Low frequency BOLD oscillations, APOE4, and plasma pTau217

Low frequency BOLD oscillations, APOE4, and plasma pTau217 | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Low frequency BOLD oscillations, APOE4, and plasma pTau 217 View ORCID Profile Trevor Lohman , View ORCID Profile Arunima Kapoor , Allison C Engstrom , Jillian Joyce , Melanie Quiring , John Paul M. Alitin , Aimee Gaubert , Amy Nguyen , Elizabeth Head , Rond Malhas , Kathleen E Rodgers , David Bradford , Basant Lashin , S. Duke Han , View ORCID Profile Mara Mather , View ORCID Profile Daniel A Nation doi: https://doi.org/10.1101/2025.11.25.25340991 Trevor Lohman 1 Department of Neurology, University of Southern California, Keck School of Medicine , Los Angeles, CA, USA 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Trevor Lohman Arunima Kapoor 3 Department of Psychological Science, University of California , Irvine, Irvine, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arunima Kapoor Allison C Engstrom 3 Department of Psychological Science, University of California , Irvine, Irvine, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jillian Joyce 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melanie Quiring 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Paul M. Alitin 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aimee Gaubert 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Nguyen 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth Head 4 Department of Pathology and Laboratory Medicine, University of California , Irvine, Irvine, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rond Malhas 4 Department of Pathology and Laboratory Medicine, University of California , Irvine, Irvine, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen E Rodgers 5 Center for Innovations in Brain Science, Department of Pharmacology, University of Arizona , Tucson, AZ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Bradford 5 Center for Innovations in Brain Science, Department of Pharmacology, University of Arizona , Tucson, AZ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Basant Lashin 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site S. Duke Han 6 Department of Psychology, University of Southern California , Los Angeles, CA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mara Mather 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA 6 Department of Psychology, University of Southern California , Los Angeles, CA 7 Department of Biomedical Engineering, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mara Mather Daniel A Nation 2 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA 8 University of Southern California, Keck School of Medicine , Los Angeles, CA, USA ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel A Nation For correspondence: danation{at}usc.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Intrinsic low frequency oscillations in BOLD signal (BOLD-LFOs) are generally considered nuisance signal in connectivity analysis and discarded. However, recent evidence suggests BOLD-LFOs may shed light on cerebrovascular dysfunction and early Alzheimer’s disease pathophysiology, but the mechanisms remain unclear. No investigations to date have assessed the relationship between BOLD-LFOs and plasma pTau 217 , or how this relationship differs in apolipoprotein-e4 ( APOE4 ) carriers who are vulnerable to cerebrovascular dysfunction and predisposed to AD pathophysiology. Methods Independently living older adults (N=118) without major neurological or psychiatric disorder were recruited from the community. Participants underwent resting-state brain functional MRI and venipuncture. Total BOLD-LFOs were quantified as signal power within the 0.01–0.10 Hz frequency range. Plasma level of pTau 217 was assessed and linear regression was used to quantify the interactive effect of APOE4 carrier status and BOLD-LFOs on plasma pTau 217 . 2×2 ANCOVA was used to compare BOLD-LFOs across APOE4 carrier and amyloid positivity statuses based on previously reported pTau 217 cutoffs. Results The interactive effect of APOE4 carrier status and BOLD-LFO power was significantly associated with plasma pTau 217 (β=−.65, p=.004). This relationship was driven by an inverse relationship between BOLD-LFOs and plasma pTau 217 in APOE4 carriers (β=−.49, p=.003). Amyloid-β (+) APOE4 carriers displayed lower BOLD-LFOs than amyloid-β (−) APOE4 carriers (p=.02) and amyloid-β (+) APOE4 non-carriers (p=.04). All models were adjusted for age and sex. Conclusion Present study findings suggests that BOLD-LFOs are implicated early in AD pathophysiology in an APOE4 dependent manner, adding support for the continued study of BOLD-LFOs in the context of cerebrovascular contributions to AD genetic risk. Background Spontaneous blood-oxygen-level-dependent signal low-frequency oscillations (BOLD-LFOs) in the 0.01-0.1 Hz frequency range were once considered nuisance signal in fMRI connectivity analysis, caused by cardiac pulsations and respirations 1 . However, more recent work has demonstrated that other neurophysiological processes of potential relevance to brain health contribute to BOLD-LFOs, including spontaneous cerebrovascular reactivity 2 , 3 , astrocyte-mediated vasomotion 4 – 6 and neuron-astrocyte crosstalk 7 . A growing body of evidence also indicates that there is disease-dependent variation in BOLD-LFOs 8 , 9 , suggesting changes in BOLD-LFOs may have relevance to the pathophysiology of some brain diseases and could hold unique diagnostic and clinical value relative to standard fMRI 9 . For example, widespread dampening of the amplitude of BOLD-LFOs has been observed in Alzheimer’s Disease (AD) dementia patients 8 , where it correlates specifically with neuronal hypometabolism on 18 F fluorodeoxyglucose-PET (FDG-PET). Another recent study found that BOLD-LFOs are altered early in the preclinical stages of AD, even in the absence of macrostructural atrophy 9 . This study also found that decreased amplitude of BOLD-LFOs correlates with the degree of diffuse cerebral amyloidosis and with accumulation of tau pathophysiological change in the entorhinal cortex 9 . Together these findings suggest that BOLD-LFO power attenuation occurs in the early stages of AD pathophysiology and is related to key amyloid, tau, and neurodegeneration markers of the disease. However, the molecular basis of these associations between BOLD-LFOs and AD remains unknown, and no studies to date have investigated how BOLD-LFOs relate to AD pathophysiology in the context of the AD risk gene, apolipoprotein-ε4 ( APOE4 ). Finally, no studies have examined whether the amplitude of BOLD-LFOs are related to blood-based biomarkers of AD, including plasma pTau 217 . The present study aims to address this knowledge gap by investigating the relationship between BOLD-LFOs and plasma pTau 217 , a reliable diagnostic and prognostic marker of AD progression 10 , in older APOE4 carriers compared to non-carriers. The present investigation also sought to examine early changes in BOLD-LFOs by comparing BOLD-LFO power between APOE4 carriers who have not yet developed pTau 217 abnormality to APOE4 carriers who have developed pTau 217 based on previously established cutoffs for detecting cerebral amyloidosis 11 . Methods Participants Participants were recruited from Los Angeles County and Orange County communities through outreach events, mailing lists, word-of-mouth, online portals, a research volunteer registry, and through the local Alzheimer’s Disease Research Center (ADRC). All procedures were conducted as part of the Vascular Senescence and Cognition (VaSC) Study at the University of Southern California (USC) and the University of California Irvine (UCI). Older adults aged 60 to 89 years who were living independently were included ( Table 1 ). Study exclusions were a prior diagnosis of dementia, history of clinical stroke, family history of dominantly inherited neurodegenerative disorders, current neurological or major psychiatric disorders that may impact cognitive function, history of moderate-to-severe traumatic brain injury, active substance abuse, current use of medications impairing the central nervous system, current organ failure or other uncontrolled systemic illness, and contraindications for brain MRI. Eligibility for the study was verified by a structured clinical health interview and review of current medications with the participant and, when available, a knowledgeable informant study partner. This study was approved by the USC and UCI Institutional Review Board, and all participants gave informed consent. The anonymous data that support the findings of this study are available upon reasonable request from the corresponding author, DAN, through appropriate data sharing protocols. View this table: View inline View popup Download powerpoint Table 1: Participant characteristics and demographics grouped by APOE4 carrier status Measures Neuroimaging All participants underwent brain MRI scans conducted on a 3T Siemens Prisma scanner with 20-channel head coil. High-resolution 3D T1-weighted anatomical (Scan parameters: TR□=□2300 ms; TE□=□2.98 ms; TI□=□900 ms; flip angle□=□9 deg; FOV□=□256 mm; resolution□=□1.0□×□1.0□×□1.2 mm 3 ; Scan time□=□9 min) images were acquired, using 3-dimensional magnetization-prepared rapid gradient-echo (MPRAGE) sequences. Resting state fMRI scans comprised 140 contiguous echo-planar imaging (EPI) functional volumes (TR□=□3,000 ms, TE□=□30 ms, FA□=□80°, 3.3□×□3.3□×□3.3 mm voxels, matrix□=□64□×□64, FoV□=□212 mm, 48 slices). Resting-state functional MRI data were preprocessed using a custom Python pipeline that integrates FMRIB Software Library (FSL v6.0), NiBabel, and SciPy. Preprocessing was conducted on each participant’s BOLD image series and corresponding T1-weighted anatomical scan. Each participant’s T1-weighted anatomical image was skull-stripped using FSL’s BET and segmented into gray matter and white matter probability maps using the FAST tool. These maps were linearly registered to the participant’s functional space using FLIRT 12 , and binary tissue masks were generated by thresholding the probabilistic segmentations at 0.5. The fully preprocessed functional images were then registered to MNI152 standard space (2 mm resolution) using affine registration with 12 degrees of freedom. The first 10 volumes of each BOLD time series were discarded. Slice timing correction was applied to adjust for inter-slice acquisition delays, as well as motion correction using FSL’s MCFLIRT tool to realign all volumes to reference image. After realignment, linear trends were removed from each voxel’s time series using a Python-based detrending procedure implemented with SciPy. The detrended data were spatially smoothed with a Gaussian kernel (4 mm full width at half maximum, approximated by sigma = 1.7 voxels) and temporally band-pass filtered to extract frequencies between 0.01-0.10 Hz using FSL’s fslmaths command. Mean BOLD time series were extracted from the filtered functional images using the previously created gray matter mask. For each extracted time series, power spectral density (PSD) was calculated via fast Fourier transform, and total power within the 0.01–0.10 Hz frequency band was computed. Plasma AD Biomarkers pTau 217 concentrations were quantified in human plasma samples using the ultra-sensitive Simoa (Single Molecule Array) assay platform developed by Quanterix (Lexington, MA, USA). The pTau 217 assay utilized a bead-based sandwich 3 step immunoassay format run on the Quanterix HD-X Analyzer, which allows for digital quantification of low-abundance biomarkers in biological fluids. Plasma samples were collected in K2EDTA tubes, centrifuged at 2000 x g for 10 minutes at 4°C, and stored at −80°C until analysis. Prior to assay, samples were thawed on ice and centrifuged again to remove any debris. The pTau-217 assay was performed according to the manufacturer’s instructions (Quanterix pTau-217 Advantage Kit, Catalog # 104588), using 100 μL of plasma per well. Capture antibodies specific for pTau-217 were immobilized on paramagnetic beads, while detector antibodies were labeled with a proprietary enzyme tag. Following incubation and washing steps, individual immunocomplexes were transferred to a microwell array, allowing for single-molecule detection. A chemiluminescent substrate was added, and signals were captured digitally by the HD-X Analyzer. A plasma pTau 217 cut off of 0.44 pg/ml has previously displayed high combination specificity and sensitivity (>85%) for detecting cerebral amyloidosis based on cerebrospinal fluid Aβ42/40 11 .This cutoff was used in the present study to categorize participants as either amyloid-β positive or negative. APOE Genotyping Fasted blood samples were obtained by venipuncture and used to determine participant APOE genotype. Genomic DNA was extracted using the PureLink Genomic DNA Mini Kit (Thermo). APOE genotyping was performed as previously described 13 – 15 . APOE4 carriers were defined as participants with at least one copy of the apolipoprotein-ε4 allele. All analyses were performed at the same lab at the University of Arizona (KR). Statistical Analyses 121 participants underwent brain fMRI, anatomical T1w MRI, and venipuncture. All data were screened for outliers (±3 SD) and three gray matter BOLD-LFO outliers were identified and removed with values of +4.84 SD, +4.22 SD, and +3.79 SD. After outlier screen, the total analyzed sample was N=118. All assessed variables were compared between APOE4 carriers and non-carriers using independent t-tests. Data analyses were performed in R. Hayes PROCESS Model 1 (simple moderation) was used to assess the potential moderating effect of APOE4 carrier status on the relationship between BOLD-LFOs and plasma pTau 217 , where x=BOLD-LFO, Y=pTau 217 , and w= APOE4 carrier status. This analysis was repeated excluding participants with a copy of the APOE2 allele (n=4). Linear regression assumptions regarding linearity, multicollinearity (VIF<5), and homoscedasticity (Breusch-Pagan test) were met. Lastly, within group analyses were performed to assess the relationship between BOLD-LFOs and pTau 217 within the APOE4 carrier and non-carrier groups. All models were adjusted for age and sex. 2X2 ANCOVA was used to compare BOLD-LFOs by amyloid-β positivity and APOE4 carrier status adjusted for age and sex, and pairwise comparisons were also performed. For illustration purposes, power spectral density plots were generated to compare BOLD-LFOs power between amyloid-β (+) and amyloid-β (−) APOE4 carriers. Minimum detectable effect sizes given a power of .80, alpha of .05, two covariates, and a sample size of N=118 was calculated using the pwr package in R as .4R 2 =.05 for the BOLD-LFO* APOE4 interaction effect. False discovery rate correction was used to account for multiple comparisons in the primary analysis including APOE4 *BOLD-LFOs interaction effect and subgroup analyses (within APOE4 carrier and non-carrier group effects) 16 . Results 118 participants were included for analysis, participant characteristics and demographics for this sample are shown in Table 1 grouped by APOE4 carrier status. BOLD-LFOs summary z-map displayed in Figure 1 . Download figure Open in new tab Figure 1: 0.01-0.1Hz BOLD low frequency oscillations (BOLD-LFO) summary Z-map (N=118). BOLD-LFO power z-map registered to the MNI152 2 mm template. Individual subject z-maps were computed by band-limited Fourier analysis of preprocessed BOLD time series, excluding CSF voxels and non-brain regions. The resulting subject maps were normalized within brain masks and averaged across the cohort. Warmer colors indicate higher relative low-frequency power compared to the whole-brain mean. The interactive effect of APOE4 carrier status and gray matter BOLD-LFOs was significantly associated with plasma pTau 217 (β=−.65, p=.004). This was driven by an inverse relationship between BOLD-LFOs and pTau 217 in APOE4 carriers (β=−.49, p=.003) as shown in Figure 2 . This analysis was repeated excluding participants with a copy of the APOE2 allele (n=4). The interaction effect was similar for this analysis (β=−66 p=.01), and when adjusted for age and sex (β=−.67 p=.007). Additional sensitivity analyses were performed for all tests adjusting for default mode network connectivity which did not attenuate the observed significant relationships. The BOLD-LFO* APOE4 interaction term’s relationship with pTau 217 , and subsequent within group analysis results survived correction for multiple comparisons. Download figure Open in new tab Figure 2: The relationship between 0.01-0.1Hz gray matter BOLD low frequency oscillations (BOLD-LFO) and pTau 217 in APOE4 carriers (n=49) compared to APOE3 homozygotes (n=69). The effect of BOLD-LFOs on pTau 217 conditional upon APOE4 carrier status (interaction effect) is reported as standardized regression coefficient (β) and p-value in the total sample(N=118). Within group statistics reported in figure legend as β and p-value within APOE4 carrier status groups. To visualize this effect across pTau 217 cutoffs and APOE4 carrier status groups, a 2X2 ANCOVA with pairwise comparisons was performed. The amyloid-β positivity status* APOE4 carrier status interaction was statistically significant (p=.006). In pairwise comparisons amyloid-β positive APOE4 carriers displayed lower BOLD-LFO power than amyloid-β positive APOE3 homozygotes (p=.04), and amyloid-β negative APOE4 carriers (p=.02) ( Figure 3 ). Download figure Open in new tab Figure 3: Gray matter blood oxygen level dependent low frequency 0.01-0.10 Hz oscillations (BOLD-LFO) comparisons of APOE4 status ( APOE3 homozygotes vs. APOE4 carriers) by amyloid-β positivity status. 2X2 ANCOVA interaction model and subsequent pairwise comparisons are adjusted for age and sex. Amyloid-β status determined based on previously established pTau 217 cutoff of .44 pg/ml which displays high combination sensitivity/specificity for detecting cerebral amyloidosis based on cerebrospinal fluid Aβ42/40 11 . A power spectral density plot is shown in Figure 4 to visualize the BOLD-LFO differences between amyloid-β positive and negative APOE4 carriers across the full spectrum of low frequency oscillations. Download figure Open in new tab Figure 4: Gray matter blood oxygen level dependent low frequency oscillations (BOLD-LFO) power spectral density plot. Comparisons across amyloid-β positivity status (amyloid-β positive= pTau 217 >.44 pg/ml) in APOE4 carriers. Amyloid-β positive n=17, amyloid-β negative n=32. Amyloid-β status determined based on previously reported pTau217 cutoff of .44 pg/ml which displays high combination sensitivity/specificity for detecting cerebral amyloidosis based on cerebrospinal fluid Aβ42/40 11 . Discussion The present study finds that BOLD-LFO power is inversely related to plasma pTau 217 levels in APOE4 carriers but not in non-carriers, suggesting a role for the APOE4 gene in the association between BOLD-LFOs and AD pathophysiological change. Findings also suggest this alteration in BOLD-LFOs may be observed in the very earliest stages of AD pathophysiological change, as evidenced by decreased BOLD-LFO power in amyloid-β positive APOE4 carriers relative to amyloid-β negative APOE4 carriers. Together these results are consistent with prior work suggesting changes in BOLD-LFOs are related to early AD pathophysiological changes on PET and CSF biomarkers and extend prior findings by demonstrating the relationship between decreased BOLD-LFOs and early stage plasma pTau 217 in APOE4 carriers specifically. APOE4 is associated with vascular, neuroinflammatory, and metabolic changes in neurons 17 , 18 and neuroglia 19 – 22 . One or more of these neurophysiological changes could be implicated in the relationship between BOLD-LFOs and pTau 217 in APOE4 carriers specifically. For example, APOE4 conveys susceptibility to cerebrovascular dysfunction 23 – 25 , which could contribute to the observed decreased in BOLD-LFOs through decreased pulsatile or intrinsic vasomotion 2 , 3 . Astrocytes are also increasingly thought to be important contributors to BOLD-LFO signal 7 , 26 and APOE4 is associated with numerous changes to astrocyte function caused by an accumulation of lipid droplets and a buildup of unsaturated fatty acids within astrocytes 19 . This accumulation of lipid droplets is sufficient to induce astrocyte reactivity, triggering the secretion of inflammatory chemokines and cytokines 27 , 28 . An examination of BOLD-LFO differences in amyloid-β positive and amyloid-β negative APOE4 carriers in the present study suggests that the largest differences in power exist in the lower frequency ranges, which are associated with astrocyte-mediated vasomotion 4 , 5 , 7 , 26 , 29 , particularly vasodilation 6 . Thus, the observed relationship between reduced BOLD-LFOs and AD pathophysiology in APOE4 carriers may be related to changes in vascular function, astrocyte reactivity, or astrocyte-vascular interactions 30 . However, other systemic physiological processes like cardiac pulsations and respiratory dynamics contribute to BOLD-LFO signal, and further studies are needed to determine the specific mechanistic contributions to BOLD-LFOs that may be implicated in APOE4 associated AD pathophysiology. Strengths of the present study include the study of BOLD-LFOs in early AD pathophysiological change by comparing APOE4 carriers to non-carriers with and without pTau 217 abnormality. Limitations include the cross-sectional nature of the study and the lack of a universally accepted plasma pTau 217 cutoff for determining cerebral amyloidosis. The present study findings suggest there should be further investigation into the potential role of decreased amplitude of BOLD-LFOs in APOE4 -related AD pathophysiological changes with potential implications for our understanding of early-stage AD and related diagnostic approaches. Data Availability The anonymous data that support the findings of this study are available upon reasonable request from the corresponding author, DAN, through appropriate data sharing protocols. Footnotes Funding: This research was supported by the Southern California Clinical and Translational Science Institute (TL: KL2TR001854), the National Institutes of Health grants (DAN: R01AG064228, R01AG060049, R01AG082073, P01AG052350, P30AG066530), (SDH: K24AG081325), (EH: P30AG066519), and the American Heart Association (AK: 23PRE1014192). References 1. ↵ Tong Y , Hocke LM , Frederick BB . 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