Quantifying blood-brain barrier permeability in patients with ischemic stroke using non-contrast MRI.

Increased blood-brain barrier permeability (BBBP) after ischemic stroke predisposes patients to hemorrhagic conversion. While altered BBBP can impact patient recovery, it is not routinely assessed during the workup of acute ischemic stroke (AIS). We study the effectiveness of the non-contrast MRI sequences diffusion-prepared pseudocontinuous arterial spin labeling (DP-pCASL) and Neurite Orientation Dispersion and Density Imaging (NODDI) in assessing BBBP and correlating to tissue microstructure after ischemic insult. Twelve patients with AIS were prospectively enrolled to undergo our multimodal MR imaging, which generated the DP-pCASL-derived cerebral blood flow (CBF), arterial transit time (ATT), and water exchange rate (kw) and the NODDI-derived b0, mean diffusivity (MD), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and isotropic volume fraction (ISO) parametric maps. The mean age of the patients was 70.2 ± 14.8 with an average NIHSS of 13.0 (7.3-19.8). MR imaging was performed on average at 53.7 (27.8-93.3) hours from stroke symptom onset. The water exchange rate (kw) of the infarcted area and its contralateral territory were 89.7 min-1 (66.7-121.9) and 89.9 min-1 (65.9-106.0) respectively (p = 0.887). Multivariable linear regression analysis showed that b0, ODI, ISO and mechanical thrombectomy were significant predictors of kw. DP-pCASL and NODDI are promising non-contrast sequences for the routine assessment of BBBP.

Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-Frequency Correlations: A Retrospective Study.

BACKGROUND: Positive correlations between low- and high-frequency spectra from stereotactic electroencephalogram (SEEG) recordings have been implicated in pathological brain activity interictally and have been used for ictal detection in both focal and network models. OBJECTIVE: We evaluated SEEG signals in patients who ultimately underwent temporal lobectomy to evaluate their utility in seizure localization and prediction of seizure freedom post-resection. METHODS: We retrospectively analyzed cross-frequency correlations between beta and high gamma (HG) interictal SEEG signals from 22 patients. We compared signals based on temporal versus extra-temporal locations, seizure-free (SF) versus non-seizure-free (NSF) outcomes, and mesial (M) versus mesial temporal-plus (M+) onset. RESULTS: Positive cross-correlations were increased in temporal areas. NSF patients showed a higher proportion of positive electrodes in temporal areas. SF patients had a greater proportion of significant channels in mesial versus lateral temporal areas. HG/Beta correlations in mesial versus lateral temporal areas predicted seizure freedom better than ictal SEEG seizure onset localization to M or M+ locations. CONCLUSIONS: We present preliminary data that local HG/Beta correlations may predict epilepsy focus and surgical outcome and may have utility as adjunct methods to conventional SEEG analysis. Further studies are needed to determine strategies for prospective studies and clinical use.

Chemovoltaic effect for renewable liquid and vapor fuels on semiconductor surfaces.

The chemovoltaic effect - generation of electronic excitation by exergonic redox reactions - has been observed on metallic surfaces of Schottky junctions and is proving to be pivotal in explaining in detail the momentum conservation relations of chemically active collisions. As shown in this work, it can hold keys for direct chemical energy harvesting by semiconductor solar cells. To study the possibilities of chemovoltaic energy conversion by semiconductors, we have modeled and designed an 'electrolyte-free fuel cell' formed by a GaAs diode that can host electrochemical fuel oxidation and oxidant reduction reactions on its conduction and valence bands and as a result convert renewable chemical energy (as well as light) into electricity. The experimental results show that exposing the surface of a suitably designed solar cell to methanol liquid or vapor in the presence of oxygen or hydrogen peroxide leads to the generation of electrical power.

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study.

Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI.

Exploring Functional Connectivity in Chronic Spinal Cord Injury Patients With Neuropathic Pain Versus Without Neuropathic Pain.

The great majority of spinal cord injury (SCI) patients have debilitating chronic pain. Despite decades of research, these pain pathways of neuropathic pain (NP) are unknown. SCI patients have been shown to have abnormal brain pain pathways. We hypothesize that SCI NP patients' pain matrix is altered compared to SCI patients without NP. This study examines the functional connectivity (FC) in SCI patients with moderate-severe chronic NP compared to SCI patients with mild-no NP. These groups were compared to control subjects. The Neuropathic Pain Questionnaire and neurological evaluation based on the International Standard Neurological Classification of SCI were utilized to define the severity and level of injury. Of the 10 SCI patients, 7 (48.6 ± 17.02 years old, 6 male and 1 female) indicated that they had NP and 3 did not have NP (39.33 ± 8.08 years old, 2 male and 1 female). Ten uninjured neurologically intact participants were used as controls (24.8 ± 4.61 years old, 5 male and 5 female). FC metrics were obtained from the comparisons of resting-state functional magnetic resonance imaging among our various groups (controls, SCI with NP, and SCI without NP). For each comparison, a region-of-interest (ROI)-to-ROI connectivity analysis was pursued, encompassing a total of 175 ROIs based on a customized atlas derived from the AAL3 atlas. The analysis accounted for covariates such as age and sex. To correct for multiple comparisons, a strict Bonferroni correction was applied with a significance level of p < 0.05/NROIs. When comparing SCI patients with moderate-to-severe pain to those with mild-to-no pain, specific thalamic nuclei had altered connections. These nuclei included: medial pulvinar; lateral pulvinar; medial geniculate nucleus; lateral geniculate nucleus; and mediodorsal magnocellular nucleus. There was increased FC between the lateral geniculate nucleus and the anteroventral nucleus in NP post-SCI. Our analysis additionally highlights the relationships between the frontal lobe and temporal lobe with pain. This study successfully identifies thalamic neuroplastic changes that occur in patients with SCI who develop NP. It additionally underscores the pain matrix and involvement of the frontal and temporal lobes as well. Our findings complement that the development of NP post-SCI involves cognitive, emotional, and behavioral influences.

A computational investigation of DMSO/water separation through functionalized GO multilayer nanosheet membrane using molecular dynamics simulation and deep neural network model for membrane performance prediction.

In this molecular dynamics (MD) simulation study, the separation of dimethyl sulfoxide (DMSO) from water was investigated using multilayer functionalized graphene oxide (GO) membranes. The GO nanosheets were modified with chemical groups (-F, -H) to alter their properties. The study analyzed the influence of pressure and functional groups on the separation rate. Additionally, a deep neural network (DNN) model was developed to predict membrane behavior under different conditions in water treatment processes. Results revealed that the fluorine-functionalized membrane exhibited higher permeation compared to the hydrogen-functionalized one, with potential of mean force (PMF) analysis indicating higher energy barriers for water molecules passing through the hydrogen-functionalized membrane. The study used density profile, water density map analysis, and radial distribution function (RDF) analysis to understand water and DMSO molecule interactions. The diffusion coefficient of water molecules was also calculated, showing higher diffusion in the fluorine-functionalized system. Overall, the findings suggest that functionalized GO membranes are effective for DMSO-water separation, with the fluorine-functionalized membrane showing superior performance. The DNN model accurately predicts membrane behavior, contributing to the optimization of membrane separation systems.

Understanding the performance of RHO type zeolite membrane for CH4/N2 separation based on molecular dynamics and deep neural network methods.

This study shows a molecular dynamics (MD) simulation study on the performance of the RHO zeolite membrane for separating nitrogen from methane/nitrogen gas mixtures. The contamination of natural gas, predominantly composed of methane, with nitrogen diminishes its value. Zeolite membranes offer promising prospects for gas separation due to their stability, rigid pore structure, and molecular sieving properties. The study investigates the impact of pressure difference (up to 30 MPa), feed composition, and membrane thickness on the separation rate at a system temperature of 298 K. Results demonstrate that the RHO zeolite membrane exhibits high permeability and selectivity for N2 separation, surpassing the upper limit defined by Robson with a maximum permeability of 2.14 × 105 GPU (Gas Permeation Units). Exceptional selectivity of N2 over CH4 molecules is observed. Additionally, altering the feed composition and membrane thickness positively influences the membrane's separation performance, thereby enhancing its efficiency. The findings contribute to the advancement of separation technologies, providing valuable insights into the potential application of zeolite membranes for efficient N2 separation from CH4/N2 gas mixtures in natural gas processing. Furthermore, the study explores the use of Deep Neural Network (DNN) models to predict the membrane's performance under diverse operating conditions. The DNN models, trained using simulation data from MD simulations, exhibit high accuracy with a coefficient of determination (R2) exceeding 0.9, ensuring reliable predictions. The integration of DNN models facilitates the optimization of zeolite membrane-based gas separation systems, improving their design and operation.

Metal artifact reduction around cervical spine implant using diffusion tensor imaging at 3T: A phantom study.

PURPOSE: Diffusion MRI continues to play a key role in non-invasively assessing spinal cord integrity and pre-operative injury evaluation. However, post-operative Diffusion Tensor Imaging (DTI) acquisition of patients with metal implants results in severe geometric distortion. We propose and demonstrate a method to alleviate the technical challenges facing the acquisition of DTI on post-operative cases and longitudinal evaluation of therapeutics. MATERIAL AND METHODS: The described technique is based on the combination of the reduced Field-Of-View (rFOV) strategy and the phase segmented EPI, termed rFOV-PS-EPI. A custom-built phantom based on a cervical spine model with metal implants was used to collect DTI data at 3 Tesla scanner using: rFOV-PS-EPI, reduced Field-Of-View single-shot EPI (rFOV-SS-EPI), and conventional full FOV techniques including SS-EPI, PS-EPI, and readout-segmented EPI (RS-EPI). Geometric distortion, SNR, and signal void were assessed to evaluate images and compare the sequences. A two-sample t-test was performed with p-value of 0.05 or less to indicate statistical significance. RESULTS: The reduced FOV techniques showed better capability to reduce distortions compared to the Full FOV techniques. The rFOV-PS-EPI method provided DTI images of the phantom at the level of the hardware whereas the conventional rFOV-SS-EPI is useful only when the metal is approximately 20 mm away. In addition, compared to the rFOV-SS-EPI technique, the suggested approach produced smaller signal voids area as well as significantly reduced geometric distortion in Circularity (p < 0.005) and Eccentricity (p < 0.005) measurements. No statistically significant differences were found for these geometric distortion measurements between the rFOV-PS-EPI DTI sequence and conventional structural T2 images (p > 0.05). CONCLUSION: The combination of rFOV and a phase-segmented acquisition approach is effective for reducing metal-induced distortions in DTI scan on spinal cord with metal hardware at 3 T.

Copper iodide nanoparticles supported on modified graphene-based nanocomposite catalyzed CO2 conversion into oxazolidinone derivatives.

We report on the preparation of copper iodide nanoparticles (NPs) immobilized on vitamin B3-modified graphene (CuI/GO-VB) nanocomposite and its application for the synthesis of oxazolidinone compounds using a remarkable carboxylative cyclization method via the reaction of arylacetylene, aldehyde and benzylamine derivatives under an atmospheric pressure of CO2 gas. The CuI/GO-VB catalyst was prepared from graphene oxide (GO), vitamin B3 (VB) and CuI using a two-step procedure; firstly graphene-based composite (GO-VB) was synthesized by the reaction of GO and nicotinoyl chloride, followed by the immobilization of CuI NPs on GO-VB. The CuI/GO-VB nanocomposite was fully identified with X-ray diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), inductively coupled plasma optical emission spectroscopy (ICP-OES), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy (XPS). The catalytic performance of the CuI/GO-VB heterogeneous catalyst was investigated in carboxylative cyclization for the synthesis of oxazolidinone compounds under an atmospheric pressure of CO2 gas at 100οC in solvent-, base-, and additive-free conditions; the corresponding oxazolidinone compounds were obtained in 79-94% yield. The hot filtration results indicated that CuI/GO-VB nanocomposite was a heterogeneous catalyst and showed a good reusability for 5 runs without a significant decrease in its catalytic performance.

Characteristic dynamic functional connectivity during sevoflurane-induced general anesthesia.

General anesthesia (GA) during surgery is commonly maintained by inhalational sevoflurane. Previous resting state functional MRI (rs-fMRI) studies have demonstrated suppressed functional connectivity (FC) of the entire brain networks, especially the default mode networks, transitioning from the awake to GA condition. However, accuracy and reliability were limited by previous administration methods (e.g. face mask) and short rs-fMRI scans. Therefore, in this study, a clinical scenario of epilepsy patients undergoing laser interstitial thermal therapy was leveraged to acquire 15 min of rs-fMRI while under general endotracheal anesthesia to maximize the accuracy of sevoflurane level. Nine recruited patients had fMRI acquired during awake and under GA, of which seven were included in both static and dynamic FC analyses. Group independent component analysis and a sliding-window method followed by k-means clustering were applied to identify four dynamic brain states, which characterized subtypes of FC patterns. Our results showed that a low-FC brain state was characteristic of the GA condition as a single featuring state during the entire rs-fMRI session; In contrast, the awake condition exhibited frequent fluctuations between three distinct brain states, one of which was a highly synchronized brain state not seen in GA. In conclusion, our study revealed remarkable dynamic connectivity changes from awake to GA condition and demonstrated the advantages of dynamic FC analysis for future studies in the assessments of the effects of GA on brain functional activities.

Profiles of resting state functional connectivity in temporal lobe epilepsy associated with post-laser interstitial thermal therapy seizure outcomes and semiologies.

Introduction: It is now understood that in focal epilepsy, impacted neural regions are not limited to the epileptogenic zone. As such, further investigation into the underlying functional connectivity (FC) patterns in those enduring Temporal Lobe Epilepsy (TLE) with Mesial Temporal Sclerosis (MTS) is imperative to understanding the intricacies of the disease. Methods: The rsfMRIs of 17 healthy participants, 10 left-sided TLE-MTS patients with a pre-operative history of focal impaired awareness seizures (FIA), and 13 left-sided TLE-MTS patients with a pre-operative history of focal aware seizures (FA) were compared to determine the existence of distinct FC patterns with respect to seizure types. Similarly, the rsfMRIs of the above-mentioned healthy participants, 16 left-sided TLE-MTS individuals who were seizure-free (SF) 12 months postoperatively, and 16 left-sided TLE-MTS persons without seizure freedom (nSF) were interrogated. The ROI-to-ROI connectivity analysis included a total of 175 regions of interest (ROIs) and accounted for both age and duration of epileptic activity. Significant correlations were determined via two-sample t-tests and Bonferroni correction (α = 0.05). Results: Comparisons of FA and FIA groups depicted significant correlations between the contralateral anterior cingulate gyrus, subgenual region, and the contralateral cerebellum, lobule III (p-value = 2.26e-4, mean z-score = -0.05 ± 0.28, T = -4.23). Comparisons of SF with nSF depicted two significantly paired-ROIs; the contralateral amygdala and the contralateral precuneus (p-value = 2.9e-5, mean z-score = -0.12 ± 0.19, T = 4.98), as well as the contralateral locus coeruleus and the ipsilateral intralaminar nucleus (p-value= 1.37e-4, mean z-score = 0.06 ± 0.17, T = -4.41). Significance: FC analysis proves to be a lucrative modality for exploring unique signatures with respect to seizure types and postoperative outcomes. By furthering our understanding of the differences between epileptic phenotypes, we can achieve improvement in future treatment modalities not limited to targeting advancements.

Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging.

Background and purpose: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. Materials and methods: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. Results: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. Conclusion: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.

Case report: Utilizing diffusion-weighted MRI on a patient with chronic low back pain treated with spinal cord stimulation.

Diffusion-weighted magnetic resonance imaging (dwMRI) has increasingly demonstrated greater utility in analyzing neuronal microstructure. In patients with chronic low back pain (cLBP), using dwMRI to observe neuronal microstructure can lead to non-invasive biomarkers which could provide clinicians with an objective quantitative prognostic tool. In this case report, we investigated dwMRI for the development of non-invasive biomarkers by conducting a region-based analysis of a 55-year-old male patient with failed back surgery syndrome (FBSS) treated with spinal cord stimulation (SCS). We hypothesized that dwMRI could safely generate quantitative data reflecting cerebral microstructural alterations driven by neuromodulation. Neuroimaging was performed at 6- and 12- months post-SCS implantation. The quantitative maps generated included diffusion tensor imaging (DTI) parameters; fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) computed from whole brain tractography. To examine specific areas of the brain, 44 regions of interest (ROIs), collectively representing the pain NeuroMatrix, were extracted and registered to the patient's diffusion space. Average diffusion indices were calculated from the ROIs at both 6- and 12- months. Regions with >10% relative change in at least 3 of the 4 maps were reported. Using this selection criterion, 8 ROIs demonstrated over 10% relative changes. These ROIs were mainly located in the insular gyri. In addition to the quantitative data, a series of questionnaires were administered during the 6- and 12-month visits to assess pain intensity, functional disability, and quality of life. Overall improvements were observed in these components, with the Pain Catastrophizing Scale (PCS) displaying the greatest change. Lastly, we demonstrated the safety of dwMRI for a patient with SCS. In summary, the results from the case report prompt further investigation in applying dwMRI in a larger cohort to better correlate the influence of SCS with brain microstructural alterations, supporting the utility of dwMRI to generate non-invasive biomarkers for prognostication.

Automated subfield volumetric analysis of amygdala, hippocampus, and thalamic nuclei in mesial temporal lobe epilepsy.

Purpose: Identifying relationships between clinical features and quantitative characteristics of the amygdala-hippocampal and thalamic subregions in mesial temporal lobe epilepsy (mTLE) may offer insights into pathophysiology and the basis for imaging prognostic markers of treatment outcome. Our aim was to ascertain different patterns of atrophy or hypertrophy in mesial temporal sclerosis (MTS) patients and their associations with post-surgical seizure outcomes. To assess this aim, this study is designed in 2 folds: (1) hemispheric changes within MTS group and (2) association with postsurgical seizure outcomes. Methods and materials: 27 mTLE subjects with mesial temporal sclerosis (MTS) were scanned for conventional 3D T1w MPRAGE images and T2w scans. With respect to 12 months post-surgical seizure outcomes, 15 subjects reported being seizure free (SF) and 12 reported continued seizures. Quantitative automated segmentation and cortical parcellation were performed using Freesurfer. Automatic labeling and volume estimation of hippocampal subfields, amygdala, and thalamic subnuclei were also performed. The volume ratio (VR) for each label was computed and compared between (1) between contralateral and ipsilateral MTS using Wilcoxon rank-sum test and (2) SF and not seizure free (NSF) groups using linear regression analysis. False Discovery rate (FDR) with significant level of 0.05 were used in both analyses to correct for multiple comparisons. Results: Amygdala: The medial nucleus of the amygdala was the most significantly reduced in patients with continued seizures when compared to patients who remained seizure free. Hippocampus: Comparison of ipsilateral and contralateral volumes with seizure outcomes showed volume loss was most evident in the mesial hippocampal regions such as CA4 and hippocampal fissure. Volume loss was also most explicit in the presubiculum body in patients with continued seizures at the time of their follow-up. Ipsilateral MTS compared to contralateral MTS analysis showed the heads of the ipsilateral subiculum, presubiculum, parasubiculum, dentate gyrus, CA4, and CA3 were more significantly affected than their respective bodies. Volume loss was most noted in mesial hippocampal regions. Thalamus: VPL and PuL were the most significantly reduced thalamic nuclei in NSF patients. In all statistically significant areas, volume reduction was observed in the NSF group. No significant volume reductions were noted in the thalamus and amygdala when comparing ipsilateral to contralateral sides in mTLE subjects. Conclusions: Varying degrees of volume loss were demonstrated in the hippocampus, thalamus, and amygdala subregions of MTS, especially between patients who remained seizure-free and those who did not. The results obtained can be used to further understand mTLE pathophysiology. Clinical relevance/application: In the future, we hope these results can be used to deepen the understanding of mTLE pathophysiology, leading to improved patient outcomes and treatments.

A Novel and Simple Method Using Computed Tomography Streak Artifact to Determine the Orientation of Directional Deep Brain Stimulation Leads.

BACKGROUND AND OBJECTIVES: Directional leads have garnered widespread use in deep brain stimulation (DBS) because of the ability to steer current and maximize the therapeutic window. Accurate identification of lead orientation is critical to effective programming. Although directional markers are visible on 2-dimensional imaging, precise orientation may be difficult to interpret. Recent studies have suggested methods of determining lead orientation, but these involve advanced intraoperative imaging and/or complex computational algorithms. Our objective is to develop a precise and reliable method of determining orientation of directional leads using conventional imaging techniques and readily available software. METHODS: We examined postoperative thin-cut computed tomography (CT) scans and x-rays of patients who underwent DBS with directional leads from 3 vendors. Using commercially available stereotactic software, we localized the leads and planned new trajectories precisely overlaying the leads visualized on CT. We used trajectory view to locate the directional marker in a plane orthogonal to the lead and inspected the streak artifact. We then validated this method with a phantom CT model by acquiring thin-cut CT images orthogonal to 3 different leads in various orientations confirmed under direct visualization. RESULTS: The directional marker creates a unique streak artifact that reflects the orientation of the directional lead. There is a hyperdense symmetric streak artifact parallel to the axis of the directional marker and a symmetric hypodense dark band orthogonal to the marker. This is often sufficient to infer the direction of the marker. If not, it at least renders 2 opposite possibilities for the direction of the marker, which can then be easily reconciled by comparison with x-ray images. CONCLUSION: We propose a method to determine orientation of directional DBS leads in a precise manner on conventional imaging and readily available software. This method is reliable across DBS vendors, and it can simplify this process and aid in effective programming.

Resting-State fMRI Can Detect Alterations in Seizure Onset and Spread Regions in Patients with Non-Lesional Epilepsy: A Pilot Study.

Introduction: Epilepsy is defined as non-lesional (NLE) when a lesion cannot be localized via standard neuroimaging. NLE is known to have a poor response to surgery. Stereotactic electroencephalography (sEEG) can detect functional connectivity (FC) between zones of seizure onset (OZ) and early (ESZ) and late (LSZ) spread. We examined whether resting-state fMRI (rsfMRI) can detect FC alterations in NLE to see whether noninvasive imaging techniques can localize areas of seizure propagation to potentially target for intervention. Methods: This is a retrospective study of 8 patients with refractory NLE who underwent sEEG electrode implantation and 10 controls. The OZ, ESZ, and LSZ were identified by generating regions around sEEG contacts that recorded seizure activity. Amplitude synchronization analysis was used to detect the correlation of the OZ to the ESZ. This was also done using the OZ and ESZ of each NLE patient for each control. Patients with NLE were compared to controls individually using Wilcoxon tests and as a group using Mann-Whitney tests. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DoC), and voxel-mirrored homotopic connectivity (VMHC) were calculated as the difference between NLE and controls and compared between the OZ and ESZ and to zero. A general linear model was used with age as a covariate with Bonferroni correction for multiple comparisons. Results: Five out of 8 patients with NLE showed decreased correlations from the OZ to the ESZ. Group analysis showed patients with NLE had lower connectivity with the ESZ. Patients with NLE showed higher fALFF and ReHo in the OZ but not the ESZ, and higher DoC in the OZ and ESZ. Our results indicate that patients with NLE show high levels of activity but dysfunctional connections in seizure-related areas. Discussion: rsfMRI analysis showed decreased connectivity directly between seizure-related areas, while FC metric analysis revealed increases in local and global connectivity in seizure-related areas. FC analysis of rsfMRI can detect functional disruption that may expose the pathophysiology underlying NLE.

The role of the insula in chronic pain following spinal cord injury: A resting-state fMRI study.

BACKGROUND AND PURPOSE: Spinal cord injury (SCI) results in the loss of motor and sensory function from disconnections between efferent and afferent pathways. Most SCI patients are affected with chronic neuropathic pain, but there is a paucity of data concerning neuroplastic changes following SCI. Chronic pain disrupts default networks and is associated with abnormal insular connectivity. The posterior insula (PI) is associated with the degree of pain and intensity of pain. The anterior insula (AI) is related to signal changes. Comprehension of SCI pain mechanisms is essential to elucidate effective treatment options. METHODS: This study examines the insular gyri functional connectivity (FC) of seven (five male, two female) SCI participants with moderate-severe chronic pain compared to 10 (five male, five female) healthy controls (HC). All subjects had 3-Tesla MRI performed and resting-state functional MRI (fMRI) was acquired. FC metrics were obtained from the comparisons of resting-state fMRI among our various groups. A seed-to-voxel analysis was pursued, encompassing six gyri of the insula. For multiple comparisons, a correction was applied with a significance level of p < .05. RESULTS: There were significant differences in FC of the insula between SCI participants with chronic pain compared with HC. In the SCI participants, there was hyperconnectivity of the AI and PI to the frontal pole. In addition, there was increased FC noted between the PI and the anterior cingulate cortex. Hyperconnectivity was also observed between the AI and the occipital cortex. CONCLUSIONS: These findings illustrate that there is a complex hyperconnectivity and modulation of pain pathways after traumatic SCI.

Effect of Enlarged Perivascular Spaces in Reliable Distinction of Prospective Targeting During Deep Brain Stimulation in Patients With Advanced Parkinson's Disease: A Study of Deterministic and Probabilistic Tractography.

BACKGROUND: Precise electrode position is vital for effective deep brain stimulation in treating motor symptoms in Parkinson's disease (PD). Enlarged perivascular spaces (PVSs) are associated with pathophysiology of neurodegenerative diseases including PD and may affect the microstructure of surrounding brain tissue. OBJECTIVE: To quantify the clinical implications of enlarged PVS on tractography-based stereotactic targeting in patients with advanced PD selected to undergo deep brain stimulation. METHODS: Twenty patients with PD underwent MRI scanning. The PVS areas were visualized and segmented. Based on the size of the PVS areas, the patient group was split into 2 categories of large vs small PVSs. Probabilistic and deterministic tractography methods were applied to a diffusion-weighted data set. Fiber assignment was performed using motor cortex as an initiation seed and the globus pallidus interna and subthalamic nucleus, separately, as inclusion masks. Two exclusion masks used consisted of cerebral peduncles and the PVS mask. The center of gravity of the tract density map was measured and compared between the tracts generated with and without consideration of the PVS mask. RESULTS: The average differences between the center of gravity of the tracts made by excluding PVS and without excluding PVS using deterministic and probabilistic tractography methods were less than 1 mm. Statistical analysis showed nonsignificant differences between deterministic and probabilistic methods and differences between patients with large and small PVSs ( P > .05). CONCLUSION: This study demonstrated that the presence of enlarged PVS is unlikely to affect targeting of basal ganglia nuclei based on tractography.

Metal Artifact Reduction Around Cervical Spine Implant Using Diffusion Tensor Imaging at 3T: A Phantom Study.

Diffusion MRI continues to play a key role in non-invasively assessing spinal cord integrity and pre-operative injury evaluation. However, post-operative Diffusion Tensor Imaging (DTI) acquisition of a patient with a metal implant results in severe geometric image distortion. A method has been proposed here to alleviate the technical challenges facing the acquisition of DTI in post-operative cases and to evaluate longitudinal therapeutics. The described technique is based on the combination of the reduced Field-Of-View (rFOV) strategy and the phase segmented acquisition scheme (rFOV-PS-EPI) for significantly mitigating metal-induced distortions. A custom-built phantom based on spine model with metal implant was used to collect high-resolution DTI data at 3 Tesla scanner using a home-grown diffusion MRI pulse sequence, rFOV-PS-EPI, single-shot (rFOV-SS-EPI), and the conventional full FOV techniques including SS-EPI, PS-EPI, and the readout-segmented (RS-EPI). This newly developed method provides high-resolution images with significant reduced metal-induced artifacts. In contrast to the other techniques, the rFOV-PS-EPI allows DTI measurement at the level of the metal hardware whereas the current rFOV-SS-EPI is useful when the metal is approximately 20 mm away. The developed approach enables high-resolution DTI in patients with metal implant.

Automatic Segmentation of Parkinson Disease Therapeutic Targets Using Nonlinear Registration and Clinical MR Imaging: Comparison of Methodology, Presence of Disease, and Quality Control.

INTRODUCTION: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson's disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. METHODS: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as "ground truth" data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. RESULTS: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). CONCLUSION: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.