Detecting neuroimaging biomarkers for schizophrenia:a meta-analysis of multivariate pattern recognition studies

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7–83.5%) and a specificity of 80.3% (95% CI: 76.9–83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9–88.2%) and similar specificity (76.9%, 95% CI: 71.3–81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9–80.4%, specificity of 79.0%, 95% CI: 74.6–82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity

Genetic modifiers of cognitive maintenance among older adults

OBJECTIVE: Identify genetic factors associated with cognitive maintenance in late life and assess their association with gray matter (GM) volume in brain networks affected in aging. METHODS: We conducted a genome-wide association study of ∼2.4 M markers to identify modifiers of cognitive trajectories in Caucasian participants (N = 7,328) from two population-based cohorts of non-demented elderly. Standardized measures of global cognitive function (z-scores) over 10 and 6 years were calculated among participants and mixed model regression was used to determine subject-specific cognitive slopes. “Cognitive maintenance” was defined as a change in slope of ≥ 0 and was compared with all cognitive decliners (slope < 0). In an independent cohort of cognitively normal older Caucasians adults (N = 122), top association findings were then used to create genetic scores to assess whether carrying more cognitive maintenance alleles was associated with greater GM volume in specific brain networks using voxel-based morphometry. RESULTS: The most significant association was on chromosome 11 (rs7109806, P = 7.8 × 10(−8)) near RIC3. RIC3 modulates activity of α7 nicotinic acetylcholine receptors, which have been implicated in synaptic plasticity and beta-amyloid binding. In the neuroimaging cohort, carrying more cognitive maintenance alleles was associated with greater volume in the right executive control network (RECN; P(FWE) = 0.01). CONCLUSIONS: These findings suggest that there may be genetic loci that promote healthy cognitive aging and that they may do so by conferring robustness to GM in the RECN. Future work is required to validate top candidate genes such as RIC3 for involvement in cognitive maintenance

METHODS OF SEARCHING FOR DIAMOND DEPOSITS ON THE EXAMPLE OF NOVOHRAD-VOLYNSKYI AREA OF UKRAINIAN SHIELD

The article gives an analysis and an assessment of the effectiveness of the methods of studying availability of diamonds of the territory of the Novohrad-Volynskyі block of the Ukrainian Shield. The geological-geophysical and petrographic conditions of the perspective Novohrad-Volynskyi area are described in detail and the search criteria of diamond content are determined. An analysis of the application of modern materials of space surveys in the study of the thermal field of the earth’s surface, structural deciphering and morphostructural analysis is presented in order to predict the manifestations of kimberlite magmatism

Learning Guided by Others

Abstract. Graph matching is one of the principal methods to formulate the correspondence between two set of points in computer vision and pattern recognition. However, most formulations are based on the minimization of a difficult energy function which is known to be NP-hard. Traditional methods solve the minimization problem approximately. In this paper, we show that an efficient solution can be obtained by exactly solving an approximated problem instead of approximately solving the original problem. We derive an exact minimization algorithm and successfully applied to action recognition in videos. In this context, we take advantage of special properties of the time domain, in particular causality and the linear order of time, and propose a novel spatio-temporal graphical structure. Keywords: Space-time graph, Hyper-graph matching, Action recognition

Accelerating Infinite Ensemble of Clustering by Pivot Features

The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system with the high time complexity in proportion to O(d3) where d is the concatenated dimension of many clustering results. Inspired by the cognitive characteristic of human memory that can pay attention to the pivot features in a more compressed data space, we propose an acceleration version of IEC (AIEC) by extracting the pivot features and learning the multiple mappings to reconstruct them, where the linear equation system can be solved with the time complexity O(dr2) (r ≪ d). Experimental results on the standard datasets including image and text ones show that our algorithm AIEC improves the running time of IEC greatly but achieves the comparable clustering performance

Multimodal manifold-regularized transfer learning for MCI conversion prediction

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods