Topographic diversity of structural connectivity in schizophrenia

Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest with Brain Imaging and Genetic Data

Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.

Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data

Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.

Acute conceptual disorganization in untreated first-episode psychosis: a combined magnetic resonance spectroscopy and diffusion imaging study of the cingulum

BACKGROUND

Disorganized thinking is a core feature of acute psychotic episodes that is linked to social and vocational functioning. Several lines of evidence implicate disrupted cognitive control, excitatory overdrive and oxidative stress relating to the anterior cingulate cortex as mechanisms of conceptual disorganization (CD). We examined 3 candidate mechanistic markers related to CD in firstepisode psychosis: glutamate excess, cortical antioxidant (glutathione) status and the integrity of the cingulum bundle that connects regions implicated in cognitive control.

METHODS

We used fractional anisotropy maps from 7 T diffusion-weighted imaging to investigate the bilateral cingulum based on a probabilistic white matter atlas. We compared high CD, low CD and healthy control groups and performed probabilistic fibre tracking from the identified clusters (regions of interest within the cingulum) to the rest of the brain. We quantified glutamate and glutathione using magnetic resonance spectroscopy (MRS) in the dorsal anterior cingulate cortex.

RESULTS

We found a significant fractional anisotropy reduction in a cluster in the left cingulum in the high CD group compared to the low CD group (Cohen's d = 1.39; p < 0.001) and controls (Cohen's d = 0.86; p = 0.009). Glutamate levels did not vary among groups, but glutathione levels were higher in the high CD group than in the low CD group. We also found higher glutathione related to lower fractional anisotropy in the cingulum cluster in the high CD group.

LIMITATIONS

The MRS measures of glutamine were highly uncertain, and MRS was acquired from a single voxel only.

CONCLUSION

Acute CD relates to indicators of oxidative stress, as well as reduced white matter integrity of the cingulum, but not to MRI-based glutamatergic excess. We propose that both oxidative imbalance and structural dysconnectivity underlie acute disorganization.