Magnetoencephalography in Alzheimer Disease

There are two basic ways Magnetoencephalography (MEG) has been applied. The most typical way is recording brain signals related to specific stimuli and tasks or signals indicative of focal pathology as in presurgical brain mapping and epilepsy localization. The second way is recording patterns of spontaneous activity characteristic of particular states or traits. An example of the latter application is described in this chapter that details efforts of deriving brain activity patterns characteristic of Alzheimer’s dementia. The derivation of such patterns will be of great value in diagnosis, prognosis, as well as monitoring progress (or the process of amelioration) of diseases.

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Hype, hyperscanning and embodied social neuroscience

10.31234/osf.io/rc9wp ◽

2020 ◽

Cited By ~ 2

Author(s):

Antonia Hamilton

Keyword(s):

Social Behaviour ◽

Brain Activity ◽

Activity Patterns ◽

Social Neuroscience ◽

Action Perception ◽

Second Person ◽

Common Input ◽

Brain Signals ◽

The Social ◽

Coherence Patterns

Hyperscanning has been hailed as a game-changing method which will allow us to understand the neuroscience of multi-person social interactions and create ‘second person neuroscience’. Here, I present a critical review of fNIRS hyperscanning studies, examining what they can and cannot tell us about social neuroscience. A key problem is that many current hyperscanning methods cannot distinguish a pure pattern of interpersonal coherence from effects driven by a common input. Limited data on participant behaviour during testing sessions compounds this problem, because it is not clear what behaviours might mediate the brain coherence patterns that are reported. Some studies respond to this problem by retreating from strong cognitive interpretations of hyperscanning data and measuring how overall levels of coherence differ with individual factors (such as age, gender, social relationships between people or clinical diagnosis). Here, I suggest that there is a better way to analyse and interpret hyperscanning studies. By tracking behaviour in detail, and analysing behaviour and brain activity patterns together, it will be possible to define what types of action, perception and mutual prediction arising in the interaction of two or more people can lead to coherent brain signals and why. This approach acknowledges that social brains are embodied and that coherent brain activity arises from the social behaviour of two people in an interaction. By integrating our study of the social brain and social behaviour, we will be able to strengthen the science of both.

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Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

10.1101/2020.05.22.111021 ◽

2020 ◽

Cited By ~ 1

Author(s):

Navvab Afrashteh ◽

Samsoon Inayat ◽

Edgar Bermudez Contreras ◽

Artur Luczak ◽

Bruce L. McNaughton ◽

...

Keyword(s):

Spontaneous Activity ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Stimulation ◽

Wide Field ◽

Evoked Responses ◽

Evoked Activity ◽

Cortical Regions ◽

The One ◽

Sensory Evoked

AbstractBrain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

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Relations of EEG-brain mapping and HMPAO-SPECT with the severity of Alzheimer's dementia (DAT)

Psychiatry Research Neuroimaging ◽

10.1016/s0925-4927(97)81558-2 ◽

1997 ◽

Vol 68 (2-3) ◽

pp. 172 ◽

Cited By ~ 1

Author(s):

Th.J. Mueller ◽

W.K. Strik ◽

J. Thome ◽

T. Dierks ◽

M. Scheubeck ◽

...

Keyword(s):

Brain Mapping ◽

Alzheimer’S Dementia ◽

Alzheimer's Dementia ◽

Hmpao Spect

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Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli

10.1101/518712 ◽

2019 ◽

Author(s):

DoHyun Kim ◽

Tomer Livne ◽

Nicholas V. Metcalf ◽

Maurizio Corbetta ◽

Gordon L. Shulman

Keyword(s):

Visual Cortex ◽

Functional Connectivity ◽

Spontaneous Activity ◽

Brain Activity ◽

Activity Patterns ◽

Visual Stimuli ◽

Correlation Coefficients ◽

Temporal Correlation ◽

Stimulus Category ◽

Human Visual Cortex

AbstractThe function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli and tasks. We compared in human visual cortex multi-vertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g. phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g. extra-striate body area for bodies), measured multi-vertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multi-vertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, i.e. both positive and negative, was significantly greater for the preferred stimulus category of an ROI (e.g. body category in body-preferring ROIs). The relationship between spontaneous and stimulus-evoked multi-vertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and patterns related to different categories fluctuated independently within their respective preferred ROIs (e.g. body- and scene-related multi-vertex patterns within body- and scene-preferring ROIs). These results support the general proposal that spontaneous multi-vertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.

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