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Journal: Brain connectivity


We sought to determine whether reading a novel causes measurable changes in resting-state connectivity of the brain and how long these changes persist. Incorporating a within-subjects design, participants received resting-state fMRI scans on 19 consecutive days. First, baseline resting state data for a “wash-in” period was taken for each participant for five days. For the next nine days participants read 1/9th of a novel during the evening and resting state data was taken the following morning. Finally, resting state data for a “wash-out” period was taken for five days after the conclusion of the novel. On the days following the reading, significant increases in connectivity were centered on hubs in the left angular/supramarginal gyri and right posterior temporal gyri. These hubs corresponded to regions previously associated with perspective taking and story comprehension, and the changes exhibited a timecourse that decayed rapidly after the completion of the novel. Longterm changes in connectivity, which persisted for several days after the reading, were observed in bilateral somatosensory cortex, suggesting a potential mechanism for “embodied semantics.”

Concepts: Brain, Human brain, Cognition, Cerebral cortex, Cerebrum, Hippocampus, Cerebellum, Neocortex


Resting-state functional magnetic resonance imaging (rs-fMRI) allows one to study brain functional connectivity, partly motivated by evidence that complex disorders, such as Alzheimer’s disease, may have altered functional brain connectivity patterns as compared to healthy subjects. A functional connectivity network describes statistical associations of the neural activities among distinct and distant brain regions. Recently, there is a major interest in group-level functional network analysis, however, there is a relative lack of studies on statistical inference, such as significance testing for group comparisons. In particular, it is still debatable which statistic should be used to measure pairwise associations as the connectivity weights. Many functional connectivity studies have used either (full or marginal) correlations or partial correlations for pairwise associations. This paper investigates the performance of using either correlations or partial correlations for testing group differences in brain connectivity, and {how sparsity levels and topological structures of the connectivity} would influence statistical power to detect group differences. Our results suggest that, in general, testing group differences in networks deviates from estimating networks. For example, high regularization on both covariance matrices and precision matrices may lead to higher statistical power; in particular, optimally selected regularization (e.g. by cross-validation or even at the true sparsity level) on the precision matrices with small estimation errors may have low power. Most importantly, and perhaps surprisingly, using either correlations or partial correlations may give very different testing results, depending on which of the covariance matrices and the precision matrices are sparse. Specifically, if the precision matrices are sparse, presumably and arguably a reasonable assumption, then using correlations often yields much higher powered and more stable testing results than using partial correlations; the conclusion is reversed if the covariance matrices, not the precision matrices, are sparse. These results may have useful implications to future studies on testing functional connectivity differences.

Concepts: Brain, Statistics, Statistical significance, Brain tumor, Magnetic resonance imaging, Statistical hypothesis testing, Effect size, Estimation


Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for Autism Spectrum Disorder (ASD). In this study, we examined the structural connectome of children with ASD using Edge Density Imaging (EDI); and then applied machine leaning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8 to 12 years were included: 14 with ASD and 33 typically developing children (TDC). The Edge Density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging (HARDI). Tract-Based Spatial Statistics (TBSS) was used for voxel-wise comparison and coregistration of ED maps in addition to conventional DTI metrics of Fractional Anisotropy (FA), Mean Diffusivity (MD), and Radial Diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine learning models: naïve Bayes, random forest, support vector machines (SVM), neural networks. For these models, cross-validation was performed with stratified random sampling (×1000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared to those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%), and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD; and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.


Cortical reorganization after stroke is thought to underlie functional improvement. Patterns of reorganization may differ depending on the amount of time since the stroke or the degree of improvement. We investigated these issues in a study of brain connectivity changes with aphasia therapy. Twelve individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement on a repetition test and analyzed effective connectivity during functional magnetic resonance imaging (fMRI) of a speech observation task before and after therapy. Using structural equation modeling, patient networks were compared to a model derived from healthy controls performing the same task. Independent of the amount of time since stroke, patients demonstrating behavioral improvement had networks that reorganized to be more similar to controls in two functional pathways in the left hemisphere. Independent of behavioral improvement, patients with remote infarcts (2 to 7 years post stroke; n=5) also reorganized to more closely resemble controls in one of these pathways. Patients with far-removed injury (>10 years post stroke; n=3) did not show behavioral improvement and, despite similarities to the normative model and overall network heterogeneity, reorganized to be less similar to controls following therapy in a distinct right lateralized pathway. Behavioral improvement following aphasia therapy was associated with connectivity more closely approximating that of healthy controls. Individuals who had a stroke more than a decade before testing also showed plasticity, with a few pathways becoming less like controls, possibly representing compensation. Better understanding of these mechanisms may help direct targeted brain stimulation.

Concepts: Medicine, Brain, Stroke, Traumatic brain injury, Brain tumor, Nuclear magnetic resonance, Magnetic resonance imaging, Structural equation modeling


Abstract We previously demonstrated with fMRI that religious belief depends upon three cognitive dimensions, which can be mapped to activation of specific brain regions. In the present study, we considered the co-activated regions as nodes of three networks corresponding to each Dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory-of-mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses enumerated above. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of Supernatural Agents (SAs) (intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded on semantic knowledge for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca’s to Wernicke’s language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing.

Concepts: Belief, Faith, Human brain, Cerebral cortex, Cerebrum, Frontal lobe, Religion, Semantic memory


Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, though it can cause adverse effects. Recent data suggests that brain RT is associated with dose-dependent cortical atrophy which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n=54, p<0.0001) were all observed in patients post-RT. Decreases in local efficiency (n=54, p = 0.007) and clustering coefficient (n=54, p = 0.005) were seen in regions receiving higher RT doses, including the inferior parietal lobule and rostral anterior cingulate. These findings demonstrate alterations in global and local network topology following RT, characterized by increased segregation of brain regions critical to cognition. These pathological network changes may contribute to the late delayed cognitive impairments observed in many patients following brain RT.

Concepts: Cancer, Oncology, Brain tumor, Cognition, Cerebral cortex, Graph theory, Computer network, Network theory


Working memory is often compromised after traumatic brain injury (TBI). A number of functional and effective connectivity studies investigated the interaction between brain regions during working memory task performance. However, previously used working memory tasks did not allow differentiation of working memory subprocesses such as capacity and manipulation. We used a novel working memory paradigm, CapMan, to investigate effective connectivity associated with the capacity and manipulation subprocesses of working memory in individuals with TBI relative to healthy controls (HCs). CapMan allows independent investigation of brain regions associated with capacity and manipulation, while minimizing the influence of other working memory related sub-processes. Areas of the fronto-parietal working memory network, previously identified in healthy individuals as engaged in capacity and manipulation during CapMan, were analyzed with the Independent Multiple-sample Greedy Equivalence Search (IMaGES; Ramsey et al., 2010) method to investigate the differences in information flow between healthy individuals and individuals with TBI. We predicted that diffuse axonal injury that often occurs after TBI might lead to changes in task-based effective connectivity and result in hyperconnectivity between the regions engaged in task performance. In accordance with this hypothesis, TBI participants showed greater inter-hemispheric connectivity and less coherent information flow from posterior to anterior brain regions compared to HC participants. Thus, the current study provides much needed evidence about the potential mechanism of neurocognitive impairments in individuals affected by TBI.

Concepts: Medicine, Traumatic brain injury, Cognitive psychology, Cognition, The Independent, Task, Diffuse axonal injury, Concussion


Fibromyalgia (FM) is a syndrome characterized by chronic pain without known peripheral causes. Previously we have reported dysfunctional pain inhibitory mechanisms for FM patients during pain administration. In the current study we employed a seed correlation analysis (SCA), independent component analysis (ICA), and an analysis of fractional amplitude of low frequency fluctuations (fALFF) to study differences between a cohort of female fibromyalgia patients and an age- and sex matched healthy control group during a resting state condition. FM patients showed decreased connectivity between thalamus and premotor areas, between the right insula and primary sensorimotor areas, as well as between supramarginal and prefrontal areas. Individual sensitivity to painful pressure was associated with increased connectivity between pain related regions (e.g. insula and thalamus) and midline regions of the default mode network (including posterior cingulate cortex and medial prefrontal cortex) among patients and controls. However, neither ICA nor fALFF revealed any group differences. Our findings suggest that abnormal connectivity patterns between pain related regions and the remaining brain during rest reflect an impaired central mechanism of pain modulation in FM. Weaker coupling between pain regions and prefrontal- and sensorimotor areas might indicate a less efficient system level control of pain circuits. Moreover, our results show that multiple, complementary analytical approaches are valuable for obtaining a more comprehensive characterization of deviant resting state activity. In conclusion, our findings show that FM primarily is associated with decreased connectivity, e.g. between several pain related areas and sensorimotor regions, which could reflect a deficiency in pain regulation.

Concepts: Brain, Cerebrum, Pain, Limbic system, Frontal lobe, Premotor cortex, Prefrontal cortex, Insular cortex


In patients with Alzheimer’s disease (AD) prominent hypometabolism has been observed in brain-regions with minor amyloid load. These hypometabolism only (HO)-areas cannot be explained merely as consequence of local amyloid-toxicity. Aim of this multimodal imaging study was to explore if such HO-phenomenon may be related to pathologies in functionally connected, remote brain-regions. 19 AD-patients and 15 matched controls underwent examinations with [11C]PiB-PET and [18F]FDG-PET. Voxel-based statistical group comparisons were performed to obtain maps of significantly elevated amyloid-burden and reduced cerebral glucose metabolism respectively in patients. An HO-area was identified by subtraction of equally thresholded result-maps (hypometabolism minus amyloid-burden). To identify the network typically functionally connected to this HO-area, it was used as seed-region for a functional connectivity analysis in resting state fMRI data of 17 elderly healthy controls, The resulting intrinsic connectivity network (HO-ICN) was retransferred into the brains of AD-patients to be able to analyze pathologies within this network in the PET-datasets. Most prominent HO-area was detected in the left middle frontal gyrus of AD-patients. The HO-ICN in healthy controls showed major overlap with brain areas significantly affected by both, amyloid-deposition and hypometabolism in patients. This association was substantiated by the results of ROI-based and voxel-wise correlation-analyses, which revealed strong correlations between the degree of hypometabolism within the HO-region and within the HO-ICN. These results support the notion that hypometabolism in brain regions not strongly affected by locoregional amyloid-pathology may be related to on-going pathologies in remote but functionally connected regions, i.e. by reduced neuronal input from these regions.

Concepts: Neuron, Neuroimaging, Human brain, Cerebral cortex, Cerebrum, Frontal lobe, Beta amyloid, Inferior frontal gyrus


Resting-state functional connectivity is one promising biomarker for Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution, and compared performance for AD and MCI identification with multi-voxel pattern analysis. The multi-voxel pattern analysis using a connectivity map of the default mode network showed good performance, with an accuracy of 81.9% for AD and MCI identification within the ADNI, but the classification model obtained from the ADNI failed to classify AD, MCI, and healthy elderly adults from our site, with an accuracy of only 43.1%. In contrast, a functional connectivity index of the medial temporal lobe based on the frequency distribution showed moderate performance, with an accuracy of 76.5 - 80.3% for AD identification within the ADNI. The performance of this index was similar for our data, with an accuracy of 73.9 - 82.6%. The frequency distribution-based index of functional connectivity could be a good biomarker for AD across multiple sites.

Concepts: Alzheimer's disease, Temporal lobe, Face perception, Identification, Lobe, Mild cognitive impairment