PMID- 36155266
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 150
DP  - 2022 Nov
TI  - Atlas-guided parcellation: Individualized functionally-homogenous parcellation in 
      cerebral cortex.
PG  - 106078
LID - S0010-4825(22)00786-7 [pii]
LID - 10.1016/j.compbiomed.2022.106078 [doi]
AB  - Resting-state Magnetic resonance imaging-based parcellation aims to group the 
      voxels/vertices non-invasively based on their connectivity profiles, which has 
      achieved great success in understanding the fundamental organizational principles 
      of the human brain. Given the substantial inter-individual variability, the 
      increasing number of studies focus on individual parcellation. However, current 
      methods perform individual parcellations independently or are based on the group 
      prior, requiring expensive computational costs, precise parcel alignment, and 
      extra group information. In this work, an efficient and flexible parcellation 
      framework of individual cerebral cortex was proposed based on a region growing 
      algorithm by merging the unassigned and neighbor vertex with the 
      highest-correlated parcel iteratively. It considered both consistency with prior 
      atlases and individualized functional homogeneity of parcels, which can be 
      applied to a single individual without parcel alignment and group information. 
      The proposed framework was leveraged to 100 unrelated subjects for functional 
      homogeneity comparison and individual identification, and 186 patients with 
      Parkison's disease for symptom prediction. Results demonstrated our framework 
      outperformed other methods in functional homogeneity, and the generated 
      parcellations provided 100% individual identification accuracy. Moreover, the 
      default mode network (DMN) exhibited higher functional homogeneity, intra-subject 
      parcel reproducibility and fingerprinting accuracy, while the sensorimotor 
      network did the opposite, reflecting that the DMN is the most representative, 
      stable, and individual-identifiable network in the resting state. The correlation 
      analysis showed that the severity of the disease symptoms was related negatively 
      to the similarity of individual parcellation and the atlases of healthy 
      populations. The disease severity can be correctly predicted using machine 
      learning models based on individual topographic features such as parcel 
      similarity and parcel size. In summary, the proposed framework not only 
      significantly improves the functional homogeneity but also captures 
      individualized and disease-related brain topography, serving as a potential tool 
      to explore brain function and disease in the future.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Li, Yu
AU  - Li Y
AD  - Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of 
      Life Sciences and Medicine, University of Science and Technology of China, Hefei, 
      230001, China; School of Information Science and Technology, University of 
      Science and Technology of China, Hefei, 230026, China.
FAU - Liu, Aiping
AU  - Liu A
AD  - Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of 
      Life Sciences and Medicine, University of Science and Technology of China, Hefei, 
      230001, China; School of Information Science and Technology, University of 
      Science and Technology of China, Hefei, 230026, China. Electronic address: 
      aipingl@ustc.edu.cn.
FAU - Fu, Xueyang
AU  - Fu X
AD  - School of Information Science and Technology, University of Science and 
      Technology of China, Hefei, 230026, China.
FAU - Mckeown, Martin J
AU  - Mckeown MJ
AD  - Pacific Parkinson's Research Centre, Vancouver, British Columbia, V6E 2M6, 
      Canada; Department of Medicine (Neurology), University of British Columbia, 
      Vancouver, British Columbia, V6T 2B5, Canada.
FAU - Wang, Z Jane
AU  - Wang ZJ
AD  - Department of Electrical and Computer Engineering, University of British 
      Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.
FAU - Chen, Xun
AU  - Chen X
AD  - Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of 
      Life Sciences and Medicine, University of Science and Technology of China, Hefei, 
      230001, China; School of Information Science and Technology, University of 
      Science and Technology of China, Hefei, 230026, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220910
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - Humans
MH  - Reproducibility of Results
MH  - *Brain/diagnostic imaging
MH  - *Magnetic Resonance Imaging/methods
MH  - Brain Mapping/methods
MH  - Cerebral Cortex/diagnostic imaging
OTO - NOTNLM
OT  - Individual identification
OT  - Individualized parcellation
OT  - Magnetic resonance imaging
OT  - Region growing
OT  - Symptom prediction
COIS- Declaration of competing interest The authors declare no competing financial 
      interests.
EDAT- 2022/09/27 06:00
MHDA- 2023/10/23 12:43
CRDT- 2022/09/26 15:26
PHST- 2022/07/28 00:00 [received]
PHST- 2022/08/23 00:00 [revised]
PHST- 2022/09/03 00:00 [accepted]
PHST- 2023/10/23 12:43 [medline]
PHST- 2022/09/27 06:00 [pubmed]
PHST- 2022/09/26 15:26 [entrez]
AID - S0010-4825(22)00786-7 [pii]
AID - 10.1016/j.compbiomed.2022.106078 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Nov;150:106078. doi: 10.1016/j.compbiomed.2022.106078. Epub 
      2022 Sep 10.