title: Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.
abstract: Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key gray matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson's disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only gray matter landmarks. Finally, we describe how this approach could be used in planning DBS treatment of psychiatric disorders.
authors.affiliation: Brain Imaging Center, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai , New York, NY , USA.
authors.forename: Rafael
authors.initials: R
authors.lastname: O'Halloran
authors.affiliation: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
authors.forename: Brian H
authors.initials: BH
authors.lastname: Kopell
authors.affiliation: Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY , USA.
authors.forename: Emma
authors.initials: E
authors.lastname: Sprooten
authors.affiliation: Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
authors.forename: Wayne K
authors.initials: WK
authors.lastname: Goodman
authors.affiliation: Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY , USA.
authors.forename: Sophia
authors.initials: S
authors.lastname: Frangou
keywords: deep brain stimulation; individual variability; machine learning applied to neuroscience; multimodal imaging; precision psychiatry