Dataset Information

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

ABSTRACT: Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N?=?100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N?=?100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Jaspers E

PROVIDER: S-EPMC6866797 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

Jaspers Ellen E Balsters Joshua H JH Kassraian Fard Pegah P Mantini Dante D Wenderoth Nicole N

Human brain mapping 20161112 3

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected a ...[more]

PMID: 27859903

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Project description:Compulsive behaviors in obsessive-compulsive disorder (OCD) may be related to deficits in reward processing mediated by corticostriatal circuitry, a brain network implicated in the pathophysiology of OCD. Performing compulsive actions can be perceived as a reward to OCD patients because it temporarily reduces the anxiety provoked by obsessions. Although most OCD literature provides evidence of altered regional activity in these corticostriatal circuits, very little is known about the connectivity between individual regions of the corticostriatal-limbic circuits, including the cognitive and affective neural circuitry associated with OCD. Thus, this study investigated the differences in functional connectivity (FC) patterns in this network during resting-state and incentive processing. Nineteen patients with OCD and 18 well-matched healthy controls were scanned during resting-state and a monetary incentive delay task (task state). FC was assessed using both voxel-wise and region-of-interest (ROI)-wise analyses. Voxel-wise FC analysis with the nucleus accumbens seed revealed that patients with OCD exhibited increased FC between the nucleus accumbens and the lateral orbitofrontal cortex during resting-state. Additionally, these patients showed decreased FC between the nucleus accumbens and limbic areas such as the amygdala during incentive processing. Exploratory ROI-wise FC analysis revealed that OCD patients demonstrated enhanced FC between the nucleus accumbens and the lateral orbitofrontal cortex and increased total connectivity of the lateral orbitofrontal cortex during resting-state. Additionally, patients showed alterations in FC between resting and task state. This study provides evidence that patients with OCD have altered FC in the corticostriatal-limbic network, particularly in striatal-amygdala and striatal-orbitofrontal circuitry, during incentive processing and resting-state. These findings also emphasize that functional connections in the network are modulated by affective/motivational states and further suggest that OCD patients may have abnormalities of such modulation in this network.

Project description:BackgroundPatients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.MethodsThis study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).ResultsFND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).ConclusionsThe results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Dataset Information

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

Publications

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

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