An open, longitudinal resource for mapping interindividual variation in the aging connectome

An open, longitudinal resource for mapping interindividual variation in the aging connectome | 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 An open, longitudinal resource for mapping interindividual variation in the aging connectome View ORCID Profile Anna MacKay-Brandt , View ORCID Profile Yunglin Gazes , Daniel Garcia-Barnett , Lauren Grebe , Olivia Ripley , Kai Xuan Gan , Kristin T. Trautman , Melissa Kramer , Melissa Breland , View ORCID Profile Russell Tobe , View ORCID Profile Alexandre Franco , View ORCID Profile Vilma Gabbay , View ORCID Profile Michael Milham , View ORCID Profile Stan Colcombe doi: https://doi.org/10.1101/2025.10.10.25337774 Anna MacKay-Brandt 1 Brain Aging and Mental Health Laboratory, Clinical Research, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anna MacKay-Brandt Yunglin Gazes 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yunglin Gazes Daniel Garcia-Barnett 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren Grebe 3 Communication Sciences and Disorders, St. John’s University , Queens, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olivia Ripley 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kai Xuan Gan 1 Brain Aging and Mental Health Laboratory, Clinical Research, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristin T. Trautman 1 Brain Aging and Mental Health Laboratory, Clinical Research, Nathan Kline Institute , Orangeburg, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa Kramer 4 Clinical Research, Nathan Kline Institute , Orangeburg, NY, 10962, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa Breland 4 Clinical Research, Nathan Kline Institute , Orangeburg, NY, 10962, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Russell Tobe 4 Clinical Research, Nathan Kline Institute , Orangeburg, NY, 10962, USA 5 Child Mind Institute , New York, NY, USA 6 Department of Psychiatry, New York University Grossman School of Medicine , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Russell Tobe Alexandre Franco 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA 5 Child Mind Institute , New York, NY, USA 6 Department of Psychiatry, New York University Grossman School of Medicine , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexandre Franco Vilma Gabbay 4 Clinical Research, Nathan Kline Institute , Orangeburg, NY, 10962, USA 7 University of Miami Leonard M. Miller School of Medicine , Miami, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vilma Gabbay Michael Milham 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA 5 Child Mind Institute , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Milham Stan Colcombe 2 Design Acquisition and Neuromodulation Laboratories, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY, USA 5 Child Mind Institute , New York, NY, USA 6 Department of Psychiatry, New York University Grossman School of Medicine , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stan Colcombe For correspondence: scolcombe{at}nki.rfmh.org Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Trajectories of age-related neurocognitive decline are not uniform, and are impacted by numerous environmental and physiological factors. Earlier life phases set the stage for later life neurocognitive function, with midlife marking a critical transition characterized by increasing variability in cognitive, affective, and physiological functioning. Despite its importance, this turbulent period remains underrepresented in open neuroimaging and phenotypic data resources. To address this gap, the Nathan Kline Institute - Rockland Sample (NKI-RS) initiative created the ‘Mapping Interindividual Variation in the Aging Connectome’ (MIVAC) substudy—an openly shared, multimodal dataset designed to map brain aging trajectories beginning in midlife and assess the influence of modifiable factors such as cardiorespiratory fitness. This longitudinal investigation includes 348 community-ascertained participants aged 38 to 71 years at baseline. Data collection incorporated deep phenotyping across cognitive, behavioral, medical, and cardiorespiratory fitness domains, along with multimodal neuroimaging (resting-state fMRI, diffusion MRI, morphometric MRI, and arterial spin labeling) and biospecimen collection. The protocol harmonizes with prior NKI-RS substudies while incorporating age-specific considerations for cognitive and neural aging. The full dataset is openly available. BACKGROUND Psychiatric and cognitive disorders are prevalent throughout the lifespan and pose a significant burden on individuals, families, and public health systems. Considerable recent effort has been directed toward understanding disruptions in brain function underlying early-onset psychiatric illness 1 – 4 and late-life cognitive decline 3 . However, midlife - despite the emerging recognition that it sets the stage for later life neurocognition 5 – 7 - remains a relatively underrepresented period in lifespan neuroscience research 8 . This stage of life, typically defined as the period between the fourth and seventh decades, is characterized by increased variation—both between and within individuals—in cognitive 9 , 10 , brain 11 , and physiological 12 functioning. It is also a critical period in which modifiable risk and protective factors may influence later life brain health and cognition 13 , 14 . Yet, few open-access neuroimaging datasets exist that characterize brain structure, function, and behavior across mid-to-late-life, either cross-sectionally 15 or longitudinally 16 , 17 . The Nathan Kline Institute - Rockland Sample (NKI-RS) - Mapping Interindividual Variation in the Aging Connectome’ (MIVAC) substudy was designed to emphasize cardiorespiratory fitness and physiological function across multiple timepoints to understand the impact of natural variation of key modifiable factors on brain and cognitive function in community samples. The current dataset was designed to complement the previously described pediatric longitudinal protocol of the NKI-RS 4 , which emphasized brain development and the emergence of psychiatric illness from ages 6 to 17. This substudy extends the community-ascertained, transdiagnostic, and deeply phenotyped framework of the NKI-RS program 18 for a longitudinal characterization of interindividual variation across midlife and older adulthood (ages 38–71). The MIVAC substudy supports efforts to map normative and atypical brain aging trajectories and to evaluate modifiable determinants of cognitive and neural health. Specifically, this study incorporates cardiorespiratory fitness assessment, medical and behavioral phenotyping, and high-resolution multimodal imaging to investigate risk and protective factors that affect brain health and cognition. To achieve these goals, we implemented a multi-cohort longitudinal design, prospectively harmonized our core protocol with prior NKI-RS substudies, and maintained the NKI-RS commitment to open science and data sharing. Imaging protocols include multiband functional and high-angular resolution diffusion magnetic resonance imaging (MRI), arterial spin labeling, and structural morphometry 4 . Comprehensive phenotyping includes psychiatric diagnostic interviews, cognitive and behavioral assessments, medical history, laboratory tests, physical measures, and the addition of gold-standard cardiorespiratory fitness assessment (VO 2 max). Protocol harmonization across prior lifespan cross-sectional and pediatric longitudinal NKI-RS substudies allows for unique, deeply-phenotyped, lifespan neuroimaging analyses. This data descriptor outlines the rationale, design, implementation, and available data for the NKI-RS MIVAC cohort and provides guidance for researchers interested in the use of these data for the study of brain aging from midlife to older adulthood. METHODS Recruitment and Retention Strategy The target enrollment was 434, with 400 participants recruited between January 2016 and April 2018. The last follow-up assessment completed before the coronavirus pandemic-related shutdown was in March 2020. Recruitment followed the strategies established across all NKI-RS substudies to approximate a community-representative enrollment for the local region. Detailed recruitment descriptions common to all NKI-RS studies can be found in 18 and 4 . In addition to program-wide approaches, the MIVAC substudy included unique recruitment, retention, and feedback strategies. Study investigators with expertise in healthy aging hosted community talks at NKI and in local community spaces. Limited clinically-relevant feedback was provided to participants upon request to support the individual health goals of our participants when shared with their healthcare providers (See 4 for more detail). However, due to concerns about sampling bias, feedback was not highlighted as part of MIVAC study recruitment and potential participants who stated concern about memory impairment were referred for a free memory evaluation through the NKI Geriatric Psychiatry Research Program and not enrolled. Building upon lessons learned from three preceding NKI-RS substudies 4 , 18 , 19 , procedures adopted or implemented to promote retention included: Birthday cards sent to participants to help maintain a positive connection and track address changes. Open house events and “coffee talks” hosted by the study investigators and designed to create a dialogue to translate preliminary findings, report on study progress, and answer any general questions about healthy aging raised by the participant community. Newsletters emailed to participants. Written and verbal participant satisfaction collected at each study visit. Prior to MIVAC data collection, the core protocol across all prior substudies was reviewed by the NKI-RS leadership team and reduced to increase efficiency and decrease participant burden. Of the 400 participants enrolled, 348 (87%) completed a baseline visit. Among those who completed baseline visits, 5 (1.4%) were identified to have an MRI contraindication and were excluded. Of the remaining participants, 264 (77%) completed a first annual follow-up. A second annual follow-up was completed by 219 (64%) participants. The coronavirus pandemic interrupted the third annual follow-up period, with 106 (31%) completed, 16 (5%) due for assessment but lost to follow-up, and 97 (28%) scheduled but administratively canceled for health safety reasons related to pandemic precautions. Of note, 285 (82%) study participants completed an online survey during the first three months of the coronavirus pandemic-related shutdown (May 2020 - August 2020) reporting on the impact of the public health mandated shutdown on their daily life activities, physical and psychological health. See Figure 1 for visualizations of age by visit and a Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting diagram for observational studies 20 . Download figure Open in new tab Figure 1. Overview of participant age distribution and retention. A. Timeline for each participant’s visits plotted against their age. Each visit is color-coded for visit number. B. Histogram of participant age. C. STROBE reporting diagram for observational studies. D. Histogram of each visit. Hatched purple bar indicates the date of pandemic shutdown on March 22, 2020. 285 out of 348 participants from BAS (82%) participated in the COVID survey during the pandemic shutdown. Participant Procedures. All NKI-RS substudies followed the same procedures, with staff trained across all contemporaneous studies and supervised by the NKI-RS leadership team. Common procedures are detailed in 4 , 18 and available on the NKI-RS website ( https://rocklandsample.org/for-researchers ). The MIVAC substudy specific procedures are described in detail below. Screening Potential participants completed a pre-screening phone interview with an intake coordinator. The screening interview assessed eligibility across all NKI-RS substudies according to common and study specific inclusion criteria (See Figure 2 ). Individuals meeting study criteria were invited to participate. Download figure Open in new tab Figure 2. Inclusion and exclusion criteria for the aggregate NKI-RS cohort and each substudy. Shared selection criteria among the four studies are presented in the center boxes. Study specific criteria are listed under each NKI-RS substudy. IRB approval This study was approved by the Nathan Kline Institute for Psychiatric Research Institutional Review Board. Prior to conducting the research, written informed consent was obtained from the participants. Experimental Design The MIVAC study design included 9 visits across four years. In the first year, participants attended a brief consent visit, a baseline full-day characterization visit, and a separate one-hour cardiorespiratory fitness assessment. Each annual follow-up included separate characterization and cardiorespiratory fitness assessment visits. Visits were conducted on consecutive days, when possible, with no more than 2 weeks separation if necessary. Adjustments to the schedule were made to complete assessments on an additional visit when necessary to decrease participant burden. Datasets include a variable that codes for interval time between each assessment (i.e. “day lag”) to allow researchers greater precision when considering analyses within a data collection epoch (e.g. baseline visit). All assessments are listed in Table 1 . An example schedule is provided in Table 2 ; See also https://rocklandsample.org/mivac-full-end-user-protocol-2 for full details. View this table: View inline View popup Download powerpoint Table 1. Assessments by visit. View this table: View inline View popup Download powerpoint Table 2. Sample Baseline Characterization Schedule. Annual follow-up appointments maintained the same visit schedule, see Table 1 for follow-up assessment details. Clinical Assessments Detailed procedures for clinical assessments common across the NKI-RS substudies can be found in 4 and https://rocklandsample.org/mivac-full-end-user-protocol-2 . Please note that some self-report assessments developed for specific age cohorts were extended to younger or older ages across the NKI-RS aggregate sample (e.g., Montreal Cognitive Assessment: MoCA, Comprehensive Addiction Severity Index for Adolescents: CASI-A), while age-specific versions of measures were also chosen and matched to the participant age (e.g., Achenbach System: Adult Self Report / Older Adult Self Report: ASR, OASR; Geritaric Depression Scale: GDS). The procedure was to assign sets of surveys to participants based on age that balanced breadth of coverage for the aggregate NKI-RS lifespan sample while also recognizing the importance of age-specific framing for some measures. Note that some items of the ASR and OASR overlap, allowing for item-level coverage across middle to older adulthood. See Supplementary Tables 1 and 2 for details. Unique to the MIVAC substudy was the characterization of global cognitive function using the MoCA 21 . Research technicians, under the supervision of the study neuropsychologist, administered the MoCA at baseline and each annual follow-up visit. Administration and scoring followed the standardized protocol. All scores below 26 were sent to the neuropsychologist for review. Scores were compared with all other available study data and any contextual information. MRI All NKI-RS substudies shared the same core neuroimaging protocol, and collection was performed using the same 3.0T Siemens TIM Trio scanner at the Nathan Kline Institute with a 32-channel head coil for all acquisitions. During each imaging session, participants underwent a comprehensive protocol consisting of two high resolution morphometric sequences (T1-weighted and T2-SPACE), one diffusion MRI sequence, six functional MRI runs, one arterial spin labeling (ASL), and a T2-FLAIR sequence. Imaging parameters are presented in Table 3 . See also 4 for a detailed description of this core NKI-RS protocol. Full imaging protocols can be downloaded here: https://rocklandsample.org/for-researchers-rockland-sample-i-2011-2022-neuroimaging-protocol View this table: View inline View popup Download powerpoint Table 3. Parameters for MR Sequences. MPRAGE = Magnetization Prepared Rapid Gradient Echo; SPACE = Sampling Perfection, Application optimized Contrast; FLAIR = Fluid-Attenuated Inversion Recovery; dMRI = diffusion MRI; R-fMRI = Resting state functional MRI; mb = Multiband; sb = Singleband; pCASL = Pseudo-Continuous Arterial Spin Labeling; FOV = Field of View; TR = Repetition time; TE = Echo time; TI = Inversion time. Physical Health Assessments All NKI-RS substudies followed the same procedures for assessing height, weight, waist/hip circumference, heart rate, blood pressure, grip strength, vision, and activity levels/sleep. Routine venipuncture assays were acquired at each visit as part of the physical-health protocol for laboratory analysis and to contribute to the biospecimen bank at the NIMH Genetics Repository. See 4 for a detailed description of those procedures. Unique to the MIVAC substudy, participant cardiorespiratory fitness and walking speed were assessed at baseline and each follow-up visit. Trained study research technicians, under the supervision of MPI Colcombe, administered gold standard submaximal VO 2 max assessments using a recumbent stationary bike connected to the Parvo Medics True One 2400 metabolic measurement system 22 . During the testing session, participants wore a fitted silicon mask attached to the metabolic measurement system, quantifying the relative concentrations of oxygen and CO 2 in their exhaled breath gasses, as well as a sensor to measure heart rate. Participants pedaled with the aim of reaching at least 90% of their age-predicted maximum heart rate (220-age) and achieving a respiratory exchange ratio (RER; CO 2 to O 2 ratio) of 1.02 or higher. During administration, medical support was provided by on-site clinicians. Cardiorespiratory fitness data include data quality criteria. Walking speed alone and under cognitive load was assessed as a low tech but clinically scalable counterpart to the gold-standard cardiorespiratory fitness assessment and has been shown to predict falls, disability, and cognitive decline 23 . Using a protocol developed by researchers at the Albert Einstein College of Medicine 24 , participants were instructed to walk at their normal pace along a 20-foot distance marked off with tape, recite alternate letters of the alphabet, and walk while reciting alternate letters of the alphabet. Two trials were completed for each condition. Walking time, total correct letters, and total errors were recorded by a trained research technician for each trial across single and dual task conditions. See https://rocklandsample.org/mivac-full-end-user-protocol-2 for full details of both physical assessments. Data Distribution and Use Maintenance of End User Inquiry Mechanism As of July 2025, 349 collaborative research sites have obtained full access to the enhanced NKI-RS dataset by signing a Data Use Agreement (DUA). An inquiry response mechanism to ensure “End Users” appropriately understand and access data was developed and retained after study completion. End users submit inquiries to rocklandsample.enduser{at}nki.rfmh.org for support with data access, data quality, or documentation; to provide feedback if a technical error is identified; and to document publications using the data resource. An inquiry mechanism is critical for effective use of these data resources. Accordingly, an End User Response Panel—composed of NKI-RS program investigators with expertise in phenotyping, clinical characterization, database management, and imaging—was developed. NKI-RS research staff monitor email at least weekly and forward questions to the Panel. Since implementation, the Panel has received and jointly responded to 598 unique inquiry threads (2017: 83; 2018: 62; 2019: 41; 2020: 60; 2021: 81; 2022: 108; 2023: 58; 2024: 56; 2025: 49). DATA RECORDS Dataset Deposition All NKI-RS data files, including the MIVAC substudy, are accessible via the International Neuroimaging Data-Sharing Initiative (INDI) under the title “Enhanced Nathan Kline Institute - Rockland Sample (NKI-RS)” 25 . Data Privacy Across all NKI-RS studies, participants provided informed consent for their data to be shared via IRB-approved protocols 4 . Informed consent for sharing via INDI was obtained prior to participation in the study. Given the critical priority of protection of participant privacy, no protected health information is ever released via the INDI. For imaging data, face information was removed from anatomical scans, and protected/identifying information was removed from all image headers and databases. Random 7-digit INDI identifiers were assigned to all datasets. Recognizing the risks associated with sharing phenotypic data, which is high dimensional and personal in nature, the NKI-RS utilizes two release versions: NKI-Rockland Lite This release version contains only de-identified imaging data and limited phenotyping (e.g. age, sex, handedness). The NKI-Rockland Lite is immediately available without DUA requirements. To access the NKI-RS Lite release, please visit the study website ( rocklandsample.org ). NKI-Rockland Full Phenotypic Release This release contains the full high-dimensional phenotypic protocol common across all NKI-RS substudies. Users of the NKI-Rockland Full Phenotypic Release complete a DUA and obtain appropriate institutional approval prior to accessing data ( https://rocklandsample.org/phenotypic-data ). The entire phenotypic dataset is available to approved users via the COINS database system 26 (used for data collection) and the Longitudinal Online Research and Imaging System 27 (LORIS) database (maintained by NKI-RS program staff as a curated resource). Data acknowledgement A guiding principle for data-sharing across all NKI-Rockland Sample studies is to provide researchers with the greatest flexibility to carry out scientific inquiries. In this regard: We do not require authorship on any manuscripts generated using NKI-Rockland datasets; citation as a data-source is sufficient. We do not require review of manuscripts generated using NKI-Rockland datasets; we do strongly encourage careful documentation of selection criteria applied to the NKI-RS in choosing datasets for analysis. We do not require specification of data analyses prior to data use. Distribution for use The primary website for all NKI-Rockland Initiative studies, including MIVAC, is located at: https://rocklandsample.org . Phenotypic data may be accessed through either an NKI-RS dedicated instance of LORIS located at https://data.rocklandsample.rfmh.org/ and supported by the NKI-RS research program staff or through the COINS Data Exchange ( https://coins.trendscenter.org/ ). Except for the limited phenotypic release, which is publicly available with the imaging, NKI-RS phenotypic data are protected by a DUA. Investigators must complete a DUA (found at https://rocklandsample.org/phenotypic-data ) and have it approved by an authorized institutional official before receiving access. The intent of the DUA is to ensure that data users (1) agree to protect participant confidentiality when handling the high dimensional NKI-RS phenotypic data and (2) agree to take the necessary measures to prevent breaches of privacy. The DUA does not place any constraints on the range of analyses that can be carried out using shared data, nor does it include requirements for review or co-authorship by the originators of the NKI-RS. Imaging data All imaging data can be accessed through the Rockland Sample portal ( https://rocklandsample.org/accessing-the-neuroimaging-data-releases ). This website provides instructions for users to directly download data from an Amazon Simple Storage Service (S3) bucket. These data are organized in the Brain Imaging Data Structure (BIDS) format. We provide two scripts to perform batch download of these imaging files, in Bash script and Python. The Python script has the capability to perform some basic filtering prior to downloading data: for example, sex = female, age >=45, session = BAS1, image type = anat. Instructions are also provided to download individual files using open-source S3 bucket browsers. Partial and missing data Some participants were not able to successfully complete all components of the NKI-RS protocol due to a variety of factors (e.g.,unexpected discomfort in the scanner, family emergency interrupting a testing day). To prevent data loss due to participant discomfort during an MRI session, we included a mock MRI scanner experience during the first visit, when possible. Overall, we attempted to collect as much data as possible within the allotted data collection intervals and logged data losses as they occurred. Data license All NKI-Rockland Sample data are distributed using the Creative Commons-Attribution-Noncommercial license, which is described at: https://creativecommons.org/licenses/by-nc/4.0/legalcode . For the high-dimensional phenotypic data, all terms specified by the DUA must be complied with. TECHNICAL VALIDATION Sample Composition Geographic Distribution The Nathan Kline Institute (NKI) is located 25 miles north of New York City in Rockland County. From 2013 through 2019, the NKI-RS initiative enrolled residents of Rockland and neighboring counties (Bergen County, NJ; Orange County, NY; and Westchester County, NY). A previously published report compared the aggregate NKI-RS dataset (n = 1,610) with the demography of Rockland County, New York State, and the United States based on census data and demonstrated similar distributions across the research sample, local, regional, and national demography in most domains 4 . Here we present the MIVAC cohort compared with US census data for additional comparison ( Table 4 ). In the current study, similar to reporting on the aggregate dataset, individuals who lived closer to NKI were more likely to enroll. See Supplementary Figure 1 . View this table: View inline View popup Download powerpoint Table 4. Demographics of the NKI-RS MIVAC sub-study (enrollment years 2016-2019) and United States Census Data. Age, Sex, and Clinical Characterization Age distributions ( Figure 1b ) were relatively even across the middle to older adult age range, despite known challenges recruiting working age adults into time intensive research protocols. The contemporaneous recruitment effort for the pediatric longitudinal substudy allowed parents to learn about the MIVAC study and gain direct experience with the research staff, facilitating enrollment across both substudies. Of the 348 participants who completed baseline assessments, 248 (71%) were female. Midlife and older adult participation among men tends to lag behind female participation in studies of healthy aging. 28 Cross enrollment with the pediatric substudy may have further contributed to this gender gap, as mothers were often the accompanying guardian. Clinical characterization of global cognition was conducted as a brief screening using the MoCA. Across 808 total assessments, there were 543 MoCA scores above 25 (67%), 259 scores between 25 and 18 (32%), and 6 scores below 18 (.7%). There was no supporting evidence for dementia among the 4 participants with scores below 18. According to standard cutoff criteria, 21 223 (65%) participants were characterized as cognitively normal at baseline (MoCA > 25), and 120 (35%) participants scored in the range suggestive of mild or moderate cognitive impairment at baseline. No study participants scored in the severe range (0-9). Of the 257 participants with baseline and at least one follow-up visit, change of classification was observed in 27% of participants, 28 (11%) fell from “normal” to “mild,” 39 (15%) rose from “mild” to “normal” classification, and 3 (.01%) rose from “moderate” to “mild.” All 4 participants who scored in the “moderate” range (10-17) scored higher at their final follow-up visit. See Figure 3A for an illustration of classification change from baseline to final follow-up visit. Download figure Open in new tab Figure 3. Diagnostic classifications of cognition and major psychiatric categories. A. Change in cognitive classification based on MoCA for all participants with baseline and at least one follow-up visit (n = 257). Scores at baseline (left) are compared with the last follow-up session for each participant (right). No follow-up information was available for 86 participants classified “normal” (n= 56) and “mild impairment” (n= 30) at baseline. B. DSM-IV diagnostic status at baseline (via Structured Clinical Interview for DSM-IV-TR Axis I Disorders – Non-Patient Edition (SCID-I/NP), 29 n = 341) and the relative percent of mood, substance, anxiety, and other diagnoses. Includes past and present DSM-IV diagnoses. “Other” diagnoses included: Hypochondriasis: n = 2; Anorexia Nervosa: n=5; Eating Disorder NOS: n=6; Bulimia Nervosa: n=3; Attention-Deficit/Hyperactivity Disorder: n=2; ADHD, Predominantly Inattentive Type: n=2; ADHD, Combined Type: n=1; ADHD NOS: n=1; Bereavement: n=4. Multiple diagnoses per participant were low (two = 42, three = 19, four or more = 17) and not included in the figure. Psychiatric characterization via semi-structured SCID-I/NP interviews determined approximately half of the participants (n = 172, 51%) had no DSM-IV diagnosis at baseline. Among those with a diagnosis (n = 169, 49%), major diagnostic categories were relatively evenly split among mood (38%), substance abuse (32%), and anxiety (20%). There was a relatively low frequency of other disorders (10%) and multiple diagnoses per participant (21% with three or more diagnoses). See Figure 3b for more detail. Quality assessment Phenotypic data Prior to each NKI-RS release, scatterplots by age and boxplots comparing past releases to each progressive release were reviewed. Phenotypic data were not “cleaned” of outliers unless overt technical errors were identified in pre-release reviews. Data review and cleaning is expected to be incorporated into each independent research analysis. Figures 4 – 6 summarize score distributions and age-associated performance trends for core cognitive and mental health measures. Figure 4 illustrates both roughly symmetric (e.g., WASI-II index scores, WIAT-II Numerical Operations) and skewed distributions (e.g., WIAT-II Reading and Spelling, MoCA, BDI, STAI). Figure 5 shows modest but statistically significant age effects for the Attention Network Task in alerting, conflict, and grand mean reaction time. Figure 6 (top row) demonstrates age-related performance declines on D-KEFS Category Fluency, Trail Making Test–Switching, and Color Word Interference–Inhibition. Figure 6 (bottom row) highlights relatively stable performance across age for Letter Fluency, Trail Making Test–Sequencing, and a composite score derived from the baseline conditions of the Color Word Interference Test (Color Naming and Word Reading). Greater variability in performance was observed for conditions with longer completion times (Trail Making Test–Switching, Color Word Interference–Inhibition) and a greater number of total words generated (Category Fluency). Download figure Open in new tab Figure 4. Distributions of cognitive abilities, academic achievement, and mental health measures. Top row : composite scores from WASI-II. Middle row : raw scores from WIAT. Bottom row left to right : MoCA education-adjusted total score, BDI total score, and STAI raw Anxiety trait score. Only baseline data are included and no filtering was done. Download figure Open in new tab Figure 5. Scatter plots of Attention Network Task subscores and grand mean against age. Alert: t(332) = -2.73, p = .007; Orienting: t(332) = 1.23, p = .220; Conflict: t(330) = 2.12, p = .034; Grand Mean: t(330) = 5.33, p<.001. Plots include all unfiltered baseline data. Download figure Open in new tab Figure 6. Delis-Kaplan Executive Function Scale Subtest Scores across longitudinal timepoints, color coded for three age tertile groups (Younger (blue): 50.5 and 61). Left panel shows performance on category and letter fluency (CF, LF). Middle panel shows Trail Making Test switching and sequencing conditions (Switch, SeQ). Right panel shows Color Word Interference Test interference and averaged baseline conditions (INT, BASE). Three bolded lines are regression lines for the three age groups in each plot. Significant age effects were observed for: CF Older: t(256) = −2.60, p = .010; Switch Middle: t(263) = 2.10, p = .037; Switch Older: t(263) = 2.43, p = .016; SeQ Older: t(280) = 4.15, p < .001; INT Middle: t(268) = 2.23, p = .026; BASE Older: t(270) = 3.33, p = .001. Figure 7 presents the histogram and kernel density estimate for distributions of physical health measures, including grooved pegboard, grip strength, walk time, and VO₂ max. Download figure Open in new tab Figure 7. Distributions of physical measures: Top: left - Grooved pegboard completion time for dominant hand, right - Grip strength for dominant hand. Bottom : left - Single task walking time from Walking While Talking Task, right - Estimated VO 2 max from submaximal cardiorespiratory fitness test. Only baseline data are included and no filtering was done. Supplementary Figure 2 depicts boxplots with kernel-density overlays for each analyte from the blood sample collection. Distributions were largely unimodal and centered within expected adult ranges, with several measures showing right-tailed skew typical of metabolic/inflammatory indices. Extreme values are visible in the tails; consistent with our phenotypic-data policy, these observations were retained (no outlier trimming) to maximize transparency and enable users to apply domain-appropriate handling (e.g., robust estimators, winsorization) in downstream analyses. Missingness was modest and chiefly attributable to occasional deferred phlebotomy or scheduling constraints. See also Supplementary Table 3 for a summary by assay (sample sizes, median, IQR, 5th–95th percentiles). To evaluate the long term stability of key phenotypic measures over time, intraclass correlation coefficients (ICCs) were calculated using participants with at least three annual longitudinal assessments. Cognitive indices from the WASI-II showed good stability, with agreement/consistency values ranging from .67/.71 (Similarities) to .77/.77 (Matrix Reasoning) to .79/.83 (Vocabulary) to .90/.90 (Block Design). The MoCA demonstrated moderate stability (.63/.64), while affective measures were higher (Beck Depression Inventory; BDI = .75/.75; State Trait Anxiety Inventory-Trait; STAI-T= .81/.81). Practice-related gains were observed for the WASI-II and MoCA, whereas BDI and STAI-T scores remained stable (See Supplementary Table 4 for details here and below). Estimates for the Attention Network Task, consistent with prior reports, 30 showed moderate test-retest stability for conflict (.48/.50) and grand mean reaction time (.66/.67), but lower values for alerting (.38/.39) and orienting (.16/.17). Practice effects included increases in alerting and orienting effects, decreases in conflict effects, and faster overall response times across sessions. D-KEFS tasks demonstrated moderate to high retest stability, with Color Word Interference scores ranging from .85 to .87 across baseline and interference conditions. Trail Making Test sequencing conditions showed moderate retest stability (number = .57/.58; letter = .64/.65), while the switching condition was higher (.74/.75) and most sensitive to practice effects, t(196) = −2.78, p=.006. Tower total achievement score showed moderate retest stablility (.53/.55) with improved scores over time (t(197)= 6.88, p <.0001, and verbal fluency measures were higher (letter = .83/.84; category = .72/.73) with increasing word production across sessions, t(177) = 4.27, p<.001 and t(178)=2.60, p=.01 for letter and category, respectively. Motor praxis reaction time, conditional exclusion test reaction time, two-back total correct, and continuous performance test d-prime from the Penn Computerized Neurocognitive Battery (Penn CNB) 31 ranged in test-retest stability from a low of .47 consistency for two-back to a high of .74 agreement for the continuous performance test. Performance gains over annual follow-up visits were observed for motor praxis, two-back, and the continuous performance test, but not for reaction time on the conditional exclusion test (see Supplementary Table 4 ). Physical measures ranged from relatively high test-retest stability (grip strength = .78/.78; VO₂ max = .83/.83) to moderate (pegboard completion = .68/.69; walk time = .46/.47). There were no observed practice effects for grip strength or VO₂ max, while pegboard times decreased with repeated testing and walk times increased. Overall, most measures demonstrated moderate to high longitudinal stability, with practice effects most pronounced in cognitive and motor speed tasks. ICCs and mean slopes for a sampling of the measures are reported in detail in Supplementary Table 4 . We next examined correlations among phenotypic variables to facilitate evaluation of measures ( Figure 8 ). All correlations were corrected for false discovery rate. To ensure transparency, outliers were not removed for this descriptor, though we recommend careful data review and outlier handling in subsequent analyses. Broad patterns of association across cognitive, health, and demographic domains were consistent with established findings 32 , 33 . For example, (1) age correlated with fluid (BD, MR) but not crystallized (VOC,SIM) WASI-II subtests 34 – 36 ,was negatively associated with RAVLT delayed recall 37 , 38 and category fluency, 39 , 40 and positively associated with Trail Making Test completion time; 40 – 42 (2) age was related to poorer physical but not mental health 43 – 45 ; (3) self-reported mood, sleep, and subjective cognitive impairment were intercorrelated; 46 , 47 (4) the Attention Network Task conflict effect and grand mean response times correlated with other cognitive measures, whereas alerting and orienting did not; 48 (5) the MoCA was associated with episodic memory, 49 executive function, 50 performance on WASI-II subtests, 51 academic achievement, 52 and physical health measures (BMI, 53 pegboard, 54 walk time, 23 , 54 – 56 VO₂max 6 ), but not with age 21 , 57 or subjective complaints; 58 and (6) VO₂max cardiorespiratory fitness was correlated with all measures of cognition except ANT alerting and orienting 59 , 60 , social network size 61 , and grip strength 62 , and was negatively associated with BMI 62 and blood glucose 63 . With respect to MoCA performance and age, some prior reports did not find an association for age in healthy adults between 55 and 85 years old 21 , while others did. 64 Age cohort differences in education level may also be an important factor to consider. 65 See Supplementary Figure 4 for MoCA scatterplots by age. See also, Supplementary Figure 3 for additional correlations between participants’ demographic and phenotypic characteristics split by age 65 and older (OASR) and under 65 (ASR). Download figure Open in new tab Figure 8. Heat map showing r values for bivariate correlations among demographic and phenotypic measures. Values that did not survive False-Discovery Rate multiple-comparisons correction are shown in gray. Four correlations with the most missing data were: WWT WalkTime & HbA1c (missing n=136), WWT WalkTime & Cholesterol (missing n= 132), HbA1c & VO 2 max (missing n= 118), and Cholesterol & VO 2 max (missing n=113). WASI BD : Wechsler Abbreviated Scale of Intelligence Board Design; WASI VOC : Vocabulary; WASI MR : Matrix Reasoning; WASI SIM: WASI Similarities; WIAT Reading: Wechsler Individual Achievement Test Word Reading Raw Score; WIAT NumOP : Numerical Operations Raw Score; WIAT Spelling : Spelling Raw Score; MoCA : Montreal Cognitive Assessment total score adjusted for education; RAVLT: Rey Auditory Verbal Learning Test - Long-Delay Recall; DKEFS CF : Delis-Kaplan Executive Function System Category Fluency Raw Score; DKEFS TMT : DKEFS Trail Making Test; ANT Grd Mean : Attentional Network Test - Grand Mean; MPraxis : PENN Computerized Neurobehavioral Battery (CNB) - Motor Praxis Reaction Time; CET : PENN CNB Conditional Exclusion Task RT; 2Back : PENN CNB 2-Back Total Correct; CPT : PENN CNB Continuous Performance Test Number + Letter d-prime; BMI : Body Mass Index; HbA1c : Hemoglobin A1c; Grip : Grip Strength in kg for dominant hand; WWT WalkTime : Walking While Talking - First trial of single task walking time; VO 2 max : estimated from the submaximal test; BDI : Beck Depression Inventory Total; STAI : State-Trait Anxiety Inventory - Total trait raw score; PSQI : Pittsburgh Sleep Quality Index total sleep score; CogFailureQuest : Cognitive Failures Questionnaire Total Score; Social Network : Social Network Size. Only baseline data are included and no filtering was done. Neuroimaging data All NKI-RS imaging datasets are released to users without quality filtering, as there is no universal agreement on quality standards in the field. Additionally, including datasets of varying quality serves two important purposes: it enables researchers to develop new methods for artifact correction and allows for assessment of how real-world data imperfections affect reliability and reproducibility of findings. Structural MRI T1-weighted images were processed with Mindboggle 66 , a software that combines functionalities from Advanced Normalization Tools (ANTS) 67 and FreeSurfer 68 to parcellate brain regions using the Desikan-Killiany-Tourville atlas. 69 Euler number was calculated in FreeSurfer to assess data quality, with poorer quality indicated by more negative values. 70 Out of 975 T1 images across all timepoints, 4.0% had Euler numbers more than 2SD below the mean. As shown in Figure 9 , Euler numbers correlated negatively with age (r = −.196, p<.001) and, except for dMRI, also correlated with FD from all other sequences (r between −.165 to −.363, all p < .01). Download figure Open in new tab Figure 9. Correlations of mean Framewise Displacement (FD) for MRI sequences with age and FD for other sequences. Lower triangle shows scatterplots with log-transformed FDs, and points were color-coded by participant age. Upper triangle displays the corresponding Pearson correlation coefficients (r) for each correlation, with significance indicated by asterisks (*p < 0.05; **p < 0.01; ***p < 0.001) based on FDR-corrected p-values. Diagonal panels show the distribution of FD values for age and each sequence. Diffusion MRI Diffusion MRI data were processed in QSIPrep and QSIRecon. 71 Detailed description of the full pipeline is described in Supplementary Information. Briefly, QSIPrep performed standard preprocessing using MRtrix3 tools, 72 including denoising, Gibbs unringing, and bias correction. Head motion and Eddy current corrections were then performed with FSL’s eddy tool. 73 Average Framewise Displacement (FD) and age effects are shown in Figure 9 . After preprocessing, QSIRecon’s autotrack pipeline 74 was used to reconstruct 56 white matter tracts and mean microstructural measures (FA) were calculated for each tract and averaged between the two hemispheres. With a total of 839 dMRI images, 3.34% had FD larger than 2 SD from the mean and 68% of the scans had no bad slices. There was no correlation between age and FD. Age differences in FA for the whole brain and select white matter tracts are presented in row 3 of Figure 10 . Consistent with current literature, FA declines in older age and at the steepest rate in the oldest age group. Download figure Open in new tab Figure 10. Structural measures from MRI for individual participants collapsing across all timepoints and color coded for three age tertile groups (Younger: 50.5 and 61). Row 1 shows the mean cortical thickness for the whole brain and for the Superior Frontal and Entorhinal Cortices (WBt, SFt, ERt). Row 2 shows White Matter Hyperintensity and volumes for the Superior Frontal and Entorhinal Cortices (WMH, SFv, ERv). Row 3 shows the mean fractional anisotropy (FA) across 28 major white matter tracts, for the Superior Longitudinal Fasciculus, and for the Fornix (WBfa, SLFfa, Fxfa). Three bolded lines are age regressions in each plot. Significant age effects were observed for: WBt - Younger: t(282) = −4.25, Middle: t(291) = −3.46, Older: t(313) = −5.09, all p<.0001; SFt - Younger: t(282) = −4.34, p < .0001, Middle: t(291) = −3.15, p = .0018, Older: t(313) = −6.14, p<.0001; ERt - Younger: t(282) = 2.81, p = .0053, Older: t(313) = −2.91, p = .0039; WMH: Younger: t(196) = −2.28, p = .0238, Older: t(252) = 5.28, p<.0001; SFv: Younger: t(282) = −2.70, p = .0074, Middle: t(291) = −2.50, p = .0131, Older: t(313) = −2.25, p = .0251; WBfa: Younger: t(263) = −2.16, p = .0314, Middle: t(264) = −3.63, p = .0003, Older: t(290) = −5.21, p <.0001; SLFfa: Younger: t(263) = −3.06, p = .0024, Middle: t(264) = −4.25, Older: t(290) = −6.37, both p<.0001; Fxfa: Middle: t(263) = −4.92, Older: t(290) = −6.02, both p<.0001. Functional MRI Jenkinson’s FD 75 was calculated for task and resting fMRI scans using AFNI’s 3dvolreg function. 76 Figure 9 shows the cross-correlations among mean FDs for fMRI scans and each with age and other sequences. Among the six fMRI sequences, out of a range of 684 to 696 total images across all timepoints, the percent of data with FD greater than 2 SD than the mean was between 2% to 4%. Among all fMRI sequences, only FD for the Breath-hold sequence with TR of 1400ms was correlated with age. Along with other NKI-RS datasets, preprocessed fMRI, T1 and T2-weighted structural MRI images from MIVAC are provided through the Reproducible Brain Charts (RBC) project ( https://reprobrainchart.github.io/ ). Data in RBC was preprocessed using the Configurable Pipeline for the Analysis of Connectomes (C-PAC). 77 Details about preprocessing are provided in. 78 Pseudo-Continuous Arterial Spin Labeling (pCASL) FD for pCASL data were quantified with AFNI’s 3dvolreg function. WIth a total of 680 images across all timepoints, 3.4% of participant data exceeded 2 SD above the mean FD across subjects. Fluid-attenuated inversion recovery (FLAIR) MRI FLAIR data were segmented into normal and hyperintense regions with the Lesion Growth Algorithm from the Lesion Segmentation Toolbox (LST v3.0; 79 ) with a kappa threshold of 0.3. As expected, Figure 10 shows that the oldest age group follows the steepest increase in total WMH volume. USAGE NOTES The MIVAC dataset extends the NKI-RS initiative into midlife and older adulthood, offering a deeply phenotyped, openly available resource for examining normative and atypical trajectories of brain aging. The dataset is designed for broad application across neuroscience, psychology, gerontology, and public health, among other fields. Since its inception, the NKI-RS has supported the training of emerging scientists at the undergraduate, master’s, and doctoral levels, as well as advanced research by established investigators—accelerating discovery and fostering the next generation of basic and clinical research. Below we outline key considerations for effective use. Data access and formats All imaging data are distributed in BIDS format and can be downloaded directly via Amazon S3, using either bulk download scripts or open-source clients. Phenotypic and physiological data are available via the LORIS and COINS platforms, with different access levels depending on whether users request the full phenotypic release (requiring a DUA) or the Lite release (limited phenotyping, no agreement required). Data quality In keeping with NKI-RS open science principles, all data are released without exclusion on the basis of quality. This allows researchers to apply their own quality-control thresholds and to develop or benchmark artifact-correction methods. Imaging data include quality metrics such as framewise displacement and Euler numbers to aid downstream decisions. Phenotypic data are released without removal of outliers, with the expectation that users apply study-specific cleaning and screening procedures appropriate to their research aims. Longitudinal considerations The MIVAC substudy employed a multi-cohort longitudinal design, with repeated measures up to four years. Due to the COVID-19 pandemic, data collection was interrupted during the third annual follow-up period, resulting in substantial missingness for the 4th timepoint. Variables coding visit intervals (e.g., “day lag”) are provided to allow precise modeling of within-person change and practice effects. Users should account for attrition and missing data when conducting longitudinal analyses. Phenotypic protocols Measures were harmonized across prior NKI-RS substudies to facilitate lifespan analyses. Some measures (e.g., CASI-A, MoCA) were extended to broader age ranges and include item level data for methodological analyses. Users are advised to consult the full End User Protocol ( https://rocklandsample.org/mivac-full-end-user-protocol-2 ) and detailed item level information in a downloadable file here: https://rocklandsample.org/for-researchers/rockland-sample-i-2011-2022/assessments/assessments_documentation . Cardiorespiratory fitness and physical health measures This dataset incorporates gold-standard submaximal VO₂max assessments, providing an opportunity to examine modifiable protective factors in aging. Investigators should review cardiorespiratory assessment quality flags when incorporating this assessment into analyses. For blood-based analyte analyses, we recommend (i) log-transforming long-tailed biomarkers, (ii) explicitly modeling fasting status and relevant medications where available, and (iii) reporting both robust (median/IQR) and parametric (mean/SD) summaries. End user support An established inquiry mechanism ( rocklandsample.enduser{at}nki.rfmh.org ) is available to assist with questions about access, documentation, and data interpretation. Inquiries are reviewed by an End User Response Panel composed of investigators with expertise in imaging, phenotyping, and data management. Intended use The dataset is well-suited for studies of: normative and atypical trajectories of brain aging beginning in midlife, the role of modifiable health factors (e.g., cardiorespiratory fitness, BMI, sleep, social networks) in cognitive and neural outcomes, methods development for artifact detection, denoising, or harmonization across open imaging datasets, and lifespan analyses leveraging harmonized protocols across NKI-RS substudies. Limitations Limitations include incomplete longitudinal follow-up due to pandemic-related shutdown, uneven representation of sex (female > male), and the need for users to implement quality-control and outlier handling. Researchers should interpret findings within the context of these constraints. DATA AVAILABILITY Information for researchers about the Rockland Sample study and how to obtain data can be obtained in the study website: https://rocklandsample.org/for-researchers Phenotypic data is available in two platforms: LORIS: https://data.rocklandsample.rfmh.org COINS: https://coins.trendscenter.org We recommend users obtain data through the LORIS portal, since it contains updated study codes ( https://rocklandsample.org/for-researchers-rockland-sample-i-2011-2022-documentation-study-codes ). Step-by-step instructions on how to download neuroimaging data can be found here: https://rocklandsample.org/accessing-the-neuroimaging-data-releases Neuroimaging data is hosted on an open AWS S3 bucket. BIDS organized data can be directly accessed through this S3 path: s3://fcp-indi/data/Projects/RocklandSample/RawDataBIDSLatest Data Citation Enhanced Nathan Kline Institute - Rockland Sample. International Neuroimaging Data-Sharing Initiative https://doi.org/10.15387/fcp_indi.retro.NKIRockland (2013) . CODE AVAILABILITY No custom codes or algorithms were used to generate or process the data presented in this manuscript. AUTHOR CONTRIBUTIONS Conception and Experimental Design: S.J.C., M.P.M., A.M-B., V.G., R.H.T. Implementation and Logistics: S.J.C., M.P.M., A.M-B, R.H.T., M.B., M.K., K.D.T. Data Collection: M.B., M.K., A.M-B., R.H.T., K.D.T. Data Informatics: A.R.F., D.G-B, Y.G., L.G., S.J.C., O.R., M.K., M.B., A.M-B, K.X.G., Data Analysis: S.J.C., A.M-B., D.G-B, Y.G.,L.G., O.R.,V.G., A.R.F. Initial Drafting of the Manuscript: A.M-B., S.J.C., A.R.F., K.D.T., O.R., K.X.G. Critical Review and Editing of the Manuscript: All authors contributed to the critical review and editing of the manuscript. ADDITIONAL INFORMATION Competing interests The authors declare no relevant competing financial interests. SUPPLEMENTARY MATERIALS Supplementary Figures Download figure Open in new tab Figure S1. Geographic distribution of participants by zip code. Color indicates percentages of participants in each zip code. Star indicates location of the Nathan Kline Institute. Download figure Open in new tab Figure S2. Boxplots with kernel-density overlays for each analyte from the blood sample collection. HbA1c = Hemoglobin A1C; HDL = High-density lipoprotein; LDL= Low-density lipoprotein; CRP = C-reactive protein; ESR = Erythrocyte sedimentation rate. Download figure Open in new tab Figure S3. Heat map showing coefficients for bivariate correlations among demographic and subset of phenotypic measures against subscales of Adult Self Report (ASR; administered to participants <60 years old) and Older Adult Self Report (OASR; administered to participants 60+ years old) and total score. Values that survived False-Discovery Rate multiple-comparisons correction are shown in bolded font. BDI : Beck Depression Inventory Total; GDS : Geriatric Depression Scale; STAI : State-Trait Anxiety Inventory - Total trait raw score; CogFailureQuest : Cognitive Failures Questionnaire Total Score; Social Network : Social Network Size, MoCA : Montreal Cognitive Assessment total score adjusted for education; RAVLT : Rey Auditory Verbal Learning Test - Long delay recall; DKEFS TMT: Delis-Kaplan Executive Function System Trail Making Test; ANT : Attention Network Test; VO2max : estimated from the submaximal test; WWT WalkTime : Walking While Talking - First trial of single task walking time. Only baseline data are included and no filtering was done. Download figure Open in new tab Figure S4. Scatter plot of MoCA education adjusted total score against Age, divided into three age groups by tertiles (Younger: 51 and 61). The three regression lines are specific to each age group (Younger: t (119)= 0.83, p = 0.41; Middle: t(108) = −1.66, p = 0.10; Older: t(110) = −1.49, p = 0.14). All unfiltered baseline data were included in the plot. Supplementary Tables View this table: View inline View popup Download powerpoint Table S1a. Mapping of Older Adult Self Report (OASR) and Adult Self Report (ASR) common items and subscales. View this table: View inline View popup Download powerpoint Table S1b. Mapping of Older Adult Self Report (OASR) and Adult Self Report (ASR) common items that are assigned to different subscales. View this table: View inline View popup Download powerpoint Table S2a. Questions unique to Adult Self Report (ASR). View this table: View inline View popup Download powerpoint Table S2b. Questions unique to Older Adult Self Report (OASR). View this table: View inline View popup Download powerpoint Table S3. Baseline descriptors for each analyte from the blood sample collection. HbA1c = Hemoglobin A1C; HDL = High-density lipoprotein; LDL= Low-density lipoprotein; CRP = C-reactive protein; ESR = Erythrocyte sedimentation rate. View this table: View inline View popup Download powerpoint Table S4. Intraclass Correlation Coefficients (ICCs) for phenotypic measures. CI = Confidence Intervals; See Figure 8 for definitions of variable abbreviations. Only baseline data are included and no filtering was done. Supplementary Methods Full method description for diffusion MRI processing in QSIPrep Preprocessing was performed using QSIPrep 0.23.1.dev0+g634483f.d20240830, which is based on Nipype 1.8.6 80 . A T1w-reference map was computed after registration of 2 T1w images (after INU-correction) using antsRegistration [ANTs 2.4.3]. The anatomical reference image was reoriented into AC-PC alignment via a 6-DOF transform extracted from a full Affine registration to the MNI152NLin2009cAsym template. A full nonlinear registration to the template from AC-PC space was estimated via symmetric nonlinear registration (SyN) using antsRegistration. Brain extraction was performed on the T1w image using SynthStrip 81 and automated segmentation was performed using SynthSeg 82 from FreeSurfer version 7.3.1. Diffusion data preprocessing Any images with a b-value less than 100 s/mm^2 were treated as a b =0 image. MP-PCA denoising as implemented in MRtrix3’s dwidenoise 83 was applied with a auto-voxel window. When phase data were available, this was done on complex-valued data. After MP-PCA, Gibbs unringing was performed using MRtrix3’s mrdegibbs 84 . Following unringing, the mean intensity of the DWI series was adjusted so all the mean intensity of the b=0 images matched across each separate DWI scanning sequence. B1 field inhomogeneity was corrected using dwibiascorrect from MRtrix3 with the N4 algorithm 85 after corrected images were resampled. FSL (version None)’s eddy was used for head motion correction and Eddy current correction. 86 Eddy was configured with a q -space smoothing factor of 10, a total of 5 iterations, and 1000 voxels used to estimate hyperparameters. A linear first level model and a linear second level model were used to characterize Eddy current-related spatial distortion. q -space coordinates were forcefully assigned to shells. Field offset was attempted to be separated from subject movement. Shells were aligned post-eddy. Eddy’s outlier replacement was run. 87 Data were grouped by slice, only including values from slices determined to contain at least 250 intracerebral voxels. Groups deviating by more than 4 standard deviations from the prediction had their data replaced with imputed values. Final interpolation was performed using the jac method. Several confounding time-series were calculated based on the preprocessed DWI: framewise displacement (FD) using the implementation in Nipype (following the definitions by 88 ). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. Slicewise cross correlation was also calculated. The DWI time-series were resampled to ACPC, generating a preprocessed DWI run in ACPC space with 1mm isotropic voxels. Many internal operations of QSIPrep use Nilearn 0.10.1 89 ; RRID:SCR_001362) and Dipy 90 . For more details of the pipeline, see the section corresponding to workflows in QSIPrep ’s documentation . Reconstruction was performed using QSIRecon 1.0.1.dev0+gf78c888.d20250114 (Cieslak et al. (2021)), which is based on Nipype 1.9.1 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502). Anatomical data for DWI reconstruction Brainmasks from antsBrainExtraction were used in all subsequent reconstruction steps. DSI Studio Reconstruction: Diffusion orientation distribution functions (ODFs) were reconstructed using generalized q-sampling imaging (GQI, Yeh, Wedeen, and Tseng (2010)) with a ratio of mean diffusion distance of 1.250000 in DSI Studio (version 94b9c79). DSI Studio Automatic Tractography Automatic Tractography was run in DSI Studio (version 94b9c79) and bundle shape statistics were calculated (Yeh 2020). AutoTrack attempted to reconstruct the following bundles: Association_ArcuateFasciculus Association_Cingulum Association_ExtremeCapsule Association_FrontalAslantTract Association_HippocampusAlveus Association_InferiorFrontoOccipitalFasciculus Association_InferiorLongitudinalFasciculus Association_MiddleLongitudinalFasciculus Association_ParietalAslantTract Association_SuperiorLongitudinalFasciculus Association_UncinateFasciculus Association_VerticalOccipitalFasciculus Commissure_AnteriorCommissure Commissure_CorpusCallosum ProjectionBasalGanglia_AcousticRadiation ProjectionBasalGanglia_AnsaLenticularis ProjectionBasalGanglia_AnsaSubthalamica ProjectionBasalGanglia_CorticostriatalTract ProjectionBasalGanglia_FasciculusLenticularis ProjectionBasalGanglia_FasciculusSubthalamicus ProjectionBasalGanglia_Fornix ProjectionBasalGanglia_OpticRadiation ProjectionBasalGanglia_ThalamicRadiation ProjectionBrainstem_CorticobulbarTract ProjectionBrainstem_CorticopontineTract ProjectionBrainstem_CorticospinalTract ProjectionBrainstem_DentatorubrothalamicTract-lr ProjectionBrainstem_DentatorubrothalamicTract-rl ProjectionBrainstem_MedialForebrainBundle ProjectionBrainstem_MedialLemniscus ProjectionBrainstem_NonDecussatingDentatorubrothalamicTract ProjectionBrainstem_ReticularTract Many internal operations of QSIRecon use Nilearn 0.10.1 (Abraham et al. 2014, RRID:SCR_001362) and Dipy 1.8.0(Garyfallidis et al. 2014). For more details of the pipeline, see the section corresponding to workflows in QSIRecon ’s documentation . ACKNOWLEDGMENTS We thank the research participants for their generous contributions of time and effort. We appreciate the study team members who supported data collection, data management, preliminary analyses, literature reviews, community outreach, and recruitment: Julia Beatini, Brian Bengyak, Sinead Burrows, Jerlyne Calixte, Brian Carbone, Stephanie Carelli, Galen Cassidy, Maya Charan, Jessica Cloud, Jesenya DeLeon, Lauren Futterman, Gwen Geisler, Chelsea Gessner, Alyssa Giannone, Jamie Glass, Courtney Gray, Steven Homan, Olive Hwang, Christy Joseph, Stephanie Kamiel, Alexis Lieval, George Lopez, Bryanna Mackey, Amalia McDonald, Laura Panek, John Pellman, Hayley Reed, Margaret Ryan, Sheela Sajan. We thank the CBIN Design, Acquisition, and Neuromodulation Laboratory’s MR technical staff Caixia Hu and Raj Sangoi, Tina Bermudez, Kefin Sajan of the CBIN Computational Neuroimaging Laboratory for his neuroinformatics support, and Elizabeth Zakszewski and Gabriel Schubiner of the Child Mind Institute (CMI). We also thank Nathalia Esper for image preprocessing support. We thank Amazon Web Service (AWS) for hosting our neuroimaging data through their Open Data Sponsorship Program. The Mapping Interindividual Variation in the Aging Connectome was principally supported by NIH R01AG047596 (MPIs Colcombe/Milham). A subset of participants had baseline characterizations supported by the core enhanced NKI-RS protocol NIMH BRAINS R01MH094639-01 (PI Milham) and NIH R01MH101555 (PI Craddock). Funding for key personnel was also provided in part by the New York State Office of Mental Health and Research Foundation for Mental Hygiene. REFERENCES 1. ↵ Xie , C. et al. A shared neural basis underlying psychiatric comorbidity . Nat. Med . 29 , 1232 – 1242 ( 2023 ). OpenUrl CrossRef PubMed 2. Cattarinussi , G. et al. Resting-state functional connectivity and spontaneous brain activity in early-onset bipolar disorder: A review of functional Magnetic Resonance Imaging studies . J. Affect. Disord . 311 , 463 – 471 ( 2022 ). OpenUrl PubMed 3. ↵ Lu , Y. et al. Detection of structural-functional coupling abnormalities using multimodal brain networks in Alzheimer’s disease: A comparison of three computational models . NeuroImage Clin . 46 , 103764 ( 2025 ). 4. ↵ Tobe , R. H. et al. A longitudinal resource for studying connectome development and its psychiatric associations during childhood . Sci. Data 9 , 300 ( 2022 ). 5. ↵ Yaffe , K. et al. Cardiovascular risk factors and accelerated cognitive decline in midlife: The CARDIA Study: The CARDIA Study . Neurology 95 , e839 – e846 ( 2020 ). OpenUrl PubMed 6. ↵ Wichayanrat , W. , Boripuntakul , S. , Keawtep , P. , Worakul , P. & Sungkarat , S . Obesity and Brain Health: The Impact of Metabolic Syndrome and Cardiorespiratory Fitness on Cognitive Performances in Middle-Aged Obese Women . J Prev Alzheimers Dis 9 , 701 – 707 ( 2022 ). OpenUrl PubMed 7. ↵ Whitmer , R. A. , Gunderson , E. P. , Barrett-Connor , E. , Quesenberry , C. P. , Jr. & Yaffe , K . Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study . BMJ 330 , 1360 ( 2005 ). OpenUrl Abstract / FREE Full Text 8. ↵ Tucker-Drob , E. M . Cognitive aging and dementia: A life span perspective . Annu. Rev. Dev. Psychol . 1 , 177 – 196 ( 2019 ). OpenUrl PubMed 9. ↵ Tucker-Drob , E. M . Global and domain-specific changes in cognition throughout adulthood . Dev. Psychol . 47 , 331 – 343 ( 2011 ). OpenUrl CrossRef PubMed 10. ↵ Tucker-Drob , E. M. , Brandmaier , A. M. & Lindenberger , U . Coupled cognitive changes in adulthood: A meta-analysis . Psychol. Bull . 145 , 273 – 301 ( 2019 ). OpenUrl CrossRef PubMed 11. ↵ Caspers , S. et al. Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS . Front. Aging Neurosci . 6 , 149 ( 2014 ). 12. ↵ Cole , J. H . Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors . Neurobiol. Aging 92 , 34 – 42 ( 2020 ). OpenUrl CrossRef PubMed 13. ↵ Andel , R. et al. Physical exercise at midlife and risk of dementia three decades later: a population-based study of Swedish twins . J. Gerontol. A Biol. Sci. Med. Sci . 63 , 62 – 66 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 14. ↵ Kraal , Z. & Zahodne , L. B . Physical activity in early and middle adulthood predicts later-life memory trajectories via health pathways . Innov. Aging 3 , S900 – S900 ( 2019 ). OpenUrl 15. ↵ Bookheimer , S. Y. et al. The Lifespan Human Connectome Project in Aging: An overview . Neuroimage 185 , 335 – 348 ( 2019 ). OpenUrl CrossRef PubMed 16. ↵ Littlejohns , T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions . Nat. Commun . 11 , 2624 ( 2020 ). OpenUrl CrossRef PubMed 17. ↵ Park , D. C. et al. The Dallas Lifespan Brain Study: A comprehensive adult lifespan data set of brain and cognitive aging . Sci. Data 12 , 846 ( 2025 ). 18. ↵ Nooner , K. B. et al. The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry . Frontiers in Neuroscience vol. 6 Preprint at doi: 10.3389/fnins.2012.00152 ( 2012 ). OpenUrl CrossRef PubMed 19. ↵ McDonald , A. R. et al. The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository . Neuroimage 146 , 157 – 170 ( 2017 ). OpenUrl PubMed 20. ↵ von Elm , E. , et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies . PLoS Med . 4 , e296 ( 2007 ). OpenUrl CrossRef PubMed 21. ↵ Nasreddine , Z. S. , et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment: Moca: A brief screening tool for MCI . J. Am. Geriatr. Soc . 53 , 695 – 699 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 22. ↵ Crouter , S. E. , Antczak , A. , Hudak , J. R. , DellaValle , D. M. & Haas , J. D . Accuracy and reliability of the ParvoMedics TrueOne 2400 and MedGraphics VO2000 metabolic systems . Eur. J. Appl. Physiol . 98 , 139 – 151 ( 2006 ). OpenUrl CrossRef PubMed 23. ↵ Jayakody , O. et al. Relative trajectories of gait and cognitive decline in aging . J. Gerontol. A Biol. Sci. Med. Sci . 77 , 1230 – 1238 ( 2022 ). OpenUrl PubMed 24. ↵ Verghese , J. et al. Validity of divided attention tasks in predicting falls in older individuals: a preliminary study . J. Am. Geriatr. Soc . 50 , 1572 – 1576 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 25. ↵ Enhanced Nathan Kline Institute - Rockland Sample . Preprint at doi: 10.15387/fcp_indi.retro.NKIRockland ( 2013 ). OpenUrl CrossRef 26. ↵ Scott , A. et al. COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets . Front Neuroinform 5 , 33 ( 2011 ). 27. ↵ Das , S. , Zijdenbos , A. P. , Harlap , J. , Vins , D. & Evans , A. C . LORIS: a web-based data management system for multi-center studies . Front Neuroinform 5 , 37 ( 2011 ). 28. ↵ Daskalopoulou , C. et al. Physical activity and healthy ageing: A systematic review and meta-analysis of longitudinal cohort studies . Ageing Res Rev 38 , 6 – 17 ( 2017 ). OpenUrl CrossRef PubMed 29. ↵ First , M. , Spitzer , B. , Gibbon , R. L. & Williams , M . Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient edition (SCID-I/NP, 1/2010 revision) . Biometrics Research ( 2002 ). 30. ↵ Macleod , J. W. et al. Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test . Neuropsychology 24 , 637 – 651 ( 2010 ). OpenUrl CrossRef PubMed 31. ↵ Gur , R. C. et al. A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation . J. Neurosci. Methods 187 , 254 – 262 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 32. ↵ Baltes , P. B. , Cornelius , S. W. , Spiro , A. , Nesselroade , J. R. & Willis , S. L . Integration versus differentiation of fluid/crytallized intelligence in old age . Dev. Psychol . 16 , 625 – 635 ( 1980 ). OpenUrl CrossRef Web of Science 33. ↵ Salthouse , T. A . Localizing age-related individual differences in a hierarchical structure . Intelligence 32 , 541 – 561 ( 2004 ). OpenUrl CrossRef Web of Science 34. ↵ Rabbitt , P . Does it all go together when it goes? The Nineteenth Bartlett Memorial Lecture . Q. J. Exp. Psychol. A 46 , 385 – 434 ( 1993 ). OpenUrl PubMed Web of Science 35. Eisdorfer , C. , Busse , E. W. & Cohen , L. D . The WAIS performance of an aged sample: the relationship between verbal and performance IQs . J. Gerontol . 14 , 197 – 201 ( 1959 ). OpenUrl CrossRef PubMed Web of Science 36. ↵ Park , D. C. & Bischof , G. N . The aging mind: neuroplasticity in response to cognitive training . Dialogues Clin. Neurosci . 15 , 109 – 119 ( 2013 ). OpenUrl CrossRef PubMed 37. ↵ Ivnik , R. J. et al. Mayo’s older americans normative studies: Updated AVLT norms for ages 56 to 97 . Clin. Neuropsychol . 6 , 83 – 104 ( 1992 ). OpenUrl CrossRef 38. ↵ Van der Elst , W. , van Boxtel , M. P. J. , van Breukelen , G. J. P. & Jolles , J . Rey’s verbal learning test: normative data for 1855 healthy participants aged 24-81 years and the influence of age, sex, education, and mode of presentation . J. Int. Neuropsychol. Soc . 11 , 290 – 302 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 39. ↵ Brickman , A. M. et al. Category and letter verbal fluency across the adult lifespan: relationship to EEG theta power . Arch. Clin. Neuropsychol . 20 , 561 – 573 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 40. ↵ Tombaugh , T. N. , Kozak , J. & Rees , L . Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming . Arch. Clin. Neuropsychol . 14 , 167 – 177 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 41. Waggestad , T. H. et al. New regression-based norms for the Trail Making Test on Norwegian older adults: Understanding the effect of education . Clin. Neuropsychol . 1 – 24 ( 2025 ). 42. ↵ Salthouse , T. A . What cognitive abilities are involved in trail-making performance? Intelligence 39 , 222 – 232 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 43. ↵ Kessler , R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication . Arch. Gen. Psychiatry 62 , 593 – 602 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 44. Thomas , M. L. et al. Paradoxical trend for improvement in mental health with aging: A community-based study of 1,546 adults aged 21-100 years: A community-based study of 1,546 adults aged 21-100 years . J. Clin. Psychiatry 77 , e1019 – 25 ( 2016 ). OpenUrl CrossRef PubMed 45. ↵ Carstensen , L. L. , Shavit , Y. Z. & Barnes , J. T . Age advantages in emotional experience persist even under threat from the COVID-19 pandemic . Psychol. Sci . 31 , 1374 – 1385 ( 2020 ). OpenUrl PubMed 46. ↵ Song , D. et al. Sleep disturbance mediates the relationship between depressive symptoms and cognitive function in older adults with mild cognitive impairment . Geriatr Nurs 42 , 1019 – 1023 ( 2021 ). OpenUrl PubMed 47. ↵ Cavaillès , C. et al. Multidimensional sleep health and long-term cognitive decline in community-dwelling older men . J. Alzheimers. Dis . 96 , 65 – 71 ( 2023 ). OpenUrl PubMed 48. ↵ Ishigami , Y. et al. The Attention Network Test-Interaction (ANT-I): reliability and validity in healthy older adults . Exp Brain Res 234 , 815 – 827 ( 2016 ). OpenUrl CrossRef PubMed 49. ↵ Li , X. et al. The role of the Montreal Cognitive Assessment (MoCA) and its memory tasks for detecting mild cognitive impairment . Neurol Sci 39 , 1029 – 1034 ( 2018 ). OpenUrl PubMed 50. Lam , B. et al. Criterion and convergent validity of the Montreal cognitive assessment with screening and standardized neuropsychological testing . J Am Geriatr Soc 61 , 2181 – 2185 ( 2013 ). OpenUrl CrossRef PubMed 51. ↵ Miller , J. B. , Vogel , S. , Banks , S. J. & Miller , J. B . P2-231: Factor analysis and construct validity of the Montreal cognitive assessment: Concordance with standard neuropsychological measures . Alzheimers. Dement . 10 , P559 – P560 ( 2014 ). OpenUrl 52. ↵ Borda , M. G. et al. Educational level and its Association with the domains of the Montreal Cognitive Assessment Test . Aging Ment Health 23 , 1300 – 1306 ( 2019 ). OpenUrl CrossRef PubMed 53. ↵ Mohun , S. H. , Spitznagel , M. B. , Gunstad , J. , Rochette , A. & Heinberg , L. J . Performance on the Montreal Cognitive Assessment (MoCA) in Older Adults Presenting for Bariatric Surgery . Obes Surg 28 , 2700 – 2704 ( 2018 ). OpenUrl PubMed 54. ↵ Seavey , C. V. & Walters , B. H . Motor Dual-Task Deficits and Their Associations with Executive Function in Older Adults with Cognitive Impairments . J Mot Behav 1 – 17 ( 2025 ). 55. Pieruccini-Faria , F. , Sarquis-Adamson , Y. & Montero-Odasso , M . Mild Cognitive Impairment Affects Obstacle Negotiation in Older Adults: Results from ‘Gait and Brain Study’ . Gerontology 65 , 164 – 173 ( 2019 ). OpenUrl PubMed 56. ↵ De Sanctis , P. et al. Neural signature of mobility-related everyday function in older adults at-risk of cognitive impairment . Neurobiol. Aging 122 , 1 – 11 ( 2023 ). OpenUrl CrossRef PubMed 57. ↵ Dalrymple-Alford , J. C. et al. The MoCA: well-suited screen for cognitive impairment in Parkinson disease . Neurology 75 , 1717 – 1725 ( 2010 ). OpenUrl CrossRef PubMed 58. ↵ Hess , C. et al. Subjective Versus Objective Assessment of Cognitive Functioning in Primary Care . J Am Board Fam Med 33 , 417 – 425 ( 2020 ). OpenUrl Abstract / FREE Full Text 59. ↵ May , R. W. , Hutchinson , M. , Seibert , G. S. , Fincham , F. , Sanchez-Gonzalez , M. A . Improving Cognitive Performance via High Intensity Interval Aerobic Exercise: A Randomized Controlled Trial . Journal of Exercise Physiology Online 20 , 141 – 146 ( 2017 ). OpenUrl 60. ↵ Kramer , A. F. & Colcombe , S . Fitness effects on the cognitive function of older adults: A meta-analytic study-revisited . Perspect. Psychol. Sci . 13 , 213 – 217 ( 2018 ). OpenUrl CrossRef PubMed 61. ↵ Ho , E. C. , Hawkley , L. , Dale , W. , Waite , L. & Huisingh-Scheetz , M . Social capital predicts accelerometry-measured physical activity among older adults in the U.S.: a cross-sectional study in the National Social Life, Health, and Aging Project . BMC Public Health 18 , 804 ( 2018 ). 62. ↵ Sushmitha , S. et al. Exploring the Relationship Between the Indices of Body Composition With Grip Strength Performance and Peak VO2 . Cureus 15 , e40874 ( 2023 ). OpenUrl 63. ↵ Leite , S. A. , Monk , A. M. , Upham , P. A. & Bergenstal , R. M . Low cardiorespiratory fitness in people at risk for type 2 diabetes: early marker for insulin resistance . Diabetol Metab Syndr 1 , 8 ( 2009 ). 64. ↵ Gluhm , S. et al. Cognitive performance on the mini-mental state examination and the montreal cognitive assessment across the healthy adult lifespan . Cogn Behav Neurol 26 , 1 – 5 ( 2013 ). OpenUrl CrossRef PubMed 65. ↵ Malek-Ahmadi , M. et al. Age- and education-adjusted normative data for the Montreal Cognitive Assessment (MoCA) in older adults age 70-99 . Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 22 , 755 – 761 ( 2015 ). OpenUrl PubMed 66. ↵ Klein , A. et al. Mindboggling morphometry of human brains . PLoS Comput. Biol . 13 , e1005350 ( 2017 ). OpenUrl CrossRef PubMed 67. ↵ Avants , B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration . Neuroimage 54 , 2033 – 2044 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 68. ↵ Fischl , B. , Liu , A. & Dale , A. M . Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex . IEEE Trans. Med. Imaging 20 , 70 – 80 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 69. ↵ Klein , A. & Tourville , J . 101 labeled brain images and a consistent human cortical labeling protocol . Front. Neurosci . 6 , 171 ( 2012 ). 70. ↵ Rosen , A. F. G. et al. Quantitative assessment of structural image quality . Neuroimage 169 , 407 – 418 ( 2018 ). OpenUrl CrossRef PubMed 71. ↵ Cieslak , M. et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data . Nat. Methods 18 , 775 – 778 ( 2021 ). OpenUrl CrossRef PubMed 72. ↵ Tournier , J.-D. , et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation . bioRxiv ( 2019 ) doi: 10.1101/551739 . OpenUrl Abstract / FREE Full Text 73. ↵ Smith , S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL . Neuroimage 23 Suppl 1 , S208 – 19 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 74. ↵ Yeh , F.-C . Population-based tract-to-region connectome of the human brain and its hierarchical topology . Nat. Commun . 13 , 4933 ( 2022 ). OpenUrl PubMed 75. ↵ Jenkinson , M. , Bannister , P. , Brady , M. & Smith , S . Improved optimization for the robust and accurate linear registration and motion correction of brain images . Neuroimage 17 , 825 – 841 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 76. ↵ Cox , R. W . AFNI: what a long strange trip it’s been . Neuroimage 62 , 743 – 747 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 77. ↵ Li , X. et al. Moving beyond processing- and analysis-related variation in resting-state functional brain imaging . Nat Hum Behav 8 , 2003 – 2017 ( 2024 ). OpenUrl PubMed 78. ↵ Shafiei , G. , et al. Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health . bioRxivorg ( 2025 ) doi: 10.1101/2025.02.24.639850 . OpenUrl Abstract / FREE Full Text 79. ↵ Schmidt , P. et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis . Neuroimage 59 , 3774 – 3783 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 80. ↵ Gorgolewski , K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python . Front. Neuroinform . 5 , 13 ( 2011 ). 81. ↵ Hoopes , A. , Mora , J. S. , Dalca , A. V. , Fischl , B. & Hoffmann , M . SynthStrip: skull-stripping for any brain image . Neuroimage 260 , 119474 ( 2022 ). 82. ↵ Billot , B. et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining . Med. Image Anal . 86 , 102789 ( 2023 ). 83. ↵ Veraart , J. et al. Denoising of diffusion MRI using random matrix theory . Neuroimage 142 , 394 – 406 ( 2016 ). OpenUrl CrossRef PubMed 84. ↵ Kellner , E. , Dhital , B. , Kiselev , V. G. & Reisert , M . Gibbs-ringing artifact removal based on local subvoxel-shifts . Magn. Reson. Med . 76 , 1574 – 1581 ( 2016 ). OpenUrl CrossRef PubMed 85. ↵ Tustison , N. J. et al. N4ITK: improved N3 bias correction . IEEE Trans. Med. Imaging 29 , 1310 – 1320 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 86. ↵ Andersson , J. L. R. & Sotiropoulos , S. N . An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging . Neuroimage 125 , 1063 – 1078 ( 2016 ). OpenUrl CrossRef PubMed 87. ↵ Andersson , J. L. R. , Graham , M. S. , Zsoldos , E. & Sotiropoulos , S. N . Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images . Neuroimage 141 , 556 – 572 ( 2016 ). OpenUrl CrossRef PubMed 88. ↵ Power , J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI . Neuroimage 84 , 320 – 341 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 89. ↵ Abraham , A. et al. Machine learning for neuroimaging with scikit-learn . Front. Neuroinform . 8 , 14 ( 2014 ). 90. ↵ Garyfallidis , E. et al. Dipy, a library for the analysis of diffusion MRI data . Front. Neuroinform . 8 , 8 ( 2014 ). References Alexandre , A. , et al. Machine Learning for Neuroimaging with Scikit-Learn . Front Neuroinform 8 , ( 2014 ). Cieslak , M. et al. QSIPrep: An Integrative Platform for Preprocessing and Reconstructing Diffusion Mri Data . Nature Methods 18 ( 7 ), 775 – 78 ( 2021 ). OpenUrl PubMed Garyfallidis , E. et al. Dipy, a Library for the Analysis of Diffusion Mri Data . Front Neuroinform 8 , ( 2014 ). Gorgolewski , K. C. , et al. Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python . Front Neuroinform 5 , 13 ( 2011 ). Gorgolewski , K. J. , et al. “ Nipype .” Software. Zenodo ( 2018 ). Yeh , F-C . Shape Analysis of the Human Association Pathways . Neuroimage 223 , Elsevier : 117329 ( 2020 ). OpenUrl CrossRef PubMed Yeh , F-C , Wedeen , V. J. , & Tseng , W-Y. I . Generalized q -Sampling Imaging . IEEE Transactions on Medical Imaging 29 ( 9 ), 1626 – 35 ( 2010 ). OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted October 13, 2025. Download PDF Email Thank you for your interest in spreading the word about medRxiv. 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, longitudinal resource for mapping interindividual variation in the aging connectome Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv 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, longitudinal resource for mapping interindividual variation in the aging connectome Anna MacKay-Brandt , Yunglin Gazes , Daniel Garcia-Barnett , Lauren Grebe , Olivia Ripley , Kai Xuan Gan , Kristin T. Trautman , Melissa Kramer , Melissa Breland , Russell Tobe , Alexandre Franco , Vilma Gabbay , Michael Milham , Stan Colcombe medRxiv 2025.10.10.25337774; doi: https://doi.org/10.1101/2025.10.10.25337774 Share This Article: Copy Citation Tools An open, longitudinal resource for mapping interindividual variation in the aging connectome Anna MacKay-Brandt , Yunglin Gazes , Daniel Garcia-Barnett , Lauren Grebe , Olivia Ripley , Kai Xuan Gan , Kristin T. 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