Bernardi, D. | Search bioRxiv

bioRxiv2025

Multivariate empirical mode decomposition reveals markers of Alzheimer’s Disease in the oscillatory response to transcranial magnetic stimulation

ObjectiveTo investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer’s Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.MethodsWe employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.ResultsAD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).ConclusionsAD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.SignificanceEarly detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology.

neuroscience↗

bioRxiv2020

A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation.

The stimulation of a single neuron in the rat somatosensory cortex can elicit a behavioral response. The probability of a behavioral response does not depend appreciably on the duration or intensity of a constant stimulation, whereas the response probability increases significantly upon injection of an irregular current. Biological mechanisms that can potentially suppress a constant input signal are present in the dynamics of both neurons and synapses and seem ideal candidates to explain these experimental findings. Here, we study a large network of integrate-and-fire neurons with several salient features of neuronal populations in the rat barrel cortex. The model includes cellular spike-frequency adaptation, experimentally constrained numbers and types of chemical synapses endowed with short-term plasticity, and gap junctions. Numerical simulations of this model indicate that cellular and synaptic adaptation mechanisms alone may not be sufficient to account for the experimental results if the local network activity is read out by an integrator. However, a differentiator circuit can detect the single-cell stimulation with a reliability that barely depends on the length or intensity of the stimulus, but that increases when an irregular signal is used. This finding is in accordance with the experimental results obtained for the stimulation of a regularly-spiking excitatory cell. Author summaryIt is widely assumed that only a large group of neurons can encode a stimulus or control behavior. This tenet of neuroscience has been challenged by experiments in which stimulating a single cortical neuron has had a measurable effect on an animals behavior. Recently, theoretical studies have explored how a single-neuron stimulation could be detected in a large recurrent network. However, these studies missed essential biological mechanisms of cortical networks and are unable to explain more recent experiments in the barrel cortex. Here, to describe the stimulated brain area, we propose and study a network model endowed with many important biological features of the barrel cortex. Importantly, we also investigate different readout mechanisms, i.e. ways in which the stimulation effects can propagate to other brain areas. We show that a readout network which tracks rapid variations in the local network activity is in agreement with the experiments. Our model demonstrates a possible mechanism for how the stimulation of a single neuron translates into a signal at the population level, which is taken as a proxy of the animals response. Our results illustrate the power of spiking neural networks to properly describe the effects of a single neurons activity.

neuroscience↗