![]() ![]() ![]() Since gamma activity is directly tied to hippocampal interneuron function and memory processes in AD, this is a potentially relevant technical advantage. Furthermore, detection of activity in higher frequency bands such as gamma (> 30 Hz) is more reliable in MEG than in EEG, because of the reduced interference of underlying scalp tissue (e.g., muscle artefacts) on the MEG signal. Using source reconstruction methods, MEG can reliably reconstruct activity in subcortical regions, including the hippocampus. Of all current non-invasive methods, MEG has the highest spatiotemporal resolution to record brain activity. On the other hand, with the traditional scalp EEG, accurately capturing subcortical, hippocampus-specific oscillations is difficult because of the relatively low spatial resolution, and predilection for cortical activity. However, the BOLD signal used for fMRI is a rather indirect measure of neuronal activity, with a low temporal resolution compared to neurophysiological techniques such as EEG and magnetoencephalography (MEG). Functional MRI (fMRI) studies have reported abnormal hippocampal activation in prodromal AD patients, and even in asymptomatic persons at risk to develop AD such as APOE4 and PSEN1 carriers. However, investigating hippocampal activity non-invasively in humans is a considerable technical challenge. So, while neuronal dysfunction is not confined to the hippocampus, it is recognized as an early AD hotspot.Ī relevant clinical question is therefore whether quantitative analysis of hippocampal dysfunction might produce more powerful biomarkers in early AD stages. Even before extensive synapse and neuron loss occurs, these pathological processes lead to altered behavioral repertoires of those neurons, and their combined effects are presumed to trigger a further detrimental cascade of neuronal excitation/inhibition imbalance with excitotoxic neurodegeneration, neuronal circuit malfunction, synchronization failure between larger neuronal assemblies, oscillatory changes, and ultimately long-distance functional and structural brain network disruption, resulting in cognitive decline. Relating information from animal models to knowledge from human studies can be challenging, for example due to many human studies being done on sporadic instead of familial AD, but doing so may give us a more complete picture of the present pathological processes, as there appears to be a general consensus that neuronal hyperexcitability plays a role. In fact, in vitro and in vivo research in AD animal models describes marked early changes in AD pathophysiology: one of the most striking abnormalities being neuronal hyperexcitability and hyperactivity, presumably mediated through amyloid-β-induced mechanisms of glutamate excitotoxicity of pyramidal neurons, as well as interneuron dysfunction. Hippocampal neuronal and synaptic activity and function is also heavily disrupted in AD. Structural pathology (e.g., tau phosphorylation and atrophy) is prominent in this region, correlating with the typical episodic memory impairment in AD patients. The hippocampus is one of the archetypical early sites of AD pathophysiology. ![]() How can we further improve the early neurophysiological detection of AD? Recently developed advanced machine learning-based classification methods are improving neurophysiological marker performance, but at this point the role of EEG markers in AD diagnosis is still supportive and context dependent. A main reason for this is individual variability: in early AD, the background pattern can still remain relatively intact, complicating reliable early detection through EEG. While these changes in the resting-state background pattern have been known for decades and correlate with cognitive impairment in AD, measures that quantify oscillatory slowing (e.g., based on power spectral density), have so far produced suboptimal diagnostic markers of AD. The dominant posterior rhythm, often referred as “alpha rhythm”, which is centered around☑0 Hz in healthy adults, can slow down to below 8 Hz. There is an early increase in slow activity in the theta band (4–8 Hz), followed by decreases in the faster beta (13–30 Hz) and alpha (8–13 Hz) bands, and ultimately an increase in slow delta activity (0.5–4 Hz). Human neuronal activity in Alzheimer’s disease (AD) is characterized by a gradual, diffuse slowing of cortical oscillatory activity as the disease progresses, as can be assessed with scalp electroencephalography (EEG). ![]()
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