Miao Cao

Topological organization of the human brain functional connectome across the lifespan

Graphical abstract

Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopment disorders in childhood. Clinically, the core symptoms of this disorder include inattention, hyperactivity, and impulsivity. Previous studies have documented that these behavior deficits in ADHD children are associated with not only regional brain abnormalities but also changes in functional and structural connectivity among regions. In the past several years, our understanding of how ADHD affects the brain’s connectivity has been greatly advanced by mapping topological alterations of large-scale brain networks (i.e., connectomes) using noninvasive neurophysiological and neuroimaging techniques (e.g., electroencephalograph, functional MRI, and diffusion MRI) in combination with graph theoretical approaches. In this review, we summarize the recent progresses of functional and structural brain connectomics in ADHD, focusing on graphic analysis of large-scale brain systems. Convergent evidence suggests that children with ADHD had abnormal small-world properties in both functional and structural brain networks characterized by higher local clustering and lower global integrity, suggesting a disorder-related shift of network topology toward regular configurations. Moreover, ADHD children showed the redistribution of regional nodes and connectivity involving the default-mode, attention, and sensorimotor systems. Importantly, these ADHD-associated alterations significantly correlated with behavior disturbances (e.g., inattention and hyperactivity/impulsivity symptoms) and exhibited differential patterns between clinical subtypes. Together, these connectome-based studies highlight brain network dysfunction in ADHD, thus opening up a new window into our understanding of the pathophysiological mechanisms of this disorder. These works might also have important implications on the development of imaging-based biomarkers for clinical diagnosis and treatment evaluation in ADHD.

Age-related changes in the topological organization of the white matter structural connectome across the human lifespan.

Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age‐related microstructural changes in specific white matter tracts during development and aging. However, the age‐related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9–85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high‐resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U‐shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich‐club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age‐related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r = 0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age. Hum Brain Mapp 36:3777–3792, 2015.

Abnormal autonomic and associated brain activities during rest in autism spectrum disorder

Autism spectrum disorders are associated with social and emotional deficits, the aetiology of which are not well understood. A growing consensus is that the autonomic nervous system serves a key role in emotional processes, by providing physiological signals essential to subjective states. We hypothesized that altered autonomic processing is related to the socio-emotional deficits in autism spectrum disorders. Here, we investigated the relationship between non-specific skin conductance response, an objective index of sympathetic neural activity, and brain fluctuations during rest in high-functioning adults with autism spectrum disorder relative to neurotypical controls. Compared with control participants, individuals with autism spectrum disorder showed less skin conductance responses overall. They also showed weaker correlations between skin conductance responses and frontal brain regions, including the anterior cingulate and anterior insular cortices. Additionally, skin conductance responses were found to have less contribution to default mode network connectivity in individuals with autism spectrum disorders relative to controls. These results suggest that autonomic processing is altered in autism spectrum disorders, which may be related to the abnormal socio-emotional behaviours that characterize this condition.

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain

Abstract Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small‐worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting‐state functional MRI and voxel‐based graph theory analysis to systematically investigate the topological organization of whole‐brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high‐order default‐mode and executive‐control regions. The hub and rich‐club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.

Toward Developmental Connectomics of the Human Brain.

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders.

Developmental Connectomics from Infancy through Early Childhood

The human brain undergoes rapid growth in both structure and function from infancy through early childhood, and this significantly influences cognitive and behavioral development in later life. A newly emerging research framework, developmental connectomics, provides unprecedented opportunities for exploring the developing brain through non-invasive mapping of structural and functional connectivity patterns. Within this framework, we review recent neuroimaging and neurophysiological studies investigating connectome development from 20 postmenstrual weeks to 5 years of age. Specifically, we highlight five fundamental principles of brain network development during the critical first years of life, emphasizing strengthened segregation/integration balance, a remarkable hierarchical order from primary to higher-order regions, unparalleled structural and functional maturations, substantial individual variability, and high vulnerability to risk factors and developmental disorders.

Connectomics in psychiatric research: advances and applications

Psychiatric disorders disturb higher cognitive functions and severely compromise human health. However, the pathophysiological mechanisms underlying psychiatric disorders are very complex, and understanding these mechanisms remains a great challenge. Currently, many psychiatric disorders are hypothesized to reflect “faulty wiring” or aberrant connectivity in the brains. Imaging connectomics is arising as a promising methodological framework for describing the structural and functional connectivity patterns of the human brain. Recently, alterations of brain networks in the connectome have been reported in various psychiatric disorders, and these alterations may provide biomarkers for disease diagnosis and prognosis for the evaluation of treatment efficacy. Here, we summarize the current achievements in both the structural and functional connectomes in several major psychiatric disorders (eg, schizophrenia, attention-deficit/hyperactivity disorder, and autism) based on multi-modal neuroimaging data. We highlight the current progress in the identification of these alterations and the hypotheses concerning the aberrant brain networks in individuals with psychiatric disorders and discuss the research questions that might contribute to a further mechanistic understanding of these disorders from a connectomic perspective.

Structural and Functional Brain Changes in the Default Mode Network in Subtypes of Amnestic Mild Cognitive Impairment

Background: Various amnestic mild cognitive impairment (aMCI) subtypes have been identified as single domain (SD) or multiple domain (MD), with differential probabilities of progression to Alzheimer disease (AD). Detecting the differences in the alterations in gray matter (GM) and intrinsic brain activity between the subtypes of aMCI help to understand their pathophysiological mechanisms and was conducive to construct such potential biomarkers to monitor the progression of aMCI. Methods: In all, 22 normal controls (NCs), 18 patients with SD-aMCI, and 17 patients with MD-aMCI participated in the study. The amplitude of low-frequency fluctuations (ALFFs) during rest represented intrinsic brain activity. Voxel-based morphometry analysis was used to measure the GM volume. Results: The MD-aMCI showed reduced GM in hippocampus (Hip), parahippocampal gyrus (PHG), and other regions than SD-aMCI. The SD-aMCI had reduced GM only in Hip and PHG than in NC. The MD-aMCI showed decreased ALFF in posterior cingulate cortex (PCC) and precuneus and increased ALFF in anterior cingulate cortex (ACC), PHG, and Hip compared with both SD-aMCI and NC. However, no ALFF difference was found between SD-aMCI and NC. Neuropsychological measures were correlated with ALFF in PCC and ACC only in the MD-aMCI. Conclusions: Patients with MD-aMCI displayed more severe GM atrophy and ALFF changes than patients with SD-aMCI. The results suggested that aMCI is heterogeneous and that MD-aMCI may be a prodromal stage which is more close to AD.

Individual differences and time-varying features of modular brain architecture

ABSTRACT Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter‐connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group‐level network modularity analyses using “static” functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting‐state functional MRI data (N=105) from the Human Connectome Project and a graph‐based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto‐parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra‐subject dynamic modular variability largely overlapped with that of inter‐subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large‐scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors. HighlightsIntrinsic modular structure of the human brain remarkably varied across individuals.Higher inter‐subject modular variability primarily located in the association cortex.The association cortex showed large variability in module affiliations over time.Regions with higher modular variability tend to have higher cognitive flexibility.