Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies

Building a Science of Individual Differences from fMRI

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity, reliability and statistical assessment that arise when the focus shifts to individual subjects and that are applicable also to other imaging modalities. We emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability as well as sources of within-subject variability

Neural mechanisms of cognitive reserve in Alzheimer's disease

Alzheimer’s disease (AD) is the most common cause of age-related dementia, where neuropathological changes develop gradually over years before the onset of dementia symptoms. Yet, despite the progression of AD pathology, the decline in cognitive abilities such as episodic memory can be relatively slow. A slower decline of cognition and delayed onset of dementia relative to the progression of neuropathology has been associated with particular intellectual and lifestyle factors such as more years of education and IQ. Thus education and IQ are seen as protective factors that are associated with an increased ability to cope with brain pathology, i.e. cognitive reserve. While numerous studies showed that education, IQ and other lifestyle factors are associated with relatively high cognitive abilities in AD, little is known about the underlying brain mechanisms of reserve. Most previous studies tested the association between protective factors such as education or IQ and differences in brain structure and function in order to identify brain mechanisms underlying reserve. Since such protective factors are global in nature and unspecific with regard to reserve, the results were highly variable. So far, there is a lack of knowledge of brain features that are associated with a higher ability to maintain cognition in the face of AD pathology. The overall aim of this dissertation was to test a priori selected functional network features that may underlie cognitive reserve. We focused on resting-state functional networks, and in particular the fronto-parietal control network as correlate of cognitive reserve. Such functional networks are thought to be composed of brain regions that are co-activated during a particular task, where the interaction between brain regions may be critical to support cognitive function. During task-free resting-state periods, the different and often distant brain regions of such network show correlated activity, i.e. functional connectivity. For the fronto-parietal control network, and in particular its globally connected hub in the left frontal cortex (LFC), higher resting-state connectivity has been previously shown to be associated with higher cognitive abilities as well as higher education and IQ, i.e. protective factors associated with reserve. Since that network and its LFC hub are relatively spared in AD, in contrast to more posterior parietal networks, we investigated whether higher connectivity of the fronto-parietal control network is associated with higher reserve in AD. We argued that the fronto-parietal control network is relatively stable during the initial stages of AD and may thus be well posited to subserve reserve in AD. In contrast, networks like the default mode network (DMN) that cover midline brain structures including the medial frontal lobe and the posterior cingulate may be highly vulnerable to AD pathology, given the previous observations of altered DMN connectivity and posterior parietal FDG-PET hypometabolism in AD. In particular, the resting-state connectivity between the DMN and the dorsal attention network (DAN) may be predictive of lower episodic memory in AD. Both networks interact in a competitive (i.e. anti-correlated) way during task and resting-state, which is critical for cognitive processes such as episodic memory. In a first step, we tested whether the resting-state connectivity between the DMN and theDAN (i.e. anti-correlated activity) is associated with lower episodic memory in subjects with amnestic mild cognitive impairment (MCI), i.e. subjects at increased risk to convertto AD dementia. Furthermore, we tested whether protective factors such as higher education moderate the association between the DMN-DAN anti-correlation andcognition. Here, the DMN-DAN anti-correlation was a measure of AD relatedpathological change rather than a substrate of reserve.We could show in two independent samples of patients at risk of AD dementia that a weaker DMN-DAN anti-correlation was associated with lower episodic memory, where the decrements in episodic memory were however weaker in subjects with higher education or IQ (interaction DMN-DAN x education/IQ). These results suggest that MCI subjects with higher protective factors (education, IQ) maintain episodic memory relatively well at a given level of AD-related brain changes. In the second step, we sought to identify those network differences that support cognitive reserve, i.e. that may explain the association between higher education and milder cognitive impairment in AD. Here, we could show that greater resting-state fMRI assessed global connectivity of the LFC, i.e. a key hub of the fronto-parietal control network, was associated with greater education and attenuated effects of neurodegeneration (measured by parietal FDG-PET hypometabolism) on memory in prodromal AD. Together, these results support the idea that global connectivity of a fronto-parietal control network hub supports cognitive reserve in AD. Based on this finding, we developed a novel restingstate fMRI index of fronto-parietal control network connectivity as a functional imaging marker of cognitive reserve. This marker is highly correlated with education and may thus be used as an imaging-based index of cognitive reserve. Together, our results provide for the first time evidence that cognitive reserve in AD is supported by higher functional connectivity of the fronto-parietal control network, in particular its LFC hub

Into the Multiverse: Methods for Studying Developmental Neuroscience

One major challenge in developmental neuroscience research is the sheer number of choices researchers face when addressing even a single research question. Even once data collection is complete, the journey from raw data to interpretation of findings may depend on numerous decisions. To address this issue, this dissertation explores “multiverse” analysis techniques for following many analytical paths at once in the same dataset. In chapter 1, multiverses are used to examine which analyses of age-related change in amygdala-medial prefrontal cortex circuitry are robust versus sensitive to researcher decisions. Chapter 2 uses multiverse analysis to identify optimal solutions for mitigating breathing-induced artifacts in resting-state functional magnetic resonance imaging data. Chapter 3 uses a variety of model specifications to characterize simultaneous reward learning strategies in youth contingent on both visual task cues and spatial-motor information. Despite varied approaches and goals, each of the three studies highlight the benefits of conducting multiple parallel analyses for both addressing questions in developmental neuroscience and deepening understanding of the methods used to address them

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

Investigating the role of Gamma-aminobutyric acid (GABA) in sedation: a combinedelectrophysiological, haemodynamicand spectroscopic study in humans

A better understanding of the mechanisms of anaesthesia and sedation are expected not only to improve the understanding of the neural correlates of consciousness but also to help improve safety from the complications of anaesthesia/ sedation and develop safer drugs and objective brain function monitoring systems. Neuroimaging modalities such as functional MRI, magnetoencephalography and MR spectroscopy provide complimentary information about brain functions and can help interrogate brain activity in a living human brain. Most anaesthetic drugs act by enhancing the inhibitory actions of GABA in the brain. Most neuroimaging research has focused on anaesthetic-induced unconsciousness, with only few investigating the earliest levels of sedation-induced altered consciousness. The work in this thesis used a range of advanced neuroimaging modalities to investigate the role of GABA (through a GABA-ergic drug, propofol), during mild sedation, in humans. This was performed as a series of experiments within two, sequential, scanning sessions, MEG followed by fMRI, in the same participants. Propofol resulted in a dissociation of the visual gamma band response (decreased evoked, increased induced power). This was related to a reduced BOLD fMRI response but there were no changes in MRS detectable GABA concentration. Response to multisensory stimulation also revealed interesting changes with MEG and fMRI. Functional connectivity analyses showed changes in connectivities of the posterior cingulate cortex (key hub of default-mode network) and thalamus with each other and other key brain regions. Resting state networks were identified with MEG too, which revealed interesting increases in connectivity in certain band- limited networks while motor networks showed no change. Perfusion fMRI using arterial spin labelling revealed a global and regional reduction in perfusion, highlighting some of the key regions (frontal cortex, precuenus, PCC and thalamus) involved in sedation