ORCID Profile
0000-0002-4906-2646
Current Organisations
CISPA Helmholtz-Zentrum für Informationssicherheit
,
University of Sydney
,
University of Melbourne
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Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 04-2014
Publisher: Springer Science and Business Media LLC
Date: 27-11-2015
Publisher: Wiley
Date: 26-10-2022
DOI: 10.1002/MRM.29496
Abstract: Brain templates provide an essential standard space for statistical analysis of brain structure and function. Despite recent advances, diffusion MRI still lacks a template of fiber orientation distribution (FOD) and tractography that is unbiased for both white and gray matter. Therefore, we aim to build up a set of such templates for better white‐matter analysis and joint structural and functional analysis. We have developed a multimodal registration method to leverage the complementary information captured by T 1 ‐weighted, T 2 ‐weighted, and diffusion MRI, so that a coherent transformation is generated to register FODs into a common space and average them into a template. Consequently, the anatomically constrained fiber‐tracking method was applied to the FOD template to generate a tractography template. Fiber‐centered functional connectivity analysis was then performed as an ex le of the benefits of such an unbiased template. Our FOD template preserves fine structural details in white matter and also, importantly, clear folding patterns in the cortex and good contrast in the subcortex. Quantitatively, our templates show better in idual‐template agreement at the whole‐brain scale and segmentation scale. The tractography template aligns well with the gray matter, which led to fiber‐centered functional connectivity showing high cross‐group consistency. We have proposed a novel methodology for building a tissue‐unbiased FOD and anatomically constrained tractography template based on multimodal registration. Our templates provide a standard space and statistical platform for not only white‐matter analysis but also joint structural and functional analysis, therefore filling an important gap in multimodal neuroimage analysis.
Publisher: Springer Science and Business Media LLC
Date: 10-02-2022
DOI: 10.1038/S41380-022-01460-7
Abstract: Brain morphology differs markedly between in iduals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each in idual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes (2) endothelial cells (3) oligodendrocyte progenitor cells (OPCs) (4) excitatory neurons and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same in iduals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells ( r = −0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes ( r = −0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-in idual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Cold Spring Harbor Laboratory
Date: 19-09-2017
DOI: 10.1101/190660
Abstract: Childhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD. We first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the in idual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype. Adults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain-symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD. Overall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain-symptom mediators.
Publisher: Springer Science and Business Media LLC
Date: 03-03-2015
Publisher: Wiley
Date: 24-01-2022
DOI: 10.1002/HBM.25780
Abstract: Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, erse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.
Publisher: Elsevier BV
Date: 10-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119455
Abstract: Complex cognitive abilities are thought to arise from the ability of the brain to adaptively reconfigure its internal network structure as a function of task demands. Recent work has suggested that this inherent flexibility may in part be conferred by the widespread projections of the ascending arousal systems. While the different components of the ascending arousal system are often studied in isolation, there are anatomical connections between neuromodulatory hubs that we hypothesise are crucial for mediating key features of adaptive network dynamics, such as the balance between integration and segregation. To test this hypothesis, we estimated the strength of structural connectivity between key hubs of the noradrenergic and cholinergic arousal systems (the locus coeruleus [LC] and nucleus basalis of Meynert [nbM], respectively). We then asked whether the strength of structural LC and nbM inter-connectivity was related to in idual differences in the emergent, dynamical signatures of functional integration measured from resting state fMRI data, such as network and attractor topography. We observed a significant positive relationship between the strength of white-matter connections between the LC and nbM and the extent of network-level integration following BOLD signal peaks in LC relative to nbM activity. In addition, in iduals with denser white-matter streamlines interconnecting neuromodulatory hubs also demonstrated a heightened ability to shift to novel brain states. These results suggest that in iduals with stronger structural connectivity between the noradrenergic and cholinergic systems have a greater capacity to mediate the flexible network dynamics required to support complex, adaptive behaviour. Furthermore, our results highlight the underlying static features of the neuromodulatory hubs can impose some constraints on the dynamic features of the brain.
Publisher: Wiley
Date: 31-08-2022
DOI: 10.1002/MRM.29413
Abstract: Characterization of cerebral cortex is challenged by the complexity and heterogeneity of its cyto‐ and myeloarchitecture. This study evaluates quantitative MRI metrics, measured across two cortical depths and in subcortical white matter (WM) adjacent to cortex (juxtacortical WM), indicative of myelin content, neurite density, and diffusion microenvironment, for a comprehensive characterization of cortical microarchitecture. High‐quality structural and diffusion MRI data ( N = 30) from the Human Connectome Project were processed to compute myelin index, neurite density index, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from superficial cortex, deep cortex, and juxtacortical WM. The distributional patterns of these metrics were analyzed in idually, correlated to one another, and were compared to established parcellations. Our results supported that myeloarchitectonic and the coexisting cytoarchitectonic structures influence the diffusion properties of water molecules residing in cortex. Full cortical thickness showed myelination patterns similar to those previously observed in humans. Higher myelin indices with similar distributional patterns were observed in deep cortex whereas lower myelin indices were observed in superficial cortex. Neurite density index and other diffusion MRI derived parameters provided complementary information to myelination. Reliable and reproducible correlations were identified among the cortical microarchitectural properties and fiber distributional patterns in proximal WM structures. We demonstrated gradual changes across the cortical sheath by assessing depth‐specific cortical micro‐architecture using anatomical and diffusion MRI. Mutually independent but coexisting features of cortical layers and juxtacortical WM provided new insights towards structural organizational units and variabilities across cortical regions and through depth.
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119699
Abstract: The potential of normative modeling to make in idualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy in iduals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from in iduals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: Cold Spring Harbor Laboratory
Date: 18-01-2020
DOI: 10.1101/2020.01.17.911032
Abstract: The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for in idual patients and evaluate whether the loci of in idual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n=195). In iduals with schizophrenia (n=322) were classified as either within the normative range for healthy in iduals of the same age and sex (5-95% percentiles), infra-normal ( % percentile) or supra-normal ( % percentile). Repeating this classification for each tract and region yielded a deviation map for each in idual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region, whereas 79% of patients showed infra-normal deviations for at least one locus (healthy in iduals: 59±2%, p .001). Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r=-0.17, p=0.006). Anatomical loci of schizophrenia-related changes are highly heterogeneous across in iduals to the extent that group-consensus pathological maps are not representative of most in idual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.
Publisher: Oxford University Press (OUP)
Date: 20-12-2011
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.NEURES.2021.06.006
Abstract: Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population s le (n = 992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Cold Spring Harbor Laboratory
Date: 08-05-2020
DOI: 10.1101/2020.05.04.20091140
Abstract: This prospective cohort study, “Prospective Imaging Study of Ageing: Genes, Brain and Behaviour” (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer’s disease (AD). In particular, we are recruiting mid-life Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide s le currently totalling nearly 3,000 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for lifestyle monitoring, detailed neurocognitive testing, blood s le donation, plus functional, structural and molecular neuroimaging. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics. All authors have seen and approved this manuscript.
Publisher: Springer Science and Business Media LLC
Date: 11-12-2018
Publisher: IEEE
Date: 03-2011
Publisher: Cold Spring Harbor Laboratory
Date: 10-06-2021
DOI: 10.1101/2021.06.08.447489
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify in idual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io , derived from any current or future s le of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 showed high stability of in idual centile scores over longitudinal assessments and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of in idual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of erse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.
Publisher: eLife Sciences Publications, Ltd
Date: 08-11-2022
DOI: 10.7554/ELIFE.76143
Abstract: The hippoc us supports multiple cognitive functions including episodic memory. Recent work has highlighted functional differences along the anterior–posterior axis of the human hippoc us, but the neuroanatomical underpinnings of these differences remain unclear. We leveraged track-density imaging to systematically examine anatomical connectivity between the cortical mantle and the anterior–posterior axis of the in vivo human hippoc us. We first identified the most highly connected cortical areas and detailed the degree to which they preferentially connect along the anterior–posterior axis of the hippoc us. Then, using a tractography pipeline specifically tailored to measure the location and density of streamline endpoints within the hippoc us, we characterised where these cortical areas preferentially connect within the hippoc us. Our results provide new and detailed insights into how specific regions along the anterior–posterior axis of the hippoc us are associated with different cortical inputs/outputs and provide evidence that both gradients and circumscribed areas of dense extrinsic anatomical connectivity exist within the human hippoc us. These findings inform conceptual debates in the field and emphasise the importance of considering the hippoc us as a heterogeneous structure. Overall, our results represent a major advance in our ability to map the anatomical connectivity of the human hippoc us in vivo and inform our understanding of the neural architecture of hippoc al-dependent memory systems in the human brain.
Publisher: Springer Science and Business Media LLC
Date: 23-07-2013
Publisher: Springer Science and Business Media LLC
Date: 11-01-2021
DOI: 10.1186/S12888-021-03040-5
Abstract: Despite the effectiveness of methylphenidate for treating ADHD, up to 30% of in iduals with ADHD show poor responses to methylphenidate. Neuroimaging biomarkers to predict medication responses remain elusive. This study characterized neuroanatomical features that differentiated between clinically good and poor methylphenidate responders with ADHD. Using a naturalistic observation design selected from a larger cohort, we included 79 drug-naive in iduals (aged 6–42 years) with ADHD without major psychiatric comorbidity, who had acceptable baseline structural MRI data quality. Based on a retrospective chart review, we defined responders by in iduals’ responses to at least one-month treatment with methylphenidate. A nonparametric mass-univariate voxel-based morphometric analysis was used to compare regional gray matter volume differences between good and poor responders. A multivariate pattern recognition based on the support vector machine was further implemented to identify neuroanatomical indicators to predict an in idual’s response. 63 and 16 in iduals were classified in the good and poor responder group, respectively. Using the small-volume correction procedure based on the hypothesis-driven striatal and default-mode network masks, poor responders had smaller regional volumes of the left putamen as well as larger precuneus volumes compared to good responders at baseline. The machine learning approach identified that volumetric information among these two regions alongside the left frontoparietal regions, occipital lobes, and posterior/inferior cerebellum could predict clinical responses to methylphenidate in in iduals with ADHD. Our results suggest regional striatal and precuneus gray matter volumes play a critical role in mediating treatment responses in in iduals with ADHD.
Publisher: IEEE
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Springer Science and Business Media LLC
Date: 06-06-2014
DOI: 10.1007/S10548-014-0357-7
Abstract: An important application of resting state fMRI data has been to identify resting state networks (RSN). The conventional RSN studies attempted to discover consistent networks through functional connectivity analysis over the whole scan time, which implicitly assumes that RSNs are static. However, the brain undergoes dynamic functional state changes and the functional connectome patterns vary along with time, even in resting state. Hence, this study aims to characterize temporal brain dynamics in resting state. It utilizes the temporally dynamic functional connectome patterns to extract a set of resting state clusters and their corresponding RSNs based on the large-scale consistent, reproducible and predictable whole-brain reference system of dense in idualized and common connectivity-based cortical landmarks (DICCCOL). Especially, an effective multi-view spectral clustering method was performed by treating each dynamic functional connectome pattern as one view, and this procedure was also applied on static multi-subject functional connectomes to obtain the static clusters for comparison. It turns out that some dynamic clusters exhibit high similarity with static clusters, suggesting the stability of those RSNs including the visual network and the default mode network. Moreover, two motor-related dynamic clusters show correspondence with one static cluster, which implies substantially more temporal variability of the motor resting network. Particularly, four dynamic clusters exhibited large differences in comparison with their corresponding static networks. Thus it is suggested that these four networks might play critically important roles in functional brain dynamics and interactions during resting state, offering novel insights into the brain function and its dynamics.
Publisher: EDP Sciences
Date: 04-2018
DOI: 10.1051/0004-6361/201731169
Abstract: Context. The large jet kinetic power and non-thermal processes occurring in the microquasar SS 433 make this source a good candidate for a very high-energy (VHE) gamma-ray emitter. Gamma-ray fluxes above the sensitivity limits of current Cherenkov telescopes have been predicted for both the central X-ray binary system and the interaction regions of SS 433 jets with the surrounding W50 nebula. Non-thermal emission at lower energies has been previously reported, indicating that efficient particle acceleration is taking place in the system. Aim. We explore the capability of SS 433 to emit VHE gamma rays during periods in which the expected flux attenuation due to periodic eclipses ( P orb ~ 13.1 days) and precession of the circumstellar disk ( P pre ~ 162 days) periodically covering the central binary system is expected to be at its minimum. The eastern and western SS 433/W50 interaction regions are also examined using the whole data set available. We aim to constrain some theoretical models previously developed for this system with our observations. Methods. We made use of dedicated observations from the Major Atmospheric Gamma Imaging Cherenkov telescopes (MAGIC) and High Energy Spectroscopic System (H.E.S.S.) of SS 433 taken from 2006 to 2011. These observation were combined for the first time and accounted for a total effective observation time of 16.5 h, which were scheduled considering the expected phases of minimum absorption of the putative VHE emission. Gamma-ray attenuation does not affect the jet/medium interaction regions. In this case, the analysis of a larger data set amounting to ~40–80 h, depending on the region, was employed. Results. No evidence of VHE gamma-ray emission either from the central binary system or from the eastern/western interaction regions was found. Upper limits were computed for the combined data set. Differential fluxes from the central system are found to be ≲ 10 −12 –10 −13 TeV −1 cm −2 s −1 in an energy interval ranging from ~few × 100 GeV to ~few TeV. Integral flux limits down to ~ 10 −12 –10 −13 ph cm −2 s −1 and ~ 10 −13 –10 −14 ph cm −2 s −1 are obtainedat 300 and 800 GeV, respectively. Our results are used to place constraints on the particle acceleration fraction at the inner jetregions and on the physics of the jet/medium interactions. Conclusions. Our findings suggest that the fraction of the jet kinetic power that is transferred to relativistic protons must be relatively small in SS 433, q p ≤ 2.5 × 10 −5 , to explain the lack of TeV and neutrino emission from the central system. At the SS 433/W50 interface, the presence of magnetic fields ≳10 μ G is derived assuming a synchrotron origin for the observed X-ray emission. This also implies the presence of high-energy electrons with E e − up to 50 TeV, preventing an efficient production of gamma-ray fluxes in these interaction regions.
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.NEUROBIOLAGING.2019.02.014
Abstract: Age-related changes in the interactive behavior of default mode network (DMN) with other resting state networks are poorly understood. We hypothesized that age would positively correlate with inter-network connectivity in late life and intellectual functioning was expected to moderate this relationship. The s le consisted of 48 community-dwelling older adults with resting state functional magnetic resonance imaging data. Global inter-connectivity between DMN and 9 other resting state networks was calculated using a novel computational framework based on machine learning. Intellectual functioning (intelligence) was estimated using the Wechsler Test of Adult Reading. A significant, positive relationship was found between age and global inter-network connectivity (r = 0.31, p = 0.029). Moderation analyses yielded a significant age × intelligence interaction term (p = 0.003), such that intelligence attenuated the relationship between age and global inter-network connectivity. Taken together, these results suggest that age is positively associated with global DMN desegregation, possibly due to dedifferentiation or compensation. Intellectual functioning moderates this relationship, such that more intelligent older adults maintain a segregated DMN profile.
Publisher: Springer Berlin Heidelberg
Date: 2010
DOI: 10.1007/978-3-642-15745-5_18
Abstract: Recently, inference of functional connectivity between brain regions using resting state fMRI (rsfMRI) data has attracted significant interests in the neuroscience community. This paper proposes a novel fiber-centered approach to study the functional connectivity between brain regions using high spatial resolution diffusion tensor imaging (DTI) and rsfMRI data. We measure the functional coherence of a fiber as the time series' correlation of two gray matter voxels that this fiber connects. The functional connectivity strength between two brain regions is defined as the average functional coherence of fibers connecting them. Our results demonstrate that: 1) The functional coherence of fibers is correlated with the brain regions they connect 2) The functional connectivity between brain regions is correlated with structural connectivity. And these two patterns are consistent across subjects. These results may provide new insights into the brain's structural and functional architecture.
Publisher: Elsevier BV
Date: 2020
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Publisher: Cold Spring Harbor Laboratory
Date: 19-11-2021
DOI: 10.1101/2021.11.17.469032
Abstract: The hippoc us supports multiple cognitive functions including episodic memory. Recent work has highlighted functional differences along the anterior-posterior axis of the human hippoc us but the neuroanatomical underpinnings of these differences remain unclear. We leveraged track-density imaging to systematically examine anatomical connectivity between the cortical mantle and the anterior-posterior axis of the in-vivo human hippoc us. We first identified the most highly connected cortical areas and detailed the degree to which they preferentially connect along the anterior-posterior axis of the hippoc us. Then, using a tractography pipeline specifically tailored to measure the location and density of streamline endpoints within the hippoc us, we characterised where, within the hippoc us, these cortical areas preferentially connect. Our results were striking in showing that different parts of the hippoc us preferentially connect with distinct cortical areas. Furthermore, we provide evidence that both gradients and circumscribed areas of dense extrinsic anatomical connectivity exist within the human hippoc us. These findings inform conceptual debates in the field by unveiling how specific regions along the anterior-posterior axis of the hippoc us are associated with different cortical inputs/outputs. Overall, our results represent a major advance in our ability to map the anatomical connectivity of the human hippoc us in-vivo and inform our understanding of the neural architecture of hippoc al dependent memory systems in the human brain. This detailed characterization of how specific portions of the hippoc us anatomically connect with cortical brain regions may promote a better understanding of its role in cognition and we emphasize the importance of considering the hippoc us as a heterogeneous structure.
Publisher: Mary Ann Liebert Inc
Date: 04-2023
Abstract: In older people with Mild Cognitive Impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased in N-Acetylaspartate (NAA) in the hippoc us, indicative of decreased neuronal integrity, and changes in the Default Mode Network (DMN) functional network. If and how these measures interrelate is yet to be established, and this understanding may provide insight into the processes underpinning observed cognitive decline. To determine the relationship between NAA levels in the left hippoc us and functional connectivity within the DMN in an aging cohort. In a s le of 51 MCI participants and 30 controls, hippoc al NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting state functional MRI. The association between hippoc al NAA and the DMN functional connectivity was tested independently within in the MCI and separately within the control group. In the DMN, we showed a significant inverse association between functional connectivity and hippoc al NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone. This study suggests that decreased neuronal integrity in the hippoc us is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 23-09-2022
Publisher: Springer International Publishing
Date: 2015
Publisher: Cold Spring Harbor Laboratory
Date: 18-11-2020
DOI: 10.1101/2020.11.17.20233601
Abstract: Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population s le (n=992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Publisher: Cambridge University Press (CUP)
Date: 07-02-2018
DOI: 10.1017/S0033291718000028
Abstract: Childhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD. We first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the in idual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype. Adults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain–symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD. Overall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain–symptom mediators.
Publisher: IEEE
Date: 04-2014
Publisher: Oxford University Press (OUP)
Date: 08-03-2021
Abstract: Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia. A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 in iduals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient’s symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of in iduals with diffusion-weighted imaging (DWI) data. Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an in idual’s symptoms was significantly associated with shorter illness duration (t = 2.7 P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = −5.5 P & 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P & 0.05). Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in in iduals with schizophrenia.
Publisher: Wiley
Date: 10-02-2023
DOI: 10.1002/MRM.29607
Publisher: Cold Spring Harbor Laboratory
Date: 19-01-2021
DOI: 10.1101/2021.01.17.427042
Abstract: Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome / tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b = 1000 s/mm 2 ) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography / connectome analysis from existing clinical MRI environments. Our code is freely available at uizengalways/FOD-Net .
Publisher: IEEE
Date: 04-2014
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40760-4_77
Abstract: Establishment of structural and functional correspondences across different brains is one of the most fundamental issues in the human brain mapping field. Recently, several multimodal DTI/fMRI studies have demonstrated that consistent white matter fiber connection patterns can predict brain function and represent common brain architectures across in iduals and populations, and along this direction, several approaches have been proposed to discover large-scale cortical landmarks with common structural connection profiles. However, an important limitation of previous approaches is that the rich anatomical information such as gyral/sulcal folding patterns has not been incorporated into the landmark discovery procedure yet. In this paper, we present a novel anatomy-guided discovery framework that defines and optimizes a dense map of cortical landmarks that possess group-wise consistent anatomical and fiber connectional profiles. This framework effectively integrates reliable and rich anatomical, morphological, and fiber connectional information for landmark initialization, optimization and prediction, which are formulated and solved as an energy minimization problem. Validation results based on fMRI data demonstrate that the identified 555 cortical landmarks are producible, predictable and exhibit accurate structural and functional correspondences across in iduals and populations, offering a universal and in idualized brain reference system for neuroimaging research.
Publisher: Springer Berlin Heidelberg
Date: 2011
DOI: 10.1007/978-3-642-23629-7_19
Abstract: Fiber clustering is a prerequisite step towards tract-based analysis of white mater integrity via diffusion tensor imaging (DTI) in various clinical neuroscience applications. Many methods reported in the literature used geometric or anatomic information for fiber clustering. This paper proposes a novel method that uses functional coherence as the criterion to guide the clustering of fibers derived from DTI tractography. Specifically, we represent the functional identity of a white matter fiber by two resting state fMRI (rsfMRI) time series extracted from the two gray matter voxels to which the fiber connects. Then, the functional coherence or similarity between two white matter fibers is defined as their rsfMRI time series' correlations, and the data-driven affinity propagation (AP) algorithm is used to cluster fibers into bundles. At current stage, we use the corpus callosum (CC) fibers that are the largest fiber bundle in the brain as an ex le. Experimental results show that the proposed fiber clustering method can achieve meaningful bundles that are reasonably consistent across different brains, and part of the clustered bundles was validated via the benchmark data provided by task-based fMRI data.
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40760-4_76
Abstract: The human brain function involves complex processes with population codes of neuronal activities. Neuroscience research has demonstrated that when representing neuronal activities, sparsity is an important characterizing property. Inspired by this finding, significant amount of efforts from the scientific communities have been recently devoted to sparse representations of signals and patterns, and promising achievements have been made. However, sparse representation of fMRI signals, particularly at the population level of a group of different brains, has been rarely explored yet. In this paper, we present a novel group-wise sparse representation of task-based fMRI signals from multiple subjects via dictionary learning methods. Specifically, we extract and pool task-based fMRI signals for a set of cortical landmarks, each of which possesses intrinsic anatomical correspondence, from a group of subjects. Then an effective online dictionary learning algorithm is employed to learn an over-complete dictionary from the pooled population of fMRI signals based on optimally determined dictionary size. Our experiments have identified meaningful Atoms of Interests (AOI) in the learned dictionary, which correspond to consistent and meaningful functional responses of the brain to external stimulus. Our work demonstrated that sparse representation of group-wise fMRI signals is naturally suitable and effective in recovering population codes of neuronal signals conveyed in fMRI data.
Publisher: Center for Open Science
Date: 28-09-2022
Abstract: Background. In older people with Mild Cognitive Impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased in N-Acetylaspartate (NAA) in the hippoc us, indicative of decreased neuronal integrity, and changes in the Default Mode Network (DMN) functional network. If and how these measures interrelate is yet to be established, and this understanding may provide insight into the processes underpinning observed cognitive decline. Objectives. To determine the relationship between NAA levels in the left hippoc us and functional connectivity within the DMN in an aging cohort.Methods. In a s le of 51 MCI participants and 30 controls, hippoc al NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting state functional MRI. The association between hippoc al NAA and the DMN functional connectivity was tested independently within in the MCI and separately within the control group.Results. In the DMN, we showed a significant inverse association between functional connectivity and hippoc al NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone. Conclusions. This study suggests that decreased neuronal integrity in the hippoc us is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline.
Publisher: Elsevier BV
Date: 05-2012
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40760-4_78
Abstract: Traditional task-based fMRI activation detection methods, e.g., the widely used general linear model (GLM), assume that the brain's hemodynamic responses follow the block-based or event-related stimulus paradigm. Typically, these activation detections are performed voxel-wise independently, and then are usually followed by statistical corrections. Despite remarkable successes and wide adoption of these methods, it remains largely unknown how functional brain regions interact with each other within specific networks during task performance blocks and in the baseline. In this paper, we present a novel algorithmic pipeline to statistically infer and sparsely represent higher-order functional interaction patterns within the working memory network during task performance and in the baseline. Specifically, a collection of higher-order interactions are inferred via the greedy equivalence search (GES) algorithm for both task and baseline blocks. In the next stage, an effective online dictionary learning algorithm is utilized for sparse representation of the inferred higher-order interaction patterns. Application of this framework on a working memory task-based fMRI data reveals interesting and meaningful distributions of the learned sparse dictionary atoms in task and baseline blocks. In comparison with traditional voxel-wise activation detection and recent pair-wise functional connectivity analysis, our framework offers a new methodology for representation and exploration of higher-order functional activities in the brain.
Publisher: Springer Singapore
Date: 2020
Publisher: IEEE
Date: 04-2014
Publisher: Elsevier BV
Date: 02-2015
DOI: 10.1016/J.MEDIA.2014.10.011
Abstract: There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification.
Publisher: IEEE
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.MEDIA.2022.102518
Abstract: Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in in idual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the in idual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.
Publisher: Springer International Publishing
Date: 2020
Publisher: Wiley
Date: 14-10-2015
DOI: 10.1002/HBM.23013
Publisher: IEEE
Date: 04-2018
Publisher: Elsevier BV
Date: 2021
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Berlin Heidelberg
Date: 2011
DOI: 10.1007/978-3-642-22092-0_47
Abstract: In task-based fMRI, the generalized linear model (GLM) is widely used to detect activated brain regions. A fundamental assumption in the GLM model for fMRI activation detection is that the brain's response, represented by the blood-oxygenation level dependent (BOLD) signals of volumetric voxels, follows the shape of stimulus paradigm. Based on this same assumption, we use the dynamic functional connectivity (DFC) curves between two ends of a white matter fiber, instead of the BOLD signal, to represent the brain's response, and apply the GLM to detect Activated Fibers (AFs). Our rational is that brain regions connected by white matter fibers tend to be more synchronized during stimulus intervals than during baseline intervals. Therefore, the DFC curves for fibers connecting active brain regions should be positively correlated with the stimulus paradigm, which is verified by our extensive experiments using multimodal task-based fMRI and diffusion tensor imaging (DTI) data. Our results demonstrate that the detected AFs connect not only most of the activated brain regions detected via traditional voxel-based GLM method, but also many other brain regions, suggesting that the voxel-based GLM method may be too conservative in detecting activated brain regions.
Publisher: Oxford University Press (OUP)
Date: 08-2021
DOI: 10.1093/GIGASCIENCE/GIAB051
Abstract: As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many in iduals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for in iduals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly erse participation: the tools used can be inaccessible for some in iduals the scheduling choices can favour some geographical locations the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for in iduals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a erse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 24-01-2019
DOI: 10.1007/S11682-018-00034-Y
Abstract: The carotenoids lutein (L) and zeaxanthin (Z) accumulate in retinal regions of the eye and have long been shown to benefit visual health. A growing literature suggests cognitive benefits as well, particularly in older adults. The present randomized controlled trial sought to investigate the effects of L and Z on brain function using resting state functional magnetic resonance imaging (fMRI). It was hypothesized that L and Z supplementation would (1) improve intra-network integrity of default mode network (DMN) and (2) reduce inter-network connectivity between DMN and other resting state networks. 48 community-dwelling older adults (mean age = 72 years) were randomly assigned to receive a daily L (10 mg) and Z (2 mg) supplement or a placebo for 1 year. Resting state fMRI data were acquired at baseline and post-intervention. A dictionary learning and sparse coding computational framework, based on machine learning principles, was used to investigate intervention-related changes in functional connectivity. DMN integrity was evaluated by calculating spatial overlap rate with a well-established DMN template provided in the neuroscience literature. Inter-network connectivity was evaluated via time series correlations between DMN and nine other resting state networks. Contrary to expectation, results indicated that L and Z significantly increased rather than decreased inter-network connectivity (Cohen's d = 0.89). A significant intra-network effect on DMN integrity was not observed. Rather than restoring what has been described in the available literature as a "youth-like" pattern of intrinsic brain activity, L and Z may facilitate the aging brain's capacity for compensation by enhancing integration between networks that tend to be functionally segregated earlier in the lifespan.
Publisher: ACM
Date: 10-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 09-06-2017
DOI: 10.1007/S11682-017-9733-8
Abstract: Recently, a growing body of studies have demonstrated the simultaneous existence of erse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those erse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore erse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the ersity of concurrent brain activities in tfMRI datasets.
Publisher: Springer Science and Business Media LLC
Date: 09-06-2017
Publisher: IEEE
Date: 04-2016
Publisher: IEEE
Date: 04-2017
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 04-09-2018
Publisher: IEEE
Date: 04-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 04-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 27-02-2018
DOI: 10.1007/S12021-018-9358-0
Abstract: In recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e.g., the general linear model (GLM)). Moreover, traditional data-driven methods (e.g., independent component analysis (ICA)) lack quantitative modeling of stimuli, which may limit the power of analysis models. In this paper, we propose a sparse representation based decoding framework to explore the neural correlates between the computational audio features and functional brain activities under free listening conditions. First, we adopt a biologically-plausible auditory saliency feature to quantitatively model the audio excerpts and meanwhile develop sparse representation/dictionary learning method to learn an over-complete dictionary basis of brain activity patterns. Then, we reconstruct the auditory saliency features from the learned fMRI-derived dictionaries. After that, a group-wise analysis procedure is conducted to identify the associated brain regions and networks. Experiments showed that the auditory saliency feature can be well decoded from brain activity patterns by our methods, and the identified brain regions and networks are consistent and meaningful. At last, our method is evaluated and compared with ICA method and experimental results demonstrated the superiority of our methods.
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.MEDIA.2022.102665
Abstract: Deep learning approaches have been widely adopted in the medical image analysis field. However, a most of existing deep learning approaches focus on achieving promising performances such as classification, detection, and segmentation, and much less effort is devoted to the explanation of the designed models. Similarly, in the brain imaging field, many deep learning approaches have been designed and applied to characterize and predict human brain states. However, these models lack interpretation. In response, we propose a novel domain knowledge informed self-attention graph pooling-based (SAGPool) graph convolutional neural network to study human brain states. Specifically, the dense in idualized and common connectivity-based cortical landmarks system (DICCCOL, structural brain connectivity profiles) and holistic atlases of functional networks and interactions system (HAFNI, functional brain connectivity profiles) are integrated with the SAGPool model to better characterize and interpret the brain states. Extensive experiments are designed and carried out on the large-scale human connectome project (HCP) Q1 and S1200 dataset. Promising brain state classification performances are observed (e.g., an average of 93.7% for seven-task classification and 100% for binary classification). In addition, the importance of the brain regions, which contributes most to the accurate classification, is successfully quantified and visualized. A thorough neuroscientific interpretation suggests that these extracted brain regions and their importance calculated from self-attention graph pooling layer offer substantial explainability.
Publisher: ACM
Date: 25-10-2010
Publisher: ACM
Date: 03-11-2014
Publisher: IEEE
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 22-09-2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Wiley
Date: 18-02-2018
DOI: 10.1002/HBM.24005
Publisher: Wiley
Date: 03-12-2022
DOI: 10.1002/HBM.25732
Abstract: Behavioral traits are rarely considered in task‐evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an in idual engages with the task, and thus lead to heterogeneity in task‐evoked brain responses. We aimed to investigate whether interin idual variation in behavior associates with the accuracy of predicting task‐evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi‐timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large‐scale networks enabled in idual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interin idual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for in iduals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interin idual variation in behavior when investigating context‐dependent dynamic functional connectivity.
Publisher: Springer Science and Business Media LLC
Date: 19-11-2018
DOI: 10.1038/S41467-018-07325-4
Abstract: Human interactions with the world are influenced by memories of recent events. This effect, often triggered by perceptual cues, occurs naturally and without conscious effort. However, the neuroscience of involuntary memory in a dynamic milieu has received much less attention than the mechanisms of voluntary retrieval with deliberate purpose. Here, we investigate the neural processes driven by naturalistic cues that relate to, and presumably trigger the retrieval of recent experiences. Viewing the continuation of recently viewed clips evokes greater bilateral activation in anterior hippoc us, precuneus and angular gyrus than naïve clips. While these regions manifest reciprocal connectivity, continued viewing specifically modulates the effective connectivity from the anterior hippoc us to the precuneus. The strength of this modulation predicts participants’ confidence in later voluntary recall of news details. Our study reveals network mechanisms of dynamic, involuntary memory retrieval and its relevance to metacognition in a rich context resembling everyday life.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 29-04-2014
Publisher: Springer Science and Business Media LLC
Date: 06-04-2022
DOI: 10.1038/S41586-022-04554-Y
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify in idual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future s le of MRI data ( www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the ersity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of in iduals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of in idual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Publisher: IEEE
Date: 04-2016
Publisher: IEEE
Date: 04-2016
Publisher: IEEE
Date: 04-2016
Publisher: Wiley
Date: 05-02-2021
DOI: 10.1002/HBM.25330
Abstract: Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC‐related network architecture shows considerable interin idual variation across the spatial extent of the DLPFC, indicating that connectivity‐based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intrain idual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute in idualized connectivity‐guided stimulation targets. In resting‐state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intrain idual distance between coordinates of only 2.7 mm. Interin idual spatial variation in personalized targets exceeded intrain idual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group‐average site. Moreover, personalized targets were heritable, suggesting that connectivity‐guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity‐guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.
Publisher: IEEE
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Elsevier BV
Date: 03-2022
Publisher: IEEE
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 29-06-2017
DOI: 10.1007/S00429-017-1460-9
Abstract: Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways, and/or anatomical regions.
Publisher: Cold Spring Harbor Laboratory
Date: 11-02-2021
DOI: 10.1101/2021.02.09.430363
Abstract: The potential of normative modeling to make in idualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy in iduals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from in iduals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90 % of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modelling where the primary interest is in accurate modelling of inter-subject variation and statistical quantification of deviations from a reference model. 1 Development and presentation of normative modeling approach based on hierarchical Bayesian modeling that can be applied to large multi-site neuroimaging data sets. Comparison of performance of Hierarchical Bayesian model including site as predictor to several common ways to harmonize for multi-site effects. Presentation of normative modeling as site correction tool.
Publisher: Springer International Publishing
Date: 2013
Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 02-2016
Publisher: Frontiers Media SA
Date: 13-02-2023
Publisher: Springer Berlin Heidelberg
Date: 2013
DOI: 10.1007/978-3-642-40763-5_82
Abstract: Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and statistical power to deal with variability of in idual brains. However, current group-wise fMRI activation detection methods typically rely on the spatial alignment established by coregistration of in idual brains' fMRI images into the same template space, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignment among multiple brains could substantially degrade the accuracy and specificity of group-wise fMRI activation detection. To address these challenges, this paper presents a novel methodology to detect group-wise fMRI activation based on a publicly released dense map of DTI-derived structural cortical landmarks, which possess intrinsic correspondences across in iduals and populations. The basic idea here is that a first-level general linear model (GLM) analysis is performed on fMRI signals of each corresponding cortical landmark in each in idual brain's own space, and then the single-subject effect size of the same landmark from a group of subjects are statistically integrated and assessed at the group level using the mixed-effects model. As a result, the consistently activated cortical landmarks are determined and declared group-wisely in response to external block-based stimuli. Our experimental results demonstrated that the proposed approach can map meaningful group-wise activation patterns on the atlas of cortical landmarks without image registration between subjects and spatial smoothing.
Publisher: Elsevier BV
Date: 11-2014
Publisher: SAGE Publications
Date: 05-2023
DOI: 10.1177/15459683231172752
Abstract: Exercise has many benefits for people with Parkinson’s disease (PD) and has been suggested to modify PD progression, but robust evidence supporting this is lacking. This systematic review (PROSPERO registration: CRD42020169999) investigated whether exercise may have neuroplastic effects indicative of attenuating PD progression. Six databases were searched for randomized controlled trials (RCTs) that compared the effect of exercise to control (no or sham exercise) or to another form of exercise, on indicators of PD progression (eg, brain-derived neurotrophic factor [BDNF], brain activation, “off” Unified Parkinson’s Disease Rating Scale [UPDRS] scores). Trial quality was assessed using the Physiotherapy Evidence Database Scale. Random-effects meta-analyses were performed where at least 3 comparable trials reported the same outcome remaining results were synthesized narratively. Forty-nine exercise trials involving 2104 PD participants were included. Compared to control, exercise improved “off” UPDRS motor scores (Hedge’s g −0.39, 95% CI: −0.65 to −0.13, P = .003) and BDNF concentration (Hedge’s g 0.54, 95% CI: 0.10-0.98, P = .02), with low to very low certainty of evidence, respectively. Narrative synthesis for the remaining outcomes suggested that compared to control, exercise may have neuroplastic effects. The exercise versus exercise comparisons were too heterogenous to enable pooling of results. This review provides limited evidence that exercise may have an attenuating effect on potential markers of PD progression. Further large RCTs are warranted to explore differential effects by exercise type, dose and PD stage, and should report on a core set of outcomes indicative of PD progression.
Publisher: Frontiers Media SA
Date: 18-01-2023
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 04-2014
Publisher: Frontiers Media SA
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Public Library of Science (PLoS)
Date: 22-12-2017
Publisher: Springer Science and Business Media LLC
Date: 16-02-2021
DOI: 10.1186/S12888-021-03096-3
Abstract: An amendment to this paper has been published and can be accessed via the original article.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2016
Publisher: American Medical Association (AMA)
Date: 03-2021
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2014
DOI: 10.1007/978-3-319-10470-6_89
Abstract: An unrelenting human quest regarding the brain science is: what is the intrinsic relationship between the brain's structural and functional architectures, which partly defines what we are and who we are. Recent studies suggest that each brain's cytoarchitectonic region has a unique set of extrinsic inputs and outputs, named as "connectional fingerprint", which largely determines the functions that each brain area performs. However, their explicit connections are largely unknown. For ex le, in what extent they are inclined to be coherent with each other and otherwise they will intend to show more heterogeneity? In this work, based on a widely used brain structural atlas which represents the most consistent structural connectome across different populations, we proposed a novel group-wise optimization framework to computationally model the functional homogeneity behind them. The optimization procedure is conducted under the joint structural and functional regulations and therefore the achieved common brain landmarks reflect the consistency of brain structure and function simultaneously. The Human Connectome Project (HCP) Q1 dataset, which includes 68 subjects with high quality imaging data, was used as test bed and the results imply that there exists extraordinary accordance between brain structural and functional architectures.
Publisher: eLife Sciences Publications, Ltd
Date: 23-06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.MEDIA.2022.102431
Abstract: Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 08-2015
Publisher: Cold Spring Harbor Laboratory
Date: 14-04-2022
DOI: 10.1101/2022.04.13.488117
Abstract: A brain template provides a standard space for statistical analysis of brain structure and function. For decades, the T1- and T2-weighted brain templates have been widely used for brain grey matter anatomical and functional analysis. However, T1- and T2-weighted templates provide very limited information about the axonal organization within the white matter. Recent advances in Diffusion MRI have enabled the detailed modelling of the axonal fibre orientation distribution (FOD) in white matter. Therefore, building a FOD template is essential for more robust white matter anatomy related analysis however, it is important that this template aligns well with the cortical and subcortical structures. From such a FOD template, a tractography template can be also generated by fibre tracking algorithms, which can be used for subsequent applications, such as to perform the joint structural and functional analysis while ensuring rigorous fibre-to-fibre correspondence. In this paper, we explore the potential of generating the FOD template based on multimodal registration, in order to constrain the tempalte unbiased to both white and grey matter. We combine the information from T1-weighted, T2-weighted and Diffusion MRI to generate a coherent transformation for FOD registration and template generation. Our FOD template preserves the structural details at the white-grey matter boundary. To illustrate the benefit of this new approach, the resulting tractography template was used for joint structural-functional connectivity analysis.
Publisher: Springer Science and Business Media LLC
Date: 08-12-2015
Publisher: Frontiers Media SA
Date: 11-02-2022
DOI: 10.3389/FBIOE.2021.706229
Abstract: Recognition accuracy and response time are both critically essential ahead of building the practical electroencephalography (EEG)-based brain–computer interface (BCI). However, recent approaches have compromised either the classification accuracy or the responding time. This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional long short-term memory (BiLSTM) with the attention mechanism is employed, and the graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. Particularly, this method is trained and tested on the short EEG recording with only 0.4 s in length, and the result has shown effective and efficient prediction based on in idual and groupwise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw and almost-instant EEG signals, which paves the road to translate the EEG-based MI recognition to practical BCI systems .
Publisher: IEEE
Date: 04-2016
Publisher: Elsevier BV
Date: 2021
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jinglei Lv.