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1.
J Neurosci Methods ; 335: 108598, 2020 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-32004594

RESUMO

BACKGROUND: Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising aid in the diagnosis of such diseases. However, there are various models of DW-MRI and the interpretation of diffusion metrics depends on the model used in fitting data. Most previous studies are entirely based on parameters calculated from a single diffusion model. NEW METHOD: We employ a data fusion framework wherein diffusion metrics from different models such as diffusion tensor imaging, diffusion kurtosis imaging and constrained spherical deconvolution model are fused using well known blind source separation approach to investigate white matter microstructural changes in population comprising of controls and VCID subgroups. Multiple comparisons between subject groups and prediction analysis using features from individual models and proposed fusion model are carried out to evaluate performance of proposed method. RESULTS: Diffusion features from individual models successfully distinguished between controls and disease groups, but failed to differentiate between disease groups, whereas fusion approach showed group differences between disease groups too. WM tracts showing significant differences are superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation and corticospinal tract. COMPARISON WITH EXISTING METHOD: ROC analysis showed increased AUC for fusion (AUC = 0.913, averaged across groups and tracts) compared to that of uni-model features (AUC = 0.77) demonstrating increased sensitivity of proposed method. CONCLUSION: Overall our results highlight the benefits of multi-model fusion approach, providing improved sensitivity in discriminating VCID subgroups.

2.
Transl Psychiatry ; 10(1): 65, 2020 Feb 12.
Artigo em Inglês | MEDLINE | ID: mdl-32066697

RESUMO

Attention-deficit/hyperactivity disorder (ADHD) often persists into adulthood, with a shift of symptoms including less hyperactivity/impulsivity and more co-morbidity of affective disorders in ADHDadult. Many studies have questioned the stability in diagnosing of ADHD from childhood to adulthood, and the shared and distinct aberrant functional connectivities (FCs) between ADHDchild and ADHDadult remain unidentified. We aim to explore shared and distinct FC patterns in ADHDchild and ADHDadult, and further investigated the cross-cohort predictability using the identified FCs. After investigating the ADHD-discriminative FCs from healthy controls (HCs) in both child (34 ADHDchild, 28 HCs) and adult (112 ADHDadult,77 HCs) cohorts, we identified both shared and distinct aberrant FC patterns between cohorts and their association with clinical symptoms. Moreover, the cross-cohort predictability using the identified FCs were tested. The ADHD-HC classification accuracies were 84.4% and 81.0% for children and male adults, respectively. The ADHD-discriminative FCs shared in children and adults lie in the intra-network within default mode network (DMN) and the inter-network between DMN and ventral attention network, positively correlated with total scores of ADHD symptoms. Particularly, inter-network FC between somatomotor network and dorsal attention network was uniquely impaired in ADHDchild, positively correlated with hyperactivity index; whereas the aberrant inter-network FC between DMN and limbic network exhibited more adult-specific ADHD dysfunction. And their cross-cohort predictions were 70.4% and 75.6% between each other. This work provided imaging evidence for symptomatic changes and pathophysiological continuity in ADHD from childhood to adulthood, suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.

3.
Artigo em Inglês | MEDLINE | ID: mdl-31944934

RESUMO

OBJECTIVE: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). METHODS: We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method. RESULTS: Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components. CONCLUSION: we identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality. SIGNIFICANCE: The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.

4.
Trends Cogn Sci ; 24(2): 135-149, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31983607

RESUMO

There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial patterns of functional structures are not well characterized. Here, we present the concepts of spatially, temporally, and spatiotemporally dynamics and use this terminology to categorize existing approaches. We review current spatially dynamic connectivity work, emphasizing that explicit incorporation of space into dynamic analyses can expand our understanding of brain function and disorder.

5.
J Neurosci Methods ; 334: 108600, 2020 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-31978489

RESUMO

BACKGROUND: Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC. NEW METHOD: Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC. RESULTS: Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls. COMPARISON WITH EXISTING METHODS: We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower. CONCLUSIONS: As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

6.
Hum Brain Mapp ; 2020 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-31904902

RESUMO

Electroconvulsive therapy is regarded as the most effective antidepressant treatment for severe and treatment-resistant depressive episodes. Despite the efficacy of electroconvulsive therapy, the neurobiological underpinnings and mechanisms underlying electroconvulsive therapy induced antidepressant effects remain unclear. The objective of this investigation was to identify electroconvulsive therapy treatment responsive multimodal biomarkers with the 17-item Hamilton Depression Rating Scale guided brain structure-function fusion in 118 patients with depressive episodes and 60 healthy controls. Results show that reduced fractional amplitude of low frequency fluctuations in the prefrontal cortex, insula and hippocampus, linked with increased gray matter volume in anterior cingulate, medial temporal cortex, insula, thalamus, caudate and hippocampus represent electroconvulsive therapy responsive covarying functional and structural brain networks. In addition, relative to nonresponders, responder-specific electroconvulsive therapy related brain networks occur in frontal-limbic network and are associated with successful therapeutic outcomes. Finally, electroconvulsive therapy responsive brain networks were unrelated to verbal declarative memory. Using a data-driven, supervised-learning method, we demonstrated that electroconvulsive therapy produces a remodeling of brain functional and structural covariance that was unique to antidepressant symptom response, but not linked to memory impairment.

7.
Schizophr Bull ; 46(1): 202-210, 2020 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-31174212

RESUMO

Catatonia is a nosologically unspecific syndrome, which subsumes a plethora of mostly complex affective, motor, and behavioral phenomena. Although catatonia frequently occurs in schizophrenia spectrum disorders (SSD), specific patterns of abnormal brain structure and function underlying catatonia are unclear at present. Here, we used a multivariate data fusion technique for multimodal magnetic resonance imaging (MRI) data to investigate patterns of aberrant intrinsic neural activity (INA) and gray matter volume (GMV) in SSD patients with and without catatonia. Resting-state functional MRI and structural MRI data were collected from 87 right-handed SSD patients. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). A multivariate analysis approach was used to examine co-altered patterns of INA and GMV. Following a categorical approach, we found predominantly frontothalamic and corticostriatal abnormalities in SSD patients with catatonia (NCRS total score ≥ 3; n = 24) when compared to SSD patients without catatonia (NCRS total score = 0; n = 22) matched for age, gender, education, and medication. Corticostriatal network was associated with NCRS affective scores. Following a dimensional approach, 33 SSD patients with catatonia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision were identified. NCRS behavioral scores were associated with a joint structural and functional system that predominantly included cerebellar and prefrontal/cortical motor regions. NCRS affective scores were associated with frontoparietal INA. This study provides novel neuromechanistic insights into catatonia in SSD suggesting co-altered structure/function-interactions in neural systems subserving coordinated visuospatial functions and motor behavior.

8.
Neuroimage ; 207: 116370, 2020 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-31751666

RESUMO

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

9.
Hum Brain Mapp ; 41(3): 617-631, 2020 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-31633256

RESUMO

The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury. Outcome was measured using the Glasgow Outcome Scale Extended (GOS-E) at 6 months postinjury. The 57 patients who completed the GOS-E were classified as recovered completely (GOS-E = 8; n = 37) or incompletely (GOS-E < 8; n = 20). Neuropsychological test scores were similar for all groups. Patients with incomplete recovery spent less time in a segregated brain state compared to recovered patients during the second visit. Also, these patients moved less frequently from one meta-state to another as compared to healthy controls and recovered patients. Furthermore, incomplete recovery was associated with disruptions in cyclic state transition patterns, called attractors, during both visits. This study demonstrates that poor long-term functional recovery is associated with alterations in dynamics between brain networks, which becomes more marked as a function of time. These results could be related to psychological processes rather than injury-effects, which is an interesting area for further work. Another natural progression of the current study is to examine whether these dynamic measures can be used to monitor treatment effects.

10.
IEEE Trans Biomed Eng ; 67(1): 110-121, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30946659

RESUMO

OBJECTIVE: We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. METHODS: It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. RESULTS: Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. CONCLUSION: N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. SIGNIFICANCE: The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

11.
J Neurosci Methods ; 332: 108531, 2020 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-31830544

RESUMO

BACKGROUND: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings. METHODS: We propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using both the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. RESULTS: Our results indicate that females and males in young adult group possess substantial difference related to visual network. In addition, some other consistent conclusions have been reached by using these two datasets. Furthermore, the GICA-TVGL model indicated that females had a higher probability to stay in a stable state. Males had a higher tendency to remain in a globally disconnected mode. COMPARISON WITH EXISTING METHOD: The performance of sliding window approach is largely affected by the window size selection. In addition, it also assumes temporal locality hypothesis. CONCLUSION: Our proposed framework provides a feasible method to investigate brain dynamics and has the potential to become a widely used tool in neuroimaging studies.

12.
Comput Methods Programs Biomed ; 183: 105073, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31525548

RESUMO

BACKGROUND AND OBJECTIVE: Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. METHODS: In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. RESULTS: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. CONCLUSIONS: Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.

13.
J Neurosci Methods ; 329: 108418, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31630085

RESUMO

BACKGROUND: In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load. NEW METHOD: Our research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy. RESULTS: We show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods. COMPARISON WITH EXISTING METHODS: Many decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy. CONCLUSIONS: We show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.

15.
Artigo em Inglês | MEDLINE | ID: mdl-31825864

RESUMO

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.

16.
PLoS One ; 14(12): e0224744, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31825974

RESUMO

From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as "polarization." These are whole-brain states in which the voxelwise-normalized BOLD (vn-BOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.

17.
Artigo em Inglês | MEDLINE | ID: mdl-31796393

RESUMO

Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus characterizes highorder relations among multiple ROIs that cannot be uncovered by a simple graph in the traditional graph based FCN construction methods. Unlike the existing hypergraph based methods where all hyperedges are assumed to have equal weights and only certain topological features are extracted from the hypergraphs, we propose a hypergraph learning based method for FCN construction in this paper. Specifically, we first generate hyperedges from fMRI time series based on sparse representation, then employ hypergraph learning to adaptively learn hyperedge weights, and finally define a hypergraph similarity matrix to represent the FCN. In our proposed method, weighting hyperedges results in better discriminative FCNs across subjects, and the defined hypergraph similarity matrix can better reveal the overall structure of brain network than using those hypergraph topological features. Moreover, we propose a multi-hypergraph learning based method by integrating multi-paradigm fMRI data, where the hyperedge weights associated with each fMRI paradigm are jointly learned and then a unified hypergraph similarity matrix is computed to represent the FCN. We validate the effectiveness of the proposed method on the Philadelphia Neurodevelopmental Cohort dataset for the classification of individuals' learning ability from three paradigms of fMRI data. Experimental results demonstrate that our proposed approach outperforms the traditional graph based methods (i.e., Pearson's correlation and partial correlation with the graphical Lasso) and the existing unweighted hypergraph based methods, which sheds light on how to optimize estimation of FCNs for cognitive and behavioral study.

18.
Hum Brain Mapp ; 2019 Dec 26.
Artigo em Inglês | MEDLINE | ID: mdl-31876339

RESUMO

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

19.
Schizophr Bull ; 2019 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-31682728

RESUMO

Cortical volumetric asymmetry (CVA) has been widely observed in individuals with psychosis, and is associated with etiological risk factors (e.g., genetics, neuromaturation) and treatment response. However, it is unclear whether CVA abnormalities emerge before psychotic illness onset. Understanding whether CVA manifests in clinical high-risk (CHR)-compared with healthy controls and schizophrenia patients (SCZ)-over time may inform our understanding of pathogenic factors. A total of 233 individuals: 73 CHR, 112 healthy controls, and 48 SCZ underwent an MRI and clinical interviews. Ninety-four individuals including healthy volunteers (HV) (n = 49) and CHR (n = 45), completed another scan at 12-months. CVA was compared by lobe in a repeated-measure design across groups, then nested by time in a longitudinal model. CHR and SCZ groups showed reduced global CVA compared with the healthy control groups but the CHR and SCZ group did not differ from each other. A group by lobe interaction indicated the presence of lobe specific reductions in frontal and cingulate CVA. Cingulate CVA was reduced in CHR and SCZ groups compared to HC groups but did not differ from each other. Frontal CVA was reduced in the older healthy controls compared with younger-HC and CHR, but did not differ from the similarly aged SZ group. CVA is similarly impacted in SCZ and CHR groups, potentially reflecting pathogenic processes. Longitudinal analyses provided further support for the neurodevelopmental hypothesis as CHR exhibited longitudinal changes in opposite directions from normative neuromaturation in HV, which was related to increasing risk for psychosis in the CHR.

20.
Proc IEEE Int Symp Biomed Imaging ; 2019: 418-421, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31687092

RESUMO

Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iteratively updates the unmixing matrix by infomax (for independence) and the sources by Hoyer projection (for sparsity), feeding the sparse sources back as input data for the next iteration. Consequently, sparseness propagates effectively through infomax iterations, producing sources with more desirable properties. Simulation results on both brain imaging and genetic data demonstrate that the proposed algorithm yields improved pattern recovery, particularly under low signal-to-noise ratio conditions, as well as improved sparseness compared to traditional infomax.

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