Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study’s primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.The data-driven method–ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds.This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

[1]  Hengjin Ke,et al.  ADHD identification and its interpretation of functional connectivity using deep self-attention factorization , 2022, Knowl. Based Syst..

[2]  Daniel Durstewitz,et al.  Deep learning for small and big data in psychiatry , 2020, Neuropsychopharmacology.

[3]  G. V. van Wingen,et al.  Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis , 2020, NeuroImage: Clinical.

[4]  Eduardo Alonso,et al.  DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI , 2020, Journal of Neuroscience Methods.

[5]  Ying Chen,et al.  ADHD classification by dual subspace learning using resting-state functional connectivity , 2020, Artif. Intell. Medicine.

[6]  Naixue Xiong,et al.  Spatio-temporal deep learning method for ADHD fMRI classification , 2019, Inf. Sci..

[7]  Y. Zang,et al.  Inconsistency in Abnormal Functional Connectivity Across Datasets of ADHD-200 in Children With Attention Deficit Hyperactivity Disorder , 2019, Front. Psychiatry.

[8]  Lizhen Shao,et al.  Deep Forest in ADHD Data Classification , 2019, IEEE Access.

[9]  Paul J. Hoffman,et al.  Comprehensive Integration of Single-Cell Data , 2018, Cell.

[10]  Muhammad Asad,et al.  Deep fMRI: AN end-to-end deep network for classification of fMRI data , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[11]  R. McGrath,et al.  The Neurocognitive Profile of Attention-Deficit/Hyperactivity Disorder: A Review of Meta-Analyses , 2018, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[12]  Chunyan Miao,et al.  3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI , 2017, IEEE Access.

[13]  Eduardo Alonso,et al.  FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI , 2017, CNI@MICCAI.

[14]  L. Rohde,et al.  Adult attention-deficit hyperactivity disorder: key conceptual issues. , 2016, The lancet. Psychiatry.

[15]  Daniel S. Margulies,et al.  The Neuro Bureau ADHD-200 Preprocessed repository , 2016, NeuroImage.

[16]  Aaron Kucyi,et al.  Disrupted functional connectivity of cerebellar default network areas in attention‐deficit/hyperactivity disorder , 2015, Human brain mapping.

[17]  Z. Yao,et al.  A review of structural and functional brain networks: small world and atlas , 2015, Brain Informatics.

[18]  R. Buckner,et al.  Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases , 2014, Proceedings of the National Academy of Sciences.

[19]  A. R. Rao,et al.  Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects , 2014, Front. Neural Circuits.

[20]  Cheryl Missiuna,et al.  Psychological distress in children with developmental coordination disorder and attention-deficit hyperactivity disorder. , 2014, Research in developmental disabilities.

[21]  D. Dewey,et al.  Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder , 2014, NeuroImage: Clinical.

[22]  J. Swanson,et al.  Clinical practice: Adult attention deficit-hyperactivity disorder. , 2013, The New England journal of medicine.

[23]  J. Shimony,et al.  Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.

[24]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

[25]  H. Hart,et al.  Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD) , 2012, Neuroscience & Biobehavioral Reviews.

[26]  Paolo Avesani,et al.  ADHD diagnosis from multiple data sources with batch effects , 2012, Front. Syst. Neurosci..

[27]  Chien-Chang Ho,et al.  ADHD classification by a texture analysis of anatomical brain MRI data , 2012, Front. Syst. Neurosci..

[28]  M. B. Nebel,et al.  Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging , 2012, Front. Syst. Neurosci..

[29]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[30]  W. Pelham,et al.  When diagnosing ADHD in young adults emphasize informant reports, DSM items, and impairment. , 2012, Journal of consulting and clinical psychology.

[31]  R. Halari,et al.  A review of fronto-striatal and fronto-cortical brain abnormalities in children and adults with Attention Deficit Hyperactivity Disorder (ADHD) and new evidence for dysfunction in adults with ADHD during motivation and attention , 2012, Cortex.

[32]  F. Xavier Castellanos,et al.  Large-scale brain systems in ADHD: beyond the prefrontal–striatal model , 2012, Trends in Cognitive Sciences.

[33]  Sarah Durston,et al.  Differentiating Frontostriatal and Fronto-Cerebellar Circuits in Attention-Deficit/Hyperactivity Disorder , 2011, Biological Psychiatry.

[34]  Conor Liston,et al.  Atypical Prefrontal Connectivity in Attention-Deficit/Hyperactivity Disorder: Pathway to Disease or Pathological End Point? , 2011, Biological Psychiatry.

[35]  Kerstin Konrad,et al.  Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder , 2010, Human brain mapping.

[36]  Kaustubh Supekar,et al.  Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI , 2010, Front. Syst. Neurosci..

[37]  Erin C Callen,et al.  Current strategies in the diagnosis and treatment of childhood attention-deficit/hyperactivity disorder. , 2009, American family physician.

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

[39]  S. Mostofsky,et al.  Abnormal cerebral cortex structure in children with ADHD , 2009, Human brain mapping.

[40]  Tianzi Jiang,et al.  Enhanced resting-state brain activities in ADHD patients: A fMRI study , 2008, Brain and Development.

[41]  B. Biswal,et al.  Network homogeneity reveals decreased integrity of default-mode network in ADHD , 2008, Journal of Neuroscience Methods.

[42]  Yufeng Wang,et al.  Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder , 2008, NeuroImage.

[43]  B. Biswal,et al.  Cingulate-Precuneus Interactions: A New Locus of Dysfunction in Adult Attention-Deficit/Hyperactivity Disorder , 2008, Biological Psychiatry.

[44]  M. Mehta,et al.  Functional MRI in ADHD: a systematic literature review , 2007, Expert review of neurotherapeutics.

[45]  S. Strakowski,et al.  Co-occurrence of bipolar and attention-deficit hyperactivity disorders in children. , 2006, Bipolar disorders.

[46]  J. Thome,et al.  Anatomical and functional brain imaging in adult attention-deficit/hyperactivity disorder (ADHD)—A neurological view , 2006, European Archives of Psychiatry and Clinical Neuroscience.

[47]  F Xavier Castellanos,et al.  Brain development and ADHD. , 2006, Clinical psychology review.

[48]  Yufeng Zang,et al.  Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study , 2006, Neuroreport.

[49]  F. Castellanos,et al.  Characterizing cognition in ADHD: beyond executive dysfunction , 2006, Trends in Cognitive Sciences.

[50]  James J. Pekar,et al.  Atypical Motor and Sensory Cortex Activation in Attention-Deficit/Hyperactivity Disorder: A Functional Magnetic Resonance Imaging Study of Simple Sequential Finger Tapping , 2006, Biological Psychiatry.

[51]  Jan K Buitelaar,et al.  Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. , 2004, Journal of the American Academy of Child and Adolescent Psychiatry.

[52]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[53]  S. Faraone,et al.  Attention-deficit/hyperactivity disorder in adults: an overview , 2000, Biological Psychiatry.

[54]  P F Renshaw,et al.  Using MRI to examine brain-behavior relationships in males with attention deficit disorder with hyperactivity. , 2000, Journal of the American Academy of Child and Adolescent Psychiatry.

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

[56]  M. Kendall,et al.  The Problem of $m$ Rankings , 1939 .

[57]  Philippe Fortemps,et al.  A multi-level classification framework for multi-site medical data: Application to the ADHD-200 collection , 2018, Expert Syst. Appl..

[58]  M. Milham,et al.  The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience , 2012, Front. Syst. Neurosci..

[59]  L. Adler,et al.  Issues in the Diagnosis and Treatment of Adult ADHD by Primary Care Physicians , 2009 .

[60]  Melanie Stollstorff,et al.  Cognitive neuroscience of Attention Deficit Hyperactivity Disorder: current status and working hypotheses. , 2008, Developmental disabilities research reviews.

[61]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.