Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI.

Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.

Functional neuroplasticity of facilitation and interference effects on inhibitory control following 3-month physical exercise in aging.

Preservation of executive function, like inhibition, closely links to the quality of life in senior adults. Although neuroimaging literature has shown enhanced inhibitory function followed by aerobic exercise, current evidence implies inconsistent neuroplasticity patterns along different time durations of exercise. Hence, we conducted a 12-week exercise intervention on 12 young and 14 senior volunteers and repeatedly measured the inhibitory functionality of distinct aspects (facilitation and interference effects) using the numerical Stroop task and functional Magnetic Resonance Imaging. Results showcased improved accuracy and reduced reaction times (RT) after 12-week exercise, attributed to frontoparietal and default mode network effects. In young adults, the first phase (0 to six weeks) exercise increased the activation of the right superior medial frontal gyrus, associated with reduced RT in interference, but in the second intervention phase (six to twelve weeks), the decreased activation of the left superior medial frontal gyrus positively correlated with reduced RT in facilitation. In senior adults, the first six-week intervention led to reduced activations of the inferior frontal gyrus, inferior parietal gyrus, and default mode network regions, associated with the reduced RT in interference. Still, in the second intervention phase, only the visual area exhibited increased activity, associated with reduced RT in interference. Except for the distinctive brain plasticity between the two phases of exercise intervention, the between-group comparison also presented that the old group gained more cognitive benefits within the first six weeks of exercise intervention; however, the cognitive improvements in the young group occurred after six weeks of intervention. Limited by the sample size, these preliminary findings corroborated the benefits of aerobic exercise on the inhibitory functions, implying an age × exercise interaction on the brain plasticity for both facilitation and interference.

Traces of EEG-fMRI coupling reveals neurovascular dynamics on sleep inertia.

Upon emergence from sleep, individuals experience temporary hypo-vigilance and grogginess known as sleep inertia. During the transient period of vigilance recovery from prior nocturnal sleep, the neurovascular coupling (NVC) may not be static and constant as assumed by previous neuroimaging studies. Stemming from this viewpoint of sleep inertia, this study aims to probe the NVC changes as awakening time prolongs using simultaneous EEG-fMRI. The time-lagged coupling between EEG features of vigilance and BOLD-fMRI signals, in selected regions of interest, was calculated with one pre-sleep and three consecutive post-awakening resting-state measures. We found marginal changes in EEG theta/beta ratio and spectral slope across post-awakening sessions, demonstrating alterations of vigilance during sleep inertia. Time-varying EEG-fMRI coupling as awakening prolonged was evidenced by the changing time lags of the peak correlation between EEG alpha-vigilance and fMRI-thalamus, as well as EEG spectral slope and fMRI-anterior cingulate cortex. This study provides the first evidence of potential dynamicity of NVC occurred in sleep inertia and opens new avenues for non-invasive neuroimaging investigations into the neurophysiological mechanisms underlying brain state transitions.

Temporal consistency of neurovascular components on awakening: preliminary evidence from electroencephalography, cerebrovascular reactivity, and functional magnetic resonance imaging.

Sleep inertia (SI) is a time period during the transition from sleep to wakefulness wherein individuals perceive low vigilance with cognitive impairments; SI is generally identified by longer reaction times (RTs) in attention tasks immediately after awakening followed by a gradual RT reduction along with waking time. The sluggish recovery of vigilance in SI involves a dynamic process of brain functions, as evidenced in recent functional magnetic resonance imaging (fMRI) studies in within-network and between-network connectivity. However, these fMRI findings were generally based on the presumption of unchanged neurovascular coupling (NVC) before and after sleep, which remains an uncertain factor to be investigated. Therefore, we recruited 12 young participants to perform a psychomotor vigilance task (PVT) and a breath-hold task of cerebrovascular reactivity (CVR) before sleep and thrice after awakening (A1, A2, and A3, with 20 min intervals in between) using simultaneous electroencephalography (EEG)-fMRI recordings. If the NVC were to hold in SI, we hypothesized that time-varying consistencies could be found between the fMRI response and EEG beta power, but not in neuron-irrelevant CVR. Results showed that the reduced accuracy and increased RT in the PVT upon awakening was consistent with the temporal patterns of the PVT-induced fMRI responses (thalamus, insula, and primary motor cortex) and the EEG beta power (Pz and CP1). The neuron-irrelevant CVR did not show the same time-varying pattern among the brain regions associated with PVT. Our findings imply that the temporal dynamics of fMRI indices upon awakening are dominated by neural activities. This is the first study to explore the temporal consistencies of neurovascular components on awakening, and the discovery provides a neurophysiological basis for further neuroimaging studies regarding SI.

The impact of emotional context on neural substrates of respiratory sensory gating.

Psychological challenges have been found to impact respiratory symptom perception in healthy individuals as well as in patients with various neurological disorders. Human respiratory sensory gating is an objective measure to examine respiratory sensory information processing of repetitive respiratory mechanical stimuli in the central nervous system. With this electrophysiological method, patients with higher anxiety levels showed reduced respiratory sensory gating function in the cortex, and increased symptom perception. In addition, positive emotional contexts were found to increase the respiratory sensory gating function using RREPs. However, neural substrates related to emotional impacts on respiratory sensory gating remain still unclear. In the present study, we examined the emotion processing of respiratory sensory gating using functional magnetic resonance imaging. We hypothesized that positive compared with neutral stimuli would result in reduced brain activations in cortical areas with the paired occlusion paradigm. Thirty-five healthy adults participated in this event-designed fMRI experiment. Paired inspiratory occlusions (two transient occlusions with a 500 ms inter-stimulus-interval are delivered during one inspiration) were provided using an external trigger outside of the scanner. At least 40 paired inspiratory occlusions were collected for each trial. The experiment contained three runs during which participants underwent 12 min for the paired inspiratory occlusion paradigm while watching a fixation cross (the control condition), neutral and positive emotional picture series. The order of emotional picture series was randomized across the participants. Our results revealed an overall trend of reduction of brain activity from the neutral (minus fixation) condition, to the pleasant (minus fixation) condition. For bilateral thalamus and primary visual cortices, there was no significant difference in neural activation between the two contrasts of pleasant (ContrastP-F) and neutral condition (ContrastN-F). The activation of the mid-cingulate and the orbitofrontal cortex was lower in ContrastP-F compared to ContrastN-F. In conclusion, our results suggest that emotional context, especially positive valence, modulates neural correlates in middle cingulate cortex and orbitofrontal cortex in terms of respiratory sensory gating. Future studies are recommended to test emotional impacts on respiratory sensations in patients with neurological disorders.

Localizing Spectral Interactions in the Resting State Network Using the Hilbert-Huang Transform.

Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To this point, we aim to unveil the fMRI-based spectral interactions using the time-frequency (TF) analysis; however, Fourier-based TF analyses impose restrictions on revealing frequency interactions given the limited time points in fMRI signals. Instead of using the Fourier-based wavelet analysis to identify the fMRI frequency of interests, we employed the Hilbert-Huang transform (HHT) for probing the specific frequency contribution to the functional integration, called ensemble spectral interaction (ESI). By simulating data with time-variant frequency changes, we demonstrated the Hilbert TF maps with high spectro-temporal resolution and full accessibility in comparison with the wavelet TF maps. By detecting amplitude-to-amplitude frequency couplings (AAC) across brain regions, we elucidated the ESI disparity between the eye-closed (EC) and eye-open (EO) conditions in rs-fMRI. In the visual network, the strength of the spectral interaction within 0.03-0.04 Hz was amplified in EC compared with that in EO condition, whereas a canonical connectivity analysis did not present differences between conditions. Collectively, leveraging from the instantaneous frequency of HHT, we firstly addressed the ESI technique to map the fMRI-based functional connectivity in a brand-new AAC perspective. The ESI possesses potential in elucidating the functional connectivity at specific frequency bins, thereby providing additional diagnostic merits for future clinical neuroscience.

Neural substrates of respiratory sensory gating: A human fMRI study.

The involvement of neural substrates in respiratory sensory gating remained unclear. This study aimed to investigate cortical and subcortical activations associated with respiratory sensory gating by using functional magnetic resonance imaging. First, we hypothesized that paired occlusions would induce neural activation in cortical and subcortical areas, including the thalamus and sensorimotor cortices. Secondly, we hypothesized that, in terms of parameter estimates in the general linear model, the activation effect size ß ratios (ßpaired/ßsingle) would be less than 2 due to central neural gating mechanism. Forty-six healthy participants were included in the study. Our analyses showed that the ßpaired/ßsingle ratios for the supramarginal gyrus, basal ganglia, thalamus, and middle frontal gyrus were less than 2. In conclusion, our results demonstrated a non-linear relationship regarding brain neural activations in response to paired versus single occlusions, suggesting that respiratory sensory information is gated at the subcortical and cortical levels.

Mindfulness-based cognitive therapy on bereavement grief: Alterations of resting-state network connectivity associate with changes of anxiety and mindfulness.

Bereavement, the experience of losing a loved one, is one of the most catastrophic but inevitable events in life. It causes grief and intense depression-like sadness. Recent studies have revealed the effectiveness and proficiency of mindfulness-based cognitive therapy (MBCT) in emotional regulation among bereavement populations. MBCT improves the well-being of the bereaved by enhancing cognitive performances. Regarding the neural correlates of bereavement grief, previous studies focused on the alleviation of emotion-cognition interferences at specific brain regions. Here, we hypothesized that the bereavement grief fundamentally triggers global alterations in the resting-state brain networks and part of the internetwork connectivity could be reformed after MBCT intervention. We recruited 19 bereaved individuals who participated the 8-week MBCT program. We evaluated (a) the large-scale changes in brain connectivity affected by the MBCT program; as well as (b) the association between connectivity changes and self-rated questionnaire. First, after MBCT, the bereaved individuals showed the reduction of the internetwork connectivity in the salience, default-mode and fronto-parietal networks in the resting state but not under emotional arousal, implying the alleviated attention to spontaneous mind wandering after MBCT. Second, the alterations of functional connectivity between subcortical (e.g., caudate) and cortical networks (e.g., cingulo-opercular/sensorimotor) were associated with the changes of the mindfulness scale, the anxiety and the emotion regulation ability. In summary, MBCT could enhance spontaneous emotion regulation among the bereaved individuals through the internetwork reorganizations in the resting state.

Think Hard or Think Smart: Network Reconfigurations After Divergent Thinking Associate With Creativity Performance.

Evidence suggests divergent thinking is the cognitive basis of creative thoughts. Neuroimaging literature using resting-state functional connectivity (RSFC) has revealed network reorganizations during divergent thinking. Recent studies have revealed the changes of network organizations when performing creativity tasks, but such brain reconfigurations may be prolonged after task and be modulated by the trait of creativity. To investigate the dynamic reconfiguration, 40 young participants were recruited to perform consecutive Alternative Uses Tasks (AUTs) for divergent thinking and two resting-state scans (before and after AUT) were used for mapping the brain reorganizations after AUT. We split participants into high- and low-creative groups based on creative achievement questionnaire (CAQ) and targeted on reconfigurations of the two brain networks: (1) default-mode network (DMN) and (2) the network seeded at the left inferior frontal gyrus (IFG) because the between-group difference of AUT-induced brain activation located at the left IFG. The changes of post-AUT RSFCs (DMN and IFGN) indicated the prolonged effect of divergent thinking. More specifically, the alterations of RSFCIFG-AG and RSFCIFG-IPL (AG: angular gyrus, IPG: inferior parietal lobule) in the high-creative group had positive relationship with their AUT performances (originality and fluency), but not found in the low-creative group. Furthermore, the RSFC changes of DMN did not present significant relationships with AUT performances. The findings not only confirmed the possibility of brain dynamic reconfiguration following divergent thinking, but also suggested the distinct IFGN reconfiguration between individuals with different creativity levels.

Reliability of Synthetic Brain MRI for Assessment of Ischemic Stroke with Phantom Validation of a Relaxation Time Determination Method.

The reliability of relaxation time measures in synthetic magnetic resonance images (MRIs) of homemade phantoms were validated, and the diagnostic suitability of synthetic imaging was compared to that of conventional MRIs for detecting ischemic lesions. Phantoms filled with aqueous cupric-sulfate (CuSO4) were designed to mimic spin-lattice (T1) and spin-spin (T2) relaxation properties and were used to compare their accuracies and stabilities between synthetic and conventional scans of various brain tissues. To validate the accuracy of synthetic imaging in ischemic stroke diagnoses, the synthetic and clinical scans of 18 patients with ischemic stroke were compared, and the quantitative contrast-to-noise ratios (CNRs) were measured, using the Friedman test to determine significance in differences. Results using the phantoms showed no significant differences in the interday and intersession synthetic quantitative T1 and T2 values. However, between synthetic and referenced T1 and T2 values, differences were larger for longer relaxation times, showing that image intensities in synthetic scans are relatively inaccurate in the cerebrospinal fluid (CSF). Similarly, CNRs in CSF regions of stroke patients were significantly different on synthetic T2-weighted and T2-fluid-attenuated inversion recovery images. In contrast, differences in stroke lesions were insignificant between the two. Therefore, interday and intersession synthetic T1 and T2 values are highly reliable, and discrepancies in synthetic T1 and T2 relaxation times and image contrasts in CSF regions do not affect stroke lesion diagnoses. Additionally, quantitative relaxation times from synthetic images allow better estimations of ischemic stroke onset time, consequently increasing confidence in synthetic MRIs as diagnostic tools for ischemic stroke.

Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity.

PURPOSE: Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS: Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS: With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions. CONCLUSION: The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

The effect of anxiety on brain activation patterns in response to inspiratory occlusions: an fMRI study.

Respiratory sensations such as breathlessness are prevalent in many diseases and are amplified by increased levels of anxiety. Cortical activation in response to inspiratory occlusions in high- and low-anxious individuals was found different in previous studies using the respiratory-related evoked potential method. However, specific brain areas showed different activation patterns remained unknown in these studies. Therefore, the purpose of this study was to compare cortical and subcortical neural substrates of respiratory sensation in response to inspiratory mechanical occlusion stimuli between high- and low-anxious individuals using functional magnetic resonance imaging (fMRI). In addition, associations between brain activation patterns and levels of anxiety, and breathlessness were examined. Thirty-four (17 high- and 17 low-anxious) healthy non-smoking adults with normal lung function completed questionnaires on anxiety (State Trait Anxiety Inventory - State), and participated in a transient inspiratory occlusion fMRI experiment. The participants breathed with a customized face-mask while respiration was repeatedly interrupted by a transient inspiratory occlusion of 150-msec, delivered every 2 to 4 breaths. Breathlessness was assessed by self-report. At least 32 occluded breaths were collected for data analysis. The results showed that compared to the low-anxious group, the high-anxious individuals demonstrated significantly greater neural activations in the hippocampus, insula, and middle cingulate gyrus in response to inspiratory occlusions. Moreover, a significant relationship was found between anxiety levels and activations of the right inferior parietal gyrus, and the right precuneus. Additionally, breathlessness levels were significantly associated with activations of the bilateral thalamus, bilateral insula and bilateral cingulate gyrus. The above evidences support stronger recruitment of emotion-related cortical and subcortical brain areas in higher anxious individuals, and thus these areas play an important role in respiratory mechanosensation mediated by anxiety.

Extraction of large-scale structural covariance networks from grey matter volume for Parkinson's disease classification.

OBJECTIVES: To identify disease-related spatial covariance patterns of grey matter volume as an aid in the classification of Parkinson's disease (PD). METHODS: Seventy structural covariance networks (SCNs) based on grey matter volume covariance patterns were defined using independent component analysis with T1-weighted structural MRI scans (discovery sample, 70 PD patients and 70 healthy controls). An image-based classifier was constructed from SCNs using a multiple logistic regression analysis with a leave-one-out cross-validation-based feature selection scheme. A validation sample (26 PD patients and 26 healthy controls) was further collected to evaluate the generalization ability of the constructed classifier. RESULTS: In the discovery sample, 13 SCNs, including the cerebellum, anterior temporal poles, parahippocampal gyrus, parietal operculum, occipital lobes, supramarginal gyri, superior parietal lobes, paracingulate gyri and precentral gyri, had higher classification performance for PD. In the validation sample, the classifier had moderate generalization ability, with a mean sensitivity of 81%, specificity of 69% and overall accuracy of 75%. Furthermore, certain individual SCNs were also associated with disease severity. CONCLUSIONS: Although not applicable for routine care at present, our results provide empirical evidence that disease-specific, large-scale structural networks can provide a foundation for the further improvement of diagnostic MRI in movement disorders. KEY POINTS: • Disease-specific, large-scale SCNs can be identified from structural MRI. • A new network-based framework for PD classification is proposed. • An SCN-based classifier had moderate generalization ability in PD classification. • The selected SCNs provide valuable functional information regarding PD patients.

IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies.

Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.

Altered Brain Functional Connectome in Migraine with and without Restless Legs Syndrome: A Resting-State Functional MRI Study.

BACKGROUND: Migraine is frequently comorbid with restless legs syndrome (RLS), both displaying functional connectivity (FC) alterations in multiple brain networks, although the neurological basis of this association is unknown. METHODS: We performed resting-state functional magnetic resonance imaging and network-wise analysis of FC in migraine patients with and without RLS and healthy controls (CRL). Network-based statistics (NBS) and composite FC matrix analyses were performed to identify the patterns of FC changes. Correlation analyses were performed to identify associations between alterations in FC and clinical profiles. RESULTS: NBS results revealed that both migraine patients with and without RLS exhibited lower FC than CRL in the dorsal attention, salience, default mode, cingulo-opercular, visual, frontoparietal, auditory, and sensory/somatomotor networks. Further composite FC matrix analyses revealed differences in FC of the salience, default mode to subcortical and frontoparietal, auditory to salience, and memory retrieval networks between migraine patients with and without RLS. There was a trend toward a negative association between RLS severity and cross-network abnormalities in the default mode to subcortical network. DISCUSSION: Migraine patients with and without RLS exhibit disruptions of brain FC. Such findings suggest that these disorders are associated with differential neuropathological mechanisms and may aid in the future development of neuroimaging-driven biomarkers for these conditions.

Mindfulness Improves Emotion Regulation and Executive Control on Bereaved Individuals: An fMRI Study.

The grief of bereavement is recognized as a severe psychosocial stressor that can trigger a variety of mental and physical disorders, and the long-lasting unresolved grief has a detrimental effect on brain functionality. Literature has documented mindfulness-based cognitive therapy (MBCT) as an efficient treatment for improving well-being, specifically related to the mood and cognition, in a variety of populations. However, little attention has been devoted to neural mechanisms with regard to bereaved individuals' cognition after MBCT intervention. In this study, we recruited 23 bereaved participants who lost a significant relative within 6 months to 4 years to attend 8-week MBCT course. We used self-reporting questionnaires to measure emotion regulation and functional magnetic resonance imaging (fMRI) with the numerical Stroop task to evaluate the MBCT effect on executive control among the bereaved participants. The self-reported questionnaires showed improvements on mindfulness and reductions in grief, difficulties in emotion regulation, anxiety, and depression after the MBCT intervention. The fMRI analysis demonstrated two scenarios: (1) the activity of the fronto-parietal network slightly declined accompanied with significant improvements in the reaction time of incongruent trials; (2) the activities in the posterior cingulate cortex and thalamus were positively associated with the Texas Revised Inventory of Grief, implying emotional interferences on cognitive functions. Results indicated that MBCT facilitated the executive control function by alleviating the emotional interferences over the cognitive functions and suggested that the 8-week MBCT intervention significantly improved both executive control and emotion regulation in bereaved individuals.

Parkinson's disease: diagnostic utility of volumetric imaging.

PURPOSE: This paper aims to examine the effectiveness of structural imaging as an aid in the diagnosis of Parkinson's disease (PD). METHODS: High-resolution T 1-weighted magnetic resonance imaging was performed in 72 patients with idiopathic PD (mean age, 61.08 years) and 73 healthy subjects (mean age, 58.96 years). The whole brain was parcellated into 95 regions of interest using composite anatomical atlases, and region volumes were calculated. Three diagnostic classifiers were constructed using binary multiple logistic regression modeling: the (i) basal ganglion prior classifier, (ii) data-driven classifier, and (iii) basal ganglion prior/data-driven hybrid classifier. Leave-one-out cross validation was used to unbiasedly evaluate the predictive accuracy of imaging features. Pearson's correlation analysis was further performed to correlate outcome measurement using the best PD classifier with disease severity. RESULTS: Smaller volume in susceptible regions is diagnostic for Parkinson's disease. Compared with the other two classifiers, the basal ganglion prior/data-driven hybrid classifier had the highest diagnostic reliability with a sensitivity of 74%, specificity of 75%, and accuracy of 74%. Furthermore, outcome measurement using this classifier was associated with disease severity. CONCLUSIONS: Brain structural volumetric analysis with multiple logistic regression modeling can be a complementary tool for diagnosing PD.

Physiological Contribution in Spontaneous Oscillations: An Approximate Quality-Assurance Index for Resting-State fMRI Signals.

Resting-state fMRI (rs-fMRI) is receiving substantial attention for its sensitivity to functional abnormality in the brain networks of people with psychiatric and neurological disorders. However, because of the variety of rs-fMRI processing methods, the necessity of rs-fMRI quality assurance is increasing. Conventionally, the temporal signal-to-noise ratio (tSNR) is generally adopted for quality examination, but the tSNR does not guarantee reliable functional connectivity (FC) outcomes. Theoretically, intrinsic FC is supposed to reflect the spontaneous synchronization of neuronal basis, rather than that from thermal noise or non-neuronal physiological noise. Therefore, we proposed a new quality-assurance index for rs-fMRI to estimate the physiological contributions in spontaneous oscillations (PICSO). The PICSO index was designed as a voxel-wise measure for facilitating practical applications to all existing rs-fMRI data sets on the basis of two assumptions: Gaussian distributions in temporal fluctuations and ultra-slow changes of neural-based physiological fluctuations. To thoroughly validate the sensitivity of the proposed PICSO index to FC, we calibrated the preprocessing steps according to phantom data and verified the relationship between the PICSO and factors that are considered to affect FC in healthy participants (n = 12). Our results demonstrated that FC showed a significantly positive correlation with the PICSO. Moreover, for generating robust FC outcomes, directly acquiring data at a relatively large voxel size was more effective than performing smoothness on high-resolution data sets. In conclusion, compared with tSNR, the PICSO index is more sensitive to the resulting FC, providing a practical quality-assurance indicator for all existing rs-fMRI data sets.

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition.

BACKGROUND: Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience. NEW METHOD: To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise. RESULTS: We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD. COMPARISON WITH EXISTING METHOD(S): Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution. CONCLUSIONS: We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images.

There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimer's disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimer's disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using knowledge from the existing literature.