scholarly journals Age-related alterations of brain network underlying the retrieval of emotional autobiographical memories: an fMRI study using independent component analysis

2014 ◽  
Vol 8 ◽  
Author(s):  
Ruiyang Ge ◽  
Yan Fu ◽  
Dahua Wang ◽  
Li Yao ◽  
Zhiying Long
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Brain Network ◽  
Independent Component ◽  
Autobiographical Memories ◽  
Age Related ◽  
Fmri Study

TEMPORAL CORTEX ACTIVATION DURING LISTENING TO EMOTIONAL WORDS IN PATIENTS WITH NON-AFFECTIVE PSYCHOSIS: AN FMRI STUDY WITH INDEPENDENT COMPONENT ANALYSIS

2010 ◽  
Vol 117 (2-3) ◽  
pp. 237
Author(s):  
Maria J. Escarti ◽  
Maria de la Iglesia-Vaya ◽  
Eduardo J. Aguilar ◽  
Luis Marti-Bonmati ◽  
Gracian Garcia-Marti ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Temporal Cortex ◽  
Component Analysis ◽  
Independent Component ◽  
Affective Psychosis ◽  
Emotional Words ◽  
Fmri Study

scholarly journals An fMRI study of acupuncture using independent component analysis

2009 ◽  
Vol 449 (1) ◽  
pp. 6-9 ◽  
Author(s):  
Yi Zhang ◽  
Wei Qin ◽  
Peng Liu ◽  
Jie Tian ◽  
Jimin Liang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Study

scholarly journals NeuroMark: a fully automated ICA method to identify effective fMRI markers of brain disorders

2019 ◽  
Author(s):  
Yuhui Du ◽  
Zening Fu ◽  
Jing Sui ◽  
Shuang Gao ◽  
Ying Xing ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Autism Spectrum ◽  
Brain Network ◽  
Independent Component ◽  
Mental Illnesses ◽  
Fmri Data ◽  
Brain Disorders ◽  
Automated Method ◽  
Functional Brain Network

SummaryIncreasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. Standardized approaches for capturing reproducible and comparable biomarkers are greatly needed. Here, we propose a method, NeuroMark, which leverages a priori-driven independent component analysis to effectively extract functional brain network biomarkers from functional magnetic resonance imaging (fMRI) data. NeuroMark automatically estimates features adaptable to each individual and comparable across subjects by taking advantage of the replicated brain network templates extracted from 1828 healthy controls as guidance to initialize the individual-level networks. Four studies including 2454 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, depression, bipolar disorder, mild cognitive impairment and Alzheimer’s disease) to evaluate the proposed method from different perspectives (replication, cross-study comparison, subtle difference identification, and multi-disorder classification). Results demonstrate the great potential of NeuroMark in its feasibility to link different datasets/studies/disorders and enhance sensitivity in identifying biomarkers for patients with challenging mental illnesses.Significance StatementIncreasing evidence highlights that features extracted from resting fMRI data can be leveraged as potential biomarkers of brain disorders. However, it has been difficult to replicate results using different datasets, translate findings across studies, and differentiate brain disorders sharing similar clinical symptoms. It is important to systematically characterize the degree to which unique and similar impaired patterns are reflective of brain disorders. We propose a fully automated method (called NeuroMark) that leverages priori-driven independent component analysis (ICA) using replicated brain network templates to estimate individual-subject network features. Evaluated by four studies involving six different brain disorders, we show that NeuroMark can effectively link the comparison of biomarkers across different studies/datasets/disorders and enable classification between complex brain disorders, while also providing information about relevant aspects of whole brain functional connectivity.

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