Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.
Transcranial magnetic stimulation (TMS) produces a direct causal effect on brain activity that can now be studied by new approaches that simultaneously combine TMS with neuroimaging methods, such as functional magnetic resonance imaging (fMRI). In this review we highlight recent concurrent TMS-fMRI studies that illustrate how this novel combined technique may provide unique insights into causal interactions among brain regions in humans. We show how fMRI can detect the spatial topography of local and remote TMS effects and how these may vary with psychological factors such as task-state. Concurrent TMS-fMRI may furthermore reveal how the brain adapts to so-called virtual lesions induced by TMS, and the distributed activity changes that may underlie the behavioural consequences often observed during cortical stimulation with TMS. We argue that combining TMS with neuroimaging techniques allows a further step in understanding the physiological underpinnings of TMS, as well as the neural correlated of TMS-evoked consequences on perception and behaviour. This can provide powerful new insights about causal interactions among brain regions in both health and disease that may ultimately lead to developing more efficient protocols for basic research and therapeutic TMS applications.
Everyday visual scenes contain a vast quantity of information, only a fraction of which can guide our behavior. Properties such as the location, color and orientation of stimuli help us extract relevant information from complex scenes (Treisman and Gelade, 1980; Livingstone and Hubel, 1987). But how does the brain coordinate the selection of such different stimulus characteristics? Neuroimaging studies have revealed significant regions of overlapping activity in frontoparietal cortex during attention to locations and features, suggesting a global component to visual selection (Vandenberghe et al., 2001; Corbetta and Shulman, 2002; Giesbrecht et al., 2003; Slagter et al., 2007). At the same time, the neural consequences of spatial and feature-based attention differ markedly in early visual areas (Treue and Martinez-Trujillo, 2007), implying that selection may rely on more specific top-down processes. Here we probed the balance between specialized and generalized control by interrupting preparatory attention in the human parietal cortex with transcranial magnetic stimulation (TMS). We found that stimulation of the supramarginal gyrus (SMG) impaired spatial attention only, whereas TMS of the anterior intraparietal sulcus (aIPS) disrupted spatial and feature-based attention. The selection of different stimulus characteristics is thus mediated by distinct top-down mechanisms, which can be decoupled by cortical interference.
Transcranial magnetic stimulation (TMS) has been used to document some apparent interhemispheric influences behaviorally, with TMS over the right parietal cortex reported to enhance processing of touch for the ipsilateral right hand (Seyal et al., 1995). However, the neural bases of such apparent interhemispheric influences from TMS remain unknown. Here, we studied this directly by combining TMS with concurrent functional magnetic resonance imaging (fMRI). We applied bursts of 10 Hz TMS over right parietal cortex, at a high or low intensity, during two sensory contexts: either without any other stimulation, or while participants received median nerve stimulation to the right wrist, which projects to left primary somatosensory cortex (SI). TMS to right parietal cortex affected the blood oxygenation level-dependent signal in left SI, with high- versus low-intensity TMS increasing the left SI signal during right-wrist somatosensory input, but decreasing this in the absence of somatosensory input. This state-dependent modulation of SI by parietal TMS over the other hemisphere was accompanied by a related pattern of TMS-induced influences in the thalamus, as revealed by region-of-interest analyses. A behavioral experiment confirmed that the same right parietal TMS protocol of 10 Hz bursts led to enhanced detection of perithreshold electrical stimulation of the right median nerve, which is initially processed in left SI. Our results confirm directly that TMS over right parietal cortex can affect processing in left SI of the other hemisphere, with rivalrous effects (possibly transcallosal) arising in the absence of somatosensory input, but facilitatory effects (possibly involving thalamic circuitry) in the presence of driving somatosensory input.
The laboratory of Dr. Petr Cejka (IRB, Università della Svizzera italiana) studies how cells repair damaged or broken DNA. DNA can break as a result of exposure to radiation, chemicals or errors during natural cellular processes such as DNA replication. A failure to repair broken DNA may result in ...
Il laboratorio del Dr. Petr Cejka all'Istituto di Ricerca in Biomedicina (IRB, Università della Svizzera italiana) studia come le cellule riparano il DNA danneggiato o rotto. Il DNA può infatti subire danni a causa dell esposizione a radiazioni o a sostanze chimiche, o anche a causa di errori che ...
Die Steuerung der Hochschullandschaft der Schweiz und vorab die Rolle des Bundes soll
grundsätzlich reformiert werden. Ausgehend vom hochschulpolitischen Konzept der 1990er
Jahre werden Trends und damit verbundener Wandel in Lehre und Forschung beschrieben.
Dies führt zu Herausforderungen einer künftigen hochschulpolitischen Steuerung. Vor diesem
Hintergrund wird das Konzept des vorliegenden neuen Bundesgesetzes über die Förderung
der Hochschulen und die Koordination im schweizerischen Hochschulbereich (HFKG) skizziert
und werden Schwierigkeiten für dessen Verabschiedung, in Kraft Setzung und Umsetzung
diskutiert.
One of the most basic questions in economics concerns the effects of competition onnmarket prices. We show that the neglect of both fairness concerns and decision errors prevents ansatisfactory understanding of how competition affects prices. We conducted experiments whichndemonstrate that the introduction of even a very small amount of competition to a bilateralnexchange situation - by adding just one competitor - induces large behavioral changes amongnbuyers and sellers, causing large changes in market prices. Models that assume that all people arenself-interested and fully rational fail to explain these changes satisfactorily. In contrast, a modelnthat combines heterogeneous fairness concerns with decision errors predicts all comparative staticneffects of changes in competition correctly. Moreover, the combined model enables us to predictnthe entire distribution of prices in many different competitive situations remarkably well.
