Brain Connectivity · 2016 · 68 citations · 40 references
Life SciencesNeurobiologyNeuroscienceClinical NeuroscienceCognitive NeuroscienceNeuropsychologyBrain ConnectivityBrain ImagingCognitive ImpairmentConnectomicsFunctional ConnectivityGraph TheoryBrain Connectivity MappingConnectome AnalysisConnectome OrganizationDiffusion Tensor Imaging MarkersPatient Demographic VariablesSmall-world OrganizationWhite Matter AnatomyWhite Matter Pathology
Survivors of pediatric acute lymphoblastic leukemia (ALL) are at increased risk for cognitive impairments that disrupt everyday functioning and decrease quality of life. The specific biological mechanisms underlying cognitive impairment following ALL remain largely unclear, but previous studies consistently demonstrate significant white matter pathology. We aimed to extend this literature by examining the organization of the white matter connectome in young patients with a history of ALL treated with chemotherapy only. We applied graph theoretical analysis to diffusion tensor imaging obtained from 31 survivors of ALL age 5-19 years and 39 matched healthy controls. Results indicated significantly lower small-worldness (p = 0.007) and network clustering coefficient (p = 0.019), as well as greater cognitive impairment (p = 0.027) in the ALL group. Regional analysis indicated that clustered connectivity in parietal, frontal, hippocampal, amygdalar, thalamic, and occipital regions was altered in the ALL group. Random forest analysis revealed a model of connectome and demographic variables that could automatically classify survivors of ALL as having cognitive impairment or not (accuracy = 0.89, p < 0.0001). These findings provide further evidence of brain injury in young survivors of ALL, even those without a history of central nervous system (CNS) disease or cranial radiation. Efficiency of local information processing, reorganization of hub connectivity, and cognitive reserve may contribute to cognitive outcome in these children. Certain connectome properties showed U-shaped relationships with cognitive impairment suggesting an optimal range of regional connectivity.
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Complex network measures of brain connectivity: Uses and interpretations
Mikail Rubinov, Olaf Sporns · NeuroImage · 2009 · 12K citations