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.
Highlights
The neuropathology underlying psychiatric disorders is poorly defined, and, psychiatric nosology is mainly informed by clinical observation
We focus here on bipolar disorder (BD) where the current evidence base is rather limited despite the fact that BD ranks among the leading causes of disability worldwide across all age groups [11]
To illustrate the usefulness of precision mapping, we focus on the connectivity profiles of the nucleus accumbens (NAc) and the ventral prefrontal cortex
Summary
The neuropathology underlying psychiatric disorders is poorly defined, and, psychiatric nosology is mainly informed by clinical observation. Psychiatric diagnoses are likely heterogeneous, multifaceted, and overlapping in their etiology and pathophysiology [1]. This motivates efforts to characterize valid and reliable biological markers of disease expression in order to facilitate early identification and novel treatment discovery. Findings from the neuroimaging literature have improved the characterization of the biological underpinnings of psychiatric disorders but have had limited clinical utility, as they are based on group-level inferences that cannot be readily applied to single individuals [4]. The term “third-generation imaging” collectively describes the development of new paradigms in image acquisition and analysis that aim to identify brain markers that can improve diagnostic assessment and prognostic formulations and lead to personalized treatment planning [4].
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