Author
Andrea Mechelli
Other affiliations: University of Cambridge, Avon and Wiltshire Mental Health Partnership NHS Trust, University of Texas Southwestern Medical Center ...read more
Bio: Andrea Mechelli is an academic researcher from King's College London. The author has contributed to research in topics: Psychosis & Schizophrenia. The author has an hindex of 66, co-authored 202 publications receiving 18912 citations. Previous affiliations of Andrea Mechelli include University of Cambridge & Avon and Wiltshire Mental Health Partnership NHS Trust.
Papers published on a yearly basis
Papers
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TL;DR: The full hemodynamic model is presented, how its associated Volterra kernels can be derived, and the model's validity in relation to empirical nonlinear characterizations of evoked responses in fMRI and other neurophysiological constraints are addressed.
1,115 citations
TL;DR: Using network analysis of DTI data from healthy volunteers, and meta-analyses of published MRI studies in 26 brain disorders, Crossley et al. show that lesions across disorders tend to be concentrated at hubs.
Abstract: Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.
978 citations
TL;DR: Support-Vector-Machine has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data, and those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease and autistic spectrum disorder are reviewed.
872 citations
TL;DR: It is shown that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this region is modulated by the proficiency attained and the age at acquisition.
Abstract: Humans have a unique ability to learn more than one language--a skill that is thought to be mediated by functional (rather than structural) plastic changes in the brain. Here we show that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this region is modulated by the proficiency attained and the age at acquisition. This relation between grey-matter density and performance may represent a general principle of brain organization.
850 citations
TL;DR: The combined use of Bayes factors and DCM allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.
849 citations
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TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
9,441 citations
9,362 citations
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
4,948 citations
TL;DR: The evolution of long noncoding RNAs and their roles in transcriptional regulation, epigenetic gene regulation, and disease are reviewed.
4,277 citations
TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
4,182 citations