Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density

2009 ◽  
Vol 29 (3) ◽  
pp. 459-473 ◽  
Author(s):  
Szymon Łęski ◽  
Ewa Kublik ◽  
Daniel A. Świejkowski ◽  
Andrzej Wróbel ◽  
Daniel K. Wójcik
2010 ◽  
Vol 104 (1) ◽  
pp. 484-497 ◽  
Author(s):  
A. Korovaichuk ◽  
J. Makarova ◽  
V. A. Makarov ◽  
N. Benito ◽  
O. Herreras

Analysis of local field potentials (LFPs) helps understand the function of the converging neuronal populations that produce the mixed synaptic activity in principal cells. Recently, using independent component analysis (ICA), we resolved ongoing hippocampal activity into several major contributions of stratified LFP-generators. Here, using pathway-specific LFP reconstruction, we isolated LFP-generators that describe the activity of Schaffer-CA1 and Perforant-Dentate excitatory inputs in the anesthetized rat. First, we applied ICA and current source density analysis to LFPs evoked by electrical subthreshold stimulation of the pathways. The results showed that pathway specific activity is selectively captured by individual components or LFP-generators. Each generator matches the known distribution of axonal terminal fields in the hippocampus and recovers the specific time course of their activation. Second, we use sparse weak electrical stimulation to prime ongoing LFPs with activity of a known origin. Decomposition of ongoing LFPs yields a few significant LFP-generators with distinct spatiotemporal characteristics for the Schaffer and Perforant inputs. Both pathways convey an irregular temporal pattern in bouts of population activity of varying amplitude. Importantly, the contribution of Schaffer and Perforant inputs to the power of raw LFPs in the hippocampus is minor (7 and 5%, respectively). The results support the hypothesis on a sparse population code used by excitatory populations in the entorhino-hippocampal system, and they validate the separation of LFP-generators as a powerful tool to explore the computational function of neuronal circuits in real time.


2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
César Romero-Rebollar ◽  
Luis Jiménez-Ángeles ◽  
Eduardo Antonio Dragustinovis-Ruiz ◽  
Verónica Medina-Bañuelos

Emotional processing has an important role in social interaction. We report the findings about the Independent Component Analysis carried out on a fMRI set obtained with a paradigm of face emotional processing. The results showed that an independent component, mainly cerebellar-medial-frontal, had a positive modulation associated with fear processing. Also, another independent component, mainly parahippocampal-prefrontal, showed a negative modulation that could be associated with implicit reappraisal of emotional stimuli. Independent Component Analysis could serve as a method to understand complex cognitive processes and their underlying neural dynamics.


2005 ◽  
Vol 25 (3) ◽  
pp. 297-307 ◽  
Author(s):  
Baoming Hong ◽  
Godfrey D. Pearlson ◽  
Vince D. Calhoun

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Erricos M. Ventouras ◽  
Periklis Y. Ktonas ◽  
Hara Tsekou ◽  
Thomas Paparrigopoulos ◽  
Ioannis Kalatzis ◽  
...  

Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.


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