The accurate diagnosis of Alzheimers 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 (CAD) 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 multi-modal neuroimaging features in one setting and has the potential to require less labelled data. A performance gain was achieved in both binary classification and multi-class classification of AD. The advantages and limitations of the proposed framework are discussed.

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ObjectiveUsing diffusion tensor imaging (DTI), we examined chronic stable MS lesions, peri-lesional white matter (PLWM) and normal appearing white matter (NAWM) in patients with relapsing-remitting multiple sclerosis (RRMS) for evidence of progressive tissue destruction and evaluated whether diffusivity change is associated with conventional MRI parameters and clinical findings.MethodPre- and post-gadolinium T1, T2 and DTI images were acquired from 55 consecutive RRMS patients at baseline and 42.3 ± 9.7 months later. Chronic stable T2 lesions of sufficient size were identified in 43 patients (total of 134 lesions). Diffusivity parameters such as axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD) and fractional anisotropy (FA) were compared at baseline and follow-up. MRI was also performed in 20 normal subjects of similar age and gender.ResultsWithin the core of chronic MS lesions the diffusion of water molecules significantly increased over the follow-up period, while in NAWM all diffusivity indices remained stable. Since increase of AD and RD in lesional core was highly concordant, indicating isotropic nature of diffusivity change, and considering potential effect of crossing fibers on directionally-selective indices, only MD, a directionally-independent measure, was used for further analysis. The significant increase of MD in the lesion core during the follow-up period (1.29 ± 0.19 μm2/ms and 1.34 ± 0.20 μm2/ms at baseline and follow-up respectively, P < 0.0001) was independent of age or disease duration, total brain lesion volume or new lesion activity, lesion size or location and baseline tissue damage (T1 hypointensity). Change of MD in the lesion core, however, was associated with progressive brain atrophy (r = 0.47, P = 0.002). A significant gender difference was also observed: the MD change in male patients was almost twice that of female patients (0.030 ± 0.04 μm2/ms and 0.058 ± 0.03 μm2/ms in female and male respectively, P = 0.01). Sub-analysis of lesions with lesion-free surrounding revealed the largest MD increase in the lesion core, while MD progression gradually declined towards PLWM. MD in NAWM remained stable over the follow-up period.ConclusionThe significant increase of isotropic water diffusion in the core of chronic stable MS lesions likely reflects gradual, self-sustained tissue destruction in demyelinated white matter that is more aggressive in males.

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.

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Our study demonstrated different patterns of ON damage in NMOSD and MS. In MS, the ON damage was less severe, with demyelination as the main pathologic component, whereas in NMOSD, axonal loss was more severe compared with myelin loss. The disproportional mfVEP amplitude and latency changes suggested predominant axonal damage within the anterior visual pathway as the main clinical feature of NMOSD, in contrast to MS, where demyelination spreads along the entire visual pathway.

Classification performance with data augmentation and age. It has previously been demonstrated