Individual Variation in Functional Topography of Association Networks in Youth.
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ABSTRACT:
SUBMITTER: Cui Z
PROVIDER: S-EPMC7182484 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.
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Project description:Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.
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Project description:Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).
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Project description:Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child's environment ("exposome") and investigate associations with each child's unique, multidimensional pattern of functional brain network organization ("functional topography") and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9-10) and future (ages 11-12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child's exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children's complex, multidimensional environments in cognitive neurodevelopment.
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Project description:Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.
| S-EPMC6519695 | biostudies-literature
Project description:The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.
| S-EPMC10441314 | biostudies-literature
Project description:The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.
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Project description:BackgroundCACNA1C rs1006737 A allele, identified as a genetic risk variant for bipolar disorder (BD), is associated with anomalous functional connectivity in adults with and without BD. Studies have yet to investigate the association of CACNA1C rs1006737 with resting-state functional connectivity (rsFC) in youth BD.MethodsParticipants included 139 youth with BD-I, -II, or -not otherwise specified, ages 13-20 years, including 27 BD A-carriers, 41 BD non-carriers, 32 healthy controls (HC) A-carriers, and 39 HC non-carriers. Anterior cingulate cortex (ACC), amygdala, and orbitofrontal cortex (OFC) were examined as regions-of-interest in seed-to-voxel analyses. General linear models included main effects of diagnosis and rs1006737, and an interaction term, controlling for age, sex, and race.ResultsWe observed a main effect of BD diagnosis on rsFC between the right amygdala and the right occipital pole (p = 0.02), and a main effect of rs1006737 genotypes on rsFC between the right OFC and bilateral occipital cortex (p < 0.001). Two significant BD diagnosis-by-CACNA1C rs1006737 interactions were also identified. The A allele was associated with positive rsFC between the right ACC and right amygdala in BD but negative rsFC in HC (p = 0.01), and negative rsFC between the left OFC and left putamen in BD but positive rsFC in HC (p = 0.01).ConclusionThis study found that the rs1006737 A allele, identified as a genetic risk variant for BD in adults, was differentially associated with rsFC in youth with BD in regions relevant to emotion, executive function, and reward. Future task-based approaches are warranted to better understand brain connectivity in relation to CACNA1C in BD.
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Project description:Individual variation is central to species involved in complex interactions with others in an ecological system. Such ecological systems could exhibit tipping points in response to changes in the environment, consequently leading to abrupt transitions to alternative, often less desirable states. However, little is known about how individual trait variation could influence the timing and occurrence of abrupt transitions. Using 101 empirical mutualistic networks, I model the eco-evolutionary dynamics of such networks in response to gradual changes in strength of co-evolutionary interactions. Results indicated that individual variation facilitates the timing of transition in such networks, albeit slightly. In addition, individual variation significantly increases the occurrence of large abrupt transitions. Furthermore, topological network features also positively influence the occurrence of such abrupt transitions. These findings argue for understanding tipping points using an eco-evolutionary perspective to better forecast abrupt transitions in ecological systems.
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Project description:Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
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