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Role of Hippocampus in Memory

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The hippocampus, a critical brain structure, plays an essential role in memory processing, particularly in the formation and retrieval of memory. This small, seahorse-shaped region is located within the medial temporal lobe, with one hippocampus in each brain hemisphere. Experimental studies involving lesions in the hippocampi of rats have demonstrated significant impairments in tasks such as object recognition and maze navigation, indicating the hippocampus involvement in both recognition and...

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Related Experiment Video

Updated: Oct 17, 2025

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models.

Ahana Priyanka¹, Kavitha Ganesan¹

¹Department of Electronics Engineering, Madras Institute of Technology, Chennai, India.

Biomedizinische Technik. Biomedical Engineering

|October 9, 2021

Summary

This summary is machine-generated.

This study identifies significant brain structure variations in Alzheimer's disease (AD) and its early stages, including mild cognitive impairment (MCI). AlexNet accurately classified these conditions using MRI scans, highlighting key biomarkers for disease progression.

Keywords:

curve evolution dementia hippocampus pre-trained models severity

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Last Updated: Oct 17, 2025

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

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Published on: July 2, 2014

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High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

Area of Science:

Neuroimaging
Biomarker Discovery
Machine Learning in Medicine

Background:

The diagnostic overlap between early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) presents a significant challenge in dementia research.
Understanding morphological variations in brain structures is crucial for tracking disease severity and progression.

Purpose of the Study:

To examine morphological variations in the whole brain (WB), grey matter (GM), and hippocampus (HC) across different stages of cognitive decline.
To identify prominent imaging biomarkers indicative of disease severity progression using MRI.
To evaluate the efficacy of curve evolution and pre-trained models in segmenting and classifying these brain regions.

Main Methods:

Utilized curve evolution with shape constraints for accurate segmentation of complex brain structures like the hippocampus (HC) and grey matter (GM).
Employed pre-trained models to analyze morphological variations and severity differences in WB, GM, and HC regions across diagnostic classes.
Evaluated the proposed methods on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Main Results:

The curve evolution method demonstrated high correlation in segmenting HC and GM regions.
Pre-trained models effectively revealed significant severity differences in WB, GM, and HC across EMCI, MCI, LMCI, and AD.
Pronounced variations were observed between AD and EMCI, AD and MCI, and AD and LMCI in all analyzed brain regions.

Conclusions:

The AlexNet model, applied to the hippocampus (HC), achieved high classification accuracy (93% for AD vs. EMCI, 78.3% for AD vs. MCI, 91% for AD vs. LMCI).
These findings suggest that morphological analysis of brain structures using advanced imaging techniques and machine learning can aid in differentiating cognitive impairment stages and diagnosing Alzheimer's disease.