The Effectiveness of fMRI Data when Combined with Polygraph Data

The Integrated Zone Comparison Technique (IZCT) was utilized with computerized polygraph instrumentation and the Academy for Scientific Investigative Training’s Horizontal Scoring System ASIT PolySuite algorithm, as part of a blind study in the detection of deception. This paper represents a synergy analysis of combining fMRI only deception data with each of the three individual physiological parameters that are used in polygraph. They include the electro-dermal response (EDR), pneumo, and cardio measurements. In addition, we compared the detection accuracy analysis using each single parameter by itself. Th e fMRI score and each individual polygraph parameter score on individual subjects were averaged to establish an overall score

Alterations in Cerebral Glucose Metabolism Measured by FDG PET in Subjects Performing a Meditation Practice Based on Clitoral Stimulation

Background: The relationship between sexuality, or the libido, and spirituality or religion has long been debated in psychiatry. Recent studies have explored the neurophysiology of both sexual experiences and spiritual practices such as meditation or prayer. In the present study, we report changes in cerebral glucose metabolism in a unique meditation practice augmented by clitoral stimulation called, Orgasmic Meditation, in which a spiritual state is described to be attained by both male and female participants engaged in the practice as a pair. Methods: Male (N=20) and female (N=20) subjects had an intravenous catheter connected to a bag of normal saline inserted prior to the practice. During the practice, men stimulated their partner’s clitoris for exactly 15 minutes (he received no sexual stimulation). Midway through the practice, researchers injected 18F-fluorodeoxyglucose so the scan would reflect cerebral metabolism during the practice. Positron emission tomography (PET) imaging was performed approximately 30 minutes later. Results: In the female participants, the meditation state showed significant decreases in the left inferior frontal, inferior parietal, insula, middle temporal, and orbitofrontal regions as well as in the right angular gyrus, anterior cingulate and parahippocampus compared to a neutral state (p\u3c0.01). Male subjects had significant decreases in the left middle frontal, paracentral, precentral, and postcentral regions as well as the right middle frontal and paracentral regions during meditation (p\u3c0.01). Men also had significantly increased metabolism in the cerebellum and right postcentral and superior temporal regions (p\u3c0.01). Conclusions: These findings represent a distinct pattern of brain activity, for both men and women, that is a hybrid between that of other meditation practices and sexual stimulation. Such findings have potential psychotherapeutic implications and may deepen our understanding of the relationship between spiritual and sexual experience

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

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

Identification of Chronic Mild Traumatic Brain Injury Using Resting State Functional MRI and Machine Learning Techniques

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration: [clinicaltrials.gov], identifier [NCT03241732]

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79–91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings

Cerebral Blood Flow and Brain Functional Connectivity Changes in Older Adults Participating in a Mindfulness-Based Stress Reduction Program

There is a growing interest in the potential beneficial effects of mindfulness meditation training in protecting against age-related physical, emotional, and cognitive decline. The current prospective, single-center, single-arm study investigated if functional magnetic resonance imaging-based changes in cerebral blood flow and brain functional connectivity could be observed in 11 elderly adults (mean age 79) after participation in a Mindfulness-Based Stress Reduction (MBSR) program. The results showed significantly (p \u3c 0.05) altered cerebral blood flow and functional connectivity in the cingulate gyrus, limbic structures, and subregions of the temporal and frontal lobes, similar to findings of other meditation-related studies in younger populations. Furthermore, these changes were also associated with significant improvements in depression symptoms. This study suggests that the MBSR program can potentially modify cerebral blood flow and connectivity in this population

Case Report: The Promising Application of Dynamic Functional Connectivity Analysis on an Individual With Failed Back Surgery Syndrome

Failed back surgery syndrome (FBSS), a chronic neuropathic pain condition, is a common indication for spinal cord stimulation (SCS). However, the mechanisms of SCS, especially its effects on supraspinal/brain functional connectivity, are still not fully understood. Resting state functional magnetic resonance imaging (rsfMRI) studies have shown characteristics in patients with chronic low back pain (cLBP). In this case study, we performed rsfMRI scanning (3.0 T) on an FBSS patient, who presented with chronic low back and leg pain following her previous lumbar microdiscectomy and had undergone permanent SCS. Appropriate MRI safety measures were undertaken to scan this subject. Seed-based functional connectivity (FC) was performed on the rsfMRI data acquired from the FBSS subject, and then compared to a group of 17 healthy controls. Seeds were identified by an atlas of resting state networks (RSNs), which is composed of 32 regions grouped into 8 networks. Sliding-window method and k-means clustering were used in dynamic FC analysis, which resulted in 4 brain states for each group. Our results demonstrated the safety and feasibility of 3T MRI scanning in a patient with implanted SCS system. Compared to the brain states of healthy controls, the FBSS subject presented very different FC patterns in less frequent brain states. The mean dwell time of brain states showed distinct distributions: the FBSS subject seemed to prefer a single state over the others. Although future studies with large sample sizes are needed to make statistical conclusions, our findings demonstrated the promising application of dynamic FC to provide more granularity with FC changes associated with different brain states in chronic pain

Harmonization of Multi-Site Diffusion Tensor Imaging Data for Cervical and Thoracic Spinal Cord at 1.5 T and 3 T Using Longitudinal ComBat

MRI scanner hardware, field strengths, and sequence parameters are major variables in diffusion studies of the spinal cord. Reliability between scanners is not well known, particularly for the thoracic cord. DTI data was collected for the entire cervical and thoracic spinal cord in thirty healthy adult subjects with different MR vendors and field strengths. DTI metrics were extracted and averaged for all slices within each vertebral level. Metrics were examined for variability and then harmonized using longitudinal ComBat (longComBat). Four scanners were used: Siemens 3 T Prisma, Siemens 1.5 T Avanto, Philips 3 T Ingenia, Philips 1.5 T Achieva. Average full cord diffusion values/standard deviation for all subjects and scanners were FA: 0.63, σ = 0.10, MD: 1.11, σ = 0.12 × 10−3 mm2/s, AD: 1.98, σ = 0.55 × 10−3 mm2/s, RD: 0.67, σ = 0.31 × 10−3 mm2/s. FA metrics averaged for all subjects by level were relatively consistent across scanners, but large variability was found in diffusivity measures. Coefficients of variation were lowest in the cervical region, and relatively lower for FA than diffusivity measures. Harmonized metrics showed greatly improved agreement between scanners. Variability in DTI of the spinal cord arises from scanner hardware differences, pulse sequence differences, physiological motion, and subject compliance. The use of longComBat resulted in large improvement in agreement of all DTI metrics between scanners. This study shows the importance of harmonization of diffusion data in the spinal cord and potential for longitudinal and multisite clinical research and clinical trials

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 postsurgical 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