Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as defining region-specific HRFs, effciently representing a general HRF, or comparing subject-specific HRFs. Results: ForWaRD is applied to fMRI time signals, after removing low-frequency trends by a wavelet-based method, and the output of ForWaRD is a time series of volumes, containing the HRF in each voxel. Compared to more complex methods, this extraction algorithm requires few assumptions (separability of signal and noise in the frequency and wavelet domains and the general linear model) and it is fast (HRF extraction from a single fMRI data set takes about the same time as spatial resampling). The extraction method is tested on simulated event-related activation signals, contaminated with noise from a time series of real MRI images. An application for HRF data is demonstrated in a simple event-related experiment: data are extracted from a region with significant effects of interest in a first time series. A continuous-time HRF is obtained by fitting a nonlinear function to the discrete HRF coeffcients, and is then used to analyse a later time series. Conclusion: With the parameters used in this paper, the extraction method presented here is very robust to changes in signal properties. Comparison of analyses with fitted HRFs and with a canonical HRF shows that a subject-specific, regional HRF significantly improves detection power. Sensitivity and specificity increase not only in the region from which the HRFs are extracted, but also in other regions of interest.

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion

Ocena dźwięków i drgań generowanych w czasie kaszlu za pomocą Wibroakustycznego Systemu Rejestracji Kaszlu MEPIM (MEPIM VSCR)

Wstęp. Mimo że kaszel to jeden z najczęstszych objawów przewlekłych chorób i nowotworów złośliwych układu oddechowego, to nadal nie jest dostępna żadna praktyczna i dokładna metoda służąca obiektywnej ocenie tego objawu. Celem autorów niniejszej pracy było opracowanie nowej metody obiektywnej oceny ilościowej kaszlu z zastosowaniem analizy częstotliwościowo-czasowej drgań klatki piersiowej i dźwięków generowanych w czasie kaszlu. Metody i wyniki. Opracowywany Wibroakustyczny System Rejestracji Kaszlu MEPIM analizuje jednocześnie drgania klatki piersiowej i sygnały akustyczne. Czujnik przyspieszenia umieszczano na klatce piersiowej pacjenta, a mikrofon - w odległości 1 m od niego. Analizę rejestrowanych dźwięków i drgań przeprowadzano za pomocą multianalizatora B & K Pulse. Zastosowano analizę FFT obu sygnałów oraz wyznaczono ich wzajemną korelację. Wnioski. Zastosowanie technik analizy widmowej może poprawić możliwości określania charakterystycznych cech kaszlu. Należy prowadzić dalsze badania kliniczne w celu uzyskania odpowiedzi na pytanie, czy analiza częstotliwościowa sygnałów wibroakustycznych może być przydatna do określania charakterystyki kaszlu

Towards Patient-Specific Brain Networks Using Functional Magnetic Resonance Imaging

fMRI applications are rare in translational medicine and clinical practice. What can be inferred from a single fMRI scan is often unreliable due to the relative low signal-to-noise ratio compared to other neuroimaging modalities. However, the potential of fMRI is promising. It is one of the few neuroimaging modalities to obtain functional brain organisation of an individual during task engagement and rest. This work extends on current fMRI image processing approaches to obtain robust estimates of functional brain organisation in two resting-state fMRI cohorts. The first cohort comprises of young adults who were born at extremely low gestations and age-matched healthy controls. Group analysis between term- and preterm-born adults revealed differences in functional organisation, which were discovered to be predominantly caused by underlying structural and physiological differences. The second cohort comprises of elderly adults with young onset Alzheimer’s disease and age-matched controls. Their corresponding resting-state fMRI scans are short in scanning time resulting in unreliable spatial estimates with conventional dual regression analysis. This problem was addressed by the development of an ensemble averaging of matrix factorisations approach to compute single subject spatial maps characterised by improved spatial reproducibility compared to maps obtained by dual regression. The approach was extended with a haemodynamic forward model to obtain surrogate neural activations to examine the subject’s task behaviour. This approach applied to two task-fMRI cohorts showed that these surrogate neural activations matched with original task timings in most of the examined fMRI scans but also revealed subjects with task behaviour different than intended by the researcher. It is hoped that both the findings in this work and the novel matrix factorisation approach itself will benefit the fMRI community. To this end, the derived tools are made available online to aid development and validation of methods for resting-state and task fMRI experiments

Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness

Compressed Sensing For Functional Magnetic Resonance Imaging Data

This thesis addresses the possibility of applying the compressed sensing (CS) framework to Functional Magnetic Resonance Imaging (fMRI) acquisition. The fMRI is one of the non-invasive neuroimaging technique that allows the brain activity to be captured and analysed in a living body. One disadvantage of fMRI is the trade-off between the spatial and temporal resolution of the data. To keep the experiments within a reasonable length of time, the current acquisition technique sacrifices the spatial resolution in favour of the temporal resolution. It is possible to improve this trade-off using compressed sensing. The main contribution of this thesis is to propose a novel reconstruction method, named Referenced Compressed Sensing, which exploits the redundancy between a signal and a correlated reference by using their distance as an objective function. The compressed video sequences reconstructed using Referenced CS have at least 50% higher in terms of Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art conventional reconstruction methods. This thesis also addresses two issues related to Referenced CS. Firstly, the relationship between the reference and the reconstruction performance is studied. To maintain the high-quality references, the Running Gaussian Average (RGA) reference estimator is proposed. The reconstructed results have at least 3dB better PSNR performance with the use of RGA references. Secondly, the Referenced CS with Least Squares is proposed. This study shows that by incorporating the correlated reference, it is possible to perform a linear reconstruction as opposed to the iterative reconstruction commonly used in CS. This approach gives at least 19% improvement in PSNR compared to the state of the art, while reduces the computation time by at most 1200 times. The proposed method is applied to the fMRI data. This study shows that, using the same amount of samples, the data reconstructed using Referenced CS has higher resolution than the conventional acquisition technique and has on average 50% higher PSNR than state-of-the-art reconstructions. Lastly, to enhance the feature of interest in the fMRI data, the baseline independent (BI) analysis is proposed. Using the BI analysis shows up to 25% improvement in the accuracy of the Referenced CS feature

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

Hemodynamic Response Function Modeling

Functional Magnetic Resonance Imaging (fMRI) is a medical-imaging technique for studying brain function. It can be used to capture the response of the brain to various tasks. The response to a brief, intense period of neural stimulation is called the hemodynamic response function (HRF). Modeling HRF is essential to identifying the brain activation by exploring the relationship between the experimental stimulus and the response. In this dissertation, we discuss three research problems related to HRF estimation. First, when multiple types of stimuli are present, how can we capture the characteristic HRF for each stimulus? Second, is there any difference among the HRFs corresponding to multiple stimuli? Third, how can we improve the HRF estimator's efficiency? We propose a nonparametric method, transfer function estimate (TFE), to answer these three questions. Building on existing work, we extend the nonparametric approach to a multivariate form, which adapts to the multiple types of stimuli, and we develop hypothesis testing to identify the brain activation and to compare the HRFs under different stimuli. In order to improve estimation efficiency, we propose using weighted least square (WLS) in a multiple system of regression by spectral methods. The finite-sample performance of the TFE is illustrated through several simulation studies and real fRMI data sets. We also establish the asymptotic normality of the TFE, as well as the efficiency of the WLS estimator.Doctor of Philosoph

HRFs extracted from the random-ISI experiment by : whole-volume (a) and region-specific (b)

×: extracted HRF coeffcients. Dashed lines: function HRFfitted to ×. 95% prediction intervals for the fitted functions are shown as error bars.<p><b>Copyright information:</b></p><p>Taken from "Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution"</p><p>http://www.biomedcentral.com/1471-2342/8/7</p><p>BMC Medical Imaging 2008;8():7-7.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2409308.</p><p></p

HRFs extracted from the fixed-ISI data by selective averaging (top row) and (bottom row)

Left: whole-volume, right: region-specific. The extracted coeffcient are the × at each TR. Dotted lines: fits of HRFto the coeffcients. Error bars show the 95% confidence intervals for the fitted function.<p><b>Copyright information:</b></p><p>Taken from "Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution"</p><p>http://www.biomedcentral.com/1471-2342/8/7</p><p>BMC Medical Imaging 2008;8():7-7.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2409308.</p><p></p