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1. Cavefish brain atlases reveal functional and anatomical convergence across independently evolved populations

   https://doi.org/10.1126/sciadv.aba3126  

   Jaggard, James B.; Lloyd, Evan; Yuiska, Anders; Patch, Adam; Fily, Yaouen; Kowalko, Johanna E.; Appelbaum, Lior; Duboue, Erik R.; Keene, Alex C. (September 2020, Science Advances)

   null (Ed.)

   Environmental perturbation can drive behavioral evolution and associated changes in brain structure and function. The Mexican fish species, Astyanax mexicanus , includes eyed river-dwelling surface populations and multiple independently evolved populations of blind cavefish. We used whole-brain imaging and neuronal mapping of 684 larval fish to generate neuroanatomical atlases of surface fish and three different cave populations. Analyses of brain region volume and neural circuits associated with cavefish behavior identified evolutionary convergence in hindbrain and hypothalamic expansion, and changes in neurotransmitter systems, including increased numbers of catecholamine and hypocretin/orexin neurons. To define evolutionary changes in brain function, we performed whole-brain activity mapping associated with behavior. Hunting behavior evoked activity in sensory processing centers, while sleep-associated activity differed in the rostral zone of the hypothalamus and tegmentum. These atlases represent a comparative brain-wide study of intraspecies variation in vertebrates and provide a resource for studying the neural basis of behavioral evolution. 

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2. Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity

   https://doi.org/10.1162/jocn_a_01658  

   Orwig, William; Diez, Ibai; Vannini, Patrizia; Beaty, Roger; Sepulcre, Jorge (March 2021, Journal of Cognitive Neuroscience)

   null (Ed.)

   Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment—semantic distance—and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults ( n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance. 

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3. Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities

   https://doi.org/10.1002/hbm.26155  

   Wu, Lei; Calhoun, Vince (November 2022, Human Brain Mapping)

   Abstract The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data‐driven parcellation and automated‐linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion‐weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data‐driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity‐based multimodal data fusion in brain. 

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4. Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer

   https://doi.org/10.1007/978-3-031-16431-6_33  

   Zhao, Chongyue; Zhan, Liang; Thompson, Paul M.; Huang, Heng. (September 2022, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science.)

   Brain large-scale dynamics is constrained by the heterogeneity of intrinsic anatomical substrate. Little is known how the spatiotemporal dynamics adapt for the heterogeneous structural connectivity (SC). Modern neuroimaging modalities make it possible to study the intrinsic brain activity at the scale of seconds to minutes. Diffusion magnetic resonance imaging (dMRI) and functional MRI reveals the large-scale SC across different brain regions. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity and exhibits complex neurobiological temporal dynamics which could not be solved by fMRI. However, most of existing multimodal analytical methods collapse the brain measurements either in space or time domain and fail to capture the spatio-temporal circuit dynamics. In this paper, we propose a novel spatio-temporal graph Transformer model to integrate the structural and functional connectivity in both spatial and temporal domain. The proposed method learns the heterogeneous node and graph representation via contrastive learning and multi-head attention based graph Transformer using multimodal brain data (i.e. fMRI, MRI, MEG and behavior performance). The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structurefunction interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method could highlight the local properties of large-scale brain spatio-temporal dynamics and capture the dependence strength between functional connectivity and behaviors. In summary, the proposed method enables the complex brain dynamics explanation for different modal variants. 

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5. Heritability and interindividual variability of regional structure-function coupling

   https://doi.org/10.1038/s41467-021-25184-4  

   Gu, Zijin; Jamison, Keith Wakefield; Sabuncu, Mert Rory; Kuceyeski, Amy (August 2021, Nature Communications)

   Abstract White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors. 

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