Imaging neuroscience (Cambridge, Mass.)最新文献

{"title":"A unique neural signature of long-term memory encoding from EEG inter-electrode correlation.","authors":"Chong Zhao, Edward K Vogel, Monica D Rosenberg","doi":"10.1162/IMAG.a.1228","DOIUrl":"https://doi.org/10.1162/IMAG.a.1228","url":null,"abstract":"<p><p>Classic memory models proposed that the encoding process involved in visual working memory (VWM) controls the bandwidth of encoding in long-term memory (LTM). Behaviorally, VWM and LTM accuracies are reliably correlated at the behavioral level, raising the question of whether LTM encoding uniquely engages processes that are distinct from VWM encoding. To investigate this, we recorded EEG activity as participants completed recognition memory tasks with set sizes of 32 and 128, far beyond typical VWM capacity. Using interelectrode correlation (IC) analysis, we found that IC patterns reliably predicted individual differences in LTM encoding across both set sizes, indicating a robust, domain-general neural signature. Importantly, this predictive power remained even after controlling for VWM and attentional control performance, suggesting that the model captures variance specific to LTM encoding. Temporally, predictive signals emerged only after stimulus onset and persisted for 500-600 ms. Early and late encoding phases involved distinct network structures, reflecting dynamic neural processes underlying individual differences in LTM encoding. Lastly, we showed that alpha band-passed IC, but not theta or beta band-passed IC, selectively predicted individual differences in LTM performance. Together, our findings reveal a unique and temporally dynamic neural signature that supports individual differences in LTM encoding, independent of general cognitive abilities.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847040","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":"Towards the use of functional near-infrared spectroscopy as an assessment tool in disorders of consciousness.","authors":"Karnig Kazazian, Matthew Kolisnyk, Garima Gupta, Jack de Jeu, Androu Abdalmalak, Adrian M Owen","doi":"10.1162/IMAG.a.1217","DOIUrl":"https://doi.org/10.1162/IMAG.a.1217","url":null,"abstract":"<p><p>Functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool for assessing patients with disorders of consciousness (DoC). While functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have advanced the detection of covert brain function, their use is often constrained by accessibility, medical and physical contraindications, and practical limitations. fNIRS offers a portable, safe, and cost-effective alternative capable of measuring hemodynamic responses at the bedside. In this perspective, we discuss the clinical motivation for integrating fNIRS into DoC patient assessments, summarize recent advancements in the application of fNIRS for examining brain function, and outline the clinical and technical advantages. We highlight key future directions of fNIRS research, including large-scale validation, multimodal integration, and the development of fNIRS-based brain-computer interfaces. Finally, we address the ethical imperative to ensure equitable access to neurotechnologies capable of detecting covert brain function. With continued methodological refinement and standardization, fNIRS may significantly transform the diagnostic, prognostic, and communicative landscape of DoC care.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13137566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846984","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":"Independent contributions of language activations in left and right temporal cortex to aphasia outcomes after stroke.","authors":"Sarah M Schneck, Deborah F Levy, Jillian L Entrup, Melodie Yen, Dana K Eriksson, Marianne Casilio, Anna V Kasdan, Lily Walljasper, Caitlin F Onuscheck, Larry T Davis, Howard S Kirshner, Michael de Riesthal, Stephen M Wilson","doi":"10.1162/IMAG.a.1225","DOIUrl":"https://doi.org/10.1162/IMAG.a.1225","url":null,"abstract":"<p><p>Recovery from aphasia after stroke has been hypothesized to depend on neuroplasticity in surviving brain regions. Many studies have investigated this process, but progress has been impeded by methodological limitations relating to task performance confounds, contrast validity, and sample sizes. Furthermore, few studies have accounted for the complex relationships that exist between patterns of structural damage, distributed networks of functional activity, and behavioral outcomes. The present cross-sectional study aimed to overcome these critical methodological limitations and to disentangle the relationships between structure, function, and behavior. We recruited 70 individuals with post-stroke aphasia and 45 neurologically normal comparison participants. We used a valid and reliable language mapping fMRI paradigm that adapted dynamically to each participant's task performance, and carried out whole-brain permutation analyses along with hypothesis-driven analyses of individually defined functional regions of interest (ROIs). Multivariable models were constructed that incorporated lesion load estimates derived from machine learning and language activations across multiple brain regions. We found strong evidence that left posterior temporal cortex is the most critical region for language processing in post-stroke aphasia: functional activity in this region was reduced in aphasia, predictive of aphasia outcomes in a whole-brain analysis above and beyond the contribution of lesion load, and remained predictive even above and beyond other functional predictors, with a medium effect size (<i>f</i> ² = 0.15). We also found that right posterior temporal cortex made an independent contribution to aphasia outcomes: functional activity was attenuated in aphasia, suggesting diaschisis, yet was predictive of aphasia outcomes above and beyond left hemisphere lesion load and functional predictors, with a small effect size (<i>f</i> ² = 0.08). We corroborated the importance of left frontal cortex: functional activity was attenuated in aphasia and predictive of aphasia outcomes over and beyond the contribution of lesion load; however, unlike in the bilateral temporal regions, functional activity in the left frontal lobe did not remain predictive once other functional predictors were included in the model. There was no support for other potential compensatory mechanisms such as recruitment of the right frontal lobe, the bilateral multiple demand network, or perilesional regions. Taken together, our findings demonstrate that functional imaging can provide critical insights into language processing in aphasia that cannot be obtained from structural imaging alone, with the left and right posterior temporal cortices making independent contributions to aphasia outcomes after stroke.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13137568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846989","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":"A neuroscientist's guide to neural burst detection.","authors":"Lindsey Power, Idil Aydin, Timothy Bardouille, Sylvain Baillet","doi":"10.1162/IMAG.a.1226","DOIUrl":"https://doi.org/10.1162/IMAG.a.1226","url":null,"abstract":"<p><p>Neural bursts-brief episodes of heightened oscillatory activity-are increasingly studied as fundamental building blocks of brain function, with relevance to perception, cognition, and disease. As such, detecting and characterizing these bursts in electrophysiological recordings have driven rapid methodological innovation in neuroscience research. However, the growing number of analysis techniques can be overwhelming, making it difficult for researchers to select the most appropriate method for their specific goals. In this review, we provide a structured and practical guide for neuroscientists to measure and interpret neural burst data. We offer an overview of current detection methods, accompanied by a suite of tutorials, including code notebooks and data, to enable concrete implementation and critical evaluation. We then conclude with actionable recommendations to help researchers select the best burst detection strategy in diverse research contexts. This guide is intended for newcomers to the field as well as more experienced neuroscientists seeking to expand their methodological toolkit.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13146973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846978","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":"Test-retest reliability analysis of resting-state EEG measures and their association with long-term memory in children and adults.","authors":"Anastasios Ziogas, Simon Ruch, Nicole H Skieresz, Sandy C Marca, Nicolas Rothen, Thomas P Reber","doi":"10.1162/IMAG.a.1224","DOIUrl":"https://doi.org/10.1162/IMAG.a.1224","url":null,"abstract":"<p><p>EEG resting-state measures, such as spectral power and microstates, have been associated with human long-term memory (LTM) performance. However, findings across studies are inconsistent and sometimes contradictory, likely due to a low reliability of the measures employed. These inconsistencies limit the interpretability and generalizability of results, emphasizing the need for a systematic evaluation of measure reliability. In this study, we addressed this gap by identifying the most reliable EEG resting-state measures and evaluating their predictive value for LTM performance in a second-language (L2) vocabulary learning paradigm. A group of children (N = 36) and adults (N = 90) participated in two studies on second-language vocabulary learning. Participants completed a test on L2 vocabulary and a resting-state EEG recording (180 seconds eyes open) before and after learning a second language. We used Intraclass Correlation Coefficients (ICC) to identify resting-state EEG measures with satisfying test-retest reliability (ICC > = 0.75) and then assessed how these reliable measures are associated with L2 vocabulary learning representing LTM performance. Highest ICC values were found for oscillatory power in the alpha range and in the frequency of occurrences, duration, and coverages of microstates. Calculations yielded ICC values of 0.84/0.86 (children/adults) for alpha power and 0.88/0.80 for microstate measures. Of these measures, only alpha power showed a positive correlation with LTM performance, but only in the adult population (<i>r</i> = 0.38, <i>p</i> < .01). No other measures were associated with LTM (all <i>p</i> > .05). Alpha power could thus serve as a stable and reliable marker of the neural mechanisms accounting for high LTM performance in the fully developed adult brain.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13130526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824365","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":"Individualized structure-function coupling reveals behavioral signatures in the adolescent brain.","authors":"Bahram Jafrasteh, Yangzhi Wang, Qingyu Hu, Keith Jamison, Amy Kuceyeski, Qingyu Zhao","doi":"10.1162/IMAG.a.1223","DOIUrl":"https://doi.org/10.1162/IMAG.a.1223","url":null,"abstract":"<p><p>Adolescence is a critical period of brain maturation, yet how functional dynamics relate to white-matter microstructure at the individual level remains poorly understood. We developed a machine learning workflow to predict fractional anisotropy (FA) from distributed resting-state functional connectivity (FC) in two large adolescent cohorts: NCANDA (<i>n</i> = 814, 12-22 years, longitudinal) and HCP-Development (<i>n</i> = 472, 12-22 years, cross-sectional). Whole-brain FA could be modestly predicted from FC ( <math><mi>r</mi></math> = 0.16 in NCANDA; <math><mi>r</mi></math> = 0.27 in HCP-D). The accuracy of predicting regional FA significantly varied across 27 white-matter regions, with the highest structure-function coupling detected in fiber tracts subserving unimodal cortical regions. These regional accuracy scores were reproducible between datasets ( <math><mi>r</mi></math> = 0.95). Region-specific analyses also revealed tract-clustered FC predictors, highlighting distinct large-scale functional circuits underlying regional microstructural integrity. We then defined a \"structure-function gap\" as the residual between predicted and observed FA in each white-matter region. These gap measures were significantly associated with a broad constellation of cognitive and behavioral measures, particularly involving memory, impulsivity, and executive function. Notably, significant behavioral coupling emerged in Corticospinal and Cingulum pathways. Together, these findings establish individualized structure-function coupling as a reproducible, anatomically specific, and behaviorally informative marker in youth, offering a new framework to link distributed FC patterns to white-matter development and behavioral variability.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824328","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 functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity.","authors":"Xinhui Li, Eloy Geenjaar, Zening Fu, Godfrey D Pearlson, Vince D Calhoun","doi":"10.1162/IMAG.a.1220","DOIUrl":"10.1162/IMAG.a.1220","url":null,"abstract":"<p><p>Psychiatric and neurodevelopmental disorders such as schizophrenia (SZ) and autism spectrum disorder (ASD) are challenging to characterize in part due to their heterogeneous presentation in individuals, with symptoms now believed to exist on a continuum. Conventional diagnostic and neuroimaging analytical approaches rely on subjective assessment or group differences, but typically ignore progression between groups or heterogeneity within a group. To estimate the neuropsychiatric continuum and heterogeneity, we proposed a functional network connectivity (FNC) interpolation framework based on a variational autoencoder (VAE) using static FNC (sFNC) and dynamic FNC (dFNC) data from controls and patients with SZ or ASD. We demonstrated that VAEs significantly outperformed a linear baseline and a semi-supervised counterpart. For both sFNC and dFNC interpolation, the generated results effectively captured representative and generalizable properties in the original data. The interpolated continua from controls to patients in both SZ and ASD revealed group-wise gradients characterized by reduced positive correlations within the auditory, sensorimotor, and visual networks, as well as between the subcortical and cerebellar domains. In contrast, anti-correlations weakened between the subcortical domain and the sensory domains, and between the cerebellar domain and the sensory domains. Finally, we showed examples of how to generate continuous FNC data following group- or state-based trajectories in the VAE latent space. The proposed framework offers added advantages over traditional methods, including data-driven discovery of hidden relationships, visualization of individual differences, imputation of missing values along a continuous spectrum, and estimation of the stage where an individual falls within the continuum.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824342","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 the temporal dynamics and modeling of mid-level feature representations in humans.","authors":"Agnessa Karapetian, Alexander Lenders, Vanshika Bawa, Martin Pflaum, Raphael Leuner, Gemma Roig, Kshitij Dwivedi, Radoslaw M Cichy","doi":"10.1162/IMAG.a.1207","DOIUrl":"https://doi.org/10.1162/IMAG.a.1207","url":null,"abstract":"<p><p>Visual perception unfolds through a hierarchy of transformations, beginning with the extraction of low-level features, such as edges, and culminating in the representation of high-level features such as object categories. While the processing of low- and high-level features is well studied, the intermediate transformations, that is, mid-level features, remain poorly understood. Here, we introduce a stimulus set of naturalistic 3D-rendered images and videos with ground-truth annotations for five candidate mid-level features (reflectance, scene depth, world normals, lighting, and skeleton position) alongside for one low-level feature (edges) and for one high-level feature (action identity). To determine when these features are processed in the brain, we collected electroencephalography (EEG) responses during stimulus presentation and trained linearized encoding models to predict EEG responses from the annotations. We first showed that candidate mid-level features were best represented between ~100 and 250 ms post-stimulus, between low- and high-level features, and consistent with a bridging role linking sensory and semantic processing. We then assessed convolutional neural networks (CNNs) as models of mid-level feature processing in humans and observed that although their hierarchies were shallower, they exhibited a comparable processing order for mid-level but not low- or high-level features, only for videos. Together, our results support the view that mid-level features are tied to surface- and shape-related processing and establish 3D-rendered stimuli with annotations as a valuable tool for investigating mid-level vision in biological and artificial neural networks.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824362","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":"Replicability of representational similarity and its role in successful memory retrieval.","authors":"Ece Yuksel, Erica S Shafer, Madeline Netto, Rachel A Diana","doi":"10.1162/IMAG.a.1214","DOIUrl":"https://doi.org/10.1162/IMAG.a.1214","url":null,"abstract":"<p><p>Studies of hippocampal pattern similarity during event encoding and its relationship to subsequent memory retrieval have revealed inconsistent results. Our laboratory recently found evidence that differences in cognitive processing during encoding can modulate the relationship between hippocampal pattern similarity and recognition success. This finding is consistent with the theoretical proposal that hippocampal representations have a dynamic relationship to memory retrieval in which cognitive goals are influential. However, there have been few attempts to replicate representational similarity findings from functional magnetic resonance imaging (fMRI) and, to our knowledge, no evidence of successful replication in either the hippocampus or subsequent memory studies. In order to draw strong theoretical conclusions from our findings, or others in the literature, it is important to demonstrate that those findings are robust. The current study attempted a direct replication of our prior experiment with the exception of minor modifications in the neuroimaging parameters, which were intended to assess the degree to which representational similarity analyses are influenced by reasonable technical differences in data collection. We did not replicate the finding that cognitive variability interacts with future recognition success in the hippocampus. Overall, we failed to replicate 9 out of 12 significant <i>F</i>-test results from the Lim et al. study. The three findings that were replicated can be explained by minor visual differences in stimulus presentation on variable cognitive context trials. In addition, we found three new significant effects in the current study that did not previously appear in our earlier study. Therefore, we conclude that fMRI studies using representational similarity analysis of subsequent memory performance are sensitive to minor methodological variation. Further tests of the replicability of these findings are needed prior to drawing theoretical conclusions from their results. Preregistered Stage 1 protocol: https://osf.io/njzhq (date of in-principle acceptance: August 1, 2025) Final recommended Stage 2 manuscript and materials: https://doi.org/10.17605/OSF.IO/ZUYNA PCI:RR Stage 1 recommendation: https://rr.peercommunityin.org/articles/rec?id=873 PCI:RR Stage 2 recommendation: https://rr.peercommunityin.org/articles/rec?id=1278.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791163","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 network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography.","authors":"Emilia Butters, Liam Collins-Jones, Rickson C Mesquita, Deepshikha Acharya, Elizabeth McKiernan, Axel A S Laurell, Audrey Low, Sruthi Srinivasan, John T O'Brien, Li Su, Gemma Bale","doi":"10.1162/IMAG.a.1208","DOIUrl":"https://doi.org/10.1162/IMAG.a.1208","url":null,"abstract":"<p><p>Dementia is associated with altered resting-state connectivity, measures of which could aid in its early detection and monitoring. High-density diffuse optical tomography (HD-DOT) is well suited to detect these alterations at scale due to its numerous practical advantages, but it has not yet been applied to dementia. In this study, we investigated resting-state functional connectivity across the prefrontal cortex in individuals with mild cognitive impairment (MCI, <i>n</i> = 22), Alzheimer's disease (AD, <i>n</i> = 21), and in healthy controls (<i>n</i> = 22). A graph theoretical approach was taken to characterise both global and local patterns of prefrontal connectivity over a 5-minute resting period. We found that individuals with MCI exhibited denser and stronger networks with shorter path lengths, which normalised in AD, accompanied by a redistribution of network hubs that were less stable. These results perhaps reflect the recruitment of additional connections in the early stages of pathology to maintain short-term network stability, which is ultimately associated with less efficient and more fragmented network organisation in later stages. Following the demonstration of HD-DOT's capacity to detect differences between healthy ageing and AD-type cognitive impairment, this work opens up new possibilities for the use of optical imaging in the study of this clinical population and HD-DOT's potential for scalable clinical use.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791223","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}