Disrupted functional brain connectome in individuals at risk for Alzheimer's disease

Entorhinal tau pathology disrupts hippocampal-prefrontal oscillatory coupling during associative learning

A neural signature of asymptomatic preclinical Alzheimer's disease (AD) is disrupted connectivity between brain regions; however, its underlying mechanisms remain unknown. Here, we tested whether a preclinical pathologic feature, tau aggregation in the entorhinal cortex (EC) is sufficient to disrupt the coordination of local field potentials (LFPs) between its efferent regions. P301L-mutant human tau or green fluorescent protein (GFP) was virally overexpressed in the EC of adult rats. LFPs were recorded from the dorsal hippocampus and prelimbic medial prefrontal cortex while the rats underwent trace eyeblink conditioning where they learned to associate 2 stimuli separated by a short time interval. In GFP-expressing rats, the 2 regions strengthened phase-phase and amplitude-amplitude couplings of theta and gamma oscillations during the interval separating the paired stimuli. Despite normal memory acquisition, this learning-related, inter-region oscillatory coupling was attenuated in the tau-expressing rats while prefrontal phase-amplitude theta-gamma cross-frequency coupling was elevated. Thus, EC tau aggregation caused aberrant long-range circuit activity during associative learning, identifying a culprit for the neural signature of preclinical AD stages.

Abnormal global functional network connectivity and its relationship to medial temporal atrophy in patients with amnestic mild cognitive impairment

Background

Amnestic mild cognitive impairment (aMCI), which is recently considered as a high risk status for developing Alzheimer’s disease (AD), manifests with gray matter atrophy and increased focal functional activity in the medial temporal lobe (MTL). However, the abnormalities of whole-brain functional network connectivity in aMCI and its relationship to medial temporal atrophy (MTA) remain unknown.

Methods

In this study, thirty-six aMCI patients and thirty-five healthy controls (HCs) were recruited. Neuropsychological assessments and MTA visual rating scaling were carried out on all participants. Furthermore, whole brain functional network was constructed at voxel level, and functional connectivity strength (FCS) was computed as the sum of the connections for each node to capture its global integrity. General linear model was used to analyze the FCS values differences between aMCI and HCs. Then, the regions showing significant FCS differences were adopted as the imaging markers for discriminative analysis. Finally, the relationship between FCS values and clinical cognitive scores was correlated in patients with aMCI.

Results

Comparing to HCs, aMCI exhibited significant atrophy in the MTL, while higher FCS values within the bilateral MTL regions and orbitofrontal cortices. Notably, the right hippocampus had the highest classification power, with the area under receiver operating characteristics (ROC) curve (AUC) of 0.790 (confidence interval: 0.678, 0.901). Moreover, FCS values of the right hippocampus and the left temporal pole were positively correlated with the cognitive performance in aMCI.

Conclusion

This study demonstrated significantly structural atrophy and raised global functional integrity in the MTL, suggesting simultaneous disruption and compensation in prodromal AD. Increased intrinsic functional connectivity in the MTL may have the potential to discriminate subjects with tendency to develop AD.

Tackling variability: A multicenter study to provide a gold-standard network approach for frontotemporal dementia

Biomarkers represent a critical research area in neurodegeneration disease as they can contribute to studying potential disease-modifying agents, fostering timely therapeutic interventions, and alleviating associated financial costs. Functional connectivity (FC) analysis represents a promising approach to identify early biomarkers in specific diseases. Yet, virtually no study has tested whether potential FC biomarkers prove to be reliable and reproducible across different centers. As such, their implementation remains uncertain due to multiple sources of variability across studies: the numerous international centers capable conducting FC research vary in their scanning equipment and their samples' socio-cultural background, and, more troublingly still, no gold-standard method exists to analyze FC. In this unprecedented study, we aim to address both issues by performing the first multicenter FC research in the behavioral-variant frontotemporal dementia (bvFTD), and by assessing multiple FC approaches to propose a gold-standard method for analysis. We enrolled 52 bvFTD patients and 60 controls from three international clinics (with different fMRI recording parameters), and three additional neurological patient groups. To evaluate FC, we focused on seed analysis, inter-regional connectivity, and several graph-theory approaches. Only graph-theory analysis, based on weighted-matrices, yielded consistent differences between bvFTD and controls across centers. Also, graph metrics robustly discriminated bvFTD from the other neurological conditions. The consistency of our findings across heterogeneous contexts highlights graph-theory as a potential gold-standard approach for brain network analysis in bvFTD. Hum Brain Mapp 38:3804-3822, 2017. © 2017 Wiley Periodicals, Inc.

MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.

Disrupted Topology of Frontostriatal Circuits Is Linked to the Severity of Insomnia

Insomnia is one of the most common health complaints, with a high prevalence of 30~50% in the general population. In particular, neuroimaging research has revealed that widespread dysfunctions in brain regions involved in hyperarousal are strongly correlated with insomnia. However, whether the topology of the intrinsic connectivity is aberrant in insomnia remains largely unknown. In this study, resting-state functional magnetic resonance imaging (rsfMRI) in conjunction with graph theoretical analysis, was used to construct functional connectivity matrices and to extract the attribute features of the small-world networks in insomnia. We examined the alterations in global and local small-world network properties of the distributed brain regions that are predominantly implicated in the frontostriatal network between 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without insomnia symptoms (NIS). Correlations between the small-world properties and clinical measurements were also generated to identify the differences between the two groups. Both the IS group and the NIS group exhibited a small-worldness topology. Meanwhile, the global topological properties didn't show significant difference between the two groups. By contrast, participants in the IS group showed decreased regional degree and efficiency in the left inferior frontal gyrus (IFG) compared with subjects in the NIS group. More specifically, significantly decreased nodal efficiency in the IFG was found to be negatively associated with insomnia scores, whereas the abnormal changes in nodal betweenness centrality of the right putamen were positively correlated with insomnia scores. Our findings suggested that the aberrant topology of the salience network and frontostriatal connectivity is linked to insomnia, which can serve as an important biomarker for insomnia.

Amyloid and tau PET demonstrate region-specific associations in normal older people

β-amyloid (Aβ) and tau pathology become increasingly prevalent with age, however, the spatial relationship between the two pathologies remains unknown. We examined local (same region) and non-local (different region) associations between these 2 aggregated proteins in 46 normal older adults using [18F]AV-1451 (for tau) and [11C]PiB (for Aβ) positron emission tomography (PET) and 1.5T magnetic resonance imaging (MRI) images. While local voxelwise analyses showed associations between PiB and AV-1451 tracer largely in the temporal lobes, k-means clustering revealed that some of these associations were driven by regions with low tracer retention. We followed this up with a whole-brain region-by-region (local and non-local) partial correlational analysis. We calculated each participant's mean AV-1451 and PiB uptake values within 87 regions of interest (ROI). Pairwise ROI analysis demonstrated many positive PiB-AV-1451 associations. Importantly, strong positive partial correlations (controlling for age, sex, and global gray matter fraction, p<.01) were identified between PiB in multiple regions of association cortex and AV-1451 in temporal cortical ROIs. There were also less frequent and weaker positive associations of regional PiB with frontoparietal AV-1451 uptake. Particularly in temporal lobe ROIs, AV-1451 uptake was strongly predicted by PiB across multiple ROI locations. These data indicate that Aβ and tau pathology show significant local and non-local regional associations among cognitively normal elderly, with increased PiB uptake throughout the cortex correlating with increased temporal lobe AV-1451 uptake. The spatial relationship between Aβ and tau accumulation does not appear to be specific to Aβ location, suggesting a regional vulnerability of temporal brain regions to tau accumulation regardless of where Aβ accumulates.

Comparison of connectivity analyses for resting state EEG data

OBJECTIVE

In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated.

APPROACH

The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory.

MAIN RESULTS

The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization.

SIGNIFICANCE

Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

APOE Genotype Effects on Intrinsic Brain Network Connectivity in Patients with Amnestic Mild Cognitive Impairment

Whether and how the apolipoprotein E (APOE) ε4 genotype specifically modulates brain network connectivity in patients with amnestic mild cognitive impairment (aMCI) remain largely unknown. Here, we employed resting-state (‘task-free’) functional MRI and network centrality approaches to investigate local (degree centrality, DC) and global (eigenvector centrality, EC) functional integrity in the whole-brain connectome in 156 older adults, including 66 aMCI patients (27 ε4-carriers and 39 non-carriers) and 90 healthy controls (45 ε4-carriers and 45 non-carriers). We observed diagnosis-by-genotype interactions on DC in the left superior/middle frontal gyrus, right middle temporal gyrus and cerebellum, with higher values in the ε4-carriers than non-carriers in the aMCI group. We further observed diagnosis-by-genotype interactions on EC, with higher values in the right middle temporal gyrus but lower values in the medial parts of default-mode network in the ε4-carriers than non-carriers in the aMCI group. Notably, these genotype differences in DC or EC were absent in the control group. Finally, the network connectivity DC values were negatively correlated with cognitive performance in the aMCI ε4-carriers. Our findings suggest that the APOE genotype selectively modulates the functional integration of brain networks in patients with aMCI, thus providing important insight into the gene-connectome interaction in this disease.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM

BACKGROUND

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI).

NEW METHOD

Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features.

RESULTS

Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD.

COMPARISON WITH EXISTING METHOD(S)

To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC.

CONCLUSION

Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.

Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder

Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group-level data. Such disruptions have not yet been identified in single-patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross-sectional structural brain network study of 33 treatment-naive, first-episode MDD patients and 33 age-, gender-, and education-matched healthy controls (HCs). Weighted graph-theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole-brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default-mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex-striatum-thalamus-cerebellum and thalamo-hippocampal circuitry. MDD-related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment-naive, first episode MDD patients. Circuit-specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482-2494, 2017. © 2017 Wiley Periodicals, Inc.

What can the topology of white matter structural networks tell us about mild cognitive impairment?

The focus of investigation in cognitive disorders has shifted from single regional motifs toward brain networks. White matter connections collectively form the connectome, and provide the underpinnings of distributed patterns of brain activity. We examine findings about large-scale properties of structural networks in mild cognitive impairment (MCI), discuss these in terms of the mechanism of cognitive decline and evaluate potential clinical implications. Networks of patients with MCI exhibit reduced global efficiency, which associates with cognitive performance. The structural core of the connectome remains relatively unperturbed. Some global measures of network structure in MCI lie on a spectrum between healthy aging and Alzheimer's dementia. Connectomics seems ill-equipped to guide diagnosis, but provides measures suitable for monitoring disease progression and treatment effect.

Modulatory effects of acupuncture on brain networks in mild cognitive impairment patients

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong (EX-HN1), Yintang (EX-HN3), Neiguan (PC6), Taixi (KI3), Fenglong (ST40), and Taichong (LR3), or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Modular-level alterations of structure-function coupling in schizophrenia connectome

Convergent evidences have revealed that schizophrenia is associated with brain dysconnectivity, which leads to abnormal network organization. However, discrepancies were apparent between the structural connectivity (SC) and functional connectivity (FC) studies, and the relationship between structural and functional deficits in schizophrenia remains largely unknown. In this study, resting-state functional magnetic resonance imaging and structural diffusion tensor imaging were performed in 20 patients with schizophrenia and 20 matched healthy volunteers (patients/controls = 19/17 after head motion rejection). Functional and structural brain networks were obtained for each participant. Graph theoretical approaches were employed to parcellate the FC networks into functional modules. The relationships between the entries of SC and FC were estimated within each module to identify group differences and their correlations with clinical symptoms. Although five common functional modules (including the default mode, occipital, subcortical, frontoparietal, and central modules) were identified in both groups, the patients showed a significantly reduced modularity in comparison with healthy participants. Furthermore, we found that schizophrenia-related aberrations of SC-FC coupling exhibited complex patterns among modules. Compared with controls, patients showed an increased SC-FC coupling in the default mode and the central modules. Moreover, significant SC-FC decoupling was demonstrated in the occipital and the subcortical modules, which was associated with longer duration of illness and more severe clinical manifestations of schizophrenia. Taken together, these findings demonstrated that altered module-dependent SC-FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia. Hum Brain Mapp 38:2008-2025, 2017. © 2017 Wiley Periodicals, Inc.

Resting state functional network disruptions in a kainic acid model of temporal lobe epilepsy

We studied the graph topological properties of brain networks derived from resting-state functional magnetic resonance imaging in a kainic acid induced model of temporal lobe epilepsy (TLE) in rats. Functional connectivity was determined by temporal correlation of the resting-state Blood Oxygen Level Dependent (BOLD) signals between two brain regions during 1.5% and 2% isoflurane, and analyzed as networks in epileptic and control rats. Graph theoretical analysis revealed a significant increase in functional connectivity between brain areas in epileptic than control rats, and the connected brain areas could be categorized as a limbic network and a default mode network (DMN). The limbic network includes the hippocampus, amygdala, piriform cortex, nucleus accumbens, and mediodorsal thalamus, whereas DMN involves the medial prefrontal cortex, anterior and posterior cingulate cortex, auditory and temporal association cortex, and posterior parietal cortex. The TLE model manifested a higher clustering coefficient, increased global and local efficiency, and increased small-worldness as compared to controls, despite having a similar characteristic path length. These results suggest extensive disruptions in the functional brain networks, which may be the basis of altered cognitive, emotional and psychiatric symptoms in TLE.

Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure

Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra-network connectivity of multiple resting-state networks (RSNs); however, whether impairment is present in inter-network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic-clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting-state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE-GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, k-mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state-specific FNC disruptions were observed in IGE-GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE-GTCS from controls with an accuracy of 77.91% (P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE-GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957-973, 2017. © 2016 Wiley Periodicals, Inc.

Simulating the Evolution of Functional Brain Networks in Alzheimer's Disease: Exploring Disease Dynamics from the Perspective of Global Activity

Functional brain connectivity is altered during the pathological processes of Alzheimer's disease (AD), but the specific evolutional rules are insufficiently understood. Resting-state functional magnetic resonance imaging indicates that the functional brain networks of individuals with AD tend to be disrupted in hub-like nodes, shifting from a small world architecture to a random profile. Here, we proposed a novel evolution model based on computational experiments to simulate the transition of functional brain networks from normal to AD. Specifically, we simulated the rearrangement of edges in a pathological process by a high probability of disconnecting edges between hub-like nodes, and by generating edges between random pair of nodes. Subsequently, four topological properties and a nodal distribution were used to evaluate our model. Compared with random evolution as a null model, our model captured well the topological alteration of functional brain networks during the pathological process. Moreover, we implemented two kinds of network attack to imitate the damage incurred by the brain in AD. Topological changes were better explained by 'hub attacks' than by 'random attacks', indicating the fragility of hubs in individuals with AD. This model clarifies the disruption of functional brain networks in AD, providing a new perspective on topological alterations.

A Triple Network Connectivity Study of Large-Scale Brain Systems in Cognitively Normal APOE4 Carriers

The triple network model, consisting of the central executive network (CEN), salience network (SN) and default mode network (DMN), has been recently employed to understand dysfunction in core networks across various disorders. Here we used the triple network model to investigate the large-scale brain networks in cognitively normal apolipoprotein e4 (APOE4) carriers who are at risk of Alzheimer’s disease (AD). To explore the functional connectivity for each of the three networks and the effective connectivity among them, we evaluated 17 cognitively normal individuals with a family history of AD and at least one copy of the APOE4 allele and compared the findings to those of 12 individuals who did not carry the APOE4 gene or have a family history of AD, using independent component analysis (ICA) and Bayesian network (BN) approach. Our findings indicated altered within-network connectivity that suggests future cognitive decline risk, and preserved between-network connectivity that may support their current preserved cognition in the cognitively normal APOE4 allele carriers. The study provides novel sights into our understanding of the risk factors for AD and their influence on the triple network model of major psychopathology.

Opportunities and Challenges for Psychiatry in the Connectomic Era

Most major psychiatric disorders arise from disturbances of anatomically distributed neural systems rather than isolated dysfunction of circumscribed brain regions. The past decade has witnessed rapid advances in our capacity to measure, map, and model neural connectivity in diverse species and at different resolution scales, from the level of individual neurons and synapses to large-scale systems spanning the entire brain. In this review, we consider how these techniques, when grounded in the theory and methods of network science, can contribute to a biological understanding of mental illness. We focus in particular on attempts to accurately map brain network disturbances in clinical populations and to model the mechanistic causes of these changes. This work suggests that pathology within highly connected hub regions is a consistent finding across a broad array of phenotypically diverse disorders, and that disparate changes in brain network organization can sometimes be explained by a surprisingly small and simple set of mechanisms.

Repeated acupuncture treatments modulate amygdala resting state functional connectivity of depressive patients

As a widely-applied alternative therapy, acupuncture is gaining popularity in Western society. One challenge that remains, however, is incorporating it into mainstream medicine. One solution is to combine acupuncture with other conventional, mainstream treatments. In this study, we investigated the combination effect of acupuncture and the antidepressant fluoxetine, as well as its underlying mechanism using resting state functional connectivity (rsFC) in patients with major depressive disorders. Forty-six female depressed patients were randomized into a verum acupuncture plus fluoxetine or a sham acupuncture plus fluoxetine group for eight weeks. Resting-state fMRI data was collected before the first and last treatments. Results showed that compared with those in the sham acupuncture treatment, verum acupuncture treatment patients showed 1) greater clinical improvement as indicated by Montgomery-Åsberg Depression Rating Scale (MADRS) and Self-Rating Depression Scale (SDS) scores; 2) increased rsFC between the left amygdala and subgenual anterior cingulate cortex (sgACC)/preguenual anterior cingulate cortex (pgACC); 3) increased rsFC between the right amygdala and left parahippocampus (Para)/putamen (Pu). The strength of the amygdala-sgACC/pgACC rsFC was positively associated with corresponding clinical improvement (as indicated by a negative correlation with MADRS and SDS scores). Our findings demonstrate the additive effect of acupuncture to antidepressant treatment and suggest that this effect may be achieved through the limbic system, especially the amygdala and the ACC.

RichMind: A Tool for Improved Inference from Large-Scale Neuroimaging Results

As the use of large-scale data-driven analysis becomes increasingly common, the need for robust methods for interpreting a large number of results increases. To date, neuroimaging attempts to interpret large-scale activity or connectivity results often turn to existing neural mapping based on previous literature. In case of a large number of results, manual selection or percent of overlap with existing maps is frequently used to facilitate interpretation, often without a clear statistical justification. Such methodology holds the risk of reporting false positive results and overlooking additional results. Here, we propose using enrichment analysis for improving the interpretation of large-scale neuroimaging results. We focus on two possible cases: position group analysis, where the identified results are a set of neural positions; and connection group analysis, where the identified results are a set of neural position-pairs (i.e. neural connections). We explore different models for detecting significant overrepresentation of known functional brain annotations using simulated and real data. We implemented our methods in a tool called RichMind, which provides both statistical significance reports and brain visualization. We demonstrate the abilities of RichMind by revisiting two previous fMRI studies. In both studies RichMind automatically highlighted most of the findings that were reported in the original studies as well as several additional findings that were overlooked. Hence, RichMind is a valuable new tool for rigorous inference from neuroimaging results.

Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

Genetic variants in Alzheimer disease - molecular and brain network approaches

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)—each essential parameters in wavelet-based methods—on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

Regional homogeneity changes in amnestic mild cognitive impairment patients

OBJECTIVE

Regional Homogeneity (ReHo) measures the local coherence of spontaneous brain activity, and it is sensitive to detect aberrant local functional connectivity of brain region. We tried to explore the activity of brain network by ReHo method in amnestic mild cognitive impairment (aMCI) patients and examine the impact of regional brain atrophy on the functional results.

METHODS

Data of both structural magnetic resonance images (MRI) and resting-state functional MRI scans were collected from 36 aMCI patients and 46 age-matched healthy controls.

RESULTS

Compared with the HC subjects, the aMCI patients showed significant decreased ReHo areas in the right inferior parietal lobule (IPL), left posterior cingulate cortex/precuneus (PCC/PCu), left inferior temporal gyrus (ITG), right supramarginal gyrus (SMG), right fusiform gyrus (FG), bilateral lentiform nucleus (LN) and right cerebellum posterior lobe, with the right IPL being the most significant area. In addition, the aMCI group also had some significant increased ReHo areas in the right medial frontal gyrus (MFG), bilateral postcentral gyrus (PoCG), left cuneus and right lingual gyrus (LG), possibly reflective of some underlining compensatory mechanism. Furthermore, in the aMCI patients, we found the ReHo index of the left PCC was positively correlated with the AVLT-Immediate Recall scores, while the ReHo index of the left cuneus was negatively correlated with the MMSE scores. In addition, we found that after regressing out the identified regional brain atrophy, the significant correlations between fitted ReHo index and clinical variables still remained.

CONCLUSIONS

Our study indicated that aMCI patients showed significant abnormal local coherence of biological activity in resting state and ReHo could serve as a sensitive biomarker in functional imaging studies of aMCI.

Abnormal topological organization of the white matter network in Mandarin speakers with congenital amusia

Congenital amusia is a neurogenetic disorder that mainly affects the processing of musical pitch. Brain imaging evidence indicates that it is associated with abnormal structural and functional connections in the fronto-temporal region. However, a holistic understanding of the anatomical topology underlying amusia is still lacking. Here, we used probabilistic diffusion tensor imaging tractography and graph theory to examine whole brain white matter structural connectivity in 31 Mandarin-speaking amusics and 24 age- and IQ-matched controls. Amusics showed significantly reduced global connectivity, as indicated by the abnormally decreased clustering coefficient (C_p) and increased normalized shortest path length (λ) compared to the controls. Moreover, amusics exhibited enhanced nodal strength in the right inferior parietal lobule relative to controls. The co-existence of the lexical tone deficits was associated with even more deteriorated global network efficiency in amusics, as suggested by the significant correlation between the increments in normalized shortest path length (λ) and the insensitivity in lexical tone perception. Our study is the first to reveal reduced global connectivity efficiency in amusics as well as an increase in the global connectivity cost due to the co-existed lexical tone deficits. Taken together these results provide a holistic perspective on the anatomical substrates underlying congenital amusia.

Hyper-connectivity of functional networks for brain disease diagnosis

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.

Alterations of Functional and Structural Networks in Schizophrenia Patients with Auditory Verbal Hallucinations

Background: There have been many attempts at explaining the underlying neuropathological mechanisms of auditory verbal hallucinations (AVH) in schizophrenia on the basis of regional brain changes, with the most consistent findings being that AVH are associated with functional and structural impairments in auditory and speech-related regions. However, the human brain is a complex network and the global topological alterations specific to AVH in schizophrenia remain unclear.

Methods: Thirty-five schizophrenia patients with AVH, 41 patients without AVH, and 50 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The whole-brain functional and structural networks were constructed and analyzed using graph theoretical approaches. Inter-group differences in global network metrics (including small-world properties and network efficiency) were investigated.

Results: We found that three groups had a typical small-world topology in both functional and structural networks. More importantly, schizophrenia patients with and without AVH exhibited common disruptions of functional networks, characterized by decreased clustering coefficient, global efficiency and local efficiency, and increased characteristic path length; structural networks of only schizophrenia patients with AVH showed increased characteristic path length compared with those of healthy controls.

Conclusion: Our findings suggest that less “small-worldization” and lower network efficiency of functional networks may be an independent trait characteristic of schizophrenia, and regularization of structural networks may be the underlying pathological process engaged in schizophrenic AVH symptom expression.

Diffusion tensor imaging and functional MRI

The advances in diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) over the last 20 years have vastly contributed to improving the understanding of the brain structure and function in patients with many diseases of the central nervous system (CNS). DWI is commonly used, for instance, in the diagnostic workup of stroke, CNS neoplasia, and rapidly progressive dementia cases. The new DTI methods provide more specific information about the most destructive aspects of tumors, neurodegenerative dementia, and multiple sclerosis pathology and give a more complete picture of the complex pathologic mechanisms of these conditions. More recently, fMRI has provided insight to the mechanisms of brain adaptation and plasticity to damage related to many neurologic conditions and has further extended our ability to understand the functional significance of pathologic changes in these diseases. Although at present fMRI does not have a role in the diagnosis, routine assessment, and monitoring of neurologic diseases, significant efforts are under way in order to achieve harmonization of both acquisition and postprocessing procedures, which are likely to contribute to a significant change of the clinical scenario.

Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer's Disease

Individuals diagnosed with mild cognitive impairment (MCI) are at high risk of transition to Alzheimer's disease (AD). However, little is known about functional characteristics of the conversion from MCI to AD. Resting-state functional magnetic resonance imaging was performed in 25 AD patients, 31 MCI patients, and 42 well-matched normal controls at baseline. Twenty-one of the 31 MCI patients converted to AD at approximately 24 months of follow-up. Functional connectivity strength (FCS) and seed-based functional connectivity analyses were used to assess the functional differences among the groups. Compared to controls, subjects with MCI and AD showed decreased FCS in the default-mode network and the occipital cortex. Importantly, the FCS of the left angular gyrus and middle occipital gyrus was significantly lower in MCI-converters as compared with MCI-nonconverters. Significantly decreased functional connectivity was found in MCI-converters compared to nonconverters between the left angular gyrus and bilateral inferior parietal lobules, dorsolateral prefrontal and lateral temporal cortices, and the left middle occipital gyrus and right middle occipital gyri. We demonstrated gradual but progressive functional changes during a median 2-year interval in patients converting from MCI to AD, which might serve as early indicators for the dysfunction and progression in the early stage of AD.

Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month

Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.

Different Scales of Cortical Organization are Selectively Targeted in the Progression to Alzheimer's Disease

Previous studies have shown that the topological organization of the cerebral cortex is altered in Alzheimer's disease (AD). However, it remains unknown whether different levels of the cortical hierarchy are homogeneously affected during disease progression, and which of these levels are mostly involved in the breakdown of metabolic (functional) connectivity. To fulfill these goals, we acquired structural magnetic resonance images (MRI) and positron emission tomography (PET) with the radiotracer 18F-fludeoxyglucose (FDG) in 29 healthy old (HO) adults, 29 amnestic mild cognitive impairment (aMCI) and 29 mild AD patients. Structural and metabolic connections were obtained from inter-regional correlations of cortical thickness and glucose consumption, respectively. Results showed that AD and HO groups differed at all levels of cortical organization (i.e. whole cortex, hemisphere, lobe and node), whereas differences among the three groups were only evident at the lobe and node levels. The correlation between structural and metabolic connectivity (F-S coupling) was also disturbed during AD progression, affecting to different connectivity scales: it decreased at the local level, revealing a progressive increase of metabolic connections in those local communities with fewer structural connections; whereas it increased at the global level, likely due to a parallel reduction of cortical thickness and glucose consumption between long-distance cortical regions. Collectively, these results reveal that different levels of cortical organization are selectively affected during the transition from normal aging to dementia, which could be helpful to track cortical dysfunctions in the progression to AD.

Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study

Schizophrenia is a condition marked by a disrupted brain functional network. In schizophrenia, the brain network is characterized by reduced distributed information processing efficiency; however, the correlation between information processing efficiency and the symptomatology of schizophrenia remains unclear. Few studies have examined path length efficiencies in schizophrenia. In this study, we examined small-world network metrics computed from resting state functional magnetic resonance imaging data collected from 49 patients with schizophrenia and 28 healthy people. We calculated brain network efficiency using graph theoretical analysis of the networks of brain areas, as defined by the Automated Anatomical Labeling parcellation scheme, and investigated efficiency correlations by using the 5-factor model of psychopathology, which considers the various domains of schizophrenic symptoms and might also consider discrete pathogenetic processes. The global efficiency of the resting schizophrenic brains was lower than that of the healthy controls, but local efficiency did not differ between the groups. The severity of psychopathology, negative symptoms, and depression and anxiety symptoms were correlated with global efficiency in schizophrenic brains. The severity of psychopathology was correlated with increased network efficiency from short-range connections, but not networks from long-range connections. Our findings indicate that schizophrenic psychopathology is correlated with brain network information processing efficiency.

Magnetite-Amyloid-β deteriorates activity and functional organization in an in vitro model for Alzheimer's disease

The understanding of the key mechanisms behind human brain deterioration in Alzheimer' disease (AD) is a highly active field of research. The most widespread hypothesis considers a cascade of events initiated by amyloid-β peptide fibrils that ultimately lead to the formation of the lethal amyloid plaques. Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role. To shed light on the action of magnetite and amyloid-β in the deterioration of neuronal circuits, we investigated their capacity to alter spontaneous activity patterns in cultured neuronal networks. Using a versatile experimental platform that allows the parallel monitoring of several cultures, the activity in controls was compared with the one in cultures dosed with magnetite, amyloid-β and magnetite-amyloid-β complex. A prominent degradation in spontaneous activity was observed solely when amyloid-β and magnetite acted together. Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex. Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment

BACKGROUND

The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown.

AIMS

To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy non-depressed controls.

METHODS

79 elderly participants (LLD (n=23), aMCI (n=18), comorbid LLD and aMCI (n=13), and controls (n=25)) completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity MRI data.

RESULTS

LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organisation was most variable in the comorbid group, followed by patients with LLD-only. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance.

CONCLUSIONS

Considering the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment.

Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review

The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.