The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.

The accurate diagnosis of Alzheimer's disease (AD) plays a significant role in patient care, especially at the early stage, because the consciousness of the severity and the progression risks allows the patients to take prevention measures before irreversible brain damages are shaped. Although many studies have applied machine learning methods for computer-aided-diagnosis (CAD) of AD recently, a bottleneck of the diagnosis performance was shown in most of the existing researches, mainly due to the congenital limitations of the chosen learning models. In this study, we design a deep learning architecture, which contains stacked auto-encoders and a softmax output layer, to overcome the bottleneck and aid the diagnosis of AD and its prodromal stage, Mild Cognitive Impairment (MCI). Compared to the previous workflows, our method is capable of analyzing multiple classes in one setting, and requires less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments.

Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.

The spread of neurodegeneration through the human brain network is reported as underlying the progression of neurodegenerative disorders. However, the exact mechanisms remain unknown. The human visual pathway is characterized by its unique hierarchical architecture and, therefore, represents an ideal model to study trans-synaptic degeneration, in contrast to the complexity in neural connectivity of the whole brain. Here we show in two specifically selected patient cohorts, including (i) glaucoma patients with symmetrical bilateral hemifield defects respecting the horizontal meridian (n = 25, 14 females, 64.8 ± 10.1 years; versus 13 normal controls with similar age/sex distributions); and (ii) multiple sclerosis patients without optic radiation lesions (to avoid potential effects of lesions on diffusivity measures) (n = 30, 25 females, 37.9 ± 10.8 years; versus 20 controls), that there are measurable topographic changes in the posterior visual pathways corresponding to the primary optic nerve defects. A significant anisotropic increase of water diffusion was detected in both patient cohorts in the optic radiations, characterized by changes in perpendicular (radial) diffusivity (a measure of myelin integrity) that extended more posteriorly than those observed in parallel (axial) diffusivity (reflecting axonal integrity). In glaucoma, which is not considered a demyelinating disease, the observed increase in radial diffusivity within the optic radiations was validated by topographically linked delay of visual evoked potential latency, a functional measure of demyelination. Radial diffusivity change in the optic radiations was also associated with an asymmetrical reduction in the thickness of the calcarine cortex in glaucoma. In addition, 3 years longitudinal observation of the multiple sclerosis patient cohort revealed an anterograde increase of radial diffusivity in the anterior part of optic radiations which again was retinotopically associated with the primary damage caused by optic neuritis. Finally, in an animal model of optic nerve injury, we observed early glial activation and demyelination in the posterior visual projections, evidenced by the presence of myelin-laden macrophages. This occurred prior to the appearance of amyloid precursor protein accumulation, an indicator of disrupted fast axonal transport. This study demonstrated strong topographical spread of neurodegeneration along recognized neural projections and showed that myelin and glial pathology precedes axonal loss in the process, suggesting that the mechanism of trans-synaptic damage may be at least partially mediated by glial components at the cellular level. The findings may have broad biological and therapeutic implications for other neurodegenerative disorders.