All individuals of this sub-study participated in two longitudinal cohort studies measuring factors associated with cognitive performance in the elderly, and parts of this dataset have already been published (Riese et al., 2015; Michels et al., 2016, 2017). Cognitively healthy subjects needed to fulfill the following criteria: MMSE score ≥ 27 as well as a clinical and neuropsychological examination not suggestive of MCI or any cognitive disorders. Amnestic MCI (aMCI) was diagnosed by standard criteria (Winblad et al., 2004). Participants were ≥ 55 years of age and did not show significant depression, as assessed by a specialist in psychiatry. Exclusion criteria were medication or drug abuse that may affect cognition. In addition, subjects with MCI were excluded if they had evidence of a clinically significant neurological, psychiatric, or general medical condition that would significantly disturb cognition. Similarly, individuals were omitted if they had contraindications for MRI or PET imaging or cerebrovascular lesions in memory-related brain structures.

The neuropsychological and clinical examination have been published previously (Riese et al., 2015; Michels et al., 2016, 2017). All participants received a detailed clinical assessment including medical history as well as physical and neurological examination. Mini Mental Status Examination was used to assess global cognition (Folstein et al., 1975). The CERAD- test battery was applied for neuropsychological assessment (Morris et al., 1989; Thalmann et al., 1997) including digit span, trail making tests A and B, letter fluency, category fluency, Stroop interference, Boston naming test, verbal learning, recall and recognition, figure copy and recall. In addition, participants did the German version of the Rey Auditory Verbal Learning Test (VLMT) (Helmstaedter et al., 2001), the Visual Paired Associates test from the Wechsler Memory Scale Revised (WMS-R) (Haerting et al., 2000). All raw values were z-transformed according to test-specific normative data adjusted for age, gender, and education where possible. Cognitive impairment was defined if at least one score per domain was 1.5 standard deviations below group means. The mean period between the neuropsychological evaluation and MRI scanning was 8.6 months for HCS (range: 2–13 months) and 3.6 months for aMCI (range: 0–8 months).

Categorical data were analyzed using Chi-square (χ²) test [e.g., PiB-positivity (yes/no) or APOE status (carrier/non-carrier)]. T-Tests and Mann-Whitney U-tests were applied to compare normally/not normally distributed continuous variables (e.g., age and test scores).

The full methods to assess cerebral amyloid-load were described previously (Steininger et al., 2014; Riese et al., 2015; Michels et al., 2017) and shall only be described in brief: Following antecubital injection of ~350 MBq of [11C]-PiB, a dynamic scan was acquired for > 70 min or a static image 50–70 min post-injection. Time activity curves were acquired from predefined volumes of interest using a maximum probability atlas (Hammers N30R83) [37, 38] that were intersected with the individuals' gray matter masks. The PiB uptake was measured in cortical and cerebellar volumes of interest from frames 50–70 min based on a volume-weighted averaging procedure. To normalize between patients, a cortical/cerebellar uptake ratio was calculated (i.e., global PiB). The cortical SUVR cut-off for defining a subject as amyloid-positive (PiB+) was derived from 93 healthy volunteers and was ≥1.265 using an established method for determination of the cut-off (Vandenberghe et al., 2010). PMOD (PNEURO V3.4) software was used for image analysis.

Apolipoprotein E genotyping followed standard procedures (Hixson and Vernier, 1990). Participants were classified according to APOE ε4 allele status (carriers: APOEε4+ or non-carriers; APOEε4-).

MRI data was acquired on a 3 Tesla Ingenia scanner (Philips Healthcare, Best, The Netherlands) using a 15-channel head coil. Headphones and cushions helped to minimize subjects' head motion. Prior to the EEG-fMRI scan, an anatomical T1-weighted 3D-MPRAGE scan was recorded (time of repetition /time of echo: 8.2 ms/3.8 ms, resolution: 1 × 1 × 1 mm, field of view: 240 mm, 170 slices). The fMRI data was recorded with a T2^*-sensitive multi-slice echo planar imaging sequence (time of repetition = 2.1 s; time of echo = 35 ms; field of view = 230 cm; image matrix = 64 × 63; voxel dimension = 3.6 × 3.6 × 4 mm³; slice gap: 0.3 mm, flip angle = 90°, 30 axial slices). One fMRI run comprised 185 volumes (length 2.1 s). Four additional volumes were acquired at the beginning of the sequence, but removed during reconstruction to avoid magnetic saturation effects. Before the fMRI run, a shimming procedure was applied to reduce susceptibility distortions produced by static (local) magnetic field inhomogeneities.

EEG and fMRI were acquired simultaneously in a 6 min 30sec eyes-closed session (no task) and during various task conditions (here we only report results for the resting state session). Custom made felt cushion were used, filling the space between the electrodes at the back-part of the head. The head was immobilized using foam pads and participants were provided with earplugs. Before each scan, participants were instructed to relax, to refrain from any movements, and to not fall asleep. Participants were personally checked after the anatomical scan (i.e., before the fMRI scan) for tiredness or any indisposition.

EEG recording the scanner were recorded with a 5 kHz sampling rate (32 mV input range, 0.1–250 Hz bandpass filters). Two MR-compatible BrainAmp amplifiers (Brain Products, Gilching, Germany), located outside the scanner room, were used for data transmission via optic fibers. Sixty scalp electrodes were recorded using MR-compatible caps (Easycap, Munich, Germany) with twisted and fixed electrode cables. Electrode positions were from the 10–20 system and additionally from the 10-10 system: FPz, AFz FCz, CPz, POz, Oz, Iz, F5/6, FC1/2/3/4/5/6, FT7/8/9/10, C1/2/5/6, CP1/2/3/4/5/6, TP7/8/9/10, P5/6, PO1/2/9/10, OI1/2). O1/2 and FP1/2 were placed 2 cm next to the standard positions, ensuring an even coverage (Maurer et al., 2007; Brem et al., 2010). F1 was used as recording reference, F2 served as ground electrode. Two electrodes were mounted below the outer canthus of each eye. To record the electrocardiogram, two electrodes were attached right to the sternum and on the left chest close to the heart. Electrode impedances was always <20 kΩ. The EEG signal quality was checked during scanning by an online correction software (RecView, Brain Products, Gilching, Germany).

For each subject, EEG preprocessing and analysis was performed with BrainVision Analyzer 2.2 (Brain Products GmbH, Gilching, Germany). Gradient artifacts were eliminated by average artifact subtraction (Allen et al., 1998, 2000), downsampled to 500 Hz and low pass-filtered at 70 Hz cutoff. Ballistocardiogram related artifacts were attenuated using a very similar subtraction method with artifact windows aligned on QRS complexes detected in the electrocardiogram traces (Allen et al., 1998, 2000). First, the EEG was digitally bandpass-filtered (0.5–70 Hz, 24 dB/oct and 50 Hz Notch) and downsampled to 256 Hz. The residual slice artifact at 18 Hz was blocked by a narrow band rejection filter ± 0.5 Hz (Sadaghiani et al., 2010). Using independent component analysis based decomposition (Delorme and Makeig, 2004) with selective back projection, muscle-, movement-, residual gradient-, and ballistocardiogram artifacts were eliminated. Finally, visual inspection was performed to remove any remaining bad intervals and faulty epochs were replaced by neighboring artifact-free EEG epochs (length 2.1 s) to ensure that the total number of epochs was constant for each participant (n = 185). Next, channels were transformed to the average reference (Lehmann and Skrandies, 1980).

For each fMRI volume (n = 185, length 2.1 s), the respective absolute spectral power was calculated on each channel using a Fast Fourier Transformation (FFT) (Hanning window: 10%, zero padded, 0.5 Hz resolution). Mean absolute spectral band value was computed for delta (1–3 Hz), theta (4–7.5 Hz), alpha1 (8–10 Hz), alpha2 (10.5–13 Hz), and beta (14–30 Hz) bands. The EEG amplitude was calculated as the mean of each FFT-transformed epoch. We computed two EEG outputs for the subsequent EEG-fMRI signal correlations. First, the global spectral power (GSP) (Jann et al., 2009; Michels et al., 2010, 2012; Luchinger et al., 2011, 2012) was calculated for each FFT-transformed epoch as the root mean square across all channel, as a measure of the total oscillatory activity over the scalp. Second, a regional EEG regressors was built in order to account for conventional locally defined EEG rhythms (Luchinger et al., 2011). Here, we focused on the alpha rhythm—as it is the dominant brain rhythm during resting state (Palva and Palva, 2007)—and extracted the mean alpha EEG power from three midline parieto-occipital (Oz, POz, and Pz) electrodes for local alpha EEG-fMRI signal correlation analysis.

All fMRI data were post-processed and analyzed with SPM8 (Wellcome Department of Cognitive Neurology, London, UK). First, images were realigned to the first scan using affine transformation. The realigned images were then normalized into Montreal Neurological Institute (MNI) standard space. Next, all images were resampled to 3 mm cubic voxels, followed by spatial smoothing (8 mm FWHM Gaussian kernel). Voxel-wise EEG-BOLD signal correlations were analyzed by a general linear model (GLM) approach for each band. The model consisted of a boxcar function modeling the eyes closed condition, which was parametrically modulated by the aforementioned GSP-based (or local alpha) EEG power fluctuations. The hemodynamic response function was used to convolve the model regressors. A first-order autoregressive model accounted for serial correlations and a 128 s high-pass filter removed slow signal drifts. For each participant, the GLM contained cerebrospinal fluid, white matter signal as well as the six head motion parameters (nuisance variables). As a result of the GLM, the contrast image of the GSP and regional alpha regressor was written for each subject for subsequent group statistics. For each frequency band (delta, theta, alpha1, alpha2, and beta) and group, we calculated EEG-fMRI signal coupling in positive correlations and anticorrelations.

Group differences were calculated as F-contrasts, i.e., testing for “HCS > < MCI,” to identify all brain regions showing abnormal undirected EEG-fMRI signal coupling. We refrained from running between group t-tests, as EEG-fMRI signal correlations are bidirectional, and thus, any stronger correlation in one group could be the result of either a stronger positive in this group or a weaker anticorrelation. However, within-group EEG-fMRI signal correlations were calculated (Supplementary Figure 1), to gain a better insight into the frequency-band specific group differences. Results were reported at height threshold p < 0.005, corrected for multiple comparison by extent threshold of minimal cluster size of k = 30 voxels (Forman et al., 1995), corresponding to a p < 0.05 cluster corrected. The voxel extent threshold based on Monte Carlo simulation using 1,000 permutations (Slotnick et al., 2003). This method was recently validated, revealing acceptable false-positive rates for fMRI inferences (Slotnick, 2017).

While the first analysis set was related to the distinction between HCS and MCI, which is indicative of a disturbed brain function that can be caused by heterogeneous pathologies, we performed a second analysis set focused on the question whether we can identify an EEG- fMRI pattern that is associated with amyloid pathology specifically. Here we compared all PiB+ individuals - MCI-due to AD and HCS—(n = 12) to PiB- HCS (n = 15). For each analysis, results were only restricted to gray matter volume.

We used the REST toolbox V1.8 (http://restfmri.net) to extract the BOLD signal spectral power from the same preprocessed fMRI images assigned to the EEG-BOLD signal correlation analysis using. Before the analysis, all motion parameters were residualized from the each image. After linear trends removal, ALFF (amplitude of low frequency fluctuation) (Yang et al., 2007; Zang et al., 2007) was calculated for the frequency range of 0.01–0.08 Hz for each participant separately from REST, representing the band average of the square-rooted FFT (taper percent = 0, length = shortest). BOLD spectral power is typically normalized spatially or spectrally to decrease inter-subject variance. Yet, the spatially normalized ALFF [mALFF = ALFF – mean (ALFF)] demonstrates a higher sensitivity to regional differences, and thus, mALFF is the reported measure in our study to illustrate any group difference in BOLD signal amplitude. First, we computed group differences on the whole brain (voxel-to-voxel) level, using two sample unpaired t-tests for both contrasts: “HCS > < MCI” and “PiB- > < PiB+” (p < 0.005, corrected for multiple comparison by extent threshold with a cluster size of k ≥ 30 voxels (Forman et al., 1995), corresponding to a p < 0.05 cluster corrected.

As a subsequent analysis, we examined mALFF change in brain regions showing group differences in EEG-fMRI signal coupling for both contrasts. Here, we created a masked image, covering all group differences across all frequency bands (p < 0.05, cluster corrected) before running the statistical mALFF group comparison. This analysis was done for two reasons: (1) to minimize multiple comparisons and (2) to link the brain regions showing the strongest BOLD signal and EEG-fMRI signal group difference. All analyses only report fMRI signals differences in gray matter.

The sample characteristics of the HCS and MCI is presented in Table 1. Years of education and age was comparable between the 14 MCI and 21 HCS. MCI had lower MMSE scores and scored lower on various cognitive tests. As expected, differences were more pronounced in memory-related scores. Twelve out of 35 subjects were PiB+ (six HCS and six aMCI; Table 1), and 15 HCS were PiB-. In total, nine participants were APOE ε4 carriers (five HCS and four MCI; Table 1).

As shown in Table 2, PiB+ (MCI and HCS combined) demonstrated compared to PiB-(HCS) higher amyloid load, higher presence of APOE ε4+ carriers, lower level of education, as well as lower performance in some neuropsychological test scores, especially in the memory related tests.

On the whole-brain level (Figure 3A), higher BOLD signal was seen in patients with MCI in the left supramarginal gyrus (MNI:−60,−25, 35), posterior cingulate cortex (PCC) (MNI:−3,−45, 11), left MTG (MNI:−57, 62, 9), right parahippocampal gyrus (MNI: 32,−13,−33), left inferior temporal gyrus (MNI:−54,−38,−16), left middle frontal gyrus (MNI:−48, 17, 30), midline sub-genual anterior cingulate cortex (MNI: 4, 11,−16), and right occipital pole (MNI: 21,−98,−7). In contrast, HCS showed stronger BOLD signal in the medial prefrontal cortex (MPFC) (MNI: 8, 42,−12), right frontal operculum (MNI: 47, 4, 5), left middle frontal gyrus (MNI:−28, 47, 30), and left inferior occipital gyrus (MNI:−31,−92, -19).

PiB- demonstrated a stronger BOLD signal (Figure 3B) in the right inferior occipital gyrus (MNI: 39,−80,−9), right cerebellum exterior (MNI: 40,−61,−40). As for patients with MCI, PiB+ revealed higher BOLD signal compared to PiB- in the right parahippocampal gyrus (MNI: 32,−27,−18), right (instead of left) supramarginal gyrus (MNI: 55,−22, 39), PCC (MNI: 4,−48, 15), right (not left) MTG (MNI: 66,−41, 0) and additional brain areas, i.e., right precentral- (MNI: 36,−20, 54) and postcentral gyrus (MNI: 51,−21, 50), and right centro-parietal operculum (MNI: 54,−20, 14).

When we restricted the analysis to regions showing EEG-fMRI signal group differences (across all frequency bands), we did not observe group differences for the contrast “HCS vs. MCI.” For the contrast “PiB- vs. PiB+” (Figure 4), we found heighted fMRI signal responses in the PiB- group in the left inferior frontal gyrus (IFG) (MNI:−50, 34, 10). In contrast, PiB+ showed a higher BOLD signal in a cluster comprising the right parahippocampal gyrus (MNI: 20,−28,−14) and hippocampus (MNI: 14, 36, 2).

Based on developmental EEG-fMRI data, a positive thalamic-BOLD coupling appears to be a marker for brain development (Luchinger et al., 2011, 2012), as it is only consistently present in adults. For example, Laufs et al. and others reported positive thalamic alpha-BOLD signal correlations in healthy adults and inverse correlations in occipital, temporal, and parietal cortex (Goldman et al., 2002; Laufs et al., 2003b; Moosmann et al., 2003; Goncalves et al., 2006; de Munck et al., 2007; Difrancesco et al., 2008; Tyvaert et al., 2008a). Our study is the first demonstrating that a positive thalamic alpha-BOLD signal coupling is also a neurophysiological signature of healthy elderly but not of individuals with MCI, indicative of disturbed thalamo-cortical coupling. Besides alpha, lower frequencies of the EEG power spectrum have also been associated with neural peculiarities of MCI. More specifically, while delta power has been inversely associated with cortical atrophy (Babiloni et al., 2006), both theta band-power and (interhemispheric) coherence could predict the decline from MCI to AD (Rossini et al., 2006). Recently, it has also been shown that instable MCI differ in delta and theta coherence involving the temporal-frontal connections compared to stable MCI (Musaeus et al., 2019). Beason-Held found in a longitudinal study that healthy aging is associated with stable DMN activity measured by [¹⁵O]PET at two points (Beason-Held et al., 2008). Our findings of abnormal delta and theta EEG-fMRI signal coupling in MCI (Supplementary Figure 1) in parietal (PCC) and frontal (IFG and frontal operculum) brain regions could reflect a neurophysiological impairment in parts of the DMN.

The presence of beta-amyloid-pathology has been associated with altered functional connectivity of the DMN in normal controls (Sperling et al., 2009; Sheline et al., 2010a; Mormino et al., 2011; Quevenco et al., 2020) and in MCI (Sperling et al., 2009). This has also been observed in cognitively healthy elderly individuals without cortical amyloidosis (Sheline et al., 2010b) and in individuals with mild AD but unknown amyloid status (Greicius et al., 2004). As we found abnormal anticorrelations (and BOLD signal) in regions of the DMN, we conclude that patients with MCI display a disturbed neurophysiological coupling in parts of the DMN, In particular, the anticorrelation in the delta band in the MCI group suggests that higher neuronal activity is associated with a decrease in BOLD-fMRI derived signal or vice versa Together with our finding of high BOLD signal in the PCC this could indicate a reduced ability of the neurovascular unit to extract oxygen from the blood. Such an effect might result from a reduced metabolic demand as a consequence of neuronal and synaptic loss together with impaired neurovascular coupling. As in MCI, especially MCI due to AD, hypometabolism has been found in various brain regions including the PCC (Alsop et al., 2000; Chen et al., 2011), we consider this a likely possibility. In such a scenario, despite an overall reduction in CBF as a consequence of less demand, the remaining active neurons would still receive enough oxygen supply. Many pathologies have been described that are associated with a dysfunction of the neurovascular unit including various forms of neurodegenerative diseases as reviewed in Iadecola (2017). However, a recent study found that subjects with MCI display a reduced oxygen extraction fraction compared to elderly controls despite unchanged cerebral blood-flow and cerebrovascular reactivity (Thomas et al., 2017). The authors proposed that in MCI neurons are less able to extract oxygen as a consequence of mitochondrial dysfunction. As we see an anticorrelation of BOLD-fMRI with delta-power such a scenario would be well consistent with our data and could even hint to a pathological mechanism of a mismatch between energy demand and oxygen utilization with the consequence of cognitive impairment. A methodological limitation of our data in this context is that even as we are using EEG-fMRI the exact temporal/causal relationship between neuronal activity and BOLD signal is only feasible in an invasive approach, e.g., in non-human primates (Logothetis et al., 2001).

Another region that showed abnormal (theta) EEG-fMRI signal coupling was the (posterior) insula, which, together with the ACC and supplementary motor area, is part of the SAN. The studies by Sammer et al. (2007) and Mizuhara et al. (2004) commonly reported a positive association between theta-BOLD signal in the hippocampus, superior temporal cortex, cingulate cortex and frontal areas, including the insular cortex, while subjects performed a mental arithmetic task (Mizuhara et al., 2004; Sammer et al., 2007). The posterior insula predominantly connects to the dorsal part of the lateral and central amygdaloid nuclei and connects mutually with the secondary somatosensory cortex. Further, this region has input from the posterior parts of the thalamus. As we examined task-unrelated, i.e. resting-state, EEG-fMRI signal correlations, our results indicate that parts of the SAN (insular cortex) have a disturbed neurophysiological coupling in patients with MCI in a mind-wandering condition that does not require particular attention or cognitive efforts. The SAN is a relevant network, “mediating” between the DMN and the FPN, and thus, disruption of this network could either parallel or even cause the observed abnormalities in the DMN and FPN in this study.

Abnormal alpha EEG/ fMRI signal coupling was seen in the left DLPFC. This region—a frontal part of the FPN—is involved in various executive functions and connect with the orbitofrontal cortex, thalamus, basal ganglia, hippocampus, and primary and secondary association areas (e.g., parietal and occipital areas). Also in the study by Balsters et al., a decrease of coupling was seen in the left FPN (Balsters et al., 2013). Previous fMRI resting-state examinations have produced inconsistent findings related to the extent to which prefrontal cortex activity changes with age. Damoiseaux et al. (2008) could not show age-related differences in the FPN, while Allen et al. (2011) demonstrated significant age-related decreases in BOLD spectra in frontal resting-state networks and the bilateral FPN. Though it has been shown that the FPN showed significant decreases with age (Biswal et al., 2010; Allen et al., 2011), it was the parietal activity and not the prefrontal activity which was reduced. Our findings replicate this observation, as reflected by a significant reduction in alpha1-related coupling of the left FPN, but only in the frontal cortex. Simultaneous EEG/fMRI studies consistently demonstrated correlations between fMRI and beta EEG band power in young adults (Laufs et al., 2003b; Mantini et al., 2007; Balsters et al., 2011) but also in older healthy adults (Balsters et al., 2013) in the DMN. Finally, the observed beta EEG-fMRI signal coupling group differences further underline the abnormal coupling in MCI in parts of the SAN (left insular cortex), FPN (precuneus) but also of the auditory cortex (transverse temporal gyrus).

In a regional (EEG power was taken from midline parieto-occipital electrodes) alpha-band based analysis, Brueggen et al. reported weaker (compared to controls) positive alpha-BOLD signal associations in patients with mild AD in the thalamus, frontal cortex and inferior temporal gyrus in AD patients, and abnormal negative associations in the hippocampus, putamen, and cerebellum (Brueggen et al., 2017). For the HCS-MCI comparison, we could replicate most of the findings, e.g., abnormal coupling in the thalamus (alpha band), frontal cortex (alpha1, local alpha, and beta). However, alterations in the hippocampus and cerebellum were not seen for this comparison. This could be related to the difference in the sample, i.e., MCI vs. patients with mild AD. Yet, focusing on the pathological aspect in our group of subjects, i.e., comparing all PiB+ subjects with PiB- (amyloid negative MCI and HCS), PiB+ additionally demonstrate alterations in the hippocampus and cerebellum, as also seen by Brueggen et al. (2017), and as discussed further below.

We found abnormally high BOLD signal in patients with MCI in the PCC, ACC, MTG, supramarginal gyrus, and parahippocampal gyrus. A meta-analysis of (12) rs-fMRI studies that reported differences in ALFF (but not mALFF) between aMCI patients and HCS, found that patients with aMCI had—apart from some regional decreases - increased ALFFs in the lingual gyrus, hippocampus, middle occipital gyrus, and inferior temporal gyrus (Pan et al., 2017). This is partially in line with our findings. As for the EEG-fMRI signal findings, our results indicate that some parts of important networks (DMN with PCC, FPN with SMG) demonstrate abnormal high BOLD signal power, whereas other parts of these (DMN with MPFC) and other networks (SAN with IFG) demonstrate abnormally low BOLD signal amplitudes. Strikingly, some of the regions (SMG, PCC, HC) appear not only be altered (in a similar way) by the presence of a cognitive impairment but also by the presence of amyloid when we combine amyloid-positive MCI and HCS thus reflecting the early spectrum of AD. The increase of BOLD signal power in hippocampal regions and adjacent areas (parahippocampal gyrus) could be explained by either a reduced capacity of the brain to extract oxygen from that regions combined with altered neurovascular coupling or hippocampal hypermetabolism. Metabolic alterations in the hippocampus were frequently demonstrated in MCI. A study using task fMRI found that initial hippocampal hyperactivity in MCI and reduction thereof using levetiracetam improved task performance (Bakker et al., 2012). Reduced intrinsic connectivity between hippocampus and precuneus was linked to higher hippocampal glucose metabolism indicating disinhibition like changes of hippocampal activity (Tahmasian et al., 2015). Furthermore, glucose metabolism in the hippocampus was negatively correlated with cognitive performance in a sample of MCI or mild dementia with varying etiological diagnoses thus suggesting rather a detrimental effect than a physiological adaptation (Apostolova et al., 2018). The value of ALFF and related measures (e.g., fractional ALFF) for the identification of individuals with subjective cognitive decline, amnestic MCI, and AD has been shown in a recent machine learning study (Yang et al., 2018). Importantly, the identified regions—leading to group separation—were overlapping with our study, i.e., the ACC, hippocampus, prefrontal brain regions, and more posterior (cingulate) brain regions. The study by Yang et al. reported elevated fMRI signal in the hippocampus in the disease progression groups. Our results add to this finding by demonstrating that also a group of subjects with MCI or PiB+ show this functional alteration. In addition, the hippocampal BOLD signal alteration was still preserved when we focus only on regional clusters, showing an EEG-fMRI signal coupling difference. This indicates that PiB+ participants show an abnormal neurophysiological signature which has its expression in both oscillatory power and fMRI signal alterations.

Several brain regions demonstrated amyloid-related whole-brain EEG-fMRI signal coupling and fMRI signal alterations, highlighting the impact of this pathology on oscillatory power and brain function (reflected by the fMRI signal). Most of the coupling differences were seen in deeper brain structures such as the brain stem, cerebellum, hippocampus, parahippocampus, and thalamus, which might not be detectable with (simple) 2D topographic mapping and would require statistical 3D analysis (Michels et al., 2017). Michels et al. (2017) showed that the undirected and directed (effective) EEG alpha and gamma connectivity strength, which was derived from source and not scalp power, was inversely related to amyloid deposition, indicating that amyloid beta load is associated with EEG metrics (Michels et al., 2017). In our study, a new finding was that EEG-fMRI signal coupling partially deviates between the “PiB-contrast” and the contrast focusing on the diagnosis (HCS vs. MCI). For the “PiB-contrast,” we also observe abnormal thalamo-cortical (seen in alpha1/2) and FPN coupling (seen in local and regional alpha bands) but we additionally found consistent (across alpha sub-bands and regional alpha band) coupling alterations in the Pib+ group in the hippocampus and brain stem. The amyloid load is specifically enhanced in the brain stem in late stages of the AD disease, as revealed e.g., by light microscopical analysis (Cole et al., 1993). Our results are not sufficient to directly link local amyloid plaques to neurophysiological alterations. However, the findings could indicate an EEG-fMRI signal effect that reflects an alteration of current along projection axons (originating from the brain stem). Using PiB-PET and FLAIR-MRI, Schreiner et al. (2018) reported a high correlation of (deviated) signal intensity in the hippocampus, basal ganglia and brain stem in cognitively healthy individuals, suggesting that regional amyloid load is associated with tissue edema (Schreiner et al., 2014). Our results indicate that fMRI and EEG-fMRI are sensitive to disease related functional alterations from cortical and subcortical regions in individuals with abnormally-high amyloid levels. The cerebellum is less often reported or discussed (compared to cerebral structures) in fMRI studies of MCI or AD, which is surprising. In fact, an immunohistochemical study demonstrated that cerebellar amyloid-beta plaques—which are primarily of the diffuse type—and amyloid-beta related proteins are less prominent in brains from non-demented patients compared to AD brains (Zhan et al., 1995). Still, some authors reported an equivalent PiB retention (by PiB-PET) by AD and controls in the cerebellum or brain stem, whereas PiB retention was especially high in temporal, frontal, and parietal brain regions in AD (Klunk, 2004; Price et al., 2005), rendering the cerebellum less relevant in AD pathology. Yet, PiB-PET uptake also critically depends on the selection of the scaling region, which is typically the cerebellum, and thus, this region is difficulty to address by PiB-PET. However, it is also known that—even in cognitively healthy individuals - cortical amyloid-beta load had positive effects on cerebro-cerebellar coupling, including regions in the (inferior) temporal gyrus, thalamus, frontal cortex, hippocampus, and parahippocampal gyrus (Steininger et al., 2014). As indicated by our study, we concluded that brain amyloidosis is associated with an abnormal neurophysiological coupling along the cerebro-cerebellar axis.