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Proceedings 2018

Proceedings 2018 are being published on a special topic of Frontiers in Physics (Biomedical Physics section) with crosslisting on Frontiers in Physiology (Biomedical Physics section) Frontiers in Neuroscience (Brain Imaging Methods section) and Frontiers in Neural Circuits. Contributors will have the choice of the journal. 

Frontiers in are open-access jounals, and a fee is required. Papers participating to special topics are granted a discount.

Speakers and young scientists presenting a poster will be invited directly. If you think your contribution can fall within the scope of the proceedings. please contact one of the Editors or submit directly your abstract.

The Proceedings of the 2016 Workshop are available as an eBook:

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Otherwise, single papers can be checked or downloaded below (press url or doi link to browse online and download).

  1. Federico Giove and Itamar Ronen.
    Editorial: Proceedings of the International School on Magnetic Resonance and Brain Function – XII Workshop. Frontiers in Physics, 2018.
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  2. Silvia Mangia, Alena Svatkova, Daniele Mascali, Mikko J Nissi, Philip C Burton, Petr Bednarik, Edward J Auerbach, Federico Giove, Lynn E Eberly, Michael J Howell, Igor Nestrasil, Paul J Tuite and Shalom Michaeli.
    Multi-modal Brain MRI in Subjects with PD and iRBD. Frontiers in Neuroscience 11:709, 2017.
    Abstract Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a condition that often evolves into Parkinson’s disease (PD). Therefore, by monitoring iRBD it is possible to track the neurodegeneration of individuals who may progress to PD. Here we aimed at piloting the characterization of brain tissue properties in mid-brain subcortical regions of 10 healthy subjects, 8 iRBD, and 9 early-diagnosed PD. We used a battery of magnetic resonance imaging (MRI) contrasts at 3 T, including adiabatic and non-adiabatic rotating frame techniques developed by our group, along with diffusion tensor imaging and resting-state fMRI. Adiabatic T1 and T2, and non-adiabatic RAFF4 (Relaxation Along a Fictitious Field in the rotating frame of rank 4) were found to have lower coefficient of variations and higher sensitivity to detect group differences as compared to DTI parameters such as fractional anisotropy and mean diffusivity. Significantly longer T1 were observed in the amygdala of PD subjects versus controls, along with a trend of lower functional connectivity as measured by network regional homogeneity, thereby supporting the notion that amygdalar dysfunction occurs in PD. Significant abnormalities in reward networks occurred in iRBD subjects, who manifested lower network strength of the accumbens. In agreement with previous studies, significantly longer T1 occurred in the substantia nigra compacta of PD versus controls, indicative of neuronal degeneration, while regional homogeneity was lower in the network of the substantia nigra reticulata. Finally, other trend-level findings were observed, i.e., lower RAFF4 and T2 in the midbrain of iRBD subjects vs controls, possibly indicating changes in non-motor features as opposed to motor function in the iRBD group. We conclude that rotating frame relaxation methods along with functional connectivity measures are valuable to characterize iRBD and PD subjects, and with proper validation in larger cohorts may provide pathological signatures of iRBD and PD.
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  3. Martin Havlicek, Dimo Ivanov, Alard Roebroeck and Kamil Uludağ.
    Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model. Frontiers in Neuroscience 11:616, 2017.
    Abstract Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated P-DCM’s ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of P-DCM to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting P-DCM to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, P-DCM that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.
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  4. Hassan B Hawsawi, David W Carmichael and Louis Lemieux.
    Safety of Simultaneous Scalp or Intracranial EEG during MRI: A Review. Frontiers in Physics 5:42, 2017.
    Abstract Understanding the brain and its activity is one of the great challenges of modern science. Normal brain activity (cognitive processes, etc.) has been extensively studied using electroencephalography (EEG) since the 1930’s, in the form of spontaneous fluctuations in rhythms, and patterns, and in a more experimentally-driven approach in the form of event-related potentials allowing us to relate scalp voltage waveforms to brain states and behaviour. The use of EEG recorded during functional magnetic resonance imaging (EEG-fMRI) is a more recent development that has become an important tool in clinical neuroscience, for example for the study of epileptic activity. The purpose of this review is to explore the magnetic resonance imaging safety aspects specifically associated with the use of scalp EEG and other brain-implanted electrodes such as intracranial EEG electrodes when they are subjected to the MRI environment. We provide a theoretical overview of the mechanisms at play specifically associated with the presence of EEG equipment connected to the subject in the MR environment, and of the resulting health hazards. This is followed by a survey of the literature on the safety of scalp or invasive EEG-fMRI data acquisitions across field strengths, with emphasis on the practical implications for the safe application of the techniques; in particular, we attempt to summarize the findings in terms of acquisition protocols when possible.
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  5. Petr Bednarik, Amir A Moheet, Heidi Grohn, Anjali F Kumar, Lynn E Eberly, Elizabeth R Seaquist and Silvia Mangia.
    Type 1 Diabetes and Impaired Awareness of Hypoglycemia Are Associated with Reduced Brain Gray Matter Volumes. Frontiers in Neuroscience 11:529, 2017.
    Abstract In this study, we retrospectively analyzed the anatomical MRI data acquired from 52 subjects with type 1 diabetes (26M/26F, 36±11 years old A1C=7.2%±0.9%) and 50 age, sex and BMI frequency-matched non-diabetic controls (25M/25F, 36±14 years old). The T1D group was further sub-divided based on whether subjects had normal, impaired, or indeterminate awareness of hypoglycemia (n=31, 20, and 1, respectively). Our goals were to test whether the grey matter volumes of selected brain regions were associated with diabetes status as well as with the status of hypoglycemia awareness. T1D subjects were found to have slightly smaller volume of the whole cortex as compared to controls (-2.7% p=0.016), with the most affected brain region being the frontal lobe (-3.6% p=0.024). Similar differences of even larger magnitude were observed among the T1D subjects based on their hypoglycemia awareness status. Indeed, compared to the patients with normal awareness of hypoglycemia, patients with impaired awareness had smaller volume of the whole cortex (-7.9% p=0.0009), and in particular of the frontal lobe (-9.1% p=0.006), parietal lobe (-8.0% p=0.015) and temporal lobe (-8.2% p=0.009). Such differences were very similar to those observed between patients with impaired awareness and controls (-7.6% p=0.0002 in whole cortex, -9.1% p=0.0003 in frontal lobe, -7.8% p=0.002 in parietal lobe, and -6.4% p=0.019 in temporal lobe). On the other hand, patients with normal awareness did not present significant volume differences compared to controls. No group-differences were observed in the occipital lobe or in the anterior cingulate, posterior cingulate, hippocampus and thalamus. We conclude that diabetes status is associated with a small but statistically significant reduction of the whole cortex volume, mainly in the frontal lobe. The most prominent structural effects occurred in patients with impaired awareness of hypoglycemia as compared to those with normal awareness, perhaps due to the long-term exposure to recurrent episodes of hypoglycemia. Future studies aimed at quantifying relationships of structural outcomes with functional outcomes, with cognitive performance, as well as with parameters describing glucose variability and severity of hypoglycemia episodes, will be necessary to further understand the impact of T1D on the brain.
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  6. Lauri J Lehto, Aloma A Albors, Alejandra Sierra, Laura Tolppanen, Lynn E Eberly, Silvia Mangia, Antti Nurmi, Shalom Michaeli and Olli Gröhn.
    Lysophosphatidyl Choline Induced Demyelination in Rat Probed by Relaxation along a Fictitious Field in High Rank Rotating Frame. Frontiers in Neuroscience 11:433, 2017.
    Abstract In this work a new MRI modality entitled Relaxation Along a Fictitious Field in the rotating frame of rank 4 (RAFF4) was evaluated in its ability to detect lower myelin content in lysophosphatidyl choline (LPC)-induced demyelinating lesions. The lesions were induced in two areas of the rat brain with either uniform or complex fiber orientations, i.e., in the corpus callosum (cc) and dorsal tegmental tract (dtg), respectively. RAFF4 showed excellent ability to detect demyelinated lesions and good correlation with myelin content in both brain areas. In comparison, diffusion tensor imaging metrices, fractional anisotropy, mean diffusivity and axonal and radial diffusivity, and magnetization transfer (MT) metrices, longitudinal relaxation during off-resonance irradiation and MT ratio, either failed to detect demyelination in dtg or showed lower correlation with myelin density quantified from gold chloride stained histological sections. Good specifity of RAFF4 to myelin was confirmed by its low correlation with cell density assesed from Nissl stained sections as well as its lack of sensitivity to pH changes in the physiological range as tested in heat denaturated bovine serum albumin phantoms. The excellent ability of RAFF4 to detect myelin content and its insensitivity to fiber orientation distribution, gliosis and pH, together with low specific absorption rate, demonstrates the promise of RAFFn as a valuable MRI technique for non-invasive imaging of demyelinating lesions.
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  7. Cécile Bordier, Carlo Nicolini and Angelo Bifone.
    Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold. Frontiers in Neuroscience 11:441, 2017.
    Abstract Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks’ community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman’s modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
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  8. Seungkyu Nam and Dae-Shik Kim.
    Reconstruction of Arm Movement Directions from Human Motor Cortex Using fMRI. Frontiers in Neuroscience 11:434, 2017.
    Abstract Recent advances in functional magnetic resonance imaging (fMRI) have been used to reconstruct cognitive states based on brain activity evoked by sensory or cognitive stimuli. To date, such decoding paradigms were mostly used for visual modalities. On the other hand, reconstructing functional brain activity in motor areas was primarily achieved through more invasive electrophysiological techniques. Here, we investigated whether non-invasive fMRI responses from human motor cortex can also be used to predict individual arm movements. To this end, we conducted fMRI studies in which participants moved their arm from a center position to one of eight target directions. Our results suggest that arm movement directions can be distinguished from the multivoxel patterns of fMRI responses in motor cortex. Furthermore, compared to multivoxel pattern analysis, encoding models were able to also reconstruct unknown movement directions from the predicted brain activity. We conclude for our study that non-invasive fMRI signal can be utilized to predict directional motor movements in human motor cortex.
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  9. Silvia Tommasin, Daniele Mascali, Tommaso Gili, Ibrahim Eid Assan, Marta Moraschi, Michela Fratini, Richard G Wise, Emiliano Macaluso, Silvia Mangia and Federico Giove.
    Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network. Frontiers in Physics 5:31, 2017.
    Abstract Spontaneous low-frequency Blood–Oxygenation Level–Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33±6 years, 8F/12M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the steady-state execution of a sustained working memory n-back task. We found that the steady state execution of such a task impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to steady-state task execution, can contribute to a better understanding of how brain networks rearrange themselves in response of a task.
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  10. Jeroen Van Schependom and Guy Nagels.
    Targeting Cognitive Impairment in Multiple Sclerosis—The Road toward an Imaging-based Biomarker. Frontiers in Neuroscience 11:380, 2017.
    Abstract Multiple Sclerosis (MS) is a neurodegenerative and –inflammatory disease leading to physical and cognitive impairment, pathological fatigue and depression, and affecting patients’ quality of life and employment status. The combination of inflammation, demyelination and neurodegeneration leads to the emergence of MS lesions, reduced white and grey matter brain volumes, a reduced conduction velocity and microstructural changes in the so-called Normal Appearing White Matter (NAWM). Currently, there are very limited options to treat cognitive impairment and its origin is only poorly understood. Therefore, several studies have attempted to relate clinical scores with features calculated either using T1- and/or FLAIR weighted MR images or using neurophysiology. The aim of those studies is not only to provide an improved understanding of the processes that underlie the different symptoms, but also to develop a biomarker – sensitive to therapy induced change – that could be used to speed up therapeutic developments (e.g. cognitive training / drug discovery / …). Here, we provide an overview of studies that have established relationships between either neuro-anatomical or –neurophysiological measures and cognitive outcome scores. We discuss different avenues that may help to improve the prediction of cognitive impairment, and how well we can expect them to predict cognitive scores.
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  11. Sarah Sonnay, Rolf Gruetter and João M N Duarte.
    How Energy Metabolism Supports Cerebral Function: Insights from 13C Magnetic Resonance Studies In vivo. Frontiers in Neuroscience 11:288, 2017.
    Abstract Cerebral function is associated with exceptionally high metabolic activity, and requires continuous supply of oxygen and nutrients from the blood stream. Since the mid-20th century the idea that brain energy metabolism is coupled to neuronal activity has emerged, and a number of studies supported this hypothesis. Moreover, brain energy metabolism was demonstrated to be compartmentalized in neurons and astrocytes, and astrocytic glycolysis was proposed to serve the energetic demands of glutamatergic activity. Shedding light on the role of astrocytes in brain metabolism, the earlier picture of astrocytes being restricted to a scaffold-associated function in the brain is now out of date. With the development and optimization of non-invasive techniques, such as nuclear magnetic resonance spectroscopy (MRS), several groups have worked on assessing cerebral metabolism in vivo. In this context, 1H MRS has allowed the measurements of energy metabolism-related compounds, whose concentrations can vary under different brain activation states. 1H-[13C] MRS, i.e. indirect detection of signals from 13C-coupled 1H, together with infusion of 13C-enriched glucose has provided insights into the coupling between neurotransmission and glucose oxidation. Although these techniques tackle the coupling between neuronal activity and metabolism, they lack chemical specificity and fail in providing information on neuronal and glial metabolic pathways underlying those processes. Currently, the improvement of detection modalities (i.e. direct detection of 13C isotopomers), the progress in building adequate mathematical models along with the increase in magnetic field strength now available, render possible detailed compartmentalized metabolic flux characterization. In particular, direct 13C MRS offers more detailed dataset acquisitions and provide information on metabolic interactions between neurons and astrocytes, and their role in supporting neurotransmission. Here we review state-of-the-art MR methods to study brain function and metabolism in vivo, and their contribution to the current understanding of how astrocytic energy metabolism supports glutamatergic activity and cerebral function. In this context, recent data suggests that astrocytic metabolism has been underestimated. Namely, the rate of oxidative metabolism in astrocytes is about half of that in neurons, and it can increase as much as the rate of neuronal metabolism in response to somatosensory stimulation.
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  12. Ian D Driver, Richard G Wise and Kevin Murphy.
    Graded Hypercapnia-Calibrated BOLD: Beyond the Iso-metabolic Hypercapnic Assumption. Frontiers in Neuroscience 11:276, 2017.
    Abstract Calibrated BOLD is a promising technique that overcomes the sensitivity of conventional fMRI to the cerebrovascular state; measuring either the basal level, or the task-induced response of cerebral metabolic rate of oxygen consumption (CMRO2). The calibrated BOLD method is susceptible to errors in the measurement of the calibration parameter M, the theoretical BOLD signal change that would occur if all deoxygenated hemoglobin were removed. The original and most popular method for measuring M uses hypercapnia (an increase in arterial CO2), making the assumption that it does not affect CMRO2. This assumption has since been challenged and recent studies have used a corrective term, based on literature values of a reduction in basal CMRO2 with hypercapnia. This is not ideal, as this value may vary across subjects and regions of the brain, and will depend on the level of hypercapnia achieved. Here we propose a new approach, using a graded hypercapnia design and the assumption that CMRO2 changes linearly with hypercapnia level, such that we can measure M without assuming prior knowledge of the scale of CMRO2 change. Through use of a graded hypercapnia gas challenge, we are able to remove the bias caused by a reduction in basal CMRO2 during hypercapnia, whilst simultaneously calculating the dose-wise CMRO2 change with hypercapnia. When compared with assuming no change in CMRO2, this approach resulted in significantly lower M values in both visual and motor cortices, arising from significant dose-dependent hypercapnia reductions in basal CMRO2 of 1.5±0.6%/mmHg (visual) and 1.8±0.7%/mmHg (motor), where mmHg is the unit change in end-tidal CO2 level. Variability in the basal CMRO2 response to hypercapnia, due to experimental differences and inter-subject variability, is accounted for in this approach, unlike previous correction approaches, which use literature values. By incorporating measurement of, and correction for, the reduction in basal CMRO2 during hypercapnia in the measurement of M values, application of our approach will correct for an overestimation in both CMRO2 task-response values and absolute CMRO2.
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