cortical electrical activity in 96 drug-free AD patients and 147 healthy subjects to train the final model, in addition to activity for 21 MCIAD patients and seven healthy subjects for the purpose of its evaluation. We then applied the low-code machine learning library of PyCaret, with three preprocessing steps (subject-wise normalization, age-difference correction, and log-transformation) to the eLORETA data of AD and healthy subjects. Of the many machine learning classification models used, the linear discriminant analysis (LDA) model showed the highest accuracy, identifying 10 AD patients and 15 healthy subjects with an accuracy of 100.0 %. The LDA model of eLORETA has high transparency and we visualized the discriminant function of the LDA final model using Viewer in eLORETA. Cortical electrical activities in the delta, theta and alpha frequency bands increased in the right dorsolateral prefrontal cortex (DLPFC) regions, as the degree of AD increased (Figs. 2–4). Cortical electrical activity in the beta frequency band decreased in the posterior cingulate cortex (PCC) regions, as the degree of AD increased (Fig. 5) Furthermore, the LDA final model correctly identified 21 MCIAD patients and seven healthy subjects with an accuracy of 96.4 %. Our findings indicate that the LDA final model of eLORETA had the capacity to detect physiological features of AD in EEG data, even before the onset of the disease. Overall, PyCaret with three preprocessing steps after eLORETA source estimation can create an accurate EEG classification model, which makes a significant contribution to the early detection of AD among the many individuals in the general population who remain undiagnosed.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dominant optic atrophy (DOA) is an inherited mitochondrial disorder characterized by retinal thinning and progressive visual loss. When accompanied by additional neurological or systemic features, such as progressive external ophthalmoplegia, myopathy, or deafness, it is classified as DOA-plus (DOA+). Although central nervous system involvement has been associated with cortical and cerebellar atrophy, specific regional patterns remain unclear. This study aimed to investigate cortical lobe alterations in DOA+ patients and examine the association between retinal thinning and structural changes in the primary visual cortex (V1).
Methods
Seven DOA+ patients and seven age- and sex-matched healthy controls underwent 3T brain MRI, including 3D T1-weighted imaging, and optical coherence tomography (OCT). Cortical parameters including surface area, gray matter volume, and cortical thickness were quantified using automated whole-brain analysis. Comparisons between DOA+ patients and control groups were performed using independent t-tests, and associations between OCT metrics and V1 cortical measures were assessed with Spearman's rank correlation.
Results
DOA+ patients showed a trend toward atrophy in V1 and across all cortical lobes, with statistically significant differences observed only in V1 and occipital lobe (p < 0.001). The occipital lobe demonstrated the greatest reduction in gray matter volume (25.1%, p < 0.001). A positive correlation was observed between average RNFL thickness and average V1 thickness (ρ = 0.90, p = 0.037).
Conclusion
DOA+ patients showed significant atrophy in occipital lobe. An association between retinal thinning and average V1 thickness was observed. However, a definite causal relationship cannot be established. Further studies in larger, genetically diverse cohorts are needed to validate these findings.
{"title":"Volumetric brain analysis and associated retinal thinning in autosomal dominant optic atrophy patients","authors":"Punpath Pajareeyapong , Sittaya Buathong , Sasi Thammasarnsophon , Kanchalika Sathianvichitr , Natthapon Rattanathamsakul , Akarawit Eiamsamarng , Niphon Chirapapaisan , Chanon Ngamsombat","doi":"10.1016/j.ynirp.2025.100314","DOIUrl":"10.1016/j.ynirp.2025.100314","url":null,"abstract":"<div><h3>Introduction</h3><div>Dominant optic atrophy (DOA) is an inherited mitochondrial disorder characterized by retinal thinning and progressive visual loss. When accompanied by additional neurological or systemic features, such as progressive external ophthalmoplegia, myopathy, or deafness, it is classified as DOA-plus (DOA+). Although central nervous system involvement has been associated with cortical and cerebellar atrophy, specific regional patterns remain unclear. This study aimed to investigate cortical lobe alterations in DOA+ patients and examine the association between retinal thinning and structural changes in the primary visual cortex (V1).</div></div><div><h3>Methods</h3><div>Seven DOA+ patients and seven age- and sex-matched healthy controls underwent 3T brain MRI, including 3D T1-weighted imaging, and optical coherence tomography (OCT). Cortical parameters including surface area, gray matter volume, and cortical thickness were quantified using automated whole-brain analysis. Comparisons between DOA+ patients and control groups were performed using independent <em>t</em>-tests, and associations between OCT metrics and V1 cortical measures were assessed with Spearman's rank correlation.</div></div><div><h3>Results</h3><div>DOA+ patients showed a trend toward atrophy in V1 and across all cortical lobes, with statistically significant differences observed only in V1 and occipital lobe (p < 0.001). The occipital lobe demonstrated the greatest reduction in gray matter volume (25.1%, p < 0.001). A positive correlation was observed between average RNFL thickness and average V1 thickness (ρ = 0.90, p = 0.037).</div></div><div><h3>Conclusion</h3><div>DOA+ patients showed significant atrophy in occipital lobe. An association between retinal thinning and average V1 thickness was observed. However, a definite causal relationship cannot be established. Further studies in larger, genetically diverse cohorts are needed to validate these findings.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1016/j.ynirp.2025.100315
Mohammad Rezaei , Shaghayegh Mohammadikhaveh , Hadis Faraji , Ramin Ardalani , Mina Rezaei , Alireza Shirazinodeh
Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for example, CNNs learning mathematical patterns from statistical correlations in the data without regard to biological cues whatsoever, and merely apply filters such as max pooling, never grasping what the biological cues they are supposed to investigate are. This blind procedure can indeed be in technical language; however, it does not help to identify meaningful insights into neuroimaging, where interpretability is essential, and such inadequacies pose a grave challenge. In our research, rather than depending on the CNNs and FCNs only for the feature extractions, we have integrated biologically motivated features into voxel-based morphometry as well as deep learning. Our goal is to analyze T1-weighted MRI scans and T2-Flair images to investigate the characteristics of gray matter, white matter, cerebrospinal fluid, and white matter Hyperintensity in patients with mild cognitive impairment (MCI) who lie on the spectrum between normal aging and Alzheimer's disease (AD). So we extracted critical structural features such as white matter Hyperintensity, gray matter volume, white matter volume, cerebrospinal fluid (CSF) volume, and cortical thickness. These are biologically meaningful biomarkers that reflect the neurodegenerative alterations directly. To validate our method, after the detection of biological features, we have converted them into 3-bit, 4-bit, 8-bit, and 16-bit images. These images were used as inputs for both FCN and CNN models to investigate the early symptoms of AD from classified intracranial features.
{"title":"Early diagnosis of Alzheimer's disease based on brain morphological changes: A comprehensive approach combining voxel-based morphometry and deep learning","authors":"Mohammad Rezaei , Shaghayegh Mohammadikhaveh , Hadis Faraji , Ramin Ardalani , Mina Rezaei , Alireza Shirazinodeh","doi":"10.1016/j.ynirp.2025.100315","DOIUrl":"10.1016/j.ynirp.2025.100315","url":null,"abstract":"<div><div>Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for example, CNNs learning mathematical patterns from statistical correlations in the data without regard to biological cues whatsoever, and merely apply filters such as max pooling, never grasping what the biological cues they are supposed to investigate are. This blind procedure can indeed be in technical language; however, it does not help to identify meaningful insights into neuroimaging, where interpretability is essential, and such inadequacies pose a grave challenge. In our research, rather than depending on the CNNs and FCNs only for the feature extractions, we have integrated biologically motivated features into voxel-based morphometry as well as deep learning. Our goal is to analyze T1-weighted MRI scans and T2-Flair images to investigate the characteristics of gray matter, white matter, cerebrospinal fluid, and white matter Hyperintensity in patients with mild cognitive impairment (MCI) who lie on the spectrum between normal aging and Alzheimer's disease (AD). So we extracted critical structural features such as white matter Hyperintensity, gray matter volume, white matter volume, cerebrospinal fluid (CSF) volume, and cortical thickness. These are biologically meaningful biomarkers that reflect the neurodegenerative alterations directly. To validate our method, after the detection of biological features, we have converted them into 3-bit, 4-bit, 8-bit, and 16-bit images. These images were used as inputs for both FCN and CNN models to investigate the early symptoms of AD from classified intracranial features.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cerebral small vessel disease (CSVD) is a primary contributor to vascular cognitive impairment. Although extensive research has examined white matter alterations in CSVD, cortical mechanisms underlying cognitive dysfunction remain incompletely characterized. To address this gap, we conducted a systematic review and meta-analysis of 26 studies investigating whether structure-cognition relationships in CSVD could be interpreted through biologically defined functional brain networks. By mapping structural features to the Yeo-7 functional atlas, we offer a network-based perspective on cognitive impairment in this population. Our integrated results demonstrate significant associations between structural alterations and all cognitive domains in CSVD patients. Notably, higher-order cognitive processes (e.g., executive function, attention and processing speed) involved more extensive functional networks than other domains. These findings help synthesize heterogeneous neuroanatomical literature on CSVD through contemporary network neuroscience frameworks, suggesting structure-cognition relationships may align with functional network architecture.
{"title":"Associations between structural brain alterations and dysfunction across cognitive domains in cerebral small vessel disease: A systematic review and meta-analysis","authors":"Zhijie Zhang , Xunqi Qian , Hua Zhang , Zijun Zhao , Wei Wang , Jingpei Wei","doi":"10.1016/j.ynirp.2025.100312","DOIUrl":"10.1016/j.ynirp.2025.100312","url":null,"abstract":"<div><div>Cerebral small vessel disease (CSVD) is a primary contributor to vascular cognitive impairment. Although extensive research has examined white matter alterations in CSVD, cortical mechanisms underlying cognitive dysfunction remain incompletely characterized. To address this gap, we conducted a systematic review and meta-analysis of 26 studies investigating whether structure-cognition relationships in CSVD could be interpreted through biologically defined functional brain networks. By mapping structural features to the Yeo-7 functional atlas, we offer a network-based perspective on cognitive impairment in this population. Our integrated results demonstrate significant associations between structural alterations and all cognitive domains in CSVD patients. Notably, higher-order cognitive processes (e.g., executive function, attention and processing speed) involved more extensive functional networks than other domains. These findings help synthesize heterogeneous neuroanatomical literature on CSVD through contemporary network neuroscience frameworks, suggesting structure-cognition relationships may align with functional network architecture.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"6 1","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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