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Background Alzheimer's disease (AD) and white‐matter structural connectivity have been linked in some observational studies, although it is unknown if this is a causal relationship. The purpose of this study was to examine the impact of various white‐matter structural connectivity on AD via a two‐sample multivariate Mendelian randomization (MR) approach. Methods The genome‐wide association study (GWAS) of Wainberg et al. provided the summary data on white‐matter structural connectivity, and Bellenguez et al.’s study provided the GWAS aggregated data for AD. MR methods included inverse variance weighted, Mendelian randomization Egger, simple mode, weighted median, and weighted mode. Heterogeneity, horizontal pleiotropy, and “leave‐one‐out” analysis guaranteed the robustness of causation. Finally, reverse MR analysis was conducted on the white‐matter structural connectivity that showed positive results in the forward MR analysis. Results Among 206 white‐matter structural connections, we identified 10 connections were strongly correlated with genetic susceptibility to AD. Right‐hemisphere limbic network to thalamus white‐matter structural connectivity and Right‐hemisphere salience_ventral attention network to accumbens white‐matter structural connectivity were positively correlated with the likelihood of AD, while the remaining 8 white‐matter structural connections were negatively related with AD. None of the above 10 white‐matter structural connections have a reverse causal relationship with AD. Conclusion Our MR study reveals a certain degree of association between white‐matter structural connectivity and AD, which may provide support for future diagnosis and treatment of AD. This study used a two‐sample bidirectional MR technique to evaluate the most recent summary GWAS data and revealed a causal relationship between white‐matter structural connectivity and AD, which is the first time that the relationship between these two has been investigated at the genetic level.

Objective : Post-stroke depression (PSD) is one of the most common neuropsychiatric symptoms with high prevalence, however, the mechanism of the brain network in PSD and the relationship between the structural and functional network remain unclear. This research applies graph theory to structural networks and explores the relationship between structural and functional networks. Methods : Forty-five patients with acute ischemic stroke were divided into the PSD group and post-stroke without depression (non-PSD) group respectively and underwent the magnetic resonance imaging scans. Network construction and Module analysis were used to explore the structural connectivity-functional connectivity (SC-FC) coupling of multi-scale brain networks in patients with PSD. Results : Compared with non-PSD, the structural network in PSD was related to the reduction of clustering and the increase of path length, but the degree of modularity was lower. Conclusions : The SC-FC coupling may serve as a biomarker for PSD. The similarity in SC and FC is associated with cognitive dysfunction, retardation, and desperation. Our findings highlighted the distinction in brain structural-functional networks in PSD. Clinical Trial Registration : https://www.clinicaltrials.gov/ct2/show/NCT03256305 , NCT03256305.

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients. Significance What are the origins of resting-state functional connectivity patterns? One dominating view is that they index ongoing cognitive processes. However, this conclusion is in conflict with studies showing that long-range functional connectivity persists after loss of consciousness, possibly reflecting structural connectivity maps. In this work we respond to this question showing that in fact both sources have a clear and separable contribution to resting-state patterns. We show that under anesthesia, the dominating functional configurations have low information capacity and lack negative correlations. Importantly, they are rigid, tied to the anatomical map. Conversely, wakefulness is characterized by the dynamical exploration of a rich, flexible repertoire of functional configurations. These dynamical properties constitute a signature of consciousness.

Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity.

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations. •Resting state Functional Connectivity (FC) displays switching non-stationarity.•Previous whole-brain models reproduce average FC, but not its dynamic switching.•Enhancing the dynamic repertoire of the whole-brain model leads to FC switching.•The simulated FC states are reminiscent of known resting state networks.

•Lead-DBS v3.0 features an end-to-end solution for DBS-based neuroimaging analysis.•New preprocessing tools include WarpDrive and algorithms for electrode localization.•Three novel tools allow mapping of DBS effects to local, tract and network level.•Lead-DBS v3.0 features pathway activation modeling.•Updates are demonstrated in single patients and a retrospective cohort of 51 patients. Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of 'brain disconnection' and 'brain connectivity compensation' to 'brain connectivity decompensation'. In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated. We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network. These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity ; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a ‘common toolbox’ underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.

Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. ►Large-scale functional or structural brain connectivity can be modeled as a network, or graph. ►This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. ►The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. ►To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. ►The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. ►The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data was acquired. ►The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork.

In the published article, there was an error in the Funding statement. The funding information “the Fundamental Research Funds for the Central Universities of Central South University” was erroneously excluded from the Funding statement. The correct Funding statement appears below.