Support vector machine classification and characterization of age-related reorganization of functional brain networks

Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18-35 yrs) and 26 older adults (55-85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5mm(3) radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.

[1]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  R Cameron Craddock,et al.  Disease state prediction from resting state functional connectivity , 2009, Magnetic resonance in medicine.

[3]  Yong He,et al.  Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition , 2010, NeuroImage.

[4]  C. Calautti,et al.  Effects of Age on Brain Activation During Auditory-Cued Thumb-to-Index Opposition: A Positron Emission Tomography Study , 2001, Stroke.

[5]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

[6]  Thad A. Polk,et al.  Human Neuroscience , 2022 .

[7]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[8]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[9]  Michael Alexander,et al.  Age-Related Differences in Movement Representation , 2002, NeuroImage.

[10]  A. Cavanna,et al.  The precuneus: a review of its functional anatomy and behavioural correlates. , 2006, Brain : a journal of neurology.

[11]  Alan C. Evans,et al.  Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy , 2010, The Journal of Neuroscience.

[12]  J. Callicott,et al.  Neurophysiological correlates of age-related changes in human motor function , 2002, Neurology.

[13]  Anthony R. McIntosh,et al.  Age-related Changes in Brain Activity across the Adult Lifespan , 2006 .

[14]  J. Callicott,et al.  Age-related alterations in default mode network: Impact on working memory performance , 2010, Neurobiology of Aging.

[15]  John A. E. Anderson,et al.  A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. , 2010, Cerebral cortex.

[16]  Peter S. Jones,et al.  Does healthy aging affect the hemispheric activation balance during paced index-to-thumb opposition task? An fMRI study , 2006, NeuroImage.

[17]  Yana Suchy,et al.  Age-related changes of the functional architecture of the cortico-basal ganglia circuitry during motor task execution , 2011, NeuroImage.

[18]  Dewen Hu,et al.  Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI , 2010, NeuroImage.

[19]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[20]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[21]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[22]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[23]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[24]  S. Rombouts,et al.  Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.

[25]  James K. Nelson,et al.  Age Differences in Deactivation: A Link to Cognitive Control? , 2007, Journal of Cognitive Neuroscience.

[26]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[27]  Allen W. Song,et al.  Measurement of spontaneous signal fluctuations in fMRI: adult age differences in intrinsic functional connectivity , 2009, Brain Structure and Function.

[28]  S. Swinnen,et al.  Neural Basis of Aging: The Penetration of Cognition into Action Control , 2005, The Journal of Neuroscience.

[29]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[30]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

[31]  J. Gabrieli,et al.  Insights into the ageing mind: a view from cognitive neuroscience , 2004, Nature Reviews Neuroscience.

[32]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[33]  Richard S. J. Frackowiak,et al.  Age-related changes in the neural correlates of motor performance. , 2003, Brain : a journal of neurology.

[34]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[35]  M. Hallett,et al.  Aging influence on functional connectivity of the motor network in the resting state , 2007, Neuroscience Letters.

[36]  P. Fransson Spontaneous low‐frequency BOLD signal fluctuations: An fMRI investigation of the resting‐state default mode of brain function hypothesis , 2005, Human brain mapping.

[37]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[38]  Peter A. Bandettini,et al.  Sources of group differences in functional connectivity: An investigation applied to autism spectrum disorder , 2010, NeuroImage.

[39]  S. Swinnen,et al.  Systems Neuroplasticity in the Aging Brain: Recruiting Additional Neural Resources for Successful Motor Performance in Elderly Persons , 2008, The Journal of Neuroscience.

[40]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[41]  J. Morris,et al.  Functional deactivations: Change with age and dementia of the Alzheimer type , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[42]  U. Lindenberger,et al.  Relations between aging sensory/sensorimotor and cognitive functions , 2002, Neuroscience & Biobehavioral Reviews.

[43]  Joseph T. Gwin,et al.  Motor control and aging: Links to age-related brain structural, functional, and biochemical effects , 2010, Neuroscience & Biobehavioral Reviews.

[44]  Y. Zang,et al.  Normal aging decreases regional homogeneity of the motor areas in the resting state , 2007, Neuroscience Letters.

[45]  Kaustubh Supekar,et al.  Development of Large-Scale Functional Brain Networks in Children , 2009, NeuroImage.

[46]  Anthony Randal McIntosh,et al.  Age-related Changes in Brain Activity across the Adult Lifespan , 2006, Journal of Cognitive Neuroscience.

[47]  Otto W. Witte,et al.  Functional significance of age-related differences in motor activation patterns , 2006, NeuroImage.