Imaging neuroscience (Cambridge, Mass.)最新文献

{"title":"Functional heterogeneity of beta bursts in childhood reveals a dimensional neural signature of motor skill.","authors":"Holly Rayson, Sarah A Gerson, Jennifer Keating, Catherine R G Jones, Catherine Purcell, James J Bonaiuto, Ross E Vanderwert","doi":"10.1162/IMAG.a.1376","DOIUrl":"10.1162/IMAG.a.1376","url":null,"abstract":"<p><p>Activity in the beta frequency band appears to play a critical role in sensorimotor processing, with transient \"bursts\" exhibiting distinct waveform motifs that may reflect different neural computations. However, how these dynamics relate to motor development and neurodevelopmental conditions remains unclear. Here, we used EEG to investigate sensorimotor beta bursts in children with and without developmental coordination disorder (DCD). We characterized beta bursts during action execution and observation of gross and fine motor actions. Conventional spectral features and overall burst rates did not differ between groups. In contrast, children with DCD showed altered rate modulation of specific burst waveform motifs, including atypical hemispheric lateralization and altered interhemispheric connectivity. Critically, motif-specific burst rates were systematically related to motor performance across individuals, with similar relations observed across groups. These findings indicate that waveform-specific beta burst dynamics track graded variation in motor performance, rather than mapping selectively onto diagnostic status. Beta burst variability may, therefore, reflect the functional expression of motor performance, rather than a disorder-specific neural mechanism underlying DCD. This work highlights the importance of beta burst waveform diversity for understanding variability in sensorimotor function across development.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13595369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"fMRI reveals brainstem and cortical networks contribute to the instruction-dependent modulation of stretch-evoked motor responses.","authors":"Rebecca C Nikonowicz, Neha A Reddy, Michelle C Medina, Molly G Bright, Fabrizio Sergi","doi":"10.1162/IMAG.a.1372","DOIUrl":"10.1162/IMAG.a.1372","url":null,"abstract":"<p><p>Reliable noninvasive measurement of human brainstem activity during motor control remains challenging due to small anatomical structures and physiological noise, yet it is essential for understanding descending contributions to rapid feedback control. We used brainstem-optimized whole-brain functional magnetic resonance imaging (fMRI) to examine whether task-dependent modulation of stretch-evoked motor responses in humans is associated with changes in activation within reticulospinal regions of the human brainstem. A behavioral validation experiment (N = 10), in which participants were instructed to resist or yield to brief wrist perturbations, confirmed task-dependent modulation of long-latency responses (LLRs). In a separate imaging cohort (N = 26), participants performed the same tasks during fMRI using an MRI-compatible robotic device and a multi-echo acquisition with physiological noise compensation. Imaging analyses revealed greater blood-oxygen-level-dependent signal during Resist than during Yield while controlling for background contraction and proprioceptive input, with activation distributed bilaterally across the pons and medulla in regions consistent with major reticulospinal nuclei. Laterality analyses demonstrated a rostrocaudal gradient, with relatively ipsilateral-biased activation in the medulla shifting toward more contralateral patterns in the pons. These findings indicate that instruction-dependent modulation of stretch-evoked motor responses is associated with measurable changes in human brainstem activation, providing evidence that reticulospinal regions contribute to task-dependent feedback control.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peaks2Image: Reconstructing fMRI maps from stereotactic coordinates to enhance cognitive meta-analysis.","authors":"Raphaël Meudec, Jérôme Dockès, Fateme Ghayem, Demian Wassermann, Bertrand Thirion","doi":"10.1162/IMAG.a.1367","DOIUrl":"10.1162/IMAG.a.1367","url":null,"abstract":"<p><p>To integrate novel findings and reach a consensus on the neural correlates of cognitive functions, human neuroscientists have relied heavily on the literature to formulate, validate, or refine hypotheses. Since thousands of publications have reported the coordinates of regions identified in brain mapping, the community has largely performed meta-analyses based on these coordinates. A known problem with coordinate-based meta-analyses is that they rely on limited information from each publication, so terms appearing in few publications are not reliably mapped. Recently, the use of public images has stimulated meta-analysis, which has become possible due to the creation of large image repositories. NeuroVault is a prominent example of such a repository. These repositories allow researchers to compare images across publications, leading to image-based meta-analyses. However, image-based meta-analysis faces a major obstacle: the scarcity and inconsistency of image annotation and metadata. In this paper, we propose <i>Peaks2Image</i>, a neural network approach that reconstructs continuous spatial representations of brain activity from peak activation tables. Peaks2Image provides dense brain reconstructions that can be combined with extensive annotations from the neuroscience literature. We use these annotations to train a decoder with textual features as labels. This results in a much broader set of decoded terms than current image-based studies. We validated the decoder using 47,000 annotated images and successfully identified 91 out of 108 terms, with a model that was uniquely trained using the neuroscience literature.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional specialisation across the first five years of life: A longitudinal characterisation of social perception with fNIRS.","authors":"Johann Benerradi, Chiara Bulgarelli, Ebrima Mbye, Borja Blanco, Ebou Touray, Sam McCann, Bosiljka Milosavljevic, Sophie E Moore, Clare E Elwell, Anna Blasi, Sarah Lloyd-Fox","doi":"10.1162/IMAG.a.1368","DOIUrl":"10.1162/IMAG.a.1368","url":null,"abstract":"<p><p>Research in typically developing infants has shown robust and consistent brain activation to social versus non-social visual and auditory stimuli in a network of brain regions, including the inferior frontal, anterior temporal, and posterior superior temporal cortex. However, large-scale, longitudinal neuroimaging studies across early childhood, particularly in low- and middle-income countries, are rare, yet important, given that they offer a powerful means of capturing within-person changes in neurodevelopment and identifying targets for intervention and support. Here, we investigated brain responses to social perception longitudinally across the first 5 years of life of participants in The Gambia with functional near-infrared spectroscopy (fNIRS) within the Brain Imaging for Global Health (BRIGHT) Project. We found that social visual stimuli elicited a selective right posterior temporal response across all six age points studied here (5, 8, 12, 18, 24 months and 3-5 years of age), with concurrent selective left responses at four of the six time points (5, 8, 18 months and 3-5 years of age). Inferior frontal regions showed brain activation at multiple ages, with the youngest (5 months) and oldest (24 months to 3-5 years) time points evidencing more widespread frontal social visual responses. Conversely, auditory social stimuli elicited a significantly stronger and more prolonged response than auditory non-social stimuli across all six age points from 5 months to 3-5 years across a range of frontal and temporal areas. Finally, from individual infant trajectories of brain activation, we identified different profiles of age-dependent specialization to auditory social stimuli, with some specializing earlier than others. Future work could take advantage of such profiles to investigate the effects of adversity and resilience factors on neurodevelopment.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating hierarchical control among functional networks disrupted by opioid use disorder using effective connectivity.","authors":"Brianna Austin, Danielle L Kurtin, Katherine Herlinger, Alexandra Hayes, Lexi J Hand, Leon Fonville, Raymond G Hill, David J Nutt, Anne R Lingford-Hughes, Louise M Paterson","doi":"10.1162/IMAG.a.1373","DOIUrl":"10.1162/IMAG.a.1373","url":null,"abstract":"<p><p>Opioid use disorder (OUD) poses a significant public health challenge. Developing a better understanding of the brain mechanisms and potential markers of OUD would facilitate the development of therapeutic interventions. While we recently showed that between-network connectivity is disrupted in people with OUD compared with healthy controls, it remains unclear what mechanisms may drive these disruptions and how dysfunctional interactions propagate across large-scale functional networks. To advance the mechanistic understanding of the disrupted processes in OUD, this study used Effective Connectivity (EC) to quantify disrupted hierarchical control among functional networks governing cognition, attention, and reward. We also explored whether whole-brain patterns of EC were effective markers to distinguish people with severe OUD from controls. We hypothesized that the ventromedial network (VMN) would drive dysfunction in cognitive and attentional networks in people with OUD. Task-fMRI data were collected from healthy controls (HC; n = 22) and OUD participants on methadone maintenance treatment (OUD; n = 25), during a heroin cue reactivity (CR) and monetary incentive delay (MID) task. Following brain parcellation (214 regions) and network assignment (7 functional networks), EC was quantified using large-scale nonlinear Granger causality. Dimensionality reduction was performed using uniform manifold approximation and projection, followed by hierarchical density-based spatial clustering of applications with noise to assess whether EC patterns could form clusters corresponding to group labels. Contrary to our hypothesis, the VMN did not drive dysfunction in cognitive and attentional networks. Instead, edges with significantly stronger EC in HC vs. OUD participants were within and between the control, somatomotor, and default mode networks. EC patterns were unable to form distinct clusters that corresponded to clinical groups; instead, clusters separated by task. Little evidence was found that disrupted VMN-mediated reward drove dysregulated whole-brain function in OUD.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner.","authors":"Joseph Obriot, Zaineb Amor, Matthias Serger, Rüdiger Stirnberg, Philipp Ehses, Malte Riedel, Tony Stöcker, Klass P Prüssmann, Shajan Gunamony, Son Chu, Alexis Amadon, Alexandre Vignaud, Vincent Gras, Florent Meyniel, Franck Mauconduit, Caroline Le Ster, Nicolas Boulant","doi":"10.1162/IMAG.a.1362","DOIUrl":"10.1162/IMAG.a.1362","url":null,"abstract":"<p><p>Ultra-high-field magnetic resonance imaging (MRI) promises major gains in blood oxygen level dependent (BOLD) sensitivity but introduces severe challenges such as RF field inhomogeneity, <math> <mrow><msub><mi>B</mi> <mn>0</mn></msub> </mrow> </math> field inhomogeneity, motion and vibration-induced field variations. We report the first whole-brain resting-state and task-based fMRI experiments at 11.7T. A 3D-EPI sequence was optimized using tailored parallel-transmit RF pulses, gradient reshaping to suppress vibration-induced <math> <mrow><msub><mi>B</mi> <mn>0</mn></msub> </mrow> </math> fluctuations, and prospective motion correction using servo navigation combined with retrospective phase equalization (PEERS). Four participants were scanned at 11.7T with whole-brain 1.2 mm isotropic resolution with servo navigation and PEERS that considerably reduced residual motion and increased temporal signal-to-noise ratio (SNR). The Default Mode Network in resting state could be detected and a physiological-noise-dominated regime was identified. Four additional but different participants were scanned at 7T for tSNR comparison and validation of the protocol, data quality, and processing pipeline. Task-based fMRI likewise was conducted at 11.7T and yielded robust, spatially specific activations across motor, visual, mathematical, and language networks, again with improved sensitivity and cleaner BOLD responses when using servo navigation and PEERS. These results demonstrate the feasibility and reliability of whole-brain human fMRI at 11.7T and constitute a first-quality control milestone to further increase resolution at that field strength.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergent multiscale organisation of neural dynamics fragments in anaesthesia.","authors":"Borjan Milinkovic, Anil K Seth, Lionel Barnett, Olivia Carter, Thomas Andrillon","doi":"10.1162/IMAG.a.1364","DOIUrl":"10.1162/IMAG.a.1364","url":null,"abstract":"<p><p>Conscious experience depends on the coordinated activity of neural processes that span multiple scales: from synapses to whole-brain dynamics. A recently introduced measure, <i>dynamical independence</i> (DI), identifies, characterises, and quantifies these multi-scale relationships using an information-theoretic dimensionality reduction approach. Here, we use DI to examine changes in the emergent dynamical organisation in the human brain under three pharmacologically distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed electroencephalography (EEG), our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. Ketamine, which preserves dream-like phenomenology, shows a different pattern relative to wakefulness: reduced overall emergence yet a partial preservation of the macroscopic structure. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-induced reconfigurations of emergent dynamical structure relative to wakefulness, dissociate the <i>amount</i> of emergence from the <i>organisation</i> of emergent dynamics, and caution against equating emergence with level of consciousness. Consequently, we suggest that wakeful conscious processing depends not only on integration between neural components within a single scale, but also on integration across scales, broadening currently held assumptions of putative signatures of consciousness.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-age in ultra-low-field MRI: How well does it work?","authors":"Francesca Biondo, Carly Bennallick, Sophie A Martin, Lemuel Puglisi, Thomas C Booth, David A Wood, Juan Eugenio Iglesias, František Váša, James H Cole","doi":"10.1162/IMAG.a.1352","DOIUrl":"10.1162/IMAG.a.1352","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Brain-age estimates the brain's biological age from neuroimaging data and has been proposed as a biomarker of brain health and disease risk. While brain-age estimation commonly uses high-field (HF) magnetic resonance imaging (MRI) ( &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;&gt;&lt;/mo&gt; &lt;mn&gt;1.5&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; T), this is costly and inaccessible, limiting its applicability. Emerging ultra-low-field (ULF) MRI ( &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.1&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; T) is cheaper and more accessible, but its lower resolution may limit the reliability of biomarkers such as brain-age. We assessed different brain-age pipelines in 23 adults scanned on one HF system (GE Signa Premier at 3 T) and two identical ULF systems (Hyperfine Swoop at 64 mT) located at two different sites, hereafter referred to as ULF1 and ULF2. We used 14 distinct acquisitions defined by T1- or T2-weighting, resolution, and preprocessing: raw anisotropic orientations (axial, coronal, sagittal), isotropic scans, and super-resolution derivatives from multi-resolution registration (MRR) and SynthSR. These inputs (a total of &lt;i&gt;n&lt;/i&gt; = 573 scans) were analyzed with five brain-age software packages (BrainageR, SynthBA, MIDI, DeepBrainNet, PyBrainAge). Performance evaluation entailed validity (brain-age vs. actual age), correspondence (ULF brain-age vs. HF brain-age) and test-retest reliability (ULF1 brain-age vs. ULF2 brain-age). Overall, results were mixed across pipelines, although several ULF pipelines performed comparably to HF. The four best-performing combinations were SynthBA on T2 scans without SynthSR, MIDI on T2 scans without SynthSR, PyBrainAge on T1 scans with SynthSR and using FreeSurfer recon-all-clinical, and BrainageR on T1 scans with SynthSR. These showed moderate-to-strong validity ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.76&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.92, &lt;math&gt; &lt;mrow&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mo&gt; &lt;/mo&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.54&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.64, Mean Absolute Error [MAE] &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;6.7&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -8.21 years), moderate-to-strong correspondence to HF ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.84&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.93, Intraclass Correlation Coefficient [ICC] &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.72&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.92), and excellent test-retest reliability ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.97&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.99, ICC &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.97&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.99). Moreover, some anisotropic acquisitions achieved comparable validity and reliability to MRR images when tested with the best-performing model, SynthBA ( &lt;math&gt; &lt;mrow&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mo&gt; &lt;/mo&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.57&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; -0.62, ICC [CI] &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.99&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; [0.97-1.00], for coronal T2). This first systematic evaluation of brain-age at ULF demonstrates that accurate and reliable estimates can be achieved across multiple pipelines, without necessarily requiring image enhancement. Performance depended on the combi","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"40 Years of diffusion MRI in the brain: From history to emerging frontiers.","authors":"Denis Le Bihan","doi":"10.1162/IMAG.a.1365","DOIUrl":"10.1162/IMAG.a.1365","url":null,"abstract":"<p><p>Diffusion MRI transformed brain imaging by making MRI sensitive not only to anatomy and spin-relaxation contrast, but also to micrometer-scale water displacements over diffusion times of a few tens of milliseconds. The resulting signal is exquisitely sensitive to tissue organization, because cell membranes, myelin, axonal packing, orientation dispersion, exchange, as well as perfusion-related incoherent fluid motion all modulate the displacement distribution sampled by diffusing water molecules. In the brain, this sensitivity proved historically decisive in three domains. First, diffusion-weighted MRI enabled the early detection of acute ischemia by revealing tissue injury before it became apparent on conventional structural imaging, thereby supporting timely treatment and improving outcomes for millions of patients worldwide. Second, the strong orientational order of white matter produces diffusion anisotropy, which can be explored through a diffusion tensor imaging (DTI) framework, ultimately leading to the reconstruction of large-scale structural pathways and connectomes in vivo, relevant to psychiatric disorders. Third, advances in gradient hardware, multi-shell acquisition, and biophysical modeling extended the field beyond the original apparent diffusion coefficient (ADC) concept, revealing tissue microstructure through time-dependent and multi-compartment diffusion behavior, with applications to brain development and myelination disorders, neurodegeneration, and neurosurgery. This article reviews the brain-centered evolution of diffusion MRI from its physical foundations to its forward-looking frontiers. Throughout, the central argument is that diffusion MRI derives its power from sensitivity to microstructure, whereas its interpretation depends critically on model assumptions and acquisition design.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.","authors":"Ian Goodall-Halliwell, Jordan DeKraker, Paul Bautin, Daniel Mendelson, Donna Gift Cabalo, Ella Sahlas, Alexander Ngo, Ke Xie, Jack Lam, Meaghan Smith, Youngeun Hwang, Laura Vavassori, Pietro Milano, Judy Chen, Arielle Dascal, Rui Ding, Guan Zhou, Marlo Naish, JiaJie Mo, Fatemeh Fadaie, Raul R Cruces, Boris C Bernhardt","doi":"10.1162/IMAG.a.1366","DOIUrl":"10.1162/IMAG.a.1366","url":null,"abstract":"<p><p>MICAFlow is a fully automated MRI preprocessing pipeline designed to support translation of advanced neuroimaging workflows toward clinically feasible research and prospective clinical-evaluation settings. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, enabling same-day processing on clinically realistic hardware. Second, registration accuracy: LAMAReg achieves cutting-edge multimodal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reproducibility: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlow's combination of machine learning and efficient workflows produces research-grade preprocessing outputs with runtimes compatible with clinically oriented research workflows. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and future clinical evaluation of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/MICAFlow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}