Mika Seppä

Coinciding early activation of the human primary visual cortex and anteromedial cuneus

Proper understanding of processes underlying visual perception requires information on the activation order of distinct brain areas. We measured dynamics of cortical signals with magnetoencephalography while human subjects viewed stimuli at four visual quadrants. The signals were analyzed with minimum current estimates at the individual and group level. Activation emerged 55–70 ms after stimulus onset both in the primary posterior visual areas and in the anteromedial part of the cuneus. Other cortical areas were active after this initial dual activation. Comparison of data between species suggests that the anteromedial cuneus either comprises a homologue of the monkey area V6 or is an area unique to humans. Our results show that visual stimuli activate two cortical areas right from the beginning of the cortical response. The anteromedial cuneus has the temporal position needed to interact with the primary visual cortex V1 and thereby to modify information transferred via V1 to extrastriate cortices.

Common cortical network for first and second pain

We measured, with whole-scalp magnetoencephalography, evoked fields from 10 healthy subjects to 1-ms thulium-laser stimuli that selectively activated nociceptive nerve fibers. The stimuli were delivered to the dorsum of the subjects left hand. The earliest cortical responses peaked at 165 +/- 7 ms, agreeing with the conduction velocity of Adelta-fibers. To stimulate unmyelinated C-fibers, we modified the method of Bragard et al. [Bragard, D., Chen, A.C., Plaghki, L., 1996. Direct isolation of ultra-late (C-fibre) evoked brain potentials by CO2 laser stimulation of tiny cutaneous surface areas in man. Neurosci. Lett. 209, 81-84], by decreasing the total energy of the laser beam and by restricting the size of the stimulated skin area to 0.2-0.3 mm2. The earliest cortical responses to these stimuli peaked at 811 +/- 14 ms. Bilateral activation of the SII cortices was detected in all 10 subjects to Adelta and in 8 subjects to C stimuli, emphasizing the importance of the SII cortex in processing of pain. Additional activation was observed in the posterior parietal cortex (PPC), probably related to sensorimotor coordination targeted to produce precise motor acts that reduce or prevent the pain; the PPC activation may have been accentuated by the required continuous evaluation of the perceived pain. In contrast to some earlier studies, we did not observe activation of the primary somatosensory cortex (SI). Additional activations to both types of stimuli were detected in the cingulate cortex (three subjects) and in the bilateral insular cortex (two subjects). These results implicate that the nociceptive inputs mediated by the Adelta- and C-fibers are processed in a common cortical network in different time windows. Reliable temporospatial characterization of cortical responses to first and second pain offers a unique tool for basic and clinical neuroscience to study the two distinctive pain fiber systems at cortical level.

Three‐dimensional integration of brain anatomy and function to facilitate intraoperative navigation around the sensorimotor strip

We studied 12 patients with brain tumors in the vicinity of the sensorimotor region to provide a preoperative three‐dimensional visualization of the functional anatomy of the rolandic cortex. We also evaluated the role of cortex‐muscle coherence analysis and anatomical landmarks in identifying the sensorimotor cortex. The functional landmarks were based on neuromagnetic recordings with a whole‐scalp magnetometer, coregistred with magnetic resonance images. Evoked fields to median and tibial nerve and lip stimuli were recorded to identify hand, foot and face representations in the somatosensory cortex. Oscillatory cortical activity, coherent with surface electromyogram during isometric muscle contraction, was analyzed to reveal the hand and foot representations in the precentral motor cortex. The central sulcus was identified also by available anatomical landmarks. The source locations, calculated from the neuromagnetic data, were displayed on 3‐D surface reconstructions of the individual brains, including the veins. The preoperative data were verified during awake craniotomy by cortical stimulation in 7 patients and by cortical somatosensory evoked potentials in 5 patients. Sources of somatosensory evoked fields identified correctly the postcentral gyrus in all patients. Useful corroborative information was obtained from anatomical landmarks in 11 patients and from cortex‐muscle correlograms in 8 patients. The preoperative visualization of the functional anatomy of the sensorimotor strip assisted in designing the operational strategy, facilitated orientation of the neurosurgeon during the operation, and speeded up the selection of sites for intraoperative stimulation or mapping, thereby helping to prevent damage of eloquent brain areas during surgery. Hum. Brain Mapping 12:180–192, 2001.

The 3D topography of MEG source localization accuracy: effects of conductor model and noise

OBJECTIVE To evaluate the effect that different head conductor models have on the source estimation accuracy of magnetoencephalography (MEG) under realistic conditions. METHODS Magnetic fields evoked by current dipoles were simulated using a highly refined 3-layer realistically shaped conductor model. Noise from a real MEG measurement was added to the simulated fields. Source parameters (location, strength, orientation) were estimated from the noisy signals using 3 spherically symmetric models and several one- and 3-layer realistically shaped boundary-element models. The effect of different measurement sensors (gradiometers, magnetometers) was also tested. RESULTS The noise typically present in brain signals masked the errors due to the different conductor models so that in most situations the models gave comparable results. Active cortical areas around the vertex and in the temporal, frontoparietal, and occipital regions were typically found with 2-4 mm accuracy, whereas source localization in several anterior frontal lobe and deep brain structures yielded errors exceeding 2 cm. Localization in anterior frontal regions may benefit most from the use of realistically shaped models. CONCLUSIONS The traditionally used sphere model is an adequate model for most research purposes. Any means that increase the signal-to-noise ratio are of highest importance in attempting to improve the source estimation accuracy.

Dynamical organisation of the human visual system revealed by lifetimes of activation traces

Magnetic source imaging reveals a dynamical organisation of visual cortical areas suggesting that the participation of local memories is an essential component of visual information processing. Response recovery studies provide evidence that each responding cortical area supports a memory function with a well-defined lifetime. The areas fell into two groups, the earliest in occipital lobes with lifetimes ranging from 0.1 to 0.6 s, and the later ones in temporal, parietal, and frontal areas with lifetimes ranging from 7 to 30 s. Also, within each group the areas responding later tended to have longer lifetimes.

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Listening to an Audio Drama Activates Two Processing Networks, One for All Sounds, Another Exclusively for Speech

Earlier studies have shown considerable intersubject synchronization of brain activity when subjects watch the same movie or listen to the same story. Here we investigated the across-subjects similarity of brain responses to speech and non-speech sounds in a continuous audio drama designed for blind people. Thirteen healthy adults listened for ∼19 min to the audio drama while their brain activity was measured with 3 T functional magnetic resonance imaging (fMRI). An intersubject-correlation (ISC) map, computed across the whole experiment to assess the stimulus-driven extrinsic brain network, indicated statistically significant ISC in temporal, frontal and parietal cortices, cingulate cortex, and amygdala. Group-level independent component (IC) analysis was used to parcel out the brain signals into functionally coupled networks, and the dependence of the ICs on external stimuli was tested by comparing them with the ISC map. This procedure revealed four extrinsic ICs of which two–covering non-overlapping areas of the auditory cortex–were modulated by both speech and non-speech sounds. The two other extrinsic ICs, one left-hemisphere-lateralized and the other right-hemisphere-lateralized, were speech-related and comprised the superior and middle temporal gyri, temporal poles, and the left angular and inferior orbital gyri. In areas of low ISC four ICs that were defined intrinsic fluctuated similarly as the time-courses of either the speech-sound-related or all-sounds-related extrinsic ICs. These ICs included the superior temporal gyrus, the anterior insula, and the frontal, parietal and midline occipital cortices. Taken together, substantial intersubject synchronization of cortical activity was observed in subjects listening to an audio drama, with results suggesting that speech is processed in two separate networks, one dedicated to the processing of speech sounds and the other to both speech and non-speech sounds.

Continuous Sampling in Mutual-Information Registration

Mutual information is a popular and widely used metric in retrospective image registration. This metric excels especially with multimodal data due to the minimal assumptions about the correspondence between the image intensities. In certain situations, the mutual-information metric is known to produce artifacts that rule out subsample registration accuracy. Various methods have been developed to mitigate these artifacts, including higher order kernels for smoother sampling of the metric. This study introduces a novel concept of continuous sampling to provide new insight into the mutual-information methods currently in use. In particular, the connection between the partial volume interpolation and the recently introduced higher order partial-volume-type kernels is revealed.Mutual information is a popular and widely used metric in retrospective image registration. This metric excels especially with multimodal data due to the minimal assumptions about the correspondence between the image intensities. In certain situations, the mutual-information metric is known to produce artifacts that rule out subsample registration accuracy. Various methods have been developed to mitigate these artifacts, including higher order kernels for smoother sampling of the metric. This study introduces a novel concept of continuous sampling to provide new insight into the mutual-information methods currently in use. In particular, the connection between the partial volume interpolation and the recently introduced higher order partial-volume-type kernels is revealed.

Uncovering cortical MEG responses to listened audiobook stories

Naturalistic stimuli, such as normal speech and narratives, are opening up intriguing prospects in neuroscience, especially when merging neuroimaging with machine learning methodology. Here we propose a task-optimized spatial filtering strategy for uncovering individual magnetoencephalographic (MEG) responses to audiobook stories. Ten subjects listened to 1-h-long recording once, as well as to 48 repetitions of a 1-min-long speech passage. Employing response replicability as statistical validity and utilizing unsupervised learning methods, we trained spatial filters that were able to generalize over datasets of an individual. For this blind-signal-separation (BSS) task, we derived a version of multi-set similarity-constrained canonical correlation analysis (SimCCA) that theoretically provides maximal signal-to-noise ratio (SNR) in this setting. Irrespective of significant noise in unaveraged MEG traces, the method successfully uncovered feasible time courses up to ~120 Hz, with the most prominent signals below 20 Hz. Individual trial-to-trial correlations of such time courses reached the level of 0.55 (median 0.33 in the group) at ~0.5 Hz, with considerable variation between subjects. By this filtering, the SNR increased up to 20 times. In comparison, independent component analysis (ICA) or principal component analysis (PCA) did not improve SNR notably. The validity of the extracted brain signals was further assessed by inspecting their associations with the stimulus, as well as by mapping the contributing cortical signal sources. The results indicate that the proposed methodology effectively reduces noise in MEG recordings to that extent that brain responses can be seen to nonrecurring audiobook stories. The study paves the way for applications aiming at accurately modeling the stimulus-response-relationship by tackling the response variability, as well as for real-time monitoring of brain signals of individuals in naturalistic experimental conditions.

Visualizing human brain surface from T1-weighted MR images using texture-mapped triangle meshes.

We describe a novel method for visualizing brain surface from anatomical magnetic resonance images (MRIs). The method utilizes standard 2D texture mapping capabilities of OpenGL graphics language. It combines the benefits of volume rendering and triangle-mesh rendering, allowing fast and realistic-looking brain surface visualizations. Consequently, relatively low-resolution triangle meshes can be used while the texture images provide the necessary details. The mapping is optimized to provide good texture-image resolution for the triangles with respect to their original sizes in the 3D MRI volume. The actual 2D texture images are generated by depth integration from the original MRI data. Our method adapts to anisotropic voxel sizes without any need to interpolate the volume data into cubic voxels, and it is very well suited for visualizing brain anatomy from standard T(1)-weighted MR images. Furthermore, other OpenGL objects and techniques can be easily combined, for example, to use cut planes, to show other surfaces and objects, and to visualize functional data in addition to the anatomical information.