Site-dependent shaping of field potential waveforms

Abstract

The activity of neuron populations gives rise to field potentials (FPs) that extend beyond the sources. Their mixing in the volume dilutes the original temporal motifs in a site-dependent manner, a fact that has received little attention. And yet, it potentially rids of physiological significance the time-frequency parameters of individual waves (amplitude, phase, duration). This is most likely to happen when a single source or a local origin is erroneously assumed. Recent studies using spatial treatment of these signals and anatomically realistic modeling of neuron aggregates provide convincing evidence for the multisource origin and site-dependent blend of FPs. Thus, FPs generated in primary structures like the neocortex and hippocampus reach far and cross-contaminate each other but also, they add and even impose their temporal traits on distant regions. Furthermore, both structures house neurons that act as spatially distinct (but overlapped) FP sources whose activation is state, region, and time dependent, making the composition of so-called local FPs highly volatile and strongly site dependent. Since the spatial reach cannot be predicted without source geometry, it is important to assess whether waveforms and temporal motifs arise from a single source; otherwise, those from each of the co-active sources should be sought.

The dietary polyphenol metabolite protocatechuic acid produces acute and sustained effects on hippocampal electrical activity

“…The FP analysis presented here highlights a number of caveats derived from the multisource and spatial nature of these signals, 32,43 even though many were minimized by using an ICA that extracts spatially coherent components or FP generators. 33,44 This step is important to provide more realistic time courses by avoiding mutual contamination between spatially overlapped sources or with potentials originated in distant sources (volume-conduction), both of which affect measurements like the mean power or the frequency content.…”

Identification of an early hippocampal recognition system using intracerebral evoked potentials in humans

“…Therefore, the identified generators cannot be linked to a single pathway or process. Moreover, the location of the maximal potential may not coincide with the origin of the current source (Herreras, 2016; Herreras et al, 2022). Due to the curvature of CA1, a synchronized synaptic input to the whole coronal layer (i.e., from the boundary with CA3 to the subiculum), would generate field potentials across the layer, whose sum would be maximal in the center of the curve.…”

“…The main features that determine the field potential of one region are the geometry and the degree of synchronization of the current sources (Herreras, 2016). Blind source separation methods such as ICA has been proposed as the solution to recover the time-courses associated to specific current generators (Herreras et al, 2022(Herreras et al, , 2015. Due to its versatility, ICA has been used to remove the reference signal in intracerebral EEG (Hu et al, 2007) and to separate neural sources in EEG (Onton et al, 2006;Tang et al, 2002), magnetoencephalography (MEG; Barborica et al, 2021;Malinowska et al, 2014) and local field potentials in rats (Fernández-Ruiz et al, 2021;Makarov et al, 2010;Torres et al, 2019).…”

“…When the activities of various structures overlap in time and space, independent component analysis (ICA) may be used to separate the time-courses of the different current generators contributing to the recorded field potentials (Herreras et al, 2022, 2015; Makarova et al, 2011). The efficiency of ICA to disentangle hippocampal pathways has been well established in animal studies, where it has helped isolating the different inputs to CA1 (Benito et al, 2016; López-Madrona et al, 2020) and to the DG (Benito et al, 2014; Fernández-Ruiz et al, 2021, 2013).…”

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

“…Evidence shows that FPs recorded in many brain structures get there by passive spread in the volume and actually arise in other regions ( Carmichael et al, 2017 ; Lalla et al, 2017 ; Parabucki and Lampl, 2017 ; Bertone-Cueto et al, 2020 ). Meanwhile regions that produce significant FPs as the cortex and hippocampus also extend their potentials into each other’s domains ( Torres et al, 2019 ), making FP waveforms multisource and site-dependent ( Herreras et al, 2022 ). Significantly, electroencephalograms (EEGs) themselves are an entirely volume-conducted conglomerate of FPs ( Nunez and Srinivasan, 2006 ).…”