brain connectomics
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2021 ◽  
Author(s):  
Sven Dorkenwald ◽  
Claire E. McKellar ◽  
Thomas Macrina ◽  
Nico Kemnitz ◽  
Kisuk Lee ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisenda Bueichekú ◽  
Jose M. Gonzalez-de-Echavarri ◽  
Laura Ortiz-Teran ◽  
Victor Montal ◽  
Federico d’Oleire Uquillas ◽  
...  

AbstractThe relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities—linked to Neuron Projection—were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.


2021 ◽  
Vol 89 (9) ◽  
pp. S367
Author(s):  
Dung Hoang ◽  
Victor Zeng ◽  
Rachal Hegde ◽  
Nicolas Raymond ◽  
Olivia Lutz ◽  
...  

2021 ◽  
Author(s):  
Jessica S. Damoiseaux ◽  
Andre Altmann ◽  
Jonas Richiardi ◽  
Sepideh Sadaghiani

Structural and functional brain connectomics are considered a basis for an individual's behavior and cognition. Therefore, deviations from typical connectivity patterns may indicate disease processes, and can potentially serve as disease biomarkers. To date, the direct clinical application of brain connectivity measures for diagnostics or treatment is limited. Nonetheless, the extant literature on fundamental and clinical research applications reveals important advances in our understanding of typical and atypical brain structure and function. In this chapter we discuss the current status of the field regarding: (1) the impact of the connectome on cognitive processes and behavior, (2) the connectome across the lifespan, and (3) clinical research applications of connectomics. In addition, we highlight some limitations of connectomics for research and clinical translation.


Author(s):  
Xiao Lin ◽  
WeiKai Li ◽  
Guangheng Dong ◽  
Qiandong Wang ◽  
Hongqiang Sun ◽  
...  

ObjectiveIncreasing pieces of evidence suggest that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. As an essential strategy in psychiatric neuroscience, the research of brain connectivity-based neuroimaging biomarkers has gained increasing attention. Most of previous studies focused on a single modality of the brain connectomics. Multimodal evidence will not only depict the full profile of the brain abnormalities of patients but also contribute to our understanding of the neurobiological mechanisms of this disease.MethodsIn the current study, 99 schizophrenia patients, 69 sex- and education-matched healthy controls, and 42 unaffected first-degree relatives of patients were recruited and scanned. The brain was parcellated into 246 regions and multimodal network analyses were used to construct brain connectivity networks for each participant.ResultsUsing the brain connectomics from three modalities as the features, the multi-kernel support vector machine method yielded high discrimination accuracies for schizophrenia patients (94.86%) and for the first-degree relatives (95.33%) from healthy controls. Using an independent sample (49 patients and 122 healthy controls), we tested the model and achieved a classification accuracy of 64.57%. The convergent pattern within the basal ganglia and thalamus–cortex circuit exhibited high discriminative power during classification. Furthermore, substantial overlaps of the brain connectivity abnormality between patients and the unaffected first-degree relatives were observed compared to healthy controls.ConclusionThe current findings demonstrate that decreased functional communications between the basal ganglia, thalamus, and the prefrontal cortex could serve as biomarkers and endophenotypes for schizophrenia.


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