Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders

By Gustavo Deco, Morten L. Kringelbach. Neuron, 2014

409 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c21581a0b677f15df3d558efa617608dee99a070

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Morten L. Kringelbach
2. Kent C. Berridge
3. Michael Marmaduke Woodman
4. Huifang E. Wang
5. Christophe Bernard
6. Viktor Jirsa
7. Maxime Guye
8. Fabrice Bartolomei

In Radiology, Nuclear Medicine and Imaging

1. Morten L. Kringelbach
2. Maxime Guye
3. Viktor Jirsa
4. Fabrice Bartolomei
5. Michael Marmaduke Woodman
6. Gustavo Deco

Citations in the corpus

• Pleasure systems in the brain (2015, background): “New efforts have given birth to computational neuropsychiatry as a way to discover novel biomarkers for affective states and in neuropsychiatric disorders (Deco and Kringelbach, 2014).”
• The Virtual Epileptic Patient: Individualized whole‐brain models of epilepsy spread (2017, methodology): “, 2013), and pathologies such as schizophrenia (Deco and Kringelbach, 2014) and lesions (Falcon et al.”
• Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study (2024, unknown): “Evaluation of functional connectivity (FC) in the brain is pivotal for gaining insights into cognitive functions and the mechanisms underlying brain disorders [24–28].”