Multiple Resting-state Networks Research Articles

Resting state network plasticity related to picture naming in low-grade glioma patients before and after resection.

The dynamic connectome perspective states that brain functions arise from the functional integration of distributed and/or partly overlapping networks. Diffuse low-grade gliomas (DLGG) have a slow infiltrating character. Here we addressed whether and how anatomical disconnection following DLGG growth and resection might interfere with functional resting-state connectivity, specifically in relation to picture naming. Thirty-nine native French persons with a left DLGG were included. All underwent awake surgical resection of the tumor using direct brain electrostimulation to preserve critical eloquent regions. The anatomical disconnectivity risk following the DLGG volume and the resection, and the functional connectivity of resting-state fMRI images in relation to picture naming were evaluated prior to and three months after surgery. Resting-state connectivity patterns were compared with nineteen healthy controls. It was demonstrated that picture naming was strongly dependent on the semantic network that emerged from the integration and interaction of regions within multiple resting-state brain networks, in which their specific role could be explained in the light of the broader resting-state network they take part in. It emphasized the importance of a whole brain approach with specific clinical data input, during resting-state analysis in case of lesion. Adaptive plasticity was found in secondary regions, functionally connected to regions close to the tumor and/or cavity, marked by an increased connectivity of the right and left inferior parietal lobule with the left inferior temporal gyrus. In addition, an important role was identified for the superior parietal lobe, connected with the frontal operculum, suggesting functional compensation by means of attentional resources in order to name a picture via recruitment of the frontoparietal attention network.

Applying a network framework to the neurobiology of reading and dyslexia

BackgroundThere is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading.MethodsFirst, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain’s tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions.ResultsWe found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual’s brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas.ConclusionsThese results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

Dysconnectivity Involving Multiple Resting-State Networks Associated with Cognitive and Emotional Functions in Long-Term Sensorineural Hearing Loss Patients

Background: Sensorineural hearing loss (SNHL) is accepted to involve wide-ranging functional reorganization, which is associated with accumulating risk of cognitive and emotional dysfunction. The coordination of multiple functional networks supports normal brain functions. However, the evaluation of the functional connectivity (FC) pattern involving multiple resting-state networks (RSNs), and the correlations between the functional remodeling of RSNs and the potential cognitive or emotional impairments in SNHL have never been conducted. Methods: 30 long-term bilateral SNHL patients and 39 well-matched healthy controls were recruited for assessment of resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological test. Findings: Using independent component analysis (ICA), 11 RSNs were identified. Relative to the healthy controls, the SNHL population presented apparent abnormalities of intra-network FC involving right frontoparietal network (rFPN), posterior temporal network (pTMN) and sensory motor network (SMN). Disrupted between-network FC were also revealed in the SNHL patients across both higher-order cognitive control networks and multiple sensory networks. Eight of the eleven RSNs showed altered functional synchronization using a seed network to whole brain FC method, particularly in the ventromedial prefrontal cortex (vmPFC). Besides, these functional abnormalities were correlated with cognition- and emotion-related performances. Interpretations: These findings supported our hypotheses that long-term SNHL involves notable dysconnectivity of multiple RSNs. This study provided important insights into the pathophysiological mechanisms of SNHL, and shed more lights on the neural substrates underlying the possible cognitive and emotional dysfunctions following SNHL, which would make sense to find the potential biomarker for clinical applications. Funding: This work is funded by the National Natural Science Foundation of China (NOs. 81520108015 and 81230034), the Fundamental Research Funds for the Central Universities (NO. 2242018k30004) and the National Key Basic Research Program of China (NOs. 2013CB733800 and 2013CB733803). Declaration of Interest: None of the materials in this manuscript has been published. All the authors have approved the manuscript. There are no potential conflicts of interests to declare. Ethical Approval: This work was proved by the Ethics Committee of Affiliated Zhongda Hospital of Southeast University, and the informed consent was obtained from each of the participants before the experiments.

An Inverse U-Shaped Curve of Resting-State Networks in Individuals at High Risk of Alzheimer's Disease.

Higher functional connectivity (FC) in resting-state networks has been shown in individuals at risk of Alzheimer's disease (AD) by many studies. However, the longitudinal trajectories of the FC remain unknown. The present 35-month follow-up study aimed to explore longitudinal changes in higher FC in multiple resting-state networks in subjects with the apolipoprotein E ε4 allele (ApoE4) and/or amnestic mild cognitive impairment (aMCI). Fifty-one subjects with aMCI and 64 cognitively normal (CN) subjects underwent neuropsychological tests and resting-state functional magnetic resonance imaging (fMRI) scans twice from April 2011 to June 2015. Subjects were divided into 4 groups according to diagnosis and ApoE4 status. The CN non-ApoE4 group served as a control group, and other groups served as AD risk groups. The cross-sectional and longitudinal patterns of multiple resting-state networks, including default mode network, hippocampus network, executive control network, and salience network, were explored by comparing FC data between groups and between time points, respectively. At baseline, compared with the control group, the AD risk groups showed higher FC with 8 regions in multiple networks. At follow-up, 6 of the regions displayed longitudinally decreased FC in AD risk groups. In contrast, the FC with all of these regions was maintained in the control group. Notably, among the 3 risk groups, most of the higher FC at baseline (5 of the 8 regions) and longitudinally decreased FC at follow-up (4 of the 6 regions) were shown in the aMCI ApoE4 group. Higher resting-state FC is followed by a decline in subjects at AD risk, and this inverse U-shaped trajectory is more notable in subjects with higher risk.

Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome

ObjectiveThis study examined the altered patterns of functional connectivity in task-positive resting state networks in failed back surgery syndrome (FBSS) patients compared to healthy controls using functional magnetic resonance imaging (fMRI). This work stems from a previous study in which alterations in the task-negative default mode network were investigated. DesignParticipants underwent a 7-minute resting state fMRI scan in which they lay still, with eyes closed, in the absence of a task. SettingScanning took place at the National Research Council's 3Tesla MRI magnet in Winnipeg, Canada. SubjectsFourteen patients with FBSS and age- and gender-matched controls participated in this study. Three patients were removed from the analyses due to image artefact (n=1) and effective pain treatment (n=2). Eleven patients (5 female, mean age 52.7 years) and their matched controls were included in the final analyses. MethodsResting state fMRI data were analyzed using an independent component analysis, yielding three resting state networks of interest: the salience network (SN), involved in detection of external stimuli, central executive network (CEN), involved in cognitions, and sensorimotor network (SeN), involved in sensory and motor integration. Analysis of Variance contrasts were performed for each network, comparing functional connectivity differences between FBSS patients and healthy controls. ResultsAlterations were observed in all three resting state networks, primarily relating to pain and its processing in the FBSS group. Specifically, compared to healthy controls, FBSS patients demonstrated increased functional connectivity in the anterior cingulate cortex within the SN, medial frontal gyrus in the CEN, and precentral gyrus within the SeN. FBSS patients also demonstrated decreased functional connectivity in the medial frontal gyrus in the SeN compared to healthy controls. Interestingly, we also observed internetwork functional connectivity in the SN and SeN. ConclusionsFBSS is associated with altered patterns of functional connectivity in the SN, CEN, and SeN. Taken together with our previous work, this reveals that a chronic pain condition can have a dramatic effect on the connectivity of multiple resting state networks. ImplicationsThese data suggest that a chronic pain condition—FBSS—is associated with disruptions to networks of functional connectivity in brain areas that are involved in numerous functions, including pain processing, sensation, and movement. It is possible that the alterations in these networks may contribute to other common chronic pain comorbidities, such as disrupted cognitions or anxiety. Previous research shows that during experimentally-induced pain, these networks can return to initial levels of functioning, indicating that these functional alterations are likely not permanent.

Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury

The aim of this study was to explore modifications of functional connectivity in multiple resting-state networks (RSNs) after moderate to severe traumatic brain injury (TBI) and evaluate the relationship between functional connectivity patterns and cognitive abnormalities. Forty-three moderate/severe TBI patients and 34 healthy controls (HC) underwent resting-state fMRI. Group ICA was applied to identify RSNs. Between-subject analysis was performed using dual regression. Multiple linear regressions were used to investigate the relationship between abnormal connectivity strength and neuropsychological outcome. Forty (93%) TBI patients showed moderate disability, while 2 (5%) and 1 (2%) upper severe disability and low good recovery, respectively. TBI patients performed worse than HC on the domains attention and language. We found increased connectivity in sensorimotor, visual, default mode (DMN), executive, and cerebellar RSNs after TBI. We demonstrated an effect of connectivity in the sensorimotor RSN on attention (p < 10−3) and a trend towards a significant effect of the DMN connectivity on attention (p = 0.058). A group-by-network interaction on attention was found in the sensorimotor network (p = 0.002). In TBI, attention was positively related to abnormal connectivity within the sensorimotor RSN, while in HC this relation was negative. Our results show altered patterns of functional connectivity after TBI. Attention impairments in TBI were associated with increased connectivity in the sensorimotor network. Further research is needed to test whether attention in TBI patients is directly affected by changes in functional connectivity in the sensorimotor network or whether the effect is actually driven by changes in the DMN.

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.

Time related effects on functional brain connectivity after serotonergic and cholinergic neuromodulation.

Psychopharmacological research, if properly designed, may offer insight into both timing and area of effect, increasing our understanding of the brain's neurotransmitter systems. For that purpose, the acute influence of the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was repeatedly measured in 12 healthy young volunteers with resting state functional magnetic resonance imaging (RS‐fMRI). Eighteen RS‐fMRI scans were acquired per subject during this randomized, double blind, placebo‐controlled, crossover study. Within‐group comparisons of voxelwise functional connectivity with 10 functional networks were examined (P < 0.05, FWE‐corrected) using a non‐parametric multivariate approach with cerebrospinal fluid, white matter, heart rate, and baseline measurements as covariates. Although both compounds did not change cognitive performance on several tests, significant effects were found on connectivity with multiple resting state networks. Serotonergic stimulation primarily reduced connectivity with the sensorimotor network and structures that are related to self‐referential mechanisms, whereas galantamine affected networks and regions that are more involved in learning, memory, and visual perception and processing. These results are consistent with the serotonergic and cholinergic trajectories and their functional relevance. In addition, this study demonstrates the power of using repeated measures after drug administration, which offers the chance to explore both combined and time specific effects. Hum Brain Mapp 38:308–325, 2017. © 2016 Wiley Periodicals, Inc.

Cognitive Decline and Reorganization of Functional Connectivity in Healthy Aging: The Pivotal Role of the Salience Network in the Prediction of Age and Cognitive Performances.

Normal aging is related to a decline in specific cognitive processes, in particular in executive functions and memory. In recent years a growing number of studies have focused on changes in brain functional connectivity related to cognitive aging. A common finding is the decreased connectivity within multiple resting state networks, including the default mode network (DMN) and the salience network. In this study, we measured resting state activity using fMRI and explored whether cognitive decline is related to altered functional connectivity. To this end we used a machine learning approach to classify young and old participants from functional connectivity data. The originality of the approach consists in the prediction of the performance and age of the subjects based on functional connectivity by using a machine learning approach. Our findings showed that the connectivity profile between specific networks predicts both the age of the subjects and their cognitive abilities. In particular, we report that the connectivity profiles between the salience and visual networks, and the salience and the anterior part of the DMN, were the features that best predicted the age. Moreover, independently of the age of the subject, connectivity between the salience network and various specific networks (i.e., visual, frontal) predicted episodic memory skills either based on a standard assessment or on an autobiographical memory task, and short-term memory binding. Finally, the connectivity between the salience and the frontal networks predicted inhibition and updating performance, but this link was no longer significant after removing the effect of age. Our findings confirm the crucial role of episodic memory and executive functions in cognitive aging and suggest a pivotal role of the salience network in neural reorganization in aging.

An examination of the default mode network in individuals with autonomous sensory meridian response (ASMR)

ABSTRACTAutonomous Sensory Meridian Response (ASMR) is a perceptual condition in which specific visual and auditory stimuli consistently trigger tingling sensations on the scalp and neck, sometimes spreading to the back and limbs. These triggering stimuli are often social, almost intimate, in nature (e.g., hearing whispering, or watching someone brush her hair), and often elicit a calm and positive emotional state. Surprisingly, despite its prevalence in the general population, no published study has examined the neural underpinnings of ASMR. In the current study, the default mode network (DMN) of 11 individuals with ASMR was contrasted to that of 11 matched controls. The results indicated that the DMN of individuals with ASMR showed significantly less functional connectivity than that of controls. The DMN of individuals with ASMR also demonstrated increased connectivity between regions in the occipital, frontal, and temporal cortices, suggesting that ASMR was associated with a blending of multiple resting-state networks. This atypical functional connectivity likely influences the unique sensory-emotional experiences associated with ASMR.

Clinical Resting-state fMRI in the Preoperative Setting: Are We Ready for Prime Time?

The purpose of this manuscript is to provide an introduction to resting-state functional magnetic resonance imaging (RS-fMRI) and to review the current application of this new and powerful technique in the preoperative setting using our institute's extensive experience. RS-fMRI has provided important insights into brain physiology and is an increasingly important tool in the clinical setting. As opposed to task-based functional MRI wherein the subject performs a task while being scanned, RS-fMRI evaluates low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal while the subject is at rest. Multiple resting state networks (RSNs) have been identified, including the somatosensory, language, and visual networks, which are of primary importance for presurgical planning. Over the past 4 years, we have performed over 300 RS-fMRI examinations in the clinical setting and these have been used to localize eloquent somatosensory and language cortices before brain tumor resection. RS-fMRI is particularly useful in this setting for patients who are unable to cooperate with the task-based paradigm, such as young children or those who are sedated, paretic, or aphasic.Although RS-fMRI is still investigational, our experience indicates that this method is ready for clinical application in the presurgical setting.

Functional resting-state fMRI connectivity correlates with serum levels of the S100B protein in the acute phase of traumatic brain injury.

The S100B protein is an intra-cellular calcium-binding protein that mainly resides in astrocytes in the central nervous system. The serum level of S100B is used as biomarker for the severity of brain damage in traumatic brain injury (TBI) patients. In this study we investigated the relationship between intrinsic resting-state brain connectivity, measured 1–22 days (mean 8 days) after trauma, and serum levels of S100B in a patient cohort with mild-to-severe TBI in need of neuro-intensive care in the acute phase. In line with previous investigations, our results show that the peak level of S100B acquired during the acute phase of TBI was negatively correlated with behavioral measures (Glasgow Outcome Score, GOS) of functional outcome assessed 6 to 12 months post injury. Using a multi-variate pattern analysis-informed seed-based correlation analysis, we show that the strength of resting-state brain connectivity in multiple resting-state networks was negatively correlated with the peak of serum levels of S100B. A negative correspondence between S100B peak levels recorded 12–36 h after trauma and intrinsic connectivity was found for brain regions located in the default mode, fronto-parietal, visual and motor resting-state networks. Our results suggest that resting-state brain connectivity measures acquired during the acute phase of TBI is concordant with results obtained from molecular biomarkers and that it may hold a capacity to predict long-term cognitive outcome in TBI patients.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome.

Challenges in the reproducibility of clinical studies with resting state fMRI: An example in early Parkinson's disease

Resting state fMRI (rfMRI) is gaining in popularity, being easy to acquire and with promising clinical applications. However, rfMRI studies, especially those involving clinical groups, still lack reproducibility, largely due to the different analysis settings. This is particularly important for the development of imaging biomarkers. The aim of this work was to evaluate the reproducibility of our recent study regarding the functional connectivity of the basal ganglia network in early Parkinson's disease (PD) (Szewczyk-Krolikowski et al., 2014). In particular, we systematically analysed the influence of two rfMRI analysis steps on the results: the individual cleaning (artefact removal) of fMRI data and the choice of the set of independent components (template) used for dual regression.Our experience suggests that the use of a cleaning approach based on single-subject independent component analysis, which removes non neural-related sources of inter-individual variability, can help to increase the reproducibility of clinical findings. A template generated using an independent set of healthy controls is recommended for studies where the aim is to detect differences from a “healthy” brain, rather than an “average” template, derived from an equal number of patients and controls. While, exploratory analyses (e.g. testing multiple resting state networks) should be used to formulate new hypotheses, careful validation is necessary before promising findings can be translated into useful biomarkers.

Experimental endotoxemia induces increased functional connectivity between the thalamus and the default mode network in healthy men

While functional MRI studies revealed altered task-related BOLD responses during experimental endotoxemia, little is known how systemic inflammation affects resting state activity of the brain. In this randomized, placebo-controlled study, healthy men received either low-dose (0.4 ng/kg) lipopolysaccharide (LPS) (N = 20) or saline (N = 25). Resting state activity was measured at baseline and 3.5 h post-injection. In multi-subject independent component analysis (ICA), 13 out of 30 components were identified which displayed the common networks assigned to resting state brain activity. Dual regression analyses revealed that LPS-treated subjects exhibited distinct patterns of connectivity within multiple resting state networks, including the somatosensory network compared to baseline and the fronto-parietal network compared to placebo. Seed based resting state analysis showed greater functional connectivity between left thalamus and right posterior insula, left posterior cingulate cortex (BA 31), left precuneus, and cerebellum (bilateral) after LPS injection compared to placebo. Together, our data indicate that resting state activity of the brain is altered during systemic inflammation, including an increased functional connectivity between the thalamus and other brain regions which are part of the default mode network. This may reflect an increased integrative processing of interoceptive input arising from the vagus/brainstem, aiming to maintain homeostasis, and to promote a more internally focused self-reflection during states of inflammation. It remains to be determined if LPS-induced alterations in resting state parameters are associated with immune-mediated mood impairments.

Estimating Large-Scale Network Convergence in the Human Functional Connectome

The study of resting-state networks provides an informative paradigm for understanding the functional architecture of the human brain. Although investigating specialized resting-state networks has led to significant advances in our understanding of brain organization, the manner in which information is integrated across these networks remains unclear. Here, we have developed and validated a data-driven methodology for describing the topography of resting-state network convergence in the human brain. Our results demonstrate the importance of an ensemble of cortical and subcortical regions in supporting the convergence of multiple resting-state networks, including the rostral anterior cingulate, precuneus, posterior cingulate cortex, posterior parietal cortex, dorsal prefrontal cortex, along with the caudate head, anterior claustrum, and posterior thalamus. In addition, we have demonstrated a significant correlation between voxel-wise network convergence and global brain connectivity, emphasizing the importance of resting-state network convergence in facilitating global brain communication. Finally, we examined the convergence of systems within each of the individual resting-state networks in the brain, revealing the heterogeneity by which individual resting-state networks balance the competing demands of specialized processing against the integration of information. Together, our results suggest that the convergence of resting-state networks represents an important organizational principle underpinning systems-level integration in the human brain.

The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain

The large-scale functional MRI connectome of the human brain is composed of multiple resting-state networks (RSNs). However, the network dynamics, such as integration and segregation between and within RSNs is largely unknown. To address this question we created high-resolution “frequency graphlets”, connectivity matrices derived across the low-frequency spectrum of the BOLD fMRI resting-state signal (0.01–0.1Hz) in a cohort of 100 subjects. We then apply and compare graph theoretical measures across the frequency graphlets. Our results show that the within- and between-network connectivity and presence of functional hubs shift as a function of frequency. Furthermore, we show that the small world network property peaks at different frequencies with corresponding spatial connectivity profiles. We conclude that the frequency dependence of the network connectivity and the spatial configuration of functional hubs suggest that the dynamics of large-scale network integration and segregation operate at different time scales.

Enhanced disease characterization through multi network functional normalization in fMRI.

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Mapping distributed brain function and networks with diffuse optical tomography.

Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.