Human Brain Mapping最新文献

{"title":"Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation.","authors":"Jon Haitz Legarreta, Zhou Lan, Yuqian Chen, Fan Zhang, Edward H Yeterian, Nikos Makris, Richard J Rushmore, Yogesh Rathi, Lauren J O'Donnell","doi":"10.1002/hbm.70317","DOIUrl":"10.1002/hbm.70317","url":null,"abstract":"<p><p>The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural MRI data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA) on a set of cerebellar white and gray matter region labels obtained from the atlas-based SUIT cerebellar segmentation pipeline employing T1-weighted data. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b = 1000 s/mm² shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70317"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12332183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Network Modeling of Transcranial Magnetic Stimulation Across Individuals Suggests Therapeutic Targets and Potential for Improvement.","authors":"Wendy Sun, Anne Billot, Jingnan Du, Xiangyu Wei, Rachel A Lemley, Mohammad Daneshzand, Aapo Nummenmaa, Randy L Buckner, Mark C Eldaief","doi":"10.1002/hbm.70266","DOIUrl":"10.1002/hbm.70266","url":null,"abstract":"<p><p>Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and modeling of the TMS electric field (E-field), we asked whether various clinical TMS approaches target different functional networks between individuals. Results revealed that modeled homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. TMS coil positions over the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC most frequently target a network coupled to the ventral striatum (reward circuitry) but largely miss that network in some individuals. We further illustrate how modeling can be used to retrospectively assess the estimated targets achieved in prior TMS sessions and also used to prospectively provide coil positions that can target distinct closely localized dlPFC network regions with spatial selectivity and maximal E-field intensity. In a final study, precision targeting was found to be feasible in participants with Major Depressive Disorder using data derived from a single low-burden MRI session suggesting the methods are applicable to translational efforts where limiting patient burden and ensuring robustness are critical.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70266"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Brain Similarity Networks With Diffusion MRI.","authors":"Amir Sadikov, Hannah L Choi, Lanya T Cai, Pratik Mukherjee","doi":"10.1002/hbm.70313","DOIUrl":"10.1002/hbm.70313","url":null,"abstract":"<p><p>Structural similarity has emerged as a promising tool in mapping the network organization of an individual, living human brain. Here, we propose diffusion similarity networks (DSNs), which employ rotationally invariant spherical harmonic features derived from diffusion magnetic resonance imaging (dMRI), to map gray matter structural organization. Compared to prior approaches, DSNs showed clearer laminar, cytoarchitectural, and micro-architectural organization; greater sensitivity to age, cognition, and sex; higher heritability in a large dataset of healthy young adults; and straightforward extension to non-cortical regions. We show DSNs are correlated with functional, structural, and gene expression connectomes, and their gradients align with the sensory-fugal and sensorimotor-association axes of the cerebral cortex, including neuronal oscillatory dynamics, metabolism, immunity, and dopaminergic and glutaminergic receptor densities. DSNs can be easily integrated into conventional dMRI analysis, adding information complementary to structural white matter connectivity, and could prove useful in investigating a wide array of neurological and psychiatric conditions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70313"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144803980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Processing of Noise-Vocoded Speech Under Divided Attention: An fMRI-Machine Learning Study.","authors":"Han Wang, Rongru Chen, Josef Schlittenlacher, Carolyn McGettigan, Stuart Rosen, Patti Adank","doi":"10.1002/hbm.70312","DOIUrl":"10.1002/hbm.70312","url":null,"abstract":"<p><p>In real-life interaction, we often need to communicate under challenging conditions, such as when speech is acoustically degraded. This issue is compounded by the fact that our attentional resources are often divided when we simultaneously need to engage in other tasks. The interaction between the perception of degraded speech and simultaneously performing additional cognitive tasks is poorly understood. Here, we combined a dual-task paradigm with functional magnetic resonance imaging (fMRI) and machine learning to establish the neural network supporting degraded speech perception under divided attention. We presented 25 human participants with noise-vocoded sentences while they engaged in a concurrent visuomotor recognition task, employing a factorial design that manipulated both speech degradation and task difficulty. Participants listened to eight-band (easier) and four-band (more difficult) noise-vocoded sentences, while the Gabor task featured two difficulty levels, determined by the angular discrepancy of the target. We employed a machine learning algorithm (Extreme Gradient Boosting, XGBoost) to evaluate the set of brain areas that showed activity predicting the difficulty of the speech and dual tasks. The results illustrated intelligibility-related responses in frontal and cingulate cortices and bilateral insulae induced by divided attention. Machine learning further revealed modality-general and specific responses to speech and visual inputs, in a set of frontotemporal regions reported for domain-general cognitive functions such as attentional control, motor function, and performance monitoring. These results suggest that the management of attentional resources during challenging speech perception recruits a bilateral operculo-frontal network also associated with processing acoustically degraded speech.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70312"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Factors Affecting Quality in Structural Magnetic Resonance Images","authors":"Lisa Raoul,&nbsp;Anastasia Benedyk,&nbsp;Oksana Berhe,&nbsp;Thomas Leon Kremer,&nbsp;Malika Renz,&nbsp;Yuchen Lin,&nbsp;Niharika Roychoudhury,&nbsp;Alexander Moldavski,&nbsp;Ali Ghadami,&nbsp;Abhijit Sreepada,&nbsp;Marvin Ganz,&nbsp;Markus Sack,&nbsp;Matthias Ruf,&nbsp;Robert Becker,&nbsp;Andreas Meyer-Lindenberg,&nbsp;Heike Tost,&nbsp;Jamila Andoh","doi":"10.1002/hbm.70271","DOIUrl":"https://doi.org/10.1002/hbm.70271","url":null,"abstract":"<p>Combining Magnetic Resonance Images (MRI) from different sources is an increasingly common practice that holds high scientific value. Differences in acquisition parameters and participant characteristics can lead to variations in image quality, highlighting the importance of ensuring these variations do not result in biased statistical outcomes. Here, we investigated contributions of both technical and participant-related factors to MRI quality. We examined how technical factors (scanner hardware, software, and acquisition protocols) affect the Image Quality Rating (IQR) of anatomical MRI. We also evaluated the stability of IQR over time, examined the effects of defacing on image quality, and investigated how participant characteristics (age, sex, and mental health) influence IQR. We collected 2779 T1-weighted volumes, acquired at two different scanner sites (both Siemens 3 Tesla), using two coil array designs (64-channel and 32-channel array), and four scanner software versions (VB17, VB15, VE11, XA30), five acquisition protocols, including two different spatial resolutions (1 mm, 0.8 mm isotropic). Data were collected from 910 healthy controls (HC) (499 women, mean age 27.55 ± 11.27) and from 563 individuals (321 women, mean age 36.42 ± 12.93) with various clinical conditions (125 Major Depressive Disorder [MDD], 43 Autism Spectrum Disorder [AUT], 81 Alcohol Use Disorder [AUD], 104 Schizophrenia [SZ], 70 Chronic Pain [CP], 41 Bipolar Disorder [BD], and 100 with unspecified disease [NHC]). Structural images were preprocessed and analyzed using the quality control pipelines of the Computational Anatomy Toolbox (CAT12, https://neuro-jena.github.io/cat12-help/), which provide an image quality rating (IQR) index for each image, with higher IQR indicating a lower image quality. There was no significant effect of scanner site or coil design on IQR. We found a significant effect of scanner software, with lower image quality for VB17 compared with VB15. There was a significant effect of acquisition protocols (i.e., IQR with protocol “T1_1mm_extended” was higher than with others protocols), and image spatial resolution had a significant impact on IQR, with higher IQR values for 1 mm compared to 0.8 mm. Within participants, IQR was stable across sessions, showing minimal day-to-day variability. Defacing had no significant impact on IQR. Regarding participant characteristics, we observed a significant interaction between sex and age: IQR increased with age in men but not in women. Additionally, participants with SZ had a significant higher IQR compared to HC and MDD. This study provides a comprehensive assessment of the influence of technical and participant-related factors on MRI quality. The findings also support IQR as a robust indicator of image quality and emphasize the importance of integrating image quality metrics, both in multicentric studies and within individual research centers. Incorporating IQR as a quality metric would help minimize biase","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators","authors":"Shengxian Ding,&nbsp;Rongjie Liu,&nbsp;Anuj Srivastava,&nbsp;Richard S. Nowakowski,&nbsp;Li Shen,&nbsp;Paul M. Thompson,&nbsp;Heping Zhang,&nbsp;Chao Huang","doi":"10.1002/hbm.70297","DOIUrl":"https://doi.org/10.1002/hbm.70297","url":null,"abstract":"<p>Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome-wide exposures and brain subcortical shape mediators due to several challenges including (i) large-scale genetic exposures, that is, millions of single-nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome-wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome-wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square-root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape-on-scalar model and a scalar-on-shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salient Memory: Effects of Distributed Learning on Cortical Regions During Memory Retrieval","authors":"Cuihong Li,&nbsp;Jiongjiong Yang","doi":"10.1002/hbm.70301","DOIUrl":"https://doi.org/10.1002/hbm.70301","url":null,"abstract":"<p>Spaced or distributed learning is an efficient way to enhance memory, especially after long retention intervals, and the lag between repetition influences memory retention. Studies have suggested that various cortical regions are involved in the spacing effect, but how the cortical regions are involved to support memory retrieval, especially at longer intervals, after DL with varying inter-study lags is still unclear. To address this issue, three groups of participants were asked to encode face–scene pairs at 20 min, 1 day, and 1 month before they were scanned by fMRI during an associative recognition task. The pairs were learned six times in three conditions: a massed (ML), distributed with a short lag (DL-S) and distributed with a long lag (DL-L). The results showed that the activation in the salience network, including the insula and cingulate cortex, was stronger when the participants retrieved the pairs correctly in the DL-L and DL-S conditions than in the ML condition. In addition, the inferior frontal gyrus/insula was more strongly activated when the new associations were correctly rejected in the DL-L than in the DL-S condition at 1 month. The functional connectivity between the hippocampus and prefrontal cortices was stronger in the DL-L than in the DL-S condition at 1 month. These results suggest that successful memory retrieval after distributed learning is associated with the regions that are responsible for salience detection and top-down control, especially at long-term retention. More salient and controlled representations could be established over time after DL and are supported by distributed brain networks.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Approach to Dynamic Resting State Functional Connectivity in Post-Traumatic Stress Disorder: An ENIGMA-PGC PTSD Study","authors":"Carissa W. Tomas,&nbsp;Jacklynn M. Fitzgerald,&nbsp;C. Lexi Baird,&nbsp;Courtney C. Haswell,&nbsp;Chadi G. Abdallah,&nbsp;Michael Angstadt,&nbsp;Justin T. Baker,&nbsp;Hannah Berg,&nbsp;Jennifer U. Blackford,&nbsp;Josh Cisler,&nbsp;Andrew S. Cotton,&nbsp;Judith K. Daniels,&nbsp;Nicholas D. Davenport,&nbsp;Richard J. Davidson,&nbsp;Terri A. deRoon-Cassini,&nbsp;Seth G. Disner,&nbsp;Wissam El Hage,&nbsp;Negar Fani,&nbsp;Jessie L. Frijling,&nbsp;Evan M. Gordon,&nbsp;Daniel W. Grupe,&nbsp;Xiaofu He,&nbsp;Ryan Herringa,&nbsp;David Hofmann,&nbsp;Ashley A. Huggins,&nbsp;Ahmed Hussain,&nbsp;Jonathan Ipser,&nbsp;Neda Jahanshad,&nbsp;Tanja Jovanovic,&nbsp;Milissa L. Kaufman,&nbsp;Yoojean Kim,&nbsp;Anthony King,&nbsp;Saskia B. J. Koch,&nbsp;Sheri Koopowitz,&nbsp;Amit Lazarov,&nbsp;Lauren A. M. Lebois,&nbsp;Isreal Liberzon,&nbsp;Shmuel Lissek,&nbsp;Antje Manthey,&nbsp;Geoffrey May,&nbsp;Katie A. McLaughlin,&nbsp;Laura Nawijn,&nbsp;Steven M. Nelson,&nbsp;Yuval Neria,&nbsp;Jack B. Nitschke,&nbsp;Bunmi O. Olatunji,&nbsp;Miranda Olff,&nbsp;Matthew Peverill,&nbsp;Yann Quidé,&nbsp;Orren Ravid,&nbsp;Kerry Ressler,&nbsp;Marisa Ross,&nbsp;Lauren E. Salminen,&nbsp;Kelly Sambrook,&nbsp;Chiahao Shih,&nbsp;Anika Sierk,&nbsp;Scott R. Sponheim,&nbsp;Dan J. Stein,&nbsp;Jennifer Stevens,&nbsp;Thomas Straube,&nbsp;Benjamin Suarez-Jimenez,&nbsp;Paul M. Thompson,&nbsp;Nic J. A. van der Wee,&nbsp;Steven J. A. van der Werff,&nbsp;Sanne J. H. van Rooij,&nbsp;Mirjam van Zuiden,&nbsp;Dick J. Veltman,&nbsp;Robert R. J. M. Vermeiren,&nbsp;Henrik Walter,&nbsp;Xin Wang,&nbsp;Hong Xie,&nbsp;Xi Zhu,&nbsp;Sigal Zilcha-Mano,&nbsp;Christine L. Larson,&nbsp;Rajendra Morey","doi":"10.1002/hbm.70116","DOIUrl":"https://doi.org/10.1002/hbm.70116","url":null,"abstract":"<p>Using functional magnetic resonance imaging (fMRI), symptoms of posttraumatic stress disorder (PTSD) have been associated with aberrations in brain networks in the absence of a given cognitive demand or task, called resting-state networks. Prior work has focused on disruption in the static functional connectivity (FC) among specific regions constrained by a priori hypotheses. However, dynamic FC, an approach that examines brain network characteristics over time, may provide a more sensitive measure to understand the network properties underlying dysfunction in PTSD. Further, using a data-driven analytic approach may reveal the contribution of other larger network disturbances beyond those revealed by hypothesis-driven examinations of ROIs or canonical networks. Therefore, the current study used group independent components analysis (ICA) and graph theory principles to identify, characterize, and subsequently compare brain network dynamics and recurrent connectivity states in a large sample of trauma exposed individuals (<i>N</i> = 1035) with and without PTSD from the ENIGMA-PGC PTSD workgroup. Neither static FC nor dynamic FC results showed robust differences between groups. There were also no group differences in dwell time or number of transitions of recurrent connectivity states. This multi-cohort sample with heterogenous trauma types and demographic features offers a significantly larger scale approach than prior literature with smaller homogenous trauma cohorts. Heterogeneity of PTSD, especially within diffuse brain networks, may not be captured by evaluating only diagnostic groups, further work should be done to evaluate brain network dynamics with respect to specific symptom profiles and trauma types.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneously Acquired Magnetoencephalography and Diffuse Optical Tomography Data Reveals Correlated Somatosensory Activity","authors":"Salla Autti,&nbsp;Pauliina Hirvi,&nbsp;Mariia Keitaanniemi,&nbsp;Hanna Mustaniemi,&nbsp;Kalle Kotilahti,&nbsp;Hanna Renvall,&nbsp;Ilkka Nissilä","doi":"10.1002/hbm.70293","DOIUrl":"https://doi.org/10.1002/hbm.70293","url":null,"abstract":"<p>Simultaneous measurement of electrophysiological and hemodynamic brain signals imposes special requirements on the instrumentation. Here, we developed a high-density fiberoptic probe for concurrent diffuse optical tomography (DOT) and magnetoencephalography (MEG) recordings. Transparent two-component silicone was mixed with carbon black dye to achieve a black, flexible, non-magnetic support for the dense optode arrangement and low (5 mm) probe thickness. The probe was used to record somatosensory responses to electrical right median nerve stimulation at 0.5, 1, 2, and 4 Hz in 18 adult human subjects. Brain activity was simultaneously measured with a commercial whole-head MEG system and with the DOT optode arrangement covering approximately 40 cm² over the parietal region in the contralateral left hemisphere. Two correlation-based clustering methods were developed to find regions where the reconstructed time course of total hemoglobin concentration (HbT) changes correlated with the predicted hemodynamic activity based on time-course characteristics of the MEG sources and the canonical hemodynamic response model. Two statistically significant clusters were found based on the correlation between HbT around the postcentral gyrus and MEG primary somatosensory cortical activity at ~35 ms (P35m response). In addition, correlation between HbT and secondary somatosensory cortical activity suggested a statistically significant cluster in the postcentral gyrus and parietal operculum. These results illustrate an improvement in localization over previous DOT studies using sparse optode arrangements, and demonstrate the feasibility of the system for simultaneous HD-DOT-MEG experiments. Furthermore, the techniques described here pave the way for understanding the coupling between hemodynamic and electrophysiological responses. Further research is needed to reveal the neuronal circuits giving rise to the correlating MEG and DOT response features. Significant improvements in the technology are still expected via optimization of the detected light power in the instrumentation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Investigation of an In-Scanner Alternative to the Cold Pressor Test in Healthy Individuals","authors":"Sonia Medina,&nbsp;Sam W. Hughes","doi":"10.1002/hbm.70291","DOIUrl":"https://doi.org/10.1002/hbm.70291","url":null,"abstract":"<p>The cold pressor task (CPT) is widely used to study tonic pain during acute and chronic conditions and is often used as a conditioning stimulus to activate descending pain control systems. However, logistical challenges in magnetic resonance imaging (MRI) limit its application, hindering the understanding of CPT's neural dynamics. To address this, we acquired resting-state functional MRI (fMRI) data from 30 healthy participants before, during and after immersion in gelled-cold water, the closest in-scanner alternative to date to CPT for prolonged stimulation. Participants provided subjective pain intensity ratings after each scan, as well as average pain perceived during noxious stimulation, using a numeric rating scale (NRS). Following fMRI, participants rated their pain continuously during identical tonic noxious stimulation of the contralateral hand using a visual analogue scale (VAS). We employed three complementary methods to examine changes in brain function across fMRI conditions: a data-driven approach via independent component analysis (ICA), seed-to-whole-brain connectivity analysis with the periaqueductal grey (PAG) as seed and spectral dynamic causal modelling (spDCM) to explore effective connectivity changes across the dorsal anterior cingulate cortex (dACC), anterior insulae (AI), thalamus and PAG. NRS scores were significantly higher following tonic cold compared to baseline and recovery conditions. Continuous VAS reflected sustained mild-to-moderate pain over 6 min, with average VAS scores not significantly differing from NRS ratings recorded in the scanner. ICA identified engagement of descending pain control and sensorimotor networks during pain, with the latter persisting during recovery. Seed-based analysis revealed a disengagement between the PAG and cortical/subcortical regions involved in pain processing, such as the dACC, midcingulate cortex, AI, intraparietal sulcus and precuneus. Finally, spDCM revealed tonic pain neural signature was most likely characterised by top-down inhibitory and bottom-up excitatory connections. This study establishes the cold gelled-water paradigm as a potential in-scanner alternative to CPT. By uncovering key neural dynamics of CPT, we provide new insights into the brain and brainstem mechanisms of tonic cold pain paradigms routinely used in psychophysical pain studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}