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The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Abstract The direct access of olfactory afferents to memory-related cortical systems has inspired theories about the role of the olfactory pathways in the development of cortical neurodegeneration in Alzheimer’s disease (AD). In this study, we used baseline olfactory identification measures with longitudinal flortaucipir and PiB PET, diffusion MRI of 89 cognitively normal older adults (73.82 ± 8.44 years; 56% females), and a transcriptomic data atlas to investigate the spatiotemporal spreading and genetic vulnerabilities of AD-related pathology aggregates in the olfactory system. We find that odor identification deficits are predominantly associated with tau accumulation in key areas of the olfactory pathway, with a particularly strong predictive power for longitudinal tau progression. We observe that tau spreads from the medial temporal lobe structures toward the olfactory system, not the reverse. Moreover, we observed a genetic background of odor perception-related genes that might confer vulnerability to tau accumulation along the olfactory system.

Abstract Background Unawareness is a behavioral condition characterized by a lack of self-awareness of objective memory decline. In the context of Alzheimer’s Disease (AD), unawareness may develop in predementia stages and contributes to disease severity and progression. Here, we use in-vivo multi-modal neuroimaging to profile the brain phenotype of individuals presenting altered self-awareness of memory during aging. Methods Amyloid- and tau-PET (N = 335) and resting-state functional MRI (N = 713) imaging data of individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4)/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study were used in this research. We applied whole-brain voxel-wise and region-of-interest analyses to characterize the cortical intersections of tau, amyloid, and functional connectivity networks underlying unawareness in the aging brain compared to aware, complainer and control groups. Results Individuals with unawareness present elevated amyloid and tau burden in midline core regions of the default mode network compared to aware, complainer or control individuals. Unawareness is characterized by an altered network connectivity pattern featuring hyperconnectivity in the medial anterior prefrontal cortex and posterior occipito-parietal regions co-locating with amyloid and tau deposition. Conclusions Unawareness is an early behavioral biomarker of AD pathology. Failure of the self-referential system in unawareness of memory decline can be linked to amyloid and tau burden, along with functional network connectivity disruptions, in several medial frontal and parieto-occipital areas of the human brain.

Abstract Introduction Non-invasive diffusion-weighted imaging (DWI) to assess brain microstructural changes via cortical mean diffusivity (cMD) has been shown to be cross-sectionally associated with tau in cognitively normal older adults, suggesting that it might be an early marker of neuronal injury. Here, we investigated how regional cortical microstructural changes measured by cMD are related to the longitudinal accumulation of regional tau as well as to episodic memory decline in cognitively normal individuals harboring amyloid pathology. Methods 122 cognitively normal participants from the Harvard Aging Brain Study underwent DWI, T1w-MRI, amyloid and tau PET imaging, and Logical Memory Delayed Recall (LMDR) assessments. We assessed whether the interaction of baseline amyloid status and cMD (in entorhinal and inferior-temporal cortices) was associated with longitudinal regional tau accumulation and with longitudinal LMDR using separate linear mixed-effects models. Results We find a significant interaction effect of the amyloid status and baseline cMD in predicting longitudinal tau in the entorhinal cortex (p = 0.044) but not the inferior temporal lobe, such that greater baseline cMD values predicts the accumulation of entorhinal tau in amyloid-positive participants. Moreover, we find a significant interaction effect of the amyloid status and baseline cMD in the entorhinal cortex (but not inferior temporal cMD) in predicting longitudinal LMDR (p

Growing evidence suggests that cerebral connectivity changes its network organization by altering modular topology in response to developmental and environmental experience. However, changes in cerebral connectivity associated with visual impairment due to early neurological injury are still not fully understood. Cerebral visual impairment (CVI) is a brain-based visual disorder associated with damage and maldevelopment of retrochiasmal pathways and areas implicated in visual processing. In this study, we used a multimodal imaging approach and connectomic analyses based on structural (voxel-based morphometry; VBM) and resting state functional connectivity (rsfc) to investigate differences in weighted degree and link-level connectivity in individuals with CVI compared to controls with neurotypical development. We found that participants with CVI showed significantly reduced grey matter volume within the primary visual cortex and intraparietal sulcus (IPS) compared to controls. Participants with CVI also exhibited marked reorganization characterized by increased integration of visual connectivity to somatosensory and multimodal integration areas (dorsal and ventral attention regions) and lower connectivity from visual to limbic and default mode networks. Link-level functional changes in CVI were also associated with key clinical outcomes related to visual function and development. These findings provide early insight into how visual impairment related to early brain injury distinctly reorganizes the functional network architecture of the human brain.

Abstract The correlations between apolipoprotein epsilon 4 (APOE4) status and regional amyloid, tau, and cortical thickness in cognitively normal elderly are not fully understood. Our cross-sectional study aimed to compare regional amyloid/tau burden, and cortical thickness according to APOE4 carrier status and assess correlations between APOE4 and Alzheimer’s disease (AD)-related biomarker burdens. We analyzed 185 cognitively normal participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Participants aged 55–90 with normal cognitive function were divided into amyloid ß-positive (Aß+) APOE4 carriers (group 1, n = 27), Aß+ APOE4 non-carriers (group 2, n = 29), and Aß− normal controls (group 0, n = 129). We compared amyloid depositions, tau depositions, and cortical thickness among the three groups and assessed correlations between APOE4 existence and imaging biomarkers adjusted for age and sex. The participants in group 2 were older than those in the other groups. The regional amyloid/tau standardized uptake value ratios (SUVRs) did not differ between groups 1 and 2, but the amyloid/tau SUVRs in most regions were numerically higher after adjusting for age difference. APOE4 allele had robust correlations with increased amyloid burden in the fronto-temporo-parietal cortical areas after adjustment for age and sex, but it had weaker and mixed correlations with the regional tau burden and did not have significant correlation with cortical thickness. We identified that the presence of APOE4 allele might be more highly associated with amyloid deposition than with other AD-related biomarkers such as tau or cortical thickness in cognitively normal elderly.

Abstract The relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities—linked to Neuron Projection—were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.

Abstract Investment decisions rely on perceptions from external stimuli along with the integration of inner brain-body signals, all of which are shaped by experience. As experience is capable of molding both the structure and function of the human brain, we have used a novel neuroimaging connectomic-genetic approach to investigate the influence of investment work experience on brain anatomy. We found that senior investors display higher gray matter volume and increased structural brain connectivity in dopamine-related pathways, as well as a set of genes functionally associated with adrenaline and noradrenaline biosynthesis (SLC6A3, TH and SLC18A2), which is seemingly involved in reward processing and bodily stress responses during financial trading. These results suggest the key role of catecholamines in the way senior investors harness their emotions while raising bodily awareness as they grow in investment maturity.

Small vessel disease (SVD) is a disorder that causes vascular lesions in the entire parenchyma of the human brain. At present, it is not well understood how primary and secondary damage interact to give rise to the complex scenario of white matter (WM) and grey matter (GM) lesions. Using novel cross-sectional and longitudinal connectomic approaches, we unveil the bidirectional nature of GM and WM changes, that is, primary cortical neurodegeneration that leads to secondary alterations in vascular border zones, and WM lesions that lead to secondary neurodegeneration in cortical projecting areas. We found this GM-WM interaction to be essential for executive cognitive performance. Moreover, we also observed that the interlocked degeneration of GM and WM over time associates with prototypical expression levels of genes potentially linked to SVD. Among these connectomic-genetic intersections, we found that the Androgen Receptor (AR) gene, is a particularly central candidate gene that might confer key vulnerability for brain lesion development in SVD. In conclusion, this study advances in the understanding of the bidirectional relationships between GM and WM lesions, primary and secondary vascular neurodegeneration, and sheds light on the genetic signatures of SVD.

Abstract Objective To characterize the progression of brain structural abnormalities in adults with pediatric and adult/late onset DM1, as well as to examine the potential predictive markers of such progression. Methods 21 DM1 patients (pediatric onset: N = 9; adult/late onset: N = 12) and 18 healthy controls (HC) were assessed longitudinally over 9.17 years through brain MRI. Additionally, patients underwent neuropsychological, genetic, and muscular impairment assessment. Inter‐group comparisons of total and voxel‐level regional brain volume were conducted through Voxel Based Morphometry (VBM); cross‐sectionally and longitudinally, analyzing the associations between brain changes and demographic, clinical, and cognitive outcomes. Results The percentage of GM loss did not significantly differ in any of the groups compared with HC and when assessed independently, adult/late DM1 patients and their HC group suffered a significant loss in WM volume. Regional VBM analyses revealed subcortical GM damage in both DM1 groups, evolving to frontal regions in the pediatric onset patients. Muscular impairment and the outcomes of certain neuropsychological tests were significantly associated with follow‐up GM damage, while visuoconstruction, attention, and executive function tests showed sensitivity to WM degeneration over time. Interpretation Distinct patterns of brain atrophy and its progression over time in pediatric and adult/late onset DM1 patients are suggested. Results indicate a possible neurodevelopmental origin of the brain abnormalities in DM1, along with the possible existence of an additional neurodegenerative process. Fronto‐subcortical networks appear to be involved in the disease progression at young adulthood in pediatric onset DM1 patients. The involvement of a multimodal integration network in DM1 is discussed.

The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson’s disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex – such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.

The dynamic architecture of the human brain has been consistently observed. However, there is still limited modeling work to elucidate how neuronal circuits are hierarchically and flexibly organized in functional systems. Here we proposed a reachable probability approach based on non-homogeneous Markov chains, to characterize all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level. We proved at the theoretical level the convergence of the functional brain network system, and demonstrated that this approach is able to detect network steady states across connectivity structure, particularly in areas of the default mode network. We further explored the dynamically hierarchical functional organization centered at the primary sensory cortices. We observed smaller optimal reachable steps to their local functional regions, and differentiated patterns in larger optimal reachable steps for primary perceptual modalities. The reachable paths with the largest and second largest transition probabilities between primary sensory seeds via multisensory integration regions were also tracked to explore the flexibility and plasticity of the multisensory integration. The present work provides a novel approach to depict both the stable and flexible hierarchical connectivity organization of the human brain.

Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer’s disease (AD), exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease progression and cognitive decline. Personalized prediction of tau progression is, therefore, vital for the early diagnosis and prognosis of AD. Evidence from both animal and human studies is suggestive of tau transmission along the brains preexisting neural connectivity conduits. We present here an analytic graph diffusion framework for individualized predictive modeling of tau progression along the structural connectome. To account for physiological processes that lead to active generation and clearance of tau alongside passive diffusion, our model uses an inhomogenous graph diffusion equation with a source term and provides closed-form solutions to this equation for linear and exponential source functionals. Longitudinal imaging data from two cohorts, the Harvard Aging Brain Study (HABS) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were used to validate the model. The clinical data used for developing and validating the model include regional tau measures extracted from longitudinal positron emission tomography (PET) scans based on the 18F-Flortaucipir radiotracer and individual structural connectivity maps computed from diffusion tensor imaging (DTI) by means of tractography and streamline counting. Two-timepoint tau PET scans were used to assess the goodness of model fit. Three-timepoint tau PET scans were used to assess predictive accuracy via comparison of predicted and observed tau measures at the third timepoint. Our results show high consistency between predicted and observed tau and differential tau from region-based analysis. While the prognostic value of this approach needs to be validated in a larger cohort, our preliminary results suggest that our longitudinal predictive model, which offers an in vivo macroscopic perspective on tau progression in the brain, is potentially promising as a personalizable predictive framework for AD.

AbstractDevelopmental stuttering is a childhood onset neurodevelopmental disorder with an unclear etiology. Subtle changes in brain structure and function are present in both children and adults who stutter. It is a highly heritable disorder, and 12–20% of stuttering cases may carry a mutation in one of four genes involved in intracellular trafficking. To better understand the relationship between genetics and neuroanatomical changes, we used gene expression data from the Allen Institute for Brain Science and voxel-based morphometry to investigate the spatial correspondence between gene expression patterns and differences in gray matter volume between children with persistent stuttering (n = 26, and 87 scans) and their fluent peers (n = 44, and 139 scans). We found that the expression patterns of two stuttering-related genes (GNPTG and NAGPA) from the Allen Institute data exhibited a strong positive spatial correlation with the magnitude of between-group gray matter volume differences. Additional gene set enrichment analyses revealed that genes whose expression was highly correlated with the gray matter volume differences were enriched for glycolysis and oxidative metabolism in mitochondria. Because our current study did not examine the participants’ genomes, these results cannot establish the direct association between genetic mutations and gray matter volume differences in stuttering. However, our results support further study of the involvement of lysosomal enzyme targeting genes, as well as energy metabolism in stuttering. Future studies assessing variations of these genes in the participants’ genomes may lead to increased understanding of the biological mechanisms of the observed spatial relationship between gene expression and gray matter volume.

The ability to produce novel ideas is central to societal progress and innovation; however, little is known about the biological basis of creativity. Here, we investigate the organization of brain networks that support creativity by combining functional neuroimaging data with gene expression information. Given the multifaceted nature of creative thinking, we hypothesized that distributed connectivity would not only be related to individual differences in creative ability, but also delineate the cortical distributions of genes involved in synaptic plasticity. We defined neuroimaging phenotypes using a graph theory approach that detects local and distributed network circuits, then characterized the spatial associations between functional connectivity and cortical gene expression distributions. Our findings reveal strong spatial correlations between connectivity maps and sets of genes devoted to synaptic assembly and signaling. This connectomic-transcriptome approach thus identifies gene expression profiles associated with high creative ability, linking cognitive flexibility to neural plasticity in the human brain.

To determine the extent of metabotropic glutamate receptor type 5 (mGluR5) network abnormalities associated with focal cortical dysplasia (FCD), we performed graph theoretical analysis of [11C]ABP688 PET binding potentials (BPND), which allows for quantification of mGluR5 availability. Undirected graphs were constructed for the entire cortex in 17 FCD patients and 33 healthy controls using inter-regional similarity of [11C]ABP688 BPND. We assessed group differences in network integration between healthy controls and the ipsilateral and contralateral hemispheres of FCD patients. Compared to healthy controls, FCD patients showed reduced network efficiency and reduced small-world connectivity. The mGluR5 network of FCD patients was also less resilient to targeted removal of high centrality nodes, suggesting a less integrated network organization. In highly efficient hub nodes of FCD patients, we observed a significant negative correlation between local efficiency and duration of epilepsy only in the contralateral hemisphere, suggesting that some nodes may be more vulnerable to persistent epileptic activity. Our study provides the first in vivo evidence for a widespread reduction in cortical mGluR5 network integration in FCD patients. In addition, we find that ongoing epileptic activity may alter chemoarchitectural brain organization resulting in reduced efficiency in distant regions that are essential for network integration.

Cerebrovascular abnormality is linked to Alzheimer's disease and related dementias (ADRDs). ApoE-Ɛ4 (APOE4) is known to play a critical role in neurovascular dysfunction, however current medical imaging technologies are limited in quantification. This cross-sectional study tested the feasibility of a recently established imaging modality, quantitative ultra-short time-to-echo contrast-enhanced magnetic resonance imaging (QUTE-CE MRI), to identify small vessel abnormality early in development of human APOE4 knock-in female rat (TGRA8960) animal model. At 8 months, 48.3% of the brain volume was found to have significant signal increase (75/173 anatomically segmented regions; q

Attractor dynamics have been discovered in neural circuits, but it is not clear if they exist at the level of whole-brain activity. Here, the authors show that certain brain regions act as nodes in which many activity ‘streams’ converge, regardless of brain state. These regions show distinctive gene expression.

Tau pathology has been associated with neuronal loss at autopsy, but the temporal evolution of tau pathology and atrophy remains unclear. Here, we investigate the association between cross-sectional AV-1451-PET as a marker of tau pathology and cortical thickness cross-sectionally. We also investigated retrospective rates of cortical thinning over the three years preceding the AV-1451 scan in a clinically normal cohort of 103 older adults from the Harvard Aging Brain Study. Tau measurements were Geometric Transfer Matrix partial volume corrected standardized uptake value ratios (SUVRs) with a cerebellar gray reference region. Thirty-four FreeSurfer-defined cortical regions of interest (ROIs) were used for both thickness and AV-1451 in each hemisphere, with seven additional volumetric ROIs. We examined “local” relationships between AV-1451 and cortical thickness in the same ROI, as well as inferior temporal AV-1451 and all thickness ROIs. All models included baseline age and sex, both interacting with time in retrospective longitudinal models, as covariates. Cross-sectional models controlled for the number of days between the two scans. Cross-sectional local comparisons revealed significant associations between elevated AV-1451 and thinner cortical ROIs predominantly in temporal regions, while analyses associating inferior temporal AV-1451 with all cortical ROIs showed a widespread pattern of significant relationships, which was strongest in temporal and parietal cortices. In our retrospective longitudinal analyses, we saw significant relationships in temporal and parietal regions. Significant local relationships were seen in right superior temporal, middle temporal, temporal pole, and fusiform, as well as the left cuneus and banks of the left superior temporal sulcus. Significant relationships between inferior temporal AV-1451 and faster thinning were observed in right temporal regions (middle temporal and fusiform) and bilateral parahippocampal cortices. We observed significant negative relationships between local and inferior temporal AV-1451 signal and both cross-sectional cortical thickness and rates of thinning in lateral and medial temporal regions. This is an important early step toward elucidating the relationship between tau pathology and retrospective longitudinal atrophy in aging and preclinical AD.

The ability to accurately judge memory efficiency (meta-memory monitoring) for newly learned (episodic) information, is decreased in older adults and even worse in Alzheimer's disease (AD), whereas no differences have been found for semantic meta-memory. The pathological substrates of this phenomenon are poorly understood. Here, we examine the association between meta-memory monitoring for episodic and semantic information to the two major proteinopathies in AD: amyloid (Aβ) and tau pathology in a group of cognitively unimpaired older adults. All participants underwent multi-tracer PET and meta-memory monitoring was assessed using a feeling-of-knowing (FOK) task for non-famous (episodic) and famous (semantic) face-name pairs. Whole brain voxel-wise correlations between meta-memory and PET data were conducted (controlling for memory), as well as confirmatory region-of-interest analyses. Participants had reduced episodic FOK compared to semantic FOK. Decreased episodic FOK was related to tauopathy in the medial temporal lobe regions, including the entorhinal cortex and temporal pole, whereas decreased semantic FOK was related to increased tau in regions associated with the semantic knowledge network. No association was found with Aβ-pathology. Alterations in the ability to accurately judge memory efficiency (in the absence of memory decline) may be a sensitive clinical indicator of AD pathophysiology in the pre-symptomatic phase. Keywords: Alzheimer's disease, Amyloid, Feeling-of-knowing, Meta-memory, Tau

Background: Myotonic Dystrophy type 1 (DM1) is a slowly progressive myopathy characterized by varying multisystemic involvement. Several cerebral features such as brain atrophy, ventricular enlargement, and white matter lesions (WMLs) have frequently been described. The aim of this study is to investigate the structural organization of the brain that defines the disease through multimodal imaging analysis, and to analyze the relation between structural cerebral changes and DM1 clinical and neuropsychological profiles. Method: 31 DM1 patients and 57 healthy controls underwent an MRI scan protocol, including T1, T2 and DTI. Global gray matter (GM), global white matter (WM), and voxel-level Voxel Based Morphometry (VBM) and voxel-level microstructural WM abnormalities through Diffusion Tensor Imaging (DTI) were assessed through group comparisons and linear regression analysis with age, degree of muscular impairment (MIRS score), CTG expansion size and neuropsychological outcomes from a comprehensive assessment. Results: Compared with healthy controls, DM1 patients showed a reduction in both global GM and WM volume; and further regional GM decrease in specific primary sensory, multi-sensory and association cortical regions. Fractional anisotropy (FA) was reduced in both total brain and regional analysis, being most marked in frontal, paralimbic, temporal cortex, and subcortical regions. Higher ratings on muscular impairment and longer CTG expansion sizes predicted a greater volume decrease in GM and lower FA values. Age predicted global GM reduction, specifically in parietal regions. At the cognitive level, the DM1 group showed significant negative correlations between IQ estimate, visuoconstructive and executive neuropsychological scores and both global and regional volume decrease, mainly distributed in the frontal, parietal and subcortical regions. Conclusions: In this study, we describe the structural brain signatures that delineate the involvement of the CNS in DM1. We show that specific sensory and multi-sensory — as well as frontal cortical areas — display potential vulnerability associated with the hypothesized neurodegenerative nature of DM1 brain abnormalities. Keywords: Myotonic Dystrophy Type 1, MRI, VBM, DTI, neuropsychology

Functional connectivity MRI (fcMRI) has become instrumental in facilitating research of human brain network organization in terms of coincident interactions between positive and negative synchronizations of large-scale neuronal systems. Although there is a common agreement concerning the interpretation of positive couplings between brain areas, a major debate has been made in disentangling the nature of negative connectivity patterns in terms of its emergence in several methodological approaches and its significance/meaning in specific neuropsychiatric diseases. It is still not clear what information the functional negative correlations or connectivity provides or how they relate to the positive connectivity. Through implementing stepwise functional connectivity (SFC) analysis and studying the causality of functional topological patterns, this study aims to shed light on the relationship between positive and negative connectivity in the human brain functional connectome. We found that the strength of negative correlations between voxel-pairs relates to their positive connectivity path-length. More importantly, our study describes how the spatio-temporal patterns of positive connectivity explain the evolving changes of negative connectivity over time, but not the other way around. This finding suggests that positive and negative connectivity do not display equivalent forces but shows that the positive connectivity has a dominant role in the overall human brain functional connectome. This phenomenon provides novel insights about the nature of positive and negative correlations in fcMRI and will potentially help new developments for neuroimaging biomarkers.

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer's disease (AD) measured by amyloid-β deposits. The proposed GRM regards 11C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1-regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.

Information processing in the human brain arises from both interactions between adjacent areas and from distant projections that form distributed brain systems. Here we map interactions across different spatial scales by estimating the degree of intrinsic functional connectivity for the local (14 mm) interactions. The balance between local and distant functional interactions measured at rest forms a map that separates sensorimotor cortices from heteromodal association areas and further identifies regions that possess both high local and distant cortical-cortical interactions. Map estimates of network measures demonstrate that high local connectivity is most often associated with a high clustering coefficient, long path length, and low physical cost. Task performance changed the balance between local and distant functional coupling in a subset of regions, particularly, increasing local functional coupling in regions engaged by the task. The observed properties suggest that the brain has evolved a balance that optimizes information-processing efficiency across different classes of specialized areas as well as mechanisms to modulate coupling in support of dynamically changing processing demands. We discuss the implications of these observations and applications of the present method for exploring normal and atypical brain function.

Understanding complex diseases will benefit the recognition of the properties of the gene networks that control biological functions. Here, we set out to model the gene network that controls T-cell activation in humans, which is critical for the development of autoimmune diseases such as Multiple Sclerosis (MS). The network was established on the basis of the quantitative expression from 104 individuals of 20 genes of the immune system, as well as on biological information from the Ingenuity database and Bayesian inference. Of the 31 links (gene interactions) identified in the network, 18 were identified in the Ingenuity database and 13 were new and we validated 7 of 8 interactions experimentally. In the MS patients network, we found an increase in the weight of gene interactions related to Th1 function and a decrease in those related to Treg and Th2 function. Indeed, we found that IFN-ss therapy induces changes in gene interactions related to T cell proliferation and adhesion, although these gene interactions were not restored to levels similar to controls. Finally, we identify JAG1 as a new therapeutic target whose differential behaviour in the MS network was not modified by immunomodulatory therapy. In vitro treatment with a Jagged1 agonist peptide modulated the T-cell activation network in PBMCs from patients with MS. Moreover, treatment of mice with experimental autoimmune encephalomyelitis with the Jagged1 agonist ameliorated the disease course, and modulated Th2, Th1 and Treg function. This study illustrates how network analysis can predict therapeutic targets for immune intervention and identified the immunomodulatory properties of Jagged1 making it a new therapeutic target for MS and other autoimmune diseases.