Aberrant intra and inter network resting state functional connectivity in thyrotoxicosis

Thyroid hormones epigenetically play an important role in the regularisation of neural networks and in neural differentiation during brain development. The present study aimed to explore the intra and inter network resting state functional connectivity changes underlying the neurobehavioural symptoms in thyrotoxicosis. To understand the pathophysiological changes, we investigated the correlation between functional connectivity and clinical and behavioural measures. Twenty‐eight freshly diagnosed thyrotoxicosis patients suffering with symptoms such as palpitation, loss of weight, trembling and heat intolerance from days to weeks and 28 healthy controls were recruited for the study. Thyrotoxicosis patients showed significantly decreased functional connectivity in sensorimotor network, fronto‐temporal network, default mode network, right fronto‐parietal network, left fronto‐parietal network and salience network. Inter network functional connectivity was significantly reduced between the basal ganglia network and sensorimotor network and increased between the salience network and fronto‐temporal network in thyrotoxicosis. Cognitive functions such as visual retention, recognition of objects, mental balance and performance on neuropsychological tests (ie, the Bender Gestalt test, Nahar‐Benson test and Mini Mental State Examination) also showed significant decline in thyrotoxicosis patients. The altered intrinsic resting state functional connectivity might underlie these cognitive deficits. The increased functional connectivity between the salience network and fronto‐temporal network suggests the recruitment of additional neuronal circuitry needed to compensate for the neuropathology in the primary neural network in thyrotoxicosis.     

Dysregulation of working memory and default‐mode networks in schizophrenia using independent component analysis,an fBIRN and MCIC study

Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data‐driven approach that can identify temporally coherent networks that underlie fMRI activity. We applied ICA to an fMRI dataset for 115 patients with chronic schizophrenia and 130 healthy controls by performing the Sternberg Item Recognition Paradigm. Here, we describe the first results using ICA to identify differences in the function of WM networks in schizophrenia compared to controls. ICA revealed six networks that showed significant differences between patients with schizophrenia and healthy controls. Four of these networks were negatively task‐correlated and showed deactivation across the posterior cingulate, precuneus, medial prefrontal cortex, anterior cingulate, inferior parietal lobules, and parahippocampus. These networks comprise brain regions known as the default‐mode network (DMN), a well‐characterized set of regions shown to be active during internal modes of cognition and implicated in schizophrenia. Two networks were positively task‐correlated, with one network engaging WM regions such as bilateral DLPFC and inferior parietal lobules while the other network engaged primarily the cerebellum. Our results suggest that DLPFC dysfunction in schizophrenia might be lateralized to the left and intrinsically tied to other regions such as the inferior parietal lobule and cingulate gyrus. Furthermore, we found that DMN dysfunction in schizophrenia exists across multiple subnetworks of the DMN and that these subnetworks are individually relevant to the pathophysiology of schizophrenia. In summary, this large multsite study identified multiple temporally coherent networks, which are aberrant in schizophrenia versus healthy controls and suggests that both task‐correlated and task‐anticorrelated networks may serve as potential biomarkers. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.     

Altered intra‐ and inter‐network functional coupling of resting‐state networks associated with motor dysfunction in stroke

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra‐ and inter‐network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty‐three left subcortical chronic stroke patients and 34 healthy controls underwent resting‐state functional magnetic resonance imaging. Eleven resting‐state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl‐Meyer Assessment (FMA) scores (hand + wrist). With respect to inter‐network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra‐ and inter‐network alterations associated with motor‐visual attention and high‐order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke.     

PAGANI Toolkit: Parallel graph‐theoretical analysis package for brain network big data

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph‐theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit–graphics processing unit (CPU‐GPU) framework with a graphical user interface to facilitate the mapping and characterization of high‐resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel‐based brain networks with ～200,000 nodes that were derived from a resting‐state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ～27 h for one subject, which is markedly less than the 118 h required with a single‐thread implementation. The voxel‐based functional brain networks exhibited prominent small‐world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto‐parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto‐parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high‐resolution connectomics research in health and disease.     

Dynamic changes of ICA‐derived EEG functional connectivity in the resting state

An emerging issue in neuroscience is how to identify baseline state(s) and accompanying networks termed “resting state networks” (RSNs). Although independent component analysis (ICA) in fMRI studies has elucidated synchronous spatiotemporal patterns during cognitive tasks, less is known about the changes in EEG functional connectivity between eyes closed (EC) and eyes open (EO) states, two traditionally used baseline indices. Here we investigated healthy subjects (n = 27) in EC and EO employing a four‐step analytic approach to the EEG: (1) group ICA to extract independent components (ICs), (2) standardized low‐resolution tomography analysis (sLORETA) for cortical source localization of IC network nodes, followed by (3) graph theory for functional connectivity estimation of epochwise IC band‐power, and (4) circumscribing IC similarity measures via hierarchical cluster analysis and multidimensional scaling (MDS). Our proof‐of‐concept results on alpha‐band power demonstrate five statistically clustered groups with frontal, central, parietal, occipitotemporal, and occipital sources. Importantly, during EO compared with EC, graph analyses revealed two salient functional networks with frontoparietal connectivity: a more medial network with nodes in the mPFC/precuneus which overlaps with the “default‐mode network” (DMN), and a more lateralized network comprising the middle frontal gyrus and inferior parietal lobule, coinciding with the “dorsal attention network” (DAN). Furthermore, a separate MDS analysis of ICs supported the emergence of a pattern of increased proximity (shared information) between frontal and parietal clusters specifically for the EO state. We propose that the disclosed component groups and their source‐derived EEG functional connectivity maps may be a valuable method for elucidating direct neuronal (electrophysiological) RSNs in healthy people and those suffering from brain disorders. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.     

A MATLAB toolbox for multivariate analysis of brain networks

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed‐effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole‐brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering‐based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.     

Top‐down versus bottom‐up attention differentially modulate frontal–parietal connectivity

The moment‐to‐moment focus of our mind's eye results from a complex interplay of voluntary and involuntary influences on attention. Previous neuroimaging studies suggest that the brain networks of voluntary versus involuntary attention can be segregated into a frontal‐versus‐parietal or a dorsal‐versus‐ventral partition—although recent work suggests that the dorsal network may be involved in both bottom‐up and top‐down attention. Research with nonhuman primates has provided evidence that a key distinction between top‐down and bottom‐up attention may be the direction of connectivity between frontal and parietal areas. Whereas typical fMRI connectivity analyses cannot disambiguate the direction of connections, dynamic causal modeling (DCM) can model directionality. Using DCM, we provide new evidence that directed connections within the dorsal attention network are differentially modulated for voluntary versus involuntary attention. These results suggest that the intraparietal sulcus exerts a baseline inhibitory effect on the frontal eye fields that is strengthened during exogenous orienting and attenuated during endogenous orienting. Furthermore, the attenuation from endogenous attention occurs even with salient peripheral cues when those cues are known to be counter predictive. Thus, directed connectivity between frontal and parietal regions of the dorsal attention network is highly influenced by the type of attention that is engaged.     

Breakdown of the affective‐cognitive network in functional dystonia

Previous studies suggested that brain regions subtending affective‐cognitive processes can be implicated in the pathophysiology of functional dystonia (FD). In this study, the role of the affective‐cognitive network was explored in two phenotypes of FD: fixed (FixFD) and mobile dystonia (MobFD). We hypothesized that each of these phenotypes would show peculiar functional connectivity (FC) alterations in line with their divergent disease clinical expressions. Resting state fMRI (RS‐fMRI) was obtained in 40 FD patients (12 FixFD; 28 MobFD) and 43 controls (14 young FixFD‐age‐matched [yHC]; 29 old MobFD‐age‐matched [oHC]). FC of brain regions of interest, known to be involved in affective‐cognitive processes, and independent component analysis of RS‐fMRI data to explore brain networks were employed. Compared to HC, all FD patients showed reduced FC between the majority of affective‐cognitive seeds of interest and the fronto‐subcortical and limbic circuits; enhanced FC between the right affective‐cognitive part of the cerebellum and the bilateral associative parietal cortex; enhanced FC of the bilateral amygdala with the subcortical and posterior cortical brain regions; and altered FC between the left medial dorsal nucleus and the sensorimotor and associative brain regions (enhanced in MobFD and reduced in FixFD). Compared with yHC and MobFD patients, FixFD patients had an extensive pattern of reduced FC within the cerebellar network, and between the majority of affective‐cognitive seeds of interest and the sensorimotor and high‐order function (“cognitive”) areas with a unique involvement of dorsal anterior cingulate cortex connectivity. Brain FC within the affective‐cognitive network is altered in FD and presented specific features associated with each FD phenotype, suggesting an interaction between brain connectivity and clinical expression of the disease.     

Altered resting‐state connectivity in Huntington's Disease

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Using resting‐state fMRI (rs‐fMRI) we investigated the functional integrity of resting‐state networks (RSN) in HD. 17 HD and 19 matched control participants were examined at a 3 Tesla MR scanner. After controlling for structural degeneration by means of voxel‐based morphometry, task‐free rs‐fMRI data were analyzed using Independent Component Analysis (ICA) and a dual‐regression approach in the context of genetic and clinical parameters. Further, we evaluated HD‐related differences in interregional connectivity between networks. RSN analysis showed a significant increase in intrinsic functional connectivity in the HD sample compared with controls, including the thalamus, striatum, prefrontal, premotor, and parietal maps. A subset of the Default Mode Network (DMN) was also affected. In the HD cohort, motor impairment correlated with higher network connectivity in mainly motor and parietal cortices. Deteriorating total functional capacity was additionally associated with higher connectivity in the striatum, thalamus, insular and frontal areas. This pattern of increased activity in intrinsic functional networks might suggest a reduced ability of intra‐network differentiation with clinical disease progression in HD. Finally, results showed reduced long‐range connectivity between parietal ICA components in HD compared to controls, indicating impaired functional coupling between interregional networks in HD. Our data demonstrates that functional connectivity is profoundly altered in HD, both within and between RSN. Rs‐fMRI analysis may provide additional valuable insights into neuronal dysfunctions beyond HD‐related structural degeneration and disruptions of functional circuits in HD. Hum Brain Mapp 35:2582–2593, 2014. © 2013 Wiley Periodicals, Inc .     

Decreased limbic and increased fronto‐parietal connectivity in unmedicated patients with obsessive‐compulsive disorder

Obsessive‐compulsive disorder (OCD) is characterized by recurrent intrusive thoughts and ritualized, repetitive behaviors, or mental acts. Convergent experimental evidence from neuroimaging and neuropsychological studies supports an orbitofronto‐striato‐thalamo‐cortical dysfunction in OCD. Moreover, an over excitability of the amygdala and over monitoring of thoughts and actions involving the anterior cingulate, frontal and parietal cortex has been proposed as aspects of pathophysiology in OCD. We chose a data driven, graph theoretical approach to investigate brain network organization in 17 unmedicated OCD patients and 19 controls using resting‐state fMRI. OCD patients showed a decreased connectivity of the limbic network to several other brain networks: the basal ganglia network, the default mode network, and the executive/attention network. The connectivity within the limbic network was also found to be decreased in OCD patients compared to healthy controls. Furthermore, we found a stronger connectivity of brain regions within the executive/attention network in OCD patients. This effect was positively correlated with disease severity. The decreased connectivity of limbic regions (amygdala, hippocampus) may be related to several neurocognitive deficits observed in OCD patients involving implicit learning, emotion processing and expectation, and processing of reward and punishment. Limbic disconnection from fronto‐parietal regions relevant for (re)‐appraisal may explain why intrusive thoughts become and/or remain threatening to patients but not to healthy subjects. Hyperconnectivity within the executive/attention network might be related to OCD symptoms such as excessive monitoring of thoughts and behavior as a dysfunctional strategy to cope with threat and uncertainty. Hum Brain Mapp 35:5617–5632, 2014. © 2014 Wiley Periodicals, Inc.     

Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient

Objective

Some studies have shown that the functional electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) networks in those with major depressive disorders (MDDs) have an abnormal random topology. In this study we aimed to further investigate the characteristics of the randomized functional brain networks in MDDs by examining resting-state scalp-EEG data.

Functional anatomy of visuo-spatial working memory during mental rotation is influenced by sex, menstrual cycle, and sex steroid hormones

Recent observations indicate that sex and level of steroid hormones may influence cortical networks associated with specific cognitive functions, in particular visuo-spatial abilities.

The present study probed the influence of sex, menstrual cycle, and sex steroid hormones on 3D mental rotation and brain function using 3-T fMRI. Twelve healthy women and 12 men were investigated. Menstrual cycle and hormone levels were assessed. The early follicular and midluteal phase of the menstrual cycle were chosen to examine short-term cyclical changes.

Parietal and frontal areas were activated during mental rotation in both sexes. Significant differences between men and women were revealed in both phases of menstrual cycle. In men we observed a significant correlation of activation levels with testosterone levels in the left parietal lobe (BA 40). In women, a cycle-dependent correlation pattern was observed for testosterone: brain activation correlated with this male hormone only during the early follicular phase. In both cycle phases females’ brain activation was significantly correlated with estradiol in frontal and parietal areas.

Our study provides evidence that fMRI-related activity during performance of cognitive tasks varies across sex and phases of the menstrual cycle. The variation might be partly explained by better task performance in men, but our results indicate that further explanations like basic neuronal or neurovascular effects modulated by steroid hormones must be considered. Both estradiol and testosterone levels may influence fMRI signals of cognitive tasks, which should affect selection of subjects for future fMRI studies.     

Neural network correlates of high‐altitude adaptive genetic variants in Tibetans: A pilot,exploratory study

Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases.     

Significance of longitudinal changes in the default‐mode network for cognitive recovery after stroke

Although a considerable number of patients suffer from cognitive impairments after stroke, the neural mechanism of cognitive recovery has not yet been clarified. Repeated resting‐state functional magnetic resonance imaging (fMRI) was used in this study to examine longitudinal changes in the default‐mode network (DMN) during the 6 months after stroke, and to investigate the relationship between DMN changes and cognitive recovery. Out of 24 initially recruited right‐hemispheric stroke patients, 11 (eight males, mean age 55.7 years) successfully completed the repeated fMRI protocol. Patients underwent three fMRI sessions at 1, 3 and 6 months after stroke. Their DMNs were analysed and compared with those of 11 age‐matched healthy subjects (nine males, mean age 56.2 years). Correlations between DMN connectivity and improvement of the cognitive performance scores were also assessed. The stroke patients were found to demonstrate markedly decreased DMN connectivity of the posterior cingulate cortex, precuneus, medial frontal gyrus and inferior parietal lobes at 1 month after stroke. At 3 months after stroke, the DMN connectivity of these brain areas was almost restored, suggesting that the period is critical for neural reorganization. The DMN connectivity of the dorsolateral prefrontal cortex in the contralesional hemisphere showed a significant correlation with cognitive function recovery in stroke patients, and should be considered a compensatory process for overcoming cognitive impairment due to brain lesion. This is the first longitudinal study to demonstrate the changes in DMN during recovery after stroke and the key regions influencing cognitive recovery.     

Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements

Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually‐guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age‐related increases in functional and effective connectivity in whole‐brain, task‐related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo‐frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task‐specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo‐ and cytoarchitectural changes. Furthermore, resting‐state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network‐wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems‐level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes.     

Aberrant cerebral network topology and mild cognitive impairment in early Parkinson's disease

The aim of this study was to assess whether mild cognitive impairment (MCI) is associated with disruption in large‐scale structural networks in newly diagnosed, drug‐naïve patients with Parkinson's disease (PD). Graph theoretical analyses were applied to 3T MRI data from 123 PD patients and 56 controls from the Parkinson's progression markers initiative (PPMI). Thirty‐three patients were classified as having Parkinson's disease with mild cognitive impairment (PD‐MCI) using the Movement Disorders Society Task Force criteria, while the remaining 90 PD patients were classified as cognitively normal (PD‐CN). Global measures (clustering coefficient, characteristic path length, global efficiency, small‐worldness) and regional measures (regional clustering coefficient, regional efficiency, hubs) were assessed in the structural networks that were constructed based on cortical thickness and subcortical volume data. PD‐MCI patients showed a marked reduction in the average correlation strength between cortical and subcortical regions compared with controls. These patients had a larger characteristic path length and reduced global efficiency in addition to a lower regional efficiency in frontal and parietal regions compared with PD‐CN patients and controls. A reorganization of the highly connected regions in the network was observed in both groups of patients. This study shows that the earliest stages of cognitive decline in PD are associated with a disruption in the large‐scale coordination of the brain network and with a decrease of the efficiency of parallel information processing. These changes are likely to signal further cognitive decline and provide support to the role of aberrant network topology in cognitive impairment in patients with early PD. Hum Brain Mapp 36:2980–2995, 2015. © 2015 Wiley Periodicals, Inc.     

Neural representation of emotion regulation goals

The use of top–down cognitive control mechanisms to regulate emotional responses as circumstances change is critical for mental and physical health. Several theoretical models of emotion regulation have been postulated; it remains unclear, however, in which brain regions emotion regulation goals (e.g., the downregulation of fear) are represented. Here, we examined the neural mechanisms of regulating emotion using fMRI and identified brain regions representing reappraisal goals. Using a multimethodological analysis approach, combining standard activation‐based and pattern‐information analyses, we identified a distributed network of lateral frontal, temporal, and parietal regions implicated in reappraisal and within it, a core system that represents reappraisal goals in an abstract, stimulus‐independent fashion. Within this core system, the neural pattern‐separability in a subset of regions including the left inferior frontal gyrus, middle temporal gyrus, and inferior parietal lobe was related to the success in emotion regulation. Those brain regions might link the prefrontal control regions with the subcortical affective regions. Given the strong association of this subsystem with inner speech functions and semantic memory, we conclude that those cognitive mechanisms may be used for orchestrating emotion regulation. Hum Brain Mapp 37:600–620, 2016. © 2015 Wiley Periodicals, Inc.     

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: A meta‐analysis of 75 fMRI studies

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta‐analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task‐based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta‐analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting‐state fMRI data of 1,000 healthy participants. Thirty‐nine task‐based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI‐related meta‐analysis while 36 task‐based fMRI studies (421 AD patients and 512 healthy controls) were included in AD‐related meta‐analysis. The meta‐analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large‐scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD‐related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI‐related and AD‐related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large‐scale networks in fulfilling the cognitive tasks. These system‐level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD. Hum Brain Mapp 36:1217–1232, 2015. © 2014 Wiley Periodicals, Inc.     

Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

Despite its widespread use in cognitive studies, there is still limited understanding of whether and how transcranial direct current stimulation (tDCS) modulates brain network function. To clarify its physiological effects, we assessed brain network function using functional magnetic resonance imaging (fMRI) simultaneously acquired during tDCS stimulation. Cognitive state was manipulated by having subjects perform a Choice Reaction Task or being at “rest.” A novel factorial design was used to assess the effects of brain state and polarity. Anodal and cathodal tDCS were applied to the right inferior frontal gyrus (rIFG), a region involved in controlling activity large‐scale intrinsic connectivity networks during switches of cognitive state. tDCS produced widespread modulation of brain activity in a polarity and brain state dependent manner. In the absence of task, the main effect of tDCS was to accentuate default mode network (DMN) activation and salience network (SN) deactivation. In contrast, during task performance, tDCS increased SN activation. In the absence of task, the main effect of anodal tDCS was more pronounced, whereas cathodal tDCS had a greater effect during task performance. Cathodal tDCS also accentuated the within‐DMN connectivity associated with task performance. There were minimal main effects of stimulation on network connectivity. These results demonstrate that rIFG tDCS can modulate the activity and functional connectivity of large‐scale brain networks involved in cognitive function, in a brain state and polarity dependent manner. This study provides an important insight into mechanisms by which tDCS may modulate cognitive function, and also has implications for the design of future stimulation studies.     

Intra‐ and inter‐network functional alterations in Parkinson's disease with mild cognitive impairment

Mild cognitive impairment (MCI) is prevalent in 15%–40% of Parkinson's disease (PD) patients at diagnosis. In this investigation, we study brain intra‐ and inter‐network alterations in resting state functional magnetic resonance imaging (rs‐fMRI) in recently diagnosed PD patients and characterise them as either cognitive normal (PD‐NC) or with MCI (PD‐MCI). Patients were divided into two groups, PD‐NC (N = 62) and PD‐MCI (N = 37) and for comparison, healthy controls (HC, N = 30) were also included. Intra‐ and inter‐network connectivity were investigated from participants’ rs‐fMRIs in 26 resting state networks (RSNs). Intra‐network differences were found between both patient groups and HCs for networks associated with motor control (motor cortex), spatial attention and visual perception. When comparing both PD‐NC and PD‐MCI, intra‐network alterations were found in RSNs related to attention, executive function and motor control (cerebellum). The inter‐network analysis revealed a hyper‐synchronisation between the basal ganglia network and the motor cortex in PD‐NC compared with HCs. When both patient groups were compared, intra‐network alterations in RSNs related to attention, motor control, visual perception and executive function were found. We also detected disease‐driven negative synchronisations and synchronisation shifts from positive to negative and vice versa in both patient groups compared with HCs. The hyper‐synchronisation between basal ganglia and motor cortical RSNs in PD and its synchronisation shift from negative to positive compared with HCs, suggest a compensatory response to basal dysfunction and altered basal‐cortical motor control in the resting state brain of PD patients. Hum Brain Mapp 38:1702–1715, 2017 . © 2016 Wiley Periodicals, Inc.