Resting-state fMRI: a review of methods and clinical applications

Multiple functional networks modeling for autism spectrum disorder diagnosis

Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.

Individual Differences in the Accuracy of Judgments of Learning Are Related to the Gray Matter Volume and Functional Connectivity of the Left Mid-Insula

The judgment of learning (JOL) is an important form of prospective metamemory judgment, and the biological basis of the JOL process is an important topic in metamemory research. Although previous task-related functional magnetic resonance imaging (MRI) studies have examined the brain regions underlying the JOL process, the neural correlates of individual differences in JOL accuracy require further investigation. This study used structural and resting-state functional MRI to investigate whether individual differences in JOL accuracy are related to the gray matter (GM) volume and functional connectivity of the bilateral insula and medial Brodmann area (BA) 11, which are assumed to be related to JOL accuracy. We found that individual differences in JOL accuracy were related to the GM volume of the left mid-insula and to the functional connectivity between the left mid-insula and various other regions, including the left superior parietal lobule/precuneus, bilateral inferior parietal lobule/intraparietal sulcus, right frontal pole and left parahippocampal gyrus/fusiform gyrus/cerebellum. Further analyses indicated that the functional connectivity related to individual differences in JOL accuracy could be divided into two factors and might support information integration and selective attention processes underlying accurate JOLs. In addition, individual differences in JOL accuracy were not related to the GM volume or functional connectivity of the medial BA 11. Our findings provide novel evidence for the role of the left mid-insula and its functional connectivity in the JOL process.

Disrupted resting brain graph measures in individuals at high risk for alcoholism

Familial susceptibility to alcoholism is likely to be linked to the externalizing diathesis seen in high-risk offspring from high-density alcohol use disorder (AUD) families. The present study aimed at comparing resting brain functional connectivity and their association with externalizing symptoms and alcoholism familial density in 40 substance-naive high-risk (HR) male offspring from high-density AUD families and 30 matched healthy low-risk (LR) males without a family history of substance dependence using graph theory-based network analysis. The HR subjects from high-density AUD families compared with LR, showed significantly reduced clustering, small-worldness, and local network efficiency. The frontoparietal, cingulo-opercular, sensorimotor and cerebellar networks exhibited significantly reduced functional segregation. These disruptions exhibited independent incremental value in predicting the externalizing symptoms over and above the demographic variables. The reduction of functional segregation in HR subjects was significant across both the younger and older age groups and was proportional to the family loading of AUDs. Detection and estimation of these developmentally relevant disruptions in small-world architecture at critical brain regions sub-serving cognitive, affective, and sensorimotor processes are vital for understanding the familial risk for early onset alcoholism as well as for understanding the pathophysiological mechanism of externalizing behaviors.

Imaging Biomarkers in Alzheimer's Disease: A Practical Guide for Clinicians

Although recent developments in imaging biomarkers have revolutionized the diagnosis of Alzheimer’s disease at early stages, the utility of most of these techniques in clinical setting remains unclear. The aim of this review is to provide a clear stepwise algorithm on using multitier imaging biomarkers for the diagnosis of Alzheimer’s disease to be used by clinicians and radiologists for day-to-day practice. We summarized the role of most common imaging techniques and their appropriate clinical use based on current consensus guidelines and recommendations with brief sections on acquisition and analysis techniques for each imaging modality. Structural imaging, preferably MRI or alternatively high resolution CT, is the essential first tier of imaging. It improves the accuracy of clinical diagnosis and excludes other potential pathologies. When the results of clinical examination and structural imaging, assessed by dementia expert, are still inconclusive, functional imaging can be used as a more advanced option. PET with ligands such as amyloid tracers and ¹⁸F-fluorodeoxyglucose can improve the sensitivity and specificity of diagnosis particularly at the early stages of the disease. There are, however, limitations in using these techniques in wider community due to a combination of lack of facilities and expertise to interpret the findings. The role of some of the more recent imaging techniques including tau imaging, functional MRI, or diffusion tensor imaging in clinical practice, remains to be established in the ongoing and future studies.

Investigation of the Changes in the Power Distribution in Resting-State Brain Networks Associated with Pure Conduct Disorder

Conduct disorder (CD) is a psychiatric disorder in children and adolescence. To investigate changes in the power distribution in brain networks between CD and typically developing (TD) groups, resting-state functional magnetic resonance imaging (rsfMRI) data of thirty-six subjects were first recorded, and then the data were preprocessed using DPARSF and SPM8. Meanwhile, the power of the blood oxygenation level-dependent (BOLD) signals of ninety brain regions was acquired using the integral of the Welch power spectral density (PSD). Additionally, the powers of the brain regions that reached significance (p < 0.05) were extracted using the bootstrap statistics, in which the standardized z-scores of the powers were used as a reference. The results of the analysis of the changes in power exhibited that there were significant power differences in some pairs of brain regions between the CD and TD groups, indicating a change in the power distribution. In addition, the results also suggest that the total power consumption of brain networks in CD patients is less than that observed in the TD group. Consequently, the study provided a paradigm for establishing quantifiable indicators via the power spectrum approach for the comparison and analysis of the BOLD signal power between CD patients and healthy controls.

Overweight is associated with lower resting state functional connectivity in females after eliminating genetic effects: A twin study

Obesity is related to altered functional connectivity of resting state brain networks that are involved in reward and motivation. It is unknown to what extent these associations reflect genetic confounding and whether the obesity-related connectivity changes are associated with differences in dietary intake. In this study, resting state functional MRI was performed after an overnight fast in 16 female monozygotic twin pairs (aged 48.8 ± 9.8 years) with a mean BMI discordance of 3.96 ± 2.1 kg/m² (range 0.7-8.2). Functional connectivity of the salience, basal ganglia, default mode and anterior cingulate-orbitofrontal cortex networks was examined by independent component analysis. Dietary intake was assessed using 3-day 24-hour recalls. Results revealed that within the basal ganglia network, heavier versus leaner co-twins have decreased functional connectivity strength in bilateral putamen (P < 0.05, FWE-corrected). There were no differences in connectivity in the other networks examined. In the overall group, lower functional connectivity strength in the left putamen was correlated with higher intake of total fat (P < 0.01). It was concluded that, after eliminating genetic effects, overweight is associated with lower resting state functional connectivity in bilateral putamen in the basal ganglia network. The association between lower putamen connectivity and higher fat intake suggests an important role of the putamen in appetitive mechanisms. The cross-sectional nature of our study cannot discriminate cause and consequence, but the findings are compatible with an effect of lower putamen connectivity on increased BMI and associated higher fat intake. Hum Brain Mapp 38:5069-5081, 2017. © 2017 Wiley Periodicals, Inc.

Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 non-smokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.

Subtle blood-brain barrier leakage rate and spatial extent: Considerations for dynamic contrast-enhanced MRI

PURPOSE

Dynamic contrast-enhanced (DCE) MRI can be used to measure blood-brain barrier (BBB) leakage. In neurodegenerative disorders such as small vessel disease and dementia, the leakage can be very subtle and the corresponding signal can be rather noisy. For these reasons, an optimized DCE-MRI measurement and study design is required. To this end, a new measure indicative of the spatial extent of leakage is introduced and the effects of scan time and sample size are explored.

METHODS

Dual-time resolution DCE-MRI was performed in 16 patients with early Alzheimer's disease (AD) and 17 healthy controls. The leakage rate (K_i ) and volume fraction of detectable leaking tissue (v_L ) to quantify the spatial extent of BBB leakage were calculated in cortical gray matter and white matter using noise-corrected histogram analysis of leakage maps. Computer simulations utilizing realistic K_i histograms, mimicking the strong effect of noise and variation in K_i values, were performed to understand the influence of scan time on the estimated leakage.

RESULTS

The mean K_i was very low (order of 10^-4 min^-1 ) and highly influenced by noise, causing the K_i to be increasingly overestimated at shorter scan times. In the white matter, the K_i was not different between patients with early AD and controls, but was higher in the cortex for patients, reaching significance after 14.5 min of scan time. To detect group differences, v_L proved more suitable, showing significantly higher values for patients compared with controls in the cortex after 8 minutes of scan time, and in white matter after 15.5 min.

CONCLUSIONS

Several ways to improve the sensitivity of a DCE-MRI experiment to subtle BBB leakage were presented. We have provided v_L as an attractive and potentially more time-efficient alternative to detect group differences in subtle and widespread blood-brain barrier leakage compared with leakage rate K_i . Recommendations on group size and scan time are made based on statistical power calculations to aid future research.

Altered putamen functional connectivity is associated with anxiety disorder in Parkinson's disease

In this study, we used resting state-functional magnetic resonance imaging (rs-fMRI) to explore altered putamen functional connectivity (FC) in Parkinson's disease patients with anxiety disorder. We divided 65 Parkinson's disease patients into anxiety (PD-A; n=18) and non-anxiety (PD-NA; n=45) groups based on a Hamilton Anxiety Rating Scale cutoff score of 12. The PD-A patients exhibited altered putamen FC with cortical and subcortical regions. The PD-A patients showed enhanced putamen FC with the caudatum, which correlated with increased emotional processing during anxiety. Decreased putamen FC with the orbitofrontal gyrus and cerebellum also correlated with increased anxiety in Parkinson's disease. Our findings demonstrate that anxiety disorder in Parkinson's disease is associated with abnormal putamen FC networks, especially with caudatum, orbitofrontal gyrus and cerebellum.

Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views-such as heat maps, node link diagrams and anatomical views-using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies-exploring progressive supranuclear palsy, as well as memory encoding and retrieval.

Combining Functional Studies with Intraoperative MRI in Glioma Surgery

Maximal safe resection is the cornerstone of treatment for low-grade and high-grade gliomas. In addition to high-resolution anatomic MRI studies that highlight tumor architecture, it is important to determine the relationship of the tumor to the eloquent cortical and subcortical areas to avoid introducing or exacerbating a neurologic deficit. The goal of this review was to highlight imaging modalities that provide functional information and can be integrated with intraoperative MRI navigation to maximize the extent of resection while preserving a patient's neurologic function.

Radiological studies of fetal alcohol spectrum disorders in humans and animal models: An updated comprehensive review

Fetal Alcohol Spectrum Disorders encompass a wide range of birth defects in children born to mothers who consumed alcohol during pregnancy. Typical mental impairments in FASD include difficulties in life adaptation and learning and memory, deficits in attention, visuospatial skills, language and speech disabilities, mood disorders and motor disabilities. Multimodal imaging methods have enabled in vivo studies of the teratogenic effects of alcohol on the central nervous system, giving more insight into the FASD phenotype. This paper offers an up-to-date comprehensive review of radiological findings in the central nervous system in studies of prenatal alcohol exposure in both humans and translational animal models, including Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, Single Photon Emission Tomography and Ultrasonography.

Neural Correlates of Outcome of the Psychotherapy Compared to Antidepressant Therapy in Anxiety and Depression Disorders: A Meta-Analysis

The most prevalent mental disorders, anxiety and depression, are commonly associated with structural and functional changes in the fronto-limbic brain areas. The clinical trials investigating patients with affective disorders showed different outcome to different treatments such as psychotherapy or pharmacotherapy. It is, however, still unexplored how these interventions approach affect the functional brain. This meta-analysis aims to compare the effects of psychotherapy compared to antidepressant therapy on functional brain activity in anxiety and depression disorders. Twenty-one samples with psychotherapy and seventeen samples with antidepressant therapy were included. The main finding showed an inverse effect of the two treatments on the right paracingulate activity. The patients undergoing psychotherapy showed an increase in the right paracingulate activity while pharmacological treatment led to a decrease of activation of this area. This finding seems to support the recent studies that hypothesize how psychotherapy, through the self-knowledge and the meaning processing, involves a top-down emotional regulation.

The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers

Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life.

Screening of autism based on task-free fMRI using graph theoretical approach

Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data. Then, autism screening was developed based on characteristics of the functional networks using the following steps: A) local and global parameters of the brain functional network were calculated using graph theory. B) network parameters of the ASDs were statistically compared to the HCs. C) significantly altered parameters were used as input features of the screening system. D) performance of the system was verified using various classification techniques. The support vector machine showed superiority to others with an accuracy of 92%. Subsequently, reliability of the results was examined using an independent dataset including 20 ASDs and 20 HCs. Our findings suggest that local parameters of the brain functional network, despite the individual variability, can potentially be used for autism screening.

Resting State Effective Connectivity Allows Auditory Hallucination Discrimination

Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.

Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest

Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp 38:2454-2465, 2017. © 2017 Wiley Periodicals, Inc.

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex reduces resting-state insula activity and modulates functional connectivity of the orbitofrontal cortex in cigarette smokers

BACKGROUND

Previous studies reported that repetitive transcranial magnetic stimulation (rTMS) can reduce cue-elicited craving and decrease cigarette consumption in smokers. The mechanism of this effect however, remains unclear. We used resting-state functional magnetic resonance imaging (rsfMRI) to test the effect of rTMS in non-treatment seeking smokers.

METHODS

We used a single blinded, sham-controlled, randomized counterbalanced crossover design where participants underwent two visits separated by at least 1 week. Participants received active rTMS over the left dorsolateral prefrontal cortex (DLPFC) during one of their visits, and sham rTMS during their other visit. They had two rsFMRI scans before and after each rTMS session. We used the same rTMS stimulation parameters as in a previous study (10Hz, 5s-on, 10s-off, 100% resting motor threshold, 3000 pulses).

RESULTS

Ten non-treatment-seeking, nicotine-dependent, cigarette smokers (6 women, an average age of 39.72 and an average cigarette per day of 17.30) finished the study. rsFMRI results demonstrate that as compared to a single session of sham rTMS, a single session of active rTMS inhibits brain activity in the right insula and thalamus in fractional amplitude of low frequency fluctuation (fALFF). For intrinsic brain connectivity comparisons, active TMS resulted in significantly decreased connectivity from the site of rTMS to the left orbitomedial prefrontal cortex.

CONCLUSIONS

This data suggests that one session of rTMS can reduce activity in the right insula and right thalamus as measured by fALFF. The data also demonstrates that rTMS can reduce rsFC between the left DLPFC and the medial orbitofrontal cortex.

Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of red to near-infrared light to measure changes in cerebral blood oxygenation. Spontaneous (resting state) functional connectivity (sFC) has become a critical tool for cognitive neuroscience for understanding task-independent neural networks, revealing pertinent details differentiating healthy from disordered brain function, and discovering fluctuations in the synchronization of interacting individuals during hyperscanning paradigms. Two of the main challenges to sFC-NIRS analysis are (i) the slow temporal structure of both systemic physiology and the response of blood vessels, which introduces false spurious correlations, and (ii) motion-related artifacts that result from movement of the fNIRS sensors on the participants’ head and can introduce non-normal and heavy-tailed noise structures. In this work, we systematically examine the false-discovery rates of several time- and frequency-domain metrics of functional connectivity for characterizing sFC-NIRS. Specifically, we detail the modifications to the statistical models of these methods needed to avoid high levels of false-discovery related to these two sources of noise in fNIRS. We compare these analysis procedures using both simulated and experimental resting-state fNIRS data. Our proposed robust correlation method has better performance in terms of being more reliable to the noise outliers due to the motion artifacts.

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index

BACKGROUND AND PURPOSE

Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI.

MATERIALS AND METHODS

Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF.

RESULTS

Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 (P < .05). Regression analysis of the fALFF with the laterality index yielded an R² value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI.

CONCLUSIONS

The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI.

Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Studies using resting-state functional magnetic resonance imaging (fMRI) to investigate underlying pathophysiology of motor and non-motor symptoms in PD yielded largely inconsistent results. This quantitative neuroimaging meta-analysis aims to identify consistent abnormal intrinsic functional patterns in PD across studies. We used PubMed to retrieve suitable resting-state studies and stereotactic data were extracted from 28 individual between-group comparisons. Convergence across their findings was tested using the activation likelihood estimation (ALE) approach. We found convergent evidence for intrinsic functional disturbances in bilateral inferior parietal lobule (IPL) and the supramarginal gyrus in PD patients compared to healthy subjects. In follow-up task-based and task-independent functional connectivity (FC) analyses using two independent healthy subject data sets, we found that the regions showing convergent aberrations in PD formed an interconnected network mainly with the default mode network (DMN). Behavioral characterization of these regions using the BrainMap database suggested associated dysfunction of perception and executive processes. Taken together, our findings highlight the role of parietal cortex in the pathophysiology of PD.

The Potential for Advanced Magnetic Resonance Neuroimaging Techniques in Pediatric Stroke Research

BACKGROUND

This article was written to provide clinicians and researchers with an overview of a number of advanced neuroimaging techniques in an effort to promote increased utility and the design of future studies using advanced neuroimaging in childhood stroke. The current capabilities of advanced magnetic resonance imaging techniques provide the opportunity to build on our knowledge of the consequences of stroke on the developing brain. These capabilities include providing information about the physiology, metabolism, structure, and function of the brain that are not routinely evaluated in the clinical setting.

METHODS

During the Proceedings of the Stroke Imaging Laboratory for Children Workshop in Toronto in June 2015, a subgroup of clinicians and imaging researchers discussed how the application of advanced neuroimaging techniques could further our understanding of the mechanisms of stroke injury and repair in the pediatric population. This subgroup was established based on their interest and commitment to design collaborative, advanced neuroimaging studies in the pediatric stroke population.

RESULTS

In working toward this goal, we first sought to describe here the magnetic resonance imaging techniques that are currently available for use, and how they have been applied in other stroke populations (e.g., adult and perinatal stroke).

CONCLUSIONS

With the continued improvement in advanced neuroimaging techniques, including shorter acquisition times, there is an opportunity to apply these techniques to their full potential in the research setting and learn more about the effects of stroke in the developing brain.

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders

Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).

Cerebrospinal Fluid Clearance in Alzheimer Disease Measured with Dynamic PET

Evidence supporting the hypothesis that reduced cerebrospinal fluid (CSF) clearance is involved in the pathophysiology of Alzheimer disease (AD) comes primarily from rodent models. However, unlike rodents, in which predominant extracranial CSF egress is via olfactory nerves traversing the cribriform plate, human CSF clearance pathways are not well characterized. Dynamic PET with ¹⁸F-THK5117, a tracer for tau pathology, was used to estimate the ventricular CSF time-activity as a biomarker for CSF clearance. We tested 3 hypotheses: extracranial CSF is detected at the superior turbinates; CSF clearance is reduced in AD; and CSF clearance is inversely associated with amyloid deposition. Methods: Fifteen subjects, 8 with AD and 7 normal control volunteers, were examined with ¹⁸F-THK5117. Ten subjects additionally underwent ¹¹C-Pittsburgh compound B (¹¹C-PiB) PET scanning, and 8 were ¹¹C-PiB-positive. Ventricular time-activity curves of ¹⁸F-THK5117 were used to identify highly correlated time-activity curves from extracranial voxels. Results: For all subjects, the greatest density of CSF-positive extracranial voxels was in the nasal turbinates. Tracer concentration analyses validated the superior nasal turbinate CSF signal intensity. AD patients showed ventricular tracer clearance reduced by 23% and 66% fewer superior turbinate CSF egress sites. Ventricular CSF clearance was inversely associated with amyloid deposition. Conclusion: The human nasal turbinate is part of the CSF clearance system. Lateral ventricle and superior nasal turbinate CSF clearance abnormalities are found in AD. Ventricular CSF clearance reductions are associated with increased brain amyloid depositions. These data suggest that PET-measured CSF clearance is a biomarker of potential interest in AD and other neurodegenerative diseases.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI.

Functional disconnection of thalamic and cerebellar dentate nucleus networks in progressive supranuclear palsy and corticobasal syndrome

AIM

To assess functional rearrangement following neurodegeneration in the thalamus and dentate nucleus in patients with progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS).

METHODS

We recruited 19 patients with PSP, 11 with CBS and 14 healthy subjects. All the subjects underwent resting-state (rs) fMRI using a 3T system. Whole brain functional connectivity of the thalamus and dentate nucleus were calculated by means of a seed-based approach with FEAT script in FSL toolbox. Thalamic volume was calculated by means of FIRST, and the dentate area by means of Jim software.

RESULTS

Both thalamic volume and dentate area were significantly smaller in PSP and CBS patients than in healthy subjects. No significant difference emerged in thalamic volume between PSP and CBS patients, whereas dentate area was significantly smaller in PSP than in CBS. Thalamic functional connectivity was significantly reduced in both patient groups in various cortical, subcortical and cerebellar areas. By contrast, changes in dentate nucleus functional connectivity differed in PSP and CBS: it decreased in subcortical and prefrontal cortical areas in PSP, but increased asymmetrically in the frontal cortex in CBS.

CONCLUSIONS

Evaluating the dentate nucleus size and its functional connectivity may help to differentiate patients with PSP from those with CBS.

Longitudinal Changes in Cerebellar and Thalamic Spontaneous Neuronal Activity After Wide-Awake Surgery of Brain Tumors: a Resting-State fMRI Study

Hypometabolism has been observed in the contralesional cerebellar hemisphere after various supratentorial cortical lesions. It is unknown whether the consequences of the dee- and deafferentation subsequent to wide-awake surgery for brain diffuse low-grade glioma can be assessed within remote and unresected subcortical structures such as the cerebellum or thalamus. To answer this question, we have conducted several regional analyses. More specifically, we have performed amplitude of low-frequency fluctuations (neuronal activity magnitude) and regional homogeneity (local temporal correlations) analyses on resting state functional magnetic resonance imaging (rs-fMRI) data and at different time points, before and after surgery. Our main results demonstrated that it is possible to evaluate subtle subcortical changes using these tools dedicated to the analysis of rs-fMRI data. The observed variations of spontaneous neuronal activity were particularly significant within the cerebellum which showed altered regional homogeneity and neuronal activity intensity in very different, specialized and non-overlapping subregions, in accordance to its neuro-anatomo-functional topography. These variations were moreover observed in the immediate postoperative period and recovered after 3 months.

Resting-State Networks as Simultaneously Measured with Functional MRI and PET

Functional MRI (fMRI) studies reported disruption of resting-state networks (RSNs) in several neuropsychiatric disorders. PET with ¹⁸F-FDG captures neuronal activity that is in steady state at a longer time span and is less dependent on neurovascular coupling. Methods: In the present study, we aimed to identify RSNs in ¹⁸F-FDG PET data and compare their spatial pattern with those obtained from simultaneously acquired resting-state fMRI data in 22 middle-aged healthy subjects. Results: Thirteen and 17 meaningful RSNs could be identified in PET and fMRI data, respectively. Spatial overlap was fair to moderate for the default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks. Despite recording different aspects of neural activity, similar RSNs were detected by both imaging modalities. Conclusion: The results argue for the common neural substrate of RSNs and encourage testing of the clinical utility of resting-state connectivity in PET data.

Review of thalamocortical resting-state fMRI studies in schizophrenia

Brain circuitry underlying cognition, emotion, and perception is abnormal in schizophrenia. There is considerable evidence that the neuropathology of schizophrenia includes the thalamus, a key hub of cortical-subcortical circuitry and an important regulator of cortical activity. However, the thalamus is a heterogeneous structure composed of several nuclei with distinct inputs and cortical connections. Limitations of conventional neuroimaging methods and conflicting findings from post-mortem investigations have made it difficult to determine if thalamic pathology in schizophrenia is widespread or limited to specific thalamocortical circuits. Resting-state fMRI has proven invaluable for understanding the large-scale functional organization of the brain and investigating neural circuitry relevant to psychiatric disorders. This article summarizes resting-state fMRI investigations of thalamocortical functional connectivity in schizophrenia. Particular attention is paid to the course, diagnostic specificity, and clinical correlates of thalamocortical network dysfunction.

Effectiveness of cognitive-coping therapy and alteration of resting-state brain function in obsessive-compulsive disorder

BACKGROUND

Cognitive-coping therapy (CCT), integrating cognitive theory with stress-coping theory, is an efficacious therapy for obsessive-compulsive disorder (OCD). However, the potential brain mediation for the effectiveness remains unclear. We sought to investigate differences of resting-state brain function between OCD and healthy controls and if such differences would be changed by a four-week CCT.

PATIENTS AND METHODS

Thirty-one OCD patients were recruited and randomized into CCT (n=15) and pharmacotherapy plus CCT (pCCT, n=16) groups, together with 25 age-, gender- and education-matched healthy controls. The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) was scored to evaluate the severity in symptoms. Resting-state functional magnetic resonance imaging was scanned pre- and post-treatment.

RESULTS

For patients, Y-BOCS scores were reduced during four-week treatment for CCT and pCCT (P<0.001), but no group difference was observed. No differences in amplitude of low-frequency fluctuation (ALFF) values were found between CCT and pCCT either pre- or post-treatment. Compared to controls, ALFF in OCD patients was higher in the left hippocampus, parahippocampus, and temporal lobes, but lower in the right orbitofrontal cortex, rectus, bilateral calcarine, cuneus, lingual, occipital, left parietal, postcentral, precentral, and parietal (corrected P<0.05). The ALFF in those regions was not significantly correlated to the severity of OCD symptoms. After a 4-week treatment, the ALFF differences between OCD patients and controls disappeared.

LIMITATIONS

The pharmacotherapy group was not included since OCD patients generally do not respond to pharmacotherapy in four weeks.

CONCLUSIONS

Our data indicated that resting-state brain function was different between OCD and controls; such differences disappeared after OCD symptoms were relieved.

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA

Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work, we assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition, we explored group tests and correlation with severity measures in the patient population. We also tested the effect of removing high frequencies via filtering. Two real data cohorts were used: one consisting of 48 mTBI and one composed of 21 smokers, both with their corresponding matched controls. A simulation procedure was designed to test the classification power of each pipeline. Results show that data preprocessing can change the classification performance. In real data, regressing motion variance before gICA produced clearer group differences and stronger correlation with nicotine dependence.

Evaluating the Influence of Spatial Resampling for Motion Correction in Resting-State Functional MRI

Head motion is one of major concerns in current resting-state functional MRI studies. Image realignment including motion estimation and spatial resampling is often applied to achieve rigid-body motion correction. While the accurate estimation of motion parameters has been addressed in most studies, spatial resampling could also produce spurious variance, and lead to unexpected errors on the amplitude of BOLD signal. In this study, two simulation experiments were designed to characterize these variance related with spatial resampling. The fluctuation amplitude of spurious variance was first investigated using a set of simulated images with estimated motion parameters from a real dataset, and regions more likely to be affected by spatial resampling were found around the peripheral regions of the cortex. The other simulation was designed with three typical types of motion parameters to represent different extents of motion. It was found that areas with significant correlation between spurious variance and head motion scattered all over the brain and varied greatly from one motion type to another. In the last part of this study, four popular motion regression approaches were applied respectively and their performance in reducing spurious variance was compared. Among them, Friston 24 and Voxel-specific 12 model (Friston et al., 1996), were found to have the best outcomes. By separating related effects during fMRI analysis, this study provides a better understanding of the characteristics of spatial resampling and the interpretation of motion-BOLD relationship.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

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Simultaneous EEG-fMRI recordings revealed altered connectivity in sickle cell patients.

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Reduced activity observed in default mode network and executive control network.

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Patients' salience network strength increases with age; opposite seen in controls.

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EEG-fMRI parameters reflect disease severity in sickle cell patients.

Decreased Subcortical and Increased Cortical Degree Centrality in a Nonclinical College Student Sample with Subclinical Depressive Symptoms: A Resting-State fMRI Study

Abnormal functional connectivity (FC) at rest has been identified in clinical depressive disorder. However, very few studies have been conducted to understand the underlying neural substrates of subclinical depression. The newly proposed centrality analysis approach has been increasingly used to explore the large-scale brain network of mental diseases. This study aimed to identify the degree centrality (DC) alteration of the brain network in subclinical depressive subjects. Thirty-seven candidates with subclinical depression and 34 well-matched healthy controls (HCs) were recruited from the same sample of college students. All subjects underwent a resting-state fMRI (rs-fMRI) scan to assess the DC of the whole brain. Compared with controls, subclinical depressive subjects displayed decreased DC in the right parahippocampal gyrus (PHG), left PHG/amygdala, and left caudate and elevated DC in the right posterior parietal lobule (PPL), left inferior frontal gyrus (IFG) and left middle frontal gyrus (MFG). In addition, by using receiver operating characteristic (ROC) analysis, we determined that the DC values in the regions with altered FC between the two groups can be used to differentiate subclinical depressive subjects from HCs. We suggest that decreased DC in subcortical and increased DC in cortical regions might be the neural substrates of subclinical depression.

Effects of isoflurane anesthesia on resting-state fMRI signals and functional connectivity within primary somatosensory cortex of monkeys

INTRODUCTION

Correlated low-frequency fluctuations of resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used for inferring intrinsic brain functional connectivity (FC). In animal studies, accurate estimate of anesthetic effects on rsfMRI signals is demanded for reliable interpretations of FC changes. We have previously shown that inter-regional FC can reliably delineate local millimeter-scale circuits within digit representations of primary somatosensory cortex (S1) subregions (areas 3a, 3b, and 1) in monkeys under isoflurane anesthesia. The goals of this study are to determine (1) the general effects of isoflurane on rsfMRI signals in the S1 circuit and (2) whether the effects are functional- and regional- dependent, by quantifying the relationships between isoflurane levels, power and inter-regional correlation coefficients in digit and face regions of distinct S1 subregions.

METHODS

Functional MRI data were collected from male adult squirrel monkeys at three different isoflurane levels (1.25%, 0.875%, and 0.5%). All scans were acquired on a 9.4T magnet with a 3-cm-diameter surface transmit-receive coil centered over the S1 cortex. Power and seed-based inter-regional functional connectivity analyses were subsequently performed.

RESULTS

As anesthesia level increased, we observed (1) diminishing amplitudes of signal fluctuations, (2) reduced power of fluctuations in the low-frequency band used for connectivity measurements, (3) decreased inter-voxel connectivity around seed regions, and (4) weakened inter-regional FC across all pairs of regions of interest (digit-to-digit). The low-frequency power measures derived from rsfMRI signals from control muscle regions, however, did not exhibit any isoflurane level-related changes. Within the isoflurane dosage range we tested, the inter-regional functional connectivity differences were still detectable, and the effects of isoflurane did not differ across region-of-interest (ROI) pairs.

CONCLUSION

Our data demonstrate that isoflurane induced similar dose-dependent suppressive effects on the power of rsfMRI signals and local fine-scale FC across functionally related but distinct S1 subregions.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

In recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

Environmental factors linked to depression vulnerability are associated with altered cerebellar resting-state synchronization

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Disrupted functional connectivity affects resting state based language lateralization

Pre-operative assessment of language localization and lateralization is critical to preserving brain function after lesion or epileptogenic tissue resection. Task fMRI (t-fMRI) has been extensively and reliably used to this end, but resting state fMRI (rs-fMRI) is emerging as an alternative pre-operative brain mapping method that is independent of a patient's ability to comply with a task. We sought to evaluate if language lateralization obtained from rs-fMRI can replace standard assessment using t-fMRI. In a group of 43 patients scheduled for pre-operative fMRI brain mapping and 17 healthy controls, we found that existing methods of determining rs-fMRI lateralization by considering interhemispheric and intrahemispheric functional connectivity are inadequate compared to t-fMRI when applied to the language network. We determined that this was attributable to widespread but nuanced disturbances in the functional connectivity of the language network in patients. We found changes in interhemispheric and intrahemispheric functional connectivity that were dependent on lesion location, and particularly impacted patients with lesions in the left temporal lobe. We then tested whether a simpler measure of functional connectivity to the language network has a better relation to t-fMRI based language lateralization. Remarkably, we found that functional connectivity between the language network and the frontal pole, and superior frontal gyrus, as well as the supramarginal gyrus, significantly correlated to task based language lateralization indices in both patients and healthy controls. These findings are consistent with prior work with epilepsy patients, and provide a framework for evaluating language lateralization at rest.

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Existing methods of determining rs-fMRI lateralization are inadequate for language.

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Functional connectivity to language network correlates with task lateralization.

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Lesion location affects functional connectivity.

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Lesions exhibit some interhemispheric hyperconnectivity within language network.

Network Connectivity of the Right STS in Three Social Perception Localizers

The posterior STS (pSTS) is an important brain region for perceptual analysis of social cognitive cues. This study seeks to characterize the pattern of network connectivity emerging from the pSTS in three core social perception localizers: biological motion perception, gaze recognition, and the interpretation of moving geometric shapes as animate. We identified brain regions associated with all three of these localizers and computed the functional connectivity pattern between them and the pSTS using a partial correlations metric that characterizes network connectivity. We find a core pattern of cortical connectivity that supports the hypothesis that the pSTS serves as a hub of the social brain network. The right pSTS was the most highly connected of the brain regions measured, with many long-range connections to pFC. Unlike other highly connected regions, connectivity to the pSTS was distinctly lateralized. We conclude that the functional importance of right pSTS is revealed when considering its role in the large-scale network of brain regions involved in various aspects of social cognition.

Brain Activity in Patients With Adductor Spasmodic Dysphonia Detected by Functional Magnetic Resonance Imaging

OBJECTIVES

Spasmodic dysphonia (SD) is considered a focal dystonia. However, the detailed pathophysiology of SD remains unclear, despite the detection of abnormal activity in several brain regions. The aim of this study was to clarify the pathophysiological background of SD.

STUDY DESIGN

This is a case-control study.

METHODS

Both task-related brain activity measured by functional magnetic resonance imaging by reading the five-digit numbers and resting-state functional connectivity (FC) measured by 150 T2-weighted echo planar images acquired without any task were investigated in 12 patients with adductor SD and in 16 healthy controls.

RESULTS

The patients with SD showed significantly higher task-related brain activation in the left middle temporal gyrus, left thalamus, bilateral primary motor area, bilateral premotor area, bilateral cerebellum, bilateral somatosensory area, right insula, and right putamen compared with the controls. Region of interest voxel FC analysis revealed many FC changes within the cerebellum-basal ganglia-thalamus-cortex loop in the patients with SD. Of the significant connectivity changes between the patients with SD and the controls, the FC between the left thalamus and the left caudate nucleus was significantly correlated with clinical parameters in SD.

CONCLUSION

The higher task-related brain activity in the insula and cerebellum was consistent with previous neuroimaging studies, suggesting that these areas are one of the unique characteristics of phonation-induced brain activity in SD. Based on FC analysis and their significant correlations with clinical parameters, the basal ganglia network plays an important role in the pathogenesis of SD.

Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study

Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.

Resting-state functional MR imaging shed insights into the brain of diabetes

Diabetes mellitus is a common metabolic disease which is associated with increasing risk for multiple cognitive declines. Alterations in brain functional connectivity are believed to be the mechanisms underlying the cognitive function impairments. During the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) has been developed as a major tool to study brain functional connectivity in vivo. This paper briefly reviews the diabetes-associated cognitive impairment, analysis algorithms and clinical applications of rs-fMRI. We also provide future perspectives of rs-fMRI in diabetes.

Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data

Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.

Brain resting state functional magnetic resonance imaging in patients with takotsubo cardiomyopathy an inseparable pair of brain and heart

BACKGROUND

Takotsubo cardiomyopathy (TTC) is often triggered by emotional or physical stress factors. Psychological variables can have an impact on the physical manifestations of heart disease. TTC may reflect stunned myocardium from a neurogenic source. Anatomical connections between different parts of the brain can be visualized by diffusion tensor imaging (DTI) and thus, expressed by diffusion coefficient - fractional anisotropy (FA). A novel tool used to evaluate brain function in the absence of task is resting state functional magnetic resonance imaging (RS-fMRI).

METHODS

The study included both psychological tests and RS-fMRI examination, and was performed uniformly, in patients with takotsubo and healthy controls. The final group of patients consisted of 13 women, each who underwent a typical pattern of TTC triggered by emotionally stressful factors. The control group included thirteen healthy, age-matched women.

RESULTS

Psychological tests revealed that the Type D personality was not more likely to appear among studied patients with takotsubo cardiomyopathy than amongst the healthy population. However, the level of anxiety seen in patients with TTC was increased. There were no differences in FA values between patients and healthy controls. RS-fMRI showed that TTC patients had increased connectivity areas in the precuneus. The healthy controls, when compared to TTC patients had increased connectivity in the ventromedial prefrontal cortex.

CONCLUSIONS

Taking into account the RS-fMRI results, psychological testing may suggest that TTC patients place a greater focus on themselves (increased tendency to experience negative affectivity, greater conscientiousness) and might have problems with emotional control. Our findings lead to the hypothesis that there is a personality profile for TTC patients' reactions to stressful triggers.

Organization of the intrinsic functional network in the cervical spinal cord: A resting state functional MRI study

Resting state functional magnetic resonance imaging (rsfMRI) has been extensively applied to investigate the organization of functional networks in the brain. As an essential part of the central nervous system (CNS), the spinal cord has not been well explored about its intrinsic functional network. In this study, we aim to thoroughly investigate the characteristics of the intrinsic functional network in the spinal cord using rsfMRI. Functional connectivity and graph theory analysis were employed to evaluate the organization of the functional network, including its topology and network communication properties. Furthermore, the reproducibility of rsfMRI analysis on the spinal cord was also examined by intra-class correlation (ICC). Comprehensive evaluation of the intrinsic functional organization presented a non-uniform distribution of topological characteristics of the functional network, in which the upper levels (C2 and C3 vertebral levels) of the cervical spinal cord showed high levels of connectivity. The present results revealed the significance of the upper cervical cord in the intrinsic functional network of the human cervical spinal cord. In addition, this study demonstrated the efficiency of the cervical spinal cord functional network and the reproducibility of rsfMRI analysis on the spinal cord was also confirmed. As knowledge expansion of intrinsic functional network from the brain to the spinal cord, this study shed light on the organization of the spinal cord functional network in both normal development and clinical disorders.

Altered Resting-State Functional Connectivity in the Hand Motor Network in Glioma Patients

To examine the functional connectivity of the primary and supplementary motor areas (SMA) in glioma patients using resting-state functional MRI (rfMRI). To correlate rfMRI data with tumor characteristics and clinical information to characterize functional reorganization of resting-state networks (RSN) and the limitations of this method. This study was IRB approved and in compliance with Health Insurance Portability and Accountability Act. Informed consent was waived in this retrospective study. We analyzed rfMRI in 24 glioma patients and 12 age- and sex-matched controls. We compared global activation, interhemispheric connectivity, and functional connectivity in the hand motor RSNs using hemispheric voxel counts, pairwise Pearson correlation, and pairwise total spectral coherence. We explored the relationship between tumor grade, volume, location, and the patient's clinical status to functional connectivity. Global network activation and interhemispheric connectivity were reduced in gliomas (p < 0.05). Functional connectivity between the bilateral motor cortices and the SMA was reduced in gliomas (p < 0.01). High-grade gliomas had lower functional connectivity than low-grade gliomas (p < 0.05). Tumor volume and distance to ipsilateral motor cortex demonstrated no association with functional connectivity loss. Functional connectivity loss is associated with motor deficits in low-grade gliomas, but not in high-grade gliomas. Global reduction in resting-state connectivity in areas distal to tumor suggests that radiological tumor boundaries underestimate areas affected by glioma. Association between motor deficits and rfMRI suggests that rfMRI may accurately reflect functional changes in low-grade gliomas. Lack of association between rfMRI and clinical motor deficits implies decreased sensitivity of rfMRI in high-grade gliomas, possibly due to neurovascular uncoupling.