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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

Supervised classification using multivariate pattern recognition models. Classification training and test phase: the classifier in initially trained on a set with predefined class labels and the classification rule derived is used to assign class membership to a previously unseen dataset.
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Figure 1: Supervised classification using multivariate pattern recognition models. Classification training and test phase: the classifier in initially trained on a set with predefined class labels and the classification rule derived is used to assign class membership to a previously unseen dataset.

Mentions: The majority of neuroimaging studies in psychiatry use voxel-based statistics (e.g., general linear model), which are biased toward detecting group-level differences that are highly localized in space and linear in nature. However, structural and fMRI data are inherently multivariate since each imaging dataset contains information distributed among the thousands of its constituent voxels. Over the last 5 years, there has been increasing interest in multivariate pattern recognition methods, as these methods can capture potentially useful information embedded in the spatial pattern of the data. Multivariate pattern recognition can be achieved through several statistical models. Regardless of the model, pattern recognition tools rely on computational algorithms to discover regularities in the data, which are then used to derive rules for inferring individual-level characterization (5) (Figure 1). This feature is of translational value, as it is aligned with the person-centered nature of clinical practice. In psychiatric neuroimaging, multivariate pattern recognition methods have been mostly used to classify individuals into discreet categories according to diagnostic status (e.g., patients, healthy controls), prognosis (e.g., converters, non-converters), or treatment response (e.g., treatment responders, non-responders). Frequently used classifiers are support vector machines and Gaussian process classifiers, which use a supervised approach to classification. This means that the algorithm is first trained to identify regularities in the neuroimaging data that discriminate individuals whose status is predefined. For example, the classifier is given imaging data from patients and healthy controls and is trained to generate a classification rule that discriminates the two groups. In the next phase, the test phase, the classifier is presented with a dataset from a previously unseen individual and uses the classification rule to determine the status of this new example. Sensitivity, specificity, and accuracy are the most commonly reported measures of classifier performance in terms of the accuracy of the classification rule in determining the status of previously unseen individual datasets. In the case of binary classifiers, for example, involving patients and controls, sensitivity refers to the proportion of patients (true positives) who are correctly identified as patients, whereas specificity measures the proportion of controls (true negatives) who are correctly identified as controls. The accuracy of the classifier refers to the total proportion of patients and controls that are correctly classified. Furthermore, permutation testing is also employed to determine whether the results of the pattern recognition model deviate significantly from chance. In linear classifiers, voxels can be visualized on the basis of their contribution to classification thus producing discriminative maps (6). The relevant literature in psychiatry has recently been summarized in multiple reviews that provide a comprehensive account of the progress to date and the challenges that still lie ahead (7–10). The field is dominated by studies that sought to discriminate healthy individuals from patients with either schizophrenia (n = 51) or major depressive disorder (MDD) (n = 31) using structural, diffusion-weighted, and fMRI data; the reported accuracies of these case–control classifiers range between 71 and 96% for schizophrenia and between 61 and 96% for MDD (10).


Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

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

Supervised classification using multivariate pattern recognition models. Classification training and test phase: the classifier in initially trained on a set with predefined class labels and the classification rule derived is used to assign class membership to a previously unseen dataset.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Supervised classification using multivariate pattern recognition models. Classification training and test phase: the classifier in initially trained on a set with predefined class labels and the classification rule derived is used to assign class membership to a previously unseen dataset.
Mentions: The majority of neuroimaging studies in psychiatry use voxel-based statistics (e.g., general linear model), which are biased toward detecting group-level differences that are highly localized in space and linear in nature. However, structural and fMRI data are inherently multivariate since each imaging dataset contains information distributed among the thousands of its constituent voxels. Over the last 5 years, there has been increasing interest in multivariate pattern recognition methods, as these methods can capture potentially useful information embedded in the spatial pattern of the data. Multivariate pattern recognition can be achieved through several statistical models. Regardless of the model, pattern recognition tools rely on computational algorithms to discover regularities in the data, which are then used to derive rules for inferring individual-level characterization (5) (Figure 1). This feature is of translational value, as it is aligned with the person-centered nature of clinical practice. In psychiatric neuroimaging, multivariate pattern recognition methods have been mostly used to classify individuals into discreet categories according to diagnostic status (e.g., patients, healthy controls), prognosis (e.g., converters, non-converters), or treatment response (e.g., treatment responders, non-responders). Frequently used classifiers are support vector machines and Gaussian process classifiers, which use a supervised approach to classification. This means that the algorithm is first trained to identify regularities in the neuroimaging data that discriminate individuals whose status is predefined. For example, the classifier is given imaging data from patients and healthy controls and is trained to generate a classification rule that discriminates the two groups. In the next phase, the test phase, the classifier is presented with a dataset from a previously unseen individual and uses the classification rule to determine the status of this new example. Sensitivity, specificity, and accuracy are the most commonly reported measures of classifier performance in terms of the accuracy of the classification rule in determining the status of previously unseen individual datasets. In the case of binary classifiers, for example, involving patients and controls, sensitivity refers to the proportion of patients (true positives) who are correctly identified as patients, whereas specificity measures the proportion of controls (true negatives) who are correctly identified as controls. The accuracy of the classifier refers to the total proportion of patients and controls that are correctly classified. Furthermore, permutation testing is also employed to determine whether the results of the pattern recognition model deviate significantly from chance. In linear classifiers, voxels can be visualized on the basis of their contribution to classification thus producing discriminative maps (6). The relevant literature in psychiatry has recently been summarized in multiple reviews that provide a comprehensive account of the progress to date and the challenges that still lie ahead (7–10). The field is dominated by studies that sought to discriminate healthy individuals from patients with either schizophrenia (n = 51) or major depressive disorder (MDD) (n = 31) using structural, diffusion-weighted, and fMRI data; the reported accuracies of these case–control classifiers range between 71 and 96% for schizophrenia and between 61 and 96% for MDD (10).

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