TbCAPs: A toolbox for co-activation pattern analysis

Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique. Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics. The toolbox is available on a public academic repository at http://c4science.ch/source/CAP_Toolbox.git. In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R = 0.59).

Development and application of dynamic functional connectivity methods to elucidate the neural underpinnings of human behaviour with functional magnetic resonance imaging

Thomas William Arthur Bolton

Human behaviour is exquisitely complex, because it is staggeringly multi-facetted and subtly varies across individuals. Characterising its yet incompletely understood underlying biological mechanisms has profound clinical implications. Here, our goal was thus to study inter-individual behavioural variability. To accomplish this feat, we monitored the dynamics of brain activity through functional magnetic resonance imaging. The acquired signals only indirectly reflect neuronal mechanisms, and can be modulated by several confounding factors. A notorious one is head motion of the scanned volunteers. As an initial step, we addressed to what extent such movement would relate to inter-individual behavioural variability. We revealed strong, yet overlooked ties between a broad range of anthropometric, cognitive, sensory and clinical measures, and spatio-temporal head motion specificities. Next, as behaviour is an intrinsically dynamic process, we scrutinised the literature to pinpoint tailored time-resolved approaches for the analysis of brain activity. In a first application, we opted to track the dynamic reconfigurations of functional interactions (or dynamic functional connectivity) between brain regions over time in the context of an audiovisual stimulation paradigm. To mitigate the deleterious influences of head motion, computations were performed across subjects (a process known as inter-subject functional correlation). We discovered that some cues from the movie would be perceived homogeneously by a healthy population, while others would not. In addition, typically developing individuals exhibited more homogeneous brain activity than subjects diagnosed with autism spectrum disorder. Furthermore, the latter differed in their individual response to the movie as a function of the extent and balance of their socio-communicative and repetitive behaviour symptoms. In a second step, we attempted to link behaviour and brain activity in the absence of an overt task. We decomposed the interactions of a dorsolateral prefrontal region of interest with the rest of the brain into a set of co-activation patterns, and observed that their expression in the resting-state could predict attentional performance. As the spatio-temporal complexity of functional brain dynamics transpired from this analysis, we then set to develop a novel sparse coupled hidden Markov model framework that enables to quantify the dynamic interplays that take place between brain networks. To try and minimise spurious effects driven by head motion, we also considered an alternative methodological avenue, called graph signal processing, which enables to inject knowledge regarding brain structure (known to constrain neuronal activity) into the analyses. By studying functional brain dynamics from this viewpoint, we revealed an extensive array of links to behaviour. Furthermore, we also extended the originally static approach to the dynamic setting, and by this mean, further broadened the set of unraveled brain/behaviour relationships. Finally, we looked back at all the methods that we applied in theoretical terms and in their ability to predict inter-individual differences in behaviour. We concluded that they render complementary aspects of functional brain dynamics, and that in future years, the neuronal significance of brain/behaviour relationships should be further strengthened by pursuing more extended multimodal analyses.

EPFL2020

Psychobiological Vulnerability to Stress

Jorge Eduardo Castro Cifuentes, Maria del Carmen Sandi Perez

Several psychobiological factors are associated with vulnerability/resilience to stress-induced depression. Emerging evidence indicates behavioral traits, such as anxiety, can be related to depression-like symptoms after exposure to chronic stress. However, single traits cannot explain the variability observed in individual's vulnerability to stress, highlighting that a combination of behavioral traits might provide a better characterization of the individual's vulnerability to development of depression following prolonged stress. Hippocampal neurogenesis has been highlighted as a cellular mechanism involved in the reduction on hippocampal volume under conditions of stress and depression, suggesting that a lack of new cells and connections may underlie the cognitive and emotional symptoms of mood disorders, and modulating neurogenesis is regarded as a target/mechanism for antidepressants actions. Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine that is expressed and potentially involved in cellular proliferation in the central nervous system. MIF was shown to induce cell cycle progression via activation of the extracellular signal-regulated kinase (ERK) cascade. Stress has been shown to alter the degree of activation of the multifunctional ERK 1/2 -as indicated by its phosphorylation (p) pERK1/2- and pharmacological manipulations of the ERK pathway affected anxiety and coping behavior. Nonetheless, other mediating mechanisms are involved in the course of both depression and antidepressant actions. Even though the amygdala has been related to the etiology and recovery of depression through imaging studies showing correlations between these mood conditions and amygdala's volume and functional activation, the number of studies devoted to this brain area is still scarce. We hypothesize here an essential role of the amygdala on depression and antidepressant actions based on the implication of the basolateral complex (BLA) in emotional arousal and stress-induced modulation of cognitive processes, and on recent data showing activation of corticotropin releasing factor receptors (CRFR1) as a substrate for stress-induced alterations. Interestingly, alterations in the functional connectivity between the amygdala, the hippocampus and frontal areas have been identified in individuals with genetic vulnerability to depression. Looking for such psychobiological interactions is particularly important today because of increasingly incidence of depression and stress related disorders'. Identifying and understanding psychobiological vulnerability to stress is crucial for best oriented and more effective prevention and intervention strategies. Therefore, the aim of this thesis was: i) to identify in rats behavioral traits capable of predicting differences in vulnerability/resilience to the behavioral, endocrine, neurogenic and brain activity effects of stress, ii) to explore the role of the amygdala and personality traits in the antidepressants' effects on depression-like behavior and hippocampal neurogenesis, and iii) to study the role of MIF expression in hippocampal neurogenesis and anxiety- and depression- like behaviors. Our first study indicates that both the direction and intensity of the impact of stress on behavior corresponds to different patterns of neurogenesis impairment and brain activity; moreover, these effects are dependent on the interaction between personality traits and the length of stress exposure. Chronic stress induced progressively depressive-like symptoms associated with an increase in cell proliferation and a decrease of cell survival in the hippocampus and with a decrease in ERK1/2 activity in the prefrontal cortex, the hippocampus and a U-shape effect in the amygdala. Personality-like profiles of high exploration seem to play a resilient role for the development of stress-induced depressive-like symptoms and the combination of high anxiety and low exploration seems to lead to higher vulnerability to develop depression-like behaviors. Whereas sub chronic stress increased cell proliferation in the combined profile of high anxiety and low exploration, profiles of low exploration showed a decrease in cell survival after exposure to chronic stress. Interestingly, ERK1/2 activity was decreased in control animals of the personality profile high anxiety and low exploration and subsequently increased after sub-chronic and chronic stress in the paraventricular nucleus, the amygdala and the hippocampus but not in the orbitofrontal cortex. The second study provides evidence for an important role for the amygdala on fluoxetine-stimulated cell proliferation and survival, as well as on the establishment of a link between cell proliferation and depression-like behavior. We also show an important role for anxiety in mediating the effects of basolateral amygdala and antidepressant activity on cell proliferation and survival. Chronic fluoxetine treatment had a positive effect on hippocampal cell survival only when the BLA was lesioned. Anxiety was related to hippocampal cell survival in opposite ways in sham- and BLA-lesioned animals (i.e., negatively in sham- and positively in BLA-lesioned animals). Both BLA lesions and low anxiety were critical factors to enable a negative relationship between cell proliferation and depression-like behavior. Therefore, our study highlights a role for the amygdala on fluoxetine-stimulated cell survival and on the establishment of a link between cell proliferation and depression-like behavior. It also reveals an important modulatory role for anxiety on cell proliferation involving both BLA-dependent and -independent mechanisms. The third study demonstrates the relevance of MIF expression for adult hippocampal neurogenesis. We identify MIF expression in neurogenic cells (in stem cells, cells undergoing proliferation and in newly proliferated cells undergoing maturation) in the subgranular zone of the rodent dentate gyrus. A causal role for MIF in cell proliferation was demonstrated using genetic and pharmacological approaches. Behaviorally, genetic deletion of MIF resulted in increased anxiety- and depression-like behaviors, as well as of impaired hippocampus-dependent memory. Together, our studies provide evidence supporting a pivotal role for MIF in both basal and antidepressant-stimulated adult hippocampal cell proliferation. Moreover, loss of MIF results in a behavioral phenotype that to a large extent corresponds with alterations predicted to arise from reduced hippocampal neurogenesis. These findings underscore MIF as a potentially relevant molecular target for the development of treatments linked to deficits in neurogenesis, as well as to problems related to anxiety, depression and cognition. Altogether the results presented support the idea of high anxiety trait individuals being a particularly prone phenotype and exposure to stressful life events as a requirement for individuals with this personality trait to develop depression.

EPFL2012

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