Deep Forest with Crossshaped Window Scanning Mechanism to Extract Topological Features Deep neural networks have been successfully applied to the classification of brain networks. However, the highdimensional and smallscale properties of the brain network data limit their extensive applications. To solve this problem, this paper proposes a new deep forest framework with crossshaped window scanning mechanism (DFCWSM) to extract topological features for the classification of brain networks. The crossshaped window scanning mechanism is designed to extract the nodelevel and the edgelevel features respectively that have meaningful interpretations in terms of corresponding network topologies. Based on the classification framework, we firstly implement the feature transformation of brain networks by the multilevel topological feature extraction. Then a cascade forest structure is used to learn the hierarchical features layer by layer. And the results of the last level of cascade forests are integrated to make the final classification. We evaluated the proposed framework on the ABIDE I data set. Experimental results show that our proposed framework can not only achieve competitive classification performance but also accurately identify the abnormal brain regions associated with ASD.

Topological Characterization of the Multifeature based Network in Patients with Alzheimers Disease and Mild Cognitive Impairment Characterization of morphological organization pattern in individual brain has long been an open question. Recently, a novel singlesubject network that built upon multiple morphological features (MFN) was introduced 1, which exhibited extraordinary power in diagnosing patients with Alzheimeru0027s disease (AD) and mild cognitive impairment (MCI). This study aims to characterize the corticocortical topological organization in MCI and AD cohorts via the MFN, and uncover the structural substrate that leads to the high classification accuracy. The MFNs were constructed for 165 normal controls (NCs), 221 patients with MCI, and 142 patients with AD from the Alzheimeru0027s Disease Neuroimaging Initiative (ADNI). Results from graph theoretical analysis showed xe2x80x98smallworldxe2x80x99 property and modular organization of the MFN in three groups; as well as increased local clustering and characteristic path length, and altered hub regions in patients with AD and MCI. More importantly, we found the primary atrophic regions were accompanied by increased number of inflow connections; whereas, the connections that linked to the less atrophic regions were mostly outflow connections. This phenomenon, we speculated, might indirectly reflect the trophic supporting effects in the brain. Our results demonstrated the basic organizational principles of the MFN and its changes in patients with AD and MCI, providing important implications of what makes the MFN powerful in autodiagnosis of mental disorders.