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Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

O'Halloran R, Kopell BH, Sprooten E, Goodman WK, Frangou S - Front Psychiatry (2016)

Bottom Line: 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 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.However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment.

View Article: PubMed Central - PubMed

Affiliation: Brain Imaging Center, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai , New York, NY , USA.

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.

No MeSH data available.


Related in: MedlinePlus

Schematic illustration of the processes involved in precision mapping. Step 1: seeds (red) and targets (vPFC: yellow; nucleus accumbens: green) are identified in native space. Step 2: probabilistic tractography is performed. The tract is binarized at 1% of the maximum value (red). Step 3: the segments (red) of the vPFC that is connected to the nucleus accumbens are identified and transformed to the cortical surface in order to include the entire depth of the cortex. Step 4: the cortical segments and the nucleus accumbens segmentation are transformed to the native functional MRI space, and BOLD time series are extracted to perform functional connectivity or dynamic causal modeling (DCM) analysis of the tractography-defined network.
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Figure 4: Schematic illustration of the processes involved in precision mapping. Step 1: seeds (red) and targets (vPFC: yellow; nucleus accumbens: green) are identified in native space. Step 2: probabilistic tractography is performed. The tract is binarized at 1% of the maximum value (red). Step 3: the segments (red) of the vPFC that is connected to the nucleus accumbens are identified and transformed to the cortical surface in order to include the entire depth of the cortex. Step 4: the cortical segments and the nucleus accumbens segmentation are transformed to the native functional MRI space, and BOLD time series are extracted to perform functional connectivity or dynamic causal modeling (DCM) analysis of the tractography-defined network.

Mentions: To illustrate the usefulness of precision mapping, we focus on the connectivity profiles of the nucleus accumbens (NAc) and the ventral prefrontal cortex (vPFC). Precision mapping involves the following four steps. First, white matter seeds and gray matter targets were determined in native space on an anatomical T1-weighted scan (Figure 4, step 1). The NAc and the vPFC were defined using Freesurfer3 segmentation. For the vPFC, multiple Freesurfer regions were combined, and a superior and posterior boundary was identified in MNI coordinate space and transformed to the individual’s native space. White matter seeds were selected in MNI space, based on the white matter anterior to the NAc and knowledge from tracer studies (54, 55), and transformed to the individual diffusion space. Second, probabilistic tractography was performed using probtrackX2 in FSL (56) (Figure 4, step 2). Third, the endpoints of the tractography were used to identify subregions within the vPFC that is anatomically connected to the NAc, and projected onto the Freesurfer cortical surface to include the full depth of the cortical ribbon (Figure 4, step 3). Fourth, the tractography-determined vPFC subregions and the corresponding subcortical targets were affine transformed to the fMRI space and used for functional connectivity analyses (Figure 4, step 4). The MNI space is only used for the identification of seed regions as a starting point for tractography, but all further processing is completely template independent and therefore individual specific. Thus, the defined network regions can vary in exact location and shape depending on the morphology of the tracts and the cortical surface within each individual.


Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

O'Halloran R, Kopell BH, Sprooten E, Goodman WK, Frangou S - Front Psychiatry (2016)

Schematic illustration of the processes involved in precision mapping. Step 1: seeds (red) and targets (vPFC: yellow; nucleus accumbens: green) are identified in native space. Step 2: probabilistic tractography is performed. The tract is binarized at 1% of the maximum value (red). Step 3: the segments (red) of the vPFC that is connected to the nucleus accumbens are identified and transformed to the cortical surface in order to include the entire depth of the cortex. Step 4: the cortical segments and the nucleus accumbens segmentation are transformed to the native functional MRI space, and BOLD time series are extracted to perform functional connectivity or dynamic causal modeling (DCM) analysis of the tractography-defined network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835492&req=5

Figure 4: Schematic illustration of the processes involved in precision mapping. Step 1: seeds (red) and targets (vPFC: yellow; nucleus accumbens: green) are identified in native space. Step 2: probabilistic tractography is performed. The tract is binarized at 1% of the maximum value (red). Step 3: the segments (red) of the vPFC that is connected to the nucleus accumbens are identified and transformed to the cortical surface in order to include the entire depth of the cortex. Step 4: the cortical segments and the nucleus accumbens segmentation are transformed to the native functional MRI space, and BOLD time series are extracted to perform functional connectivity or dynamic causal modeling (DCM) analysis of the tractography-defined network.
Mentions: To illustrate the usefulness of precision mapping, we focus on the connectivity profiles of the nucleus accumbens (NAc) and the ventral prefrontal cortex (vPFC). Precision mapping involves the following four steps. First, white matter seeds and gray matter targets were determined in native space on an anatomical T1-weighted scan (Figure 4, step 1). The NAc and the vPFC were defined using Freesurfer3 segmentation. For the vPFC, multiple Freesurfer regions were combined, and a superior and posterior boundary was identified in MNI coordinate space and transformed to the individual’s native space. White matter seeds were selected in MNI space, based on the white matter anterior to the NAc and knowledge from tracer studies (54, 55), and transformed to the individual diffusion space. Second, probabilistic tractography was performed using probtrackX2 in FSL (56) (Figure 4, step 2). Third, the endpoints of the tractography were used to identify subregions within the vPFC that is anatomically connected to the NAc, and projected onto the Freesurfer cortical surface to include the full depth of the cortical ribbon (Figure 4, step 3). Fourth, the tractography-determined vPFC subregions and the corresponding subcortical targets were affine transformed to the fMRI space and used for functional connectivity analyses (Figure 4, step 4). The MNI space is only used for the identification of seed regions as a starting point for tractography, but all further processing is completely template independent and therefore individual specific. Thus, the defined network regions can vary in exact location and shape depending on the morphology of the tracts and the cortical surface within each individual.

Bottom Line: 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 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.However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment.

View Article: PubMed Central - PubMed

Affiliation: Brain Imaging Center, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai , New York, NY , USA.

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.

No MeSH data available.


Related in: MedlinePlus