# NIH grant: High-resolution, high-dimensional semantic mapping of the human anterior temporal lobe (1R01AG097884-01A1) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R01AG097884-01A1)

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NIH grant 1R01AG097884-01A1: High-resolution, high-dimensional semantic mapping of the human anterior temporal lobe
Institute: {'code': 'AG', 'abbreviation': 'NIA', 'name': 'National Institute on Aging', 'admin_org_id': '12500002', 'admin_funding_url': ''}. PI: JACK L GALLANT. Award: $634293.
Start: 2026-09-05T00:00:00.
SUMMARY
Semantic disorders profoundly impact an individual's ability to understand and interact with the world around
them, significantly reducing quality of life. Understanding and treating these disorders requires detailed
knowledge of how the brain represents and processes semantic information. Several theories about semantic
representation in the brain have been proposed previously, but none account for all of the clinical and
neuroimaging evidence. To address these limitations, we have developed the Distributed Conceptual Network
(DCN) theory, incorporating recent impactful findings from our high-resolution, high-dimensional functional
mapping studies. The DCN theory proposes that a multimodal conceptual network receives modal conceptual
information from sensory systems and combines it with information stored in long-term memory. Based on the
available clinical evidence, the anterior temporal lobes (ATL) appear to play a key role in this process. This
project aims to understand the role of the ATL in supporting semantic knowledge by combining novel functional
MRI methods, naturalistic tasks, a precision neuroimaging approach based on voxelwise encoding models, and
systematic comparisons with semantic tasks and behavioral assessments. In Aim 1 we propose to determine
whether the ATL contains multimodal semantic representations, modal representations, or a mixture of these
two. The DCN theory proposes that the ATL operates as an interface between the multimodal conceptual network
and long-term memory. If the DCN theory is correct, then the ATL should represent the same range of semantic
concepts as the rest of the multimodal conceptual network to maximize information flow between the multimodal
conceptual network and long-term memory. Therefore, in Aim 2 we propose to determine whether the range of
semantic concepts represented in the ATL matches the range of semantic concepts represented in the distributed
conceptual network. Furthermore, if conceptual representations in the ATL support semantic memory retrieval,
then they should predict brain activations during semantic retrieval tasks, and they should predict semantic
deficits in patients with ATL neurodegeneration. To test these predictions, in Aim 3 we propose to validate our
high-dimensional semantic maps through comprehensive comparisons with classic semantic tasks in healthy
participants and behavioral assessments in patients with ATL neurodegeneration. The resulting high-resolution
semantic maps and analytical framework will provide a foundation for future studies investigating early markers
of semantic impairment in neurodegenerative conditions. By revealing the organization of conceptual knowledge
in the human brain, this work will advance both basic neuroscience research and clinical research in semantic
disorders.
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