Brain Connectivity

Many researchers using many different approaches have attempted to find features discriminating between autism spectrum disorder (ASD) and typically control (TC) subjects. In this study, this attempt has been continued by analyzing global metrics of functional graphs and metrics of functional triadic interactions of the brain in the low, middle, and high-frequency bands (LFB, MFB, and HFB) of the structural graph. The graph signal processing (GSP) provided the combinatorial usage of the functional graph of resting-state fMRI and structural graph of DTI. In comparison to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and probably decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may show the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. All of these results demonstrated that the significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. In conclusion, the results demonstrate the promising perspective of GSP for attaining discriminative features and new knowledge, especially in the case of ASD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/469268v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@100df67org.highwire.dtl.DTLVardef@4b2455org.highwire.dtl.DTLVardef@13e51d5org.highwire.dtl.DTLVardef@6e789c_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

BackgroundFollowing mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction. Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. ResultsGroup comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. ConclusionsOur multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

The most widely used inputs in classification models of brain disorders such as early mild cognitive impairment (eMCI) or Alzheimers disease are estimates of static-based functional connectivity (SFC) and sliding window dynamic functional connectivity (swDFC). Although these methods are convenient for estimation and computational purposes, as it keeps the estimation tractable, they present a simplified version of a highly integrated and dynamic phenomenon. Change point dynamic functional connectivity (cpDFC) methods, which are far less commonly used, offer an alternative to swDFC approaches. In this study, we consider a classification task between controls and patients with eMCI using resting-state functional magnetic resonance imaging (fMRI) data from the Alzheimers Disease Neuroimaging Initiative (ADNI) studies, ADNI2 and ADNIGO. Our results indicate that the DFC methods are generally superior to the SFC methods when used as inputs into the classification model. Most importantly, we find that the cpDFC methods are generally superior to the widely used swDFC methods. We discuss how cpDFC methods offer many distinct advantages over swDFC methods, namely, the parsimony of network features and ease of interpretability. We validate the robustness and consistency of our results by testing the methods on an additional resting-state fMRI dataset of mild cognitive impairment patients. These findings call into question the validity of numerous fMRI studies that have utilized inferior approaches, such as SFC and swDFC, as inputs to classification models to predict various brain disorders. Finally, we present an ensemble model of the best models, which achieves an accuracy of 91.17% from leave-one-out cross-validation of subjects with eMCI. Our results suggest that the underlying functional networks are dynamic, multiscale, and that different FC methods capture distinct information for classification efficacy.

Independent brain regions in neuroanatomy achieve a specific function through connections. As one of the significant morphological features of the cerebral cortex, previous studies have found significant differences in the structure and function of the cerebral gyri and sulci, which provides a basis for us to study the functional connectivity differences between these two anatomic parts. Previous studies using fully connected functional connectivity (FC) and structural connectivity (SC) matrices found significant differences in the perspective of region or connection in gyri and sulci. However, a clear issue is that previous studies have only analyzed the differences through either FC or SC, without effectively integrating both. Meanwhile, another nonnegligible issue is that the subcortical areas, involved in various tasks, have not been systematically explored with cortical regions. Due to the strong coupling between FC and SC, we use SC-informed FC to systematically explore the functional characteristics of gyri/sulci and subcortical regions by combining deep learning method with magnetic resonance imaging (MRI) technology. Specifically, we use graph attention network (GAT) to explore the important connections in the SC-informed FC through the Human Connectome Project (HCP) dataset. With high classification results of above 99%, we have successfully discovered important connections under different tasks. We have successfully explored the importance of different types of connections. In low threshold, gyri-gyri are the most important connections. With the threshold increasing, sub-sub become the most important. Gyri have a higher importance in functional connectivity than sulci. In the seven task states, these connections are mainly distributed among the front, subcortical, and occipital. This study provides a novel way to explore the characteristics of functional connectivity at the whole brain scale.

Children and adolescents with congenital heart disease (CHD) frequently experience neurodevelopmental impairments that can impact academic performance, memory, attention, and behavioral function, ultimately affecting overall quality of life. This study aims to investigate the impact of CHD on functional brain network connectivity and cognitive function. Using resting-state fMRI data, we examined several network metrics across various brain regions utilizing weighted networks and binarized networks with both absolute and proportional thresholds. Regression models were fitted to patient neurocognitive exam scores using various metrics obtained from all three methods. Our results unveil significant differences in network connectivity patterns, particularly in temporal, occipital, and subcortical regions, across both weighted and binarized networks. Furthermore, we identified distinct correlations between network metrics and cognitive performance, suggesting potential compensatory mechanisms within specific brain regions.

The neurobiological basis of ADHD and its subtypes remains unclear, with inconsistent findings from studies using electrophysiology and neuroimaging. Some studies suggest ADHD-I is a distinct disorder, but there is also evidence of similar neural basis in ADHD-I and ADHD-C subtypes. This study investigates the neural basis of ADHD and its subtypes using a subnetwork modularity approach based on graph theoretical analysis of EEG data from 35 children aged 7-11. EEG was recorded in the eyes open condition and preprocessed. After preprocessing, data was analyzed using LORETA algorithm to estimate current densities in 84 regions of interest (ROIs) in the cortex and calculate functional connectivity between these ROIs in different EEG frequency bands. Then, we evaluated modularity of five functional brain networks (default mode, central control, salience, visual, and sensorimotor) using Newman modularity algorithm. Further, we evaluated edge betweenness centrality to assess communications between these functional brain networks. The study found that different brain networks have modularity in certain frequency bands, and ADHD groups showed reduced modularity of the visual network compared to normal groups in the alpha1 band (8-10 Hz). The communication between the visual network and other brain networks, except the salience network, was also reduced in ADHD groups (in the alpha1 band). However, there were no significant differences in the modularity of brain networks and communication among them between two ADHD subtypes. The results suggest a novel mechanism for ADHD involving lower intrinsic modularity in the visual network, disturbed communication between the visual network and other networks, and potential impact on the function of control and sensorimotor networks. Further, our results suggest that there may be a common neural basis for both subtypes, involving a shared disturbance in the modularity and connectivity of the ventral network. This supports the idea that ADHD-I and ADHD-C are subtypes within the same category and contradicts previous studies that suggest they are separate disorders.

Individuals suffering from Obsessive Compulsive Disorder (OCD) and Schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients. AUTHOR SUMMARYIndividuals suffering from Obsessive Compulsive Disorder (OCD) and Schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients.

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutation of the APC gene presenting with numerous colorectal adenomatous polyps and a near 100% risk of colon cancer. Preliminary research findings from our group indicate that FAP patients experience significant deficits across many cognitive domains. In the current study, fMRI brain metrics in a FAP population and matched controls were used to further the mechanistic understanding of reported cognitive deficits. This research identified and characterized any possible differences in resting brain networks and associations between neural network changes and cognition from 34 participants (18 FAP patients, 16 healthy controls). Functional connectivity analysis was performed using FSL with independent component analysis (ICA) to identify functional networks. Significant differences between cases and controls were observed in 8 well-established resting state networks. With the addition of an aggregate cognitive measure as a covariate, these differences were virtually non-existent, indicating a strong correlation between cognition and brain activity at the network level. The data indicate robust and pervasive effects on functional neural network activity among FAP patients and these effects are likely involved in cognitive deficits associated with this disease.

Most dynamic functional connectivity in fMRI data is focused on linear correlations, and to our knowledge, no study has studied whole brain explicitly nonlinear dynamic relationships within the data. While some approaches have attempted to study overall connectivity more generally using flexible models, we are particularly interested in whether the non-linear relationships, above and beyond linear, are capturing unique information. This study thus proposes an approach to assess the explicitly nonlinear dynamic functional network connectivity derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information. Simulations showed the proposed method accurately estimated NMI over time, even within relatively short windows of data. Results on fMRI data included 151 schizophrenia patients, and 163 healthy controls showed three unique, highly structured, mostly long-range, functional states that also showed significant group differences. This analysis identifies a higher level of explicitly nonlinear dependencies in transient connectivity within the visual network in healthy controls compared to schizophrenia patients. In particular, nonlinear relationships tend to be more widespread than linear ones. We also find highly significant differences in the relative co-occurrence of linear and explicitly nonlinear states in HC and SZ, suggesting these may be an important aspect of the disorder. Overall, this work suggests that quantifying nonlinear dependencies of dynamic functional connectivity may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

The cerebellum has been increasingly recognized to play key roles in the pathology of multiple sclerosis (MS) and spectrum disorders (NMOSD), two main demyelinating diseases with similar clinical presentations. Despite accumulating evidence from neuroimaging research for cerebellar volumetric alterations in the diseases, however, there have been no network-based studies examining convergent and divergent alterations in cerebellar connectome between MS and NMOSD. This multisite and multimodal study examined common and specific alterations in within-cerebellar coordination and cerebello-cerebral communication between MS and NMOSD by retrospectively collecting structural and resting-state functional MRI data from 208 MS patients, 200 NMOSD patients and 228 healthy controls (HCs) in seven sites in China. Morphological brain networks were constructed by estimating interregional similarity in cortical thickness and functional brain networks were formed by calculating interregional temporal synchronization in functional signals. After identifying cerebellar modular architecture and based on prior cerebral cytoarchitectonic classification and functional partition, within-cerebellar and cerebello-cerebral morphological and functional connectivity were compared among the MS, NMOSD and HC groups. Five modules were identified within the cerebellum including Primary Motor A (PMA), Primary Motor B (PMB), Primary Non-Motor (PNM), Secondary Motor (SM) and Secondary Non-Motor (SNM) modules. Compared with the HCs, the MS and NMOSD patients exhibited both increases and decreases in within-cerebellar morphological connectivity that were mainly involved in the PMA, PMB and SNM. Particularly, the two patient groups showed a common altered pattern characterized by decreases between the PMA and SNM, both of which were more densely connected with the PMB. For cerebello-cerebral morphological connectivity, widespread reductions were found in both patient groups for the SM and SNM with almost all cerebral cytoarchitectonic classes and functional systems while increases were observed only in the NMOSD patients for the PMB with cerebral areas involving motor and sensory domains. With regard to cerebellar functional connectivity, fewer alterations were observed in the patients that were all characterized by reductions and were mainly involved in cerebello-cerebral interactions between cerebellar motor modules and cerebral association cortex and high-order networks, particularly in the NMOSD patients. Cerebellar connectivity-based classification achieved around 60% accuracies to distinguish the three groups to each other with morphological connectivity as predominant features for differentiating the patients from controls while functional connectivity for discriminating the two diseases. Altogether, this study characterizes common and specific circuit dysfunctions of the cerebellum between MS and NMOSD, which provide novel insights into shared and unique pathophysiologic mechanisms underlying the two diseases.

BackgroundDynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying of functional integration between brain networks. In a typical dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters through a conventional clustering criterion computed by running the algorithm repeatedly, over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline, called iterative sparse kmeans or iSparse kmeans, to analyze large dFNC data without having access to huge computational power. MethodIn iSparse kmeans, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of iSparse kmeans, we analyzed four dFNC datasets from the human connectome project (HCP). ResultsWe found that both conventional kmeans and iSparse kmeans generate similar brain dFNC states while iSparse kmeans is 27 times faster than the traditional method in finding the optimum number of clusters. We show that the results are replicated across four different datasets from HCP. ConclusionWe developed a new analytic pipeline which facilitates analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.

Automating the diagnostic process steps has been of interest for research grounds and to help manage the healthcare systems. Improved classification accuracies, provided by ever more sophisticated algorithms, were mirrored by the loss of interpretability on the criteria for achieving accuracy. In other words, the mechanisms responsible for generating the distinguishing features are typically not investigated. Furthermore, the vast majority of the classification studies focus on the classification of one disease as opposed to matched controls. While this scenario has internal validity, concerning the appropriateness toward answering scientific questions, it does not have external validity. In other words, differentiating multiple diseases at once is a classification problem closer to many real-world scenarios. In this work, we test the hypothesis that specific data features hold most of the discriminative power across multiple neurodegenerative diseases. Furthermore, we perform an explorative analysis to compare metrics based on different assumptions (concerning the underlying mechanisms). To test this hypothesis, we leverage a large Magnetoencephalography dataset (N=109) merging four cohorts, recorded in the same clinical setting, of patients affected by multiple sclerosis, amyotrophic lateral sclerosis, Parkinsons disease, and mild cognitive impairment. Our results show that it is possible to reach a balanced accuracy of 67,1% (chance level = 35%), based on a small set of (non-disease specific) features. We show that edge metrics (defined as statistical dependencies between pairs of brain signals) perform better than nodal metrics (considering region while disregarding the interactions. Moreover, phase-based metrics slightly outperform amplitude-based metrics. In conclusion, our work shows that a small set of phase-based connectivity metrics applied to MEG data successfully distinguishes across multiple neurological diseases.

White matter degradation has been proposed as one possible explanation for age-related cognitive decline. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. Cognitive domains were measured annually (mean follow-up = 1.25 {+/-} 0.61 years), including processing speed, memory, language, visuospatial, and executive functions. Diffusion tensor imaging was performed at baseline in 90 clinically normal older adults (aged 54-86). We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity. The machine learning approach was used to predict the rate of cognitive decline from white matter connectivity data. The reduced efficacy of white matter networks could predict the performance of these cognitive domains except memory. The predicted scores were significantly correlated with the real scores. For the local regions for predicting the cognitive changes, the right precuneus, left inferior parietal lobe and cuneus are the most important regions for predicting monthly change of executive function; some left partial and occipital regions are the most important for the changed of attention; the right frontal and temporal regions are the most important for the changed of language. Our findings suggested that the global white matter connectivity characteristics are the valuable predictive index for the longitudinal cognitive decline. For the first time, topological efficiency of white matter connectivity maps which related to special domains of cognitive decline in the elderly are identified.

Graph theory provides a promising technique to investigate Alzheimers disease (AD)-related alterations in brain connectivity. However, discrepancies exist in the reported disruptions that occur to network topology across the AD continuum, which may be attributed to differences in the denoising approach used in fMRI processing to remove the effect of non-neuronal sources from signal. The current study aimed to determine if diagnostic differences in graph metrics were dependent on nuisance regression strategy. Sixty cognitively normal (CN), 60 MCI, and 40 AD matched for age, sex, and motion, were selected from the ADNI database for analysis. Resting state images were preprocessed using AFNI (v21.2.04) and 16 nuisance regression approaches were employed, which included the unique combination of four nuisance regressors (derivatives of the realignment parameters, motion censoring [euclidean norm > 0.3mm], outlier censoring [outlier fraction > .10], bandpass filtering [0.01 - 0.1 Hz]). Graph metrics representing network segregation (clustering coefficient, local efficiency, modularity), network integration (largest connected component, path length, local efficiency), and small-worldness (clustering coefficient/path length) were calculated. The results showed a significant interaction between diagnosis and nuisance approach on path length, such that diagnostic differences were only evident when motion derivatives and censoring of both motion and outlier volumes were applied. Further, regardless of the denoising approach, AD patients exhibited less segregated networks and lower small-worldness than CN and MCI. Finally, independent of diagnosis, denoising strategy significantly affected the magnitude of nearly all metrics (except local efficiency), such that models including bandpass filtering had higher graph metrics than those without. These findings suggest the relative robustness of network segregation and small-worldness properties to denoising strategy. However, caution should be taken when interpreting path length findings across studies, as subtle variations in regression approach may account for discrepancies. Continued efforts should be taken towards harmonizing preprocessing pipelines across studies to aid replication efforts and build consensus towards understanding the mechanisms underlying pathological aging.

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout this thesis we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimers disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimers Disease Neuroimaging Initiative.

Independent component analysis (ICA) is a widely used data-driven technique for investigating brain structure and function to extract intrinsic networks. However, the ability of ICA, a linear mixing model, to capture nonlinear relationships is inherently limited. While nonlinear ICA can be used to estimate nonlinear+ linear mixtures, it can be useful to study the degree to which there is nonlinearity above and beyond the widely studied linear resting networks. Here, we propose a way to divide the data into sources exhibiting linear-only or explicitly nonlinear dependencies in resting functional magnetic resonance imaging (fMRI) data. Such an approach can be very informative as it allows us to evaluate the degree to which a given network might be linear, nonlinear, or both linear and nonlinear. Here, we present an enhanced connectivity-domain ICA approach, connectivity-matrix ICA, incorporating normalized mutual information (NMI) after canceling the linear effects to measure explicitly nonlinear (EN) relationships within voxel connectivity. This integration enables the identification of brain spatial maps that exhibit pronounced explicitly nonlinear dependencies while excluding linear relationships. By eliminating linear dependencies and utilizing NMI, we discover highly structured resting networks that conventional functional connectivity methods would typically overlook. The results indicate that several maps show only linear or EN relationships, and the rest of the components display both linear and nonlinear patterns. We categorized these maps as linear-only, EN-only, and linear-EN maps. We also evaluate differences in the identified networks in a schizophrenia dataset. A significant global difference has been discovered between schizophrenia and controls in some linear-EN maps, such as the frontal lobe. Moreover, the temporal lobe and thalamus display linear group differences, while the visual and motor cortex display global differences in nonlinear relationships as their primary driver of these disparities. In sum, our findings emphasize the significance of accounting for explicitly nonlinear dependencies in functional connectivity analysis and demonstrate the effectiveness of the extended cmICA approach in revealing previously unrecognized brain dynamics.

BackgroundWhile functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. MethodsWe proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification. ResultsIn addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. ConclusionsIn sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact StatementMulti-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (MI), that calculates the nonlinear correlation between different brain regions. We demonstrate our proposed approach using simulated data, then apply it to a dataset previously studied in (Damaraju et al., 2014). This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in nonlinear dependencies between schizophrenia patients and healthy controls particularly in visual cortex, with controls showing more nonlinearity in most cases. Certain domains, including cognitive control, and default mode, appeared much less nonlinear, whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Further, we propose a method that captures both linear and nonlinear effects in a boosted approach. This method increases the sensitivity to group differences in comparison to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

Up to now, disrupted functional organization of the brain in Alzheimers disease (AD) has mostly been examined by assessing "static" functional connectivity (FC). However, recent studies have provided clear evidence that brain FC exhibits intrinsic spatiotemporal "dynamic" organization also being significantly affected in AD. However, inconsistency in former studies is a motivation for further investigation of impaired dynamic profile of brain FC networks in Alzheimers disease._In this work, we identified recurring FC states during rest (eyes-open) in 24 AD patients and 37 healthy controls (HC) by combining sliding window-based method (examining dynamic FC) and k-means clustering algorithm (identifying recurring FC states over time). To define the optimum number of recurring FC states in k-means algorithm, we employed non-supervised validity criteria such as silhouette value and Dunn index over bootstrap samples. Then, we examined differences in dynamic properties of recurring FC states including probability of occurrence, lifetime and switching profile between groups. Afterward, association between impaired dynamic profile of recurring FC states and cognitive performance was assessed in AD patients. Our findings revealed three recurring FC states and among them, FC state with the most significant differences in probability of occurrence (corrected p-value < 0.0001) included brain regions involved in default mode, frontoparietal and salience networks. This recurring FC state occurred significantly less and lasted shorter in patients with AD than in the HC. Further, the probability of occurrence in this recurring FC state significantly correlated positively with cognitive decline in patients with AD. Our findings suggest more decreased in cognitive performance in patients with AD associated with more reduced ability of AD patients to access clinically relevant impaired brain FC networks. Impaired dynamic profile of recurring FC states in AD provide greater understanding of ADs pathophysiology.

The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream models regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream models regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemispheres homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology.