Connectivity dynamics from wakefulness to sleep

Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 ​s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Laufs, Helmut. Goethe Universitat Frankfurt; AlemaniaFil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados Unido

O ensino das ciências experimentais no liceu, em Portugal, na I República (1910-1926)

O ensino das ciências experimentais (ECE) em Portugal ficou, como pretendemos demonstrar, fortemente marcado pela instauração da República, que comemorou no ano transacto o seu centenário. A República de 1910 pretendeu reformar toda a mentalidade portuguesa, através do pilar base – a educação – pela qual seria capaz de sacudir a nossa maneira de ser, lançando desta forma o país para um progresso de nível europeu. O estudo a que nos propomos, uma investigação documental no domínio da História da Ciência1, visa aprofundar os conhecimentos existentes sobre esta época e perceber o impacto da reforma do ECE, principalmente nos Liceus, caracterizando as principais figuras, políticas e docentes responsáveis pela sua conceptualização e aplicação. Através desta investigação procuraremos lançar as primeiras bases para descobrir as origens deste pensamento, querendo ainda comparar os fundamentos psicopedagógicos, epistemológicos e sociológicos da época com as principais ideias actualmente presentes no ensino da Ciência. Com este trabalho pretendemos, num primeiro momento, apresentar e divulgar o desenho da investigação e os seus objectivos, na procura de estabelecer parcerias e receber contributos da comunidade académica interessada por esta problemática

Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity

Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

In the field of neuroimaging, there is a growing interest in developing collaborative frameworks that enable researchers to address challenging questions about the human brain by leveraging data across multiple sites all over the world. Additionally, efforts are also being directed at developing algorithms that enable collaborative analysis and feature learning from multiple sites without requiring the often large data to be centrally located. In this paper, we propose two new decentralized algorithms: (1) A decentralized regression algorithm for performing a voxel-based morphometry analysis on structural magnetic resonance imaging (MRI) data and, (2) A decentralized dynamic functional network connectivity algorithm which includes decentralized group ICA and sliding-window analysis of functional MRI data. We compare results against those obtained from their pooled (or centralized) counterparts on the same data i.e., as if they are at one site. Results produced by the decentralized algorithms are similar to the pooled-case and showcase the potential of performing multi-voxel and multivariate analyses of data located at multiple sites. Such approaches enable many more collaborative and comparative analysis in the context of large-scale neuroimaging studies

PolySearch: A web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites

A particular challenge in biomedical text mining is to find ways of handling ‘comprehensive’ or ‘associative’ queries such as ‘Find all genes associated with breast cancer’. Given that many queries in genomics, proteomics or metabolomics involve these kind of comprehensive searches we believe that a web based tool that could support these searches would be quite useful. In response to this need, we have developed the PolySearch web server. PolySearch supports `50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is ‘Given X, find all Y’s’ where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. PolySearch’s performance has been assessed in tasks such as gene synonym identification, protein– protein interaction identification and disease gene identification using a variety of manually assembled ‘gold standard’ text corpuses. Its f-measure on these tasks is 88, 81 and 79%, respectively. These values are between 5 and 50% better than other published tools. The server is freely available at http://wishart. biology.ualberta.ca/polysearc

Genetic Markers of White Matter Integrity in Schizophrenia Revealed by Parallel ICA

It is becoming a consensus that white matter integrity is compromised in schizophrenia (SZ), however the underlying genetics remains elusive. Evidence suggests a polygenic basis of the disorder, which involves various genetic variants with modest individual effect sizes. In this work, we used a multivariate approach, parallel independent component analysis (P-ICA), to explore the genetic underpinnings of white matter abnormalities in SZ. A pre-filtering step was first applied to locate 6527 single nucleotide polymorphisms (SNPs) discriminating patients from controls with a nominal uncorrected p-value of 0.01. These potential susceptibility loci were then investigated for associations with fractional anisotropy (FA) images in a cohort consisting of 73 SZ patients and 87 healthy controls (HC). A significant correlation (r = −0.37, p = 1.25 × 10−6 ) was identified between one genetic factor and one FA component after controlling for scanning site, ethnicity, age, and sex. The identified FA-SNP association remained stable in a 10-fold validation. A 5000-run permutation test yielded a p-value of 2.00 × 10−4 . The FA component reflected decreased white matter integrity in the forceps major for SZ patients. The SNP component was overrepresented in genes whose products are involved in corpus callosum morphology (e.g., CNTNAP2, NPAS3, and NFIB) as well as canonical pathways of synaptic long term depression and protein kinase A signaling. Taken together, our finding delineates a part of genetic architecture underlying SZ-related FA reduction, emphasizing the important role of genetic variants involved in neural development

A Multi-site Resting State fMRI Study on the Amplitude of Low Frequency Fluctuations in Schizophrenia

Background: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC). Methods: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01–0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions. Results: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF. Conclusions: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences