Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment

Heritability of movie-evoked brain activity and connectivity

The neural bases of sensory processing are conserved across people, but no two individuals experience the same stimulus in exactly the same way. Recent work has established that the idiosyncratic nature of subjective experience is underpinned by individual variability in brain responses to sensory information. However, the fundamental origins of this individual variability have yet to be systematically investigated. Here, we establish a genetic basis for individual differences in sensory processing by quantifying (1) the heritability of high-dimensional brain responses to movies and (2) the extent to which this heritability is grounded in lower-level aspects of brain function. Specifically, we leverage 7T fMRI data collected from a twin sample to first show that movie-evoked brain activity and connectivity patterns are heritable across the cortex. Next, we use hyperalignment to decompose this heritability into genetic similarity in where vs. how sensory information is processed. Finally, we show that the heritability of brain activity patterns can be partially explained by the heritability of the neural timescale, a one-dimensional measure of local circuit functioning. These results demonstrate that brain responses to complex stimuli are heritable, and that this heritability is due, in part, to genetic control over stable aspects of brain function.

Heritability of white matter in twins: A diffusion neuroimaging review

Diffusion neuroimaging has emerged as an essential non-invasive technique to explore in vivo microstructural characteristics of white matter (WM), whose integrity allows complex behaviors and cognitive abilities. Studying the factors contributing to inter-individual variability in WM microstructure can provide valuable insight into structural and functional differences of brain among individuals. Genetic influence on this variation has been largely investigated in twin studies employing different measures derived from diffusion neuroimaging. In this context, we performed a comprehensive literature search across PubMed, Scopus and Web of Science of original twin studies focused on the heritability of WM. Overall, our results highlighted a consistent heritability of diffusion indices (i.e., fractional anisotropy, mean, axial and radial diffusivity), and network topology among twins. The genetic influence resulted prominent in frontal and occipital regions, in the limbic system, and in commissural fibers. To enhance the understanding of genetic influence on WM microstructure further studies in less heterogeneous experimental settings, encompassing all diffusion indices, are warranted.

Heritability of functional gradients in the human subcortico-cortical connectivity

The human subcortex plays a pivotal role in cognition and is widely implicated in the pathophysiology of many psychiatric disorders. However, the heritability of functional gradients based on subcortico-cortical functional connectivity remains elusive. Here, leveraging twin functional MRI (fMRI) data from both the Human Connectome Project (n = 1023) and the Adolescent Brain Cognitive Development study (n = 936) datasets, we construct large-scale subcortical functional gradients and delineate an increased principal functional gradient pattern from unimodal sensory/motor networks to transmodal association networks. We observed that this principal functional gradient is heritable, and the strength of heritability exhibits a heterogeneous pattern along a hierarchical unimodal-transmodal axis in subcortex for both young adults and children. Furthermore, employing a machine learning framework, we show that this heterogeneous pattern of the principal functional gradient in subcortex can accurately discern the relationship between monozygotic twin pairs and dizygotic twin pairs with an accuracy of 76.2% (P < 0.001). The heritability of functional gradients is associated with the anatomical myelin proxied by MRI-derived T1-weighted/T2-weighted (T1w/T2w) ratio mapping in subcortex. This study provides new insights into the biological basis of subcortical functional hierarchy by revealing the structural and genetic properties of the subcortical functional gradients.

Personalized functional brain network topography is associated with individual differences in youth cognition

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.

Altered resting-state cerebellar-cerebral functional connectivity in patients with panic disorder before and after treatment

The study aimed to investigate the functional connectivity (FC) between the cerebellum and intrinsic cerebral networks in patients with panic disorder (PD), and to observe changes in the cerebellar-cerebral FC following pharmacotherapy. Fifty-four patients with PD and 54 healthy controls (HCs) underwent clinical assessments and functional magnetic resonance imaging scans before and after a 5-week paroxetine treatment. Seed-based cerebellar-cerebral FC was compared between the PD and HC groups, as well as between patients with PD before and after treatment. Additionally, the correlations between FC and clinical features of PD were analyzed. Compared to HCs, patients with PD had altered cerebellar-cerebral FC in the default mode, affective-limbic, and sensorimotor networks. Moreover, a negative correlation between cerebellar-insula disconnection and the severity of depressive symptoms in patients with PD (Pearson correlation, r = -0.424, p = 0.001, Bonferroni corrected) was found. After treatment, most of the enhanced FCs observed in patients with PD at baseline returned to levels similar to those observed in HCs. However, the reduced FC at baseline did not significantly change after treatment. The findings suggest that patients with PD have specific deficits in resting-state cerebellar-cerebral FC and that paroxetine may improve PD by restoring the balance of cerebellar-cerebral FC. These findings emphasize the crucial involvement of cerebellar-cerebral FC in the neuropsychological mechanisms underlying PD and in the potential pharmacological mechanisms of paroxetine for treating PD.

The Cellular Underpinnings of the Human Cortical Connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cortex. The cortical sheet can be broadly divided into distinct networks, which are further embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here, using transcriptional data from the Allen Human Brain Atlas, we demonstrate that imputed cell type distributions are spatially coupled to the functional organization of cortex, as estimated through fMRI. Cortical cellular profiles follow the macro-scale organization of the functional gradients as well as the associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on post-mortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Understanding the link between functional profiles and intelligence through dimensionality reduction and graph analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interretable dimensions. Specifically, this study uses a data-driven approach focussing on areas where FC variability is highest across individuals to model different facets of intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and relative functional distance from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that with decreasing functional affinity with the latter, its functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Dissociable multi-scale patterns of development in personalized brain networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Functional cortical associations and their intraclass correlations and heritability as revealed by the fMRI Human Connectome Project

We report on the functional connectivity (FC), its intraclass correlation (ICC), and heritability among 70 areas of the human cerebral cortex. FC was estimated as the Pearson correlation between averaged prewhitened Blood Oxygenation Level-Dependent time series of cortical areas in 988 young adult participants in the Human Connectome Project. Pairs of areas were assigned to three groups, namely homotopic (same area in the two hemispheres), ipsilateral (both areas in the same hemisphere), and heterotopic (nonhomotopic areas in different hemispheres). ICC for each pair of areas was computed for six genetic groups, namely monozygotic (MZ) twins, dizygotic (DZ) twins, singleton siblings of MZ twins (MZsb), singleton siblings of DZ twins (DZsb), non-twin siblings (SB), and unrelated individuals (UNR). With respect to FC, we found the following. (a) Homotopic FC was stronger than ipsilateral and heterotopic FC; (b) average FCs of left and right cortical areas were highly and positively correlated; and (c) FC varied in a systematic fashion along the anterior-posterior and inferior-superior dimensions, such that it increased from anterior to posterior and from inferior to superior. With respect to ICC, we found the following. (a) Homotopic ICC was significantly higher than ipsilateral and heterotopic ICC, but the latter two did not differ significantly from each other; (b) ICC was highest for MZ twins; (c) ICC of DZ twins was significantly lower than that of the MZ twins and higher than that of the three sibling groups (MZsb, DZsb, SB); and (d) ICC was close to zero for UNR. Finally, with respect to heritability, it was highest for homotopic areas, followed by ipsilateral, and heterotopic; however, it did not differ statistically significantly from each other.

Machine learning in neuroimaging: from research to clinical practice

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.