Resting-state Connectivity Dynamics in the Human Brain using High-speed fMRI

Resting-state fMRI using seed-based connectivity analysis (SCA) typically involves regression of the confounding signals resulting from movement and physiological noise sources. This not only adds additional complexity to the analysis but may also introduce possible regression bias. We recently introduced a computationally efficient real-time SCA approach without confound regression, which employs sliding-window correlation analysis with running mean and standard deviation (meta-statistics). The present study characterizes the confound tolerance of this windowed seed-based connectivity analysis (wSCA), which combines efficient decorrelation of confounding signal events with high-pass filter characteristics that reduce sensitivity to drifts. The confound suppression and the strength of resting-state network (RSN) connectivity were characterized for a range of confounding signal profiles as a function of sliding-window width and scan duration, using simulation and in vivo data. The connectivity strength in six resting-state networks (RSNs) and artifactual connectivity in white matter were compared between wSCA and conventional regression-based SCA (cSCA). The wSCA approach demonstrated scalable confound suppression that increased with decreasing sliding-window width and increasing scan duration in both simulations and in vivo. The confound suppression for sliding-window widths ≤ 15 s was comparable to that of cSCA. Twenty-eight RSNs that were previously reported in a group-ICA study were detected in real-time at scan durations as short as 30 s and with sliding-window widths as short as 4 s. The inter- and intra- network connectivity dynamics of the 28 resting-state networks were studied in real-time and self-repeating connectivity patterns were identified. The wSCA is further investigated offline to study the strength and temporal fluctuations in connectivity using 28 single-region seeds and 28 multi-region seed clusters to measure inter-regional connectivity (IRC) in 140 functional brain regions and inter-network connectivity (INC) among the hubs of 28 RSNs. Multi-region seed IRC maps displayed smaller temporal fluctuations and stronger resting-state connectivity compared with single-region seed IRC maps. Dual thresholding of the meta-statistics maps demonstrated higher spatio-temporal IRC stability in auditory, sensorimotor, and visual cortices compared to other brain regions. The group averaged INC matrices for single-region seeds were consistent with the functional network connectivity matrices (FNCMs) presented in the aforementioned group-ICA study. Furthermore, we extended the mapping of functional connectivity to the whole-brain connectivity fingerprints. In combination with novel brain parcellation methods and advanced machine learning algorithms, wSCA can aid in studying the spatial and temporal connectivity dynamics of the resting-state connectivity. The robust confound tolerance, high temporal resolution, and compatibility with real-time high-speed fMRI, make this approach suitable for monitoring data quality, neurofeedback, and clinical research studies involving disease related changes in functional connectomics

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality.The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients).RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality.This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI

Hybrid-space reconstruction with add-on distortion correction for simultaneous multi-slab diffusion MRI

Purpose: This study aims to propose a model-based reconstruction algorithm for simultaneous multi-slab diffusion MRI acquired with blipped-CAIPI gradients (blipped-SMSlab), which can also incorporate distortion correction. Methods: We formulate blipped-SMSlab in a 4D k-space with kz gradients for the intra-slab slice encoding and km (blipped-CAIPI) gradients for the inter-slab encoding. Because kz and km gradients share the same physical axis, the blipped-CAIPI gradients introduce phase interference in the z-km domain while motion induces phase variations in the kz-m domain. Thus, our previous k-space-based reconstruction would need multiple steps to transform data back and forth between k-space and image space for phase correction. Here we propose a model-based hybrid-space reconstruction algorithm to correct the phase errors simultaneously. Moreover, the proposed algorithm is combined with distortion correction, and jointly reconstructs data acquired with the blip-up/down acquisition to reduce the g-factor penalty. Results: The blipped-CAIPI-induced phase interference is corrected by the hybrid-space reconstruction. Blipped-CAIPI can reduce the g-factor penalty compared to the non-blipped acquisition in the basic reconstruction. Additionally, the joint reconstruction simultaneously corrects the image distortions and improves the 1/g-factors by around 50%. Furthermore, through the joint reconstruction, SMSlab acquisitions without the blipped-CAIPI gradients also show comparable correction performance with blipped-SMSlab. Conclusion: The proposed model-based hybrid-space reconstruction can reconstruct blipped-SMSlab diffusion MRI successfully. Its extension to a joint reconstruction of the blip-up/down acquisition can correct EPI distortions and further reduce the g-factor penalty compared with the separate reconstruction.Comment: 10 figures+tables, 8 supplementary figure

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github. com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc

Estudios funcionales mediante resonancia magnética en pequeños animales

This thesis is framed within the field of preclinical biomedical imaging, and specifically devoted to the study of functional magnetic resonance imaging (fMRI) technique in small animals. The experimental and technological complexity of this modality has greatly limited its use, and therefore it is not a routine imaging modality. However, it provides valuable information both at the physiological level, to study the mechanisms of normal brain during neuronal activity, and at the pathological level, to study drugs intended for different brain dysfunctions. In this work we have studied techniques and methods that intend to alleviate these difficulties and facilitate their use by the scientific community. The work includes contributions at several stages: the experimental setup, the data acquisition and reconstruction, and the quantitative image analysis. The first section addresses the problem of using anesthesia during the experiment. In order to perform functional measurements, it is necessary to establish a protocol to induce anesthetic sedation of the animal rather than a deep anesthetic state. Moreover, the use of non-toxic drugs with fast induction and recovery is desirable. In this section of the thesis we conducted fMRI experiments in rats sedated with sevoflurane, and since this agent had not been previously reported for fMRI, it was necessary to conduct strategies in order to determine the optimum dose-response and stimulation frequency. Furthermore, the signal obtained in the cerebral cortex was compared with a more traditional protocol sedation, subdermal medetomidine. The signal obtained was similar to that obtained under medetomidine, but the animal preparation time increased considerably, which constitutes a serious practical drawback for the use of sevoflurane. The second section is devoted to the study of a compressed sensing framework that allows a substantial reduction on the acquisition time without degrading image quality. The acquisition of a much reduced amount of data, thus at high rates of acceleration that violate the Nyquist-Shannon criterion, is possible by means of a wise exploitation of the temporal information redundancy and by the use of nonlinear iterative reconstruction algorithms. In this study we evaluated the performance of three compressed-sensing reconstruction algorithms that exploit temporal redundancy to recover the BOLD contrast and which have proved successful in other applications or imaging modalities such as: X-ray tomography, dynamic cardiac MRI, and resting state MRI studies. The comparison was performed in two signal-to-noise ratio scenarios and the conclusion drawn is that the algorithm which uses an a priori image (PICCS) yields the best reconstruction. The third section deals with the post-processing and image analysis. There are several open-source tools available to this purpose, but they were originally designed for human studies. Their adaptation to rodent images requires the use of additional tools or some image transformation processing that involve programming skills. Moreover, to obtain quantitative values, the user would need to use additional extensions or external software. In this work we have studied the existing tools and proposed and developed a new software, fMRat, which automatically performs a full multi-subject analysis, from the initial format conversion to the extraction of numerical values from the regions interest chosen by the user. The tool was programmed in Matlab as an extension of the existing SPM package, and was validated with 460 real rat studies. The code has been published as "open-software" in Github website and is accessible to the neuroscience community.Esta tesis se enmarca dentro del ámbito de la imagen biomédica preclínica, y específicamente trata sobre la técnica de imagen de resonancia magnética funcional (fMRI) en pequeños animales. La complejidad de dicha técnica tanto a nivel experimental como tecnológico ha limitado considerablemente su ámbito de uso, y por ello no es una modalidad de imagen que se realice de manera habitual. Sin embargo ofrece información muy valiosa tanto a nivel fisiológico, para el estudio de los mecanismos del cerebro normal durante la actividad neuronal, como a nivel patológico, para la búsqueda y estudio de fármacos aplicables a diferentes disfunciones cerebrales. En esta tesis se han estudiado técnicas y métodos para intentar aliviar estas dificultades y facilitar su utilización por parte de la comunidad científica. El trabajo incluye aportaciones en los ámbitos de la configuración del experimento, de la adquisición de los datos y su reconstrucción, y por último del análisis cuantitativo final de las imágenes. En el primer capítulo se trata el problema del uso de anestesia durante el experimento. Para obtener medidas funcionales es necesario establecer un protocolo anestésico que facilite la sedación del animal pero sin llegar a un estado anestésico profundo. Por otra parte, es deseable que sea de rápida inducción y recuperación, y que no sea tóxico para que pueda usarse en estudios longitudinales. En esta parte de la tesis se realizaron experimentos de fMRI en rata sedada con sevofluorano, para lo cual fue necesario realizar un estudio dosis-respuesta y un barrido de frecuencias de estimulación. Además, la señal obtenida en la corteza cerebral se comparó con la de otro protocolo de sedación más tradicional, con medetomidina subdérmica. La señal obtenida fue de intensidad similar a la obtenida con medetomidina, pero el tiempo de preparación del animal se incrementó considerablemente, lo cual constituye un grave inconveniente práctico para el uso de este anestésico. El segundo capítulo está dedicado al estudio de un entorno de adquisición comprimida o “compressed sensing” que permita reducir sustancialmente el tiempo de adquisición sin degradar la calidad de la imagen, gracias a la adquisición de una cantidad mucho menor de datos. En este trabajo se muestra que sería posible acelerar la adquisición a altas tasas que incumplen el criterio de Nyquist-Shannon siempre y cuando se explote la redundancia de información temporal y al mismo tiempo se empleen algoritmos de reconstrucción de imagen iterativos no lineales. En concreto se compara la eficacia de tres algoritmos de reconstrucción que explotan la redundancia temporal para recuperar el contraste BOLD y que han arrojado buenos resultados en otras aplicaciones o modalidades de imagen: tomografía por rayos X, estudios dinámicos de corazón por resonancia magnética, y resonancia funcional en reposo o “resting state”. La comparativa se realizó en dos escenarios de relación señal a ruido y se concluye que el algoritmo que utiliza una imagen a priori (PICCS) es el que mejores resultados obtiene en la reconstrucción. El tercer capítulo aborda el postprocesado y análisis de las imágenes. Existen varias herramientas gratuitas y de código abierto para este fin, pero fueron diseñadas para imagen de cerebro humano, y su adaptación a imágenes de roedores requiere el uso de herramientas adicionales o la realización de transformaciones en la imagen que implican conocimientos de programación. Además, para obtener valores cuantitativos es imprescindible el uso de extensiones o herramientas adicionales. En este trabajo se han estudiado las herramientas existentes y se ha propuesto y desarrollado un nuevo software, fMRat, que realiza el análisis completo de varios sujetos de manera automática, desde el cambio de formato de las imágenes hasta la obtención de valores numéricos de las regiones de interés elegidas por el usuario. La herramienta está programada en Matlab como una extensión de un paquete SPM ya existente, y fue validada con 460 estudios reales de ratas. El código está publicado como “opensoftware” en el sitio web de Github y es accesible a cualquier neurocientífico que desee utilizarlo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Pedro Ramos Cabrer.- Secretario: Juan Miguel Parra Robles.- Vocal: María Jesús Ledesma Carbay

Processing strategies for functional magnetic resonance imaging data sets

Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 1999.Includes bibliographical references (leaves 108-118).by Luis Carlos Maas, III.Ph.D

Depth-Dependent Physiological Modulators of the BOLD Response in the Human Motor Cortex

This dissertation proposes a set of methods for improving spatial localization of cerebral metabolic changes using functional magnetic resonance imaging (fMRI). Blood oxygen level dependent (BOLD) fMRI estabilished itself as the most frequently used technique for mapping brain activity in humans. It is non-invasive and allows to obtain information about brain oxygenation changes in a few minutes. It was discovered in 1990 and, since then, it contributed enormously to the developments in neuroscientific research. Nevertheless, the BOLD contrast suffers from inherent limitations. This comes from the fact that the observed response is the result of a complex interplay between cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen consumption (CMRO2) and has a strong dependency on baseline blood volume and oxygenation. Therefore, the observed response is mislocalized from the site where the metabolic activity takes place and it is subject to high variability across experiments due to normal brain physiology. Since the peak of BOLD changes can be as much as 4 mm apart from the site of metabolic changes, the problem of spatial mislocalization is particularly constraining at submillimeter resolution. Three methods are proposed in this work in order to overcome this limitation and make data more comparable. The first method involves a modification of an estabilished model for calibration of BOLD responses (the dilution model), in order to render it applicable at higher resolutions. The second method proposes a model-free scaling of the BOLD response, based on spatial normalization by a purely vascular response pattern. The third method takes into account the hypothesis that the cortical vasculature could act as a low-pass filter for BOLD fluctuations as the blood is carried downstream, and investigates differences in frequency composition of cortical laminae. All methods are described and tested on a depth-dependent scale in the human motor cortex