Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's 'functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations.
Leveraging available annotated data is an essential component of many modern methods for medical image analysis. In particular, approaches making use of the "neighbourhood" structure between images for this purpose have shown significant potential. Such techniques achieve high accuracy in analysing an image by propagating information from its immediate "neighbours" within an annotated database. Despite their success in certain applications, wide use of these methods is limited due to the challenging task of determining the neighbours for an out-of-sample image.
This paper presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects.
Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by highly orchestrated cellular migration. A greater appreciation of the processes involved in human fetal corticogenesis is vital to gaining an understanding of how developmental disturbances originating in gestation could establish a variety of complex neuropathology manifesting in childhood, or even in adult life. Magnetic resonance imaging modalities offer a unique insight into anatomical structure, and increasingly infer information regarding underlying microstructure in the human brain.
Recent functional brain connectivity studies have contributed to our understanding of the neurocircuitry supporting pain perception. However, evoked-pain connectivity studies have employed cutaneous and/or brief stimuli, which induce sensations that differ appreciably from the clinical pain experience. Sustained myofascial pain evoked by pressure cuff affords an excellent opportunity to evaluate functional connectivity change to more clinically relevant sustained deep-tissue pain.
The macaque monkey is an important model for cognitive and sensory neuroscience that has been used extensively in behavioral, electrophysiological, molecular and, more recently, neuroimaging studies. However, macaque MRI has unique technical differences relative to human MRI, such as the geometry of highly parallel receive arrays, which must be addressed to optimize imaging performance. A 22-channel receive coil array was constructed specifically for rapid high-resolution anesthetized macaque monkey MRI at 3 T. A local Helmholtz transmit coil was used for excitation.
Recent neuroimaging studies implicate that both the dorsal and ventral visual pathways, as well as the middle temporal (MT) areas which are critical for the perception of visual motion, are involved in the perception of three-dimensional (3D) structure from two-dimensional (2D) motion (3D-SFM). However, the neural dynamics underlying the reconstruction of a 3D object from 2D optic flow is not known. Here we combined magnetoencephalography (MEG) and functional MRI (fMRI) measurements to investigate the spatiotemporal brain dynamics during 3D-SFM.
Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process.
Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes.
