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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

Discriminative clusters. Clusters discriminating patients with bipolar disorder from their relatives with major depressive disorder (red) or their healthy relatives (green); PFC, prefrontal cortex.
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Figure 2: Discriminative clusters. Clusters discriminating patients with bipolar disorder from their relatives with major depressive disorder (red) or their healthy relatives (green); PFC, prefrontal cortex.

Mentions: The Gaussian process classifier discriminated patients with BD from their MDD relatives with a sensitivity (true positives for patients with BD) of 53.9%, specificity (true negative for relatives with MDD) of 94.5%, and overall classification accuracy of 73.1% (pā€‰=ā€‰0.001). The largest discriminating clusters were located in the left superior frontal gyrus, right middle frontal gyrus, bilaterally in the middle/superior frontal gyrus, and the right temporal lobe (Figure 2). Previous studies have shown that different task-based fMRI classifiers can discriminate patients with BD from unselected patients with MDD with classification accuracy of approximately 80% (10). The present study shows that similar classification accuracy can be achieved even when patients have family history of BD. Importantly, patients with BD and their relatives with MDD could be classified with high specificity. This opens the possibility of excluding the BD, with a very high level of confidence after a 10-min brain scan, when assessing individuals with MDD at high risk for BD by virtue of a positive family history. Clinical application will require replication in different samples and settings and in more diverse clinical populations. Nevertheless, these findings demonstrate the potential value of neuroimaging in assisting in situations of clinical uncertainty.


Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

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

Discriminative clusters. Clusters discriminating patients with bipolar disorder from their relatives with major depressive disorder (red) or their healthy relatives (green); PFC, prefrontal cortex.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Discriminative clusters. Clusters discriminating patients with bipolar disorder from their relatives with major depressive disorder (red) or their healthy relatives (green); PFC, prefrontal cortex.
Mentions: The Gaussian process classifier discriminated patients with BD from their MDD relatives with a sensitivity (true positives for patients with BD) of 53.9%, specificity (true negative for relatives with MDD) of 94.5%, and overall classification accuracy of 73.1% (pā€‰=ā€‰0.001). The largest discriminating clusters were located in the left superior frontal gyrus, right middle frontal gyrus, bilaterally in the middle/superior frontal gyrus, and the right temporal lobe (Figure 2). Previous studies have shown that different task-based fMRI classifiers can discriminate patients with BD from unselected patients with MDD with classification accuracy of approximately 80% (10). The present study shows that similar classification accuracy can be achieved even when patients have family history of BD. Importantly, patients with BD and their relatives with MDD could be classified with high specificity. This opens the possibility of excluding the BD, with a very high level of confidence after a 10-min brain scan, when assessing individuals with MDD at high risk for BD by virtue of a positive family history. Clinical application will require replication in different samples and settings and in more diverse clinical populations. Nevertheless, these findings demonstrate the potential value of neuroimaging in assisting in situations of clinical uncertainty.

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