Angel Nevado

Reorganization of functional networks in mild cognitive impairment.

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

Brain imaging of acupuncture: Comparing superficial with deep needling

The difference between superficial and deep needling at acupuncture points has yet to be mapped with functional magnetic resonance imaging (fMRI). Using a 3T MRI, echo planar imaging data were acquired for 17 right-handed healthy volunteer participants. Two fMRI scans of acupuncture needling were taken in random order in a block design, one for superficial and one for deep needling on the right hand at the acupuncture point LI-4 (Hegu), with the participant blind to the order. For both scans needle stimulation was used. Brain image analysis tools were used to explore within-group and between-group differences in the blood oxygen level dependent (BOLD) responses. The study demonstrated marked similarities in BOLD signal responses between superficial and deep needling, with no significant differences in either activations (increases in BOLD signal) or deactivations (decreases in BOLD signal) above the voxel Z score of 2.3 with corrected cluster significance of P=0.05. For both types of needling, deactivations predominated over activations. These fMRI data suggest that acupuncture needle stimulation at two different depths of needling, superficial and deep, do not elicit significantly different BOLD responses. This data is consistent with the equivalent therapeutic outcomes that are claimed by proponents of Japanese and Chinese styles of acupuncture that utilise superficial and deep needling, respectively.

Functional Connectivity in Mild Cognitive Impairment During a Memory Task: Implications for the Disconnection Hypothesis

Mild cognitive impairment (MCI) has been considered an intermediate state between healthy aging and dementia. The early damage in anatomical connectivity and progressive loss of synapses that characterize early Alzheimers disease suggest that MCI could also be a disconnection syndrome. Here, we compare the degree of synchronization of brain signals recorded with magnetoencephalography from patients (22) with MCI with that of healthy controls (19) during a memory task. Synchronization Likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. During the memory task patients showed higher interhemispheric synchronization than healthy controls between left and right -anterior temporo-frontal regions (in all studied frequency bands) and in posterior regions in the γ band. On the other hand, the connectivity pattern from healthy controls indicated two clusters of higher synchronization, one among left temporal sensors and another one among central channels. Both of them were found in all frequency bands. In the γ band, controls showed higher Synchronization Likelihood values than MCI patients between central-posterior and frontal-posterior channels and a high synchronization in posterior regions. The inter-hemispheric increased synchronization values could reflect a compensatory mechanism for the lack of efficiency of the memory networks in MCI patients. Therefore, these connectivity profiles support only partially the idea of MCI as a disconnection syndrome, as patients showed increased long distance inter-hemispheric connections but a decrease in antero-posterior functional connectivity.

Spatio‐temporal prediction and inference by V1 neurons

In normal vision, visual scenes are predictable, as they are both spatially and temporally redundant. Evidence suggests that the visual system may use the spatio‐temporal regularities of the external world, available in the retinal signal, to extract information from the visual environment and better reconstruct current and future stimuli. We studied this by recording neuronal responses of primary visual cortex (areau2003V1) in anaesthetized and paralysed macaques during the presentation of dynamic sequences of bars, in which spatio‐temporal regularities and local information were independently manipulated. Most V1 neurons were significantly modulated by events prior to and distant from stimulation of their classical receptive fields (CRFs); many were more strongly tuned to prior and distant events than they were to CRFs bars; and several showed tuning to prior information without any CRF stimulation. Hence, V1 neurons do not simply analyse local contours, but impute local features to the visual world, on the basis of prior knowledge of a visual world in which useful information can be distributed widely in space and time.

Functional imaging and neural information coding

Measuring functional magnetic resonance imaging (fMRI) responses to parametric stimulus variations in imaging experiments can elucidate how sensory information is represented in the brain. However, a potential limitation of this approach is that fMRI responses reflect only a regional average of neuronal activity. For this reason stimulus-induced changes in fMRI signal may not always reflect how sensory information is encoded by neuronal population activity. We investigate the potential problems induced by the finite spatial resolution of the fMRI signal by combining the principles of Information Theory with a computational model of neuronal activity based on known tuning properties of sensory cortex and assuming a linear spike rate to fMRI signal relationship. We found that the relationship between neuronal information and fMRI signal is highly nonlinear. It follows that the brain voxel experiencing the largest fMRI signal change is not necessarily the voxel encoding the most sensory information. Results also show that functional imaging data can be better interpreted in terms of neural information processing if the fMRI data and some knowledge about the tuning properties of the underlying neuronal populations are incorporated into a computational model. We discuss how imaging techniques themselves may provide an estimation of neuronal tuning properties.

Increased biomagnetic activity in healthy elderly with subjective memory complaints

OBJECTIVEnSubjective memory complaints (SMCs) are frequently reported by elderly people with or without objective cognitive impairment (OMI) as assessed by neuropsychological tests. We investigate whether SMCs are associated with altered brain biomagnetic patterns even in the absence of OMI.nnnMETHODSnWe report spatio-temporal patterns of brain magnetic activity recorded with magnetoencephalography during a memory task in 51 elderly participants divided into the following groups: patients with mild cognitive impairment (MCI) with SMC and OMI, individuals with SMC but not OMI, and healthy controls without neither SMC nor OMI. Exclusion criteria for all three groups included a diagnosis of depression or any other psychiatric condition.nnnRESULTSnNo statistically significant differences were found between MCI patients and participants with SMC. However, the SMC showed higher activation, between 200 and 900 ms after stimulus onset, than the control group in posterior ventral regions and in the dorsal pathway. MCI patients showed higher activation than the control group in the posterior part of the ventral pathway.nnnCONCLUSIONSnThese findings suggest that similar physiological mechanisms may underlie SMC and MCI, which could be two stages in a cognitive continuum.nnnSIGNIFICANCEnMEG provide different neurophysiological profiles between SMC and control subjects.

Signal-to-noise ratio of the MEG signal after preprocessing

BACKGROUNDnMagnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise.nnnNEW METHODnThe effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials.nnnRESULTSnRecordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI.nnnCOMPARISON WITH EXISTING METHODSnNo previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed.nnnCONCLUSIONSnThe application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

Can we observe collective neuronal activity from macroscopic aggregate signals

The fundamental problem faced by noninvasive neuroimaging techniques such as EEG/MEG(1) is to elucidate functionally important aspects of the microscopic neuronal network dynamics from macroscopic aggregate measurements. Due to the mixing of the activities of large neuronal populations in the observed macroscopic aggregate, recovering the underlying network that generates the signal in the absence of any additional information represents a considerable challenge. Recent MEG studies have shown that macroscopic measurements contain sufficient information to allow the differentiation between patterns of activity, which are likely to represent different stimulus-specific collective modes in the underlying network (Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes, G.R., 2007. Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex. NeuroImage 35, 518-530). The next question arising in this context is whether aspects of collective network activity can be recovered from a macroscopic aggregate signal. We propose that this issue is most appropriately addressed if MEG/EEG signals are to be viewed as macroscopic aggregates arising from networks of coupled systems as opposed to aggregates across a mass of largely independent neural systems. We show that collective modes arising in a network of simulated coupled systems can be indeed recovered from the macroscopic aggregate. Moreover, we show that nonlinear state space methods yield a good approximation of the number of effective degrees of freedom in the network. Importantly, information about hidden variables, which do not directly contribute to the aggregate signal, can also be recovered. Finally, this theoretical framework can be applied to experimental MEG/EEG data in the future, enabling the inference of state dependent changes in the degree of local synchrony in the underlying network.

Primary visual cortex neurons that contribute to resolve the aperture problem.

It is traditional to believe that neurons in primary visual cortex are sensitive only or principally to stimulation within a spatially restricted receptive field (classical receptive field). It follows from this that they should only be capable of encoding the direction of stimulus movement orthogonal to the local contour, since this is the only information available in their classical receptive field aperture. This direction is not necessarily the same as the motion of the entire object, as the direction cue within an aperture is ambiguous to the global direction of motion, which can only be derived by integrating with unambiguous components of the object. Recent results, however, show that primary visual cortex neurons can integrate spatially and temporally distributed cues outside the classical receptive field, and so we reexamined whether primary visual cortex neurons suffer the aperture problem. With the stimulation of an optimally oriented bar drifting across the classical receptive field in different global directions, here we show that a subpopulation of primary visual cortex neurons (25/81) recorded from anesthetized and paralyzed marmosets is capable of integrating informative unambiguous direction cues presented by the bar ends, well outside their classical receptive fields, to encode global motion direction. Although the stimuli within the classical receptive field were identical, their directional responses were significantly modulated according to the global direction of stimulus movement. Hence, some primary visual cortex neurons are not local motion energy filters, but may encode signals that contribute directly to global motion processing.

Cortical oscillatory activity associated with the perception of illusory and real visual contours

We used magnetoencephalography (MEG) to examine the nature of oscillatory brain rhythms when passively viewing both illusory and real visual contours. Three stimuli were employed: a Kanizsa triangle; a Kanizsa triangle with a real triangular contour superimposed; and a control figure in which the corner elements used to form the Kanizsa triangle were rotated to negate the formation of illusory contours. The MEG data were analysed using synthetic aperture magnetometry (SAM) to enable the spatial localisation of task-related oscillatory power changes within specific frequency bands, and the time-course of activity within given locations-of-interest was determined by calculating time-frequency plots using a Morlet wavelet transform. In contrast to earlier studies, we did not find increases in gamma activity (>30 Hz) to illusory shapes, but instead a decrease in 10-30 Hz activity approximately 200 ms after stimulus presentation. The reduction in oscillatory activity was primarily evident within extrastriate areas, including the lateral occipital complex (LOC). Importantly, this same pattern of results was evident for each stimulus type. Our results further highlight the importance of the LOC and a network of posterior brain regions in processing visual contours, be they illusory or real in nature. The similarity of the results for both real and illusory contours, however, leads us to conclude that the broadband (<30 Hz) decrease in power we observed is more likely to reflect general changes in visual attention than neural computations specific to processing visual contours.