Identification of MCI individuals using structural and functional connectivity networks

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity

Enriched white matter connectivity networks for accurate identification of MCI patients

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer’s disease (AD), is frequently considered to be good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ1, λ2, λ3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using a SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients

Human Genome-Wide RNAi Screen Identifies an Essential Role for Inositol Pyrophosphates in Type-I Interferon Response

The pattern recognition receptor RIG-I is critical for Type-I interferon production. However, the global regulation of RIG-I signaling is only partially understood. Using a human genome-wide RNAi-screen, we identified 226 novel regulatory proteins of RIG-I mediated interferon-β production. Furthermore, the screen identified a metabolic pathway that synthesizes the inositol pyrophosphate 1-IP7 as a previously unrecognized positive regulator of interferon production. Detailed genetic and biochemical experiments demonstrated that the kinase activities of IPPK, PPIP5K1 and PPIP5K2 (which convert IP5 to1-IP7) were critical for both interferon induction, and the control of cellular infection by Sendai and influenza A viruses. Conversely, ectopically expressed inositol pyrophosphate-hydrolases DIPPs attenuated interferon transcription. Mechanistic experiments in intact cells revealed that the expression of IPPK, PPIP5K1 and PPIP5K2 was needed for the phosphorylation and activation of IRF3, a transcription factor for interferon. The addition of purified individual inositol pyrophosphates to a cell free reconstituted RIG-I signaling assay further identified 1-IP7 as an essential component required for IRF3 activation. The inositol pyrophosphate may act by β-phosphoryl transfer, since its action was not recapitulated by a synthetic phosphonoacetate analogue of 1-IP7. This study thus identified several novel regulators of RIG-I, and a new role for inositol pyrophosphates in augmenting innate immune responses to viral infection that may have therapeutic applications

The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits

PMCID: PMC3410907This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

GeneChip analyses point to novel pathogenetic mechanisms in mantle cell lymphoma

The translocation t(11;14)(q13;q32) is the genetic hallmark of mantle cell lymphoma (MCL) but is not sufficient for inducing lymphomagenesis. Here we performed genome-wide 100K GeneChip Mapping in 26 t(11;14)-positive MCL and six MCL cell lines. Partial uniparental disomy (pUPD) was shown to be a recurrent chromosomal event not only in MCL cell lines but also in primary MCL. Remarkably, pUPD affected recurrent targets of deletion like 11q, 13q and 17p. Moreover, we identified 12 novel regions of recurrent gain and loss as well as 12 high-level amplifications and eight homozygously deleted regions hitherto undescribed in MCL. Interestingly, GeneChip analyses identified different genes, encoding proteins involved in microtubule dynamics, such as MAP2, MAP6 and TP53, as targets for chromosomal aberration in MCL. Further investigation, including mutation analyses, fluorescence in situ hybridisation as well as epigenetic and expression studies, revealed additional aberrations frequently affecting these genes. In total, 19 of 20 MCL cases, which were subjected to genetic and epigenetic analyses, and five of six MCL cell lines harboured at least one aberration in MAP2, MAP6 or TP53. These findings provide evidence that alterations of microtubule dynamics might be one of the critical events in MCL lymphomagenesis contributing to chromosomal instability

Reducing inappropriate polypharmacy: the process of deprescribing

Inappropriate polypharmacy, especially in older people, imposes a substantial burden of adverse drug events, ill health, disability, hospitalization, and even death. The single most important predictor of inappropriate prescribing and risk of adverse drug events in older patients is the number of prescribed drugs. Deprescribing is the process of tapering or stopping drugs, aimed at minimizing polypharmacy and improving patient outcomes. Evidence of efficacy for deprescribing is emerging from randomized trials and observational studies. A deprescribing protocol is proposed comprising 5 steps: (1) ascertain all drugs the patient is currently taking and the reasons for each one; (2) consider overall risk of drug-induced harm in individual patients in determining the required intensity of deprescribing intervention; (3) assess each drug in regard to its current or future benefit potential compared with current or future harm or burden potential; (4) prioritize drugs for discontinuation that have the lowest benefit-harm ratio and lowest likelihood of adverse withdrawal reactions or disease rebound syndromes; and (5) implement a discontinuation regimen and monitor patients closely for improvement in outcomes or onset of adverse effects. Whereas patient and prescriber barriers to deprescribing exist, resources and strategies are available that facilitate deliberate yet judicious deprescribing and deserve wider application

Deep Sequencing of Pyrethroid-Resistant Bed Bugs Reveals Multiple Mechanisms of Resistance within a Single Population

A frightening resurgence of bed bug infestations has occurred over the last 10 years in the U.S. and current chemical methods have been inadequate for controlling this pest due to widespread insecticide resistance. Little is known about the mechanisms of resistance present in U.S. bed bug populations, making it extremely difficult to develop intelligent strategies for their control. We have identified bed bugs collected in Richmond, VA which exhibit both kdr-type (L925I) and metabolic resistance to pyrethroid insecticides. Using LD50 bioassays, we determined that resistance ratios for Richmond strain bed bugs were ∼5200-fold to the insecticide deltamethrin. To identify metabolic genes potentially involved in the detoxification of pyrethroids, we performed deep-sequencing of the adult bed bug transcriptome, obtaining more than 2.5 million reads on the 454 titanium platform. Following assembly, analysis of newly identified gene transcripts in both Harlan (susceptible) and Richmond (resistant) bed bugs revealed several candidate cytochrome P450 and carboxylesterase genes which were significantly over-expressed in the resistant strain, consistent with the idea of increased metabolic resistance. These data will accelerate efforts to understand the biochemical basis for insecticide resistance in bed bugs, and provide molecular markers to assist in the surveillance of metabolic resistance

Evaluation of high-throughput genomic assays for the Fc gamma receptor locus

Cancer immunotherapy has been revolutionised by the use of monoclonal antibodies (mAb) that function through their interaction with Fc gamma receptors (FcγRs). The low-affinity FcγR genes are highly homologous, map to a complex locus at 1p23 and harbour single nucleotide polymorphisms (SNPs) and copy number variation (CNV) that can impact on receptor function and response to therapeutic mAbs. This complexity can hinder accurate characterisation of the locus. We therefore evaluated and optimised a suite of assays for the genomic analysis of the FcγR locus amenable to peripheral blood mononuclear cells and formalin-fixed paraffin-embedded (FFPE) material that can be employed in a high-throughput manner. Assessment of TaqMan genotyping for FCGR2A-131H/R, FCGR3A-158F/V and FCGR2B-232I/T SNPs demonstrated the need for additional methods to discriminate genotypes for the FCGR3A-158F/V and FCGR2B-232I/T SNPs due to sequence homology and CNV in the region. A multiplex ligation-dependent probe amplification assay provided high quality SNP and CNV data in PBMC cases, but there was greater data variability in FFPE material in a manner that was predicted by the BIOMED-2 multiplex PCR protocol. In conclusion, we have evaluated a suite of assays for the genomic analysis of the FcγR locus that are scalable for application in large clinical trials of mAb therapy. These assays will ultimately help establish the importance of FcγR genetics in predicting response to antibody therapeutics

The Pediatric Cell Atlas:Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

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