Group-Wise FMRI Activation Detection on DICCCOL Landmarks

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 12; no. 4; pp. 513 - 534
• Main Authors: Lv, Jinglei, Guo, Lei, Zhu, Dajiang, Zhang, Tuo, Hu, Xintao, Han, Junwei, Liu, Tianming
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2014; Springer; Springer Nature B.V
• Subjects: Algorithms; Anatomic Landmarks - anatomy & histology; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Brain Mapping - methods; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Humans; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Medical sciences; Nervous system; Neurology; Neurosciences; Original Article; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Reproducibility of Results; Sensitivity and Specificity; DTI; fMRI; DICCCOL; Group-wise activation detection; Brain; Statistical analysis; Variability; Central nervous system; Activation; Nuclear magnetic resonance imaging; Linear model; Experimental result; Surveillance; Mixed model; Size effect; Beacon; Medical imagery; Noise immunity; Alignment defect; Functional imaging
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-014-9226-5

Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and its capacity to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the co-registration of individual brains’ fMRI images, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignments could significantly degrade the required inter-subject correspondences, thus substantially reducing the sensitivity and specificity of group-wise fMRI activation detection. To deal with these challenges, this paper presents a novel approach to detecting group-wise fMRI activation on our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). The basic idea here is that the first-level general linear model (GLM) analysis is first performed on the fMRI signal of each corresponding DICCCOL landmark in individual brain’s own space, and then the estimated effect sizes of the same landmark from a group of subjects are statistically assessed with the mixed-effect model at the group level. Finally, the consistently activated DICCCOL landmarks are determined and declared in a group-wise fashion in response to external block-based stimuli. Our experimental results have demonstrated that the proposed approach can detect meaningful activations.