Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces.

Prior studies have used graph analysis of resting-state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test-retest reliability and sensor versus source association of global graph measures. Atlas-based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage-corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.

Behavioral phenotypes of pediatric temporal lobe epilepsy.

OBJECTIVE: A broad spectrum of emotional-behavioral problems have been reported in pediatric temporal lobe epilepsy (TLE), but with considerable variability in their presence and nature of expression, which hampers precise identification and treatment. The present study aimed to empirically identify latent patterns or behavioral phenotypes and their correlates. METHODS: Data included parental ratings of emotional-behavioral status on the Behavior Assessment System for Children, 2nd Edition (BASC-2) of 81 children (mean age = 11.79, standard deviation [SD] = 3.93) with TLE. The nine clinical subscales were subjected to unsupervised machine learning to identify behavioral subgroups. To explore concurrent validity and the underlying composition of the identified clusters, we examined demographic factors, seizure characteristics, psychosocial factors, neuropsychological performance, psychiatric status, and health-related quality of life (HRQoL). RESULTS: Three behavioral phenotypes were identified, which included no behavioral concerns (Cluster 1, 43% of sample), externalizing problems (Cluster 2, 41% of sample), and internalizing problems (Cluster 3, 16% of sample). Behavioral phenotypes were characterized by important differences across clinical seizure variables, psychosocial/familial factors, everyday executive functioning, and HRQoL. Cluster 2 was associated with younger child age, lower maternal education, and higher rate of single-parent households. Cluster 3 was associated with older age at epilepsy onset and higher rates of hippocampal sclerosis and parental psychiatric history. Both Cluster 2 and 3 demonstrated elevated family stress. Concurrent validity was demonstrated through the association of psychiatric (i.e., rate of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) disorders and psychotropic medication) and parent-rated HRQoL variables. SIGNIFICANCE: Youth with TLE present with three distinct behavioral phenotypes that correspond with important clinical and sociodemographic markers. The current findings demonstrate the variability of behavioral presentations in youth with TLE and provide a preliminary framework for screening and targeting intervention to enhance support for youth with TLE and their families.

Global network alterations of the cognitive phenotypes in pediatric temporal lobe epilepsy.

OBJECTIVE: An emerging literature suggests that the neuropsychological sequelae of pediatric temporal lobe epilepsy (TLE) are characterized by a continuum of cognitive phenotypes that range in type and severity. The goal of the present investigation was to better characterize the neuropsychological networks that underlie these phenotypes. METHODS: The study included 59 patients with TLE who were empirically categorized into three cognitive phenotypes (normal, focal, and generalized impairment). Nine neuropsychological measures representing multiple cognitive domains (i.e., reasoning, language, visouperception, memory, and executive function) were examined by graph theory to characterize the global network properties of the cognitive phenotypes. RESULTS: Across the cognitive phenotype groups (i.e., normal, focal, generalized impaired) the following findings emerged: (1) the adjacency matrices demonstrated different patterns of association between cognitive measures within the neuropsychological network; (2) global measures including global efficiency (GE) and average clustering coefficient (aCC) showed a stepwise increase across the range of impaired pediatric TLE phenotypes; however, modularity (M) demonstrated the opposite pattern. IMPRESSIONS: Cognitive networks in pediatric TLE demonstrate stepwise perturbation in underlying neuropsychological networks. Graph theory offers a novel approach to examine cognitive abnormalities in pediatric TLE that may be applied to other pediatric epilepsies.

Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data.

Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra-hemispheric networks of healthy controls (HCs) and patients with left- or right-hemispheric temporal lobe or temporal plus extra-temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra-hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve-Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network-based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra-hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra-hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.

Mapping critical hubs of receptive and expressive language using MEG: A comparison against fMRI.

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q < 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ±â€¯0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ±â€¯0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ±â€¯0.35 and fMRI-LI = 0.46 ±â€¯0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.

Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study.

Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods-surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods-in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.

A model for visual naming based on spatiotemporal dynamics of ECoG high-gamma modulation.

OBJECTIVE: We studied spatiotemporal dynamics of electrocorticographic (ECoG) high-gamma modulation (HGM) during visual naming. METHODS: In 8 patients, aged 4-19 years, with left hemisphere subdural electrodes, propagation of ECoG HGM during overt visual naming was mapped with trial-averaged time-frequency analysis. Group-level synthesis was performed by transforming all electrodes to a standard space and assigning cortical parcels based on a reference atlas. RESULTS: After image display following cortical parcels were activated: inferior occipital, caudal angular, fusiform, and middle temporal gyri, and superior temporal sulcus [0-400 ms]; rostral pars triangularis (A45r), inferior frontal sulcus, caudal dorsolateral premotor cortex (A6cdl) [300-600 ms]; caudal ventrolateral premotor cortex (A6cvl), caudal pars triangularis (A45c), pars opercularis (A44) [400-800 ms]; primary sensorimotor cortex [600-1400 ms], with most prominent HGM in glossolaryngeal region (A4tl). Lastly, auditory cortex (A41/A42) and superior temporal gyrus (A22) were activated [900 ms-1.4 s]. After 1.5 s, HGM decreased globally, except in ventrolateral premotor cortex. CONCLUSIONS: During visual naming, ECoG HGM shows a sequential but overlapping spatiotemporal course through cortical regions. We provide neurophysiologic validation for a model of visual naming incorporating both modular and distributed cortical processing. This may explain cognitive deficits seen in some patients after surgery involving HGM naming sites outside perisylvian language cortex.

The Role of the Primary Sensory Cortices in Early Language Processing.

The results of this magnetoencephalography study challenge two long-standing assumptions regarding the brain mechanisms of language processing: First, that linguistic processing proper follows sensory feature processing effected by bilateral activation of the primary sensory cortices that lasts about 100 msec from stimulus onset. Second, that subsequent linguistic processing is effected by left hemisphere networks outside the primary sensory areas, including Broca's and Wernicke's association cortices. Here we present evidence that linguistic analysis begins almost synchronously with sensory, prelinguistic verbal input analysis and that the primary cortices are also engaged in these linguistic analyses and become, consequently, part of the left hemisphere language network during language tasks. These findings call for extensive revision of our conception of linguistic processing in the brain.

Neural Mechanism Underling Comprehension of Narrative Speech and Its Heritability: Study in a Large Population.

Comprehension of narratives constitutes a fundamental part of our everyday life experience. Although the neural mechanism of auditory narrative comprehension has been investigated in some studies, the neural correlates underlying this mechanism and its heritability remain poorly understood. We investigated comprehension of naturalistic speech in a large, healthy adult population (n = 429; 176/253 M/F; 22-36 years of age) consisting of 192 twin pairs (49 monozygotic and 47 dizygotic pairs) and 237 of their siblings. We used high quality functional MRI datasets from the Human Connectome Project (HCP) in which a story-based paradigm was utilized for the auditory narrative comprehension. Our results revealed that narrative comprehension was associated with activations of the classical language regions including superior temporal gyrus (STG), middle temporal gyrus (MTG), and inferior frontal gyrus (IFG) in both hemispheres, though STG and MTG were activated symmetrically and activation in IFG were left-lateralized. Our results further showed that the narrative comprehension was associated with activations in areas beyond the classical language regions, e.g. medial superior frontal gyrus (SFGmed), middle frontal gyrus (MFG), and supplementary motor area (SMA). Of subcortical structures, only the hippocampus was involved. The results of heritability analysis revealed that the oral reading recognition and picture vocabulary comprehension were significantly heritable (h 2 > 0.56, p < 10- 13). In addition, the extent of activation of five areas in the left hemisphere, i.e. STG, IFG pars opercularis, SFGmed, SMA, and precuneus, and one area in the right hemisphere, i.e. MFG, were significantly heritable (h 2 > 0.33, p < 0.0004). The current study, to the best of our knowledge, is the first to investigate auditory narrative comprehension and its heritability in a large healthy population. Referring to the excellent quality of the HCP data, our results can clarify the functional contributions of linguistic and extra-linguistic cortices during narrative comprehension.

Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease.

Objective: Early Alzheimer's disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline. Methods: A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-ß (Aß) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD). Results: Aß emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aß, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD. Conclusion: This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development.

Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology.

Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.

A Proposed Stereoelectroencephalography Electrode Nomenclature and Call for Standardization.

INTRODUCTION: Between 20 and 40% of patients with epilepsy are considered pharmacoresistant. Stereoelectroencephalography (sEEG) is frequently used as an invasive method for localizing seizures in patients with pharmacoresistant epilepsy who are surgical candidates; however, electrode nomenclature varies widely across institutions. This lack of standardization can have many downstream consequences, including difficulty with intercenter or intracenter interpretation, communication, and reliability. METHODS: The authors propose a novel sEEG nomenclature that is both intuitive and comprehensive. Considerations include clear/precise entry and target anatomical locations, laterality, distinction of superficial and deep structures, functional mapping, and relative labeling of electrodes in close proximity if needed. Special consideration was also given to electrodes approximating radiographically distinct lesions. The accuracy of electrode identification and the use of correct entry-target labels were assessed by neurosurgeons and epileptologists, not directly involved in each case. RESULTS: The authors' nomenclature was used in 41 consecutive sEEG cases (497 electrodes total) within their institution. After reconstruction was complete, the accuracy of electrode identification was 100%, and the correct use of entry-target labels was 98%. The last 30 sEEG cases had 100% correct use of entry-target labels. CONCLUSIONS: The proposed sEEG nomenclature demonstrated both high accuracy in electrode identification and consistent use of entry-target labeling. The authors submit this nomenclature as a model for standardization across epilepsy surgery centers. They intend to improve practicability, ease of use, and specificity of this nomenclature through collaboration with other surgical epilepsy centers.

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study.

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal, and fast-progressive neurodegenerative disease characterized by the degeneration of motor neurons. ALS patients often experience an initial misdiagnosis or a diagnostic delay due to the current unavailability of an efficient biomarker. Since impaired speech is typical in ALS, we hypothesized that functional differences between healthy and ALS participants during speech tasks can be explained by cortical pattern changes, thereby leading to the identification of a neural biomarker for ALS. In this pilot study, we collected magnetoencephalography (MEG) recordings from three early-diagnosed patients with ALS and three healthy controls during imagined (covert) and overt speech tasks. First, we computed sensor correlations, which showed greater correlations for speakers with ALS than healthy controls. Second, we compared the power of the MEG signals in canonical bands between the two groups, which showed greater dissimilarity in the beta band for ALS participants. Third, we assessed differences in functional connectivity, which showed greater beta band connectivity for ALS than healthy controls. Finally, we performed single-trial classification, which resulted in highest performance with beta band features (∼ 98%). These findings were consistent across trials, phrases, and participants for both imagined and overt speech tasks. Our preliminary results indicate that speech-evoked beta oscillations could be a potential neural biomarker for diagnosing ALS. To our knowledge, this is the first demonstration of the detection of ALS from single-trial neural signals.

An application of dynamical directed connectivity of ictal intracranial EEG recordings in seizure onset zone localization.

BACKGROUND: Identification of the seizure onset zone (SOZ) is a challenging task in epilepsy surgery. Patients with epilepsy have an altered brain network, allowing connectivity-based analyses to have a great potential in SOZ identification. We investigated a dynamical directed connectivity analysis utilizing ictal intracranial electroencephalographic (iEEG) recordings and proposed an algorithm for SOZ identification based on grouping iEEG contacts. NEW METHODS: Granger Causality was used for directed connectivity analysis in this study. The intracranial contacts were grouped into visually detected contacts (VDCs), which were identified as SOZ by epileptologists, and non-resected contacts (NRCs). The intragroup and intergroup directed connectivity for VDCs and NRCs were calculated around seizure onset. We then proposed an algorithm for SOZ identification based on the cross-correlation of intragroup outflow and inflow of SOZ candidate contacts. RESULTS: Our results revealed that the intragroup connectivity of VDCs (VDC→VDC) was significantly larger than the intragroup connectivity of NRCs (NRC→NRC) and the intergroup connectivity between NRCs and VDCs (NRC→VDC) around seizure onset. We found that the proposed algorithm had 90.1 % accuracy for SOZ identification in the seizure-free patients. COMPARISON WITH EXISTING METHODS: The existing connectivity-based methods for SOZ identification often use either outflow or inflow. In this study, SOZ contacts were identified by integrating outflow and inflow based on the cross correlation between these two measures. CONCLUSIONS: The proposed group-based dynamical connectivity analysis in this study can aid our understanding of underlying seizure network and may be used to assist in identifying the SOZ contacts before epilepsy surgery.

MEG language mapping using a novel automatic ECD algorithm in comparison with MNE, dSPM, and DICS beamformer.

Introduction: The single equivalent current dipole (sECD) is the standard clinical procedure for presurgical language mapping in epilepsy using magnetoencephalography (MEG). However, the sECD approach has not been widely used in clinical assessments, mainly because it requires subjective judgements in selecting several critical parameters. To address this limitation, we developed an automatic sECD algorithm (AsECDa) for language mapping. Methods: The localization accuracy of the AsECDa was evaluated using synthetic MEG data. Subsequently, the reliability and efficiency of AsECDa were compared to three other common source localization methods using MEG data recorded during two sessions of a receptive language task in 21 epilepsy patients. These methods include minimum norm estimation (MNE), dynamic statistical parametric mapping (dSPM), and dynamic imaging of coherent sources (DICS) beamformer. Results: For the synthetic single dipole MEG data with a typical signal-to-noise ratio, the average localization error of AsECDa was less than 2 mm for simulated superficial and deep dipoles. For the patient data, AsECDa showed better test-retest reliability (TRR) of the language laterality index (LI) than MNE, dSPM, and DICS beamformer. Specifically, the LI calculated with AsECDa revealed excellent TRR between the two MEG sessions across all patients (Cor = 0.80), while the LI for MNE, dSPM, DICS-event-related desynchronization (ERD) in the alpha band, and DICS-ERD in the low beta band ranged lower (Cor = 0.71, 0.64, 0.54, and 0.48, respectively). Furthermore, AsECDa identified 38% of patients with atypical language lateralization (i.e., right lateralization or bilateral), compared to 73%, 68%, 55%, and 50% identified by DICS-ERD in the low beta band, DICS-ERD in the alpha band, MNE, and dSPM, respectively. Compared to other methods, AsECDa's results were more consistent with previous studies that reported atypical language lateralization in 20-30% of epilepsy patients. Discussion: Our study suggests that AsECDa is a promising approach for presurgical language mapping, and its fully automated nature makes it easy to implement and reliable for clinical evaluations.

Connectivity analysis of novelty process in habitual short sleepers.

Neurophysiological processes underlying auditory memory and attention are impaired in habitually short sleepers. The aim of this study was to use dynamic causal modeling (DCM) to study the mechanisms of these impairments in short sleepers. Eight normal sleepers (total sleep time (TST)=7-8h) and nine habitual short sleepers (TST ≤ 6 h) participated. The time in bed was increased from habitual (≤ 6 h) to extended (~8.5h) for one week in the short sleep group. Event related potentials (ERPs) were collected using an auditory novelty task in "IGNORE" and "ATTEND" conditions. Fourteen DCM models were considered using different configurations of connections among the following six areas: left and right primary auditory cortices, superior temporal gyri (STG), and inferior temporal gyri (IFG). After fitting the ERPs to the 14 models (separately for the IGNORE and ATTEND conditions), the best model (across subjects) was chosen using the Bayesian model comparison. For both conditions, the connection from right-STG to right-IFG for normal sleepers was significantly greater than habitual short sleepers. This connection did not differ in habitual short sleepers before and after one week of extended sleep time. This connection for normal sleepers was not significantly greater than the habitual short sleepers after one week of extended sleep. These results show that the deficiency of novelty processing, seen in short sleepers, can be explained by the differences in connectivity of the pathway between frontal and temporal brain areas as compared to the normal sleepers. In addition, one week of extended time in bed was not enough to fully normalize this neuronal pathway between STG and IFG in short sleepers.

Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study.

PURPOSE: Resting-state functional magnetic resonance imaging (Rs-fMRI) can be used to investigate the alteration of resting-state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. METHODS: This study used Rs-fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject-specific score based on group RSNs and a dual-regression approach (namely RSN scores); (2) voxel-wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. RESULTS: The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. CONCLUSION: We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks.

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Functional Magnetic Resonance Imaging and Applications in Dermatology.

As a noninvasive imaging modality able to show the dynamic changes in neurologic activity, functional magnetic resonance imaging has revolutionized the ability to both map and further understand the functional regions of the brain. Current applications range from neurosurgical planning to an enormous variety of investigational applications across many diverse specialties. The main purpose of this article is to provide a foundational understanding of how functional magnetic resonance imaging is being used in research by outlining the underlying basic science, specific methods, and direct investigational and clinical applications. In addition, the use of functional magnetic resonance imaging in current dermatological research, especially in relation to studies concerning the skin‒brain axis, is explicitly addressed. This article also touches on the advantages and limitations concerning functional magnetic resonance imaging in comparison with other similar techniques.

Magnetometers vs Gradiometers for Neural Speech Decoding.

Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.