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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

Fiber tracks from three separate patients with electrodes targeting either the caudal zona incerta (cZi) (A), the subthalamic nucleus (STN) (B), or the globus pallidus interna (Gpi) (C). These sagittal views show the fiber tracks that pass through a 3-mm sphere centered on the active electrode and may represent fibers of passage most affected by deep brain stimulation (DBS).
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Figure 6: Fiber tracks from three separate patients with electrodes targeting either the caudal zona incerta (cZi) (A), the subthalamic nucleus (STN) (B), or the globus pallidus interna (Gpi) (C). These sagittal views show the fiber tracks that pass through a 3-mm sphere centered on the active electrode and may represent fibers of passage most affected by deep brain stimulation (DBS).

Mentions: All images were first registered to the first preoperative CT scan using FLIRT (FMRIB Linear Image Registration Tool)4 and a mutual information cost function. The electrodes were automatically segmented from the second postoperative CT scan (after both electrodes were implanted) using software written in-house in MATLAB (Mathworks, Natick, MA, USA). The segmentation consisted of using a brain mask derived from the structural T1-weighted image to mask out regions outside the brain, then thresholding at 2000 Houndsfield units to obtain only the electrodes. After segmentation, the right and left leads were separately fit to a model of the specific implant (Medtronic 3389 or 3387) to obtain segmentations of the implants including each of the four electrodes and body of the implant. From the segmentation of the electrodes, the centroid of each electrode could be determined. In the structural MR dataset, cortical reconstruction and volumetric segmentation were performed with Freesurfer. The SPGRE image was used as input to Freesurfer. DWI data were corrected for Eddy-current distortions using Eddy_correct in FSL and fit to the preoperative CT using and affine transform. Fiber tracking was performed using the MRtrix package5. Constrained spherical deconvolution (78, 79) was performed on preprocessed images to obtain fiber orientation distributions. We used a anatomically constrained probabilistic tractography algorithm (iFOD2) (80) seeded from 3 mm spheres drawn around the centroid of each of the four electrodes, for both the left and right implants, to determine the connectivity pattern of each seed to cortical and subcortical regions using the cortical parcelation and subcortical segmentation algorithms (aparc + aseg) in Freesurfer. Figure 6 shows fiber tracks from three separate patients with electrodes targeting either cZI, STN, or GPi. These sagittal views show the fiber tracks that pass through the 3-mm sphere centered on the active electrode (cathode) and thus can be interpreted as the fibers of passage most affected by DBS. The tracks from all three targets share some common features as they connect with subcortical regions in the brainstem and cerebellum and with cortical regions within superior prefrontal cortex. We then averaged connectivity matrices from the tracks originating from the active electrode separately for each patient group determined by the DBS target (cZI, STN, and GPi). We focused on the electrode associated with better efficacy and tolerability. Group connectivity plots (Figure 7) demonstrate clearly the differences between the three targets. Notably, the cZI and STN targets had strong connections to the contralateral cerebellar cortex that likely encompasses the DRT, as it has a decussation and connects to the contralateral dentate nucleus. Strong connections to the ipsilateral superior frontal cortex may be partly comprised of fibers that form the hyperdirect pathway potentially useful as a target for PD by disrupting the synchrony of sensory motor networks (81).


Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

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

Fiber tracks from three separate patients with electrodes targeting either the caudal zona incerta (cZi) (A), the subthalamic nucleus (STN) (B), or the globus pallidus interna (Gpi) (C). These sagittal views show the fiber tracks that pass through a 3-mm sphere centered on the active electrode and may represent fibers of passage most affected by deep brain stimulation (DBS).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 6: Fiber tracks from three separate patients with electrodes targeting either the caudal zona incerta (cZi) (A), the subthalamic nucleus (STN) (B), or the globus pallidus interna (Gpi) (C). These sagittal views show the fiber tracks that pass through a 3-mm sphere centered on the active electrode and may represent fibers of passage most affected by deep brain stimulation (DBS).
Mentions: All images were first registered to the first preoperative CT scan using FLIRT (FMRIB Linear Image Registration Tool)4 and a mutual information cost function. The electrodes were automatically segmented from the second postoperative CT scan (after both electrodes were implanted) using software written in-house in MATLAB (Mathworks, Natick, MA, USA). The segmentation consisted of using a brain mask derived from the structural T1-weighted image to mask out regions outside the brain, then thresholding at 2000 Houndsfield units to obtain only the electrodes. After segmentation, the right and left leads were separately fit to a model of the specific implant (Medtronic 3389 or 3387) to obtain segmentations of the implants including each of the four electrodes and body of the implant. From the segmentation of the electrodes, the centroid of each electrode could be determined. In the structural MR dataset, cortical reconstruction and volumetric segmentation were performed with Freesurfer. The SPGRE image was used as input to Freesurfer. DWI data were corrected for Eddy-current distortions using Eddy_correct in FSL and fit to the preoperative CT using and affine transform. Fiber tracking was performed using the MRtrix package5. Constrained spherical deconvolution (78, 79) was performed on preprocessed images to obtain fiber orientation distributions. We used a anatomically constrained probabilistic tractography algorithm (iFOD2) (80) seeded from 3 mm spheres drawn around the centroid of each of the four electrodes, for both the left and right implants, to determine the connectivity pattern of each seed to cortical and subcortical regions using the cortical parcelation and subcortical segmentation algorithms (aparc + aseg) in Freesurfer. Figure 6 shows fiber tracks from three separate patients with electrodes targeting either cZI, STN, or GPi. These sagittal views show the fiber tracks that pass through the 3-mm sphere centered on the active electrode (cathode) and thus can be interpreted as the fibers of passage most affected by DBS. The tracks from all three targets share some common features as they connect with subcortical regions in the brainstem and cerebellum and with cortical regions within superior prefrontal cortex. We then averaged connectivity matrices from the tracks originating from the active electrode separately for each patient group determined by the DBS target (cZI, STN, and GPi). We focused on the electrode associated with better efficacy and tolerability. Group connectivity plots (Figure 7) demonstrate clearly the differences between the three targets. Notably, the cZI and STN targets had strong connections to the contralateral cerebellar cortex that likely encompasses the DRT, as it has a decussation and connects to the contralateral dentate nucleus. Strong connections to the ipsilateral superior frontal cortex may be partly comprised of fibers that form the hyperdirect pathway potentially useful as a target for PD by disrupting the synchrony of sensory motor networks (81).

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