In light of the massive neuroimaging research, coordinate-based meta-analysis (CBMA) has emerged as a potent tool to identify neuroanatomical regions robustly involved in psychiatric disorders. However, despite this success, the application of CBMA (and of neuroimaging, in general) Findings into the clinical-diagnostic setting remains an open challenge. This is due to the fact that (i) canonical CBMA method cannot infer which neuroanatomical correlates are selectively altered in clinical conditions under examination; (ii) brain disorders tend to exhibit a common pattern of neuroanatomical variation (https://doi.org/10.1002/ hbm.25105). Here, strengths and pitfalls of CBMA method will be introduced and how its findings can be used for understand the neuroanatomy associated with psychiatric disorders. Then, the talk will present a novel CBMA data-driven Bayesian approach (https://doi.org/10.1002/hbm.25452) to determine the selective neuroanatomical landscape of brain disorders. Lastly, this approach will be illustrated by an exemplary use case dealing with the selectivity of atypical neuroanatomy in autism spectrum disorder (https://doi. org/10.1016/j.bpsc.2022.01.007).
RE-CONCEPTUALIZING THE NEUROANATOMICAL LANDSCAPE OF NEUROPSYCHIATRIC DISEASES VIA META-ANALYTIC AND BAYESIAN APPROACH. THE CASE OF AUTISM
Liloia Donato
2022-01-01
Abstract
In light of the massive neuroimaging research, coordinate-based meta-analysis (CBMA) has emerged as a potent tool to identify neuroanatomical regions robustly involved in psychiatric disorders. However, despite this success, the application of CBMA (and of neuroimaging, in general) Findings into the clinical-diagnostic setting remains an open challenge. This is due to the fact that (i) canonical CBMA method cannot infer which neuroanatomical correlates are selectively altered in clinical conditions under examination; (ii) brain disorders tend to exhibit a common pattern of neuroanatomical variation (https://doi.org/10.1002/ hbm.25105). Here, strengths and pitfalls of CBMA method will be introduced and how its findings can be used for understand the neuroanatomy associated with psychiatric disorders. Then, the talk will present a novel CBMA data-driven Bayesian approach (https://doi.org/10.1002/hbm.25452) to determine the selective neuroanatomical landscape of brain disorders. Lastly, this approach will be illustrated by an exemplary use case dealing with the selectivity of atypical neuroanatomy in autism spectrum disorder (https://doi. org/10.1016/j.bpsc.2022.01.007).File | Dimensione | Formato | |
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