Recent fMRI studies have shown that functional networks can be extracted even from resting state data, the so called “resting state networks” (RSNs) by applying independent component analysis (ICA). However, compared to fMRI, EEG and MEG have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RSNs and their interactions in all (...) frequency bands. We applied low resolution brain electromagnetic tomography-ICA (LORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RSNs in alpha, beta and gamma frequency bands. Next, taking into account these frequency properties, five RSNs were identified; 1) the visual network, 2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) and left posterior dorsal visual pathway (DVP), 3) self-referential processing network, characterized by a positive correlation between the medial PFC and right VVP, 4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus and 5) sensorimotor network. To detect aging-related changes of these five RSNs, the subjects were divided into three age groups: younger, middle aged, and elderly group, and Student's t test with Bonferroni correction on each coefficient of five independent components were performed. We found a significant attenuation in dual-process of visual perception network in elderly relative to middle aged subjects. Overall findings indicate that LORETA-ICA with EEG data can precisely identify five RSNs in their intrinsic frequency bands, and correct correlations and aging-related changes between and within RSNs. (shrink)

In 1895 in the Project for a Scientific Psychology Freud tried to integrate psychology and neurology in order to develop a neuroscientific psychology. Since 1880 Freud made no distinction between psychology and physiology. His papers from the end of the 1880s to1890 were very clear on this scientific overlap: as with many of its contemporaries, Freud thought about psychology essentially as the physiology of the brain. Years later he had to surrender, realizing a technological delay, not capable to pursue its (...) ambitious aim, and until that moment psychoanalysis would have to use its more suitable clinical method. Also, he seemed skeptical about the phrenology drift, typical of that time, in which any psychological function needed to be located in its neuroanatomical area. He could not see the progresses of neurosciences and its fruitful dialogue with psychoanalysis, occurred also thanks to the improvements in the field of neuroimaging, which has made possible a remarkable advance in the knowledge of the mind-brain system and a better observation of the psychoanalytical theories. After years of investigations, deriving from research and clinical work of the last century, the discovery of neural networks, together with the free energy principle, we are observing under a new light the psychodynamic neuroscience in its exploration of the mind-brain system. In this narrative review we summarize the important developments of the psychodynamic neuroscience, with particular regard to the free energy principle, the resting state networks, especially the Default Mode Network in its link with the Self; finally we suggest a discussion by approaching the concept of Alpha Function, proposed by the psychoanalyst Wilfred Ruprecht Bion, trying to speculate about the connection with neuroscience. (shrink)

AimResting state functional magnetic resonance imaging was used to analyze changes in functional connectivity within various brain networks and functional network connectivity among various brain regions in patients with high myopia.Methodsrs-fMRI was used to scan 82 patients with HM and 59 healthy control volunteers matched for age, sex, and education level. Fourteen resting state networks were extracted, of which 11 were positive. Then, the FCs and FNCs of RSNs in HM patients were examined by independent component (...) analysis.ResultsCompared with the HC group, FC in visual network 1, dorsal attention network, auditory network 2, visual network 3, and sensorimotor network significantly increased in the HM group. FC in default mode network 1 significantly decreased. Furthermore, some brain regions in default mode network 2, default mode network 3, auditory network 1, executive control network, and significance network increased while others decreased. FNC analysis also showed that the network connection between the default mode network and cerebellar network was enhanced in the HM group.ConclusionCompared with HCs, HM patients showed neural activity dysfunction within and between specific brain networks, particularly in the DMN and CER. Thus, HM patients may have deficits in visual, cognitive, and motor balance functions. (shrink)