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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

Precision targeting at the individual patient level. The figure illustrates the difference between patient-specific connectivity and group connectivity to guide targeting for a patient where the caudal zona incerta (cZi) has been targeted. The heat map shows where this patient’s connectivity is the closest match to the group connectivity average in patients having a good treatment response. The actual location of implants is shown on the postoperative computerized tomography (CT) scan (blue).
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Figure 8: Precision targeting at the individual patient level. The figure illustrates the difference between patient-specific connectivity and group connectivity to guide targeting for a patient where the caudal zona incerta (cZi) has been targeted. The heat map shows where this patient’s connectivity is the closest match to the group connectivity average in patients having a good treatment response. The actual location of implants is shown on the postoperative computerized tomography (CT) scan (blue).

Mentions: Figure 8 illustrates the benefits of precision targeting at the individual patient level for one of the cZI patients. In this patient, RMS difference with the average group connectivity from the cZI subjects is shown over the preoperative MRI (Figure 8, heat map), with the postoperative CT windowed to show only the implant (Figure 8, blue). The best match to the group average coincided almost exactly with the location of the implant on the right side and was approximately 1 mm lateral in the case of the left implant. Note that this surgery was planned in the conventional way without considering DWI. These results show that surgery could be aided by the use of DWI-derived connectivity, displayed in this heat map form which is easy to import into current surgical-planning software.


Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

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

Precision targeting at the individual patient level. The figure illustrates the difference between patient-specific connectivity and group connectivity to guide targeting for a patient where the caudal zona incerta (cZi) has been targeted. The heat map shows where this patient’s connectivity is the closest match to the group connectivity average in patients having a good treatment response. The actual location of implants is shown on the postoperative computerized tomography (CT) scan (blue).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 8: Precision targeting at the individual patient level. The figure illustrates the difference between patient-specific connectivity and group connectivity to guide targeting for a patient where the caudal zona incerta (cZi) has been targeted. The heat map shows where this patient’s connectivity is the closest match to the group connectivity average in patients having a good treatment response. The actual location of implants is shown on the postoperative computerized tomography (CT) scan (blue).
Mentions: Figure 8 illustrates the benefits of precision targeting at the individual patient level for one of the cZI patients. In this patient, RMS difference with the average group connectivity from the cZI subjects is shown over the preoperative MRI (Figure 8, heat map), with the postoperative CT windowed to show only the implant (Figure 8, blue). The best match to the group average coincided almost exactly with the location of the implant on the right side and was approximately 1 mm lateral in the case of the left implant. Note that this surgery was planned in the conventional way without considering DWI. These results show that surgery could be aided by the use of DWI-derived connectivity, displayed in this heat map form which is easy to import into current surgical-planning software.

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