Mikkel Schöttner Sieler, Thomas A W Bolton, Jagruti Patel, Patric Hagmann
{"title":"Comparing and scaling fMRI features for brain-behavior prediction.","authors":"Mikkel Schöttner Sieler, Thomas A W Bolton, Jagruti Patel, Patric Hagmann","doi":"10.1162/IMAG.a.141","DOIUrl":"10.1162/IMAG.a.141","url":null,"abstract":"<p><p>Predicting behavioral variables from neuroimaging modalities such as magnetic resonance imaging (MRI) has the potential to allow the development of neuroimaging biomarkers of mental and neurological disorders. A crucial processing step to this aim is the extraction of suitable features. These can differ in how well they predict the target of interest, and how this prediction scales with sample size and scan time. Here, we compare nine feature subtypes extracted from resting-state functional MRI recordings for behavior prediction, ranging from regional measures of functional activity to functional connectivity (FC) and metrics derived with graph signal processing (GSP), a principled approach for the extraction of structure-informed functional features. We study 979 subjects from the Human Connectome Project Young Adult dataset, predicting summary scores for mental health, cognition, processing speed, and substance use, as well as age and sex. The scaling properties of the features are investigated for different combinations of sample size and scan time. FC comes out as the best feature for predicting cognition, age, and sex. Graph power spectral density is the second best for predicting cognition and age, while for sex, variability-based features show potential as well. When predicting sex, the low-pass graph-filtered coupled FC slightly outperforms the simple FC variant. None of the other targets were predicted significantly. The scaling results point to higher performance reserves for the better-performing features. They also indicate that it is important to balance sample size and scan time when acquiring data for prediction studies. The results confirm FC as a robust feature for behavior prediction, but also show the potential of GSP and variability-based measures. We discuss the implications for future prediction studies in terms of strategies for acquisition and sample composition.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076763","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}
Christian Kothe, Seyed Yahya Shirazi, Tristan Stenner, David Medine, Chadwick Boulay, Matthew I Grivich, Fiorenzo Artoni, Tim Mullen, Arnaud Delorme, Scott Makeig
{"title":"The lab streaming layer for synchronized multimodal recording.","authors":"Christian Kothe, Seyed Yahya Shirazi, Tristan Stenner, David Medine, Chadwick Boulay, Matthew I Grivich, Fiorenzo Artoni, Tim Mullen, Arnaud Delorme, Scott Makeig","doi":"10.1162/IMAG.a.136","DOIUrl":"10.1162/IMAG.a.136","url":null,"abstract":"<p><p>Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area network (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis environments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios, including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076141","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}
Francesca Morfini, Aaron Kucyi, Jiahe Zhang, Clemens C C Bauer, Paul A Bloom, David Pagliaccio, Nicholas A Hubbard, Isabelle M Rosso, Anastasia Yendiki, Satrajit S Ghosh, Diego A Pizzagalli, John D E Gabrieli, Susan Whitfield-Gabrieli, Randy P Auerbach
{"title":"Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach.","authors":"Francesca Morfini, Aaron Kucyi, Jiahe Zhang, Clemens C C Bauer, Paul A Bloom, David Pagliaccio, Nicholas A Hubbard, Isabelle M Rosso, Anastasia Yendiki, Satrajit S Ghosh, Diego A Pizzagalli, John D E Gabrieli, Susan Whitfield-Gabrieli, Randy P Auerbach","doi":"10.1162/IMAG.a.145","DOIUrl":"10.1162/IMAG.a.145","url":null,"abstract":"<p><p>Identifying brain-based correlates of risk for future depression and anxiety severity in youth could improve prevention and treatment efforts. We tested whether connectome-based predictive modeling (CPM) based on resting-state functional connectivity (FC) at baseline: (a) predicts future depression and anxiety severity during childhood and (b) generalizes to adolescence. We used two independent, longitudinal datasets including children from the Adolescent Brain Cognitive Development (ABCD) study and adolescents from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD included a cohort of 11,875 children ages 9-11 years old, and BANDA enrolled 215 adolescents ages 14-17 years, of which ~70% reported a depressive or anxiety disorder. CPM with internal (within ABCD) and external validation (from ABCD to BANDA) used baseline whole-brain FC to predict depression and anxiety severity at a 1-year follow-up assessment. ABCD-derived functional connections, which we term \"Symptoms Network\", were validated within BANDA to test model applicability in adolescence, which is a peak period for the emergence of internalizing disorders. Participants with complete data were included from ABCD (n = 3,718, 52.9% girls, ages 10.0 ± 0.6) and BANDA (n = 150, 61.3% girls, ages 15.4 ± 0.9). In ABCD, we found that FC predicted 1-year follow-up symptoms severity (<i>ρ</i> = 0.058, <i>p</i> = 0.040), measured with the Child Behavior Checklist Anxious/Depressed subscale. External validation in BANDA indicated that the Symptoms Network predicted 1-year follow-up symptoms severity (<i>ρ</i> = 0.222, <i>p</i> = 0.007), measured with the Revised Child Depression and Anxiety Scale <i>t</i>-transformed total score. In both ABCD and BANDA, FC enhanced the prediction of future symptom severity beyond baseline clinical and demographic information (baseline severity, sex, and age), including when correcting for mean head motion. The ABCD-derived connections included contributions from somatomotor, attentional, and subcortical regions and were characterized by heterogeneous FC within adolescents, where the same region pairs were characterized by positive FC for some participants but by negative FC for others. In conclusion, FC may provide inroads for early identification of internalizing symptoms, which could inform preventative-intervention approaches prior to the emergence of affective disorders during a critical period of neuromaturation. However, the small effect sizes and heterogeneity in results underscore the challenges of employing brain-based biomarkers for clinical applications and emphasize the need for individualized approaches for understanding neurodevelopment and mental health.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076762","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":"Improved evaluation of waveform reconstruction in speech decoding based on invasive brain-computer interfaces.","authors":"Xiaolong Wu, Kejia Hu, Zhichun Fu, Dingguo Zhang","doi":"10.1162/IMAG.a.146","DOIUrl":"10.1162/IMAG.a.146","url":null,"abstract":"<p><p>Brain-computer interfaces (BCIs) that reconstruct speech waveforms from neural signals are a promising communication technology. However, the field lacks a standardized evaluation metric, making it difficult to compare results across studies. Existing objective metrics, such as correlation coefficient (CC) and mel cepstral distortion (MCD), are often used inconsistently and have intrinsic limitations. This study addresses the critical need for a robust and validated method for evaluating reconstructed waveform quality. Literature about waveform reconstruction from intracranial signals is reviewed, and issues with evaluation methods are presented. We collated reconstructed audio from 10 published speech BCI studies and collected Mean Opinion Scores (MOS) from human raters to serve as a perceptual ground truth. We then systematically evaluated how well combinations of existing objective metrics (STOI and MCD) could predict these MOS scores. To ensure robustness and generalizability, we employed a rigorous leave-one-dataset-out cross-validation scheme and compared multiple models, including linear and non-linear regressors. This work, for the first time, identifies a lack of a standard evaluation method, which prohibits cross-study comparison. Using 10 public datasets, our analysis reveals that a non-linear model, specifically a Random Forest regressor, provides the most accurate and reliable prediction of subjective MOS ratings (R² = 0.892). We propose this cross-validated Random Forest model, which maps STOI and MCD to a predicted MOS score, as a standardized objective evaluation metric for the speech BCI field. Its demonstrated accuracy and robust validation outperform the available methods. Moreover, it can provide the community with a reliable tool to benchmark performance, facilitate meaningful cross-study comparisons for the first time, and accelerate progress in speech neuroprosthetics.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076780","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}
Benedikt Glinski, Mohammed Ali Salehinejad, Kuri Takahashi, Asif Jamil, Fatemeh Yavari, Min-Fang Kuo, Michael A Nitsche
{"title":"Phase-synchronized 40 Hz tACS and iTBS effects on gamma oscillations.","authors":"Benedikt Glinski, Mohammed Ali Salehinejad, Kuri Takahashi, Asif Jamil, Fatemeh Yavari, Min-Fang Kuo, Michael A Nitsche","doi":"10.1162/IMAG.a.140","DOIUrl":"10.1162/IMAG.a.140","url":null,"abstract":"<p><p>Gamma oscillations play a crucial role in core cognitive functions such as memory processes. Enhancing gamma oscillatory activity, which is reduced in Alzheimer's Disease, may have therapeutic potential, but effective interventions remain to be determined. Previous studies have shown that phase-synchronized electric and magnetic stimulation boosts brain oscillatory activities at theta, alpha, and delta frequency bands in different ways. The high-frequency gamma frequency band remains to be investigated. This study applies novel noninvasive brain stimulation techniques, namely phase-locked 40-Hz intermittent theta-burst stimulation (iTBS) and transcranial alternating current stimulation (tACS), and explores gamma oscillation changes in the brain. Thirty healthy young participants randomly underwent 40-Hz tACS (1), 40-Hz iTBS (2), two combined interventions (phase-locked iTBS to tACS peak sine wave or tACS trough sine wave) (3-4), and a sham condition (5). The target regions were the left and right dorsolateral prefrontal cortex and were stimulated by simultaneous tACS and iTBS. Gamma oscillatory activities (for 2 hours after intervention) were monitored following each intervention. Our results show that all stimulation protocols enhanced 40-Hz oscillatory power. The iTBS-tACS Peak shows the most significant and stable increase in gamma oscillatory activities (up to 2 hours), followed by 40-Hz tACS and 40-Hz iTBS. 40-Hz tACS and 40-Hz iTBS had the strongest acute effects (up to 30 minutes) on induced gamma oscillations, while 40-Hz tACS most consistently induced gamma oscillations for up to 2 hours in overall resting EEG data. Phase-synchronizing iTBS with tACS at 40 Hz and the very 40 Hz tACS alone targeting the dorsolateral prefrontal cortex may be a viable approach for inducing and stabilizing gamma oscillatory activity, particularly in conditions where endogenous gamma oscillations are attenuated, such as Alzheimer's Disease.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066453","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}
Haylee J Ressa, Benjamin T Newman, Zachary Jacokes, James C McPartland, Natalia M Kleinhans, T Jason Druzgal, Kevin A Pelphrey, John Darrell Van Horn
{"title":"Widespread associations between behavioral metrics and brain microstructure in ASD suggest age mediates subtypes of ASD.","authors":"Haylee J Ressa, Benjamin T Newman, Zachary Jacokes, James C McPartland, Natalia M Kleinhans, T Jason Druzgal, Kevin A Pelphrey, John Darrell Van Horn","doi":"10.1162/IMAG.a.144","DOIUrl":"10.1162/IMAG.a.144","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by differences in social communication and repetitive behaviors. Our laboratory has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased throughout the adolescent brain in ASD. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 years through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066547","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}
Jared Deighton, Shan Zhong, Kofi Agyeman, Wooseong Choi, Charles Y Liu, Darrin J Lee, Vasileios Maroulas, Vassilios N Christopoulos
{"title":"Functional ultrasound imaging combined with machine learning for whole-brain analysis of drug-induced hemodynamic changes.","authors":"Jared Deighton, Shan Zhong, Kofi Agyeman, Wooseong Choi, Charles Y Liu, Darrin J Lee, Vasileios Maroulas, Vassilios N Christopoulos","doi":"10.1162/IMAG.a.139","DOIUrl":"10.1162/IMAG.a.139","url":null,"abstract":"<p><p>Functional ultrasound imaging (fUSI) is a cutting-edge technology that measures changes in cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells moving within its field of view (FOV). It offers high spatiotemporal resolution and sensitivity, allowing for detailed visualization of cerebral blood flow dynamics. While fUSI has been utilized in preclinical drug development studies to explore the mechanisms of action of various drugs targeting the central nervous system, many of these studies rely on predetermined regions of interest (ROIs). This focus may overlook relevant brain activity outside these specific areas, which could influence the results. To address this limitation, we compared three machine learning approaches-convolutional neural network (CNN), support vector machine (SVM), and vision transformer (ViT)-combined with fUSI to analyze the pharmacodynamics of dizocilpine (MK-801), a potent non-competitive NMDA receptor antagonist commonly used in preclinical models for memory and learning impairments. While all three machine learning techniques could distinguish between drug and control conditions, CNN proved particularly effective due to its ability to capture hierarchical spatial features while maintaining anatomical specificity. Class activation mapping revealed brain regions, including the prefrontal cortex and hippocampus, that are significantly affected by drug administration, consistent with literature reporting a high density of NMDA receptors in these areas. Overall, the combination of fUSI and CNN creates a novel analytical framework for examining pharmacological mechanisms, allowing for data-driven identification and regional mapping of drug effects while preserving anatomical context and physiological relevance.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066450","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}
Callum Simpson, Gerard Hall, John S Duncan, Yujiang Wang, Peter N Taylor
{"title":"Automated generation of epilepsy surgery resection masks: The RAMPS pipeline.","authors":"Callum Simpson, Gerard Hall, John S Duncan, Yujiang Wang, Peter N Taylor","doi":"10.1162/IMAG.a.147","DOIUrl":"10.1162/IMAG.a.147","url":null,"abstract":"<p><p>MRI-based delineation of brain tissue removed by epilepsy surgery can be challenging due to post-operative brain shift. In consequence, most studies use manual approaches which are prohibitively time-consuming for large sample sizes, require expertise, and can be prone to errors. We propose RAMPS (Resections And Masks in Preoperative Space), an automated pipeline to generate a 3D resection mask of pre-operative tissue. Our pipeline leverages existing software including FreeSurfer, SynthStrip, Sythnseg and ANTs to generate a mask in the same space as the patient's pre-operative T1 weighted MRI. We compare our automated masks against manually drawn masks and two other existing pipelines (Epic-CHOP and ResectVol). Comparing to manual masks (N = 87), RAMPS achieved a median (IQR) dice similarity of 0.86 (0.078) in temporal lobe resections, and 0.72 (0.32) in extratemporal resections. In comparison to other pipelines, RAMPS had higher dice similarities (N = 62) (RAMPS: 0.86, Epic-CHOP: 0.72, ResectVol: 0.72). We release a user-friendly, easy-to-use pipeline, RAMPS, open source for accurate delineation of resected tissue.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066444","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":"Real-time fMRI neurofeedback boosts heartbeat perception by modulating insula activation pattern during interoceptive attention.","authors":"Yusuke Haruki, Yuxiang Yang, Keisuke Suzuki, Hiroshi Imamizu, Kenji Ogawa","doi":"10.1162/IMAG.a.142","DOIUrl":"10.1162/IMAG.a.142","url":null,"abstract":"<p><p>Real-time fMRI neurofeedback (NF) has emerged as a promising method for enabling individuals to modulate specific brain regions and, consequently, their behavioural outcomes. This study examined whether the NF targeting the right insula could improve heartbeat perception ability and influence emotional response to negatively valenced stimuli, by training participants to modulate the brain activation associated with interoceptive (heartbeat-focused) and exteroceptive (visual-focused) attention. Fifty-four participants underwent a single ~40-minute NF session with contingent (NF group, n = 28) or non-contingent (Sham group, n = 26) feedback, with heartbeat perception and emotional appraisal assessed pre- and post-training. The NF group demonstrated significant improvements in heartbeat perception, with individual learning effects in neuromodulation predicting the behavioural gains. However, group-level NF scores did not differ significantly, likely reflecting variability in learnability. Despite improvements in heartbeat perception, NF training did not modulate emotional responses at either the behavioural or neural level, suggesting that targeting the insula alone is insufficient to alter affective processing within a single session. These findings provide evidence that NF can enhance heartbeat perception through targeted neuromodulation in the insular cortex.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042401","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}
Annabell Coors, Weiyi Zeng, Ulrich Ettinger, Monique M B Breteler
{"title":"Neuropathology determines whether brain systems segregation benefits cognitive performance.","authors":"Annabell Coors, Weiyi Zeng, Ulrich Ettinger, Monique M B Breteler","doi":"10.1162/IMAG.a.138","DOIUrl":"10.1162/IMAG.a.138","url":null,"abstract":"<p><p>The human brain is a large-scale network, containing multiple segregated, functionally specialized systems. With increasing age, these systems become less segregated, but the reasons and consequences of this age-related reorganization are largely unknown. Thus, after characterizing age- and sex-specific differences in the segregation of global, sensorimotor, and association systems using resting-state functional MRI data, we analyzed how segregation relates to cognitive performance in both classical and eye movement tasks across age strata and whether this is influenced by the degree of neuropathology. Our analyses included 6,455 participants (30-95 years) of the community-based Rhineland Study. System segregation indices were based on functional connectivity within and between 12 brain systems. We assessed cognitive performance with tests for memory, processing speed, executive function, and crystallized intelligence and oculomotor tasks. Multivariable regression models confirmed that brain systems become less segregated with age (e.g., global segregation: standardized regression coefficient (ß) = -0.298; 95% confidence interval [-0.299, -0.297], p < 0.001) and that in older age this effect is stronger in women compared to men. Higher segregation benefited memory (especially in young individuals) and processing speed in individuals with mild neuropathology (not significant after multiple testing correction). <i>Lower</i> segregation benefited crystallized intelligence in 46- to 55-year-olds. Associations between segregation indices and cognition were generally weak (ß ~ 0.01-0.06). This suggests that optimal brain organization may depend on the degree of brain pathology. Age-related brain reorganization could serve as a compensatory mechanism and partly explain improvements in crystallized intelligence and the decline in fluid cognitive domains from adolescence to (late) adulthood.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042370","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}