Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l1 -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.

Low CD4+ cell count nadir exacerbates the impacts of APOE ε4 on functional connectivity and memory in adults with HIV

OBJECTIVE

Nearly half of individuals living with HIV in the USA are now 50 or older. This rapidly ageing populace may be at an increasingly greater risk of Alzheimer's disease. However, the potential interaction between HIV-disease and Alzheimer's disease pathogenesis (i.e. Alzheimer's disease genetic risk factors) on brain function remains an open question. The present study aimed to investigate the impact of APOE ε4 on brain function in middle-aged to older people with HIV (PWH), as well as the putative interaction between ε4 and HIV disease severity.

METHODS

Ninety-nine PWH participated in a cross-sectional study (56.3 ± 6.5 years, range 41-70 years, 27 women, 26 ε4 carriers and 73 noncarriers). Structural MRI and resting-state functional MRI were collected to assess alterations in brain structure and functional connectivity, respectively.

RESULTS

APOE ε4 was associated with worse memory performance and reduced functional connectivity in the memory network. The functional connectivity reduction was centred at the caudate nucleus rather than hippocampus and correlated with worse memory performance. In ε4 carriers, low CD4+ cell count nadir was associated with reduced functional connectivity in the memory network, but this association was absent in noncarriers. Furthermore, there was an indirect detrimental impact of ε4 on memory performance through memory network functional connectivity. However, this indirect effect was contingent on CD4+ cell count nadir, that is the indirect effect of ε4 on memory was only significant when CD4+ cell count nadir was low.

INTERPRETATION

APOE ε4 is associated with reduced memory and reduced functional connectivity within the memory network, and low CD4+ cell count nadir -- indicating a history of severe immunosuppression -- may exacerbate the effects of ε4.

The State of Resting State Networks

Functional MRI (fMRI) is currently used for pre-surgical planning, but is often limited to information on the motor and language systems. Resting state fMRI can provide more information on multiple other networks to the neurosurgeon and neuroradiologist; however, currently, these networks are not well known among clinicians. The purpose of this manuscript is to provide an introduction to these networks for the clinician and to discuss how they could be used in the future for precise and individualized surgical planning. We provide a short introduction to resting state fMRI and discuss multiple currently accepted resting state networks with a review of the literature. We review the characteristics and function of multiple somatosensory, association, and other networks. We discuss the concept of critical nodes in the brain and how the neurosurgeon can use this information to individually customize patient care. Although further research is necessary, future application of pre-surgical planning will require consideration of networks other than just motor and language in order to minimize post-surgical morbidity and customize patient care.

Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso

Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC from fMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. The sliding window technique has been widely used in many studies to capture network dynamics, but has a number of limitations. In this study, we apply a time-varying graphical lasso (TVGL) model, an extension from the traditional graphical lasso, to address the challenge, which can greatly improve the estimation of FC. The performance of estimating dynamic FC is evaluated with the TVGL through both simulated experiments and real rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) project. Improved performance is achieved over the sliding window technique. In particular, group differences and transition behaviours between young adults and children are investigated using the estimated dynamic connectivity networks, which help us to better unveil the mechanisms underlying the evolution of the brain over time.

Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

A Unified Theory of Neuro-MRI Data Shows Scale-Free Nature of Connectivity Modes

A primary goal of many neuroimaging studies that use magnetic resonance imaging (MRI) is to deduce the structure-function relationships in the human brain using data from the three major neuro-MRI modalities: high-resolution anatomical, diffusion tensor imaging, and functional MRI. To date, the general procedure for analyzing these data is to combine the results derived independently from each of these modalities. In this article, we develop a new theoretical and computational approach for combining these different MRI modalities into a powerful and versatile framework that combines our recently developed methods for morphological shape analysis and segmentation, simultaneous local diffusion estimation and global tractography, and nonlinear and nongaussian spatial-temporal activation pattern classification and ranking, as well as our fast and accurate approach for nonlinear registration between modalities. This joint analysis method is capable of extracting new levels of information that is not achievable from any of those single modalities alone. A theoretical probabilistic framework based on a reformulation of prior information and available interdependencies between modalities through a joint coupling matrix and an efficient computational implementation allows construction of quantitative functional, structural, and effective brain connectivity modes and parcellation. This new method provides an overall increase of resolution, accuracy, level of detail, and information content and has the potential to be instrumental in the clinical adaptation of neuro-MRI modalities, which, when jointly analyzed, provide a more comprehensive view of a subject's structure-function relations, while the current standard, wherein single-modality methods are analyzed separately, leaves a critical gap in an integrated view of a subject's neuorphysiological state. As one example of this increased sensitivity, we demonstrate that the jointly estimated structural and functional dependencies of mode power follow the same power law decay with the same exponent.

Connectome-wide network analysis of youth with Psychosis-Spectrum symptoms

Adults with psychotic disorders have dysconnectivity in critical brain networks, including the default mode (DM) and the cingulo-opercular (CO) networks. However, it is unknown whether such deficits are present in youth with less severe symptoms. We conducted a multivariate connectome-wide association study examining dysconnectivity with resting state functional magnetic resonance imaging in a population-based cohort of 188 youths aged 8-22 years with psychosis-spectrum (PS) symptoms and 204 typically developing (TD) comparators. We found evidence for multi-focal dysconnectivity in PS youths, implicating the bilateral anterior cingulate, frontal pole, medial temporal lobe, opercular cortex and right orbitofrontal cortex. Follow-up seed-based and network-level analyses demonstrated that these results were driven by hyper-connectivity among DM regions and diminished connectivity among CO regions, as well as diminished coupling between frontal and DM regions. Collectively, these results provide novel evidence for functional dysconnectivity in PS youths, which show marked correspondence to abnormalities reported in adults with established psychotic disorders.

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Independent sources of spontaneous BOLD fluctuation along the visual pathway

In resting-state functional magnetic resonance imaging (fMRI) experiments, correlation analysis can be used to identify clusters of cortical regions that may be functionally connected. Although such functional connectivity is often assumed to reflect cortico-cortical connections, a potential confound is the contribution of subcortical brain regions, many of which have strong anatomical connectivity to cortical regions and may also enable cortico-cortical interactions through trans-thalamic pathways. To investigate this, we performed resting state fMRI of the human visual system, including cortical regions and subcortical nuclei of the pulvinar and lateral geniculate. Regression analysis was used to investigate the dependence of the measured inter-regional correlations upon afferents from specific retinal, thalamic and cortical regions as well as systemic global signal fluctuation. A high level of inter-hemispheric correlation (cc = 0.95) was found in the visual cortex that could not be explained by activity in the subcortical nuclei investigated; in addition a relatively low level of inter-hemispheric correlation (cc = 0.39-0.42) was found in vision-related thalamic nuclei that could not be explained by direct anatomical connections or their cortical inputs. These findings suggest that spontaneous fMRI signal correlations within the human visual system originate from a mixture of independent signal sources that may be transmitted through thalamo-cortical, cortico-thalamic, and cortico-cortical connections either trans-callosal or trans-thalamic in origin. Our findings thus call for more cautious interpretation of resting state functional connectivity in terms of any single type of anatomical connectivity.

Functional connectivity tracks clinical deterioration in Alzheimer's disease

While resting state functional connectivity has been shown to decrease in patients with mild and/or moderate Alzheimer's disease, it is not yet known how functional connectivity changes in patients as the disease progresses. Furthermore, it has been noted that the default mode network is not as homogenous as previously assumed and several fractionations of the network have been proposed. Here, we separately investigated the modulation of 3 default mode subnetworks, as identified with group independent component analysis, by comparing Alzheimer's disease patients to healthy controls and by assessing connectivity changes over time. Our results showed decreased connectivity at baseline in patients versus controls in the posterior default mode network, and increased connectivity in the anterior and ventral default mode networks. At follow-up, functional connectivity decreased across all default mode systems in patients. Our results suggest that earlier in the disease, regions of the posterior default mode network start to disengage whereas regions within the anterior and ventral networks enhance their connectivity. However, as the disease progresses, connectivity within all systems eventually deteriorates.

Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study

Recently, a great deal of interest has arisen in resting state fMRI as a measure of tonic brain function in clinical populations. Most studies have focused on the examination of temporal correlation between resting state fMRI low-frequency oscillations (LFOs). Studies on the amplitudes of these low-frequency oscillations are rarely reported. Here, we used amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) to examine the amplitude of LFO in schizophrenia. Twenty-six healthy controls and 29 patients with schizophrenia or schizoaffective disorder participated. Our findings show that patients showed reduced low-frequency amplitude in proportion to the total frequency band investigated (i.e., fALFF) in the lingual gyrus, left cuneus, left insula/superior temporal gyrus, and right caudate and increased fALFF in the medial prefrontal cortex and the right parahippocampal gyrus. ALFF was reduced in patients in the lingual gyrus, cuneus, and precuneus and increased in the left parahippocampal gyrus. These results suggest LFO abnormalities in schizophrenia. The implication of these abnormalities for schizophrenic symptomatology is further discussed.