In our previous study we investigated Masking Level Differences (MLD) using functional Magnetic Resonance Imaging (fMRI), but were unable to confirm neural correlations for the MLD within the auditory cortex and inferior colliculus. Here we have duplicated conditions from our previous study, but have included more participants and changed the study site to a new location with a newer scanner and presentation system. Additionally, Diffusion Tensor Imaging (DTI) is included to allow investigation of fiber tracts that may be involved with MLDs. Twenty participants were included and underwent audiometric testing and MRI scanning. The current study revealed regions of increased and decreased activity within the auditory cortex when comparing the combined noise and signal of the dichotic MLD stimuli (N0Sπ and NπS0) with N0S0. Furthermore, we found evidence of inferior colliculus involvement. Our DTI findings show strong correlations between DTI measures within the brainstem and signal detection threshold levels. Patterns of correlation when the signal was presented only to the right ear showed an extensive network in the left hemisphere; however, the opposite was not true for the signal presented only to the left ear. Our current study was able to confirm what we had previously hypothesized using fMRI, while extending our investigation of MLDs to include the characteristics of connecting neural pathways.
Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian–and thereby probabilistic–framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms–a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects’ performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject ‘prototypes’ (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.
The Trail Making Test (TMT) has its limitations when applied to Eastern cultures due to its reliance on the alphabet. We looked for an alternative tool that is reliable and distinguishable like the TMT and devised the Trail Making Test Black & White (TMT-B&W) as a new variant. This study identifies the applicability of the TMT-B&W as a useful neuropsychological tool and determines whether the TMT-B&W could play an equivalent role as the TMT.
The TMT-B&W uses numbers encircled by black or white circles as stimuli, instead of using the alphabet. A total of 138 participants were including containing groups of 31 cognitively normal controls (NC), 55 mild cognitive impairment (MCI), and 52 people with Alzheimer’s disease (AD). Along with the TMT-B&W, the TMT and other neuropsychological tests were administered to all subjects.
A considerably low dropout rate for TMT B&W demonstrates that all participants were more willingly engaged in the TMT B&W than the TMT. In particular, subjects with cognitive impairments or lower levels of education performed better on the TMT-B&W than the TMT. The difference in time-to-completion of the TMT-B&W was significant according to the level of cognitive impairment. The TMT-B&W revealed a high correlation with the TMT and frontal lobe function test.
The TMT-B&W is as reliable and effective as the TMT. It is worth developing a new variant of the TMT.
This article investigates a possible Brain Computer Interface (BCI) based on semantic relations. The BCI determines which prime word a subject has in mind by presenting probe words using an intelligent algorithm. Subjects indicate when a presented probe word is related to the prime word by a single finger tap. The detection of the neural signal associated with this movement is used by the BCI to decode the prime word. The movement detector combined both the evoked (ERP) and induced (ERD) responses elicited with the movement. Single trial movement detection had an average accuracy of 67%. The decoding of the prime word had an average accuracy of 38% when using 100 probes and 150 possible targets, and 41% after applying a dynamic stopping criterium, reducing the average number of probes to 47. The article shows that the intelligent algorithm used to present the probe words has a significantly higher performance than a random selection of probes. Simulations demonstrate that the BCI also works with larger vocabulary sizes, and the performance scales logarithmically with vocabulary size.
Interpretation of the EEG background pattern in routine recordings is an important part of clinical reviews. We evaluated the feasibility of an automated analysis system to assist reviewers with evaluation of the general properties in the EEG background pattern.
Quantitative EEG methods were used to describe the following five background properties: posterior dominant rhythm frequency and reactivity, anterior-posterior gradients, presence of diffuse slow-wave activity and asymmetry. Software running the quantitative methods were given to ten experienced electroencephalographers together with 45 routine EEG recordings and computer-generated reports. Participants were asked to review the EEGs by visual analysis first, and afterwards to compare their findings with the generated reports and correct mistakes made by the system. Corrected reports were returned for comparison.
Using a gold-standard derived from the consensus of reviewers, inter-rater agreement was calculated for all reviewers and for automated interpretation. Automated interpretation together with most participants showed high (kappa > 0.6) agreement with the gold standard. In some cases, automated analysis showed higher agreement with the gold standard than participants. When asked in a questionnaire after the study, all participants considered computer-assisted interpretation to be useful for every day use in routine reviews.
Automated interpretation methods proved to be accurate and were considered to be useful by all participants.
Computer-assisted interpretation of the EEG background pattern can bring consistency to reviewing and improve efficiency and inter-rater agreement.
Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.
Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming) and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to observed pain in others.
Adolescent idiopathic scoliosis (AIS) is a multifactorial disease affecting approximately 1–4% of teenagers especially girls at the age of 10–16, but its etiopathogenesis remains uncertain. Previous study has revealed that the cortical thickness in AIS patients is different from that in normal controls. Cortical thickness measurements are known to be strongly correlated between regions that are axonally connected. Hence, a hypothesis is proposed to study the possibility to demonstrate abnormal structural network revealed by cortical thickness in AIS patients. The aim of the study is to investigate abnormalities in the organization of the brain cortical network in AIS patients. This study included 42 girls with severe idiopathic scoliosis (14.7±1.3 years old) and 41 age-matched normal controls (NC, 14.6±1.4 years old). The brain cortex was partitioned into 154 cortical regions based on gyral and sulcal structure. The interregional connectivity was measured as the statistical correlations between the regional mean thicknesses across the subjects. We employed the graph theoretic analysis to examine the alteration in interregional correlation, small-world efficiency, hub distribution, and regional nodal characteristics in AIS patients. We demonstrated that the cortical network of AIS patients fully preserved the small-world architecture and organization, and further verified the hemispheric asymmetry of AIS brain. Our results indicated increased central role of temporal and occipital cortex and decreased central role of limbic cortex in AIS patients compared with controls. Furthermore, decreased structural connectivity between hemispheres and increased connectivity in several cortical regions were observed. The findings of the study reveal the pattern of structural network alteration in AIS brain, and would help in understanding the mechanism and etiopathogenesis of AIS.
Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages.
Materials and Methods
We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age.
In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5th percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects.
While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease.
We propose a linear-elastic registration method to register diffusion-weighted MRI (DW-MRI) data sets by mapping their diffusion orientation distribution functions (ODFs). The ODFs were reconstructed using a q-ball imaging (QBI) technique to resolve intravoxel fiber crossing. The registration method is based on mapping the ODF maps represented by spherical harmonics which yield analytic solutions and reduce the computational complexity. ODF reorientation is required to maintain the consistency with transformed local fiber directions. The reorientation matrices are extracted from the local Jacobian and directly applied to the coefficients of spherical harmonics. The similarity cost of the registration is defined by the ODF shape distance calculated from the spherical harmonic coefficients. The transformation fields are regularized by linear elastic constraints. The proposed method was validated using both synthetic and real data sets. Experimental results show that the elastic registration improved the affine alignment by further reducing the ODF shape difference; reorientation during the registration produced registered ODF maps with more consistent principle directions compared to registrations without reorientation or simultaneous reorientation.
Non-rigid registration of diffusion MRI is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.
Diffusion MRI; orientation distribution function (ODF); spherical harmonics; ODF reorientation; registration; diffeomorphisms
Studies addressing brain correlates of emotional personality have remained sparse, despite the involvement of emotional personality in health and well-being. This study investigates structural and functional brain correlates of psychological and physiological measures related to emotional personality. Psychological measures included neuroticism, extraversion, and agreeableness scores, as assessed using a standard personality questionnaire. As a physiological measure we used a cardiac amplitude signature, the so-called Eκ value (computed from the electrocardiogram) which has previously been related to tender emotionality. Questionnaire scores and Eκ values were related to both functional (eigenvector centrality mapping, ECM) and structural (voxel-based morphometry, VBM) neuroimaging data. Functional magnetic resonance imaging (fMRI) data were obtained from 22 individuals (12 females) while listening to music (joy, fear, or neutral music). ECM results showed that agreeableness scores correlated with centrality values in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the ventral striatum (nucleus accumbens). Individuals with higher Eκ values (indexing higher tender emotionality) showed higher centrality values in the subiculum of the right hippocampal formation. Structural MRI data from an independent sample of 59 individuals (34 females) showed that neuroticism scores correlated with volume of the left amygdaloid complex. In addition, individuals with higher Eκ showed larger gray matter volume in the same portion of the subiculum in which individuals with higher Eκ showed higher centrality values. Our results highlight a role of the amygdala in neuroticism. Moreover, they indicate that a cardiac signature related to emotionality (Eκ) correlates with both function (increased network centrality) and structure (grey matter volume) of the subiculum of the hippocampal formation, suggesting a role of the hippocampal formation for emotional personality. Results are the first to show personality-related differences using eigenvector centrality mapping, and the first to show structural brain differences for a physiological measure associated with personality.
Seeing the articulatory gestures of the speaker (“speech reading”) enhances speech perception especially in noisy conditions. Recent neuroimaging studies tentatively suggest that speech reading activates speech motor system, which then influences superior-posterior temporal lobe auditory areas via an efference copy. Here, nineteen healthy volunteers were presented with silent videoclips of a person articulating Finnish vowels /a/, /i/ (non-targets), and /o/ (targets) during event-related functional magnetic resonance imaging (fMRI). Speech reading significantly activated visual cortex, posterior fusiform gyrus (pFG), posterior superior temporal gyrus and sulcus (pSTG/S), and the speech motor areas, including premotor cortex, parts of the inferior (IFG) and middle (MFG) frontal gyri extending into frontal polar (FP) structures, somatosensory areas, and supramarginal gyrus (SMG). Structural equation modelling (SEM) of these data suggested that information flows first from extrastriate visual cortex to pFS, and from there, in parallel, to pSTG/S and MFG/FP. From pSTG/S information flow continues to IFG or SMG and eventually somatosensory areas. Feedback connectivity was estimated to run from MFG/FP to IFG, and pSTG/S. The direct functional connection from pFG to MFG/FP and feedback connection from MFG/FP to pSTG/S and IFG support the hypothesis of prefrontal speech motor areas influencing auditory speech processing in pSTG/S via an efference copy.
Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.
Higher levels of fitness or physical function are positively associated with cognitive outcomes but the potential underlying mechanisms via brain structure are still to be elucidated in detail. We examined associations between brain structure and physical function (contemporaneous and change over the previous three years) in community-dwelling older adults.
Participants from the Lothian Birth Cohort 1936 (N=694) underwent brain MRI at age 73 years to assess intracranial volume, and the volumes of total brain tissue, ventricles, grey matter, normal-appearing white matter, and white matter lesions. At ages 70 and 73, physical function was assessed by 6-meter walk, grip strength, and forced expiratory volume. A summary ‘physical function factor’ was derived from the individual measures using principal components analysis. Performance on each individual physical function measure declined across the three year interval (p<0.001). Higher level of physical function at ages 70 and 73 was associated with larger total brain tissue and white matter volumes, and smaller ventricular and white matter lesion volumes (standardized β ranged in magnitude from 0.07 to 0.17, p<0.001 to 0.034). Decline in physical function from age 70 to 73 was associated with smaller white matter volume (0.08, p<0.01, though not after correction for multiple testing), but not with any other brain volumetric measurements.
Physical function was related to brain volumes in community-dwelling older adults: declining physical function was associated with less white matter tissue. Further study is required to explore the detailed mechanisms through which physical function might influence brain structure, and vice versa.
To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.
In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).
The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12).
Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
Twist-related protein 1 (Twist1), also known as class A basic helix-loop-helix protein 38 (bHLHa38), has been implicated in cell lineage determination and differentiation. Previous studies demonstrate that Twist1 expression is up-regulated in gastric cancer with poor clinical outcomes. Besides, Twist1 is suggested to be involved in progression of human gastric cancer. However, its biological functions remain largely unexplored. In the present study, we show that Twist 1 overexpression leads to a significant up-regulation of FoxM1, which plays a key role in cell cycle progression in gastric cancer cells. In contrast, knockdown of Twist 1 reduces FoxM1 expression, suggesting that FoxM1 might be a direct transcriptional target of Twist 1. At the molecular level, we further reveal that Twist 1 could bind to the promoter region of FoxM1, and subsequently recruit p300 to induce FoxM1 mRNA transcription. Therefore, our results uncover a previous unknown Twist 1/FoxM1 regulatory pathway, which may help to understand the mechanisms of gastric cancer proliferation.
Previous studies using hierarchical clustering approach to analyze resting-state fMRI data were limited to a few slices or regions-of-interest (ROIs) after substantial data reduction.
To develop a framework that can perform voxel-wise hierarchical clustering of whole-brain resting-state fMRI data from a group of subjects.
Materials and Methods
Resting-state fMRI measurements were conducted for 86 adult subjects using a single-shot echo-planar imaging (EPI) technique. After pre-processing and co-registration to a standard template, pair-wise cross-correlation coefficients (CC) were calculated for all voxels inside the brain and translated into absolute Pearson's distances after imposing a threshold CC≥0.3. The group averages of the Pearson's distances were then used to perform hierarchical clustering with the developed framework, which entails gray matter masking and an iterative scheme to analyze the dendrogram.
With the hierarchical clustering framework, we identified most of the functional connectivity networks reported previously in the literature, such as the motor, sensory, visual, memory, and the default-mode functional networks (DMN). Furthermore, the DMN and visual system were split into their corresponding hierarchical sub-networks.
It is feasible to use the proposed hierarchical clustering scheme for voxel-wise analysis of whole-brain resting-state fMRI data. The hierarchical clustering result not only confirmed generally the finding in functional connectivity networks identified previously using other data processing techniques, such as ICA, but also revealed directly the hierarchical structure within the functional connectivity networks.
This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications. The validity, efficiency, and potential applications of DMS are evaluated through four benchmark experiments, including the simulated dMRI of white matter fibers, the multiple scattering diffusion imaging, the biophysical modeling of polar cell membranes, and the high angular resolution diffusion imaging and fiber tractography of complex fiber configurations. We expect that this novel software tool would be substantially advantageous to clarify the interrelationship between dMRI and the microscopic characteristics of brain tissues, and to advance the biophysical modeling and the dMRI methodologies.
Elevated or reduced velocity of cerebrospinal fluid (CSF) at the craniovertebral junction (CVJ) has been associated with type I Chiari malformation (CMI). Thus, quantification of hydrodynamic parameters that describe the CSF dynamics could help assess disease severity and surgical outcome. In this study, we describe the methodology to quantify CSF hydrodynamic parameters near the CVJ and upper cervical spine utilizing subject-specific computational fluid dynamics (CFD) simulations based on in vivo MRI measurements of flow and geometry. Hydrodynamic parameters were computed for a healthy subject and two CMI patients both pre- and post-decompression surgery to determine the differences between cases. For the first time, we present the methods to quantify longitudinal impedance (LI) to CSF motion, a subject-specific hydrodynamic parameter that may have value to help quantify the CSF flow blockage severity in CMI. In addition, the following hydrodynamic parameters were quantified for each case: maximum velocity in systole and diastole, Reynolds and Womersley number, and peak pressure drop during the CSF cardiac flow cycle. The following geometric parameters were quantified: cross-sectional area and hydraulic diameter of the spinal subarachnoid space (SAS). The mean values of the geometric parameters increased post-surgically for the CMI models, but remained smaller than the healthy volunteer. All hydrodynamic parameters, except pressure drop, decreased post-surgically for the CMI patients, but remained greater than in the healthy case. Peak pressure drop alterations were mixed. To our knowledge this study represents the first subject-specific CFD simulation of CMI decompression surgery and quantification of LI in the CSF space. Further study in a larger patient and control group is needed to determine if the presented geometric and/or hydrodynamic parameters are helpful for surgical planning.
Cerebral vasoreactivity (CVR) can be assessed by functional MRI (fMRI) using hypercapnia challenges. In normal subjects, studies have shown temporal variability of CVR blood oxygenation level-dependent responses among different brain regions. In the current study, we analyzed the variability of BOLD CVR dynamics by fMRI with a breath-holding task in 17 subjects with unilateral carotid stenosis before they received carotid stenting. Great heterogeneity of CVR dynamics was observed when comparing BOLD responses between ipsilateral and contralateral hemispheres in each patient, especially in middle cerebral artery (MCA) territories. While some subjects (n=12) had similar CVR responses between either hemisphere, the others (n=5) had a poorly correlated pattern of BOLD changes between ipsilateral and contralateral hemispheres. In the latter group, defined as impaired CVR, post-stenting perfusion tended to be more significantly increased. Our data provides the first observation of divergent temporal BOLD responses during breath holding in patients with carotid stenosis. The development of collateral circulation and the derangement of cerebral hemodynamics can be detected through this novel analysis of the different patterns of BOLD changes. The results also help in prediction of robust increase of perfusion or hyperperfusion after carotid stenting.
Humans are more familiar with index – thumb than with any other finger to thumb grasping. The effect of familiarity has been previously tested with complex, specialized and/or transitive movements, but not with simple intransitive ones. The aim of this study is to evaluate brain activity patterns during the observation of simple and intransitive finger movements with differing degrees of familiarity.
A functional Magnetic Resonance Imaging (fMRI) study was performed using a paradigm consisting of the observation of 4 videos showing a finger opposition task between the thumb and the other fingers (index, middle, ring and little) in a repetitive manner with a fixed frequency (1 Hz). This movement is considered as the pantomime of a precision grasping action.
Significant activity was identified in the bilateral Inferior Parietal Lobule and premotor regions with the selected level of significance (FDR [False Discovery Rate] = 0.01). The extent of the activation in both regions tended to decrease when the finger that performed the action was further from the thumb. More specifically, this effect showed a linear trend (index>middle>ring>little) in the right parietal and premotor regions.
The observation of less familiar simple intransitive movements produces less activation of parietal and premotor areas than familiar ones. The most important implication of this study is the identification of differences in brain activity during the observation of simple intransitive movements with different degrees of familiarity.
To identify perinatal clinical antecedents of white matter microstructural abnormalities in extremely preterm infants.
A prospective cohort of extremely preterm infants (N = 86) and healthy term controls (N = 16) underwent diffusion tensor imaging (DTI) at term equivalent age. Region of interest-based measures of white matter microstructure - fractional anisotropy and mean diffusivity - were quantified in seven vulnerable cerebral regions and group differences assessed. In the preterm cohort, multivariable linear regression analyses were conducted to identify independent clinical factors associated with microstructural abnormalities.
Preterm infants had a mean (standard deviation) gestational age of 26.1 (1.7) weeks and birth weight of 824 (182) grams. Compared to term controls, the preterm cohort exhibited widespread microstructural abnormalities in 9 of 14 regional measures. Chorioamnionitis, necrotizing enterocolitis, white matter injury on cranial ultrasound, and increasing duration of mechanical ventilation were adversely correlated with regional microstructure. Conversely, antenatal steroids, female sex, longer duration of caffeine therapy, and greater duration of human milk use were independent favorable factors. White matter injury on cranial ultrasound was associated with a five weeks or greater delayed maturation of the corpus callosum; every additional 10 days of human milk use were associated with a three weeks or greater advanced maturation of the corpus callosum.
Diffusion tensor imaging is sensitive in detecting the widespread cerebral delayed maturation and/or damage increasingly observed in extremely preterm infants. In our cohort, it also aided identification of several previously known or suspected perinatal clinical antecedents of brain injury, aberrant development, and neurodevelopmental impairments.
To investigate the pattern of spontaneous neural activity in patients with end-stage renal disease (ESRD) with and without neurocognitive dysfunction using resting-state functional magnetic resonance imaging (rs-fMRI) with a regional homogeneity (ReHo) algorithm.
Materials and Methods
rs-fMRI data were acquired in 36 ESRD patients (minimal nephro-encephalopathy [MNE], n = 19, 13 male, 37±12.07 years; non-nephro-encephalopathy [non-NE], n = 17, 11 male, 38±12.13 years) and 20 healthy controls (13 male, 7 female, 36±10.27 years). Neuropsychological (number connection test type A [NCT-A], digit symbol test [DST]) and laboratory tests were performed in all patients. The Kendall's coefficient of concordance (KCC) was used to measure the regional homogeneity for each subject. The regional homogeneity maps were compared using ANOVA tests among MNE, non-NE, and healthy control groups and post hoc t -tests between each pair in a voxel-wise way. A multiple regression analysis was performed to evaluate the relationships between ReHo index and NCT-A, DST scores, serum creatinine and urea levels, disease and dialysis duration.
Compared with healthy controls, both MNE and non-NE patients showed decreased ReHo in the multiple areas of bilateral frontal, parietal and temporal lobes. Compared with the non-NE, MNE patients showed decreased ReHo in the right inferior parietal lobe (IPL), medial frontal cortex (MFC) and left precuneus (PCu). The NCT-A scores and serum urea levels of ESRD patients negatively correlated with ReHo values in the frontal and parietal lobes, while DST scores positively correlated with ReHo values in the bilateral PCC/precuneus, MFC and inferior parietal lobe (IPL) (all P<0.05, AlphaSim corrected). No significant correlations were found between any regional ReHo values and disease duration, dialysis duration and serum creatinine values in ESRD patients (all P>0.05, AlphaSim corrected).
Diffused decreased ReHo values were found in both MNE and non-NE patients. The progressively decreased ReHo in the default mode network (DMN), frontal and parietal lobes might be trait-related in MNE. The ReHo analysis may be potentially valuable for elucidating neurocognitive abnormalities of ESRD patients and detecting the development from non-NE to MNE.
Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well.
fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state.
These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.