About

Randy L. Buckner is the Sosland Family Professor of Psychology and of Neuroscience at Harvard University. He is affiliated with the Center for Brain Science and serves as the Director of the Psychiatric Neuroimaging Research Division at Massachusetts General Hospital. His professional roles highlight his leadership in the fields of psychology, neuroscience, and psychiatric neuroimaging, integrating research and clinical applications at a leading academic and medical institution.

Research topics

• Psychology
• Neuroscience
• Genetics
• Medicine
• Biology
• Psychiatry
• Computer Science
• Evolutionary biology
• Clinical psychology
• Cognitive psychology
• Gerontology
• Data science
• Computational biology
• Audiology
• Social psychology
• Oncology
• Demography
• Internal medicine
• Developmental psychology

Selected publications

• Optimizing ultra-rapid compressed-sensing MPRAGE acquisitions for brain morphometry

  Frontiers in Neuroimaging · 2026-01-02 · 1 citations

  articleOpen access

  Purpose Compressed-sensing (CS) methods can decrease the acquisition time for T 1 -weighted (T 1 w) structural MRI images to 1–2 min. Rapid acquisitions reduce participant burden, reduce the risk of motion artifacts, and allow for repeat scans to be acquired within a session. This study investigated the tradeoffs of sparse sampling and CS image reconstruction for brain morphometric applications. Methods Magnetization-Prepared Rapid Gradient Echo (MPRAGE) images were acquired at 1.0 mm spatial resolution. The effects of the acceleration factor (x2 to x8) and regularization factor were examined. Subcortical volumes and regional cortical thickness estimates of brain structure were obtained for all T 1 w images. Within-sequence agreement was evaluated by comparing estimates obtained using the same protocol in the same imaging session. Between-sequence agreement was evaluated by comparing estimates from a fully sampled MPRAGE protocol to the novel CS-accelerated MPRAGE protocols within the same session. Results Higher acceleration lowered the SNR in white matter but not in gray matter. SNR could be further manipulated by the regularization parameter. Within-sequence agreement was comparable across all protocols. In fact, the spread in estimates from the 58-s CSx8 protocol was similar to those from the fully sampled protocol. Similarly, high agreement was found between estimates from the fully sampled and under-sampled protocols for all acceleration levels up to eight. Modifying the regularization factor had a quantifiable effect on image smoothness, however it had minimal impact on the agreement of morphometric estimates. Conclusion Accelerated CS imaging protocols show comparable performance to traditional longer protocols for morphometric brain estimates.

  PublisherDOI

• Vulnerability to memory decline in aging revealed by a mega-analysis of structural brain change

  Nature Communications · 2025-11-21 · 2 citations

  articleOpen access

  Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leverages 13 longitudinal datasets, including 3737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimer's Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assess whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis reveals a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations are stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthen with age, reaching moderate associations in participants in their eighties. While APOE ε4 carriers exhibit steeper brain and memory loss, genetic risk has no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

  PublisherOA PDFDOI

• Specialization of the human hippocampal long axis revisited

  Proceedings of the National Academy of Sciences · 2025-01-14 · 41 citations

  articleOpen accessSenior authorCorresponding

  The hippocampus possesses anatomical differences along its long axis. Here, we explored the functional specialization of the human hippocampal long axis using network-anchored precision functional MRI in two independent datasets (N = 11 and N = 9) paired with behavioral analysis (N = 266 and N = 238). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus selectively contained a region correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization of the anterior and posterior hippocampal regions using task data identified and replicated a functional double dissociation. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest an unexpected specialization along the long axis of the human hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

  PublisherOA PDFDOI

• Verbal versus Nonverbal Processing Leads to Generalized Hemispheric Laterality Effects that Span Multiple Networks

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-17

  articleOpen accessSenior author

  Precision neuroimaging was used to explore specialization for verbal versus nonverbal processing. Consistent with prior findings, individuals exhibited a spatially left-lateralized association language network, with regions in both hemispheres robustly responding to processing of meaning-based sentences. We next examined differential responses to verbal (words) versus nonverbal (faces) materials within the same working memory task. The right hemisphere components of the language network responded more strongly to nonverbal than to verbal materials, splitting the network's functional profile between the hemispheres. Similar patterns were observed across multiple association networks including putative cognitive-control, action-mode, and attention networks. The hemispheric laterality effect was prospectively replicated in a second independent study. These findings highlight a generalized laterality phenomenon that transcends individual specialized networks and aligns with a broad mechanism that modulates processing between the hemispheres.

  PublisherOA PDFDOI

• Within-Individual Precision Mapping of Brain Networks Exclusively Using Task Data

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-25 · 4 citations

  preprintOpen accessSenior author

  Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. The straightforward strategy involved extracting residualized data after application of a task-based general linear model (GLM) and then applying standard functional connectivity analysis. Functional correlation matrices estimated from task data were highly similar to those derived from traditional resting-state fixation data. The largest factor affecting similarity between correlation matrices was the amount of data. Networks estimated within-individual from task data displayed strong spatial overlap with those estimated from resting-state fixation data and predicted the same triple functional dissociation in independent data. The implications of these findings are that (1) existing task data can be reanalyzed to estimate within-individual network organization, (2) resting-state fixation and task data can be pooled to increase statistical power, and (3) future studies can exclusively acquire task data to both estimate networks and extract task responses. Most broadly, the present results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

  PublisherOA PDFDOI

• Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept

  medRxiv · 2025-03-17

  preprintOpen access

  Abstract Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for reliably detecting longitudinal atrophy, particularly over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning ) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual patients. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2’23’’). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over two days. Using linear mixed-effects models, phenotypically vulnerable cortical (“core atrophy”) regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

  PublisherOA PDFDOI

• Adjacent Dorsolateral Prefrontal Cortex (DLPFC) Regions Participate in Distinct Large-Scale Networks Differentially Recruited for Social and Cognitive Control Functions

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-22

  articleOpen accessSenior author

  Human prefrontal cortex (PFC) is heterogenous. In monkeys, side-by-side PFC regions, including within dorsolateral PFC (DLPFC), show distinct long-range anatomical projection profiles, raising the possibility that adjacent regions might specialize as a consequence of the distributed networks in which they are embedded. Consistent with this possibility, recent findings in humans provide evidence for PFC regions domain-specialized for scene (spatial) processing and language processing that are adjacent to distinct domain-flexible regions responding to traditional cognitive control demands. Here we tested functional specialization of PFC regions linked to another domain-specialized network recruited by certain forms of social processing (theory-of-mind, ToM, tasks). Using within-individual precision neuroimaging approaches, side-by-side DLPFC regions embedded within parallel distributed networks were identified within the idiosyncratic anatomy of each individual ( N =13). Functional responses between these distinct regions revealed a robust functional double dissociation: one DLPFC region was preferentially recruited by ToM tasks and an adjacent DLPFC region by working memory task demands. The region responding to social processing demands was small and its position varied slightly from one person to the next suggesting why it may have been underappreciated in past group-based analyses. These findings add to evidence that PFC features more functional specialization than commonly appreciated and further that the specialization of juxtaposed regions within PFC can be understood by examining the distributed networks within which the regions are embedded.

  PublisherOA PDFDOI

• Scanbuddy: fMRI motion plotting and SNR estimation at scan acquisition

  The Journal of Open Source Software · 2025-09-26

  articleOpen access

  Functional magnetic resonance imaging (fMRI) is a powerful research and clinical tool for in vivo imaging of human brain function.Scanbuddy is containerized software that produces motion plots at the time of scan acquisition.Users should have a machine separate from the scanner host PC with its own display monitor that is capable of running Linux and Docker.Users can set up auto exporting from the scanner host PC via a Samba share mount for BOLD scans.With auto exporting, the scanner will automatically send reconstructed DICOMs to both the scanner host PC and the Scanbuddy machine.Scanbuddy uses the excellent dcm2niix (Li et al., 2016) and AFNI (Cox, 1996) packages for DICOM conversion and computing motion estimates.Scanbuddy was developed in a linux environment and can handle repetition times as fast as 0.6 seconds.

  PublisherOA PDFDOI

• Topography of cognition: Evolution of parallel distributed association networks in primates

  Evolution of Nervous Systems · 2025-05-23 · 2 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Age- and Sex-Related Differences in Sleep Patterns and Their Relations to Self-Reported Sleep and Mood

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-20 · 1 citations

  preprintOpen accessSenior author

  Robust age- and sex-related differences in sleep were identified (n=38,546) and replicated (n=38,547) from week-long passive actigraphy data in UK Biobank participants ages 44-82. Sleep patterns reflected reliable non-linear interactions between age and sex. Younger women slept about 17 min more than their male counterparts, though this difference diminished with age, with both sexes reducing total sleep duration in later life. Middle-aged individuals exhibited shorter sleep durations during the week, with weekend sleep increasing by as much as 50 min. Participants in their seventh and eighth decades showed more consistent sleep patterns throughout the week. Sleep patterns also suggest maintenance of total sleep duration: individuals reporting waking too early maintain sleep duration by going to sleep earlier, while individuals reporting sleeping too much fall asleep later but also wake later, again maintaining sleep duration. Self-reported depression and anhedonia were associated with reduced total sleep duration across multiple age groups and both sexes. These collective results indicate that the timing, consistency, and overall amount of sleep differs by age and is affected by individual factors.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01AG034556

  NIH · $2.7M · 2015

• NIH Grant U01MH109589

  NIH · $17.1M · 2022

• NIH Grant S10OD020039

  NIH · $600k · 2016

• NIH Grant R01AG021910

  NIH · $1.2M · 2011

• NIH Grant U01AG052564

  NIH · $18.7M · 2022

Frequent coauthors

• Reisa A. Sperling

  Harvard University

  312 shared

• Jordan W. Smoller

  Stanley Center for Psychiatric Research

  291 shared

• Avram J. Holmes

  Rutgers Sexual and Reproductive Health and Rights

  252 shared

• Bruce Fischl

  Harvard University

  234 shared

• Joshua L. Roffman

  Harvard University

  194 shared

• Sven Cichon

  University Hospital of Basel

  153 shared

• Benedicto Crespo‐Facorro

  Centro de Investigación Biomédica en Red de Salud Mental

  153 shared

• Mert R. Sabuncu

  147 shared

Labs

• Buckner LabPI

  The Buckner Lab at Harvard University focuses on psychiatric neuroimaging research.

Education

• Ph.D., Psychology

  Harvard University

  1993

• B.A., Psychology

  University of California, Berkeley

  1988