Electrical conduction properties of soils are primarily determined by electrolytic soil water conductivity, i.e., ion concentration and mobility, and the interface conduction processes at water–mineral interfaces. Electrical polarization properties originate mainly in ion accumulation processes in constrictions of the pore network and at water–mineral interfaces. If surfaces are electrically charged and in contact with an electrolyte such as those found at mineral grain surfaces or cell membranes, electrical double layers (EDLs) form, which comprise the so-called Stern layer of bound counterions and the so-called diffusive layer. The latter forms in the equilibrium of electromigrative and diffusive ion fluxes, and is characterized by ion concentration gradients. The EDL is affected by external electric fields, manifesting an induced polarization (IP), and takes a finite time (relaxation time) to reach equilibrium again once an impressed external field is turned off e.g.,. Models of both Stern layer polarization (the build-up of counterion concentration gradients in the Stern layer in the direction of the external electric field) e.g., and diffuse layer polarization (the build-up of counterion and coion concentration gradients in the diffuse layer in the direction of the external electric field) e.g., have been developed, as well as models encompassing both the Stern layer and the diffuse layer e.g.,. In a porous system such as soil, diffuse layer polarization is also referred to as membrane polarization since the resultant ion concentration gradients, for instance along a pore constriction, have an effect similar to an ion-selective membrane e.g.,. Strength and relaxation behavior of EDL polarization are influenced by background ion concentration in the pore water and surface charge density, among other factors e.g.,. Importantly, the relaxation time relates to the spatial length scale of the polarization process and the ionic diffusion coefficient in the EDL, which may be different for Stern layer and diffuse layer polarization e.g.,. The relationship between relaxation time and characteristic length scale for induced polarization in soils and sediments has been investigated in many studies e.g.,.

Electrical methods measure the conduction and polarization properties of a medium. In the frequency domain, the measured quantity is the complex-valued impedance, with the real (ohmic) part accounting for conduction, and the imaginary part accounting for polarization (capacitive) effects.

The electrical impedance, Z^, at a measurement (angular) frequency ω is defined as the ratio of the complex voltage U^ to the current I^, and can be represented by a real part Z′ and an imaginary part Z′′: Z^(ω)=U^(ω)I^(ω)=Z′(ω)+jZ′′(ω), with j denoting the imaginary unit. The inverse of the impedance is the admittance Y^(ω)=1/Z^(ω)=Y′(ω)+jY′′(ω). Impedances, or admittances, can be translated to effective material properties by means of a (real-valued) geometrical factor K, which takes into account the geometric dimensions of the measurement (in particular electrode positions): ρ^a(ω)=KZ^(ω)=KY^(ω),σ^a(ω)=Y^(ω)K=1KZ^(ω)=1ρ^a(ω), with ρ^a and σ^a being the apparent complex resistivity and apparent complex conductivity, respectively. These quantities are referred to as “apparent” because they actually only represent the true properties if the medium under investigation is homogeneous. Otherwise, they represent an effective (average) value. Spatial discrimination of electrical properties can be achieved by the use of multiple measurements with different electrode locations, which also form the basis for tomographic processing (inversion), i.e., imaging.

Impedance measurements can be conducted using only two electrodes for a combined current- and voltage measurement, or by using four electrodes (quadrupole measurements, also called four-point spreads) with separate current and voltage electrode pairs. In the latter case, the contact impedance between electrode and medium, which becomes significant towards lower measurement frequencies, has practically no influence on the voltage measurement e.g.,.

Polarization phenomena of biomatter are commonly classified into three frequency regions with different polarization sources, namely the α, β, and γ regions e.g.,. While overlapping, the low-frequency α polarization is thought to extend into the lower kHz range, followed by the β polarization in the range up to about 100 MHz, and joined by the γ polarization at higher frequencies e.g.,. Controlled by the mobility of the charge carriers, the α range is assumed to be associated with electrochemical polarization (i.e., the build-up and relaxation of ionic concentration gradients such as those found in EDLs, in an electric, time-variable field); the β range by the Maxwell–Wagner polarization of composite media e.g.,; and the γ range by molecular, ionic, and atomic polarization. The different processes lead to different current flow paths within biomatter for different frequencies . These observations have been primarily made on cell suspensions and various kinds of tissue, which exhibit structures that are much more homogeneous than fully developed plant and root systems. Polarization processes in plant roots are assumed to originate among others in the cell membranes, the apoplast and the symplast . The frequency dependence of published multi-frequency measurements e.g., indicates multiple length scales and associated structures as the origin of electrical polarization responses.

On a cellular, or multi-cellular level, much work has been conducted to gain information about the electrical surface characteristics of cells (cell structures). A Gouy–Chapman-Stern model relating surface charges to external ion concentrations has been formulated and subsequently improved . Using this model, ion activity at membrane surfaces can be computed and analyzed for the investigation of physiological effects. These and subsequent studies regarding ion toxicity and related surface electric potential have provided further evidence that certain surface potentials can be linked to physiological states and processes, e.g., ion availability and uptake . estimated the electric potential at rice-root surfaces of macroscopic root segments using measurements of the electrokinetic zeta-potential. The zeta-potential is the experimentally accessible electric potential at a distance from the surface where slipping in the electrolyte occurs upon a flow-driving pressure gradient.

The EDL is the source of polarization responses in the low-frequency range usually measured with EIS/EIT. It is sensitive to physiological processes that affect ion (nutrient) availability in the vicinity of, and ion fluxes across, charged cell membranes. The key function of roots is the uptake of water and nutrients, which is highly dependent on nutrient availability, demand, and stress factors e.g.,. Nutrient availability can influence water (and nutrient) transport within plant systems e.g.,, and nutrient availability within roots can fluctuate in response to certain depletion situations e.g.,. Furthermore, the distribution of stress hormones such as abscisic acid (ABA) increases in response to stress situations, possibly inducing the aforementioned reactions e.g.,. The formation and properties of large-scale ion-selective structures such as endodermis and hypodermis are also directly influenced by the growth environment, and can change in response to external stress factors . In addition, noted that electrical polarization effects originate in the “active” parts of a root system only, which change according to age, nutrient availability, and other stress factors see also.

The majority of studies concerning full root systems work with equivalent electrical circuit models to describe the measured signals of various biostructures seeand references therein. The scale and composition of these models vary considerably. For example, equates root segments with cylindrical capacitors, whose conducting plates are formed by the inner xylem and the fluid surrounding the root segment, with root cortex acting as a dielectric. proposed a simplified model of cell polarization in root systems, in which the cell membrane acts as a dielectric between the conducting inner and outer regions of the cells, thus representing a classical capacitor. Equivalent circuit representations inherently depend on the assumed flow paths of the electric current. For instance, impedance measurements using the stem as one pole for current injection and the medium surrounding the roots as the other pole as frequently being done, e.g., force the current to cross all radial layers of the roots. However, even for stem injection, equivalent circuit models considerably simplify the true electrical processes in the root and root–rhizosphere system, and it is questionable whether these models can be transferred between different experimental setups as evident from the large number of slightly different models that were proposed, e.g.,. A purely phenomenological analysis is made by , who use a classification approach to analyze spectral impedance data measured on pine roots infested with mycorrhizal fungi.

We propose to describe and interpret low-frequency (< 1 kHz) polarization processes in biomatter using concepts similar to those established for soils and rocks in recent years, under the assumption that the observed responses originate from the polarization of EDLs present in the biomatter. Accordingly, it should be possible to link the polarization magnitude to the average EDL thickness, which depends on the electric potential drop between the charged surface/membrane and the background ambient electrolyte, and link characteristic relaxation times to the length scales at which the polarization processes take place.

Given the current observations and understanding of electrical polarization processes in biomatter, as reviewed in the previous section, our hypotheses are as follows:

The magnitude of the low-frequency polarization response of roots is related to the overall surface area comprised by EDLs in the root–rhizosphere system, including the inner root structure. EDLs may form at Casparian strips (e.g., hypodermis and endodermis) and plasma membranes.

In the present study, we address the second part of hypothesis three, as well as hypothesis four. Hypotheses one and two are based on the synthesis of existing works, but can neither be validated nor invalidated by the present study.

The EIS (or SIP) method involves the measurement of impedances at multiple frequencies (usually in the mHz to kHz range). It can be extended by utilizing electrode arrays consisting of tens to hundreds of electrodes to collect numerous, spatially distributed four-point impedance measurements. From these data sets images of the complex conductivity (or its inverse, complex resistivity) can be computed using tomographic inversion algorithms e.g.,. This method is called complex conductivity (or complex resistivity) imaging or electrical impedance tomography (EIT), and refers to both single- and multi-frequency (spectral) approaches. EIT images are characterized by a spatially variable resolution, which decreases with increasing distance from the electrodes e.g.,. The method's primary fields of application are in near-surface geophysics e.g., and medical imaging e.g.,.

Spectral EIT measurements presented in this study were conducted using the 40-channel EIT-40 impedance tomograph , which was configured in a monitoring setup to acquire up to seven EIT data sets (frames) from a mini rhizotron per day.

The Debye decomposition (DD) approach e.g., was used to analyze the complex conductivity spectra recovered from the multi-frequency EIT inversion results. The approach yields integral parameters describing the spectral characteristics of the SIP signature. The complex conductivity spectrum is represented as a superposition of a large number of Debye relaxation terms at relaxation times τk (suitably distributed over the range implicitly defined by the data frequency limits; see ): σ^(ω)=σ∞1-∑kmk1+jωτk, with σ∞ being the (real-valued) conductivity in the high-frequency limit, and mk the kth chargeability, describing the relative weight of the kth Debye relaxation term in the decomposition. The chargeabilities mk at the different relaxation times τk form a relaxation time distribution (RTD), from which the following descriptive parameters are computed e.g.,:

The normalized total chargeability mtotn is a measure of the overall polarization reflected in the spectrum e.g.,: mtotn=σ0∑kmk, with σ0 being the (real-valued) conductivity in the low-frequency limit.

The implementation of was used for the DD analysis. The iterative inversion scheme balances between (error-weighted) data fitting and smoothing requirements.

Photographs of the plant systems at the beginning and the end of the experiment are shown in Fig. . As is evident from the photographs, the plants significantly reacted to the nutrient stress situation (and possibly anaerobic conditions) and degraded over time. The root systems extended down to a depth of approximately 13 cm, with an approximate maximum lateral extension of 13 cm (see Fig. ).

In Fig.  the temporal evolution of the raw data spectra in terms of apparent complex conductivity (Eq. ) is shown for two example measurement configurations: a quadrupole with electrode pairs on both sides of the rhizotron located directly above the root system, i.e., with sensitivity to the root system and a quadrupole with electrodes from the horizontal electrode line at 37 cm depth, i.e., located relatively far away from the root system and thus sensitive only to the water. The real component of apparent complex conductivity shows a smooth, consistent behavior across the time and frequency domains for both responses, “with roots” and “water-only” (Fig. a, b). However, the conductivity decreases for the quadrupole around the root system, while it increases in the “water-only” quadrupole. The imaginary components, i.e., the polarization responses, with roots (Fig. c) are also consistent and show changes, especially in the lower-frequency range, over time. The water-only measurements, on the other hand, only exhibit negligible polarization responses, likely dominated by measurement errors and noise (Fig. d). The polarization magnitudes, on the one hand, lie well below the signal threshold that can be reliably measured with the EIT-40 system ; the jittery shape of these signatures (Fig. d) is attributed to the logarithmic scale of the plot. On the other hand, measured root signatures lie clearly above the measurement threshold of the system (Fig. c).

The spatial variability of the electrical response was assessed using the complex conductivity imaging results, i.e., σ′ and σ′′, at the first time step for the two frequencies 1 and 70 Hz (Fig. ). Only weak variations in the real component (in-phase conductivity) can be observed at the location of the root system (Fig. b, d). However, a significant polarization response in the imaginary component (Fig. c, e) coincides with the extension of the root system. The frequency dependence previously found in the apparent complex conductivity spectra (see Fig. ) is also revealed in the imaging results, with a stronger response at 70 than at 1 Hz. It manifests both in signal strength and in the spatial extension of the polarizable anomaly associated with the root system.

Complex conductivity spectra were extracted from the multi-frequency imaging results at three locations: near the stem area of the root system, from the lower area of the root system, and from the lower half of the rhizotron, where no root segments were present. The two locations near the root system are indicated in Fig. a. Thus, these locations represent areas with relatively thick root segments, thin root segments, and no root segments (water only) at all. Figure  shows the temporal evolution of the spectral response for the three locations. The real component of complex conductivity (σ′) increased over the course of the experiment for all three locations (Fig. a–c). The imaginary component of complex conductivity (σ′′) reveals a frequency-dependent polarization response at the root segment locations for all time steps (Fig. d, e). The polarization magnitude decreases over time, and changes in the shape of the spectra can be observed for later time steps. These changes are most pronounced for the location near the stem (Fig. a). The polarization signatures recovered at the bottom of the rhizotron (water only) show an amount that is almost 2 orders of magnitude smaller (Fig. f) compared to those in the root system areas; they contain more noise and do not exhibit a clear frequency trend.

The Debye decomposition scheme was applied to the complex conductivity spectra recovered from multi-frequency EIT to quantify the overall polarization (normalized total chargeability mtotn) and the characteristic relaxation time (mean relaxation time τmean), as well as the uniformity parameter U60,10. By means of this analysis, the intrinsic spectra can be assessed with respect to the magnitude and shape of the polarization response for all pixels at each time step. Figure  shows a decomposition result of a pixel signature from the stem area for the first time step, corresponding to the spectrum plotted in Fig. a, d at “0 h” (dark blue curve). The complex conductivity spectrum was fitted by means of 96 Debye relaxation terms (Fig. a), yielding a relaxation time distribution (RTD) (Fig. b), from which τmean, τ10, and τ60 can be determined (Fig. a, b). We note that τmean does not coincide with the RTD peak, which only happens if the RTD shows a perfect symmetry (in log scale), which is not the case here.

Images depicting the DD-derived total polarization (mtotn) results of the complex conductivity spectra (obtained from multi-frequency EIT) for selected time steps are presented in Fig. . The extension of the root system against the surrounding water (characterized by low polarization) is clearly delineated in the images, and a continuous decrease in polarization strength is observed over time.

For further analysis, the complex conductivity spectra (also referred to as pixel spectra) were classified into two categories, with and without root segments, as described in Sect. . The resulting “root system spectra” were then processed separately, and care was taken that the selected spectra exhibit a sufficiently strong and consistent polarization response to allow a reliable relaxation time analysis. Figure  shows the comparison of the mtotn results for the first time step with the extension of the root system according to the photograph. The root system area reconstructed from the spectral EIT results shows a good agreement with the known outer boundaries of the root system. Systematic changes in the overall root system response were analyzed by averaging the mtotn pixel values in the root system zone (Fig. ). This average polarization response shows a steady decrease over time.

Images of the DD-derived mean relaxation time τmean are presented in Fig.  for selected time steps. Spatial variations within the root system zone can be observed for each time step, as well as changes between time steps. A general trend from larger relaxation times (up to 18 ms) to smaller relaxation times (down to 9 ms) over the course of the experiment is noticeable. Corresponding images of the uniformity parameter U60,10 are shown in Fig. . Observed variations within images and between time steps indicate changes in the shape of the pixel spectra. Values approaching 1 indicate a stronger spectral dispersion, i.e., a focusing of the spectral polarization response in a narrower frequency band.

In the course of the experiment, a conductivity increase is observed (Fig. a–c), which originated at the bottom of the rhizotron and continuously migrated upwards (see S1 in the supplement for conductivity images of the whole rhizotron). This spatial pattern rules out the root system as the cause of the conductivity increase, and we attribute it to the dissolution and subsequent upwards diffusion of impurities at the bottom of the rhizotron frame. While a certain influence on the spectral parameter results (Figs.  and ) is possible, we believe the impact to be relatively small for two reasons: First, the observed time evolution of mtotn shows changes more or less centered around the stem region, not following the distribution pattern of the conductivity increase; second, we observe changes in the spectral behavior of the signatures (in terms of τmean), which can not be explained by an increase in conductivity. However, in future experiments the background conditions should be monitored and the plant only inserted once equilibrium of the system has been reached.

The physiological response of the root system to the imposed nutrient deprivation and possible anaerobic conditions is reflected by a decreasing overall polarization response (Figs.  and ). Note that it is highly unlikely that the dropping water table caused this decrease in polarization, as more current is forced through the main bulk of the root system with the dropping water table. Thus, it should actually increase the polarization response, assuming that this response does not change due to physiological reactions in the root system. Note that the water table always remains above the actual root system and only drops in the stem area. We attribute the observed decrease in polarization to a general weakening of the EDLs present in the root system. The cause of this EDL weakening can be manifold and can not be isolated in this study. In the following, we shortly discuss two (possibly superimposing) approaches to interpretation.

The first approach is to consider ion (nutrient) concentration in the fluid phase of the EDL. Shoot–root systems represent a hydraulically connected system whose water potential is primarily controlled by water transpiration at the leaves. In the case of intact hydraulic connectivity in the plant, a decrease in water potential due to transpiration causes water and solute uptake by the roots, and water and solute flow from the roots to the leaves . While it is possible that solutes were taken up by the plant in our experiment, no nutrients were available to the plant. Accordingly, for our experiment we expected that the root regions farther away from the stem, that is, the regions with the highest water potentials, were depleted of nutrients first, as the available nutrients were translocated to the stem and the leaf areas, following the (negative) water potential gradient. Plants can sense, and react to, stress situations and can initiate changes in solute transport and hydraulic membrane conductivity properties e.g.,, especially if dynamic responses are considered. As a result, without being able to pinpoint the exact cause, the ion concentration could have decreased in the vicinity of cell membranes in these depleted areas, leading to a weakening of associated EDLs, in turn implying a decreased polarization response. The second approach is to consider changes in the electrical surface characteristics of cell membranes in reaction to the imposed physiological stress situation. These surface characteristics could have changed in response to certain active triggers, such as stress hormones, or as a result of balancing processes across the membranes. In light of these considerations, the stem should retain a more stable electrical polarization since it is likely not affected as much by physiological responses as the other, smaller, root segments (in terms of larger nutrient storage capability, better air availability, and general metabolic activity). This is consistent with the time-lapse imaging results, which show more stable polarization responses in the stem area (Fig. ).

Another indication of the physiological stress response is the changes in the shape of the spectra (Figs.  and ). Relative changes in the relaxation time contributions suggest changes in the underlying structures that control the polarization response at certain time steps. These changes might be related to new or ceased ion fluxes and their varying pathways within the root systems, as well as to varying surface charges at various structures such as the endodermis. If these structures change, or break down, in response to stress situations, corresponding changes in the electrical properties can be expected. However, given its spatial resolution limits, EIT does not allow one to distinguish these different structures directly.

Assuming that relaxation times can be linked to length scales of the underlying polarization processes, the observed signatures indicate multiple polarizable structures. However, the methodology applied here prevents further investigations in this direction. In contrast to most of the existing studies, we did not inject current directly in the stem, and correspondingly the explicit current pathways are much less defined in our approach. This prevents (at this stage) a simple formulation of an equivalent lumped electrical circuit model. Comparison of measurements using the procedure presented here with a stem-injection approach could, however, help to elucidate the origin of polarization and its length-scale characteristics. Current injection into the stem forces the current to flow through the root system and through all radial layers of the root segments, and thus a stronger polarization response from inside the root segment can be expected, as well as the polarization of additional membrane structures. Additional experiments could focus on establishing relationships between recovered spectral polarization parameters and root-specific parameters, such as surface area and root length density. The use of sophisticated electrical models, coupled to existing macroscopic root development and nutrient uptake models e.g.,, could provide further insight to identify the key processes that control the electrical polarization signatures, of root systems. While this study focused on establishing the EIT methodology for crop root research, in future studies, physiological plant parameters such as leaf transpiration rates, which could help to identify the key processes that control electrical polarization signatures, should also be monitored.

As already pointed out by , single-frequency measurements are of limited value to determine electrical polarization properties of root systems, both in terms of spatial distribution and polarization strength, and this finding is supported by our spectral EIS results (Figs. , , , and ). This becomes even more obvious when interpreting the polarization signature as an EDL response, which typically exhibits a strong frequency dependence. It should be noted that the frequency range analyzed here in an imaging framework (0.46 to 220 Hz) does not cover the full bandwidth that could be, in principle, measured with the presented setup, and corresponding advances are within easy reach e.g.,. However, reliable measurements at lower and higher frequencies will require careful adaptations in measurement and data processing procedures.

The classification of image pixels into two classes cannot, and should not, be treated as a universal analysis procedure. For the simple conditions in this experiment a clear distinction between the root system area and the surrounding medium could be made, which facilitated the assessment of the method (e.g., Fig. ), and can potentially be used for further experiments with root systems in aqueous solutions. However, the primary results of this study do not rely on this specific classification, and likewise soil-based experiments could be conducted with the measurement setup.

This study does not involve any kind of granular substrate and thus excludes possible influences from such a background material. In fact, significant additional electrical polarization can be expected when soil surrounds the root system, which will superimpose on the root system response. Organic matter and micorrhiza may also contribute to the overall electrical signature. Finally, a variable water content can significantly influence the electrical response of the soil and the root system, either directly by influencing present EDLs or indirectly by inducing physiological processes such as nutrient uptake, which in turn can affect the electrical signatures of the EDLs.

The observed polarization response of the root system is relatively weak and its measurement requires a corresponding measurement instrument accuracy. This accuracy is provided by the EIT-40 tomograph that was used in this study . The high accuracy of the instrument was also recently demonstrated in an imaging study on soil columns .

If fixed data weighting is used, which we believe would produce more reliable and consistent results for multi-frequency time-lapse data, data selection, i.e., filtering, becomes a relevant step in the processing pipeline before the inversion and subsequent spectral analysis. While it is common to remove outliers from geophysical data prior to inversion, filtering becomes challenging if multiple time steps are to be analyzed consistently. The number of retained data points varied slightly between time steps, although the same filtering criteria were applied. This can be explained by data noise and varying contact impedances at the electrodes. However, data quality was sufficient enough to produce consistent imaging results for all time steps and frequencies, as is evident from the impedance spectra (Fig. ).

Another important issue is the data processing flow in the imaging framework, coupled with the spectral analysis based on the Debye decomposition. The inversion algorithm produces spatially smooth images; however, the images were computed for each frequency separately, and thus no smooth variation between adjacent frequencies is enforced in the inversion, although physically expected. Corresponding inversion algorithms have been developed recently and could lead to a further improvement of the multi-frequency imaging results. However, a similar constraint is introduced by the Debye decomposition, where smoothness is imposed along the relaxation time axis, which directly corresponds to the frequency axis. Minor noise components can thus be expected to be smoothed out both spatially and spectrally.

EIT applications in pseudo-2-D rhizotron containers require specific processing steps. The determination and testing of correction factors accounting for modeling errors due to an imperfect 2-D situation (Fig. ) are as important as the correct representation of the rhizotron in terms of the FE grid underlying the inversion process (Fig. ). Not taking these aspects into account can produce artifacts in the imaging results that can easily be misinterpreted in biological terms. Similarly, data errors should not be underestimated, as this can also produce misleading imaging results when data are overfitted e.g.,. Contrarily, an overestimation of data errors can mask information present in the data. Among other analysis steps, raw (impedance) data and imaging (complex conductivity) data should be checked for consistency and plausibility by taking into account the much lower spatial resolution of the raw data (see Figs.  and ).

Electrical imaging results exhibit a spatially variable resolution, which usually decreases as the distance from the electrodes increases. One could question such a method's usefulness if the resolution cannot be clearly determined. Nonetheless, even limited spatial information allows for a distinction of polarizing and non-polarizing regions in the investigated object. This is not possible with spectroscopic measurements, and so it is even more difficult to analyze spatially distributed root systems with measurements such as these. We suspect that some of the reported inconsistencies in electrical capacitance relationships with biological parameters e.g.,and references therein can be ascribed to missing spatial information in the measurement data. The resolution of EIT is not sufficient to image microscopic current flow paths in the root system, but the imaged macroscopic electrical properties can be compared for different regions of the root system, for instance the (older) top part of the root system compared to the younger lower part. Future improvements in experimental setups (electrode distribution and spacing) and measurement configurations will most probably lead to increased spatial resolution.

Another advantage of the EIT approach presented in this study is the possibility of arbitrarily placed electrodes (as long as the resulting geometrical arrangement allows for a sufficient measurement coverage of the root system), in contrast to using stem electrodes as commonly done in previous studies. If the stem of a plant is used to inject current into the root system, measurements, and resulting correlations to biological parameters, are highly sensitive to the electrode position above the stem base . Another problem is that electrodes can not be inserted into the stem if damage to the plant is to be avoided. However, injections can also be realized by use of non-invasive clamps.

The timescale of the physiological response to be monitored is also important for the experimental design. It took approximately 3.5 h to complete a single time frame of the spectral EIT measurements presented here. Thus, physiological processes taking place on a shorter time span cannot be resolved. Reducing the data acquisition time can be achieved by reducing either the number of low-frequency measurements or the number of current injections. This can result in a loss of spectral and spatial resolution if measurement configurations are not suitably optimized to compensate for the lost number of measurements.