Individual brain parcellation: Review of methods, validations and applications
- URL: http://arxiv.org/abs/2407.00984v1
- Date: Mon, 1 Jul 2024 05:48:05 GMT
- Title: Individual brain parcellation: Review of methods, validations and applications
- Authors: Chengyi Li, Shan Yu, Yue Cui,
- Abstract summary: Accurate mapping of brain functional regions at the individual level is pivotal for a comprehensive understanding of the variations in brain function and behaviors.
With the development of neuroimaging and machine learning techniques, studies on individual brain parcellation are booming.
- Score: 7.159138402684875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Individual brains vary greatly in morphology, connectivity and organization. The applicability of group-level parcellations is limited by the rapid development of precision medicine today because they do not take into account the variation of parcels at the individual level. Accurate mapping of brain functional regions at the individual level is pivotal for a comprehensive understanding of the variations in brain function and behaviors, early and precise identification of brain abnormalities, as well as personalized treatments for neuropsychiatric disorders. With the development of neuroimaging and machine learning techniques, studies on individual brain parcellation are booming. In this paper, we offer an overview of recent advances in the methodologies of individual brain parcellation, including optimization- and learning-based methods. Comprehensive evaluation metrics to validate individual brain mapping have been introduced. We also review the studies of how individual brain mapping promotes neuroscience research and clinical medicine. Finally, we summarize the major challenges and important future directions of individualized brain parcellation. Collectively, we intend to offer a thorough overview of individual brain parcellation methods, validations, and applications, along with highlighting the current challenges that call for an urgent demand for integrated platforms that integrate datasets, methods, and validations.
Related papers
- Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution [10.49121904052395]
We build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas.
Prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding.
arXiv Detail & Related papers (2024-07-19T21:05:28Z) - BrainFounder: Towards Brain Foundation Models for Neuroimage Analysis [6.5388528484686885]
This study introduces a novel approach towards the creation of medical foundation models.
Our method involves a novel two-stage pretraining approach using vision transformers.
BrainFounder demonstrates a significant performance gain, surpassing the achievements of previous winning solutions.
arXiv Detail & Related papers (2024-06-14T19:49:45Z) - MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
Brain decoding aims to reconstruct stimuli from acquired brain signals.
Currently, brain decoding is confined to a per-subject-per-model paradigm.
We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
arXiv Detail & Related papers (2024-04-11T15:46:42Z) - Aligning brain functions boosts the decoding of visual semantics in
novel subjects [3.226564454654026]
We propose to boost brain decoding by aligning brain responses to videos and static images across subjects.
Our method improves out-of-subject decoding performance by up to 75%.
It also outperforms classical single-subject approaches when fewer than 100 minutes of data is available for the tested subject.
arXiv Detail & Related papers (2023-12-11T15:55:20Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - UniBrain: Universal Brain MRI Diagnosis with Hierarchical
Knowledge-enhanced Pre-training [66.16134293168535]
We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain.
Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
arXiv Detail & Related papers (2023-09-13T09:22:49Z) - Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks.
We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
arXiv Detail & Related papers (2023-05-25T16:29:16Z) - Deep learning reveals the common spectrum underlying multiple brain
disorders in youth and elders from brain functional networks [53.257804915263165]
Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions.
Key evidence from neuroimaging data for pathological commonness remains unrevealed.
We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
arXiv Detail & Related papers (2023-02-23T09:22:05Z) - A Structure-guided Effective and Temporal-lag Connectivity Network for
Revealing Brain Disorder Mechanisms [8.459311736323572]
We propose an effective temporal-lag neural network (termedN) to infer causal relationships and the temporal-lag values between brain regions.
The evaluation results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2022-12-01T15:02:22Z) - Towards a Neural Model for Serial Order in Frontal Cortex: a Brain
Theory from Memory Development to Higher-Level Cognition [53.816853325427424]
We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals.
Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the form of ordinal patterns and use them to index information hierarchically in different parts of the brain.
By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and planning temporally ordered information.
arXiv Detail & Related papers (2020-05-22T14:29:51Z) - Towards a predictive spatio-temporal representation of brain data [0.2580765958706854]
We show that fMRI datasets are constituted by complex and highly heterogeneous timeseries.
We compare various modelling techniques from deep learning and geometric deep learning to pave the way for future research.
We hope that our methodological advances can ultimately be clinically and computationally relevant by leading to a more nuanced understanding of the brain dynamics in health and disease.
arXiv Detail & Related papers (2020-02-29T18:49:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.