Prediction of brain maturity based on cortical thickness at different spatial resolutions

Khundrakpam, Budhachandra; Tohka, Jussi; Evans, Alan C.

doi:10.1016/j.neuroimage.2015.02.046

Cited by 95 publications

(104 citation statements)

References 59 publications

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“…This enabled us to divide the problem into several sub-problems with lower complexity while better retaining the original spatial resolution of the thickness measures. We hypothesized that both of these properties are important for successful predictions: Khundrakpam et al (2015) have previously demonstrated that a fine parcellation of the cortical thickness measures was advantageous for age estimation within healthy children. However, increasing spatial resolution results in higher dimensionality, which increases the complexity of the model.…”

Section: Discussionmentioning

confidence: 99%

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

et al. 2017

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Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multiprotocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a wholePreprint submitted to NeuroImage September 4, 2017 brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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Section: Discussionmentioning

confidence: 99%

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

et al. 2017

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“…Furthermore, several previous studies which were either cortical thickness (Rodrigue and Kennedy, 2011; Khundrakpam et al, 2015) or activation fMRI studies (Dosenbach et al, 2010; Qiu et al, 2015) have reported that the SM system contributes to estimate the age of young adults and aging subjects. In this study, we found that the age of older adults could be predicted by decreased rsFC value between the mid-posterior insula and primary sensorimotor cortex, as well as decreased rsFC value of mid-posterior insula resulted from ICA analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Khundrakpam et al (2015) found that the top predictors of brain maturity were found in highly localized sensorimotor (precentral and postcentral gyrus, insula) and association areas (including middle and superior frontal gyrus) in normally growing children and adolescents. Similarly, Dosenbach et al (2010) reported that rsFC of SM network contributed in estimating chronological age in the typically developing volunteers.…”

Section: Introductionmentioning

confidence: 97%

“…Several previous structural and functional studies using magnetic resonance imaging (MRI) scans have also shown developmental trajectories in brain maturation and aging (Dosenbach et al, 2010; Rodrigue and Kennedy, 2011; Cao et al, 2014, 2016; Khundrakpam et al, 2015). Khundrakpam et al (2015) found that the top predictors of brain maturity were found in highly localized sensorimotor (precentral and postcentral gyrus, insula) and association areas (including middle and superior frontal gyrus) in normally growing children and adolescents.…”

Section: Introductionmentioning

confidence: 99%

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The Functional Integration in the Sensory-Motor System Predicts Aging in Healthy Older Adults

Chen

Chang

et al. 2017

Front. Aging Neurosci.

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Healthy aging is typically accompanied by a decrease in the motor capacity. Although the disrupted neural representations and performance of movement have been observed in older age in previous studies, the relationship between the functional integration of sensory-motor (SM) system and aging could be further investigated. In this study, we examine the impact of healthy aging on the resting-state functional connectivity (rsFC) of the SM system, and investigate as to how aging is affecting the rsFC in SM network. The SM network was identified and evaluated in 52 healthy older adults and 51 younger adults using two common data analytic approaches: independent component analysis and seed-based functional connectivity (seed at bilateral M1 and S1). We then evaluated whether the altered rsFC of the SM network could delineate trajectories of the age of older adults using a machine learning methodology. Compared with the younger adults, the older demonstrated reduced functional integration with increasing age in the mid-posterior insula of SM network and increased rsFC among the sensorimotor cortex. Moreover, the reduction in the rsFC of mid-posterior insula is associated with the age of older adults. Critically, the analysis based on two-aspect connectivity-based prediction frameworks revealed that the age of older adults could be reliably predicted by this reduced rsFC. These findings further indicated that healthy aging has a marked influence on the SM system that would be associated with a reorganization of SM system with aging. Our findings provide further insight into changes in sensorimotor function in the aging brain.

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“…Recent studies using the concept of brain maturation index have shown promising advancement of predicting individual brain maturity in healthy children, adolescents and young adults (Brown et al, 2012;Chen et al, 2007;Dosenbach et al, 2010;Erus et al, 2014;Franke, Luders, May, Wilke, and Gaser, 2012;Khundrakpam, Tohka, and Evans, 2015), as well as aging over the human lifespan (Franke, Ziegler, Klöppel, and Gaser, 2010;Mwangi, Hasan, and Soares, 2013). By using machine learning technique and cross validation among large amount of data, these studies can account for as high as 92% of variance Fig.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a brain maturation index using longitudinal neuroanatomical scans

et al. 2015

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Prediction of brain maturity based on cortical thickness at different spatial resolutions

Cited by 95 publications

References 59 publications

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

The Functional Integration in the Sensory-Motor System Predicts Aging in Healthy Older Adults

Development and validation of a brain maturation index using longitudinal neuroanatomical scans

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