Regional dynamics of the resting brain in amyotrophic lateral sclerosis using fALFF and ReHo analyses

Resting state functional magnetic resonance imaging (rs-fMRI) has been playing an important role in the study of amyotrophic lateral sclerosis (ALS). Although functional connectivity is widely studied, the patterns of spontaneous neural activity of the resting brain are important mechanisms that have been used recently to study a variety of conditions but remain less explored in ALS. Here we have used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) to study the regional dynamics of the resting brain of non-demented ALS patients compared with healthy controls. As expected, we found the sensorimotor network (SMN) with changes in fALFF and ReHo but also found the default mode (DMN), frontoparietal (FPN), salience (SN) networks altered and the cerebellum, although no structural changes between ALS patients and controls were reported in the regions with fALFF and ReHo changes. We show an altered pattern in the spontaneous low frequency oscillations that is not confined to the motor areas and reveal a more widespread involvement of non-motor regions, including those responsible for cognition

Altered cerebral neurovascular coupling in medication-overuse headache: A study combining multi-modal resting-state fMRI with 3D PCASL

AimStructural and functional changes in the brain have been identified in individuals with medication-overuse headache (MOH) using MRI. However, it has not been clearly established whether neurovascular dysfunction occurs in MOH, which could be elucidated by examining neurovascular coupling (NVC) from the viewpoints of neuronal activity and cerebral blood flow. The aim of this study was to investigate potential alterations in NVC function of the brain in individuals with MOH using resting-state functional MRI (rs-fMRI) and 3D pseudo-continuous arterial spin labeling (3D PCASL) imaging techniques.MethodsA total of 40 patients with MOH and 32 normal controls (NCs) were recruited, and rs-fMRI and 3D PCASL data were obtained using a 3.0 T MR scanner. Standard preprocessing of the rs-fMRI data was performed to generate images representing regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC); cerebral blood flow (CBF) images were generated using 3D PCASL sequence data. These functional maps were all normalized into Montreal Neurological Institute (MNI) space, and NVC was subsequently determined on the basis of Pearson correlation coefficients between the rs-fMRI maps (ReHo, fALFF, and DC) and CBF maps. The statistical significance of differences between the MOH and NC groups in terms of NVC in different brain regions was established via Z-test. Further analysis was performed to examine correlations between NVC in the brain regions with NVC dysfunction and clinical variables among patients with MOH.ResultsNVC mainly presented a negative correlation in patients with MOH and NCs. No significant difference between the two groups was detected in terms of average NVC over the entire gray matter area. However, several brain regions with significantly decreased NVC in patients with MOH compared to NCs were identified: the left orbital region of the superior frontal gyrus, the bilateral gyrus rectus, and the olfactory cortex (P < 0.05). A correlation analysis revealed that the DC of the brain regions with NVC dysfunction was significantly positively correlated with disease duration (r = 0.323, P = 0.042), and DC–CBF connectivity was negatively correlated with VAS score (r = −0.424, P = 0.035).ConclusionThe current study demonstrated that cerebral NVC dysfunction occurs in patients with MOH, and the NVC technique could function as a new imaging biomarker in headache research

Neuroimaging biomarkers in genetic frontotemporal dementia : towards a timely diagnosis

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease characterised by the progressive degeneration of the frontal and temporal lobes, which results in behavioural (behavioural variant FTD) and language (primary progressive aphasia) disorders. No effective therapies currently exist to cure FTD or slow disease progression. However, efforts are being made to develop disease modifying treatments, which aim to reverse or inhibit pathological processes leading up to neuronal cell death. Therefore, the ability to diagnose FTD before brain atrophy (i.e., irreversible brain damage) is crucial. Approximately 10–30% of all FTD patients have a familial form, often caused by mutations in the genes MAPT, GRN or a repeat expansion in the gene C9orf72. These families offer the unique opportunity to study mutation carriers in the presymptomatic stage, where early pathological changes may already occur, but subjects are cognitively healthy. In this dissertation, we used multimodal MRI and machine learning to investigate whether MRI biomarkers for FTD have diagnostic value on the single-subject level to detect FTD-related differences in the presymptomatic disease stage. Furthermore, we aimed to advance the combination of resting-state functional MRI data between scanners. Lastly, we studied potential biomarkers for the differentiation between early stages of FTD and Alzheimer’s disease. LUMC / Geneeskund

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement

Physiological and pathological modulations of intrinsic brain activity assessed via resting-state fMRI

Since its inception in 1992, functional magnetic resonance imaging (fMRI) has considerably boosted our knowledge of the human brain function, primarily due to its non-invasive nature, and its relative high spatial and temporal resolution. Among the available fMRI contrasts, blood-oxygenation level-dependent (BOLD) signal plays a leading role in this field. The contrast is based on the different magnetic properties of the haemoglobin which - combined with the specific relation existing between neuronal, vascular and metabolic activity - allows to ascribe variations in the measured signal to variations in the underlying neuronal activity. During BOLD acquisitions, the comparison of different cognitive states in task-based experiment (alternating rest states to sensory or cognitive stimulations) has revealed the modular organization of the human brain function, an operation that is commonly referred to as functional brain mapping. Surprisingly, task-induced activity requires an increase in brain’s energy consumption by less than 5 percent of the underlying baseline activity. Most of the brain’s energy demand, from 60 to 80 percent, is used to sustain intrinsic, task-unrelated, neural activity (Raichle, 2006). In this light, functional brain mapping, utilizing task-based fMRI, focuses only on the tip of the iceberg, whereas most of the brain’s activity remains largely uncharted. The notion that the brain has an intrinsic or spontaneous activity is known from early electro-encephalography (EEG) measures due to Hans Berger. However, only in recent years, after the seminal work of Biswal and colleagues (Biswal et al., 1995), the study of spontaneous brain activity has overwhelmingly emerged as a primary field of research in neuroscience. In the so called resting-state condition (i.e., when the brain is not focused on the external world), Biswal reported BOLD low-frequency (< 0.1 Hz) fluctuations (LFFs) synchronized across functionally related and anatomically connected regions. Thereafter, several studies have consistently shown that specific patterns of synchronized spontaneous LFFs identify different resting-state networks, including, but not limited to, visual, motor, auditory, and attentive network. The overall picture emerging from thousands of resting-state fMRI studies depicts a never-resting brain, continuously engaged in maintaining communications within several wide-distributed networks. Such intrinsic brain activity, reflected in spontaneous BOLD LFFs, is the focus of the present thesis. The study of LFFs in spontaneous BOLD signal can reveal much about brain’s functional organization, especially considering that signal variability has been related to variability in behaviour (Fox et al., 2007). In addition, the simplicity of data acquisition – subjects just lie in the scanner refraining from falling asleep - makes the technique particularly suited for studying pathological conditions, in which subject’s cooperation might not fulfil the demands of task-based studies. Indeed, several psychiatric and neurological disorders, including degenerative dementia, have shown altered patterns of LFFs, even in the absence of observable anatomical abnormalities (Barkhof et al., 2014). Thus, how the intrinsic brain’s activity is modulated in response to different behavioural states and in response to pathological conditions can give insights into the brain functionality and into the mechanisms behind illnesses, respectively. Importantly, correct result interpretation is highly influenced by the type of metrics adopted and how they are implemented. The resting-state approach to the study of the brain’s function has required the development of more sophisticated processing and analysis techniques compared to those commonly applied in task-based fMRI. While seeking for task-responding regions in the brain is guided by information embedded in the experimental paradigm, in steady-state fMRI no a priori cue is provided. In such experiment the extraction of relevant information is based on (i) the temporal synchronization between spatially segregated elements of the brain, feature known as functional connectivity, and on (ii) the amplitude of the oscillation per se, a measure of the strength of the intrinsic brain activity. Despite such simple classification, the field of resting-state fMRI is scattered with a disparate amount of metrics, each of which highlight different facets of spontaneous LFFs. Before turning to the study of spontaneous LFF modulations, we will provide a comprehensive and optimized mathematical framework for the extraction of relevant information from resting-state data (Chapter 2). The results of this effort is an easy-to-use matlab toolbox specifically designed for the processing and analysis of steady-state fMRI data. In principle, the information coded in functional connectivity and in oscillation amplitude are unrelated. While the former assesses the degree of cooperation between segregated elements of the brain, the latter quantifies the neural workload of each single brain’s element, independently from the activity of other regions. Nonetheless, modulations in both measurements have been reported in several pathological conditions - yet in separate studies - suggesting a possible relation between them. In this context, we sought to investigate the potential coupling between the functional connectivity and the oscillation amplitude in cohort of healthy elderly and the probable modulations induced by dementia of the Alzheimer’s type (Chapter 3). Regardless of how the brain relates the two types of measures extractable from resting-state data, their disease-induced modulations are relevant per se in uncovering the illness. Indeed, Alzheimer’s disease is known to produce alterations in spontaneous brain activity, both at the synchronization and the amplitude level (Wang et al., 2007). Since the hallmark of the pathology is a profound deficit in episodic memory, much effort has been done in characterizing the alterations in spontaneous brain activity underlying such deficit. Contrarily, little is known about another commonly reported deficit, the language related impairment (Taler and Phillips, 2008). In the second part of Chapter 3 we sought to disclose the brain regions underpinning language deficits by looking at the alterations in functional connectivity of the relevant network. While the study of LFFs in pathological conditions can contribute to reveal the mechanisms behind the pathology and how it spreads into the brain, the study of spontaneous brain activity in physiological conditions can disclose the intrinsic brain functionality. In healthy subjects the resting brain has been extensively characterized and its network topology has shown to be a consistent and reliable physiological feature (Damoiseaux et al., 2006). An intriguing issue is how the brain reorganizes its patterns of spontaneous BOLD LFF while it is focusing on the external world. Indeed, the intrinsic brain activity is not an exclusive feature of the resting condition, instead it is present also on the top of the task-evoked response. In chapter 4, with peculiar experimental paradigms we separated the task-evoked response from the intrinsic brain activity during sustained cognitive stimulations. In a first experiment we sought to characterize the spatio-temporal proprieties and the dynamic of the transition from a resting to a stimulated condition. In the second part we specifically investigated how the brain reorganizes its internal functional architecture during visuospatial attention. Indeed, besides strongly affecting the processing of visual incoming stimuli, visual spatial attention also affects brain networks. Recent studies suggest that visual attention affects functional connectivity within and between the visual network and the attention network (Spadone et al., 2015), yet modulations of attention on brain networks are still poorly understood

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia

Investigating stimulus induced metabolic changes in human visual cortex using functional magnetic resonance spectroscopy at 7T

This thesis concerns the investigation of metabolic changes in 1H metabolite levels in the human visual cortex due to visual stimulation using proton magnetic resonance spectroscopy (1H-MRS) at 7T. The work described in this thesis has been undertaken by the author and collaborators at the Sir Peter Mansfield Magnetic Resonance Centre at the University of Nottingham. Detection of functional changes in 1H metabolites may enable a greater understanding of neurotransmitter activity and metabolic pathways used for energy synthesis during activation of brain tissue. Previous 1H MRS studies of the activated human brain mainly focused on observing lactate (Lac) changes. More recent studies by Mangia et al, taking advantage of the increased signal and spectral resolution at 7T, have investigated the change in the level of Lac, glutamate (Glu), Aspartate (Asp) and Glucose (Glc) during activation. However, Mangia, did not measure significant change in the level of gamma aminobutyric acid (GABA) and Glutamine (Gln), which might be expected to change due to increased neurotransmitter cycling rates during activation. Given that the metabolite changes observed due to visual stimulation were relatively small. We used a long, intense visual stimulus, designed to retain attention, to confirm and quantify the changes in the levels of Glu, GABA, and Gln, and to further investigate the Lac and Asp response to visual stimulation. Our present results using a moving stimulus of full-screen flickering contrast-defined wedges, have demonstrated many more metabolic changes throughout two different time scales of stimulation. Small (2~11%) but significant stimulation induced increases in Lac, Glu and glutathione (GSH) were observed along with decreases in Asp, GIn and glycine (Gly). In addition, decreases in (intracellular) Glc and increases in GABA were seen but did not reach significance. The opposite changes in Glu and Asp are indicative of increased activity of the malate-aspartate shuttle, which taken together with the opposite changes in Glc and Lac reflect the expected increase in brain energy metabolism. The increases in Glu and GABA coupled with the decrease in GIn can be interpreted in terms of increased activity of the Glu/Gln and Gln/Glu/GABA neurotransmitter cycles. An entirely new observation is the increase of GSH during prolonged visual stimulation. The similarity of its time course to that of Glu suggests that it may be a response to the increased release of Glu or to the increased production of reactive oxygen species. Gly is also a precursor of GSH and a decrease on activation is consistent with increased GSH synthesis. Together these observations constitute the most detailed analysis to date of functional changes in human brain metabolites. Interestingly, the Lac response was confined to the first visual stimulus. It is possible that processes triggered during the first period of visual stimulation, could continue for a while after stimulation has ended. If this is an important mechanism of the activity-stimulated brain Lac response, shortening the duration of the first stimulus might lead to an increase in Lac response during the second period of stimulation. With this in mind, we designed a repeated visual stimulation paradigm, varying the duration of the first stimulation (shorter than 9.9-min, based on our previous results), to see the effect on the Lac response during the second visual stimulation period. A gradual increase in Lac under the prolonged stimulation, following the first brief stimulation (1s, 16s and 48s, respectively), was observed and maintained until the end of these periods. Lac responses during the second stimulation period looked similar whether the first stimulation was 1s or 16s. With the increase of first visual stimulus duration (48s), the Lac response under the second stimulation period was slightly diminished. No significant Lac accumulation can be evident to the second stimulation, when the initial stimulation was 288s. The averaged Lac level was considerably below baseline after cessation of the first 288s stimulus. It is possible that the increased glycolytic flux, triggered during the initial longer stimulation, would still continue for a while during recovery, accounting for the decreased brain Lac level during resting periods from stimulation. Further experiments are ongoing, varying the duration of the second resting periods, to see the effect on the Lac response to the second stimulation

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease