Differentiation of task complexity in long-term memory retrieval using multifractal detrended fluctuation analysis of fNIRS recordings.

The retrieval of phonological, lexical, semantic, or syntactic language information from long-term memory plays an important role in language processing. However, it remains unclear whether variability analysis of brain signals obtained using functional near-infrared spectroscopy (fNIRS) is able to separate language-related task conditions. This study employed multifractal detrended fluctuation (MFDF) analysis focusing on the width of the multifractal spectrum to elucidate whether high complexity tasks increase the fractal dynamics of brain activation signals compared to low complexity tasks. Nine Japanese college students participated in a long-term memory retrieval experiment using low (n ± 1) and high (n ± 2) complexity tasks. Our results showed that high complexity tasks induced a significantly larger multifractal spectral width in the posterior medial temporal gyri bilaterally, due to higher cognitive demands. These findings suggest that in addition to conventional techniques based on mean amplitude analysis and general linear modelling of fNIRS data, the use of MFDF analysis offers a powerful alternative methodology to gain a deeper understanding of long-term memory retrieval in language memory processing.

Native non-prototypicality in vowel perception induces prominent neuromagnetic mismatch intensities in non-native speakers: a pilot study.

Neural mismatch response resulting from the difference between prediction and observation is related to change detection and discrimination. Robust neuromagnetic brain activity of auditory mismatch-related perception occurs in response to non-prototypical vowels in across-category contrasts for first-language speakers. However, whether this non-prototypicality effect applies to within-category vowel perception remains to be elucidated. Here, healthy Japanese adults (n = 7) were subjected to magnetoencephalography (MEG) while watching a silent movie, and passively listened to synthesized English vowels /i/. We observed the source-level mismatch effect to the mid-high near-front vowel deviant [ɪ] with the most non-prototypical, unspecified feature in the participants' native language system. The mismatch effect recruited the left posterior superior temporal sulcus with a peak latency of 225 ms post-stimulus onset. We further studied whether a longer F1 distance between vowel pairs would increase mismatch-activated intensities, however, we did not observe neuromagnetic changes when the prototypical anchor standard [i] was compared with three non-prototypical deviants differing in first resonance frequency (F1) values. Our results indicate that an F1 increase in within-category upper front vowel perception is a strong activator of mismatch responses measured by source-level activated intensities for non-native listeners.

Local syntactic violations evoke fast mismatch-related neural activity detected by optical neuroimaging.

It remains to be investigated whether syntax-related mismatch activity would be evoked in event-related optical signals by syntactic violations that deviate from our language knowledge and expectations. In the current study, we have employed fast optical neuroimaging with a frequency-domain oximeter to examine whether syntactic violations of English bare infinitives in the non-finite complement clause would trigger syntax-related mismatch effects. Recorded sentences of bare or full infinitive structures (without or with the 'to' infinitival marker) with syntactically correct or incorrect versions and non-syntactic lexical items (verbs) were presented to native speakers of English (n = 8) during silent movie viewing as a passive oddball task. The analysis of source strength (i.e., minimum norm current amplitudes) revealed that the syntactic category violations of bare object infinitives led to significantly more robust optical mismatch effects than the other syntactic violation and non-structural, lexical elements. This mismatch response had a peak latency of 186 ms in the left anterior superior temporal gyrus. In combination with our prior MEG report (Kubota et al. in Neurosci Lett 662:195-204, 2018), the present optical neuroimaging findings show that syntactic marking (unmarked-to-marked) violations of the bare object infinitive against the rule of the mental grammar enhance the signal strength exactly in the same manner seen with MEG scanning, including the peak latency of mismatch activity and the activated area of the brain.

Head errors of syntactic dependency increase neuromagnetic mismatch intensities.

Mismatch-related brain activation in healthy individuals is an important area of neural investigation. Previously, we evaluated sentence-level syntactic dependencies, composed of a head and a dependent between two syntactically related words in head-initial English structures. We demonstrated that prominent mismatch effects were induced by within-category dependent errors when semantic interpretation was preserved. However, the following issues were not addressed: (1) whether head errors of syntactic dependency in head-final structures would elicit large mismatch field (MMF) intensities, and (2) whether an MMF effect of syntactic errors would be seen in the left superior temporal cortex alone. In this study, auditory MMFs were obtained by magnetocephalography (MEG) from healthy Japanese adults (n = 8) who were subjected to a passive auditory oddball paradigm with syntactically legal or illegal utterances and single words in Japanese. The results demonstrate that the source waveforms had significantly higher MMF cortical activation in response to the head error, which involved altered polarity of the predicate. This resulted in a syntactically incorrect and semantically incomprehensible expression, when compared to the syntactically correct expression and the non-structural lexical item. This mismatch effect, with a peak latency of 164 ms, was confined to the anterior region of the left superior temporal cortex. The current results clearly indicate that the representation of syntactic dependency is stored in long-term memory and tends to be activated in automatic auditory processing.

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury.

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91-97%, sensitivity: 100%, and specificity: 77-93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the ß frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

Source Connectivity Analysis Can Assess Recovery of Acute Mild Traumatic Brain Injury Patients.

In this study we investigated whether source connectivity analysis of resting-state Magnetoencephalographic (MEG) activity could separate mild traumatic brain injury (mTBI) patients from age-and sex-matched controls. For each subject, we used artifact-free data recorded on three sessions to estimate intracranial sources which were then projected onto a standardized brain atlas for common reference. The statistical and topological properties of functional brain networks, estimated using Granger causality, were analyzed using MANOVA, with group and recording session as the independent variables and number of in-going and out-going connections in each atlas region as dependent variables. Overall, mTBI subjects showed a larger number of stronger connections compared to controls. The number and topology of in-going and out-going connections were significantly different across the two groups in areas involved with spatial memory, perception of visual space, emotion formation and processing, learning, and memory. Additionally, differences between patients and controls were decreasing across the three sessions indicating patient improvement. Our results suggest that connectivity analysis may be used as a reliable biomarker of mTBI and can also help with the diagnosis and assessment of patient recovery.

Alpha desynchronization/synchronization during working memory testing is compromised in acute mild traumatic brain injury (mTBI).

Diagnosing and monitoring recovery of patients with mild traumatic brain injury (mTBI) is challenging because of the lack of objective, quantitative measures. Diagnosis is based on description of injuries often not witnessed, subtle neurocognitive symptoms, and neuropsychological testing. Since working memory (WM) is at the center of cognitive functions impaired in mTBI, this study was designed to define objective quantitative electroencephalographic (qEEG) measures of WM processing that may correlate with cognitive changes associated with acute mTBI. First-time mTBI patients and mild peripheral (limb) trauma controls without head injury were recruited from the emergency department. WM was assessed by a continuous performance task (N-back). EEG recordings were obtained during N-back testing on three occasions: within five days, two weeks, and one month after injury. Compared with controls, mTBI patients showed abnormal induced and evoked alpha activity including event-related desynchronization (ERD) and synchronization (ERS). For induced alpha power, TBI patients had excessive frontal ERD on their first and third visit. For evoked alpha, mTBI patients had lower parietal ERD/ERS at the second and third visits. These exploratory qEEG findings offer new and non-invasive candidate measures to characterize the evolution of injury over the first month, with potential to provide much-needed objective measures of brain dysfunction to diagnose and monitor the consequences of mTBI.

Magnetoencephalography Reveals Mismatch Field Enhancement from Unexpected Syntactic Category Errors in English Sentences.

The type of syntactic operations that increase neuronal activation in humans as a result of syntactically erroneous, unexpected lexical items in hearing sentences has remained unclear. In the present study, we used recordings of magnetoencephalographic (MEG) activity to compare bare infinitive and full infinitive constructions in English. This research aims to identify the type of syntactic deviance that may trigger an early syntax-related mismatch field (MMF) component when unexpected words appear in sentences. Six speakers of English as a first language were presented with auditory stimuli of sentences or words in a passive odd-ball paradigm while watching a silent movie. The experimental protocol included four sessions, specifically investigating the sentential (structural) versions of full (with the 'to' infinitival particle) and bare infinitival structures (without the particle) and the lexical (non-structure) versions of the verb either with or without the particle to determine whether the structure processing of sentences was a more crucial factor in the detection of the MMF than the simple processing of lexical items in verb-only conditions. The amplitude analysis of the resulting evoked fields showed that the presence of the syntactic category error of bare infinitival structures against syntactic predictions evoked a significantly larger MMF activation with a peak latency of approximately 200ms in the anterior superior temporal sulci in the left hemisphere, compared with the lexical items that did not have any syntactic status. These results clearly demonstrate that syntactically unexpected, illegal input in the bare infinitival structure is likely to be noticed more robustly in the brain while processing the structural information of the entire sentence than the corresponding verb-only items.

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study.

Functional brain connectivity networks exhibit "small-world" characteristics and some of these networks follow a "rich-club" organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an "attack strategy" to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the "true" underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ1, θ-γ1, and ß-γ2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ1 frequencies, and underrepresented in left occipital regions in the δ-ß, δ-γ1, θ-ß, and ß-γ2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.

Reconfiguration of dominant coupling modes in mild traumatic brain injury mediated by δ-band activity: A resting state MEG study.

During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for (δ-γ1), (δ-γ2), (θ-ß), (θ-γ2), (α-γ2), (γ1-γ2) and intra-frequency coupling for (γ1-γ1) and (δ-δ) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the (δ, θ), (δ, ß), (δ, γ1), and (δ, γ2) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects.

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury.

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

Decoding movement direction using phase-space analysis of hemodynamic responses to arm movements based on functional near-infrared spectroscopy.

In this study we applied phase-space analysis on the hemodynamic signals recorded from the motor cortex of human subjects using functional near infrared spectroscopy (fNIRS) to decode the direction of intentional hand movements. Our goal is to develop a brain-computer-interface (BCI) based on optical imaging that can control a wheelchair. To establish the relationship between the hemodynamic response and movement direction, participants were asked to perform repetitive arm movements in two orthogonal directions (right-left and front-back) on a horizontal plane, while the time course of the oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) responses were recorded. We applied phase-space analysis on oxy-Hb and deoxy-Hb signals to characterize movement direction. Our results show that movement directions taken pairwise (left vs. right, and forward vs. backward) are mapped onto different quadrants in the oxy-Hb vs. deoxy-Hb phase plane. These findings demonstrate that phase-space analysis can be used to decode the movement direction in a BCI controlling a wheelchair. In conclusion, phase-space analysis can be used to differentiate intentional movement direction without correlating the temporal movement kinematics with the hemodynamic response.

Brain activation profiles in mTBI: evidence from ERP activity of working memory response.

In this study we analyzed event related potentials (ERPs) obtained in an N-back working memory test that varied in difficulty from 0- to 2-back. We collected 21 channels of activity from 11 mild traumatic brain injury (mTBI) patients and 7 normal controls, on three different visits, and used the amplitude and latency of the P300 component to characterize the subjects. A preprocessing procedure based on independent component analysis was used first to identify and eliminate electrophysiological noise on a single trial basis. Then to obtain more reliable statistics, the recording electrodes were lumped into five main groups corresponding roughly to frontal, central, parietal, and left and right temporal brain regions. For each subject, the P300 amplitude and latency were measured after averaging the activity of all channels in each group. Group analyses showed that latencies in the central region were significantly shorter in controls, at every visit for the 2-back test. The lack of significant differences across the three visits for the mTBI group indicates that mTBI subjects are not improving at the rate that might have been expected, confirming previous reports that mTBI deficits may persist for years.

Mining cross-frequency coupling microstates from resting state MEG: An application to mild traumatic brain injury.

Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates. The resulting dynamic network revealed transitions between a limited number of microstates for mTBI subjects compared to controls. The significant differences in transition probability between the two groups, along with the limited repertoire of possible states, can form the basis for a robust dynamic connectomic biomarker for the diagnosis of mTBI.

Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

Melanoma and other skin lesion detection using smart handheld devices.

Smartphones of the latest generation featuring advanced multicore processors, dedicated microchips for graphics, high-resolution cameras, and innovative operating systems provide a portable platform for running sophisticated medical screening software and delivering point-of-care patient diagnostic services at a very low cost. In this chapter, we present a smartphone digital dermoscopy application that can analyze high-resolution images of skin lesions and provide the user with feedback about the likelihood of malignancy. The same basic procedure has been adapted to evaluate other skin lesions, such as the flesh-eating bacterial disease known as Buruli ulcer. When implemented on the iPhone, the accuracy and speed achieved by this application are comparable to that of a desktop computer, demonstrating that smartphone applications can combine portability and low cost with high performance. Thus, smartphone-based systems can be used as assistive devices by primary care physicians during routine office visits, and they can have a significant impact in underserved areas and in developing countries, where health-care infrastructure is limited.

Low frequency overactivation in dyslexia: Evidence from resting state Magnetoencephalography.

In this study, we compared the brain activation profiles obtained from resting state Magnetoencephalographic (MEG) activity in 15 dyslexic patients with the profiles of 15 normal controls, using power spectral density (PSD) analysis. We first estimated intracranial dipolar MEG sources on a dense grid on the cortical surface and then projected these sources on a standardized atlas with 68 regions of interest (ROIs). Averaging the PSD values of all sources in each ROI across all control subjects resulted in a normative database that was used to convert the PSD values of dyslexic patients into z-scores in eight distinct frequency bands. We found that dyslexic patients exhibited statistically significant overactivation in the delta band (0.1-4 Hz) in the right temporal (entorhinal and insula), left inferior frontal (Broca's area), and right inferior frontal regions. Overactivation may be interpreted as a compensatory mechanism for reading characterizing dyslexic patients. These findings suggest that resting-state MEG activation maps may be used as specific biomarkers that can help with the diagnosis of and assess the efficacy of intervention in dyslexia.

Brain activation profiles in mTBI: Evidence from combined resting-state EEG and MEG activity.

In this study, we compared the brain activation profiles obtained from resting state Electroencephalographic (EEG) and Magnetoencephalographic (MEG) activity in six mild traumatic brain injury (mTBI) patients and five orthopedic controls, using power spectral density (PSD) analysis. We first estimated intracranial dipolar EEG/MEG sources on a dense grid on the cortical surface and then projected these sources on a standardized atlas with 68 regions of interest (ROIs). Averaging the PSD values of all sources in each ROI across all control subjects resulted in a normative database that was used to convert the PSD values of mTBI patients into z-scores in eight distinct frequency bands. We found that mTBI patients exhibited statistically significant overactivation in the delta, theta, and low alpha bands. Additionally, the MEG modality seemed to better characterize the group of individual subjects. These findings suggest that resting-state EEG/MEG activation maps may be used as specific biomarkers that can help with the diagnosis of and assess the efficacy of intervention in mTBI patients.

Comparison of brain network models using cross-frequency coupling and attack strategies.

Several neuroimaging studies have suggested that functional brain connectivity networks exhibit "small-world" characteristics, whereas recent studies based on structural data have proposed a "rich-club" organization of brain networks, whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density. In this study, we adopted an "attack strategy" to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model's hubs would reveal the organization that better describes the topology of the underlying brain networks. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross-frequency coupling between the δ and ß frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.

Restoring cortical connectivity directionality and synchronization is essential to treating disorder of consciousness.

The design of neurorehabilitation therapy to treat subjects with altered consciousness provides opportunities and challenges to professionals involved with the care for these severely ill patients. While there is an increased interest in determining methods to restore consciousness in these patients, the process is complex and challenging, due in part to the diverse aetiology of these states of consciousness, and also to the intricate cerebral connectivity involved in their treatment. The present case study examines a patient who showed signs of emergence from the vegetative state after neurorehabilitation using The Combined Method Therapy (CMT). In this case, neurorehabilitation therapy was applied simultaneously with pharmacological treatment, stimulation, and neuroimaging techniques to help adjust drug dosage. The results of this study suggest that this combined approach to treatment promoted connectivity among posterior and anterior cortical regions aiding emergence from the vegetative state.