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Desikan Killiany AtlasEdit

The Desikan Killiany atlas is a widely used framework for labeling the cerebral cortex in structural brain MRI analyses. It provides a fixed set of gyral-based regions that map onto the visible folds of the cortex, enabling researchers to compare brain structure across individuals and studies in a consistent way. By partitioning the cortex into 34 regions per hemisphere (68 labels in total), the atlas offers an interpretable scheme for reporting measurements such as cortical thickness, surface area, and gray matter volume. The atlas is closely tied to the FreeSurfer software ecosystem and is often referred to in conjunction with that platform. For readers and developers, this atlas represents a pragmatic balance between anatomical interpretability and automation-friendly robustness in large-scale neuroimaging work NeuroImage and FreeSurfer.

The Desikan Killiany atlas emerged in the mid-2000s as part of a push toward automated, atlas-based labeling of cortical regions. The original work was published in a leading neuroimaging journal, providing a labeling system that could be applied consistently across datasets without requiring manual delineation for every subject NeuroImage. Since its introduction, the atlas has become a default component in many processing pipelines, helping researchers translate complex MRI data into region-level metrics that can be aggregated, compared, and meta-analyzed. It is commonly used in studies of aging, neurodegenerative disease, and developmental neuroscience, where stable, interpretable region labels facilitate cross-study communication and replication Alzheimer's Disease Neuroimaging Initiative.

The naming and labeling convention of the atlas reflect a pragmatic, anatomy-based approach. Regions are defined by recognizable gyral landmarks (for example, frontal, temporal, parietal, and occipital areas, as well as limbic and cingulate structures) rather than purely data-driven boundaries. In practice, the atlas is implemented as part of FreeSurfer’s cortical parcellation workflow, with labels assigned to vertices on the reconstructed cortical surface. Researchers extract metrics from each labeled region and then perform statistical analyses that compare groups or track changes over time. The approach is designed to be transparent and interpretable, which is valuable when communicating findings to clinicians, policymakers, or fellow researchers who rely on clear anatomical meaning for decision-making cerebral cortex and cortical parcellation.

History and development

  • Origins and purpose: The atlas was developed to provide an automated, anatomically interpretable labeling system that could be used with MRI data from diverse scanners and protocols. It was intended to support cross-subject comparisons by anchoring metrics to recognizable cortical gyri and sulci cerebral cortex.
  • Publication and adoption: The original Desikan Killiany labeling system appeared in a 2006 paper that established a practical set of 34 regions per hemisphere and demonstrated its use with automated labeling in the FreeSurfer pipeline. Since then, the atlas has become a standard component in many neuroimaging studies aiming for reproducibility and interpretability across laboratories NeuroImage.
  • Extensions and variants: A later development, the Desikan-Killiany-Tourville (DKT) atlas, builds upon the same conceptual foundation while refining boundaries and expanding regional delineations. The DKT atlas is widely referenced as an updated option for researchers seeking greater anatomical granularity while preserving the interpretability that comes from gyral-based labels Desikan-Killiany-Tourville atlas.

Technical overview

  • Data inputs and processing: The atlas operates within a surface-based processing framework. The cortical surface is reconstructed from MRI data (often T1-weighted images) and inflated to a sphere to enable robust inter-subject alignment. Labels from the atlas are then projected onto the subject’s cortical surface, allowing region-wise measurements to be computed for each labeled area Magnetic resonance imaging and cerebral cortex.
  • Labeling scheme: The 68 labels (34 per hemisphere) correspond to major gyri and adjacent regions. Boundaries are defined by gyral and sulcal landmarks, making the labels relatively easy to interpret in anatomical terms. This structure supports downstream analyses of cortical thickness, surface area, and gray matter volume at the regional level.
  • Interpretation and use: Researchers commonly report regional metrics as part of broader analyses of aging, disease progression, or developmental trajectories. The atlas’s straightforward interpretation—each label maps to a recognizable cortical landmark—facilitates communication of findings to a broad audience, including clinicians and policy stakeholders cortical parcellation.

Applications and impact

  • Standardization and comparability: The atlas provides a common reference framework that helps different studies align their region-level results. This standardization supports meta-analyses and cross-cohort comparisons, which are crucial for establishing robust brain-behavior relationships and disease biomarkers FreeSurfer.
  • Clinical and developmental research: By supplying a consistent set of labels, the atlas has supported work in cognitive aging, neurodegenerative diseases such as Alzheimer’s disease, and pediatric development. Researchers use region-specific measures to track atrophy, thinning, or growth patterns over time and in response to interventions Alzheimer's Disease Neuroimaging Initiative.
  • Practical considerations: The atlas’s balance of anatomical interpretability and automation makes it a practical choice for large datasets, epidemiological studies, and multi-site collaborations. Its integration with open-source tools reduces costs and accelerates progress, enabling researchers to leverage existing pipelines without reinventing labeling schemes NeuroImage.

Controversies and debates

  • Boundaries versus function: A frequent critique is that gyral-based boundaries do not always align with functional brain organization. Functional networks observed in other modalities or in task-based studies can cut across gyral landmarks, leading some to argue for data-driven or multimodal parcellations that better reflect functional architecture. Proponents of alternative approaches point to methods that incorporate functional MRI data or diffusion imaging to define boundaries that better capture networks like the default mode or salience networks.
  • Population and developmental considerations: The atlas is rooted in adult brain anatomy and may be less optimal for pediatric populations or brains with pathology that alters gyral patterns. While the labeling is robust, some researchers advocate for subject-specific or developmentally tailored parcellations to improve accuracy in diverse cohorts.
  • Alternatives and trade-offs: There is ongoing debate about the best balance between interpretability and granularity. Data-driven atlases (for example, those derived from resting-state functional connectivity or multi-modal data) can offer finer or differently organized parcellations but may sacrifice the clear anatomical semantics that gyral-based schemes provide. The choice often reflects a trade-off between cross-study comparability (favoring standard atlases like Desikan Killiany) and network-specific precision (favoring newer, data-driven schemes) Glasser atlas and Harvard-Oxford atlas.
  • Population bias discussions: Critics sometimes frame neuroscience as vulnerable to biases in study samples, which can shape the perceived universality of anatomical landmarks. Supporters of the standard atlas argue that while demographic representation matters, the atlas remains a transparent, reproducible tool for comparing structural brain metrics across large groups. They contend that this does not excuse ignoring diversity in data collection, but it does not undermine the practicality and historical utility of a well-established framework. Advocates for continued use emphasize the need for broader data inclusion and complementary methods rather than discarding a widely validated standard.

Adoption and alternatives

  • Widespread use: The Desikan Killiany atlas remains a cornerstone in many centers that rely on FreeSurfer for cortical parcellation. Its clear anatomical basis and ease of implementation have kept it in regular use for years, even as newer schemes have emerged.
  • Complementary approaches: Researchers often compare results across multiple parcellations or augment region-based analyses with data-driven methods to capture additional structure. Examples include newer multi-modal atlases and functional network-based parcellations, which can be used alongside the Desikan Killiany framework to provide a fuller picture of brain organization. Notable alternatives and complements include Glasser atlas and Automated Anatomical Labeling atlas.
  • Practical considerations for researchers: When planning a study, investigators weigh the need for cross-study comparability against the desire for finer-grained or functionally driven boundaries. The Desikan Killiany atlas offers a robust, interpretable baseline that works well for large-scale analyses and for reporting findings in a way that clinicians and researchers can readily understand cerebral cortex.

See also