On nodes and modes in resting state fMRI.

This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries - and simulations of neuronal fluctuations based upon the connectome. Its particular focus is on patterns or modes of distributed activity that underlie functional connectivity. We first demonstrate that the eigenmodes of functional connectivity - or covariance among regions or nodes - are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections. This symmetry constraint is motivated by appealing to proximity graphs based upon multidimensional scaling. Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory. Technically, these modes have small negative Lyapunov exponents that approach zero from below. Interestingly, the superposition of modes - whose exponents are sampled from a power law distribution - produces classical 1/f (scale free) spectra. We conjecture that the emergence of dynamical instability - that underlies intrinsic brain networks - is inevitable in any system that is separated from external states by a Markov blanket. This conjecture appeals to a free energy formulation of nonequilibrium steady-state dynamics. The common theme that emerges from these theoretical considerations is that endogenous fluctuations are dominated by a small number of dynamically unstable modes. We use this as the basis of a dynamic causal model (DCM) of resting state fluctuations - as measured in terms of their complex cross spectra. In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents - that can also be interpreted as locations in a multidimensional scaling space. Model inversion provides not only estimates of edges or connectivity but also the topography and dimensionality of the underlying scaling space. Here, we focus on conceptual issues with simulated fMRI data and provide an illustrative application using an empirical multi-region timeseries

The connected brain: Causality, models and intrinsic dynamics

Recently, there have been several concerted international efforts - the BRAIN initiative, European Human Brain Project and the Human Connectome Project, to name a few - that hope to revolutionize our understanding of the connected brain. Over the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We will highlight the theoretical advances made in the (dynamic) causal modelling of brain function - that may have escaped the wider audience of this article - and provide a brief overview of recent developments and interesting clinical applications. We hope that this article will engage the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain\u27s structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions

Construct validation of a DCM for resting state fMRI

Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems - known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for resting state fMRI - as measured in terms of their complex cross spectral density - referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden states. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs

Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on bloodoxygenation- level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a “network disease” as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions

Measuring directed functional connectivity in mouse fMRI networks using Granger Causality

Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network”. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of directed maps of functional connectivity in the human brain, providing a novel interpretative dimension that could advance our understanding of the brains’ functional organization. Here, we used Granger Causality (GC), a measure of directed causation, to investigate the direction of information flow within mouse rsfMRI networks characterized by unidirectional (i.e. frontal-hippocampal) as well as reciprocal (e.g. DMN) underlying connectional architecture. We observed robust hippocampal-prefrontal dominant connectivity along the direction of projecting ventro-subicular neurons both at single subject and population level. Analysis of key DMN nodes revealed the presence of directed functional connectivity from temporal associative cortical regions to prefrontal and retrosplenial cortex, reminiscent of directional connectivity patterns described for the human DMN. We also found robust directional connectivity from insular to prefrontal areas. In a separate study, we reproduced the same directional connectivity fingerprints and showed that mice recapitulating a mutation associated to autism spectrum disorder exhibited reduced or altered directional connectivity. Collectively, our results document converging directional connectivity towards retrosplenial and prefrontal cortical areas consistent with higher integrative functions subserved by these regions, and provide a first description of directional topology in resting-state connectivity networks that complements ongoing research in the macroscale organization of the mouse brain

Multimodal Investigation of Peripheral and Central Nervous System Pain Mechanisms in Burning Mouth Syndrome (BMS) Using Magnetic Resonance Imaging and Psychometry (MRIBMS)

Background The International Classification of Orofacial Pain (ICOP) has defined burning mouth syndrome (BMS-ICOP) as an intraoral burning or dysaesthetic sensation that reoccurs daily for more than two hours per day and more than three months without evidence of causative lesions upon clinical examination and investigation. The burning mouth symptoms can be caused by primary and secondary BMS. Primary BMS is idiopathic BMS or true BMS, while secondary BMS, also known as burning mouth disorder (BMD), is attributed to local factors or systemic conditions. The prevalence of BMS in women was significantly higher than in men and mainly occurred at the post-menopausal age between 50 and 80 years. This intense, continuous, spontaneous pain severely affects the patient’s oral function, health, and psychology, with high reported rates of anxiety and depression. The pathophysiology of primary BMS as defined by ICOP (BMS-ICOP) remains uncertain, and with no standardised treatment protocol, treatment outcomes are further complicated. Over the years, there have been reports of altered cerebral activities in various levels of the neuraxis in patients with BMS-ICOP, which implies that BMS pain has a central nervous system component. AimThis thesis aimed to characterise patients’ cerebral responses associated with chronic trigeminal pain following administration of two topical peripheral-acting analgesics, clonazepam mouthwash (CMW) and dental local anaesthetic (LA), and the difference between treatment responders and non-responders. Attenuating or escalating pain in response to peripheral medications will allow in-depth phenotyping of patients with BMS-ICOP and facilitate tailored medicine. This thesis also studied and described the characteristics of patients with BMS-ICOP and the psychological impact of BMS-ICOP.MethodsThis prospective, open-label study was conducted at King’s College Dental Institute and Clinical Research Facilities, King’s College London. In the first visit, 26 participants diagnosed with BMS-ICOP were clinically screened and psychologically assessed using psychometric questionnaires. Functional magnetic resonance imaging (fMRI) and pulsed-continuous arterial spin labelling (pCASL) imaging techniques were employed to provide quantitative measurements of the resting-state functional connectivity (FC) and regional cerebral blood flow (rCBF), respectively, that related to changes in the brain activity. Participants underwent a series of fMRI and pCASL scans and rated their pain intensity using the numerical rating scale (NRS, 0-10) and visual analogue scale (VAS, 0-100) before and after the administration of CMW and LA. A subgroup of 15 BMS-ICOP patients with burning pain across the tongue was selected from the initial 26 BMS-ICOP patients to receive LA intervention. In addition to it, patients’ grey matter volume (GMV) was quantified using voxel-based morphometry (VBM) analysis. Here, we performed seed-based FC and pCASL analysis of the regions of interest (ROIs), including the left hippocampus, ventromedial prefrontal cortex (vmPFC), left amygdala, thalamus, right anterior insula (RAI), and periaqueductal grey matter (PAG); given reports of perturbed functioning changes in this region in chronic pain. Treatment responders were defined as reporting 50% or greater pain reduction from baseline following analgesic administration. ResultsOverall, the cohort of patients had daily recurring and continuously hot burning pain, with a mean NRS intensity rating of 5.15, progressively worsening during the day.Although experiencing a high pain level, most patients had a low tendency to catastrophise the threat value of pain or pain-related thoughts and did not exhibit depression, anxiety, or somatic symptom disorders. When comparing the pain and control sites, more than 90% of patients showed no chairside qualitative sensory deficit to touch and two-point discrimination. Meanwhile, 42% and 20% of patients had pin-prick and thermal sensitivity changes, respectively. This similarity was also reflected in the quantitative mechanical detection threshold assessment, where there were no significant changes between the control and pain sites (p = 0.695, SE =0.06). We also did not observe any statistically significant correlation between behaviour changes and cerebral responses to pain (pre-intervention), such as anxiety (r=0.09, p=0.677, 95% CI= -0.31-0.46) and depression (r= -0.21, 95% CI= -0.55 – 0.2, p=0.314). ClonazepamRinsing with 2mg CMW for 10 minutes significantly reduced pain intensity across the participants. An acute 2mg dose was selected to provide an immediate state of pain relief effect, keeping in mind that the suggested maximum daily prescribed clonazepam dose for pain relief is 4mg/day. Patients experienced a mean pain intensity NRS score reduction of 2.67 (p&lt;0.001), and 15 patients responded to treatment. The study found a correlation between patients’ brain GMV and resting-state FC and pain intensity before and after rinsing with CMW. These changes were seen in the brain regions responsible for pain-related cognitive and affective processing and descending pain modulation. We also demonstrated the effect of CMW, which caused a decrease in the FC in the L hippocampus and RAI ROIs. There were alterations in the FC (∆FC) following treatment that were associated with changes in pain levels, as seen in the L hippocampus and vmPFC ROIs. In attempting to predict treatment response towards clonazepam, we tested the baseline FC with changes in pain ratings, and we did not observe any significant correlation. In addition, patients with a minimum of 50% pain reduction following CMW had a lower baseline FC than non-responders in all six ROIs. Conversely, an increased FC was noted in responders between L hippocampus-brainstem/ cerebellum and vmPFC-primary motor/somatosensory cortices. Similarly, there was a reduction in post-mouthwash rCBF compared to pre-mouthwash rCBF. No significant changes were reported upon analysis of the baseline rCBF and changes in the rCBF (∆rCBF) along with pain intensity.Dental local anaesthesiaFollowing bilateral inferior alveolar nerve block, patients achieved greater pain intensity relief than CMW with a reduction of 3.73 NRS units (p&lt;0.001, SD=1.91), with 13 patients responding to it, but two patients did not. At baseline, we also observed the FC presence between brain regions involved in cognitive and affective (emotion) pain processing and modulation, and these connectivities were associated with pain ratings and area size. Participants with a greater reduction in their pain intensity NRS (∆NRS) and VAS (∆VAS) scores after LA had weak baseline FC strength between L hippocampus-temporal lobe (p=0.024) and PAG–L amygdala (p=0.032), respectively. However, no significant association was found between the ∆FC with pain ratings and pain area size. Contrary to CMW’s pCASL analysis, no correlation was observed between LA group patients’ baseline rCBF and pain ratings and area size. However, further exploratory pCASL analysis (uncorrected initial threshold of p=0.005) showed a reduction in rCBF after LA administration in the cognitive (dorsolateral prefrontal cortex), primary motor cortex, and primary somatosensory cortex brain regions. When comparing the studies, differences in cerebral responses to pain are likely related to the context of expectancy effect and the differential in afferent nociceptive ascending trigeminothalamic inputs to the brain and descending pain inhibition modulation system.ConclusionOur cohort of patients with BMS-ICOP had a remarkable ability to engage in valued daily activities by having high pain acceptance behaviour and a low tendency to magnify the value of pain. Administration of topical peripheral analgesics during the ongoing experience of chronic pain modulated the brain’s resting state activities, such as FC and rCBF. Alterations in FC and rCBF between brain regions involved in chronic pain modulation may reflect ongoing BMS-ICOP pain symptomatology, possibly due to impaired central and/or peripheral nervous system function. Understanding the peripheral and central processes involved in BMS-ICOP pain and how analgesics alter them may provide preliminary insights into the mechanism of action of potential topical analgesics, which may be a valuable parameter in predicting treatment response and is fundamental to advancing pain medicine

Investigating the mechanism of action of Deep Brain Stimulation using functional magnetic resonance imaging

Depleted of dopamine, the dynamics of the Parkinsonian brain impact on both “action” and “resting” motor behaviour. Subthalamic nucleus deep brain stimulation (STN DBS) has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Functional magnetic resonance imaging (fMRI) using the blood oxygen level dependent (BOLD) contrast provides the opportunity to study the human brain in vivo, collecting indirect measures of neural activity across the whole brain. To date, technical difficulties and safety concerns have precluded the use of fMRI in DBS patients. Previous work from this department has demonstrated that scanning patients with certain DBS systems and MRI equipment is both safe and feasible. This thesis explores the neuromodulatory actions of STN DBS on both action and resting motor behaviours in patients with Parkinson’s disease (PD) using fMRI. In brief, I present two fMRI studies conducted on STN DBS patients, one task-based, and one resting, collected under a previously approved protocol. I then present experiments exploring the safety of scanning DBS patients using an improved protocol, and then detail two further experiments collected under this new protocol, again one task-based, and one resting. Specifically, I employ statistical parametric mapping to determine DBS-induced changes in motor evoked responses. Using dynamic causal modelling (DCM) and Bayesian model selection, I compare generative models of cortico-subcortical interactions to explain the observed data, inferring which connections DBS may be affecting, and which modulations predict efficacy. I proceed to use stochastic DCM to model the modulatory effects of DBS on endogenous (resting-state) dynamics. Abstract | 4 4 This work casts DBS in terms of modulating effective connectivity within the cortico-basal ganglia motor loops. I discuss how this may explain its current usage in PD, as well as exploratory uses to treat other pathological brain states