An open dataset of cerebral tau deposition in young healthy adults based on [18F]MK6240 positron emission tomography

An open dataset of cerebral tau deposition in young healthy adults based on [18F]MK6240 positron emission tomography | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results An open dataset of cerebral tau deposition in young healthy adults based on [ 18 F]MK6240 positron emission tomography View ORCID Profile Jack Lam , View ORCID Profile Raúl Rodriguez-Cruces , View ORCID Profile Thaera Arafat , View ORCID Profile Jessica Royer , View ORCID Profile Judy Chen , Arielle Dascal , View ORCID Profile Ella Sahlas , View ORCID Profile Raluca Pana , Robert Hopewell , Chris Hung-Hsin Hsiao , View ORCID Profile Gassan Massarweh , Jean-Paul Soucy , Sylvia Villeneuve , View ORCID Profile Lorenzo Caciagli , View ORCID Profile Matthias Koepp , Andrea Bernasconi , Neda Bernasconi , View ORCID Profile Boris Bernhardt doi: https://doi.org/10.1101/2025.06.13.655622 Jack Lam 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jack Lam Raúl Rodriguez-Cruces 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raúl Rodriguez-Cruces Thaera Arafat 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thaera Arafat Jessica Royer 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica Royer Judy Chen 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Judy Chen Arielle Dascal 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ella Sahlas 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ella Sahlas Raluca Pana 2 Montréal Neurological Institute, McGill University Health Centre , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raluca Pana Robert Hopewell 3 McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chris Hung-Hsin Hsiao 3 McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gassan Massarweh 3 McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gassan Massarweh Jean-Paul Soucy 3 McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sylvia Villeneuve 4 Douglas Mental Health University Institute, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lorenzo Caciagli 5 Queen Square Institute of Neurology, University College London , London, United Kingdom 6 Department of Neurology, Inselspital, Sleep-Wake-Epilepsy Center, Bern University Hospital, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lorenzo Caciagli Matthias Koepp 5 Queen Square Institute of Neurology, University College London , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthias Koepp Andrea Bernasconi 2 Montréal Neurological Institute, McGill University Health Centre , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Neda Bernasconi 2 Montréal Neurological Institute, McGill University Health Centre , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Boris Bernhardt 1 Multimodal Imaging and Connectome Analysis (MICA) Laboratory and Centre of Excellence in Epilepsy at the Neuro (CEEN), Montréal Neurological Institute and Hospital, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Boris Bernhardt For correspondence: boris.bernhardt{at}mcgill.ca Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Tauopathies are pathologies wherein phosphorylated insoluble tau aggregates in neurons, leading to dysfunction and degeneration. Positron emission tomography (PET) enables measurement of in vivo tau, with second-generation radiotracers such as [ 18 F]MK6240 showing high tau affinity with minimal off-target binding. While tauopathies are commonly linked to age-related neurodegenerative diseases, notably Alzheimer’s disease (AD), evidence suggests pathophysiological cascades may begin long before clinical onset. Increasingly, tau is recognized in pathologies affecting younger individuals, including autosomal dominant AD, Niemann-Pick disease type C, chronic traumatic encephalopathy, and epilepsy, thus highlighting the importance of normative data in non-geriatric populations. Here, we present a dataset of 33 young to middle-age healthy adults (mean age 34.0±10.4 years, 12 female) with [ 18 F]MK6240 PET data and T1w magnetic resonance imaging. Longitudinal data are also available in a subset of 9 participants with a minimum follow-up time of 1 year. Our dataset aims to support imaging biomarker studies on younger individuals potentially at risk for AD and to advance work in tauopathies affecting non-geriatric populations generally excluded from neurodegeneration studies. Background Tau is a protein encoded by the microtubule-associated protein tau (MAPT) gene located on chromosome 17q21 and is primarily involved in microtubule assembly and stabilization 1 Hyperphosphorylation of tau reduces its binding affinity to microtubules and promotes aggregation into insoluble intracellular filaments, leading to neural toxicity and neurodegeneration. 2 , 3 Tau aggregates in the central nervous system are the main characteristic of a group of diseases called tauopathies, which include pathologies such as Alzheimer’s disease (AD), Pick’s disease, frontotemporal dementias, progressive supranuclear palsy, and corticobasal degeneration. 4 Tauopathies are not exclusive to diseases associated with aging, however. Autosomal dominant AD with underlying mutations in presenilin 1, presenilin 2, or amyloid precursor protein genes may present as early as the third decade of life. 5 Niemann-Pick disease type C, which manifests abnormal tau protein similar to that in AD, may present at any age, however, prevalence peaks at age 19-30. 6 , 7 Chronic traumatic encephalopathy (CTE) presents at a wide age range depending on the nature of trauma and shows a spectrum of hyperphosphorylated tau pathology. 8 , 9 Additionally, increasing work looking into the bidirectional relationship between AD and seizures has sparked interest in the role of tau in epilepsy. 10 Studies on surgically resected specimens and post-mortem tissue show evidence of increased tau even in young to middle-aged adults with temporal lobe epilepsy as well as other developmental and acquired epilepsies. 11 – 14 As our understanding of various tauopathies advances, there is an increasing need for non-invasive tools to study tau in vivo , which will provide important insights into disease progression, offer prognostic information, and support the development of therapeutic interventions targeting tau. Over the past decade, numerous positron emission tomography (PET) radiopharmaceuticals have been developed to measure tau in vivo . 15 The first-generation of tau PET radiotracers has been used extensively in research and includes [ 18 F]THK5317, [ 18 F]THK5351, [ 11 C]PBB3, and [ 18 F]AV1451 (also known as [ 18 F]flortaucipir). 16 Among these, [ 18 F]flortaucipir was the first to be approved for clinical use by the Food and Drug Administration (FDA). [ 18 F]flortaucipir showed high affinity and specificity for tau and correlated well with the spatial distribution of neurofibrillary tangles described by Braak staging. 17 – 19 Despite its widespread adoption and validation for the diagnosis of AD, its utility in non-AD tauopathies is limited. Autopsy studies have shown negligible to poor flortaucipir binding in other tauopathies due to differences in tau isoforms. 20 – 22 Off-target binding to melanin-containing structures, such as the substantia nigra, and calcified structures, such as the choroid plexus has also been noted in flortaucipir studies. 15 , 23 Additionally, flortaucipir shows a relatively low effect size in non-AD tauopathies and has poor correlation with disease severity. 24 Research is ongoing into second-generation radiotracers with the aim of developing tracers with broader applicability, improved specificity, and reduced off target binding. 15 , 25 [ 18 F]MK6240 is one promising second-generation tau PET tracer that shows high cell permeability, high affinity for neurofibrillary tangles, and poor binding to amyloid plaques. 26 – 28 While there is noted meningeal and neuromelanin binding as well as weaker binding to intraparenchymal hemorrhage, [ 18 F]MK6240 is not affected by the off-target binding in basal ganglia and choroid plexus that is seen with flortaucipir. 27 , 29 Studies have demonstrated that [ 18 F]MK6240 uptake in AD patients shows patterns consistent with the known distributions of tau deposition 30 , 31 and may discriminate cognitively normal AD subjects from AD dementia patients, 30 , 32 correlating with cognitive deficits. 33 , 34 Moreover, similar to [ 18 F]flortaucipir, increased [ 18 F]MK6240 uptake in AD patients correlated with Braak staging, demonstrating its ability to detect early accumulation of neurofibrillary tangles and to follow its progression. 30 , 33 , 35 In keeping with the open science mission of the Montreal Neurological Institute (MNI), 36 we present a unique openly available dataset of healthy, young to middle-aged adult individuals with [ 18 F]MK6240 PET and T1-weighted structural MRI. We include raw 3D PET and MRI volumes, MNI152 transformation matrices, cerebellar grey matter and composite 37 (whole cerebellum, brainstem, and eroded subcortical white matter) reference regions, and partial volume corrected standard uptake maps based on these reference regions, as well as their respective surfaces in fsLR32k surface space. As brain changes may predate clinical AD symptoms by years, 38 this provided data can help support studies on younger populations at risk for later development of AD in order to better understand pathophysiological pathways and identify potential interventions. Additionally, we present a subset of participants who have a repeat [ 18 F]MK6240 PET scan with a follow-up time of at least one year, which will be useful to researchers working on presumably non-AD tauopathies affecting young to middle-aged adult individuals who have traditionally been underrepresented in research on tau. Methods Participants Thirty-three healthy controls (mean age±standard deviation (SD) 34.0±10.4, 12 female) were recruited from the local Montreal area via advertisement. All participants signed a McGill University research ethics board (REB) approved informed consent form prior to participation (project 2018-4148), including consent to share anonymized data in an openly shared repository. All participants denied a history of traumatic brain injury or neurological or psychiatric illness and had a Montreal Cognitive Assessment score of ≥26 (mean score±SD 28.1±1.7). MRI acquisition An overall schematic of the imaging processing methods is presented in Figure 1 . Protocolized structural MRI scans were carried out at the Brain Imaging Centre of the Montreal Neurological Institute using a 3T Siemens Magnetom Prisma-Fit equipped with a 64-channel head coil. The full scanning protocol can be found in Royer et al. 2022. 39 T1-weighted (T1w) imaging was obtained using a 3D magnetization-prepared rapid gradient-echo sequence (MPRAGE; 0.8mm isotropic voxels, TR = 3200ms, TE = 3.14ms, TI = 900 ms, flip angle = 9 degrees). Download figure Open in new tab Figure 1. Overview of the data processing pipeline. The T1w MRI and 3D [18F]MK6240 PET were linearly coregistered and standard uptake value ratio (SUVR) maps were calculated using either the cerebellar grey matter or composite ROI reference region. Partial volume correction was done using PETPVC and both corrected and uncorrected SUVR maps were projected to fsLR32k surface space and smoothed to 10mm full-width at half-maximum. Abbreviations: SUVR, standard uptake value ratio; PVC, partial volume correction; GM, grey matter. T1w MRI were processed using micapipe v0.2.3 ( https://micapipe.readthedocs.io/ ), an openly available robust pipeline for multimodal MRI analysis, 40 as well as FreeSurfer v6.0 ( https://surfer.nmr.mgh.harvard.edu/ ) for cortical surface reconstruction and segmentation. All surfaces and segmentations were manually inspected and corrected. Each T1w MRI was deobliqued and reoriented to LPI orientation (left-right, posterior-anterior, inferior-superior). [ 18 F]MK6240 PET acquisition and image processing All participants underwent [ 18 F]MK6240 during a separate session from the MRI. A subset of 9 participants underwent a second PET scan with the same acquisition protocol (mean interval 826±486.6 days, minimum 372 days, maximum 1797 days). [ 18 F]MK6240 was synthesized in the PET unit of the McConnell Brain Imaging Centre according to previously published protocols. 41 PET scans were acquired using a brain-dedicated Siemens high-resolution research tomograph (HRRT; Erlangen, Germany) scanner with a resolution of 2.4mm full-width at half-maximum (FWHM). A bolus of [ 18 F]MK6240 (mean dose 240±18 MBq) was injected intravenously and images were acquired 90-110min post-injection (4 frames, 300s each). An ordered subset expectation maximization (OSEM) algorithm was used to reconstruct the images. A 6min transmission scan with a rotating Cesium-137 source was used for attenuation correction. Images were corrected for dead time, decay, and random and scattered coincidences. The dynamic [ 18 F]MK6240 images were co-registered and averaged together to a single 3D volume. The [ 18 F]MK6240 PET images were linearly co-registered to the T1w MRI using Advanced Normalization Tools (ANTs). 42 Cerebellar grey matter 30 and composite (whole cerebellum, brainstem, and eroded subcortical white matter) 37 reference regions were derived from the aparc atlas from FreeSurfer outputs. The average uptake in these reference regions was then used to calculate the respective standard uptake value ratio (SUVR) maps. Partial volume correction was undertaken using Muller-Gartner correction with the PETPVC toolbox ( https://github.com/UCL/PETPVC ). 43 Corrected and uncorrected SUVR maps were sampled along each participants native cortical surface space and then resampled to fsLR32K standard space and spatially smoothed using a Gaussian kernel with 10mm FWHM for further analysis. 44 Data Records The provided raw data and derivatives are compliant with the Brain Imaging Directory Structure (BIDS) format. 45 All data is available on OSF at https://osf.io/znt9d . 46 Raw data The raw T1w MRI (defaced for anonymity), [ 18 F]MK6240 PET (4D image with 4 frames), and their respective .json files are provided in /rawdata/sub-#/ses-01/anat and /rawdata/sub-#/ses-01/pet ( Figure 2A ). Since only one MRI session was conducted, only the session-2 PET scan (and no MRI) are provided in /rawdata/sub-#/ses-02 . Both session-1 and session-2 PET scans were registered to the session-1 MRI. An affine registration file for the [ 18 F]MK6240 PET to the T1w MRI, derived from ANTs, is provided in /rawdata/sub-#/ses-0#/pet . In addition, non-linear registration files for the T1w MRI to MNI152 space can be found in /rawdata/sub-#/ses-0#/anat . Download figure Open in new tab Figure 2. Directory structure of the [18F]MK6240 healthy control dataset for an example subject. ( a ) The rawdata directory contains anonymized, defaced T1w MRI (session 1) and 4D [18F]MK6240 PET images (session 1 and, when available, session 2). We also include a linear registration file for the PET to T1w MRI as well as nonlinear registration files for the T1w MRI to MNI152 standard space. ( b ) The derivatives directory contains the cerebellar grey matter and composite reference region masks as well as a 4D grey matter and white matter mask input for partial volume correction with PVCPET. In the pet subdirectory, we include the standard uptake value ratio (SUVR) volumes using the cerebellar grey matter and composite reference regions, both corrected and non-corrected for partial volume using Muller-Gartner (MG) correction. Smoothed (10mm full-width at half-maximum) and non-smoothed maps are included. A volume without normalization or partial volume correction corresponding to the 3D [18F]MK6240 image is also included. The surf subdirectory holds the individual fsLR32k midthickness surface as well as the previous volumes projected to fsLR32k space. ( c ) Parcellated SUVR maps normalized to the composite ROI reference region in a number of parcellation schemes and resolutions are included as comma-separated value (csv) files. Derivatives The cerebellar grey matter and composite ROI masks are contained in /derivatives/sub-#/ses-01/anat ( Figure 2B ). We also include an eroded pons mask for those who wish to use the pons as a reference region. Additionally, a two-frame image ( /derivatives/sub-#/ses-01/anat/sub-#_ses-01_space-T1w-desc-GMWM_mask . nii . gz ) containing probabilistic maps of grey matter and white matter, respectively, is included as input for the Muller-Gartner partial volume correction in PETPVC toolbox. Both Muller-Gartner partial volume corrected and non-partial volume corrected data (pvc-MG and pvc-none) normalized to both cerebellar grey matter and composite reference regions (ref-cerebellarGM and ref-compositeROI) are included in /derivatives/sub-#/ses-0#/pet . Additionally, the non-corrected and non-normalized PET image ( i . e ., the raw 4D [ 18 F]MK6240 PET image averaged over time to a single 3D image) can be found as sub-s#_ses-#_pvc-none_ref-none_trc-MK6240_pet . nii . gz . In the /derivatives/sub-#/ses-0#/surf branch, we have included the individual mid-thickness surface file in fsLR32k resolution ( sub-s#_ses-01_hemi-#_space-T1w_surf-fsLR-32k_label-midthickness . surf . gii ) as well as surfaces derived from the five partial volume corrected and non-corrected and cerebellar grey matter, composite ROI normalized, and non-normalized volumes from the /pet subfolder. Raw and smoothed (10mm FWHM) surface-mapped data are also provided. The /derivatives/sub-#/ses-02 branch contains the same PET volumes and surfaces but for session-2. Again, no anatomically derived volumes and surfaces are provided in the session-2 subfolder as only one MRI session was available, and all session-2 PET data were registered to the session-1 MRI. Parcellations Parcellated session 1 non-partial volume corrected [ 18 F]MK6240 PET data normalized to the composite ROI reference region in comma separated value (csv) format are provided in the parcellations folder. We include the following parcellation schemes: Desikan-Killany 47 (aparc), Destrieux 48 (aparc-a2009s), Von Economo, 49 Schaefer 50 (in a range of resolutions from 100-1000 nodes), Glasser 51 (derived from the Human Connectome Project), and subparcellations of aparc 52 (in a range of resolutions from 100-400). Technical Validation Reference region and signal-to-noise Previous studies have suggested that cerebellar grey matter is an ideal reference region for tau PET tracers due to its lack of involvement in neurodegenerative pathophysiology. 21 , 53 , 54 One study examining [ 18 F]MK6240 in AD patients and healthy controls both cross-sectionally and longitudinally showed that uptake in cerebellar grey matter remain similar across groups and across time. 54 Another study on [ 18 F]MK6240 comparing cerebellar grey matter and eroded white matter reference regions showed no advantage of eroded white matter over cerebellar grey matter and, in fact, it was prone to spill-over in AD patients. 32 Landau and colleagues described a composite reference region consisting of the whole cerebellum, brainstem, and eroded subcortical white matter. 37 They demonstrated that this approach enhanced sensitivity to longitudinal changes in beta-amyloid over time using florbetapir PET, compared to using the cerebellum or pons alone. Similar findings have been noted with tau tracers, showing an advantage to including supratentorial white matter for improved repeatability. 55 – 58 Here, we included SUVR maps using either the cerebellar grey matter reference region or the composite reference region described by Landau and colleagues. 37 The mean and standard deviation surface maps for non-corrected and Muller-Gartner corrected PET data normalized to the cerebellar grey matter and composite reference regions are shown in Figure 3A-B . While [ 18 F]MK6240 may show considerable spill over from meningeal uptake, our processing pipeline limits this effect by employing a partial volume correction technique, sampling to the midthickness surface, and spatially smoothing on the surface. Download figure Open in new tab Figure 3. Mean standard uptake value ratio (SUVR) and standard deviation (SD) maps for ( a ) cerebellar grey matter and ( b ) composite ROI reference regions are shown. Partial volume corrected (PVC) data using the Muller-Gartner method are shown in the bottom rows. Ridgeline plots of the distribution of individual SUVRs for both cerebellar grey matter and composite ROI reference regions are shown in Figure 4A-B . Overall, the SUVRs skewed higher using the composite ROI reference region due to a smaller normalization factor. We calculated the signal to noise ratio (SNR) maps by dividing the raw PET maps by the average signal outside the head and then normalizing by the composite ROI reference region. We projected the SNR maps to the surface with the resulting average surface map shown in Figure 4C . There was generally high SNR, especially in inferior frontal, posterior temporal, and occipital areas. Download figure Open in new tab Figure 4. Ridgeline plots showing the distribution of standard uptake value ratio (SUVR) maps of individual participants are shown for the cerebellar grey matter ( a ) and composite ( b ) reference regions. The average signal to noise ratio (SNR) map is shown in ( c ) demonstrating high signal in occipital, posterior temporal, and inferior frontal areas. Intra- and inter-subject reliability and identifiability Previous work has shown excellent 6-month test-retest reliability of [ 18 F]MK6240 in healthy controls. 59 In our cohort of 9 healthy individuals who had at least 1-year follow up [ 18 F]MK6240 PET scan, we examined both the intra- and inter-subject reliability (session 1 vs session 1 and session 1 vs session 2). A correlation matrix shows the session 1 vs session 2 correlation for the same individual along the diagonal (intra-subject reliability) ( Figure 5A ). The bottom-left half of the correlation matrix represents the session 1 vs session 1 correlations between different individuals, while the upper-right half shows the session 1 vs session 2 correlations (inter-subject reliability). Median intra-subject correlation across the 9 participants was 0.78±0.06 ( Figure 5B ), while the mean inter-subject correlation, calculated by averaging the correlations between subjects in session 1 vs session 1 and session 1 vs session 2, was 0.57±0.13. All individuals had test-retest correlation coefficients at or above 0.65. Download figure Open in new tab Figure 5. Correlation matrix of the nine subjects with retest data is shown in ( a ). The bottom-left half represents the inter-subject correlations for the session (ses) 1 scans while the top-right half represents the correlations between the session 1 vs session 2 scans. Outlined in black in the diagonal represents the intra-subject reliability, correlating each patient’s session 1 vs session 2 scan. Boxplots showing the median intra- and inter-subject correlations with whiskers extending to the minimum or maximum value within 1.5 times the interquartile range are shown in ( b ). The mean intra-subject (intra) and inter-subject (inter) correlations were 0.78 and 0.57, respectively. The identifiability was calculated to be 1.69. The intra-subject correlation boxplot is shown zoomed up in ( c ) with the session 1 and session 2 [18F]MK6240 maps of the highest and lowest correlated subjects (s002 and s007, respectively) shown to the right. Intraclass correlation was estimated based on a single-measurement, consistency, two-way mixed effects model using the Pingouin python statistical package. 60 – 62 The intraclass correlation coefficient was 0.76, indicating good reliability. 61 Next, we assessed how well [ 18 F]MK6240 PET measures were reproducible across sessions while preserving individual differences by calculating the identifiability, 63 – 65 which reflects the effect size of the difference between intra- and inter-subject reliability. This measure is calculated as the absolute difference in mean intra- and inter-subject correlations divided by the pooled standard deviation. The identifiability was calculated to be 1.69, which is comparable with previously reported neuroimaging biomarker, such as myelination, 64 and 7T microstructural profile covariance derived from quantitative T1 relaxometry and default mode network resting state functional MRI. 66 Illustrative surface maps for the highest and the lowest correlating cases are displayed to the right of the boxplot ( Figure 5C ). Strength of test-retest correlation did not appear to be related to the length of interval between sessions (r = -0.08, p = 0.84). Overall, in this small subcohort of participants with a follow up scan, [ 18 F]MK6240 shows generally good identifiability and test-retest reliability. Code availability The T1w MRI processing pipeline, micapipe v0.2.3, is openly available on GitHub ( https://github.com/MICA-MNI/micapipe ) and extensively documented on ReadTheDocs ( https://micapipe.readthedocs.io/ ). PET partial volume correction was done using the PETPVC toolbox ( https://github.com/UCL/PETPVC ). Author contributions Study design and conception: JL, LC, MK, AB, NB, BCB; data acquisition, analysis, interpretation: JL, RRC, TA, JR, JC, AD, ES, RH, CH, GM, BCB; manuscript drafting and data organization: JL, RRC, BCB. All authors provided feedback and approved of the final manuscript. Disclosures The authors declare no competing interests. Acknowledgements We would like to thank the participants for their efforts to take part in the study and to be willing to contribute to the Montreal Neurological Institute’s open science initiative. We also thank the members of the McConnell Brain Imaging Centre’s MRI and PET units for their help in data acquisition and processing. RRC received support from the Fonds de la Recherche du Québec – Santé (FRQ-S), the Montreal Neurological Institute Jeanne Timmins Costello Fellowship, and the Healthy Brains, Healthy Lives – Entrepreneur Postdoctoral Fellowship. JR received support from the Canadian Open Neuroscience Platform (CONP) and Canadian Institutes of Health Research (CIHR). LC acknowledges prior support from Brain Research UK (Award 14181). BCB acknowledges support from CIHR (FDN-154298, PJT-174995, PJT-191853), SickKids Foundation (NI17-039), Natural Sciences and Engineering Research Council (NSERC RGPIN-2025-05932), Azrieli Center for Autism Research of the Montreal Neurological Institute (ACAR), BrainCanada, FRQ-S, the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), the Canada Research Chairs Program (CRC), and the Centre of Excellence in Epilepsy at the Neuro (CEEN). Funder Information Declared Fonds de Recherche du Québec – Santé, https://ror.org/02eqrsj93 Montreal Neurological Institute and Hospital Healthy Brains, Healthy Lives Canadian Open Neuroscience Platform (CONP) Canadian Institutes of Health Research, https://ror.org/01gavpb45 , FDN-154298 , PJT-174995 , PJT-191853 SickKids Foundation, https://ror.org/04374qe70 , NI17-039 Natural Sciences and Engineering Research Council, https://ror.org/01h531d29 , RGPIN-2025-05932 Azrieli Foundation Brain Canada Foundation Helmholtz International BigBrain Analytics and Learning Laboratory Canada Research Chairs, https://ror.org/0517h6h17 Centre of Excellence in Epilepsy at the Neuro (CEEN) Footnotes ↵ * co-senior author https://osf.io/znt9d References 1. ↵ Cleveland DW , Hwo SY , Kirschner MW . Purification of tau, a microtubule-associated protein that induces assembly of microtubules from purified tubulin . J Mol Biol . 1977 ; 116 ( 2 ): 207 – 225 . doi: 10.1016/0022-2836(77)90213-3 OpenUrl CrossRef PubMed Web of Science 2. ↵ Xia Y , Prokop S , Gorion KMM , et al. Tau Ser208 phosphorylation promotes aggregation and reveals neuropathologic diversity in Alzheimer’s disease and other tauopathies . Acta Neuropathol Commun . 2020 ; 8 ( 1 ): 88 . doi: 10.1186/s40478-020-00967-w OpenUrl CrossRef PubMed 3. ↵ Götz J , Halliday G , Nisbet RM . Molecular Pathogenesis of the Tauopathies . Annu Rev Pathol Mech Dis . 2019 ; 14 (Volume 14, 2019): 239 – 261 . doi: 10.1146/annurev-pathmechdis-012418-012936 OpenUrl CrossRef PubMed 4. ↵ Creekmore BC , Watanabe R , Lee EB . Neurodegenerative Disease Tauopathies . Annu Rev Pathol Mech Dis . 2024 ; 19 ( 1 ): 345 – 370 . doi: 10.1146/annurev-pathmechdis-051222-120750 OpenUrl CrossRef 5. ↵ Bateman RJ , Aisen PS , De Strooper B , et al. Autosomal-dominant Alzheimer’s disease: a review and proposal for the prevention of Alzheimer’s disease . Alzheimers Res Ther . 2011 ; 3 ( 1 ): 1 . doi: 10.1186/alzrt59 OpenUrl CrossRef PubMed 6. ↵ Burton BK , Ellis AG , Orr B , et al. Estimating the prevalence of Niemann-Pick disease type C (NPC) in the United States . Mol Genet Metab . 2021 ; 134 ( 1 ): 182 – 187 . doi: 10.1016/j.ymgme.2021.06.011 OpenUrl CrossRef PubMed 7. ↵ Auer IA , Schmidt ML , Lee VM , et al. Paired helical filament tau (PHFtau) in Niemann-Pick type C disease is similar to PHFtau in Alzheimer’s disease . Acta Neuropathol (Berl) . 1995 ; 90 ( 6 ): 547 – 551 . doi: 10.1007/BF00318566 OpenUrl CrossRef PubMed Web of Science 8. ↵ Alosco ML , Iaccarino L , Asken BM , et al. 18F-MK-6240 Tau PET as a Biomarker for Chronic Traumatic Encephalopathy: Case Series of 10 Symptomatic Former National Football League Players . Alzheimers Dement . 2022 ; 18 ( S6 ): e066995 . doi: 10.1002/alz.066995 OpenUrl CrossRef 9. ↵ McKee AC , Stein TD , Nowinski CJ , et al. The spectrum of disease in chronic traumatic encephalopathy . Brain . 2013 ; 136 ( 1 ): 43 – 64 . doi: 10.1093/brain/aws307 OpenUrl CrossRef PubMed Web of Science 10. ↵ Lam AD , Thibault EG , Mayblyum DV , et al. Association of Seizure Foci and Location of Tau and Amyloid Deposition and Brain Atrophy in Patients With Alzheimer Disease and Seizures . Neurology . 2024 ; 103 ( 9 ): e209920 . doi: 10.1212/WNL.0000000000209920 OpenUrl CrossRef PubMed 11. ↵ Thom M , Liu JYW , Thompson P , et al. Neurofibrillary tangle pathology and Braak staging in chronic epilepsy in relation to traumatic brain injury and hippocampal sclerosis: a postmortem study . Brain J Neurol . 2011 ; 134 ( Pt 10 ): 2969 – 2981 . doi: 10.1093/brain/awr209 OpenUrl CrossRef PubMed Web of Science 12. Tai XY , Koepp M , Duncan JS , et al. Hyperphosphorylated tau in patients with refractory epilepsy correlates with cognitive decline: a study of temporal lobe resections . Brain . 2016 ; 139 ( 9 ): 2441 – 2455 . doi: 10.1093/brain/aww187 OpenUrl CrossRef PubMed 13. Gourmaud S , Shou H , Irwin DJ , et al. Alzheimer-like amyloid and tau alterations associated with cognitive deficit in temporal lobe epilepsy . Brain J Neurol . 2020 ; 143 ( 1 ): 191 – 209 . doi: 10.1093/brain/awz381 OpenUrl CrossRef PubMed 14. ↵ Mrzyglod A , Mebrouk A , Bartkiewicz J , et al. Patterns of phosphorylated tau accumulation in a spectrum of acquired and developmental brain lesions associated with refractory epilepsy . Epilepsia . Published online April 29, 2025. doi: 10.1111/epi.18418 OpenUrl CrossRef 15. ↵ Cassinelli Petersen G , Roytman M , Chiang GC , Li Y , Gordon ML , Franceschi AM . Overview of tau PET molecular imaging . Curr Opin Neurol . 2022 ; 35 ( 2 ): 230 . doi: 10.1097/WCO.0000000000001035 OpenUrl CrossRef PubMed 16. ↵ Leuzy A , Chiotis K , Lemoine L , et al. Tau PET imaging in neurodegenerative tauopathies-still a challenge . Mol Psychiatry . 2019 ; 24 ( 8 ): 1112 – 1134 . doi: 10.1038/s41380-018-0342-8 OpenUrl CrossRef PubMed 17. ↵ Xia CF , Arteaga J , Chen G , et al. [(18)F]T807, a novel tau positron emission tomography imaging agent for Alzheimer’s disease . Alzheimers Dement J Alzheimers Assoc . 2013 ; 9 ( 6 ): 666 – 676 . doi: 10.1016/j.jalz.2012.11.008 OpenUrl CrossRef PubMed Web of Science 18. ↵ Schwarz AJ , Yu P , Miller BB , et al. Regional profiles of the candidate tau PET ligand18 F-AV-1451 recapitulate key features of Braak histopathological stages . Brain . 2016 ; 139 ( 5 ): 1539 – 1550 . doi: 10.1093/brain/aww023 OpenUrl CrossRef PubMed 19. ↵ Marquié M , Siao Tick Chong M , Antón-Fernández A , et al. [F-18]-AV-1451 binding correlates with postmortem neurofibrillary tangle Braak staging . Acta Neuropathol (Berl) . 2017 ; 134 ( 4 ): 619 – 628 . doi: 10.1007/s00401-017-1740-8 OpenUrl CrossRef PubMed 20. ↵ Cummings JL , Gonzalez MI , Pritchard MC , May PC , Toledo-Sherman LM , Harris GA . The therapeutic landscape of tauopathies: challenges and prospects . Alzheimers Res Ther . 2023 ; 15 ( 1 ): 168 . doi: 10.1186/s13195-023-01321-7 OpenUrl CrossRef PubMed 21. ↵ Marquié M , Normandin MD , Vanderburg CR , et al. Validating novel tau positron emission tomography tracer [F-18]-AV-1451 (T807) on postmortem brain tissue . Ann Neurol . 2015 ; 78 ( 5 ): 787 – 800 . doi: 10.1002/ana.24517 OpenUrl CrossRef PubMed 22. ↵ Sander K , Lashley T , Gami P , et al. Characterization of tau positron emission tomography tracer [18F]AV-1451 binding to postmortem tissue in Alzheimer’s disease, primary tauopathies, and other dementias . Alzheimers Dement J Alzheimers Assoc . 2016 ; 12 ( 11 ): 1116 – 1124 . doi: 10.1016/j.jalz.2016.01.003 OpenUrl CrossRef 23. ↵ Lockhart SN , Ayakta N , Winer JR , La Joie R , Rabinovici GD , Jagust WJ . Elevated 18F-AV-1451 PET tracer uptake detected in incidental imaging findings . Neurology . 2017 ; 88 ( 11 ): 1095 – 1097 . doi: 10.1212/WNL.0000000000003724 OpenUrl CrossRef PubMed 24. ↵ Burnham SC , Iaccarino L , Pontecorvo MJ , et al. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles . Brain Commun . 2024 ; 6 ( 1 ): fcad305 . doi: 10.1093/braincomms/fcad305 OpenUrl CrossRef 25. ↵ Bischof GN , Dodich A , Boccardi M , et al. Clinical validity of second-generation tau PET tracers as biomarkers for Alzheimer’s disease in the context of a structured 5-phase development framework . Eur J Nucl Med Mol Imaging . 2021 ; 48 ( 7 ): 2110 – 2120 . doi: 10.1007/s00259-020-05156-4 OpenUrl CrossRef PubMed 26. ↵ Hostetler ED , Walji AM , Zeng Z , et al. Preclinical Characterization of 18F-MK-6240, a Promising PET Tracer for In Vivo Quantification of Human Neurofibrillary Tangles . J Nucl Med Off Publ Soc Nucl Med . 2016 ; 57 ( 10 ): 1599 – 1606 . doi: 10.2967/jnumed.115.171678 OpenUrl Abstract / FREE Full Text 27. ↵ Betthauser TJ , Cody KA , Zammit MD , et al. In Vivo Characterization and Quantification of Neurofibrillary Tau PET Radioligand18 F-MK-6240 in Humans from Alzheimer Disease Dementia to Young Controls . J Nucl Med . 2019 ; 60 ( 1 ): 93 – 99 . doi: 10.2967/jnumed.118.209650 OpenUrl Abstract / FREE Full Text 28. ↵ Levy JP , Bezgin G , Savard M , et al. 18F-MK-6240 tau-PET in genetic frontotemporal dementia . Brain J Neurol . 2022 ; 145 ( 5 ): 1763 – 1772 . doi: 10.1093/brain/awab392 OpenUrl CrossRef PubMed 29. ↵ Aguero C , Dhaynaut M , Normandin MD , et al. Autoradiography validation of novel tau PET tracer [F-18]-MK-6240 on human postmortem brain tissue . Acta Neuropathol Commun . 2019 ; 7 ( 1 ): 37 . doi: 10.1186/s40478-019-0686-6 OpenUrl CrossRef PubMed 30. ↵ Pascoal TA , Shin M , Kang MS , et al. In vivo quantification of neurofibrillary tangles with [18F]MK-6240 . Alzheimers Res Ther . 2018 ; 10 ( 1 ): 74 . doi: 10.1186/s13195-018-0402-y OpenUrl CrossRef PubMed 31. ↵ Lohith TG , Bennacef I , Vandenberghe R , et al. Brain Imaging of Alzheimer Dementia Patients and Elderly Controls with 18F-MK-6240, a PET Tracer Targeting Neurofibrillary Tangles . J Nucl Med Off Publ Soc Nucl Med . 2019 ; 60 ( 1 ): 107 – 114 . doi: 10.2967/jnumed.118.208215 OpenUrl Abstract / FREE Full Text 32. ↵ Krishnadas N , Doré V , Robertson JS , et al. Rates of regional tau accumulation in ageing and across the Alzheimer’s disease continuum: an AIBL 18F-MK6240 PET study . eBioMedicine . 2023 ; 88 : 104450 . doi: 10.1016/j.ebiom.2023.104450 OpenUrl CrossRef PubMed 33. ↵ Kreisl WC , Lao PJ , Johnson A , et al. Patterns of tau pathology identified with 18 F-MK-6240 PET imaging . Alzheimers Dement J Alzheimers Assoc . 2022 ; 18 ( 2 ): 272 – 282 . doi: 10.1002/alz.12384 OpenUrl CrossRef 34. ↵ Krishnadas N , Huang K , Schultz SA , et al. Visually Identified Tau 18F-MK6240 PET Patterns in Symptomatic Alzheimer’s Disease . J Alzheimers Dis JAD . 2022 ; 88 ( 4 ): 1627 – 1637 . doi: 10.3233/JAD-215558 OpenUrl CrossRef PubMed 35. ↵ Pascoal TA , Benedet AL , Tudorascu DL , et al. Longitudinal 18F-MK-6240 tau tangles accumulation follows Braak stages . Brain . 2021 ; 144 ( 11 ): 3517 – 3528 . doi: 10.1093/brain/awab248 OpenUrl CrossRef PubMed 36. ↵ Poupon V , Seyller A , Rouleau GA . The Tanenbaum Open Science Institute: Leading a Paradigm Shift at the Montreal Neurological Institute . Neuron . 2017 ; 95 ( 5 ): 1002 – 1006 . doi: 10.1016/j.neuron.2017.07.026 OpenUrl CrossRef PubMed 37. ↵ Landau SM , Fero A , Baker SL , et al. Measurement of Longitudinal β-Amyloid Change with 18F-Florbetapir PET and Standardized Uptake Value Ratios . J Nucl Med Off Publ Soc Nucl Med . 2015 ; 56 ( 4 ): 567 – 574 . doi: 10.2967/jnumed.114.148981 OpenUrl Abstract / FREE Full Text 38. ↵ Strikwerda-Brown C , Hobbs DA , Gonneaud J , et al. Association of Elevated Amyloid and Tau Positron Emission Tomography Signal With Near-Term Development of Alzheimer Disease Symptoms in Older Adults Without Cognitive Impairment . JAMA Neurol . 2022 ; 79 ( 10 ): 975 – 985 . doi: 10.1001/jamaneurol.2022.2379 OpenUrl CrossRef PubMed 39. ↵ Royer J , Rodríguez-Cruces R , Tavakol S , et al. An Open MRI Dataset For Multiscale Neuroscience . Sci Data . 2022 ; 9 ( 1 ): 569 . doi: 10.1038/s41597-022-01682-y OpenUrl CrossRef PubMed 40. ↵ Cruces RR , Royer J , Herholz P , et al. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis . NeuroImage . 2022 ; 263 : 119612 . doi: 10.1016/j.neuroimage.2022.119612 OpenUrl CrossRef PubMed 41. ↵ Hopewell R , Ross K , Kostikov A , et al. A simplified radiosynthesis of [18 F]MK-6240 for tau PET imaging . J Label Compd Radiopharm . 2019 ; 62 ( 2 ): 109 – 114 . doi: 10.1002/jlcr.3695 OpenUrl CrossRef PubMed 42. ↵ Tustison NJ , Cook PA , Holbrook AJ , et al. The ANTsX ecosystem for quantitative biological and medical imaging . Sci Rep . 2021 ; 11 ( 1 ): 9068 . doi: 10.1038/s41598-021-87564-6 OpenUrl CrossRef PubMed 43. ↵ Thomas BA , Cuplov V , Bousse A , et al. PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography . Phys Med Biol . 2016 ; 61 ( 22 ): 7975 – 7993 . doi: 10.1088/0031-9155/61/22/7975 OpenUrl CrossRef PubMed 44. ↵ Van Essen DC , Glasser MF , Dierker DL , Harwell J , Coalson T. Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases . Cereb Cortex . 2012 ; 22 ( 10 ): 2241 – 2262 . doi: 10.1093/cercor/bhr291 OpenUrl CrossRef PubMed Web of Science 45. ↵ Gorgolewski KJ , Auer T , Calhoun VD , et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments . Sci Data . 2016 ; 3 ( 1 ): 160044 . doi: 10.1038/sdata.2016.44 OpenUrl CrossRef PubMed 46. ↵ Lam J , Thaera Arafat , Sahlas E , et al. An open dataset of cerebral tau deposition in young healthy adults based on [18F]MK6240 positron emission tomography . Published online 2025. doi: 10.17605/OSF.IO/ZNT9D OpenUrl CrossRef 47. ↵ Desikan RS , Ségonne F , Fischl B , et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest . NeuroImage . 2006 ; 31 ( 3 ): 968 – 980 . doi: 10.1016/j.neuroimage.2006.01.021 OpenUrl CrossRef PubMed Web of Science 48. ↵ Destrieux C , Fischl B , Dale A , Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature . NeuroImage . 2010 ; 53 ( 1 ): 1 – 15 . doi: 10.1016/j.neuroimage.2010.06.010 OpenUrl CrossRef PubMed Web of Science 49. ↵ Von Economo C , Koskinas G. Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. (The Cyto-Architectonics of the Cerebral Cortex of Adult Man .). Arch Neurol Psychiatry . 1926 ; 16 ( 6 ): 816 . doi: 10.1001/archneurpsyc.1926.02200300136013 OpenUrl CrossRef 50. ↵ Schaefer A , Kong R , Gordon EM , et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI . Cereb Cortex N Y N 1991 . 2018 ; 28 ( 9 ): 3095 – 3114 . doi: 10.1093/cercor/bhx179 OpenUrl CrossRef PubMed 51. ↵ Glasser MF , Coalson TS , Robinson EC , et al. A multi-modal parcellation of human cerebral cortex . Nature . 2016 ; 536 ( 7615 ): 171 – 178 . doi: 10.1038/nature18933 OpenUrl CrossRef PubMed 52. ↵ Vos de Wael R, Benkarim O , Paquola C , et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets . Commun Biol . 2020 ; 3 ( 1 ): 1 – 10 . doi: 10.1038/s42003-020-0794-7 OpenUrl CrossRef PubMed 53. ↵ Lemoine L , Saint-Aubert L , Marutle A , et al. Visualization of regional tau deposits using 3H-THK5117 in Alzheimer brain tissue . Acta Neuropathol Commun . 2015 ; 3 ( 1 ): 40 . doi: 10.1186/s40478-015-0220-4 OpenUrl CrossRef PubMed 54. ↵ Tissot C , Servaes S , Lussier FZ , et al. The Association of Age-Related and Off-Target Retention with Longitudinal Quantification of [18 F]MK6240 Tau PET in Target Regions . J Nucl Med . 2023 ; 64 ( 3 ): 452 – 459 . doi: 10.2967/jnumed.122.264434 OpenUrl Abstract / FREE Full Text 55. ↵ Schwarz CG , Therneau TM , Weigand SD , et al. Selecting software pipelines for change in flortaucipir SUVR: Balancing repeatability and group separation . NeuroImage . 2021 ; 238 : 118259 . doi: 10.1016/j.neuroimage.2021.118259 OpenUrl CrossRef PubMed 56. Devous MD , Joshi AD , Navitsky M , et al. Test–Retest Reproducibility for the Tau PET Imaging Agent Flortaucipir F 18 . J Nucl Med . 2018 ; 59 ( 6 ): 937 – 943 . doi: 10.2967/jnumed.117.200691 OpenUrl Abstract / FREE Full Text 57. Harrison TM , La Joie R , Maass A , et al. Longitudinal tau accumulation and atrophy in aging and alzheimer disease . Ann Neurol . 2019 ; 85 ( 2 ): 229 – 240 . doi: 10.1002/ana.25406 OpenUrl CrossRef PubMed 58. ↵ Southekal S , Devous MD , Kennedy I , et al. Flortaucipir F 18 Quantitation Using Parametric Estimation of Reference Signal Intensity . J Nucl Med . 2018 ; 59 ( 6 ): 944 – 951 . doi: 10.2967/jnumed.117.200006 OpenUrl Abstract / FREE Full Text 59. ↵ Vanderlinden G , Mertens N , Michiels L , et al. Long-term test-retest of cerebral [18F]MK-6240 binding and longitudinal evaluation of extracerebral tracer uptake in healthy controls and amnestic MCI patients . Eur J Nucl Med Mol Imaging . 2022 ; 49 ( 13 ): 4580 – 4588 . doi: 10.1007/s00259-022-05907-5 OpenUrl CrossRef PubMed 60. ↵ Vallat R. Pingouin: statistics in Python . J Open Source Softw . 2018 ; 3 ( 31 ): 1026 . doi: 10.21105/joss.01026 OpenUrl CrossRef 61. ↵ Koo TK , Li MY . A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research . J Chiropr Med . 2016 ; 15 ( 2 ): 155 – 163 . doi: 10.1016/j.jcm.2016.02.012 OpenUrl CrossRef PubMed 62. ↵ Baumgartner R , Joshi A , Feng D , Zanderigo F , Ogden RT . Statistical evaluation of test-retest studies in PET brain imaging . EJNMMI Res . 2018 ; 8 ( 1 ): 13 . doi: 10.1186/s13550-018-0366-8 OpenUrl CrossRef PubMed 63. ↵ Mansour L S , Seguin C , Smith RE , Zalesky A. Connectome spatial smoothing (CSS): Concepts, methods, and evaluation . NeuroImage . 2022 ; 250 : 118930 . doi: 10.1016/j.neuroimage.2022.118930 OpenUrl CrossRef PubMed 64. ↵ Mansour L S , Tian Y , Yeo BTT , Cropley V , Zalesky A. High-resolution connectomic fingerprints: Mapping neural identity and behavior . NeuroImage . 2021 ; 229 : 117695 . doi: 10.1016/j.neuroimage.2020.117695 OpenUrl CrossRef PubMed 65. ↵ Amico E , Goñi J. The quest for identifiability in human functional connectomes . Sci Rep . 2018 ; 8 ( 1 ): 8254 . doi: 10.1038/s41598-018-25089-1 OpenUrl CrossRef PubMed 66. ↵ Cabalo DG , Leppert IR , Thevakumaran R , et al. Multimodal precision MRI of the individual human brain at ultra-high fields . Sci Data . 2025 ; 12 ( 1 ): 526 . doi: 10.1038/s41597-025-04863-7 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 14, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following An open dataset of cerebral tau deposition in young healthy adults based on [18F]MK6240 positron emission tomography Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share An open dataset of cerebral tau deposition in young healthy adults based on [ 18 F]MK6240 positron emission tomography Jack Lam , Raúl Rodriguez-Cruces , Thaera Arafat , Jessica Royer , Judy Chen , Arielle Dascal , Ella Sahlas , Raluca Pana , Robert Hopewell , Chris Hung-Hsin Hsiao , Gassan Massarweh , Jean-Paul Soucy , Sylvia Villeneuve , Lorenzo Caciagli , Matthias Koepp , Andrea Bernasconi , Neda Bernasconi , Boris Bernhardt bioRxiv 2025.06.13.655622; doi: https://doi.org/10.1101/2025.06.13.655622 Share This Article: Copy Citation Tools An open dataset of cerebral tau deposition in young healthy adults based on [ 18 F]MK6240 positron emission tomography Jack Lam , Raúl Rodriguez-Cruces , Thaera Arafat , Jessica Royer , Judy Chen , Arielle Dascal , Ella Sahlas , Raluca Pana , Robert Hopewell , Chris Hung-Hsin Hsiao , Gassan Massarweh , Jean-Paul Soucy , Sylvia Villeneuve , Lorenzo Caciagli , Matthias Koepp , Andrea Bernasconi , Neda Bernasconi , Boris Bernhardt bioRxiv 2025.06.13.655622; doi: https://doi.org/10.1101/2025.06.13.655622 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7633) Biochemistry (17680) Bioengineering (13889) Bioinformatics (41928) Biophysics (21445) Cancer Biology (18585) Cell Biology (25491) Clinical Trials (138) Developmental Biology (13374) Ecology (19897) Epidemiology (2067) Evolutionary Biology (24308) Genetics (15606) Genomics (22496) Immunology (17736) Microbiology (40385) Molecular Biology (17175) Neuroscience (88583) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4822) Physiology (7641) Plant Biology (15149) Scientific Communication and Education (2045) Synthetic Biology (4293) Systems Biology (9822) Zoology (2271)