There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain’s functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test–retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9–12 min, while improvements in intrasession reliability plateaued around 12–16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.
Resting-state fMRI (rs-fMRI) has been demonstrated to have moderate to high reliability and produces consistent patterns of connectivity across a wide variety of subjects, sites, and scanners. However, there is no one agreed upon method to acquire rs-fMRI data. Some sites instruct their subjects, or patients, to lie still with their eyes closed, while other sites instruct their subjects to keep their eyes open or even fixating on a cross during scanning. Several studies have compared those three resting conditions based on connectivity strength. In our study, we assess differences in metrics of test–retest reliability (using an intraclass correlation coefficient), and consistency of the rank-order of connections within a subject and the ranks of subjects for a particular connection from one session to another (using Kendall's W tests). Twenty-five healthy subjects were scanned at three different time points for each resting condition, twice the same day and another time two to three months later. Resting-state functional connectivity measures were evaluated in motor, visual, auditory, attention, and default-mode networks, and compared between the different resting conditions. Of the networks examined, only the auditory network resulted in significantly higher connectivity in the eyes closed condition compared to the other two conditions. No significant between-condition differences in connectivity strength were found in default mode, attention, visual, and motor networks. Overall, the differences in reliability and consistency between different resting conditions were relatively small in effect size but results were found to be significant. Across all within-network connections, and within default-mode, attention, and auditory networks statistically significant greater reliability was found when the subjects were lying with their eyes fixated on a cross. In contrast, primary visual network connectivity was most reliable when subjects had their eyes open (and not fixating on a cross).
Deep brain stimulation of the subthalamic nucleus (STN) is a widely performed surgical treatment for patients with Parkinson’s disease. The goal of the surgery is to place an electrode centered in the motor region of the STN while lowering the effects of electrical stimulation on the non-motor regions. However, distinguishing the motor region from the neighboring associative and limbic areas in individual patients using imaging modalities was until recently difficult to obtain in vivo. Here, using ultra-high field MR imaging, we have performed a dissection of the subdivisions of the STN of individual Parkinson’s disease patients. We have acquired 7 T diffusion-weighted images of seventeen patients with Parkinson’s disease scheduled for deep brain stimulation surgery. Using a structural connectivity-based parcellation protocol, the STN’s connections to the motor, limbic, and associative cortical areas were used to map the individual subdivisions of the nucleus. A reproducible patient-specific parcellation of the STN into a posterolateral motor and gradually overlapping central associative area was found in all STNs, taking up on average 55.3% and 55.6% of the total nucleus volume. The limbic area was found in the anteromedial part of the nucleus. Our results suggest that 7T MR imaging may facilitate individualized and highly specific planning of deep brain stimulation surgery of the STN.
BackgroundDeep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports.ObjectiveProvide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation.MethodsIntegration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson’s disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution.ResultsPathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings.ConclusionParameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.
The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac-and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points-two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.
Background Adult post-traumatic stress disorder (PTSD) has been characterized by altered fear network connectivity. Childhood trauma is a major risk factor for adult PTSD, yet its contribution to fear network connectivity in PTSD remains unexplored. We examined, within a single model, the contribution of childhood maltreatment, combat exposure, and combat-related post-traumatic stress symptoms (PTSS) to resting-state connectivity (rs-FC) of the amygdala and hippocampus in military veterans. Methods Medication-free male veterans (n=27, average 26.6 years) with a range of PTSS completed resting-state fMRI. Measures including the Clinician-Administered PTSD Scale (CAPS), Childhood Trauma Questionnaire (CTQ), and Combat Exposure Scale (CES) were used to predict rs-FC using multi-linear regression. Fear network seeds included the amygdala and hippocampus. Results Amygdala: CTQ predicted lower connectivity to ventromedial prefrontal cortex (vmPFC), but greater anticorrelation with dorsal/lateral PFC. CAPS positively predicted connectivity to insula, and loss of anticorrelation with dorsomedial/dorsolateral (dm/dl)PFC. Hippocampus CTQ predicted lower connectivity to vmPFC, but greater anticorrelation with dm/dlPFC. CES predicted greater anticorrelation, while CAPS predicted less anticorrelation with dmPFC. Conclusions Childhood trauma, combat exposure, and PTSS differentially predict fear network rs-FC. Childhood maltreatment may weaken ventral prefrontal-subcortical circuitry important in automatic fear regulation, but, in a compensatory manner, may also strengthen dorsal prefrontal-subcortical pathways involved in more effortful emotion regulation. PTSD symptoms, in turn, appear to emerge with the loss of connectivity in the latter pathway. These findings suggest potential mechanisms by which developmental trauma exposure leads to adult PTSD, and which brain mechanisms are associated with the emergence of PTSD symptoms.
Neuroanatomists posit that the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST) comprise two major nodes of a macrostructural forebrain entity termed the extended amygdala. The extended amygdala is thought to play a critical role in adaptive motivational behavior and is implicated in the pathophysiology of maladaptive fear and anxiety. Resting functional connectivity of the Ce was examined in 107 young anesthetized rhesus monkeys and 105 young humans using standard resting-state functional magnetic resonance imaging (fMRI) methods to assess temporal correlations across the brain. The data expand the neuroanatomical concept of the extended amygdala by finding, in both species, highly significant functional coupling between the Ce and the BST. These results support the use of in vivo functional imaging methods in nonhuman and human primates to probe the functional anatomy of major brain networks such as the extended amygdala.
ObjectiveDeep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients.Methods72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model’s accuracy and its clinical relevancy.ResultsWe successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61).ConclusionThe results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient’s clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.
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