Hippocampal Research: History, Methods, and Perspectives for the Neurosciences
A recent review by researchers at the Institute of Neurosciences and Medicine (INM-1) examines the development of methodology—and thus the progress—in the neurosciences using the hippocampus as an example, the brain region responsible for learning, memory, and spatial orientation. For more than 150 years, innovative methods coupled with meticulous neuroanatomical analyses have enabled increasingly detailed insights into the structure and function of the brain. The study, now published in Anatomical Science International, concludes that future methodological advances in brain research must necessarily be comparative and interdisciplinary—incorporating the expertise of physicists, computational neuroscientists, and, above all, classical neuroanatomists.
The work of the Jülich scientists focuses on the structural organization of the hippocampus in both the human brain and in animal models frequently used in neuroscience research. It addresses various components such as cellular structure, molecular diversity, and connectivity. A broad overview is provided of the different research methods. It begins with the Golgi staining technique, developed in the 1870s, and covers further methods such as immunohistochemical staining and receptor autoradiography. Modern invasive and non-invasive tractography techniques, such as magnetic resonance imaging (MRI), round off the overview.
The authors also look to the future with the digitization of the neurosciences. To analyse the massive, highly complex data sets emerging from research, neuroscientists increasingly rely on artificial intelligence (AI) methods. For instance, deep learning algorithms and specially prepared training data from neuroanatomy have been used to automatically segment the entire "BigBrain"—an extremely high-resolution 3D model of a human brain—into its individual layers.
New AI-supported approaches are also being applied in hippocampal research. Here, state-of-the-art imaging data are combined with statistical learning techniques to capture the fine neural circuitry within the hippocampus. The results align with classical anatomical subdivisions.
The researchers attribute particular significance to the comparative approach: the hippocampus has evolved over time in terms of size, neuronal connectivity, and synaptic plasticity. Comparing different species makes it possible to better understand structural adaptations and to improve the transfer of findings from animal experiments to humans—an important step in developing targeted therapies for neurological and psychiatric diseases.
To analyse the resulting large and complex data sets, the researchers propose the "Common Space" concept. Originally developed to overcome methodological limitations between different species, this method allows integration of data from various measurement techniques—from molecular profiles and connectivity analyses to functional imaging—within a unified analysis framework. Applied to the hippocampus, this approach could help integrate high-resolution anatomical data, temporally resolved data, and computational models, thus enabling comprehensive deciphering of the relationship between its microstructural organization and large-scale brain dynamics.
Original publication:
Zhao, L., Palomero-Gallagher, N. Hippocampal architecture viewed through the eyes of methodological development. Anat Sci Int (2025). https://doi.org/10.1007/s12565-025-00878-7
Contact
apl-.Prof. Dr. rer. nat. Nicola Palomero-Gallagher
Working Group Leader "Receptors"
- Institute of Neurosciences and Medicine (INM)
- Structural and Functional Organisation of the Brain (INM-1)
Room 2005b
Press Contact
Erhard Zeiss
Wissenschaftlicher Kommunikationsreferent
- Institute of Neurosciences and Medicine (INM)
- Structural and Functional Organisation of the Brain (INM-1)
Room 3033