Human Brain Mapping最新文献

{"title":"Effects of Aging, Estimated Fitness, and Cerebrovascular Status on White Matter Microstructural Health","authors":"Grace M. Clements,&nbsp;Paul Camacho,&nbsp;Daniel C. Bowie,&nbsp;Kathy A. Low,&nbsp;Bradley P. Sutton,&nbsp;Gabriele Gratton,&nbsp;Monica Fabiani","doi":"10.1002/hbm.70168","DOIUrl":"10.1002/hbm.70168","url":null,"abstract":"<p>White matter (WM) microstructural health declines with increasing age, with evidence suggesting that improved cardiorespiratory fitness (CRF) may mitigate this decline. Specifically, higher fit older adults tend to show preserved WM microstructural integrity compared to their lower fit counterparts. However, the extent to which fitness and aging <i>independently</i> impact WM integrity across the adult lifespan is still an open question, as is the extent to which cerebrovascular health mediates these relationships. In a large sample (<i>N</i> = 125, aged 25–72), we assessed the impact of age and estimated cardiorespiratory fitness on fractional anisotropy (FA, derived using diffusion weighted imaging, dwMRI) and probed the mediating role of cerebrovascular health (derived using diffuse optical tomography of the cerebral arterial pulse, pulse-DOT) in these relationships. After orthogonalizing age and estimated fitness and computing a PCA on whole brain WM regions, we found several WM regions impacted by age that were independent from the regions impacted by estimated fitness (hindbrain areas, including brainstem and cerebellar tracts), whereas other areas showed interactive effects of age and estimated fitness (midline areas, including fornix and corpus callosum). Critically, cerebrovascular health mediated <i>both</i> relationships suggesting that vascular health plays a linking role between age, fitness, and brain health. Secondarily, we assessed potential sex differences in these relationships and found that, although females and males generally showed the same age-related FA declines, males exhibited somewhat steeper declines than females. Together, these results suggest that age and fitness impact specific WM regions and highlight the mediating role of cerebrovascular health in maintaining WM health across adulthood.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673526","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":"Investigating the Human Brain's Integration of Internal and External Reference Frames: The Role of the Alpha and Beta Bands in a Modified Temporal Order Judgment Task","authors":"Xianhao Wei,&nbsp;Jian Zhang,&nbsp;Jinyan Zhang,&nbsp;Zimo Li,&nbsp;Qi Li,&nbsp;Jinglong Wu,&nbsp;Jingjing Yang,&nbsp;Zhilin Zhang","doi":"10.1002/hbm.70196","DOIUrl":"10.1002/hbm.70196","url":null,"abstract":"<p>The integration of the internal and external reference frames of the human brain is crucial for achieving accurate tactile spatial localization. However, the mechanisms underlying this integration have yet to be fully elucidated. This study adopted a modified temporal order judgment paradigm with an advanced weighted phase lag index method to investigate brain network interactions when the internal and external reference frames were integrated. We found that when the brain integrated internal and external reference frames, alpha oscillations decreased, beta oscillations increased, and inter-hemispheric connectivity increased. Specifically, compared with the match condition: first, the alpha band oscillation predominantly contributed to processing the internal reference frame mismatch; second, the alpha and late beta band oscillation predominantly contributed to processing the external reference frame mismatch; third, the early alpha and late beta band oscillation predominantly contributed to processing the internal and external reference frame mismatch. These findings suggest that the neural oscillation of the alpha and beta bands plays an essential role in tactile spatial localization.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669518","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":"The Neural Association Between Symptom and Cognition in Major Depressive Disorder: A Network Control Theory Study","authors":"Aoxiang Zhang,&nbsp;Qian Zhang,&nbsp;Ziyuan Zhao,&nbsp;Qian Li,&nbsp;Fei Li,&nbsp;Yongbo Hu,&nbsp;Xiaoqi Huang,&nbsp;Weihong Kuang,&nbsp;Graham J. Kemp,&nbsp;Youjin Zhao,&nbsp;Qiyong Gong","doi":"10.1002/hbm.70198","DOIUrl":"10.1002/hbm.70198","url":null,"abstract":"<p>Major depressive disorder (MDD) is characterized by intercorrelated clinical symptoms and cognitive deficits, whose neural mechanisms in relation to these disturbances remain unclear. To elucidate this, we applied the relatively new approach of Network Control Theory (NCT), which considers how network topology informs brain dynamics based on white matter connectivity data. We used the NCT parameter of average controllability (AC) to assess the potential control that brain network nodes have on brain-state transitions associated with clinical and cognitive symptoms in MDD. DTI and high-resolution T1-weighted anatomical imaging were performed on 170 MDD patients (mean age 31.6 years; 72 males, 98 females) and 137 healthy controls (HC; mean age 33.4 years; 64 males, 73 females). We used an NCT approach to compare AC between the groups. We then performed partial Spearman's rank correlation and moderation/mediation analyses for AC and cognition and clinical symptom scores. Compared with HC, MDD patients had lower AC in the left precuneus and superior parietal lobule and higher AC in the right precentral gyrus (preCG) and superior frontal gyrus (SFG), predominantly in the default-mode, somatomotor, and attention networks. In the HC group, AC of right preCG was positively associated with processing speed. While in the MDD group, AC of right SFG was negatively associated with memory function and also negatively moderated the association between memory and anxiety symptoms. The current study highlighted that the altered brain controllability may provide a novel understanding of the neural substrate underlying cognitive control in MDD. Disrupted control of right SFG during state transitions may partially explain the variable relationship between memory and anxiety symptoms in MDD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663543","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":"Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants","authors":"Andrea Gondová,&nbsp;Sara Neumane,&nbsp;Tomoki Arichi,&nbsp;Jessica Dubois","doi":"10.1002/hbm.70186","DOIUrl":"https://doi.org/10.1002/hbm.70186","url":null,"abstract":"<p>Functional networks characterized by coherent neural activity across distributed brain regions have been observed to emerge early in neurodevelopment. Synchronized maturation across regions that relate to functional connectivity (FC) could be partially reflected in the developmental changes in underlying microstructure. Nevertheless, covariation of regional microstructural properties, termed “microstructural connectivity” (MC), and its relationship to the emergence of functional specialization during the early neurodevelopmental period remain poorly understood. We investigated the evolution of MC and FC postnatally across a set of cortical and subcortical regions, focusing on 45 preterm infants scanned longitudinally, and compared to 45 matched full-term neonates as part of the developing Human Connectome Project (dHCP) using direct comparisons of grey-matter connectivity strengths as well as network-based analyses. Our findings revealed a global strengthening of both MC and FC with age, with connection-specific variability influenced by the connection maturational stage. Prematurity at term-equivalent age was associated with significant connectivity disruptions, particularly in FC. During the preterm period, direct comparisons of MC and FC strength showed a positive linear relationship, which seemed to weaken with development. On the other hand, overlaps between MC- and FC-derived networks (estimated with Mutual Information) increased with age, suggesting a potential convergence towards a shared underlying network structure that may support the co-evolution of microstructural and functional systems. Our study offers novel insights into the dynamic interplay between microstructural and functional brain development and highlights the potential of MC as a complementary descriptor for characterizing brain network development and alterations due to perinatal insults such as premature birth.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638785","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 Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset","authors":"Yilan Dong,&nbsp;Dafnis Batalle,&nbsp;Maria Deprez","doi":"10.1002/hbm.70190","DOIUrl":"https://doi.org/10.1002/hbm.70190","url":null,"abstract":"<p>Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures, and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), autoencoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities, and evaluation pipelines rather than different machine learning models may explain variations in accuracy in the published literature. The highest accuracy in our framework was obtained when using ensemble models (<i>p</i> &lt; 0.001), leading to an accuracy of 72.2% and AUC = 0.77 using GCN classifiers. However, an SVM classifier performed with an accuracy of 70.1% and AUC = 0.77, just marginally below GCN, and significant differences were not found when comparing different algorithms under the same testing conditions (<i>p</i> &gt; 0.05). Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability in selecting relevant features contributing to model decision making. The code is available at https://github.com/YilanDong19/Machine-learning-with-ABIDE.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632732","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":"Machine Learning-Based Clustering of Layer-Resolved fMRI Activation and Functional Connectivity Within the Primary Somatosensory Cortex in Nonhuman Primates","authors":"Arabinda Mishra,&nbsp;Feng Wang,&nbsp;Li Min Chen,&nbsp;John C. Gore","doi":"10.1002/hbm.70193","DOIUrl":"https://doi.org/10.1002/hbm.70193","url":null,"abstract":"<p>Delineating the functional organization of mesoscale cortical columnar structure is essential for understanding brain function. We have previously demonstrated a high spatial correspondence between BOLD fMRI and LFP responses to tactile stimuli in the primary somatosensory cortex area 3b of nonhuman primates. This study aims to explore how 2D spatial profiles of the functional column vary across cortical layers (defined by three cortical depths) in both tactile stimulation and resting states using fMRI. At 9.4 T, we acquired submillimeter-resolution oblique fMRI data from cortical areas 3b and 1 of anesthetized squirrel monkeys and obtained fMRI signals from three cortical layers. In both areas 3b and 1, the tactile stimulus-evoked fMRI activation foci were fitted with point spread functions (PSFs), from which shape parameters, including full width at half maximum (FWHM), were derived. Seed-based resting-state fMRI data analysis was then performed to measure the spatial profiles of resting-state connectivity within and between areas 3b and 1. We found that the tactile-evoked fMRI response and local resting-state functional connectivity were elongated at the superficial layer, with the major axes oriented in lateral to medial (from digit 1 to digit 5) direction. This elongation was significantly reduced in the deeper (middle and bottom) layers. To assess the robustness of these spatial profiles in distinguishing cortical layers, shape parameters describing the spatial extents of activation and resting-state connectivity profiles were used to classify the layers via self-organizing maps (SOM). A minimal overall classification error (~13%) was achieved, effectively classifying the layers into two groups: the superficial layer exhibited distinct features from the two deeper layers in the rsfMRI data. Our results support distinct 2D spatial profiles for superficial versus deeper cortical layers and reveal similarities between stimulus-evoked and resting-state configurations.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632731","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":"White Matter Microstructure Among Straight and Gay Cisgender Men, Sao Praphet Song, and Straight Cisgender Women in Thailand","authors":"Lindsey T. Thurston,&nbsp;Artit Rodkong,&nbsp;Pongpun Saokhieo,&nbsp;Taweewat Supindham,&nbsp;Oranitcha Kaewthip,&nbsp;Kittichai Wantanajittikul,&nbsp;Malvina N. Skorska,&nbsp;Meng-Chuan Lai,&nbsp;Suwat Chariyalertsak,&nbsp;Suwit Saekho,&nbsp;Doug P. VanderLaan","doi":"10.1002/hbm.70188","DOIUrl":"https://doi.org/10.1002/hbm.70188","url":null,"abstract":"<p>White matter (WM) microstructure is differentiated in relation to sex/gender, psychosexuality, and, among transgender people, gender-affirming hormone (GAH) use. Prior research focused on Western samples, which limits generalizability to other populations. Here, diffusion tensor imaging (DTI) was used to assess WM microstructure in a Thai sample (<i>N</i> = 128) of straight cisgender men, straight cisgender women, gay cisgender men, and <i>sao praphet song</i> (i.e., transfeminine individuals assigned male at birth and sexually attracted to cisgender men). <i>Sao praphet song</i> were further grouped by GAH use. Groups were compared on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) using whole-brain tract-based spatial statistics (TBSS). FA, AD, and RD were further examined via multivariate analysis to assess covariance across WM microstructural indices and participant groups. A significant multivariate pattern differentiated the feminine- from masculine-identifying groups irrespective of sex assigned at birth and suggested WM tissue organization was greater among the latter in the bilateral cingulum, anterior corona radiata, left corpus callosum, and right superior longitudinal fasciculus, forceps minor, and corticospinal tracts. TBSS analyses reinforced that WM differed by gender identity in various regions. Among <i>sao praphet song</i>, GAH use was associated with lower regional FA, suggesting less WM organization bilaterally in the corpus callosum, cingulum, and anterior corona radiata. The findings aligned with prior studies in Western samples, indicating cross-population generalizability of WM microstructural differentiation in relation to sex/gender, psychosexuality, and GAH use.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632809","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":"Brain Morphometrics Correlations With Age Among 350 Participants Imaged With Both 3T and 7T MRI: 7T Improves Statistical Power and Reduces Required Sample Size","authors":"Cong Chu,&nbsp;Tales Santini,&nbsp;Jr-Jiun Liou,&nbsp;Ann D. Cohen,&nbsp;Pauline M. Maki,&nbsp;Anna L. Marsland,&nbsp;Rebecca C. Thurston,&nbsp;Peter J. Gianaros,&nbsp;Tamer S. Ibrahim","doi":"10.1002/hbm.70195","DOIUrl":"https://doi.org/10.1002/hbm.70195","url":null,"abstract":"<p>Magnetic resonance imaging (MRI) at 7T has a superior signal-to-noise ratio to 3T but also presents higher signal inhomogeneities and geometric distortions. A key knowledge gap is to robustly investigate the sensitivity and accuracy of 3T and 7T MRI in assessing brain morphometrics. This study aims to (a) aggregate a large number of paired 3T and 7T scans to evaluate their differences in quantitative brain morphological assessment using a widely available brain segmentation tool, FreeSurfer, as well as to (b) examine the impact of normalization methods for subject variability and smaller sample sizes on data analysis. A total of 401 healthy participants aged 29–68 were imaged at both 3T and 7T. Structural T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images were processed and segmented using FreeSurfer. To account for head size variability, the brain volumes underwent intracranial volume (ICV) correction using the Residual (regression model) and Proportional (simple division to ICV) methods. The resulting volumes and thicknesses were correlated with age using Pearson's correlation and false discovery rate correction. The correlations were also calculated in increasing sample size from three to the whole sample to estimate the sample size required to detect aging-related brain variation. Three hundred and fifty subjects (208 females) passed the image quality control, with 51 subjects excluded due to excessive motion artifacts on 3T, 7T, or both. 7T MRI showed an overall stronger correlation between morphometrics and age and a larger number of significantly correlated brain volumes and cortical thicknesses. While the ICV is consistent between both field strengths, the Residual normalization method shows markedly higher correlation with age for 3T when compared with the Proportional normalization method. The 7T results are consistent regardless of the normalization method used. In a large cohort of healthy participants with paired 3T and 7T scans, we compared the statistical performance in assessing age-related brain morphological changes. Our study reaffirmed the inverse correlation between brain volumes and cortical thicknesses and age and highlighted varying correlations in different brain regions and normalization methods at 3T and 7T. 7T imaging significantly improves statistical power and thus reduces the required sample size.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612542","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":"Age- and Sex-Specific Patterns in Adult Brain Network Segregation","authors":"Abhijot Singh Sidhu,&nbsp;Kaue T. N. Duarte,&nbsp;Talal H. Shahid,&nbsp;Rachel J. Sharkey,&nbsp;M. Louis Lauzon,&nbsp;Marina Salluzzi,&nbsp;Cheryl R. McCreary,&nbsp;Andrea B. Protzner,&nbsp;Bradley G. Goodyear,&nbsp;Richard Frayne","doi":"10.1002/hbm.70169","DOIUrl":"https://doi.org/10.1002/hbm.70169","url":null,"abstract":"&lt;p&gt;The human brain is organized into several segregated associative and sensory functional networks, each responsible for various aspects of cognitive and sensory processing. These functional networks become less segregated over the adult lifespan, possibly contributing to cognitive decline that is observed during advanced age. To date, a comprehensive understanding of decreasing network segregation with age has been hampered by (1) small sample sizes, (2) lack of investigation at different spatial scales, (3) the limited age range of participants, and more importantly (4) an inadequate consideration of sex (biological females and males) differences. This study aimed to address these shortcomings. Resting-state functional magnetic resonance imaging data were collected from 357 cognitively intact participants (18.2–91.8 years; 49.9 ± 17.1 years; 27.70 ± 1.72 MoCA score, 203 [56.8%] females), and the segregation index (defined as one minus the ratio of between-network connectivity to within-network connectivity) was calculated at three spatial scales of brain networks: whole-brain network, intermediate sensory and associative networks, as well as core visual (VIS), sensorimotor (SMN), frontoparietal (FPN), ventral attention (VAN), dorsal attention (DAN), and default mode networks (DMN). Where applicable, secondary within-, between-, and pairwise connectivity analyses were also conducted to investigate the origin of any observed age and sex effects on network segregation. For any given functional metric, linear and quadratic age effects, sex effects, and respective age by sex interaction effects were assessed using backwards iterative linear regression modeling. Replicating previous work, brain networks were found to become less segregated across adulthood. Specifically, negative quadratic decreases in whole-brain network, intermediate associative network, VAN, and DMN segregation index were observed. Intermediate sensory networks, VIS, and SMN exhibited negative linear decreases in segregation index. Secondary analysis revealed that this process of age-related functional reorganization was preferential as functional connectivity was observed to increase either between anatomically adjacent associative networks (DMN-DAN, FPN-DAN) or between anterior associative and posterior sensory networks (VIS-DAN, VIS-DMN, VIS-FPN, SMN-DMN, and SMN-FPN). Inherent sex differences in network segregation index were also observed. Specifically, whole-brain, associative, DMN, VAN, and FPN segregation index was greater in females compared to males, irrespective of age. Secondary analysis found that females have reduced functional connectivity between associative networks (DAN-VAN, VAN-FPN) compared to males and independent of age. A notable linear age-related decrease in FPN SI was also only observed for females and not males. The observed findings support the notion that functional networks reorganize across the adult lifespan, becoming less segregated. This decline ","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612522","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":"Music-Evoked Nostalgia Activates Default Mode and Reward Networks Across the Lifespan","authors":"Sarah Hennessy,&nbsp;Petr Janata,&nbsp;Talia Ginsberg,&nbsp;Jonas Kaplan,&nbsp;Assal Habibi","doi":"10.1002/hbm.70181","DOIUrl":"https://doi.org/10.1002/hbm.70181","url":null,"abstract":"<p>Nostalgia is a mixed emotion that is often evoked by music. Nostalgic music may induce temporary improvements in autobiographical memory in individuals with cognitive decline. However, the neural mechanism underlying music-evoked nostalgia and its associated memory improvements is unclear. With the ultimate goal of understanding how nostalgia-evoking music may help retrieve autobiographical memories in individuals with cognitive impairment, we first sought to understand the neural underpinnings of these processes in healthy younger and older adults. Methodological constraints, including the lack of personally tailored and experimentally controlled stimuli, have impeded our understanding of this mechanism. Here, we utilized an innovative machine-learning-based method to construct three categories of songs, all matched for musical features: (1) personalized nostalgic, (2) familiar non-nostalgic, and (3) unfamiliar non-nostalgic. In 57 participants (29 aged 18–35; 28 aged 60 and older), we investigated the functional neural correlates of music-evoked nostalgia using fMRI. Four main findings emerged: (1) Listening to nostalgic music, more than familiar non-nostalgic or unfamiliar music, was associated with bilateral activity in the default mode network, salience network, reward network, medial temporal lobe, and supplementary motor regions, (2) Psychophysiological interaction (PPI) models indicated that listening to nostalgic music involved increased functional connectivity of self-referential (posteromedial cortex) and affect-related regions (insula), (3) Older adults had stronger BOLD signals than younger adults in nostalgia-related regions during nostalgic listening, (4) While the BOLD response to nostalgic music in younger adults was associated with trait-level factors of nostalgia proneness and cognitive ability, the response in older adults was related to affective responses to the music. Overall, our findings serve as a foundation for understanding the neural basis of music-evoked nostalgia and its potential use in future clinical interventions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612391","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}