EEG To FMRI Synthesis: Is Deep Learning a Candidate?

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from $N=80$ participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure

Brain connectivity dynamics in cisgender and transmen people with gender incongruence before gender affirmative hormone treatment

Large-scale brain network interactions have been described between trans- and cis-gender binary identities. However, a temporal perspective of the brain's spontaneous fuctuations is missing. We investigated the functional connectivity dynamics in transmen with gender incongruence and its relationship with interoceptive awareness. We describe four states in native and meta-state spaces: (i) one state highly prevalent with sparse overall connections; (ii) a second with strong couplings mainly involving components of the salience, default, and executive control networks. Two states with global sparse connectivity but positive couplings (iii) within the sensorimotor network, and (iv) between salience network regions. Transmen had more dynamical fuidity than cismen, while cismen presented less meta-state fuidity and range dynamism than transmen and ciswomen. A positive association between attention regulation and fuidity and meta-state range dynamism was found in transmen. There exist gender diferences in the temporal brain dynamism, characterized by distinct interrelations of the salience network as catalyst interacting with other networks. We ofer a functional explanation from the neurodevelopmental cortical hypothesis of a gendered-self

Quantitative comparisons on hand motor functional areas determined by resting state and task BOLD fMRI and anatomical MRI for pre-surgical planning of patients with brain tumors

AbstractFor pre-surgical planning we present quantitative comparison of the location of the hand motor functional area determined by right hand finger tapping BOLD fMRI, resting state BOLD fMRI, and anatomically using high resolution T1 weighted images. Data were obtained on 10 healthy subjects and 25 patients with left sided brain tumors. Our results show that there are important differences in the locations (i.e., >20mm) of the determined hand motor voxels by these three MR imaging methods. This can have significant effect on the pre-surgical planning of these patients depending on the modality used. In 13 of the 25 cases (i.e., 52%) the distances between the task-determined and the rs-fMRI determined hand areas were more than 20mm; in 13 of 25 cases (i.e., 52%) the distances between the task-determined and anatomically determined hand areas were >20mm; and in 16 of 25 cases (i.e., 64%) the distances between the rs-fMRI determined and anatomically determined hand areas were more than 20mm. In just three cases, the distances determined by all three modalities were within 20mm of each other. The differences in the location or fingerprint of the hand motor areas, as determined by these three MR methods result from the different underlying mechanisms of these three modalities and possibly the effects of tumors on these modalities

Increased Global Functional Connectivity Correlates with LSD-Induced Ego Dissolution.

Lysergic acid diethylamide (LSD) is a non-selective serotonin-receptor agonist that was first synthesized in 1938 and identified as (potently) psychoactive in 1943. Psychedelics have been used by indigenous cultures for millennia [1]; however, because of LSD's unique potency and the timing of its discovery (coinciding with a period of major discovery in psychopharmacology), it is generally regarded as the quintessential contemporary psychedelic [2]. LSD has profound modulatory effects on consciousness and was used extensively in psychological research and psychiatric practice in the 1950s and 1960s [3]. In spite of this, however, there have been no modern human imaging studies of its acute effects on the brain. Here we studied the effects of LSD on intrinsic functional connectivity within the human brain using fMRI. High-level association cortices (partially overlapping with the default-mode, salience, and frontoparietal attention networks) and the thalamus showed increased global connectivity under the drug. The cortical areas showing increased global connectivity overlapped significantly with a map of serotonin 2A (5-HT2A) receptor densities (the key site of action of psychedelic drugs [4]). LSD also increased global integration by inflating the level of communication between normally distinct brain networks. The increase in global connectivity observed under LSD correlated with subjective reports of "ego dissolution." The present results provide the first evidence that LSD selectively expands global connectivity in the brain, compromising the brain's modular and "rich-club" organization and, simultaneously, the perceptual boundaries between the self and the environment.This research received financial support from the Safra Foundation (who fund DJN as the Edmond J. Safra Professor of Neuropsychopharmacology) and the Beckley Foundation (it was conducted as part of the Beckley-Imperial research programme). ET is supported by a postdoctoral fellowship of the AXA Research Fund. RCH is supported by an MRC clinical development scheme grant. SDM is supported by a Royal Society of New Zealand Rutherford Discovery Fellowship. KM is supported by a Wellcome Trust Fellowship (WT090199). The researchers would like to thank supporters of the Walacea.com crowd-funding campaign for helping to secure the funds required to complete the study. This report presents independent research carried out at the NIHR/Wellcome Trust Imperial Clinical Research Facility. Authors declare no conflict of interest.This is the author accepted manuscript. The final version is available from Cell Press via http://dx.doi.org/10.1016/j.cub.2016.02.01

National instruments labVIEW and video imaging technique for health status monitoring

No Abstract