Retrieving the hemodynamic response function in resting state fMRI: methodology and application

In this paper we present a procedure to retrieve the hemodynamic response function (HRF) from resting state functional magnetic resonance imaging (fMRI) data. The fundamentals of the procedures are further validated by considering simultaneous electroencephalographic (EEG) recordings. The typical HRF shape at rest for a group of healthy subject is presented. Then we present the modifications to the shape of the HRF at rest following two physiological modulations: eyes open versus eyes closed and propofol-induced modulations of consciousness

Intermittent theta burst stimulation increases reward responsiveness in individuals with higher hedonic capacity

Background: Repetitive transcranial magnetic stimulation over the left dorsolateral prefrontal cortex (DLPFC) has been documented to influence striatal and orbitofrontal dopaminergic activity implicated in reward processing. However, the exact neuropsychological mechanisms of how DLPFC stimulation may affect the reward system and how trait hedonic capacity may interact with the effects remains to be elucidated. Objective: In this sham-controlled study in healthy individuals, we investigated the effects of a single session of neuronavigated intermittent theta burst stimulation (iTBS) on reward responsiveness, as well as the influence of trait hedonic capacity. Methods: We used a randomized crossover single session iTBS design with an interval of 1 week. We assessed reward responsiveness using a rewarded probabilistic learning task and measured individual trait hedonic capacity (the ability to experience pleasure) with the temporal experience of pleasure scale questionnaire. Results: As expected, the participants developed a response bias towards the most rewarded stimulus (rich stimulus). Reaction time and accuracy for the rich stimulus were respectively shorter and higher as compared to the less rewarded stimulus (lean stimulus). Active or sham stimulation did not seem to influence the outcome. However, when taking into account individual trait hedonic capacity, we found an early significant increase in the response bias only after active iTBS. The higher the individuals trait hedonic capacity, the more the response bias towards the rich stimulus increased after the active stimulation. Conclusion: When taking into account trait hedonic capacity, one active iTBS session over the left DLPFC improved reward responsiveness in healthy male participants with higher hedonic capacity. This suggests that individual differences in hedonic capacity may influence the effects of iTBS on the reward system

Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state fMRI data from the Human Connectome Project, showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1403.515

Information transfer of an Ising model on a brain network

We implement the Ising model on a structural connectivity matrix describing the brain at a coarse scale. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the number of incoming links

Statistical approaches for resting state fMRI data analysis

This doctoral dissertation investigates the methodology to explore brain dynamics from resting state fMRI data. A standard resting state fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. There are two main objectives in the analysis of these noisy data: establishing the link between neural activity and the measured signal, and determining distributed brain networks that correspond to brain function. These measures can then be used as indicators of psychological, cognitive or pathological states. Two main issues will be addressed: retrieving and interpreting the hemodynamic response function (HRF) at rest, and dealing with the redundancy inherent to fMRI data. Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions

Accelerated iTBS treatment applied to the left DLPFC in depressed patients results in a rapid volume increase in the left hippocampal dentate gyrus, not driven by brain perfusion

Background: Accelerated intermittent Theta Burst Stimulation (aiTBS) has been shown to be an effective antidepressant treatment. Although neurobiological changes shortly after this intervention have been reported, whether aiTBS results in structural brain changes must still be determined. Furthermore, it possible that rapid volumetric changes are driven by factors other than neurotrophic processes. Objectives: We examined whether possible grey matter volumetric (GMV) increases after aiTBS treatment could be driven by increased brain perfusion, measured by Arterial Spin Labeling (ASL). Methods: 46 treatment-resistant depressed patients were randomized to receive 20 sessions of active or sham iTBS applied to the left dorsolateral prefrontal cortex. All sessions were delivered over 4 days at 5 sessions per day (trial registration: http://clinicaltrials.gov/show/NCT01832805). Patients were scanned the day before starting stimulation and three days after aiTBS. Results: There was a significant cluster of increased left hippocampal GMV in the dentate gyrus related to HRSD changes after active aiTBS, but not after sham stimulation. These GMV increases became more pronounced when accounting for changes in cerebral perfusion. Conclusions: Active, but not sham, aiTBS, resulted in acute volumetric changes in parts of the left dentate gyrus, suggesting a connection with adult neurogenesis. Furthermore, taking cerebral perfusion measurements into account impacts on detection of the GMV changes. Whether these hippocampal volumetric changes produced by active aiTBS are necessary for long-term clinical improvement remains to be determined. (C) 2020 The Author(s). Published by Elsevier Inc

Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

Antioxidant and anti-inflammatory effects of rhamnazin on lipopolysaccharide-induced acute lung injury and inflammation in rats

Background: Acute Lung Injury (ALI) results into severe inflammation and oxidative stress to the pulmonary tissue. Rhamnazin is a natural flavonoid and known for its antioxidant and anti-inflammatory properties.Materials and methods: The antioxidative and anti-inflammatory properties rhamnazin were tested for protection against the acute lung injury. We investigated whether rhamnazin improves the lipopolysaccharide (LPS)-induced ALI in an animal model (rat). We also studied the probable molecular mechanism of action of rhamnazin. Rhamnazin was injected intraperitoneally (i.p.) (5, 10 and 20 mg/kg) two days before intratracheal LPS challenge (5mg/kg). The changes in lung wet-to-dry weight ratio, LDH activity, pulmonary histopathology, BALF protein concentration, MPO activity, oxidative stress, cytokine production were estimated.Results: The results showed a significant attenuation of all the inflammatory parameters and a marked improvement in the pulmonary histopathology in the animal groups pretreated with rhamnazin. The rhamnazin pretreated group also showed activation of Nrf2 pathway and attenuation of ROS such as H2O2, MDA and hydroxyl ion. These results indicated that rhamnazin could attenuate the symptoms of ALI in rats due to its strong antioxidant and anti-inflammatory properties.Conclusion: The results strongly demonstrated that rhamnazin provides protection against LPS-induced ALI. The underlying mechanisms of its anti-inflammatory action may include inhibition of Nrf2 mediated antioxidative pathway.Keywords: acute lung injury, inflammation, cytokine, BALF, flavonoi