Recent advances have substantially increased the number of genes that are statistically associated with complex genetic disorders of the CNS such as autism and schizophrenia. It is now clear that there will likely be hundreds of distinct loci contributing to these disorders, underscoring a remarkable genetic heterogeneity. It is unclear whether this genetic heterogeneity indicates an equal heterogeneity of cellular mechanisms for these diseases. The commonality of symptoms across patients suggests there could be a functional convergence downstream of these loci upon a limited number of cell types or circuits that mediate the affected behaviors. One possible mechanism for this convergence would be the selective expression of at least a subset of these genes in the cell types that comprise these circuits. Using profiling data from mice and humans, we have developed and validated an approach, cell type-specific expression analysis, for identifying candidate cell populations likely to be disrupted across sets of patients with distinct genetic lesions. Using human genetics data and postmortem gene expression data, our approach can correctly identify the cell types for disorders of known cellular etiology, including narcolepsy and retinopathies. Applying this approach to autism, a disease where the cellular mechanism is unclear, indicates there may be multiple cellular routes to this disorder. Our approach may be useful for identifying common cellular mechanisms arising from distinct genetic lesions.

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The progression of Parkinson’s disease (PD) seems to vary according to the disease stage, which greatly influences the management of PD patients. However, the underlying mechanism of progression in PD remains unclear. This study was designed to explore the progressive pattern of iron accumulation at different stages in PD patients. Sixty right-handed PD patients and 40 normal controls were recruited. According to the disease stage, 45 patients with Hoehn-Yahr stage ≤ 2.5 and 15 patients with Hoehn-Yahr stage ≥ 3 were grouped into early-stage PD (EPD) and late-stage PD (LPD) groups, respectively. The iron content in the cardinal subcortical nuclei covering the cerebrum, cerebellum and midbrain was measured using quantitative susceptibility mapping (QSM). The substantia nigra pars compacta (SNc) showed significantly increased QSM values in the EPD patients compared with the controls. In the LPD patients, while the SNc continued to show increased QSM values compared with the controls and EPD patients, the regions showing increased QSM values spread to include the substantia nigra pars reticulata (SNr), red nucleus (RN) and globus pallidus (GP). Our data also indicated that iron deposition in the GP internal segment (GPi) was more significant than in the GP external segment. No other regions showed significant changes in QSM values among the groups. Therefore, we were able to confirm a regionally progressive pattern of iron accumulation in the different stages of PD, indicating that iron deposition in the SNc is affected exclusively in the early stages of the disease while the SNr, RN and GP, and particularly the GPi segment, become involved in advanced stages of the disease. This is a preliminary study providing objective evidence of the iron-related progression in PD.

BackgroundIn recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals.Materials and MethodsNineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.ResultsUsing the proposed methods to classify the data set, good results (accuracy  = 86.96%, sensitivity  = 78.95%, specificity  = 92.59%) were obtained.ConclusionsThis method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.

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