Real-Time fMRI Neurofeedback: Methods and Application to Chronic Tinnitus

In the last twenty years, advances in real-time functional magnetic resonance imaging (rt-fMRI) have offered exciting new tools to study the human brain. One of them, termed rt-fMRI neurofeedback (NF), has turned the MRI scanner environment into an interactive brain-computer interface (BCI), allowing to position brain activity as the independent variable in the relationship ranging from brain function to observed behavior. This paradigm immediately gained considerable interest in the fMRI research community, as the idea of grasping volitional control over a precisely targeted brain area suddenly became feasible. Since then, a multitude of studies have used this technique in drastic efforts to normalize pathological brain activity (or connectivity) in a wide range of neurological disorders, ultimately seeking clinical and behavioral improvement.Among these, chronic tinnitus, commonly referred to as "ringing in the ears", has been considered a promising condition for NF interventions. Tinnitus has been linked to wide disruptions in brain functions involving attention, cognition, emotional distress, and auditory and sensory processing, among others. It has also been associated with hyperactivity in the primary auditory cortex after acoustic trauma. Currently, no cure exists for a complete relief of symptoms. A previous proof-of-concept study suggested that learned down-regulation of the auditory cortex slightly improved short-term subjective reports of tinnitus severity in a small group of participants. Here, we present the longest NF clinical trial to date in chronic tinnitus (NeuroTin), in which 60 patients are enrolled across 3 different interventions: electroencephalography NF (EEG NF), fMRI NF, and cognitive behavioral therapy (CBT), nowadays considered as standard care for tinnitus. The aim is to assess whether any of the two NF approaches can outperform standard CBT training for alleviation of tinnitus distress. In this dissertation, we present clinical outcomes and neuroimaging findings for the fMRI NF group.Twenty-one participants learned to down-regulate the activity of their primary auditory cortex over 15 weeks of fMRI NF training. While only a few participants managed to learn down-regulation, we found a significant clinical relief in tinnitus distress lasting up to one year in the fMRI NF group, as opposed to the CBT group. We demonstrate the superiority of fMRI NF training over standard care CBT and show, for the first time, resulting long-term benefits in quality of life. Furthermore, we bring additional evidence regarding the implication of a highly modulated small brain region in tinnitus, bilaterally located in the parietal operculum (OP3). We describe its effective connectivity with regions pertaining to the auditory pathway and discuss its implication in a possibly altered multisensory integration mechanism in tinnitus. Then, using a co-activation patterns analysis, we unveil 4 functional brain networks co-occurring with the deactivation of OP3. We identify them as (1) an auditory-visual network comprising an important node of the auditory pathway (the medial geniculate body), (2) a part of the NF control network, (3) the default mode network, and (4) a somatosensory network likely linked to other sensory integration modalities. Lastly, we characterize the average dynamics of these 4 functional networks and discuss new avenues for fMRI NF interventions and treatments in chronic tinnitus.

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Fikret Isik Karahanoglu

Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) contrast as a proxy for neuronal activity. The impact of fMRI on neurosciences, medicine, and psychology is ever increasing and has been mainly focussing on (1) understanding brain organization in terms of segregation (i.e., localized processing) and integration (i.e., distributed processing), specifically, related to sensory processing and cognition; (2) exploring temporal characteristics of brain processes. FMRI provides large spatiotemporal datasets, typically one whole-brain volume with spatial resolution of 1–3 mm in each dimension, every 1–4 seconds during several minutes. The structure of neurophysiological contributions in these data is complex and therefore requires advanced data processing. Conventional fMRI analysis is exploiting timing properties of a stimulation or task paradigm designed by the experimenter; i.e., evidence is sought for the presence of a hypothetical BOLD response. More recently, the community has shown increasing interest in spontaneous brain activity acquired during resting-state fMRI (RS-fMRI). In the absence of any task, data-driven or exploratory methods have found great use. In particular, blind source separation such as independent component analysis (ICA) has been widely applied to RS-fMRI data. One limitation of current data-driven methods is the lack of incorporating knowledge about the hemodynamic system, which governs any activity-related signal component in the fMRI measurements. In this dissertation, we build upon the latest advances in convex optimization and propose a novel framework that can reveal activity-inducing signals at the fMRI timescale. In particular, our regularization strategy, termed “total activation” (TA), allows deconvolving the fMRI signal to remove hemodynamic blur and to improve spatial contrast of activation patterns by incorporating knowledge about meaningful brain regions. The contribution of our method lies in adapting and tailoring state-of-the-art signal processing techniques with specific domain knowledge from fMRI and neurosciences. First, we extend “total variation” (TV), which is a well-recognized method in image processing for edge-preserving regularization. TV favors signals that are piecewise constant, and, therefore, whose derivatives are sparse. We generalize this concept for signals of which the derivative of an additional linear differential operator is sparse, and build a variational formulation for the denoising problem. The recovered signal can be also studied after applying the regularizing operators; e.g., applying the differential operator will lead to the piecewise constant driving signal, while applying an additional derivative reveals a sparse “innovation” signal. Fast and efficient schemes from convex optimization are deployed to solve the variational problem at hand. Second, we apply TA for fMRI data analysis to explore the underlying “deblurred” activity-inducing signals. The temporal regularization is based on generalization of TV where the differential operator is chosen to invert the (linearized) hemodynamic system. Consequently, this will favor block-type activity-inducing signals without restrictions on timing nor duration. The spatial regularization uses a mixed-norm regularization to favor coherent activity-inducing signals in brain regions chosen from an anatomical brain atlas. After demonstrating the feasibility of the proposed method using simulated data, we show results on experimental fMRI data consisting of long resting-state periods and a few unanticipated visual stimuli. The method is able to readily recover plausible activation patterns for the visual stimuli without any prior knowledge about their timing. More interestingly, we also recovered complex spatiotemporal patterns of spontaneous activity that were organized in “resting-state networks”, as such providing a new approach to study non-stationary dynamics of RS-fMRI, a research direction that will be of major interest in the coming years. Finally, we include convincing results for localizing epileptogenic brain regions in patients using simultaneous EEG-fMRI recordings.

EPFL2013

Dynamic brain networks explored by structure-revealing methods

Nora Leonardi

The human brain is a complex system able to continuously adapt. How and where brain activity is modulated by behavior can be studied with functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique with excellent spatial resolution and whole-brain coverage. FMRI scans of healthy adults completing a variety of behavioral tasks have greatly contributed to our understanding of the functional role of individual brain regions. However, by statistically analyzing each region independently, these studies ignore that brain regions act in concert rather than in unison. Thus, many studies since have instead examined how brain regions interact. Surprisingly, structured interactions between distinct brain regions not only occur during behavioral tasks but also while a subject rests quietly in the MRI scanner. Multiple groups of regions interact very strongly with each other and not only do these groups bear a striking resemblance to the sets of regions co-activated in tasks, but many of these interactions are also progressively disrupted in neurological diseases. This suggests that spontaneous fluctuations in activity can provide novel insights into fundamental organizing principles of the human brain in health and disease. Many techniques to date have segregated regions into spatially distinct networks, which ignores that any brain region can take part in multiple networks across time. A more natural view is to estimate dynamic brain networks that allow flexible functional interactions (or connectivity) over time. The estimation and analysis of such dynamic functional interactions is the subject of this dissertation. We take the perspective that dynamic brain networks evolve in a low-dimensional space and can be described by a small number of characteristic spatiotemporal patterns. Our proposed approaches are based on well-established statistical methods, such as principal component analysis (PCA), sparse matrix decompositions, temporal clustering, as well as a multiscale analysis by novel graph wavelet designs. We adapt and extend these methods to the analysis of dynamic brain networks. We show that PCA and its higher-order equivalent can identify co-varying functional interactions, which reveal disturbed dynamic properties in multiple sclerosis and which are related to the timing of stimuli for task studies, respectively. Further we show that sparse matrix decompositions provide a valid alternative approach to PCA and improve interpretability of the identified patterns. Finally, assuming an even simpler low-dimensional space and the exclusive temporal expression of individual patterns, we show that specific transient interactions of the medial prefrontal cortex are disturbed in aging and relate to impaired memory.

EPFL2014

Visuo-vestibular mechanisms of bodily self-consciousness

Joachim Jean-Marie Forget

Bodily self-consciousness is linked to multisensory integration and is particularly dependent on vestibular perception providing the brain with the main sensory cues about body motion and location in space. Vestibular and visual inputs are permanently balanced and integrated to encode the most optimal representation of the external world and of the observer in the central nervous system. Vection, an illusory self-motion experience induced only by visual stimuli, illustrates the fact that the visual and the vestibular systems share common neural underpinnings and a similar phenomenology. Optokinetic stimulation inducing vection and direct vestibular stimulation induce whole-body motion sensations that can be used to explore multisensory interactions. A failure in visuo-vestibular integration, artificially induced by the methods of cognitive psychology or in pathological conditions, has also been reported to altered own body perception and bodily self-consciousness. The respective contributions of the vestibular and visual systems to bodily self-consciousness amongst other polymodal sensory mechanisms, and the neural correlates of visuo-vestibular convergence, should be better understood. We first performed a neuroimaging study of brain regions where optokinetic and vestibular stimuli converge, using 7T functional magnetic resonance imaging in individual subjects. We identified three main regions of convergence: (1) the depth of supramarginal gyrus or retroinsular cortex, (2) the surface of supramarginal gyrus at the temporo-parietal junction, (3) and the posterior part of middle temporal gyrus and superior temporal sulcus. Then, we aimed to induce the embodiment of an external fake rubber hand through visuo-tactile conflict - the so-called rubber hand illusion paradigm, and studied how this integration is modulated by vection. Subjects experiencing vection in the direction of the rubber hand mislocalised the position of their real hand towards the rubber hand indicating that visuo-vestibular stimuli can enhance visuo-tactile integration. We also investigated if visuo-proprioceptive and tactile integration in peripersonal space could be dynamically updated based on the congruency of visual and proprioceptive feedback. A pair of rubber hands or feet provided visual feedback. Fake and real limbs were crossed or uncrossed. We showed that sensory cues were integrated in peripersonal space, dynamically reshaped but only for hands. Finally, we investigated a rare case of an illusory own body perception in an epileptic patient suffering from multiple daily disembodiments during seizures. Seizures were associated to a focal cortical microdysplasia juxtaposed to a developmental venous anomaly in the left angular gyrus, a brain region known to be important for visuo-vestibular integration and bodily self-consciousness. Our results characterize the inferior parietal lobule as a crucial structure in merging visual, vestibular, tactile and proprioceptive inputs, allowing the emergence of the global and unified experience of being âI.â Multisensory body representation can be reshaped transiently using visual and vestibular signals or in relation to a medical condition affecting the temporo-parietal junction. The integration of visual and vestibular signals, aims to adapt dynamically our internal representations to constant changes occurring in our environment.

EPFL2015