Human Connectome Project Data Research Articles

Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting

High angular resolution diffusion imaging (HARDI) is a promising method for advanced analysis of brain microstructure. However, comprehensive HARDI analysis requires multiple acquisitions of diffusion images (multi-shell HARDI), which is time consuming and often impractical in clinical settings. This study aimed to establish neural network models that can predict new diffusion datasets from clinically feasible brain diffusion MRI for multi-shell HARDI. The development included 2 algorithms: multi-layer perceptron (MLP) and convolutional neural network (CNN). Both followed a voxel-based approach for model training (70%), validation (15%), and testing (15%). The investigations involved 2 multi-shell HARDI datasets: 1) 11 healthy subjects from the Human Connectome Project (HCP); and 2) 10 local subjects with multiple sclerosis (MS). To assess outcomes, we conducted neurite orientation dispersion and density imaging using both predicted and original data and compared their orientation dispersion index (ODI) and neurite density index (NDI) in different brain tissues with 2 measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results showed that both models achieved robust predictions, which provided competitive ODI and NDI, especially in brain white matter. The CNN outperformed MLP with the HCP data on both PSNR (p < 0.001) and SSIM (p < 0.01). With the MS data, the models performed similarly. Overall, the optimized neural networks can help generate non-acquired brain diffusion MRI, which will make advanced HARDI analysis possible in clinical practice following further validation. Enabling detailed characterization of brain microstructure will allow enhanced understanding of brain function in both health and disease.

Human habit neural circuitry may be perturbed in eating disorders.

Circuit-based mechanisms mediating the development and execution of habitual behaviors involve complex cortical-striatal interactions that have been investigated in animal models and more recently in humans. However, how human brain circuits implicated in habit formation may be perturbed in psychiatric disorders remains unclear. First, we identified the locations of the sensorimotor putamen and associative caudate in the human brain using probabilistic tractography from Human Connectome Project data. We found that multivariate connectivity of the sensorimotor putamen was altered in humans with binge eating disorder and bulimia nervosa and that the degree of alteration correlated with severity of disordered eating behavior. Furthermore, the extent of this circuit aberration correlated with mean diffusivity in the sensorimotor putamen and decreased basal dopamine D2/3 receptor binding potential in the striatum, consistent with previously reported microstructural changes and dopamine signaling mediating habit learning in animal models. Our findings suggest a neural circuit that links habit learning and binge eating behavior in humans, which could, in part, explain the treatment-resistant behavior common to eating disorders and other psychiatric conditions.

Diffusion Measures of Subcortical Structures Using High-Field MRI

The pathology of Parkinson's disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.

Lifestyle risks associated with brain functional connectivity and structure.

Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.

Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter.

To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network. We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data. The proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model. Combing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate.

Identifying the midline thalamus in humans in vivo.

The midline thalamus is critical for flexible cognition, memory, and stress regulation in humans and its dysfunction is associated with several neurological and psychiatric disorders, including Alzheimer's disease, schizophrenia, and depression. Despite the pervasive role of the midline thalamus in cognition and disease, there is a limited understanding of its function in humans, likely due to the absence of a rigorous noninvasive neuroimaging methodology to identify its location. Here, we introduce a new method for identifying the midline thalamus in vivo using probabilistic tractography and k-means clustering with diffusion weighted imaging data. This approach clusters thalamic voxels based on data-driven cortical and subcortical connectivity profiles and then segments the midline thalamus according to anatomical connectivity tracer studies in rodents and macaques. Results from two different diffusion weighted imaging sets, including adult data (22-35years) from the Human Connectome Project (n = 127) and adolescent data (9-14years) collected at Florida International University (n = 34) showed that this approach reliably classifies midline thalamic clusters. As expected, these clusters were most evident along the dorsal/ventral extent of the third ventricle and were primarily connected to the agranular medial prefrontal cortex (e.g., anterior cingulate cortex), nucleus accumbens, and medial temporal lobe regions. The midline thalamus was then bisected based on a human brain atlas into a dorsal midline thalamic cluster (paraventricular and paratenial nuclei) and a ventral midline thalamic cluster (rhomboid and reuniens nuclei). This anatomical connectivity-based identification of the midline thalamus offers the opportunity for necessary investigation of this region in vivo in the human brain and how it relates to cognitive functions in humans, and to psychiatric and neurological disorders.

3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to phenotypic traits has lagged behind. Our previous work developed a novel analytic framework to assess the relationship between brain network architecture and phenotypic differences while controlling for confounding variables. More specifically, this innovative regression framework related distances (or similarities) between brain network features from a single task to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. Here we extend that work to the multitask and multisession context to allow for multiple brain networks per individual. We explore several similarity metrics for comparing distances between connection matrices and adapt several standard methods for estimation and inference within our framework: standard F test, F test with scan-level effects (SLE), and our proposed mixed model for multitask (and multisession) BrAin NeTwOrk Regression (3M_BANTOR). A novel strategy is implemented to simulate symmetric positive-definite (SPD) connection matrices, allowing for the testing of metrics on the Riemannian manifold. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Bayesian hierarchical modeling on covariance valued data

Analysis of structural and functional connectivity (FC) of human brains is of pivotal importance for diagnosis of cognitive ability. The Human Connectome Project (HCP) provides an excellent source of neural data across different regions of interest (ROIs) of the living human brain. Individual specific data were available in the form of time varying covariance matrices representing the brain activity as the subjects perform a specific task. As a preliminary objective of studying the heterogeneity of brain connectomics across the population, we develop a probabilistic model for a sample of covariance matrices using a scaled Wishart distribution. We stress here that our data units are available in the form of covariance matrices, and we use the Wishart distribution to create our likelihood function rather than its more common usage as a prior on covariance matrices. Based on empirical explorations suggesting the data matrices to have a low effective rank, we further model the center of the Wishart distribution using an orthogonal factor model type decomposition. We encourage shrinkage toward a low rank structure through a novel shrinkage prior and discuss strategies to sample from the posterior distribution using a combination of Gibbs and slice sampling. The efficacy of the approach is explored in various simulation settings and exemplified on several case studies including our motivating HCP data. We extend our modeling framework to a dynamic setting to detect change points.

MRI-visible Dilated Perivascular Space in the Brain by Age: The Human Connectome Project.

Background Dilated perivascular spaces (dPVS) are associated with aging and various disorders; however, the effect of age on dPVS burden in young populations and normative data have not been fully evaluated. Purpose To investigate the dPVS burden and provide normative data according to age in a healthy population, including children. Materials and Methods In this retrospective study, three-dimensional T2-weighted brain MRI scans from the Human Connectome Project data sets were used for visual grading (grade 0, 1, 2, 3, 4 for 0, 1-10, 11-20, 21-40, and >40 dPVS on a single section of either hemispheric region) and automated volumetry of dPVS in basal ganglia (BGdPVS) and white matter (WMdPVS). Linear and nonlinear regression were performed to assess the association of dPVS volume with age. Optimal cutoff ages were determined with use of the maximized continuous-scale C-index. Participants were grouped by cutoff values. Linear regression was performed to assess the age-dPVS volume relationship in each age group. Normative data of dPVS visual grades were provided per age decade. Results A total of 1789 participants (mean age, 35 years; age range, 8-100 years; 1006 female participants) were evaluated. Age was related to dPVS volume in all regression models (R2 range, 0.41-0.55; P < .001). Age-dPVS volume relationships were altered at the mid-30s and age 55 years; BGdPVS and WMdPVS volumes negatively correlated with age until the mid-30s (β, -1.2 and -7.8), then positively until age 55 years (β, 3.3 and 54.1) and beyond (β, 3.9 and 42.8; P < .001). The 90th percentile for dPVS grades was grade 1 for age 49 years and younger, grade 2 for age 50-69 years, and grade 3 for age 70 years and older (overall, grade 2) for BGdPVS, and grade 3 for age 49 years and younger and grade 4 for age 50 years and older (overall, grade 3) for WMdPVS. Conclusion Dilated perivascular spaces (dPVS) showed a biphasic volume pattern with brain MRI, lower volumes until the mid-30s, then higher afterward. Grades of 3 or higher and 4 might be considered pathologic dPVS in basal ganglia and white matter, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Bapuraj and Chaudhary in this issue.

Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.

Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension.

Reliability and sensitivity to altered hemodynamics measured with resting-state fMRI metrics: Comparison with 123I-IMP SPECT

Blood oxygenation level-dependent (BOLD) contrast is sensitive to local hemodynamic changes and thus is applicable to imaging perfusion or vascular reactivity. However, knowledge about its measurement characteristics compared to reference standard perfusion imaging is limited. This study longitudinally evaluated perfusion in patients with steno-occlusive disease using resting-state functional MRI (rsfMRI) acquired before and within nine days of anterior circulation revascularization in patients with large cerebral artery steno-occlusive diseases. The reliability and sensitivity to longitudinal changes of rsfMRI temporal correlation (Rc) and time delay (TDc) relative to the cerebellar signal were examined voxel-wise in comparison with single-photon emission CT (SPECT) cerebral blood flow (CBF) using the within-subject standard deviation (Sw) and intraclass correlation coefficients (ICCs). For statistical comparisons, the standard deviation (SD) of longitudinal changes within the cerebellum, the number of voxels with significant changes in the left middle cerebral artery territory ipsilateral to surgery, and their average changes relative to the cerebellar SD were evaluated. The test-retest reliability of the fMRI metrics was also similarly evaluated using the human connectome project (HCP) healthy young adult dataset. The test-retest time interval was 31 ± 18 days. Test-retest reliability was significantly higher for SPECT (cerebellar SD: −2.59 ± 0.20) than for fMRI metrics (cerebellar SD: Rc, −2.34 ± 0.24, p = 0.04; TDc, −2.19 ± 0.21, p = 0.003). Sensitivity to postoperative changes, which was evaluated as the number of voxels, was significantly higher for fMRI TDc (8.78 ± 0.72) than for Rc (7.42 ± 1.48, p = 0.03) or SPECT CBF (6.88 ± 0.67, p < 0.001). The ratio between the average Rc, TDc, and SPECT CBF changes within the left MCA target region and cerebellar SD was also significantly higher for fMRI TDc (1.21 ± 0.79) than Rc (0.48 ± 0.94, p = 0.006) or SPECT CBF (0.23 ± 0.57, p = 0.001). The measurement variability of time delay was also larger than that of temporal correlation in HCP data within the cerebellum (t = −8.7, p < 0.001) or in the whole-brain (t = −27.4, p < 0.001) gray matter. These data suggest that fMRI time delay is more sensitive to the hemodynamic changes than SPECT CBF, although the reliability is lower. The implication for fMRI connectivity studies is that temporal correlation can be significantly decreased due to altered hemodynamics, even in cases with normal CBF.

DACO: Distortion/artefact correction for diffusion MRI data

In this paper, we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion-weighted (DW) magnetic resonance images (MRI). The registration in DACO is accomplished by means of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of mean apparent propagator (MAP)-MRI. DACO corrects (1) the susceptibility-induced distortions and (2) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (3) the eddy current-induced distortions and (4) the head motions by registering each image in the real dMRI data to the corresponding image in the pseudo dMRI data. DACO estimates the models of artefacts simultaneously in an iterative and interleaved manner. The mathematical formulation of the models and the estimation procedures are detailed in this paper. Using the human connectome project (HCP) data the evaluation shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, omitting the additional acquisitions needed to conduct the algorithm. Therefore, our method should be beneficial to most dMRI data, particularly to those acquired without field maps or reverse phase-encoding images.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis.

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10−4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Searching high and low for meaningful and replicable morphometric correlates of personality.

Recent personality neuroscience research in large samples suggests that personality traits tend to bear null-to-small relations to morphometric (i.e., brain structure) regions of interest (ROIs). In this preregistered, two-part study using Human Connectome Project data (N = 1,105), we address the possibility that these null-to-small relations are due, in part, to the "level" (i.e., hierarchical placement) of personality and/or morphometry examined. We used a Five-Factor Model framework and operationalized personality in terms of meta-traits, domains, facets, and items; we operationalized morphometry in terms of omnibus measures (e.g., total brain volume), and cortical thickness and area in the ROIs of the Desikan and Destrieux atlases. First, we compared the patterns of effect sizes observed between these levels using mixed effects modeling. Second, we used a machine learning framework for estimating out-of-sample predictability. Results highlight that personality-morphometry relations are generally null-to-small no matter how they are operationalized. Relatively, the largest mean effect sizes were observed at the domain level of personality, but the largest individual effect sizes were observed at the facet and item level, particularly for the Ideas facet of Openness and its constituent items. The largest effect sizes observed were at the omnibus level of morphometry, and predictive models containing only omnibus variables were comparably predictive to models including both omnibus variable and ROIs. We conclude by encouraging researchers to search across levels of analysis when investigating relations between personality and morphometry and consider prioritizing omnibus measures, which appear to yield the largest and most consistent effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Optimized Diffusion Imaging for Brain Structural Connectome Analysis.

High angular resolution diffusion imaging (HARDI) is a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space. It has been widely used in data acquisition for human brain structural connectome analysis. To more accurately estimate the structural connectome, dense samples in q-space are often acquired, potentially resulting in long scanning times and logistical challenges. This paper proposes a statistical method to select q-space directions optimally and estimate the local diffusion function from sparse observations. The proposed approach leverages relevant historical dMRI data to calculate a prior distribution to characterize local diffusion variability in each voxel in a template space. For a new subject to be scanned, the priors are mapped into the subject-specific coordinate and used to help select the best q-space samples. Simulation studies demonstrate big advantages over the existing HARDI sampling and analysis framework. We also applied the proposed method to the Human Connectome Project data and a dataset of aging adults with mild cognitive impairment. The results indicate that with very few q-space samples (e.g., 15 or 20), we can recover structural brain networks comparable to the ones estimated from 60 or more diffusion directions with the existing methods.

Modes of cognition: Evidence from metastable brain dynamics

Managing cognitive load depends on adequate resource allocation by the human brain through the engagement of metastable substates, which are large-scale functional networks that change over time. We employed a novel analysis method, deep autoencoder dynamical analysis (DADA), with 100 healthy adults selected from the Human Connectome Project (HCP) data set in rest and six cognitive tasks. The deep autoencoder of DADA described seven recurrent stochastic metastable substates from the functional connectome of BOLD phase coherence matrices. These substates were significantly differentiated in terms of their probability of appearance, time duration, and spatial attributes. We found that during different cognitive tasks, there was a higher probability of having more connected substates dominated by a high degree of connectivity in the thalamus. In addition, compared with those during tasks, resting brain dynamics have a lower level of predictability, indicating a more uniform distribution of metastability between substates, quantified by higher entropy. These novel findings provide empirical evidence for the philosophically motivated cognitive theory, suggesting on-line and off-line as two fundamentally distinct modes of cognition. On-line cognition refers to task-dependent engagement with the sensory input, while off-line cognition is a slower, environmentally detached mode engaged with decision and planning. Overall, the DADA framework provides a bridge between neuroscience and cognitive theory that can be further explored in the future.

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.

Empirical estimation of the eigenmodes of macroscale cortical dynamics: Reconciling neural field eigenmodes and resting-state networks

Neural field theories successfully describe normal, awake mesoscale to macroscale brain activity using linearized partial differential equations that involve a Laplacian operator. These neural field theories therefore predict spatial brain activity patterns that are simple, smooth eigenfunctions of the Laplacian operator that satisfy the Helmholtz equation and extend across the entire cortex. However, complex, localized spatial patterns such as resting-state networks have been proposed from data analysis of brain activity measurements, primarily fMRI. Using Human Connectome Project data, the Helmholtz eigenmode prediction of neural field theory is shown to be consistent with eigenvectors of both fMRI and MEG covariance matrices. It is explained why these Helmholtz equation eigenmodes have been difficult to observe due to their similar spectra, effects of covariant input stimuli to the brain, and measurement noise. This implies that complex spatial patterns like resting-state networks may be the result of complex input to the brain but simple intrinsic brain dynamics, rather than simple stimuli and complex brain dynamics, as assumed in many existing models.

MRI Analysis: Optimization of parameters for diffusion MRI to enhance hippocampal subfield analysis and segmentation (Preliminary Data)

IntroductionThe hippocampus is an important, complex limbic structure anatomically embedded in the medial temporal lobe of each cerebral cortex, which has been implicated in the pathogenesis of neuro-inflammatory disease conditions. Few studies have focused on the characterization of the MRI neuroimaging signatures of highly physio- pathologically relevant subfields of the hippocampus (CA1, CA4-DG, CA2/CA3, SLRM).ObjectivesUsing self-guided manually segmented, Diffusion weighted and NODDI maps created from data obtained from the Human Connectome Project (HCP) we intend to test whether Diffusion MRI-based quantitative imaging parameters (MD, FA, ODI, ISOVF, ICVF), indicative of microstructural characteristics of major hippocampal subfields (CA1, CA2/CA3, CA4-DG and SLRM), correspond to predictions for animal literature and imaging-histology correlations. We will also explore the correlations between these parameters and age.MethodsWe used images from the Public connectome data (updated April 2018), exploring subjects with the 3T MRI sessions obtainable from the WU-Minn HCP Data section. For the purpose of this study, we selected and downloaded 10 preliminary imaging data (6 females and 4 males) based on age variability in the following ranges (26-30, 31-35 and 36+). We manually segmented, and computed quantitative parameters.ResultsConverging and consistent literature allude to decreasing volumes with increasing age. Analyzing the volumes from the diffusion maps (pilot data), this was also the case, with volumes computed from CA1 and DG-CA4 sub regions. IQT also allowed for better appreciation of neuroanatomical boundaries and land marks, hence allowing more regions to be easily manually segmented (addition of CA2/CA3).ConclusionsApplication to Neuroinflammatory imaging data.DisclosureNo significant relationships.