Frontoparietal network topology as a neuromarker of music perceptual abilities

Frontoparietal network topology as a neuromarker of music perceptual abilities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Frontoparietal network topology as a neuromarker of music perceptual abilities Massimo Lumaca, Peter Keller, Giosuè Baggio, Victor Pando-Naude, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3930575/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Why are some humans more musical than others? Neither fully cognitive testing nor classical localizationist neuroscience alone can provide the whole picture. Here we test how the interplay of brain organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. Our network-based study tests how human connectome variations affect music perception. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional connectomes' organization in relation to musical scores. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays a solid groundwork for future investigations into the neurobiological roots of musical culture(s). Biological sciences/Neuroscience/Cognitive neuroscience/Perception Biological sciences/Neuroscience/Computational neuroscience/Network models Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Can human neurocognitive architecture partly explain human culture? A central goal in cognitive neuroscience is to determine how neurocognitive variability creates interindividual differences in cultural capacities, like linguistic and musical abilities 1–4 . Interindividual neurocognitive differences may impact the emergence and propagation of novel cultural variants, thereby shaping cultural diversity 5 . This perspective underscores the complex interplay between individual neurocognitive abilities, cultural capacities, and the broader mosaic of human culture. Music, a ubiquitous cultural element of human societies that is deeply grounded in the human nervous system 6,7 , serves as an effective proxy to explore the brain-culture nexus. Music not only exhibits near-universal characteristics 8 but also displays significant diversity both within and across human populations 9 . The perception of music is closely tied to activity of neurocognitive systems like attention and memory 10,11 . Minor interindividual differences in the properties of these systems can manifest in music perceptual behaviour, potentially leading to large system-level effects when magnified and propagated through cultural transmission 12 . How neurocognitive variability translates into diverse music perceptual competencies remains an area of ongoing investigation 1 . Our research employs network neuroscience and graph theory on a comprehensive MRI, cognitive, and behavioral dataset (n > 200) to examine the neurocognitive markers of music perceptual abilities within the general population. Musical competence, the ability to perceive, remember, and discriminate music 13 , depends on core perceptuo-cognitive skills, including sensory integration, auditory discrimination, attention and working memory. As such, this competence is underpinned by the activity of extensive sensory and cognitive networks 14,15 . As a universal human ability that emerges early in human development 16,17 , it differs from music production, which requires specialized skill acquisition. It displays significant variability within the general population, especially in the pitch and rhythmic domains 18,19 . The neural investigation of perceptual abilities that are minimally dependent on formal musical training may offer deeper insights into the biological foundations of musical culture(s) 20 . Despite being grounded on the activity of complex networks, musical abilities have been mainly addressed either focusing on the neural activation 21,22 and morphology 23,24 of single brain regions, or on the functional connections 25,26 and white matter bundles 27–30 linking these areas. A more holistic understanding of the neural underpinnings of musical abilities would benefit from a network-level approach on brain data in combination with an objective test of music perception. Network neuroscience, recognizing the distributed nature of music perceptual faculty, offers a compelling framework for this endeavour 31,32 . It conceptualizes the brain as a connectome, a network of nodes (neural populations) linked by edges (axonal pathways for structural connectomes or signaling pathways for functional connectomes). Leveraging graph theory 33,34 , network neuroscience provides a framework to quantify three key network properties of brain connectivity: integration (enabling efficient processing of distributed information), segregation (supporting specialized processing within localized clusters), and centrality (highlighting the importance of hub regions in functional integration). By employing graph theory on both structural and functional connectomes, this approach effectively bridges the domains of neuroanatomy and brain dynamics. The static architecture of functional and structural brain networks may thus provide the basis to understand the links between neuroanatomy, information processing, mental representations, and behaviour 35,36 . Research studying functional connectomes from resting-state fMRI (rs-fMRI) and structural connectomes from diffusion MRI (dMRI) indicates that nuances in such network properties relate to differences in general cognitive abilities, including intelligence, WM capacity, and cognitive control 37–42 . Network neuroscience has the potential to establish itself as a leading approach to study how variation in the relatively static brain organization shapes variability in human cognition and behaviour. The main standardized objective tests of music perceptual abilities are the Musical Ear Test (MET) 43 , the Advanced Measures of Music Audiation 44 , the Profile of Music Perception Skills 45 , and the Swedish Musical Discrimination Test 46 . These tests specifically assess abilities in detecting pitch and timing variations in musical sequences, providing reliable measures of music competence that can be effectively related with brain network configurations. Among these tests, the MET, a well-established test of musical aptitude, has undergone extensive validation in large-scale studies 19,47 . It is notable for its openly accessible format, correlating robustly with musical imitation scores used in musical academies, without being influenced by demographic factors such as age, gender, socio-economic status 47 or personality traits 19 . Despite its shorter duration (~ 20 minutes) compared to the other tests (> 40 minutes), the MET maintains robust psychometric properties 43 . Unlike aptitude, or talent, 'competence' is a term that remains neutral, not favoring either innate qualities (nature) or learned skills (nurture). This test is effective in assessing interindividual differences in nonmusicians, accounting for latent musical capacities that can lead individuals without formal training to outperform the average musician’s MET scores 19 . It also shows positive correlations with implicit (self-reported) measures of general musical sophistication, like the Goldsmiths Musical Sophistication Index 19,48 . Consequently, the MET, when used in combination with network neuroscience, can be pivotal in exploring the network-level neural foundations of the human capacity for music, and in assessing how subtle neural variations affect musical skills in the general population. The biological roots of the human capacity for music have gathered increased attention in the past decade within the fields of cognitive science and biomusicology 49,50 . This faculty is thought to arise from the complex synergy of various perceptuo-cognitive elements, each with unique neurobiological foundations and evolutionary histories. Working memory, a cognitive system with limited capacity crucial for temporary storage and manipulation of sensory information 51 , is a fundamental component of music perceptual faculty 52 . Research in music psychology and neuroscience indicates a hierarchical process in music perception involving serial-to-parallel conversion, integrating auditory elements into increasingly complex musical structures, from basic chunks to complete melodies 53 . Such a process hinges on the WM system's ability to retain lower-level units while integrating new information to form more elaborate musical constructs. Music abilities, as assessed with the MET, correlate significantly with WM capacity; greater WM capacity often translates to superior musical skills 13,54 . Children with musical training exhibit improved cognitive flexibility compared to their non-trained counterparts, a phenomenon linked to increased brain activations in frontoparietal regions 10 . Frontoparietal connectivity is associated with better working memory performance 55 . This suggests that frontoparietal connections are crucial for supporting the high cognitive demands of musical listening and practice. Specifically focusing on the intrinsic organization of WM neural systems, both structurally and functionally, could shed light on the impact of domain-general neurocognitive systems on music perceptual behaviors and how they help shaping individual differences 56 . Our study employs graph theory analyses to investigate the impact of functional and structural WM neural organization on music competence, integrating multimodal neuroimaging with behavioral and cognitive data 34,57,58 . While previous research has focused on the effects of music listening and training on brain network configuration 59–63 , our investigation reverses this viewpoint. We specifically explore how subtle interindividual differences in the frontoparietal network (FPN), a crucial network underpinning WM and other high-level cognitive processes 64–68 , relate to differences in music competence, assessed using the MET. Working memory relies on the integration of past and current sensory information from a large-scale network 69 , whose core infrastructure comprises bilateral dorsolateral prefrontal cortices and posterior parietal cortices 70–72 . Higher WM scores are associated with highly integrative networks, promoting efficient inter-regional communication and rapid combination of information from distributed regions 69,73–75 . Our hypothesis is that more globally efficient functional and structural FPNs may enhance the hierarchical processing and integration of musical elements, their retention in a temporary buffer, and their comparison with previous musical information, thereby impacting musical competence. Participants also completed the Goldsmiths Musical Sophistication Index 18 , a self-report questionnaire about formal and informal musical behaviours, experience, and skills, and the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV) 76 , which includes a WM index. Individual differences research requires large sample sizes (n > 100) for making findings reproducible 77 . Also, graph theory should be applied to both functional and structural modalities for achieving a comprehensive understanding of how the organization of functional and structural brain networks differently contribute to human behaviour. The large sample size of this study, in combination with a network-of-interest approach that relies on an a priori hypotheses, aims to address the issue of low statistical power in individual differences study. Our use of graph theory in both functional and structural connectomes further help in assessing their distinctive role in the support of human behaviour. The anticipated results have the potential to examine the influence of brain network variability on the large spectrum of musical abilities observed in humans and to deepen our comprehension of the biological foundations of music culture(s) 49 . Results Overview of the experimental design and analysis pipeline Our dataset comprised MET 43 , Goldsmiths Musical Sophistication Index (Gold-MSI) 18 , and Wechsler Adult Intelligence Scale (WAIS-IV) 76 scores, along with functional and structural scans, from a large number of healthy adults (Table 1 ). Participants were non-musicians (Suppl. Figure 1). Our study aimed to elucidate the interplay between the structural and functional architecture of the frontoparietal network (FPN) and musical competence. We assessed the relationship between individual propensities for integration or segregation within the FPN and their musical ability. This entailed examining how musical competence correlates with FPN nodes' organization, either in facilitating efficient communication and integration across distal nodes (indicated by higher global efficiency and centrality) or in forming specialized, segregated clusters (reflected by a higher clustering coefficient and local efficiency). Global efficiency is the most commonly used measure of functional integration, while in structural connectomes efficiency and centrality are the relevant metrics 78 . Additionally, we probed the potential mediating role of a domain-general cognitive feature, namely working memory, in these brain-behaviour correlations. Musical competence was quantified using the percentage of MET total scores. Table 1 Descriptive statistics for demographic information (age and gender), MET, and Gold-MSI subscales (N = 241) Total Mean Std Range Demographics Age - 24.44 4.70 18–49 Gender 135 females (56%) - - - MET Score Range Total - 73.53 9.16 38–94 Melody - 35.99 5.52 24–49 Rhythm - 37.80 4.59 25–48 Gold-MSI Score Range (theoretical max) Active Engagement - 30.32 9.17 25–48 (63) Perceptual abilities - 41.95 8.09 17–63 (63) Musical Training - 14.04 9.28 2–47 (49) Emotions - 28.97 6.67 9–43 (49) Singing Abilities - 24.10 7.46 9–49 (42) General Sophistication - 59.61 16.90 27–121 (126) Items Gold-MSI Compliments - 3.99 2.30 1–7 Identity - 5.30 1.97 1–7 Hours of daily practice - 2.89 2.16 1–7 Music Theory - 3.45 1.89 1–7 Instruments played - 3.10 2.06 1–7 The analysis pipeline of this study is partly depicted in Fig. 1 . Figure 2 shows the brain networks analysed with graph theory. A core FPN, including the bilateral dorsolateral prefrontal cortex (middle and superior frontal gyrus and sulcus) 79,80 and bilateral posterior parietal cortex (inferior and superior parietal lobule, and intraparietal sulcus) 81,82 was selected for the main analysis. An occipital network with an equivalent number of nodes was included for control analysis. Graph theory results for the structural networks Figure 3a shows the results for the structural FPN (Suppl. Table 5). We observed a positive correlation between percentage of MET total scores and centrality in the right superior frontal gyrus (SupFG) (F = 4.40, pFDR = 0.0002) and the right superior parietal lobule (SupPL) (F = 3.70, pFDR = 0.002). Additionally, the right SupPL was associated positively with global efficiency and percentage of MET total scores (F = 3.16, pFDR = 0.029). In contrast, a negative association was found between percentage of MET total scores with segregation measures in the right SupFG (local efficiency: F = -4.34, pFDR = 0.0003; clustering coefficient: F = -4.34, pFDR = 0.00004) and SupPL (local efficiency: F = -4.21, pFDR = 0.0003; clustering coefficient: F = -4.61, pFDR = 0.00006). These results suggest that individuals exhibit superior music perceptual abilities when the topology of FPN’s physical pathways, within these two brain regions, implies stronger potential for functional integration. Conversely, a potential for functional segregation in these two nodes is associated with worse musical abilities. No significant results were found between graph theory metrics in the occipital control network and MET scores (Suppl. Table 6). Graph theory results for the functional networks Figure 3b displays the results for the FPN (Suppl. Table 7). A positive correlation was found between the global efficiency of the right middle frontal gyrus (MFG) and percentage of MET total scores (F = 3.06, pFDR = 0.043), indicating that individuals with higher music perceptual abilities have a right middle frontal gyrus that efficiently communicates with, and most likely integrates specialized information from, other FPN regions. Notably, no significant correlations emerged between the graph theory metrics of the occipital control network and MET scores (Suppl. Table 8). Relationship between WM, music competence, and FPN topology A significant relationship (r = 0.21, pFDR < 0.01) was observed between WMI scores from the WAIS-IV and percentage of MET total scores (Suppl. Figure 3), indicating that higher WM scores are linked to superior music perceptual abilities in the MET. To explore the association between FPN topology and WM performance, we examined the impact of graph theory metrics (independent variable), alongside WMI scores (dependent variable), age, sex, and Musical Training Index (nuisance regressors), in a linear multiple regression framework. Our analysis of functional connectomes revealed a positive correlation between WMI scores and the global efficiency of the right MFG (F = 3.18, r = 0.20, pFDR = 0.02), supramarginal gyrus (F = 2.70, r = 0.19, pFDR = 0.03), and superior frontal sulcus (F = 2.71, r = 0.17, pFDR = 0.03) (Suppl. Table 9). This result suggests that an efficient communication and integration capabilities of these brain regions within the FPN are conducive to enhanced WM performance. Conversely, no significant associations were observed with WMI scores using nodal metrics of the functional occipital control network or metrics from structural networks (FPN and occipital) (Suppl. Tables 10–12). WM mediates the relationship between rMFG efficiency and music competence A mediation model was developed to explore the relationship between the efficiency of the right MFG (rMFG) within the functional FPN, WM performance, and music competence, hypothesizing WM as a mediator in the rMFG efficiency-musical competence relationship. To test this hypothesis, we constructed a structural equation model (SEM). The model included rMFG global efficiency as predictor and the percentage MET total scores as dependent variable. Results were adjusted for the covariates of age, sex, and Musical Training Index from the Gold-MSI. Bootstrap resampling procedures were leveraged to derive robust bias-corrected accelerated confidence intervals around the parameters of interest. Table 2 and Fig. 4 display the statistics of mediation effects in the SEM. The SEM indicated a significant direct impact of rMFG efficiency on WMI scores (standardized beta = 0.46, p = 0.002). The direct path from WMI to percentage of MET total score was also significant (standardized beta = 0.18, p = 0.010). Additionally, the direct effect of rMFG efficiency on musicality was significant (standardized beta = 0.17, p = 0.013). Critically, the indirect effect of rMFG efficiency on musicality mediated through WMI was significant (standardized beta = 0.21, 95% CI [1.180 to 12.477]). In summary, the results demonstrate both direct effects of global neural efficiency on WM and musicality, as well as an indirect pathway linking neural function to musical competence through domain-general cognitive abilities. Table 2 Results from the structural equation model (SEM). Path Nodes connected Est. β SE 95% CI p-value c Global E rMFG -> WMI 42.265 15.014 [17.981, 75837] 0.002 a WMI -> MET 0.115 0.044 [0.011, 0.197] 0.010 b Global E rMFG -> MET 24.518 9.913 [5.056, 44.023] 0.013 Indirect c*a 5.335 2.028 [1.180, 12.477] 0.043 Total c*a + b 29.853 9.654 [11.434, 47.269] 0.002 Discussion This research employed graph theory to analyze a large dataset of over 200 brain images (diffusion and resting-state fMRI), cognitive and musical ability assessments, seeking to determine how domain-general memory networks influence music perception skills. We focused this investigation on a frontoparietal network (FPN), a brain network known to be pivotal in WM. We found that higher global communication efficiency in the structural and functional FPNs, notably in key areas of the right dorsolateral prefrontal cortex and the right superior parietal lobule, correlated positively with enhanced music perceptual skills. Critically, differences emerged when comparing results for functional and structural networks. For functional networks, global efficiency in right middle frontal gyrus (rMFG) was significantly associated with both musical competence and WM, the latter serving as a mediator in the direct influence of FPN organization on musical aptitude. Conversely, such direct association with WM performance was not observed for key regions of the structural networks. These results were based on brain images acquired during rs-fMRI and diffusion MRI scans, not during a musical or cognitive task. The findings suggest that the inherent organization of FPN’s core components may serve as a neuromarker for music perception abilities in the general population, with distinct roles played by functional and structural network organization. Musicality, a multifaceted human trait, is the product of domain-specific perceptual skills such as pitch and metrical perception, and broader cognitive functions like attention and WM 86 . Research into its biological roots necessitates a divide-and-conquer approach, deconstructing musicality into its fundamental perceptual and cognitive components for isolated examination 49,87 . A pivotal discovery from this study is the significant role played by the functional topology of the rMFG, a key region of the dorsolateral prefrontal cortex (DLPFC), in predicting music perceptual abilities in a large sample of individuals. We found this effect being partly mediated by WM. The rMFG is critical for higher-level cognitive processes, including executive functions and WM operations 88–90 . Accordingly, functional studies linking WM and music perception often report DLPFC activity 91–93 . Platel and colleagues 94 observed bilateral activation in Brodmann areas 9 and 10 of the middle frontal gyri during an episodic music memory task, using PET scans. They attributed this DLPFC activity to the perceptual analysis of melodies in WM 95 . Notably, the rMFG is also implicated in the perception of rhythmic structure in music 96,97 , underscoring the critical role of WM functions in the perception of durations in auditory stimuli. One hypothesis is that DLPFC might mediate the critical memory processes required during music perception (encoding, maintenance and integration) via interaction with more posterior parietal regions, likely the seat of internal sensory representations 64 . Our findings indicate that this operation is more effective when an efficient functional organization is in place, such as more direct functional routes between rMFG and the rest of the network. This would explain the superior music abilities in participants with a higher intrinsic global efficiency in the prefrontal region. Our finding corroborates network neuroscience research indicating that a network's information processing performance can be augmented by a sparse functional configuration that yields disproportionately high efficiency 38,98 . Here, we show that this organization in FPNs can provide a neuromarker of music competence in the general population. The lack of a direct one-to-one correspondence between structural and functional networks 99 may account for the divergent outcomes observed in our graph theory analyses of these networks. We observed that the integration capabilities and centrality of the right superior parietal lobule within the structural FPN network predict musical competence but do not correlate with WM performance. This finding is consistent with prior research indicating a stronger association of cognitive performance with functional rather than structural connectivity 100–103 . Resting-state functional connectivity is more closely associated with high-level cognitive tasks such as WM likely due to its dynamic and flexible nature 104,105 , which aligns with the complex and temporally integrated demands required by these tasks. Conversely, structural connectivity is more closely linked to tasks with significant sensory components, such as language perception 103,106,107 , reflecting its role in establishing stable physical pathways for sensory information processing. In our study, we extend this finding to music perceptual abilities. The superior parietal lobule, recognized as a higher-order association area and an integrative hub 108,109 is thought to encode and combine past and current sensory information, influencing integrated representations for guiding subsequent adaptive behaviour. An alternative, not mutually exclusive, interpretation is that the right superior parietal lobule may be implicated in sensory-related attentional processes essential for music perception. This region, together with the superior frontal gyrus, is a key area of the dorsal attention network 110 . Activity in this dorsal FPN reflects active goal-directed control of attention 111 . Working memory is thought to encompass two distinct operations with different neuroanatomical locations: firstly, a selection mechanism that retrieves pertinent items, and secondly, an updating function that redirects attentional focus 112 . This updating process is characterized by transient activation in the superior frontal and posterior parietal cortices. This observation aligns with our discovery of a high centrality of these regions within the structural connectome, correlating with enhanced musical performance. It may imply their crucial function in updating sensory representations for the redirection of attentional focus to relevant items within the stimuli, enhancing their discrimination. Previous research has shown that the microstructural organization of dorsal fronto-parietal white matter pathways, such as the anterior subdivision of the right superior longitudinal fasciculus (SLF I), are related to music perceptual abilities in nonmusicians. Increased white matter coherence in this tract is positively correlated with the speed of musical learning 113 . In both interpretations, higher centrality and communication efficiency of this region might aid sensory integration, attentional focus towards pertinent stimuli, and comparison between auditory sequences. The structural organization of the superior parietal lobule may be specialized for the attentional processing and integration of complex sensory inputs, such as those required in music perception, rather than the more abstract and manipulative cognitive processes involved in working memory. Neurocognitive variability, a hallmark of the human brain, plays a pivotal role in shaping the diverse range of abilities observed across various cognitive and cultural domains. Although extensive research has explored the interplay of brain function, cognitive abilities, and cultural skills, these elements have largely been studied in isolation, leaving a unified theoretical framework elusive. The neuronal recycling hypothesis 114 provides a potential solution, positing that the brain repurposes its older circuits—initially evolved for general cognitive functions—to accommodate evolutionarily more recent cultural skills, all while maintaining their original constraints. Consequently, the spectrum of individual proficiencies within cultural domains is closely linked to the structural and functional nuances of the neural circuits they co-opt, as well as to the cognitive functions these circuits originally support 115 . Studies corroborating this theory reveal a significant correlation between general cognitive functions and cultural behaviour proficiencies, highlighting a neural overlap across these domains 116 . Individual network-level constraints in neurocognitive systems may provide a unique neuronal niche through which cultural material is filtered and to which it may eventually adapt. During the cultural transmission of music, minor inter-individual differences in neural information processing can manifest themselves in differences in musical behaviour 117 . Amplified and spread through cultural evolutionary mechanisms, minor neurocognitive difference can have large system-level effects, such as diversity within and across human cultures. Under this framework, music can be seen as a useful model system to investigate the link between variability in cultural capacities, variation in our innate neurocognitive machinery, and large-scale cultural phenomena. To conclude, our study uses a network science approach to elucidate the complex interrelationship between neurocognitive variability and the spectrum of musical abilities seen across humans. Our results suggest intrinsic communication efficiency and integration capacity within FPN core circuitry may aid music perception faculties, with distinct contributions from functional and structural network configurations. The functional topology of right prefrontal regions may facilitate domain-general cognitive functions like WM that support musicality. In contrast, structural properties of superior parietal cortices may subserve sensory or attentional processes more directly tied to auditory capabilities. Overall these findings contribute to elucidating the distinct role of functional and structural neurocognitive variability in support of musical abilities, providing a framework for future explorations into the neurobiological foundations of human culture. Methods Participants Data were acquired across multiple sessions at Aarhus University and Aarhus University Hospital from healthy individuals as part of the EU COST Action CA18106 The Neural Architecture of Consciousness . The project protocol received ethical approval from De Videnskabsetiske Komitéer for Region Midtjylland, Denmark. The scanning session included the collection of resting-state fMRI, high-angular resolution diffusion imaging (HARDI) and multi-parameter mapping data 118 . Approximately one week prior to undergoing scans, participants completed the Goldsmiths Musical Sophistication Index (Gold-MSI) questionnaire in an online session. Typically within a few weeks of the scans, in an optional session, they completed the Musical Ear Test (MET) and Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV). Recruitment of participants was conducted via the Center of Functionally Integrative Neuroscience (CFIN) at Aarhus University, leveraging both the university's participant database and local advertising. A total of 300 adult participants, with no personal history of neurological or psychiatric disorders and no hearing deficits, consented to the study, were financially compensated for their participation and completed the MRI scanning session (see “MRI acquisition”) as well as the Gold-MSI questionnaire (which were both mandatory for study participation). A subset of these participants (n = 241; 135 females, 18–49 years of age) completed the optional MET test session. In terms of musical training, 60% had no music lessons (n = 145), 36% had up to 5 years of training (n = 88), and only a small subset (n = 8) had over 6 years of training, and were classified as musicians 119 (Suppl. Figure 1). Following multivariate outlier analysis, 9 participants were excluded due to significant deviations in Gold-MSI and MET scores, identified via PCA and Euclidean distance criteria (Suppl. Figure 2). Structural connectome construction failed in 7 more participants. Consequently, the analysis on functional and structural connectomes in relation to MET scores was conducted with 232 (Suppl. Table 1) and 225 participants (Suppl. Table 2), respectively. Additionally, a subset of these participants (n = 201) had their domain-general cognitive abilities assessed using the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV) 76 . Only the Working Memory Index (WMI) was used for this study. Graph theory analysis incorporating WMI scores included 201 participants for functional (Suppl. Tables 3) and 195 for structural connectome analyses (Suppl. Tables 4). Musical abilities Musical Ear Test (MET) The Musical Ear Test (MET) comprises 104 trials: 52 melodic phrase pairs in the Melody subtest and 52 rhythmic phrase pairs in the Rhythm subtest. Before testing began, participants were instructed to use headphones and minimize distractions. Participants evaluated whether sequences in each trial — piano tones for Melody and drum beats for Rhythm — were identical, with deviations involving at least one tone (Melody) or inter-onset interval (Rhythm). Feedback was restricted to initial practice trials. Inter-trial intervals in the audio were capped at 1500 ms for Melody and range from 1659 to 3230 ms for Rhythm, thus standardizing MET duration. Scoring awards one point for each correct response, with Melody and Rhythm subtest scores each calculated as the percentage of correct answers out of 52. The percentage of MET total score was calculated as the percentage of correct answers out of the sum of these subtest scores (i.e., 104). Percentage of MET total score, strongly correlating with melodic (r = 0.89) and rhythmic scores (r = 0.85) (Suppl. Figure 3), was the primary metric in subsequent analyses, representing musical competence. Goldsmiths Musical Sophistication Index (Gold-MSI) The Gold-MSI is a 38-item self-report questionnaire assessing musical behaviours, experiences, and skills. It comprises five subscales: Active Engagement (9 items, e.g., daily attentive music listening duration), Perceptual Abilities (9 items, e.g., identifying out-of-tune singing or playing), Music Training (7 items, e.g., years of formal music theory training), Singing Abilities (7 items, e.g., accuracy in matching recorded notes while singing), and Emotions (6 items, e.g., selecting music for mood enhancement). Additionally, a General Factor score is derived from 18 representative items across these subscales. Responses are rated on a 7-point Likert scale, ranging from complete disagreement to complete agreement, with the last seven items featuring variable response options. Working memory abilities Wechsler Adult Intelligence Scale In the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV), our analysis focused solely on the Digit Span and Arithmetic subtests to assess working memory in participants. The Digit Span subtest comprises three distinct tasks: Digit Span Forwards, Digit Span Backwards, and Digit Span Sequencing. The Digit Span Forwards task involves the oral presentation of number sequences by the experimenter, which participants are required to replicate verbatim. Performance is measured by the number of sequences accurately recalled. In the Digit Span Backwards task, participants must reverse and repeat the sequences, with scores reflecting the count of sequences correctly reproduced in reverse order. The Digit Sequencing task necessitates rearranging spoken numbers in ascending order, scored based on the number of sequences correctly ordered. The Arithmetic subtest involves mentally solving arithmetic problems, presented verbally, as their difficulty and memory load increases. For each subtest, raw scores were normalized to age-corrected z scores, with a mean of zero and standard deviation of one, wherein higher scores signify enhanced performance. The Working Memory Index (WMI) is derived by summing the scaled scores from these tasks and subsequently converting this aggregate into an index score using a standard conversion table. MRI acquisition Data were acquired using a Siemens Magnetom Prisma-fit 3T MRI scanner. Following an initial scout scan, two resting-state fMRI sequences (12 and 6 minutes) were run, accompanied by quantitative multi-parameter mapping 118 (around 20 minutes) —used here for synthetically generated T1-weighted images— and high-angular resolution diffusion imaging (HARDI) (around 10 minutes), within a one-hour scanning session. For each participant, 1500 functional volumes were acquired (TR, 700 ms; TE, 30 ms; voxel size 2.5 mm 3 ). The MPM protocol was implemented based on the Siemens vendor sequence. Three-dimensional (3D) data acquisition consisted of three multi-echo spoiled gradient echo scans (i.e. fast low angle shot [FLASH] sequences with MT, T1, and PD contrast weighting). Additional reference radio-frequency (RF) scans were acquired. The acquisition protocol had the following parameters): TR of PD- and T1-weighted contrasts: 18 ms; TR of MT-weighted contrast: 37 ms; minimum/maximum TE of PD-, T1- and MT-weighted contrasts: 2.46/14.76 ms; flip angles for MT-, PD- and T1-weighted contrasts: 6°, 4°, 25°, respectively; six equidistant echoes; 1 mm isotropic reconstruction voxel size; Field of view 224 ´ 256 ´ 176 mm; AP phase encoding direction; GRAPPA parallel imaging speedup factor of 2; T1w, PDw and MTw acquisition times: 3:50, 3.50, 7.52. The acquisition of low-resolution 3D spoiled gradient echo volumes was executed using both the RF head coil and the body coil. This dual acquisition facilitated the generation of a relative net RF receive field sensitivity (B1−) map for the head coil 120–122 . The approach obtained rapid acquisition by maintaining a low isotropic spatial resolution of 4^3 mm^3, a short echo time (TE) approx 2ms, and a reduced flip angle of 6°, avoiding the use of parallel imaging acceleration or partial Fourier. This procedure of capturing volume pairs with the head and body coils was systematically repeated prior to the acquisition of each of the MT, PD, and T1 contrasts. The sequence used to collect HARDI images included: 75 diffusion directions at b = 2500 s/mm 2 ; 60 directions at b = 1500 s/mm 2 ; 21 directions at b = 1200 s/mm 2 ; 30 directions at b = 1000 s/mm 2 ; 15 directions at b = 700/mm 2 ; 10 directions at b = 5 s/mm 2 , with the different b-shells acquired in the same series (flip angle = 90◦, TR/TE = 2850/71 ms, voxel size = 2 mm 3 ; matrix size = 100 x 100, number of slices = 84). The phase-encoding direction was anterior to posterior (AP). An opposite phase-encoding direction (PA) was also acquired (b = 0 s/mm 2 ) to allow EPI distortion correction 123 . Neuroanatomical data processing Synthetic T1-weighted images were generated using the longitudinal relaxation rate (R1) and effective proton density (PD) high resolution maps (acquired during the MPM sequence protocol). First, both maps were thresholded in order to achieve the required FreeSurfer units. The R1 map was converted to a T1 map by taking its reciprocal and thresholded at zero. This was scaled by a factor of 1000. The PD map was thresholded by zero and scaled by 100. All manipulations were performed using FSL maths commands. Subsequently, the "mri_synthesize" FreeSurfer command was applied to create a synthetic FLASH image based on previously calculated T1 (thresholded R1 map) and proton density map. The optional flagged argument for optimal gray and white matter contrast weighting was used with the following parameters 20, 30, and 2.5. Finally, the synthetic T1-weighted image was divided by four to achieve the scale that FreeSurfer expects. The synthetic T1-weighted image was preprocessed using fMRIPrep 21.0.2 124 (RRID:SCR_016216), which is based on Nipype 1.6.1 125 (RRID:SCR_002502). The T1-weighted image were corrected for intensity non-uniformity (INU) using the N4BiasFieldCorrection 126 , part of the ANTs 2.3.3 127 (RRID:SCR_004757). This corrected image served as the T1w-reference throughout the preprocessing workflow. Skull stripping was performed on this reference image using a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), with the OASIS30ANTs as the target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast 128 (FSL 6.0.5.1; RRID:SCR_002823). Brain surface reconstruction was carried out using FreeSurfer’s recon-all function 129 (version 6.0.1; RRID:SCR_001847), and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle 130 (RRID:SCR_002438). Volume-based spatial normalization of the brain images to the two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was executed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of the T1w reference and the T1w template. The templates employed for this normalization included the ICBM 152 Nonlinear Asymmetrical template version 2009c (RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym) and FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model 131 (RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym). dMRI processing and structural connectome construction The diffusion MRI (dMRI) data was preprocessed using custom MATLAB scripts developed internally at the Center of Functionally Integrative Neuroscience (CFIN). The preprocessing steps included noise reduction adapted from the approach by Veraart et al. 132 , correction of Gibbs ringing artifacts following the method described by Kellner et al. 133 , and motion, eddy currents, and field distortion corrections using the top-up and eddy tools from the FSL toolbox 134 . The generation of structural connectomes was performed using the MRtrix3 software toolkit. The analysis involved several steps per subject. We first created a 5-tissue-type (5tt) image, which contained masks of different tissue types (cortical grey matter, deep grey matter, white matter, CSF. and “other”) within the brain and is essential for Anatomically-Constrained Tractography (ACT). Co-registration was then performed to align T1-weighted and DWI images. A response function was created for each major tissue type (white matter, grey matter, cerebrospinal fluid) for each subject. The individual subject response functions were used to create group-level response functions. Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD), was used to estimate Fiber Orientation Distributions (FODs) within each voxel of the brain followed by normalisation. Next, whole-brain probabilistic tractography was performed using the ACT framework and backtracking. The maximum attempted number of streamlines was 1*10^ qi9 streamlines with 10 million streamlines per connectome being selected. Each seed was determined dynamically from the FOD image using the SIFT model. The FOD cutoff was 0.06, the maximum length of each selected streamlines was 250mm while the minimum was 20mm. The SIFT2 model was then applied to the data. Connectomes were then generated using the Destrieux parcellation for the cortex and the FSL FIRST segmentations for the subcortical structures. Each connectome was multiplied by the SIFT proportionality coefficient (mu). Finally a custom automated pipeline for visualising the diffusion data in a structured and standardised way was run for quality control. We generated jpegs of the 5TT images alongside GIFs of the registration of the T1w image to the B = 0 image. These visualisations were used to ensure that the processing pipeline worked correctly. rsfMRI processing and functional connectome construction The processing of resting-state (rs-fMRI) volumes was implemented by using default surface-based preprocessing routines from the SPM CONN toolbox (Whitfield-Gabrieli ( http://www.nitrc.org/projects/conn ) 135 , implemented in Matlab (2016b). Functional data was realigned and unwarped without field maps using SPM12 (r7487), employing a 6-parameter transformation for alignment and b-spline interpolation for resampling. Outliers were identified using ART 136 based on framewise displacement and global BOLD signal deviations, and an average reference BOLD image was created for each participant excluding all outlier volumes. Coregistration of functional and anatomical data was achieved using mutual information. Functional images were then mapped onto the cortical surface, averaging data across layers between the pial and white matter surfaces. Finally, surface-level functional data were smoothed using 40 iterative diffusion steps. Our denoising process involved a standard pipeline, regressing out confounds like white matter and cerebrospinal fluid (CSF) signals, motion artifacts, outlier scans, session effects, and linear trends. This included the use of CompCor for noise component extraction from white matter and CSF. Bandpass frequency filtering was applied to the BOLD timeseries to retain frequencies between 0.008 Hz and 0.09 Hz. The effective degrees of freedom of the BOLD signal post-denoising were estimated for all participants. We estimated region-to-region connectivity matrices across 16 regions of interest (ROIs) by calculating the functional connectivity strength (Fig. 2 ). This was represented by Fisher-transformed bivariate correlation coefficients derived from a weighted general linear model (GLM) and stored in a functional connectivity matrix. The GLM accounted for associations between BOLD signal timeseries of ROI pairs, with weighting to mitigate transient magnetization effects at the start of each run. The connectivity matrix only included Destrieux’s cortical nodes (148x148). From this matrix, we selected 16 nodes for one frontoparietal network of interest and one occipital control network (16x16) (Fig. 2 ). Graph theory analyses Graph theory analyses for functional and structural connectivity matrices, and for both frontoparietal and occipital networks, followed the same pipeline. Connectivity matrices (16×16) were thresholded at a fixed network-level cost range (k) (0 < k < 1), resulting in binarized, undirected adjacency matrices. The analysis incorporated both positive and negative rs-FC values. To avoid reliance on specific and arbitrary threshold values (e.g., k = 0.15) 137 , graph metrics were aggregated across multiple thresholds (k = 0.15–0.30, interval 0.01) 138 . Within this range, brain networks show small-world features (GE and LE have, respectively, larger values than lattice and random graphs of equal size and cost values; Supplementary Figs. 3–4). From the matrices, four node-level graph theory metrics were computed using the Brain Connectivity Toolbox (BCT) 78 : clustering coefficient, local efficiency, global efficiency, and betweenness centrality. All these metrics offer clear interpretability and are prevalent in network studies. A second-level General Linear Model (GLM) included MET as an explanatory variable, controlling for age, gender, and musical training (Gold-MSI questionnaire) as nuisance regressors. Node-level p-values were adjusted for multiple comparisons using a false discovery rate of q < 0.05 (two-tailed), for each graph metric. 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F., Meyer, H., Buckley, C. & Weiskopf, N. Correction of inter-scan motion artifacts in quantitative R1 mapping by accounting for receive coil sensitivity effects. Magn. Reson. Med. 76 , 1478–1485 (2016). Tabelow, K., Balteau, E., Ashburner, J. & Callaghan, M. F. hMRI–A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage (2019). Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20 , 870–888 (2003). Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16 , 111–116 (2019). Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5 , 13 (2011). Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29 , 1310–1320 (2010). Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12 , 26–41 (2008). Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20 , 45–57 (2001). Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9 , 179–194 (1999). Klein, A. et al. Mindboggling morphometry of human brains. PLoS Comput. Biol. 13 , e1005350 (2017). Evans, A. C., Janke, A. L., Collins, D. L. & Baillet, S. Brain templates and atlases. Neuroimage 62 , 911–922 (2012). Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. Neuroimage 142 , 394–406 (2016). Kellner, E., Dhital, B., Kiselev, V. G. & Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76 , 1574–1581 (2016). Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125 , 1063–1078 (2016). Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2 , 125–141 (2012). Mozes, S. & Whitfield-Gabrieli, S. Artifact detection toolbox (ART). Gabrieli Laboratory: MIT (2011). Langer, N., Pedroni, A. & Jäncke, L. The problem of thresholding in small-world network analysis. PLoS One 8 , e53199 (2013). Lumaca, M., Vuust, P. & Baggio, G. Network Analysis of Human Brain Connectivity Reveals Neural Fingerprints of a Compositionality Bias in Signaling Systems. Cereb. Cortex (2021) doi:10.1093/cercor/bhab307. Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3930575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":273874211,"identity":"b02ea98b-fdda-4d4f-9371-454ef38c9a8e","order_by":0,"name":"Massimo Lumaca","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLCCBCDmgzBtGPiYISzGGYS0sEGYaQxsRGlhQGg5DGPg1qLbfvzxh4c5NgxsEtmJH37UnJdnY+c9wPCzjUF2ZgN2LWZncswkErcB3SORu1my59htwzZmvgTG3jYG49k4bDE7kMPGkLjtMEjLBmkGttsJbMw8Bgy8bQyJ83BpOf/88Qeols2/Gf6dA2th/ItPy40EAwmolm3SjG0HwFqYQbbgdNiNN2C/8LDxvN1m2duXDPQLj8FhmXMSxji9fz798cef22zk+NlzN9/48c1Onp//jOHDN2U2sjMO4LAGCnhQeEDFEvjVj4JRMApGwSjACwD8uVJNTg9EhgAAAABJRU5ErkJggg==","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Lumaca","suffix":""},{"id":273874212,"identity":"a5e0e5ff-f8ff-4cc8-a83f-6cc08d441461","order_by":1,"name":"Peter Keller","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Keller","suffix":""},{"id":273874213,"identity":"d8e44a54-9b3e-4c1b-ae4c-16427d487cb2","order_by":2,"name":"Giosuè Baggio","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Giosuè","middleName":"","lastName":"Baggio","suffix":""},{"id":273874214,"identity":"3d77c6bb-118c-4827-8e70-a4378c1d40d4","order_by":3,"name":"Victor Pando-Naude","email":"","orcid":"https://orcid.org/0000-0003-1963-5068","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Pando-Naude","suffix":""},{"id":273874215,"identity":"fa686b0b-08c6-4966-8612-33cf9fa7e7ba","order_by":4,"name":"Claude Bajada","email":"","orcid":"https://orcid.org/0000-0001-6138-4851","institution":"University of Malta","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"","lastName":"Bajada","suffix":""},{"id":273874216,"identity":"431df9b6-6e3b-4d00-a261-a9fc5ba50e7b","order_by":5,"name":"Mie Martinez","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Mie","middleName":"","lastName":"Martinez","suffix":""},{"id":273874217,"identity":"b2d095b5-01ca-453a-8d79-942b717758fd","order_by":6,"name":"Josephine Hillebrand Hansen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Josephine","middleName":"Hillebrand","lastName":"Hansen","suffix":""},{"id":273874218,"identity":"bc95616a-9faf-4f58-8f78-e1ec0c0dcef9","order_by":7,"name":"Andrea Ravignani","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Ravignani","suffix":""},{"id":273874219,"identity":"66d9e5ad-7847-4e44-b2c5-22b9f6dcab2a","order_by":8,"name":"Nikita Joe","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Nikita","middleName":"","lastName":"Joe","suffix":""},{"id":273874220,"identity":"eb22c56e-e25a-405a-ae9e-107f299112c8","order_by":9,"name":"Peter Vuust","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Vuust","suffix":""},{"id":273874221,"identity":"0d9a6b05-8843-45e5-95dd-f9f54f8bb2e2","order_by":10,"name":"Katharina Vulic","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Vulic","suffix":""},{"id":273874222,"identity":"b35b1827-2ed1-4990-91b9-b418bcfbde13","order_by":11,"name":"Kristian Sandberg","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Kristian","middleName":"","lastName":"Sandberg","suffix":""}],"badges":[],"createdAt":"2024-02-05 09:58:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3930575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3930575/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-52479-z","type":"published","date":"2024-09-17T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51451907,"identity":"fd7fde6d-f1a7-4391-bc42-aa0485212dcc","added_by":"auto","created_at":"2024-02-21 21:40:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139239,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the connectomic pipeline for single-subject data. Thick arrows denote outputs feeding into subsequent steps within the same pipeline, while dashed arrows indicate parallel processing for structural and functional connectomes. Tbe anatomical image underwent preprocessing, segmentation and parcellation in fMRIPrep. The generated Destrieux atlas\u003ca href=\"https://paperpile.com/c/3cKvUj/b3GZ+ueNA\"\u003e\u003csup\u003e83,84\u003c/sup\u003e\u003c/a\u003e, including 74 homologue cortical regions, was fed into the pipeline for the construction of functional and structural connectomes. The functional connectome (148x148) was created from rs-fMRI images with a standard surface-based processing pipeline, and included Fisher’s z-transformed Pearson’s correlation coefficients between all node pairs in the parcellation. The structural connectome (148x148) was obtained from the processing of HARDI images, after combining a whole-brain probabilistic anatomically constrained tractogram (10 million streamlines) with Destrieux cortical parcels, and included Fiber Bundle Capacity (FBC) at the edge-level\u003ca href=\"https://paperpile.com/c/3cKvUj/5Fdo\"\u003e\u003csup\u003e85\u003c/sup\u003e\u003c/a\u003e. This metric of structural connectivity quantifies the capacity of white-matter bundles to relay information between brain regions. To perform targeted graph theory analyses, sixteen cortical nodes from functional and structural connectomes were selected to create one frontoparietal network and one occipital control network (16 x 16 nodes) (Fig. 2). In separate analyses, functional and structural matrices were filtered using a multi-threshold approach and were binarized to create adjacency matrices. Graph theory metrics of global efficiency, local efficiency, clustering coefficient, and betweenness centrality\u003ca href=\"https://paperpile.com/c/3cKvUj/S4zp\"\u003e\u003csup\u003e78\u003c/sup\u003e\u003c/a\u003e were calculated from these matrices, and entered as predictors in a multiple regression analysis. Lastly, a mediation analysis using structural equation modeling (SEM) assessed the indirect influence of working memory (red dashed arrow) on the effect of topological nodal metrics on percentage of MET total scores. Abbreviations: ACT - anatomically constrained tractography; HARDI - High Angular Resolution Diffusion Imaging; rs-fMRI - resting-state functional MRI; MET - musical ear test; WMI - Working Memory Index.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/bdf951a039bc3ea90e0fcb80.jpeg"},{"id":51451908,"identity":"a79dad89-b575-4acc-8d5b-e41f75c7679c","added_by":"auto","created_at":"2024-02-21 21:40:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67378,"visible":true,"origin":"","legend":"\u003cp\u003eTarget networks for graph theory analysis. \u003cstrong\u003eA)\u003c/strong\u003e The frontoparietal network included frontal cortical nodes (yellow) subserving high-order cognition and posterior parietal nodes (green) involved in sensory integration. The occipital control network consisted of occipital, occipito-temporal and occipito-parietal cortical nodes supporting visual information processing. Each network contained eight homologous nodes per hemisphere.\u003cstrong\u003e B) \u003c/strong\u003eConnectome diagrams for functional (left) and structural networks (right).\u003cstrong\u003e \u003c/strong\u003eThe color intensity of each edge reflects the proportion of participants exhibiting a connection between two nodes (range 0:1). The graph only displays the top 50% of the most robust connections. Darker blue signifies connections present in all or nearly all participants, a consistency maintained across all proportional thresholds from 0.15 to 0.30 in 0.01 increments. Complementing heatmaps are shown below each diagram. Abbreviations: LH - Left Hemisphere; RH - Right Hemisphere; MFG - middle frontal gyrus; SupFG - superior frontal gyrus; MFS - middle frontal sulcus; SupFS - superior frontal sulcus; SuMarG - supramarginal gyrus; IntPS - intraparietal sulcus; SupPL - superior parietal lobule; AngG - angular gyrus; InfOcG/S - inferior occipital gyrus (O1) and sulcus; MOcG - middle occipital gyrus (O2, lateral occipital gyrus); SupOcG - superior occipital gyrus (O1); FuG - lateral occipito-temporal gyrus (fusiform gyrus, O4-T4); Cos/Lins - medial occipito-temporal sulcus and lingual sulcus; OcPo - occipital pole; AoCs - anterior occipital sulcus and preoccipital notch (temporo-occipital incisure); PoCs - parieto-occipital sulcus (or fissure).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/aeb6533b61643601688d579b.jpeg"},{"id":51452270,"identity":"23d176eb-12b6-4b18-a4c4-4506611bb84f","added_by":"auto","created_at":"2024-02-21 21:48:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68502,"visible":true,"origin":"","legend":"\u003cp\u003eNeurobehavioural correlations for the structural and functional frontoparietal networks. \u003cstrong\u003eA)\u003c/strong\u003e Scatterplots showing significant relationships between graph theory metrics of integration (global efficiency), segregation (clustering coefficient and local efficiency) and centrality (betweenness centrality) (x-axis) and percentage of MET total scores (% MET total; y-axis) for the right superior parietal lobule (light green color; one the left) and the right superior frontal gyrus (cyan color; on the right) within the frontoparietal network (N=232). \u003cstrong\u003eB) \u003c/strong\u003eScatterplots for the significant association between the global efficiency of the right middle frontal gyrus (rMFG) within the functional frontoparietal network (yellow color) and percentage MET total score. In the scatterplots, the shaded grey area represents the 95% confidence interval. Each point on a scatterplot is one participant.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/5bc4713e768150f762132af0.jpeg"},{"id":51451911,"identity":"b55dbff5-42e3-414a-9659-dc06e1e28732","added_by":"auto","created_at":"2024-02-21 21:40:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53169,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram for the mediation analysis using structural equation modeling (SEM). Standardized coefficients are shown for each path. The bootstrap statistical significance of the direct and indirect paths is presented in Table 2. Results of the proposed model confirm that the global efficiency of the right middle frontal gyrus within the functional frontoparietal network is positively associated with percentage of MET total scores, through greater working memory abilities. Abbreviations: rMFG - right middle frontal gyrus; GE - global efficiency; MET - percentage of MET total score; WMI - Working Memory Index\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/28457fdc58c473126d800f75.jpeg"},{"id":65431277,"identity":"f8d3cfba-7057-485a-864a-b8f9911ea8cf","added_by":"auto","created_at":"2024-09-27 11:55:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/26e37752-a199-4b28-8d13-1e5f9150df87.pdf"},{"id":51451910,"identity":"15b542b8-2d27-412b-8190-26f1539d7b29","added_by":"auto","created_at":"2024-02-21 21:40:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1439288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3930575/v1/be82bccac85adb0a24be2bf9.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Frontoparietal network topology as a neuromarker of music perceptual abilities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCan human neurocognitive architecture partly explain human culture? A central goal in cognitive neuroscience is to determine how neurocognitive variability creates interindividual differences in cultural capacities, like linguistic and musical abilities\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. Interindividual neurocognitive differences may impact the emergence and propagation of novel cultural variants, thereby shaping cultural diversity\u003csup\u003e5\u003c/sup\u003e. This perspective underscores the complex interplay between individual neurocognitive abilities, cultural capacities, and the broader mosaic of human culture. Music, a ubiquitous cultural element of human societies that is deeply grounded in the human nervous system\u003csup\u003e6,7\u003c/sup\u003e, serves as an effective proxy to explore the brain-culture nexus. Music not only exhibits near-universal characteristics\u003csup\u003e8\u003c/sup\u003e but also displays significant diversity both within and across human populations\u003csup\u003e9\u003c/sup\u003e. The perception of music is closely tied to activity of neurocognitive systems like attention and memory\u003csup\u003e10,11\u003c/sup\u003e. Minor interindividual differences in the properties of these systems can manifest in music perceptual behaviour, potentially leading to large system-level effects when magnified and propagated through cultural transmission\u003csup\u003e12\u003c/sup\u003e. How neurocognitive variability translates into diverse music perceptual competencies remains an area of ongoing investigation\u003csup\u003e1\u003c/sup\u003e. Our research employs network neuroscience and graph theory on a comprehensive MRI, cognitive, and behavioral dataset (n\u0026thinsp;\u0026gt;\u0026thinsp;200) to examine the neurocognitive markers of music perceptual abilities within the general population.\u003c/p\u003e \u003cp\u003eMusical competence, the ability to perceive, remember, and discriminate music\u003csup\u003e13\u003c/sup\u003e, depends on core perceptuo-cognitive skills, including sensory integration, auditory discrimination, attention and working memory. As such, this competence is underpinned by the activity of extensive sensory and cognitive networks\u003csup\u003e14,15\u003c/sup\u003e. As a universal human ability that emerges early in human development\u003csup\u003e16,17\u003c/sup\u003e, it differs from music production, which requires specialized skill acquisition. It displays significant variability within the general population, especially in the pitch and rhythmic domains\u003csup\u003e18,19\u003c/sup\u003e. The neural investigation of perceptual abilities that are minimally dependent on formal musical training may offer deeper insights into the biological foundations of musical culture(s)\u003csup\u003e20\u003c/sup\u003e. Despite being grounded on the activity of complex networks, musical abilities have been mainly addressed either focusing on the neural activation\u003csup\u003e21,22\u003c/sup\u003e and morphology\u003csup\u003e23,24\u003c/sup\u003e of single brain regions, or on the functional connections\u003csup\u003e25,26\u003c/sup\u003e and white matter bundles\u003csup\u003e27\u0026ndash;30\u003c/sup\u003e linking these areas. A more holistic understanding of the neural underpinnings of musical abilities would benefit from a network-level approach on brain data in combination with an objective test of music perception.\u003c/p\u003e \u003cp\u003eNetwork neuroscience, recognizing the distributed nature of music perceptual faculty, offers a compelling framework for this endeavour\u003csup\u003e31,32\u003c/sup\u003e. It conceptualizes the brain as a connectome, a network of nodes (neural populations) linked by edges (axonal pathways for structural connectomes or signaling pathways for functional connectomes). Leveraging graph theory\u003csup\u003e33,34\u003c/sup\u003e, network neuroscience provides a framework to quantify three key network properties of brain connectivity: integration (enabling efficient processing of distributed information), segregation (supporting specialized processing within localized clusters), and centrality (highlighting the importance of hub regions in functional integration). By employing graph theory on both structural and functional connectomes, this approach effectively bridges the domains of neuroanatomy and brain dynamics. The static architecture of functional and structural brain networks may thus provide the basis to understand the links between neuroanatomy, information processing, mental representations, and behaviour\u003csup\u003e35,36\u003c/sup\u003e. Research studying functional connectomes from resting-state fMRI (rs-fMRI) and structural connectomes from diffusion MRI (dMRI) indicates that nuances in such network properties relate to differences in general cognitive abilities, including intelligence, WM capacity, and cognitive control\u003csup\u003e37\u0026ndash;42\u003c/sup\u003e. Network neuroscience has the potential to establish itself as a leading approach to study how variation in the relatively static brain organization shapes variability in human cognition and behaviour.\u003c/p\u003e \u003cp\u003eThe main standardized objective tests of music perceptual abilities are the Musical Ear Test (MET)\u003csup\u003e43\u003c/sup\u003e, the Advanced Measures of Music Audiation\u003csup\u003e44\u003c/sup\u003e, the Profile of Music Perception Skills\u003csup\u003e45\u003c/sup\u003e, and the Swedish Musical Discrimination Test\u003csup\u003e46\u003c/sup\u003e. These tests specifically assess abilities in detecting pitch and timing variations in musical sequences, providing reliable measures of music competence that can be effectively related with brain network configurations. Among these tests, the MET, a well-established test of musical aptitude, has undergone extensive validation in large-scale studies\u003csup\u003e19,47\u003c/sup\u003e. It is notable for its openly accessible format, correlating robustly with musical imitation scores used in musical academies, without being influenced by demographic factors such as age, gender, socio-economic status\u003csup\u003e47\u003c/sup\u003e or personality traits\u003csup\u003e19\u003c/sup\u003e. Despite its shorter duration (~\u0026thinsp;20 minutes) compared to the other tests (\u0026gt;\u0026thinsp;40 minutes), the MET maintains robust psychometric properties\u003csup\u003e43\u003c/sup\u003e. Unlike aptitude, or talent, 'competence' is a term that remains neutral, not favoring either innate qualities (nature) or learned skills (nurture). This test is effective in assessing interindividual differences in nonmusicians, accounting for latent musical capacities that can lead individuals without formal training to outperform the average musician\u0026rsquo;s MET scores\u003csup\u003e19\u003c/sup\u003e. It also shows positive correlations with implicit (self-reported) measures of general musical sophistication, like the Goldsmiths Musical Sophistication Index\u003csup\u003e19,48\u003c/sup\u003e. Consequently, the MET, when used in combination with network neuroscience, can be pivotal in exploring the network-level neural foundations of the human capacity for music, and in assessing how subtle neural variations affect musical skills in the general population.\u003c/p\u003e \u003cp\u003eThe biological roots of the human capacity for music have gathered increased attention in the past decade within the fields of cognitive science and biomusicology\u003csup\u003e49,50\u003c/sup\u003e. This faculty is thought to arise from the complex synergy of various perceptuo-cognitive elements, each with unique neurobiological foundations and evolutionary histories. Working memory, a cognitive system with limited capacity crucial for temporary storage and manipulation of sensory information\u003csup\u003e51\u003c/sup\u003e, is a fundamental component of music perceptual faculty\u003csup\u003e52\u003c/sup\u003e. Research in music psychology and neuroscience indicates a hierarchical process in music perception involving serial-to-parallel conversion, integrating auditory elements into increasingly complex musical structures, from basic chunks to complete melodies\u003csup\u003e53\u003c/sup\u003e. Such a process hinges on the WM system's ability to retain lower-level units while integrating new information to form more elaborate musical constructs. Music abilities, as assessed with the MET, correlate significantly with WM capacity; greater WM capacity often translates to superior musical skills\u003csup\u003e13,54\u003c/sup\u003e. Children with musical training exhibit improved cognitive flexibility compared to their non-trained counterparts, a phenomenon linked to increased brain activations in frontoparietal regions\u003csup\u003e10\u003c/sup\u003e. Frontoparietal connectivity is associated with better working memory performance\u003csup\u003e55\u003c/sup\u003e. This suggests that frontoparietal connections are crucial for supporting the high cognitive demands of musical listening and practice. Specifically focusing on the intrinsic organization of WM neural systems, both structurally and functionally, could shed light on the impact of domain-general neurocognitive systems on music perceptual behaviors and how they help shaping individual differences\u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study employs graph theory analyses to investigate the impact of functional and structural WM neural organization on music competence, integrating multimodal neuroimaging with behavioral and cognitive data\u003csup\u003e34,57,58\u003c/sup\u003e. While previous research has focused on the effects of music listening and training on brain network configuration\u003csup\u003e59\u0026ndash;63\u003c/sup\u003e, our investigation reverses this viewpoint. We specifically explore how subtle interindividual differences in the frontoparietal network (FPN), a crucial network underpinning WM and other high-level cognitive processes\u003csup\u003e64\u0026ndash;68\u003c/sup\u003e, relate to differences in music competence, assessed using the MET. Working memory relies on the integration of past and current sensory information from a large-scale network\u003csup\u003e69\u003c/sup\u003e, whose core infrastructure comprises bilateral dorsolateral prefrontal cortices and posterior parietal cortices\u003csup\u003e70\u0026ndash;72\u003c/sup\u003e. Higher WM scores are associated with highly integrative networks, promoting efficient inter-regional communication and rapid combination of information from distributed regions\u003csup\u003e69,73\u0026ndash;75\u003c/sup\u003e. Our hypothesis is that more globally efficient functional and structural FPNs may enhance the hierarchical processing and integration of musical elements, their retention in a temporary buffer, and their comparison with previous musical information, thereby impacting musical competence. Participants also completed the Goldsmiths Musical Sophistication Index\u003csup\u003e18\u003c/sup\u003e, a self-report questionnaire about formal and informal musical behaviours, experience, and skills, and the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV)\u003csup\u003e76\u003c/sup\u003e, which includes a WM index. Individual differences research requires large sample sizes (n\u0026thinsp;\u0026gt;\u0026thinsp;100) for making findings reproducible\u003csup\u003e77\u003c/sup\u003e. Also, graph theory should be applied to both functional and structural modalities for achieving a comprehensive understanding of how the organization of functional and structural brain networks differently contribute to human behaviour. The large sample size of this study, in combination with a network-of-interest approach that relies on an a priori hypotheses, aims to address the issue of low statistical power in individual differences study. Our use of graph theory in both functional and structural connectomes further help in assessing their distinctive role in the support of human behaviour. The anticipated results have the potential to examine the influence of brain network variability on the large spectrum of musical abilities observed in humans and to deepen our comprehension of the biological foundations of music culture(s)\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eOverview of the experimental design and analysis pipeline\u003c/h2\u003e\n \u003cp\u003eOur dataset comprised MET\u003csup\u003e43\u003c/sup\u003e, Goldsmiths Musical Sophistication Index (Gold-MSI)\u003csup\u003e18\u003c/sup\u003e, and Wechsler Adult Intelligence Scale (WAIS-IV)\u003csup\u003e76\u003c/sup\u003e scores, along with functional and structural scans, from a large number of healthy adults (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were non-musicians (Suppl. Figure\u0026nbsp;1). Our study aimed to elucidate the interplay between the structural and functional architecture of the frontoparietal network (FPN) and musical competence. We assessed the relationship between individual propensities for integration or segregation within the FPN and their musical ability. This entailed examining how musical competence correlates with FPN nodes\u0026apos; organization, either in facilitating efficient communication and integration across distal nodes (indicated by higher global efficiency and centrality) or in forming specialized, segregated clusters (reflected by a higher clustering coefficient and local efficiency). Global efficiency is the most commonly used measure of functional integration, while in structural connectomes efficiency and centrality are the relevant metrics\u003csup\u003e78\u003c/sup\u003e. Additionally, we probed the potential mediating role of a domain-general cognitive feature, namely working memory, in these brain-behaviour correlations. Musical competence was quantified using the percentage of MET total scores.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics for demographic information (age and gender), MET, and Gold-MSI subscales (N\u0026thinsp;=\u0026thinsp;241)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135 females (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u0026ndash;94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMelody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGold-MSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore Range (theoretical max)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActive Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;48 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceptual abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;63 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMusical Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;47 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;43 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinging Abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;49 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral Sophistication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u0026ndash;121 (126)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems Gold-MSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompliments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdentity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHours of daily practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMusic Theory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruments played\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe analysis pipeline of this study is partly depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the brain networks analysed with graph theory. A core FPN, including the bilateral dorsolateral prefrontal cortex (middle and superior frontal gyrus and sulcus)\u003csup\u003e79,80\u003c/sup\u003e and bilateral posterior parietal cortex (inferior and superior parietal lobule, and intraparietal sulcus)\u003csup\u003e81,82\u003c/sup\u003e was selected for the main analysis. An occipital network with an equivalent number of nodes was included for control analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eGraph theory results for the structural networks\u003c/h2\u003e\n \u003cp\u003eFigure 3a shows the results for the structural FPN (Suppl. Table 5). We observed a positive correlation between percentage of MET total scores and centrality in the right superior frontal gyrus (SupFG) (F\u0026thinsp;=\u0026thinsp;4.40, pFDR\u0026thinsp;=\u0026thinsp;0.0002) and the right superior parietal lobule (SupPL) (F\u0026thinsp;=\u0026thinsp;3.70, pFDR\u0026thinsp;=\u0026thinsp;0.002). Additionally, the right SupPL was associated positively with global efficiency and percentage of MET total scores (F\u0026thinsp;=\u0026thinsp;3.16, pFDR\u0026thinsp;=\u0026thinsp;0.029). In contrast, a negative association was found between percentage of MET total scores with segregation measures in the right SupFG (local efficiency: F = -4.34, pFDR\u0026thinsp;=\u0026thinsp;0.0003; clustering coefficient: F = -4.34, pFDR\u0026thinsp;=\u0026thinsp;0.00004) and SupPL (local efficiency: F = -4.21, pFDR\u0026thinsp;=\u0026thinsp;0.0003; clustering coefficient: F = -4.61, pFDR\u0026thinsp;=\u0026thinsp;0.00006). These results suggest that individuals exhibit superior music perceptual abilities when the topology of FPN\u0026rsquo;s physical pathways, within these two brain regions, implies stronger potential for functional integration. Conversely, a potential for functional segregation in these two nodes is associated with worse musical abilities. No significant results were found between graph theory metrics in the occipital control network and MET scores (Suppl. Table 6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eGraph theory results for the functional networks\u003c/h2\u003e\n \u003cp\u003eFigure 3b displays the results for the FPN (Suppl. Table\u0026nbsp;7). A positive correlation was found between the global efficiency of the right middle frontal gyrus (MFG) and percentage of MET total scores (F\u0026thinsp;=\u0026thinsp;3.06, pFDR\u0026thinsp;=\u0026thinsp;0.043), indicating that individuals with higher music perceptual abilities have a right middle frontal gyrus that efficiently communicates with, and most likely integrates specialized information from, other FPN regions. Notably, no significant correlations emerged between the graph theory metrics of the occipital control network and MET scores (Suppl. Table\u0026nbsp;8).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship between WM, music competence, and FPN topology\u003c/h2\u003e\n \u003cp\u003eA significant relationship (r\u0026thinsp;=\u0026thinsp;0.21, pFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was observed between WMI scores from the WAIS-IV and percentage of MET total scores (Suppl. Figure\u0026nbsp;3), indicating that higher WM scores are linked to superior music perceptual abilities in the MET. To explore the association between FPN topology and WM performance, we examined the impact of graph theory metrics (independent variable), alongside WMI scores (dependent variable), age, sex, and Musical Training Index (nuisance regressors), in a linear multiple regression framework. Our analysis of functional connectomes revealed a positive correlation between WMI scores and the global efficiency of the right MFG (F\u0026thinsp;=\u0026thinsp;3.18, r\u0026thinsp;=\u0026thinsp;0.20, pFDR\u0026thinsp;=\u0026thinsp;0.02), supramarginal gyrus (F\u0026thinsp;=\u0026thinsp;2.70, r\u0026thinsp;=\u0026thinsp;0.19, pFDR\u0026thinsp;=\u0026thinsp;0.03), and superior frontal sulcus (F\u0026thinsp;=\u0026thinsp;2.71, r\u0026thinsp;=\u0026thinsp;0.17, pFDR\u0026thinsp;=\u0026thinsp;0.03) (Suppl. Table\u0026nbsp;9). This result suggests that an efficient communication and integration capabilities of these brain regions within the FPN are conducive to enhanced WM performance. Conversely, no significant associations were observed with WMI scores using nodal metrics of the functional occipital control network or metrics from structural networks (FPN and occipital) (Suppl. Tables\u0026nbsp;10\u0026ndash;12).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eWM mediates the relationship between rMFG efficiency and music competence\u003c/h2\u003e\n \u003cp\u003eA mediation model was developed to explore the relationship between the efficiency of the right MFG (rMFG) within the functional FPN, WM performance, and music competence, hypothesizing WM as a mediator in the rMFG efficiency-musical competence relationship. To test this hypothesis, we constructed a structural equation model (SEM). The model included rMFG global efficiency as predictor and the percentage MET total scores as dependent variable. Results were adjusted for the covariates of age, sex, and Musical Training Index from the Gold-MSI. Bootstrap resampling procedures were leveraged to derive robust bias-corrected accelerated confidence intervals around the parameters of interest. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e display the statistics of mediation effects in the SEM. The SEM indicated a significant direct impact of rMFG efficiency on WMI scores (standardized beta\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;0.002). The direct path from WMI to percentage of MET total score was also significant (standardized beta\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.010). Additionally, the direct effect of rMFG efficiency on musicality was significant (standardized beta\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.013). Critically, the indirect effect of rMFG efficiency on musicality mediated through WMI was significant (standardized beta\u0026thinsp;=\u0026thinsp;0.21, 95% CI [1.180 to 12.477]). In summary, the results demonstrate both direct effects of global neural efficiency on WM and musicality, as well as an indirect pathway linking neural function to musical competence through domain-general cognitive abilities.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults from the structural equation model (SEM).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNodes connected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEst. \u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal E rMFG -\u0026gt; WMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[17.981, 75837]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWMI -\u0026gt; MET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.011, 0.197]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal E rMFG -\u0026gt; MET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[5.056, 44.023]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec*a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.180, 12.477]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec*a\u0026thinsp;+\u0026thinsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[11.434, 47.269]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research employed graph theory to analyze a large dataset of over 200 brain images (diffusion and resting-state fMRI), cognitive and musical ability assessments, seeking to determine how domain-general memory networks influence music perception skills. We focused this investigation on a frontoparietal network (FPN), a brain network known to be pivotal in WM. We found that higher global communication efficiency in the structural and functional FPNs, notably in key areas of the right dorsolateral prefrontal cortex and the right superior parietal lobule, correlated positively with enhanced music perceptual skills. Critically, differences emerged when comparing results for functional and structural networks. For functional networks, global efficiency in right middle frontal gyrus (rMFG) was significantly associated with both musical competence and WM, the latter serving as a mediator in the direct influence of FPN organization on musical aptitude. Conversely, such direct association with WM performance was not observed for key regions of the structural networks. These results were based on brain images acquired during rs-fMRI and diffusion MRI scans, not during a musical or cognitive task. The findings suggest that the inherent organization of FPN’s core components may serve as a neuromarker for music perception abilities in the general population, with distinct roles played by functional and structural network organization.\u003c/p\u003e \u003cp\u003eMusicality, a multifaceted human trait, is the product of domain-specific perceptual skills such as pitch and metrical perception, and broader cognitive functions like attention and WM\u003csup\u003e86\u003c/sup\u003e. Research into its biological roots necessitates a divide-and-conquer approach, deconstructing musicality into its fundamental perceptual and cognitive components for isolated examination\u003csup\u003e49,87\u003c/sup\u003e. A pivotal discovery from this study is the significant role played by the functional topology of the rMFG, a key region of the dorsolateral prefrontal cortex (DLPFC), in predicting music perceptual abilities in a large sample of individuals. We found this effect being partly mediated by WM. The rMFG is critical for higher-level cognitive processes, including executive functions and WM operations\u003csup\u003e88–90\u003c/sup\u003e. Accordingly, functional studies linking WM and music perception often report DLPFC activity\u003csup\u003e91–93\u003c/sup\u003e. Platel and colleagues\u003csup\u003e94\u003c/sup\u003e observed bilateral activation in Brodmann areas 9 and 10 of the middle frontal gyri during an episodic music memory task, using PET scans. They attributed this DLPFC activity to the perceptual analysis of melodies in WM\u003csup\u003e95\u003c/sup\u003e. Notably, the rMFG is also implicated in the perception of rhythmic structure in music\u003csup\u003e96,97\u003c/sup\u003e, underscoring the critical role of WM functions in the perception of durations in auditory stimuli. One hypothesis is that DLPFC might mediate the critical memory processes required during music perception (encoding, maintenance and integration) via interaction with more posterior parietal regions, likely the seat of internal sensory representations\u003csup\u003e64\u003c/sup\u003e. Our findings indicate that this operation is more effective when an efficient functional organization is in place, such as more direct functional routes between rMFG and the rest of the network. This would explain the superior music abilities in participants with a higher intrinsic global efficiency in the prefrontal region. Our finding corroborates network neuroscience research indicating that a network's information processing performance can be augmented by a sparse functional configuration that yields disproportionately high efficiency\u003csup\u003e38,98\u003c/sup\u003e. Here, we show that this organization in FPNs can provide a neuromarker of music competence in the general population.\u003c/p\u003e \u003cp\u003eThe lack of a direct one-to-one correspondence between structural and functional networks\u003csup\u003e99\u003c/sup\u003e may account for the divergent outcomes observed in our graph theory analyses of these networks. We observed that the integration capabilities and centrality of the right superior parietal lobule within the structural FPN network predict musical competence but do not correlate with WM performance. This finding is consistent with prior research indicating a stronger association of cognitive performance with functional rather than structural connectivity\u003csup\u003e100–103\u003c/sup\u003e. Resting-state functional connectivity is more closely associated with high-level cognitive tasks such as WM likely due to its dynamic and flexible nature\u003csup\u003e104,105\u003c/sup\u003e, which aligns with the complex and temporally integrated demands required by these tasks. Conversely, structural connectivity is more closely linked to tasks with significant sensory components, such as language perception\u003csup\u003e103,106,107\u003c/sup\u003e, reflecting its role in establishing stable physical pathways for sensory information processing. In our study, we extend this finding to music perceptual abilities. The superior parietal lobule, recognized as a higher-order association area and an integrative hub\u003csup\u003e108,109\u003c/sup\u003e is thought to encode and combine past and current sensory information, influencing integrated representations for guiding subsequent adaptive behaviour. An alternative, not mutually exclusive, interpretation is that the right superior parietal lobule may be implicated in sensory-related attentional processes essential for music perception. This region, together with the superior frontal gyrus, is a key area of the dorsal attention network\u003csup\u003e110\u003c/sup\u003e. Activity in this dorsal FPN reflects active goal-directed control of attention\u003csup\u003e111\u003c/sup\u003e. Working memory is thought to encompass two distinct operations with different neuroanatomical locations: firstly, a selection mechanism that retrieves pertinent items, and secondly, an updating function that redirects attentional focus\u003csup\u003e112\u003c/sup\u003e. This updating process is characterized by transient activation in the superior frontal and posterior parietal cortices. This observation aligns with our discovery of a high centrality of these regions within the structural connectome, correlating with enhanced musical performance. It may imply their crucial function in updating sensory representations for the redirection of attentional focus to relevant items within the stimuli, enhancing their discrimination. Previous research has shown that the microstructural organization of dorsal fronto-parietal white matter pathways, such as the anterior subdivision of the right superior longitudinal fasciculus (SLF I), are related to music perceptual abilities in nonmusicians. Increased white matter coherence in this tract is positively correlated with the speed of musical learning\u003csup\u003e113\u003c/sup\u003e. In both interpretations, higher centrality and communication efficiency of this region might aid sensory integration, attentional focus towards pertinent stimuli, and comparison between auditory sequences. The structural organization of the superior parietal lobule may be specialized for the attentional processing and integration of complex sensory inputs, such as those required in music perception, rather than the more abstract and manipulative cognitive processes involved in working memory.\u003c/p\u003e \u003cp\u003eNeurocognitive variability, a hallmark of the human brain, plays a pivotal role in shaping the diverse range of abilities observed across various cognitive and cultural domains. Although extensive research has explored the interplay of brain function, cognitive abilities, and cultural skills, these elements have largely been studied in isolation, leaving a unified theoretical framework elusive. The neuronal recycling hypothesis\u003csup\u003e114\u003c/sup\u003e provides a potential solution, positing that the brain repurposes its older circuits—initially evolved for general cognitive functions—to accommodate evolutionarily more recent cultural skills, all while maintaining their original constraints. Consequently, the spectrum of individual proficiencies within cultural domains is closely linked to the structural and functional nuances of the neural circuits they co-opt, as well as to the cognitive functions these circuits originally support\u003csup\u003e115\u003c/sup\u003e. Studies corroborating this theory reveal a significant correlation between general cognitive functions and cultural behaviour proficiencies, highlighting a neural overlap across these domains\u003csup\u003e116\u003c/sup\u003e. Individual network-level constraints in neurocognitive systems may provide a unique neuronal niche through which cultural material is filtered and to which it may eventually adapt. During the cultural transmission of music, minor inter-individual differences in neural information processing can manifest themselves in differences in musical behaviour\u003csup\u003e117\u003c/sup\u003e. Amplified and spread through cultural evolutionary mechanisms, minor neurocognitive difference can have large system-level effects, such as diversity within and across human cultures. Under this framework, music can be seen as a useful model system to investigate the link between variability in cultural capacities, variation in our innate neurocognitive machinery, and large-scale cultural phenomena.\u003c/p\u003e \u003cp\u003eTo conclude, our study uses a network science approach to elucidate the complex interrelationship between neurocognitive variability and the spectrum of musical abilities seen across humans. Our results suggest intrinsic communication efficiency and integration capacity within FPN core circuitry may aid music perception faculties, with distinct contributions from functional and structural network configurations. The functional topology of right prefrontal regions may facilitate domain-general cognitive functions like WM that support musicality. In contrast, structural properties of superior parietal cortices may subserve sensory or attentional processes more directly tied to auditory capabilities. Overall these findings contribute to elucidating the distinct role of functional and structural neurocognitive variability in support of musical abilities, providing a framework for future explorations into the neurobiological foundations of human culture.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eData were acquired across multiple sessions at Aarhus University and Aarhus University Hospital from healthy individuals as part of the EU COST Action CA18106 \u003cem\u003eThe Neural Architecture of Consciousness\u003c/em\u003e. The project protocol received ethical approval from De Videnskabsetiske Komitéer for Region Midtjylland, Denmark. The scanning session included the collection of resting-state fMRI, high-angular resolution diffusion imaging (HARDI) and multi-parameter mapping data\u003csup\u003e118\u003c/sup\u003e. Approximately one week prior to undergoing scans, participants completed the Goldsmiths Musical Sophistication Index (Gold-MSI) questionnaire in an online session. Typically within a few weeks of the scans, in an optional session, they completed the Musical Ear Test (MET) and Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV). Recruitment of participants was conducted via the Center of Functionally Integrative Neuroscience (CFIN) at Aarhus University, leveraging both the university's participant database and local advertising. A total of 300 adult participants, with no personal history of neurological or psychiatric disorders and no hearing deficits, consented to the study, were financially compensated for their participation and completed the MRI scanning session (see “MRI acquisition”) as well as the Gold-MSI questionnaire (which were both mandatory for study participation).\u003c/p\u003e\u003cp\u003eA subset of these participants (n = 241; 135 females, 18–49 years of age) completed the optional MET test session. In terms of musical training, 60% had no music lessons (n = 145), 36% had up to 5 years of training (n = 88), and only a small subset (n = 8) had over 6 years of training, and were classified as musicians\u003csup\u003e119\u003c/sup\u003e (Suppl. Figure\u0026nbsp;1). Following multivariate outlier analysis, 9 participants were excluded due to significant deviations in Gold-MSI and MET scores, identified via PCA and Euclidean distance criteria (Suppl. Figure\u0026nbsp;2). Structural connectome construction failed in 7 more participants. Consequently, the analysis on functional and structural connectomes in relation to MET scores was conducted with 232 (Suppl. Table\u0026nbsp;1) and 225 participants (Suppl. Table\u0026nbsp;2), respectively. Additionally, a subset of these participants (n = 201) had their domain-general cognitive abilities assessed using the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV)\u003csup\u003e76\u003c/sup\u003e. Only the Working Memory Index (WMI) was used for this study. Graph theory analysis incorporating WMI scores included 201 participants for functional (Suppl. Tables\u0026nbsp;3) and 195 for structural connectome analyses (Suppl. Tables\u0026nbsp;4).\u003c/p\u003e\u003ch2\u003eMusical abilities\u003c/h2\u003e\u003ch2\u003eMusical Ear Test (MET)\u003c/h2\u003e\u003cp\u003eThe Musical Ear Test (MET) comprises 104 trials: 52 melodic phrase pairs in the Melody subtest and 52 rhythmic phrase pairs in the Rhythm subtest. Before testing began, participants were instructed to use headphones and minimize distractions. Participants evaluated whether sequences in each trial — piano tones for Melody and drum beats for Rhythm — were identical, with deviations involving at least one tone (Melody) or inter-onset interval (Rhythm). Feedback was restricted to initial practice trials. Inter-trial intervals in the audio were capped at 1500 ms for Melody and range from 1659 to 3230 ms for Rhythm, thus standardizing MET duration. Scoring awards one point for each correct response, with Melody and Rhythm subtest scores each calculated as the percentage of correct answers out of 52. The percentage of MET total score was calculated as the percentage of correct answers out of the sum of these subtest scores (i.e., 104). Percentage of MET total score, strongly correlating with melodic (r = 0.89) and rhythmic scores (r = 0.85) (Suppl. Figure\u0026nbsp;3), was the primary metric in subsequent analyses, representing musical competence.\u003c/p\u003e\u003ch2\u003eGoldsmiths Musical Sophistication Index (Gold-MSI)\u003c/h2\u003e\u003cp\u003eThe Gold-MSI is a 38-item self-report questionnaire assessing musical behaviours, experiences, and skills. It comprises five subscales: Active Engagement (9 items, e.g., daily attentive music listening duration), Perceptual Abilities (9 items, e.g., identifying out-of-tune singing or playing), Music Training (7 items, e.g., years of formal music theory training), Singing Abilities (7 items, e.g., accuracy in matching recorded notes while singing), and Emotions (6 items, e.g., selecting music for mood enhancement). Additionally, a General Factor score is derived from 18 representative items across these subscales. Responses are rated on a 7-point Likert scale, ranging from complete disagreement to complete agreement, with the last seven items featuring variable response options.\u003c/p\u003e\u003ch2\u003eWorking memory abilities\u003c/h2\u003e\u003ch2\u003eWechsler Adult Intelligence Scale\u003c/h2\u003e\u003cp\u003eIn the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV), our analysis focused solely on the Digit Span and Arithmetic subtests to assess working memory in participants. The Digit Span subtest comprises three distinct tasks: Digit Span Forwards, Digit Span Backwards, and Digit Span Sequencing. The Digit Span Forwards task involves the oral presentation of number sequences by the experimenter, which participants are required to replicate verbatim. Performance is measured by the number of sequences accurately recalled. In the Digit Span Backwards task, participants must reverse and repeat the sequences, with scores reflecting the count of sequences correctly reproduced in reverse order. The Digit Sequencing task necessitates rearranging spoken numbers in ascending order, scored based on the number of sequences correctly ordered. The Arithmetic subtest involves mentally solving arithmetic problems, presented verbally, as their difficulty and memory load increases. For each subtest, raw scores were normalized to age-corrected z scores, with a mean of zero and standard deviation of one, wherein higher scores signify enhanced performance. The Working Memory Index (WMI) is derived by summing the scaled scores from these tasks and subsequently converting this aggregate into an index score using a standard conversion table.\u003c/p\u003e\u003ch2\u003eMRI acquisition\u003c/h2\u003e\u003cp\u003eData were acquired using a Siemens Magnetom Prisma-fit 3T MRI scanner. Following an initial scout scan, two resting-state fMRI sequences (12 and 6 minutes) were run, accompanied by quantitative multi-parameter mapping\u003csup\u003e118\u003c/sup\u003e (around 20 minutes) —used here for synthetically generated T1-weighted images— and high-angular resolution diffusion imaging (HARDI) (around 10 minutes), within a one-hour scanning session. For each participant, 1500 functional volumes were acquired (TR, 700 ms; TE, 30 ms; voxel size 2.5 mm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eThe MPM protocol was implemented based on the Siemens vendor sequence. Three-dimensional (3D) data acquisition consisted of three multi-echo spoiled gradient echo scans (i.e. fast low angle shot [FLASH] sequences with MT, T1, and PD contrast weighting). Additional reference radio-frequency (RF) scans were acquired. The acquisition protocol had the following parameters): TR of PD- and T1-weighted contrasts: 18 ms; TR of MT-weighted contrast: 37 ms; minimum/maximum TE of PD-, T1- and MT-weighted contrasts: 2.46/14.76 ms; flip angles for MT-, PD- and T1-weighted contrasts: 6°, 4°, 25°, respectively; six equidistant echoes; 1 mm isotropic reconstruction voxel size; Field of view 224 ´ 256 ´ 176 mm; AP phase encoding direction; GRAPPA parallel imaging speedup factor of 2; T1w, PDw and MTw acquisition times: 3:50, 3.50, 7.52. The acquisition of low-resolution 3D spoiled gradient echo volumes was executed using both the RF head coil and the body coil. This dual acquisition facilitated the generation of a relative net RF receive field sensitivity (B1−) map for the head coil\u003csup\u003e120–122\u003c/sup\u003e. The approach obtained rapid acquisition by maintaining a low isotropic spatial resolution of 4^3 mm^3, a short echo time (TE) approx 2ms, and a reduced flip angle of 6°, avoiding the use of parallel imaging acceleration or partial Fourier. This procedure of capturing volume pairs with the head and body coils was systematically repeated prior to the acquisition of each of the MT, PD, and T1 contrasts.\u003c/p\u003e\u003cp\u003eThe sequence used to collect HARDI images included: 75 diffusion directions at b = 2500 s/mm\u003csup\u003e2\u003c/sup\u003e; 60 directions at b = 1500 s/mm\u003csup\u003e2\u003c/sup\u003e; 21 directions at b = 1200 s/mm\u003csup\u003e2\u003c/sup\u003e; 30 directions at b = 1000 s/mm\u003csup\u003e2\u003c/sup\u003e; 15 directions at b = 700/mm\u003csup\u003e2\u003c/sup\u003e; 10 directions at b = 5 s/mm\u003csup\u003e2\u003c/sup\u003e, with the different b-shells acquired in the same series (flip angle = 90◦, TR/TE = 2850/71 ms, voxel size = 2 mm\u003csup\u003e3\u003c/sup\u003e; matrix size = 100 x 100, number of slices = 84). The phase-encoding direction was anterior to posterior (AP). An opposite phase-encoding direction (PA) was also acquired (b = 0 s/mm\u003csup\u003e2\u003c/sup\u003e) to allow EPI distortion correction\u003csup\u003e123\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eNeuroanatomical data processing\u003c/h2\u003e\u003cp\u003eSynthetic T1-weighted images were generated using the longitudinal relaxation rate (R1) and effective proton density (PD) high resolution maps (acquired during the MPM sequence protocol). First, both maps were thresholded in order to achieve the required FreeSurfer units. The R1 map was converted to a T1 map by taking its reciprocal and thresholded at zero. This was scaled by a factor of 1000. The PD map was thresholded by zero and scaled by 100. All manipulations were performed using FSL maths commands. Subsequently, the \"mri_synthesize\" FreeSurfer command was applied to create a synthetic FLASH image based on previously calculated T1 (thresholded R1 map) and proton density map. The optional flagged argument for optimal gray and white matter contrast weighting was used with the following parameters 20, 30, and 2.5. Finally, the synthetic T1-weighted image was divided by four to achieve the scale that FreeSurfer expects.\u003c/p\u003e\u003cp\u003eThe synthetic T1-weighted image was preprocessed using fMRIPrep 21.0.2\u003csup\u003e124\u003c/sup\u003e (RRID:SCR_016216), which is based on Nipype 1.6.1\u003csup\u003e125\u003c/sup\u003e (RRID:SCR_002502). The T1-weighted image were corrected for intensity non-uniformity (INU) using the N4BiasFieldCorrection\u003csup\u003e126\u003c/sup\u003e, part of the ANTs 2.3.3\u003csup\u003e127\u003c/sup\u003e (RRID:SCR_004757). This corrected image served as the T1w-reference throughout the preprocessing workflow. Skull stripping was performed on this reference image using a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), with the OASIS30ANTs as the target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast\u003csup\u003e128\u003c/sup\u003e (FSL 6.0.5.1; RRID:SCR_002823). Brain surface reconstruction was carried out using FreeSurfer’s recon-all function\u003csup\u003e129\u003c/sup\u003e (version 6.0.1; RRID:SCR_001847), and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle\u003csup\u003e130\u003c/sup\u003e (RRID:SCR_002438). Volume-based spatial normalization of the brain images to the two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was executed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of the T1w reference and the T1w template. The templates employed for this normalization included the ICBM 152 Nonlinear Asymmetrical template version 2009c (RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym) and FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model\u003csup\u003e131\u003c/sup\u003e (RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym).\u003c/p\u003e\u003ch2\u003edMRI processing and structural connectome construction\u003c/h2\u003e\u003cp\u003eThe diffusion MRI (dMRI) data was preprocessed using custom MATLAB scripts developed internally at the Center of Functionally Integrative Neuroscience (CFIN). The preprocessing steps included noise reduction adapted from the approach by Veraart et al. \u003csup\u003e132\u003c/sup\u003e, correction of Gibbs ringing artifacts following the method described by Kellner et al. \u003csup\u003e133\u003c/sup\u003e, and motion, eddy currents, and field distortion corrections using the top-up and eddy tools from the FSL toolbox \u003csup\u003e134\u003c/sup\u003e. The generation of structural connectomes was performed using the MRtrix3 software toolkit. The analysis involved several steps per subject. We first created a 5-tissue-type (5tt) image, which contained masks of different tissue types (cortical grey matter, deep grey matter, white matter, CSF. and “other”) within the brain and is essential for Anatomically-Constrained Tractography (ACT). Co-registration was then performed to align T1-weighted and DWI images. A response function was created for each major tissue type (white matter, grey matter, cerebrospinal fluid) for each subject. The individual subject response functions were used to create group-level response functions. Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD), was used to estimate Fiber Orientation Distributions (FODs) within each voxel of the brain followed by normalisation.\u003c/p\u003e\u003cp\u003eNext, whole-brain probabilistic tractography was performed using the ACT framework and backtracking. The maximum attempted number of streamlines was 1*10^ qi9 streamlines with 10\u0026nbsp;million streamlines per connectome being selected. Each seed was determined dynamically from the FOD image using the SIFT model. The FOD cutoff was 0.06, the maximum length of each selected streamlines was 250mm while the minimum was 20mm. The SIFT2 model was then applied to the data. Connectomes were then generated using the Destrieux parcellation for the cortex and the FSL FIRST segmentations for the subcortical structures. Each connectome was multiplied by the SIFT proportionality coefficient (mu). Finally a custom automated pipeline for visualising the diffusion data in a structured and standardised way was run for quality control. We generated jpegs of the 5TT images alongside GIFs of the registration of the T1w image to the B = 0 image. These visualisations were used to ensure that the processing pipeline worked correctly.\u003c/p\u003e\u003ch2\u003ersfMRI processing and functional connectome construction\u003c/h2\u003e\u003cp\u003eThe processing of resting-state (rs-fMRI) volumes was implemented by using default surface-based preprocessing routines from the SPM CONN toolbox (Whitfield-Gabrieli (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/conn\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/conn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e135\u003c/sup\u003e, implemented in Matlab (2016b). Functional data was realigned and unwarped without field maps using SPM12 (r7487), employing a 6-parameter transformation for alignment and b-spline interpolation for resampling. Outliers were identified using ART\u003csup\u003e136\u003c/sup\u003e based on framewise displacement and global BOLD signal deviations, and an average reference BOLD image was created for each participant excluding all outlier volumes. Coregistration of functional and anatomical data was achieved using mutual information. Functional images were then mapped onto the cortical surface, averaging data across layers between the pial and white matter surfaces. Finally, surface-level functional data were smoothed using 40 iterative diffusion steps. Our denoising process involved a standard pipeline, regressing out confounds like white matter and cerebrospinal fluid (CSF) signals, motion artifacts, outlier scans, session effects, and linear trends. This included the use of CompCor for noise component extraction from white matter and CSF. Bandpass frequency filtering was applied to the BOLD timeseries to retain frequencies between 0.008 Hz and 0.09 Hz. The effective degrees of freedom of the BOLD signal post-denoising were estimated for all participants.\u003c/p\u003e\u003cp\u003eWe estimated region-to-region connectivity matrices across 16 regions of interest (ROIs) by calculating the functional connectivity strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This was represented by Fisher-transformed bivariate correlation coefficients derived from a weighted general linear model (GLM) and stored in a functional connectivity matrix. The GLM accounted for associations between BOLD signal timeseries of ROI pairs, with weighting to mitigate transient magnetization effects at the start of each run. The connectivity matrix only included Destrieux’s cortical nodes (148x148). From this matrix, we selected 16 nodes for one frontoparietal network of interest and one occipital control network (16x16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eGraph theory analyses\u003c/h2\u003e\u003cp\u003e Graph theory analyses for functional and structural connectivity matrices, and for both frontoparietal and occipital networks, followed the same pipeline. Connectivity matrices (16×16) were thresholded at a fixed network-level cost range (k) (0 \u0026lt; k \u0026lt; 1), resulting in binarized, undirected adjacency matrices. The analysis incorporated both positive and negative rs-FC values. To avoid reliance on specific and arbitrary threshold values (e.g., k = 0.15)\u003csup\u003e137\u003c/sup\u003e, graph metrics were aggregated across multiple thresholds (k = 0.15–0.30, interval 0.01)\u003csup\u003e138\u003c/sup\u003e. Within this range, brain networks show small-world features (GE and LE have, respectively, larger values than lattice and random graphs of equal size and cost values; Supplementary Figs.\u0026nbsp;3–4). From the matrices, four node-level graph theory metrics were computed using the Brain Connectivity Toolbox (BCT)\u003csup\u003e78\u003c/sup\u003e: clustering coefficient, local efficiency, global efficiency, and betweenness centrality. All these metrics offer clear interpretability and are prevalent in network studies. A second-level General Linear Model (GLM) included MET as an explanatory variable, controlling for age, gender, and musical training (Gold-MSI questionnaire) as nuisance regressors. Node-level p-values were adjusted for multiple comparisons using a false discovery rate of q \u0026lt; 0.05 (two-tailed), for each graph metric.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is based upon work from COST Action CA18106 (The Neural Architecture of Consciousness), supported by COST (European Cooperation in Science and Technology). We thank Signe Kirk Brødbæk, Simon Durand, Nina Dyrberg, Sara Kolding, Audrey Mazancieux, Dunja Paunovic, Bianka Rumi, \u0026nbsp;and Povilas Tarailis for their assistance in data collection. The Center for Music in the Brain (MIB) is funded by the Danish National Research Foundation (project number DNRF117).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZatorre, R. J. 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Cortex\u003c/em\u003e (2021) doi:10.1093/cercor/bhab307.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3930575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3930575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhy are some humans more musical than others? Neither fully cognitive testing nor classical localizationist neuroscience alone can provide the whole picture. Here we test how the interplay of brain organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. Our network-based study tests how human connectome variations affect music perception. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional connectomes' organization in relation to musical scores. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays a solid groundwork for future investigations into the neurobiological roots of musical culture(s).\u003c/p\u003e","manuscriptTitle":"Frontoparietal network topology as a neuromarker of music perceptual abilities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 21:40:22","doi":"10.21203/rs.3.rs-3930575/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f9d997b-7bba-4c34-ac69-2118440e3a02","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28864870,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"},{"id":28864871,"name":"Biological sciences/Neuroscience/Computational neuroscience/Network models"}],"tags":[],"updatedAt":"2024-09-27T10:28:57+00:00","versionOfRecord":{"articleIdentity":"rs-3930575","link":"https://doi.org/10.1038/s41467-024-52479-z","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-09-17 04:00:00","publishedOnDateReadable":"September 17th, 2024"},"versionCreatedAt":"2024-02-21 21:40:22","video":"","vorDoi":"10.1038/s41467-024-52479-z","vorDoiUrl":"https://doi.org/10.1038/s41467-024-52479-z","workflowStages":[]},"version":"v1","identity":"rs-3930575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3930575","identity":"rs-3930575","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}