Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients

Combinatorial Mismatch Scan (CMS) for loci associated with dementia in the Amish

BACKGROUND: Population heterogeneity may be a significant confounding factor hampering detection and verification of late onset Alzheimer's disease (LOAD) susceptibility genes. The Amish communities located in Indiana and Ohio are relatively isolated populations that may have increased power to detect disease susceptibility genes. METHODS: We recently performed a genome scan of dementia in this population that detected several potential loci. However, analyses of these data are complicated by the highly consanguineous nature of these Amish pedigrees. Therefore we applied the Combinatorial Mismatch Scanning (CMS) method that compares identity by state (IBS) (under the presumption of identity by descent (IBD)) sharing in distantly related individuals from such populations where standard linkage and association analyses are difficult to implement. CMS compares allele sharing between individuals in affected and unaffected groups from founder populations. Comparisons between cases and controls were done using two Fisher's exact tests, one testing for excess in IBS allele frequency and the other testing for excess in IBS genotype frequency for 407 microsatellite markers. RESULTS: In all, 13 dementia cases and 14 normal controls were identified who were not related at least through the grandparental generation. The examination of allele frequencies identified 24 markers (6%) nominally (p ≤ 0.05) associated with dementia; the most interesting (empiric p ≤ 0.005) markers were D3S1262, D5S211, and D19S1165. The examination of genotype frequencies identified 21 markers (5%) nominally (p ≤ 0.05) associated with dementia; the most significant markers were both located on chromosome 5 (D5S1480 and D5S211). Notably, one of these markers (D5S211) demonstrated differences (empiric p ≤ 0.005) under both tests. CONCLUSION: Our results provide the initial groundwork for identifying genes involved in late-onset Alzheimer's disease within the Amish community. Genes identified within this isolated population will likely play a role in a subset of late-onset AD cases across more general populations. Regions highlighted by markers demonstrating suggestive allelic and/or genotypic differences will be the focus of more detailed examination to characterize their involvement in dementia

Expression of Neuron-Specific Enolase in Multiple Myeloma and Implications for Clinical Diagnosis and Treatment

OBJECTIVE: To determine the expression of neuron-specific enolase (NSE) in patients with multiple myeloma (MM) and to evaluate its clinical value as a tumor marker and, an indicator of disease progression and treatment efficacy. METHODS: Using electrochemiluminescence immunoassay (ECLIA), we measured the serum levels of NSE in 47 healthy subjects (control group), 25 patients with small cell lung cancer (lung cancer group), and 52 patients with MM (MM group). For the MM group, serum NSE levels were measured and other disease indicators and related symptoms were monitored before and after chemotherapy. The relationship between NSE expression and other MM-related factors was analyzed. In addition, immunohistochemical staining was performed on bone marrow biopsy specimens from patients with MM. RESULTS: In the control group, serum NSE levels were within the normal range as previously reported, while the lung cancer group and the untreated MM group exhibited NSE levels that were significantly higher relative to the control group (P<0.05). The difference in NSE expression between the lung cancer group and untreated MM group was statistically significant (P<0.05). NSE levels were significantly decreased in MM patients after chemotherapy and were positively correlated with an MM disease index [beta-2 microglobulin (β2-MG)]. Changes in NSE were not related to the response rate to chemotherapy but rather were correlated with progression-free survival. CONCLUSIONS: Patients with MM may have increased serum NSE levels, and changes in NSE may provide insight into treatment efficacy of chemotherapy and disease progression. Perhaps NSE expression is a viable biomarker for MM and can be a useful reference for the design and adjustment of clinical MM treatment programs

Evaluation of a summary score of cognitive performance for use in trials in perioperative and critical care.

Background/Aims: Cognitive dysfunction after medical treatment is increasingly being recognized. Studies on this topic require repeated cognitive testing within a short time. However, with repeated testing, practice effects must be expected. We quantified practice effects in a demographically corrected summary score of a neuropsychological test battery repeatedly administered to healthy elderly volunteers. Methods: The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Neuropsychological Assessment Battery (for which a demographically corrected summary score was developed), phonemic fluency tests, and trail-making tests were administered in healthy volunteers aged 65 years or older on days 0, 7, and 90. This battery allows calculation of a demographically adjusted continuous summary score. Results: Significant practice effects were observed in the CERAD total score and in the word list (learning and recall) subtest. Based on these volunteer data, we developed a threshold for diagnosis of postoperative cognitive dysfunction (POCD) with the CERAD total score. Conclusion: Practice effects with repeated administration of neuropsychological tests must be accounted for in the interpretation of such tests. Ignoring practice effects may lead to an underestimation of POCD. The usefulness of the proposed demographically adjusted continuous score for cognitive function will have to be tested prospectively in patients