Visual motion direction is represented in population-level neural response as measured by magnetoencephalography

Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli

Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.

Near-real-time feature-selective modulations in human cortex

For neural activity to be linked with cognitive function, information is needed about both the temporal dynamics and the content of neural codes. Traditionally, recording of single neurons in animals has been the primary means of obtaining high temporal resolution and precise information about neural tuning properties such as selectivity for different sensory features. Recent functional magnetic resonance imaging (fMRI) studies in humans have been able to measure feature selectivity within specific subregions of sensory cortex (e.g., orientation selectivity in primary visual cortex, or V1) [1, 2]. However, investigating the neural mechanisms that support cognitive processing-which often occurs rapidly on a subsecond scale-with a temporally insensitive method such as fMRI severely limits the types of inferences that can be drawn. Here, we describe a new method for tracking the rapid temporal evolution of feature-selective information processing with scalp recordings of electroencephalography (EEG). We generate orientation-selective response profiles based on the spatially distributed pattern of steady-state visual evoked potential (SSVEP) responses to flickering visual stimuli. Using this approach, we report a multiplicative attentional modulation of these feature-selective response profiles with a temporal resolution of 24-120 ms, which is far faster than that achieved with fMRI. Finally, we show that behavioral performance on a discrimination task can be predicted based on the amplitude of these temporally precise feature-selective response profiles. This method thus provides a high-temporal-resolution metric that can be used to track the influence of cognitive manipulations on feature-selective information processing in human cortex.

Early Alzheimer's disease blocks responses to accelerating self-movement

We assessed the cortical processing of self-movement stimuli in aging and Alzheimer's disease (AD). Our goal was to identify distinguishing effects on neural mechanisms related to driving and navigation. Young (YNC) and older (ONC) normal controls, and early AD patients (EAD) viewed real-world videos and dot motion stimuli simulating self-movement scenes. We recorded visual motion event related potentials (VMERPs) to stimulus motion coherence and speed. Aging delays motion evoked N200s, whereas AD diminishes response amplitudes. Early Alzheimer's disease patients respond to increments in motion coherence, but they are uniquely unresponsive to increments in motion speed that simulate accelerating self-movement. AD-related impairments of self-movement processing may have grave consequences for driving safety and navigational independence.