Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication‐class of response in complex patients

This study determined the clinical utility of an fMRI classification algorithm predicting medication‐class of response in patients with challenging mood diagnoses.

[1]  P. Williamson,et al.  Correlation of brain default mode network activation with bipolarity index in youth with mood disorders. , 2013, Journal of affective disorders.

[2]  H. Akiskal.  The emergence of the bipolar spectrum: validation along clinical-epidemiologic and familial-genetic lines. , 2007, Psychopharmacology bulletin.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Daniel D. Lee,et al.  Grassmann discriminant analysis: a unifying view on subspace-based learning , 2008, ICML '08.

[5]  Anna Manelis,et al.  Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach. , 2016, Brain : a journal of neurology.

[6]  J. Mccombs,et al.  The impact of unrecognized bipolar disorders for patients treated for depression with antidepressants in the fee-for-services California Medicaid (Medi-Cal) program. , 2004, Journal of affective disorders.

[7]  Tülay Adali,et al.  A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework , 2009, Human brain mapping.

[8]  Teresa A. Victor,et al.  Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders , 2017, Brain Structure and Function.

[9]  V. Calhoun,et al.  Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery , 2012, IEEE Reviews in Biomedical Engineering.

[10]  J. Nurnberger,et al.  Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. , 1994, Archives of general psychiatry.

[11]  V. Menon.  Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[12]  Gene H. Golub,et al.  Numerical methods for computing angles between linear subspaces , 1971, Milestones in Matrix Computation.

[13]  Y Li,et al.  Clinical utility of a short resting‐state MRI scan in differentiating bipolar from unipolar depression , 2017, Acta psychiatrica Scandinavica.

[14]  R. Kessler,et al.  Clinical and Economic Effects of Unrecognized or Inadequately Treated Bipolar Disorder , 2008, Journal of psychiatric practice.

[15]  Vince D. Calhoun,et al.  Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data , 2015, IEEE Transactions on Autonomous Mental Development.

[16]  Tianzi Jiang,et al.  Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. , 2016, Journal of affective disorders.

[17]  Hao He,et al.  Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches , 2016, Human brain mapping.

[18]  Mario Dzemidzic,et al.  Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression , 2009, Psychiatry Research: Neuroimaging.

[19]  Peter Dechent,et al.  Differentiating unipolar and bipolar depression by alterations in large‐scale brain networks , 2016, Human brain mapping.

[20]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[21]  H. Akiskal,et al.  Toward a validation of a new definition of agitated depression as a bipolar mixed state (mixed depression) , 2004, European Psychiatry.

[22]  John A. Rice,et al.  The long-term natural history of the weekly symptomatic status of bipolar I disorder. , 2002, Archives of general psychiatry.

[23]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I : clinical version : scoresheet , 1997 .

[24]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[25]  Jingyu Liu,et al.  A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders , 2015, NeuroImage.

[26]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[27]  Yuhui Du,et al.  Group information guided ICA for fMRI data analysis , 2013, NeuroImage.

[28]  Alex Fornito,et al.  Voxelwise Meta-Analysis of Gray Matter Abnormalities in Bipolar Disorder , 2010, Biological Psychiatry.

[29]  F. Goodwin,et al.  Antidepressants worsen rapid-cycling course in bipolar depression: A STEP-BD randomized clinical trial. , 2015, Journal of affective disorders.

[30]  P. Fox,et al.  Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies , 2017, JAMA psychiatry.

[31]  Tianzi Jiang,et al.  Discriminant analysis of functional connectivity patterns on Grassmann manifold , 2011, NeuroImage.