Behavioral and neural markers of cigarette-craving regulation in young-adult smokers during abstinence and after smoking

Cigarette craving contributes substantially to the maintenance of tobacco use disorder. Behavioral strategies to regulate craving may facilitate smoking cessation but remain underexplored. We adapted an emotion-regulation strategy, using proximal/distal self-positioning, to the context of cigarette craving to examine craving regulation in 42, daily smokers (18-25 years old). After overnight abstinence from smoking, before and after smoking their first cigarette of the day, participants viewed videos of natural scenes presenting young adults who were either smoking cigarettes ("smoke") or not ("non-smoke"). Before each video, participants were instructed to imagine themselves either immersed in the scene ("close") or distanced from it ("far"). They rated their craving after each video. Task-based fMRI data are presented for a subsample of participants (N = 21). We found main effects of smoking, instruction, and video type on craving-lower ratings after smoking than before, following the "far" vs. "close" instructions, and when viewing non-smoke vs. smoke videos. Before smoking, "smoke" vs. "non-smoke" videos elicited activation in, orbitofrontal cortex, anterior cingulate, lateral parietal cortex, mid-occipital cortex, ventral striatum, dorsal caudate, and midbrain. Smoking reduced activation in anterior cingulate, left inferior frontal gyrus, and bilateral temporal poles. Activation was reduced in the ventral striatum and medial prefrontal cortex after the "far" vs. the "close" instruction, suggesting less engagement with the stimuli during distancing. The results indicate that proximal/distal regulation strategies impact cue-elicited craving, potentially via downregulation of the ventral striatum and medial prefrontal cortex, and that smoking during abstinence may increase cognitive control capacity during craving regulation

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities

fMRI activation detection with EEG priors

The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

Issues in the processing and analysis of functional NIRS imaging and a contrast with fMRI findings in a study of sensorimotor deactivation and connectivity

Includes abstract.~Includes bibliographical references.The first part of this thesis examines issues in the processing and analysis of continuous wave functional linear infrared spectroscopy (fNIRS) of the brain usung the DYNOT system. In the second part, the same sensorimotor experiment is carried out using functional magnetic resonance imaging (fMRI) and near infrared spectroscopy in eleven of the same subjects, to establish whether similar results can be obtained at the group level with each modality. Various techniques for motion artefact removal in fNIRS are compared. Imaging channels with negligible distance between source and detector are used to detect subject motion, and in data sets containing deliberate motion artefacts, independent component analysis and multiple-channel regression are found to improve the signal-to-noise ratio

Hum Brain Mapp

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.F30 MH094032/MH/NIMH NIH HHS/United StatesF30 MH940322/MH/NIMH NIH HHS/United StatesR01 HD057076/HD/NICHD NIH HHS/United StatesR01 ND057076/ND/ONDIEH CDC HHS/United StatesR01 NS046424/NS/NINDS NIH HHS/United StatesR01 NS26424/NS/NINDS NIH HHS/United StatesR21 NS061144/NS/NINDS NIH HHS/United StatesR21 NS61144/NS/NINDS NIH HHS/United StatesT32 GM007200/GM/NIGMS NIH HHS/United States2014-05-01T00:00:00Z23861343PMC389510

Characterisation of the Haemodynamic Response Function (HRF) in the neonatal brain using functional MRI

Background: Preterm birth is associated with a marked increase in the risk of later neurodevelopmental impairment. With the incidence rising, novel tools are needed to provide an improved understanding of the underlying pathology and better prognostic information. Functional Magnetic Resonance Imaging (fMRI) with Blood Oxygen Level Dependent (BOLD) contrast has the potential to add greatly to the knowledge gained through traditional MRI techniques. However, it has been rarely used with neonatal subjects due to difficulties in application and inconsistent results. Central to this is uncertainity regarding the effects of early brain development on the Haemodynamic Response Function (HRF), knowledge of which is fundamental to fMRI methodology and analysis. Hypotheses: (1) Well localised and positive BOLD functional responses can be identified in the neonatal brain. (2) The morphology of the neonatal HRF differs significantly during early human development. (3) The application of an age-appropriate HRF will improve the identification of functional responses in neonatal fMRI studies. Methods: To test these hypotheses, a systematic fMRI study of neonatal subjects was carried out using a custom made somatosensory stimulus, and an adapted study design and analysis pipeline. The neonatal HRF was then characterised using an event related study design. The potential future application of the findings was then tested in a series of small experiments. Results: Well localised and positive BOLD functional responses were identified in neonatal subjects, with a maturational tendency towards an increasingly complex pattern of activation. A positive amplitude HRF was identified in neonatal subjects, with a maturational trend of a decreasing time-to-peak and increasing positive peak amplitude. Application of the empirical HRF significantly improved the precision of analysis in further fMRI studies. Conclusions: fMRI can be used to study functional activity in the neonatal brain, and may provide vital new information about both development and pathology

Integrated Real-Time Control And Processing Systems For Multi-Channel Near-Infrared Spectroscopy Based Brain Computer Interfaces

This thesis outlines approaches to improve the signal processing and anal- ysis of Near-infrared spectroscopy (NIRS) based brain-computer interfaces (BCI). These approaches were developed in conjunction with the implemen- tation of a new customized exible multi-channel NIRS based BCI hardware system (Soraghan, 2010). Using a comparable functional imaging modality the assumptions on which NIRS-BCI have been reassessed, with regard to cognitive task selection, active area locations and lateralized motor cortex activation separability. This dissertation will also present methods that have been implemented to allow reduced hardware requirements in future NIRS-BCI development. We will also examine the sources of homeostatic physiological interference and present new approaches for analysis and at- tenuation within a real-time NIRS-BCI paradigm

A Comparative Assessment of Statistical Approaches for fMRI Data to Obtain Activation Maps

Functional Magnetic Resonance Imaging (fMRI) lets us peek into the human mind and try to identify which brain areas are associated with certain tasks without the need for an invasive procedure. However, the data collected during fMRI sessions is complex; this 4 dimensional sequence of 3 dimensional volumes as images of the brain does not allow for straightforward inference. Multiple models have been developed to analyze this data and each comes with its intricacies and problems. Two of the most common ones are 2-step General Linear Model (GLM) and Independent Component Analysis (ICA). We compare these approaches empirically by fitting the models to real fMRI data using packages developed and readily available in R. The real data, obtained from an open source database openneuro.org, is named BOLD5000. The task of interest for this thesis is image viewing versus fixation cross (resting state). We found that both the first-level GLM and ICA revealed significant activation located in the occipital lobe which is consistent with the literature on visual tasks. The second-level GLM results were consistent with the first level and found activation located in the occipital lobe as well. The Group ICA results however found activation located mainly in the temporal lobe.No embargoAcademic Major: Statistic