2,115 research outputs found
Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients
We aim to develop clustering models to obtain behavioral representations from
continuous multimodal mobile sensing data towards relapse prediction tasks. The
identified clusters could represent different routine behavioral trends related
to daily living of patients as well as atypical behavioral trends associated
with impending relapse.
We used the mobile sensing data obtained in the CrossCheck project for our
analysis. Continuous data from six different mobile sensing-based modalities
(e.g. ambient light, sound/conversation, acceleration etc.) obtained from a
total of 63 schizophrenia patients, each monitored for up to a year, were used
for the clustering models and relapse prediction evaluation. Two clustering
models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were
used to obtain behavioral representations from the mobile sensing data. The
features obtained from the clustering models were used to train and evaluate a
personalized relapse prediction model using Balanced Random Forest. The
personalization was done by identifying optimal features for a given patient
based on a personalization subset consisting of other patients who are of
similar age.
The clusters identified using the GMM and PAM models were found to represent
different behavioral patterns (such as clusters representing sedentary days,
active but with low communications days, etc.). Significant changes near the
relapse periods were seen in the obtained behavioral representation features
from the clustering models. The clustering model based features, together with
other features characterizing the mobile sensing data, resulted in an F2 score
of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation
setting. This obtained F2 score is significantly higher than a random
classification baseline with an average F2 score of 0.042
Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics
Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach
Social and spatial heterogeneity in psychosis proneness in a multilevel case-prodrome-control study
To test whether spatial and social neighbourhood patterning of people at ultra-high risk (UHR) of psychosis differs from first-episode psychosis (FEP) participants or controls and to determine whether exposure to different social environments is evident before disorder onset
Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders
Personality is influenced by genetic and environmental factors1
and associated with mental health. However, the underlying
genetic determinants are largely unknown. We identified six
genetic loci, including five novel loci2,3, significantly associated
with personality traits in a meta-analysis of genome-wide
association studies (N = 123,132–260,861). Of these genomewide
significant loci, extraversion was associated with variants
in WSCD2 and near PCDH15, and neuroticism with variants
on chromosome 8p23.1 and in L3MBTL2. We performed a
principal component analysis to extract major dimensions
underlying genetic variations among five personality traits
and six psychiatric disorders (N = 5,422–18,759). The first
genetic dimension separated personality traits and psychiatric
disorders, except that neuroticism and openness to experience
were clustered with the disorders. High genetic correlations
were found between extraversion and attention-deficit–
hyperactivity disorder (ADHD) and between openness and
schizophrenia and bipolar disorder. The second genetic
dimension was closely aligned with extraversion–introversion
and grouped neuroticism with internalizing psychopathology
(e.g., depression or anxiety)
Hierarchical organization of functional connectivity in the mouse brain: a complex network approach
This paper represents a contribution to the study of the brain functional
connectivity from the perspective of complex networks theory. More
specifically, we apply graph theoretical analyses to provide evidence of the
modular structure of the mouse brain and to shed light on its hierarchical
organization. We propose a novel percolation analysis and we apply our approach
to the analysis of a resting-state functional MRI data set from 41 mice. This
approach reveals a robust hierarchical structure of modules persistent across
different subjects. Importantly, we test this approach against a statistical
benchmark (or null model) which constrains only the distributions of empirical
correlations. Our results unambiguously show that the hierarchical character of
the mouse brain modular structure is not trivially encoded into this
lower-order constraint. Finally, we investigate the modular structure of the
mouse brain by computing the Minimal Spanning Forest, a technique that
identifies subnetworks characterized by the strongest internal correlations.
This approach represents a faster alternative to other community detection
methods and provides a means to rank modules on the basis of the strength of
their internal edges.Comment: 11 pages, 9 figure
Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates.
Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants
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