The human brain can be studied as a hierarchy of complex networks on different temporal and spatial scales. On each scale, from gene, protein, synapse, neuron and microcircuit, to area, pathway and ...the whole brain, many advances have been made with the development of related techniques. Brain network studies on different temporal and spatial scales are booming. However, such studies have focused on single levels, and can only reflect limited aspects of how the brain is formed and how it works. Therefore, it is increasingly urgent to integrate a variety of techniques, methods and models, and to merge fragmented findings into a uniform research framework or platform. To this end, we have proposed the concept of the brainnetome and several related programs/projects have been launched in China. In this paper, we offer a brief review on the methodologies of the brainnetome, which include techniques on different scales, the brainnetome atlas, and methods of brain network analysis. We then take Alzheimer's disease and schizophrenia as examples to show how the brainnetome can be studied in neurological and psychiatric disorders. We also review the studies of how risk genes for brain diseases affect the brain networks. Finally, we summarize the challenges for the brainnetome, and what actions and measures have been taken to address these challenges in China. It is envisioned that the brainnetome will open new avenues and some long-standing issues may be solved by combining the brainnetome with other “omes”.
•The brainnetome is a new “-ome” in which brain network is basic research unit.•We review brainnetome techniques, brainnetome atlas, and brain network methods.•We illustrate the essential role of the brainnetome in neuropsychiatric disorders.•We demonstrate how the brainnetome meets genome.•We summarize the challenges in the brainnetome and strategic priorities in China.
The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing ...multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.
Disorders of consciousness (DoC) are acquired conditions of severe altered consciousness. During the past decades, some prognostic models for DoC have been explored on the basis of a variety of ...predictors, including demographics, neurological examinations, clinical diagnosis, neurophysiology and brain images. In this article, a systematic review of pertinent literature was conducted. We identified and evaluated 21 prognostic models involving a total of 1201 DoC patients. In terms of the reported accuracies of predicting the prognosis of DoC, these 21 models vary widely, ranging from 60 to 90%. Using improvement of consciousness level as favorable outcome criteria, we performed a quantitative meta-analysis, and found that the pooled sensitivity and specificity of the hybrid model that combined more than one technique were both superior to those of any single technique, including EEG and fMRI at the tasks and resting state. These results support the view that any single technique has its own advantages and limitations; and the integrations of multiple techniques, including diverse brain images and different paradigms, have the potential to improve predictive accuracy for DoC. Then, we provide methodological points of view and some prospects about future research. Totally, in comparison to a great many diagnostic methods for the DoC, the research of prognostic models is sparse and preliminary, still largely in its infancy with many challenges and opportunities.
The superior frontal gyrus (SFG) is located at the superior part of the prefrontal cortex and is involved in a variety of functions, suggesting the existence of functional subregions. However, ...parcellation schemes of the human SFG and the connection patterns of each subregion remain unclear. We firstly parcellated the human SFG into the anteromedial (SFGam), dorsolateral (SFGdl), and posterior (SFGp) subregions based on diffusion tensor tractography. The SFGam was anatomically connected with the anterior and mid-cingulate cortices, which are critical nodes of the cognitive control network and the default mode network (DMN). The SFGdl was connected with the middle and inferior frontal gyri, which are involved in the cognitive execution network. The SFGp was connected with the precentral gyrus, caudate, thalamus, and frontal operculum, which are nodes of the motor control network. Resting-state functional connectivity analysis further revealed that the SFGam was mainly correlated with the cognitive control network and the DMN; the SFGdl was correlated with the cognitive execution network and the DMN; and the SFGp was correlated with the sensorimotor-related brain regions. The SFGam and SFGdl were further parcellated into three and two subclusters that are well corresponding to Brodmann areas. These findings suggest that the human SFG consists of multiple dissociable subregions that have distinct connection patterns and that these subregions are involved in different functional networks and serve different functions. These results may improve our understanding on the functional complexity of the SFG and provide us an approach to investigate the SFG at the subregional level.
•The human SFG is parcellated into three subregions based on DTT.•The SFGam connected with the cognitive control and default mode networks.•The SFGdl was connected with the cognitive execution network.•The SFGp was connected with the motor control network.
Visual deprivation during different developmental periods leads to different structural and functional alterations in the brain; however, the effects of visual deprivation on the spontaneous ...functional organization of the brain remain largely unknown. In this study, we used voxel-based functional connectivity density (FCD) analyses to investigate the effects of visual deprivation during different developmental periods on the spontaneous functional organization of the brain. Compared with the sighted controls (SC), both the congenitally blind (CB) and the late blind (LB) exhibited decreased short- and long-range FCDs in the primary visual cortex (V1) and decreased long-range FCDs in the primary somatosensory and auditory cortices. Although both the CB and LB exhibited increased short-range FCD in the dorsal visual stream, the CB exhibited greater increases in the short- and long-range FCDs in the ventral visual stream and hippocampal complex compared with the LB. Moreover, the short-range FCD of the left V1 exhibited a significant positive correlation with the duration of blindness in the LB. Our findings suggest that visual deprivation before the developmental sensitive period can induce more extensive brain functional reorganization than does visual deprivation after the sensitive period, which may underlie an enhanced capacity for processing nonvisual information in the CB.
Schizophrenia is hypothesized to arise from disrupted brain connectivity. This “dysconnectivity hypothesis” has generated interest in discovering whether there is anatomical and functional ...dysconnectivity between the prefrontal cortex (PFC) and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain
in vivo
and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.
Previous meta-analyses found abnormal brain activations in schizophrenia patients compared with normal controls when performing working memory tasks. Although most studies focused on dysfunction of ...the working memory activation network in schizophrenia patients, deactivation abnormalities of the working memory in the default mode network have also been reported in schizophrenia but have received less attention. Our goal was to discover whether deactivation abnormalities can also be consistently found in schizophrenia during working memory tasks and, further, to consider both activation and deactivation abnormalities. Fifty-two English language peer-reviewed studies were included in this meta-analysis. Compared with normal controls, the schizophrenia patients showed activation dysfunction of the bilateral dorsolateral prefrontal cortex and posterior parietal cortex as well as the anterior insula, anterior cingulate cortex, and supplementary motor area, which are core nodes of the central executive and salience network. In addition to dysfunction of the activation networks, the patients showed deactivation abnormalities in the ventral medial prefrontal cortex and posterior cingulate cortex, which are core nodes of the default mode network. These results suggest that both activation and deactivation abnormalities exist in schizophrenia patients and that these abnormalities should both be considered when investigating the pathophysiological mechanism of schizophrenia.
Abstract
Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging ...variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions—particularly prefrontal–parietal and basal ganglia—whereas the network pattern differs between genders.
Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia
. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia ...identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners (n = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders.
Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. ...In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.
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