Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance.

(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.

Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation.

(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.

Quantification of Upper Limb Motor Recovery and EEG Power Changes after Robot-Assisted Bilateral Arm Training in Chronic Stroke Patients: A Prospective Pilot Study.

Background: Bilateral arm training (BAT) has shown promise in expediting progress toward upper limb recovery in chronic stroke patients, but its neural correlates are poorly understood. Objective: To evaluate changes in upper limb function and EEG power after a robot-assisted BAT in chronic stroke patients. Methods: In a within-subject design, seven right-handed chronic stroke patients with upper limb paresis received 21 sessions (3 days/week) of the robot-assisted BAT. The outcomes were changes in score on the upper limb section of the Fugl-Meyer assessment (FM), Motricity Index (MI), and Modified Ashworth Scale (MAS) evaluated at the baseline (T0), posttraining (T1), and 1-month follow-up (T2). Event-related desynchronization/synchronization were calculated in the upper alpha and the beta frequency ranges. Results: Significant improvement in all outcomes was measured over the course of the study. Changes in FM were significant at T2, and in MAS at T1 and T2. After training, desynchronization on the ipsilesional sensorimotor areas increased during passive and active movement, as compared with T0. Conclusions: A repetitive robotic-assisted BAT program may improve upper limb motor function and reduce spasticity in the chronically impaired paretic arm. Effects on spasticity were associated with EEG changes over the ipsilesional sensorimotor network.

Assessment of Event-Related EEG Power After Single-Pulse TMS in Unresponsive Wakefulness Syndrome and Minimally Conscious State Patients.

In patients without a behavioral response, non-invasive techniques and new methods of data analysis can complement existing diagnostic tools by providing a method for detecting covert signs of residual cognitive function and awareness. The aim of this study was to investigate the brain oscillatory activities synchronized by single-pulse transcranial magnetic stimulation (TMS) delivered over the primary motor area in the time-frequency domain in patients with the unresponsive wakefulness syndrome or in a minimally conscious state as compared to healthy controls. A time-frequency analysis based on the wavelet transform was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS in patients as compared to healthy controls. The pattern of EEG changes in the patients differed from that of healthy controls. In the controls there was an early synchronization of slow waves immediately followed by a desynchronization of alpha and beta frequency bands over the frontal and centro-parietal electrodes, whereas an opposite early synchronization, particularly over motor areas for alpha and beta and over the frontal and parietal electrodes for beta power, was seen in the patients. In addition, no relevant modification in slow rhythms (delta and theta) after TMS was noted in patients. The clinical impact of these findings could be relevant in neurorehabilitation settings for increasing the awareness of these patients and defining new treatment procedures.

Time-frequency modulation of ERD and EEG coherence in robot-assisted hand performance.

A better understanding of cortical modifications related to movement preparation and execution after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Electroencephalography (EEG) modifications of cortical activity in healthy subjects were evaluated using time-frequency event-related EEG and task-related coherence (TRCoh). Twenty-one channel EEG was recorded in eight subjects during protocols of active, passive, and imagined movements. The subjects performed robot-assisted tasks using the Bi-Manu-Track robot-assisted arm trainer. We applied time-frequency event-related synchronization/desynchronization (ERS/ERD) and TRCoh approaches to investigate where movement-related decreases in power were localized and to study the functional relationships between areas. Our results showed ERD of sensorimotor (SM) area over the contralateral side before the movement and bilateral ERD during execution of the movement. ERD during passive movements was similar in topography to that observed during voluntary movements, but without pre-movement components. No significant difference in time course ERD was observed among the three types of movement over the two SM areas. The TRCoh topography was similar for active and imagined movement; before passive movement, the frontal regions were uncoupled from the SM regions and did not contribute to task performance. This study suggests new perspectives for the evaluation of brain oscillatory activity and the neurological assessment of motor performance by means of quantitative EEG to better understand the planning and execution of movement.

Coherence and Consciousness: Study of Fronto-Parietal Gamma Synchrony in Patients with Disorders of Consciousness.

Evaluation of consciousness needs to be supported by the evidence of brain activation during external stimulation in patients with unresponsive wakefulness syndrome (UWS). Assessment of patients should include techniques that do not depend on overt motor responses and allow an objective investigation of the spontaneous patterns of brain activity. In particular, electroencephalography (EEG) coherence allows to easily measure functional relationships between pairs of neocortical regions and seems to be closely correlated with cognitive or behavioral measures. Here, we show the contribution of higher order associative cortices of patients with disorder of consciousness (N = 26) in response to simple sensory stimuli, such as visual, auditory and noxious stimulation. In all stimulus modalities an increase of short-range parietal and long-range fronto-parietal coherences in gamma frequencies were seen in the controls and minimally conscious patients. By contrast, UWS patients showed no significant modifications in the EEG patterns after stimulation. Our results suggest that UWS patients can not activate associative cortical networks, suggesting a lack of information integration. In fact, fronto-parietal circuits result to be connectively disrupted, conversely to patients that exhibit some form of consciousness. In the light of this, EEG coherence can be considered a powerful tool to quantify the involvement of cognitive processing giving information about the integrity of fronto-parietal network. This measure can represent a new neurophysiological marker of unconsciousness and help in determining an accurate diagnosis and rehabilitative intervention in each patient.

Combining ESI, ASL and PET for quantitative assessment of drug-resistant focal epilepsy.

When localization of the epileptic focus is uncertain, the epileptic activity generator may be more accurately identified with non-invasive imaging techniques which could also serve to guide stereo-electroencephalography (sEEG) electrode implantation. The aim of this study was to assess the diagnostic value of perfusion magnetic resonance imaging with arterial spin labeling (ASL) in the identification of the epileptogenic zone, as compared to the more invasive positron-emission tomography (PET) and other established investigation methods for source imaging of electroencephalography (EEG) data. In 6 patients with drug-resistant focal epilepsy, standard video-EEG was performed to identify clinical seizure semeiology, and high-density EEG, ASL and FDG-PET to non-invasively localize the epileptic focus. A standardized source imaging procedure, low-resolution brain electromagnetic tomography constrained to the individual matter, was applied to the averaged spikes of high-density EEG. Quantification of current density, cerebral blood flow, and standardized uptake value were compared over the same anatomical areas. In most of the patients, source in the interictal phase was associated with an area of hypoperfusion and hypometabolism. Conversely, in the patients presenting with early post-ictal discharges, the brain area identified by electrical source imaging (ESI) as the generating zone appeared to be hyperperfused. In 2 patients in whom the focus remained uncertain, the postoperative follow-up showed the disappearance of epileptic activity. As an innovative and more comprehensive approach to the study of epilepsy, the combined use of ESI, perfusion MRI, and PET may play an increasingly important role in the non-invasive evaluation of patients with refractory focal epilepsy.

Effect of voluntary repetitive long-lasting muscle contraction activity on the BOLD signal as assessed by optimal hemodynamic response function.

OBJECTIVE: Among other neuroimaging techniques, functional magnetic resonance imaging (fMRI) can be useful for studying the development of motor fatigue. The aim of this study was to identify differences in cortical neuronal activation in nine subjects on three motor tasks: right-hand movement with minimum, maximum, and post-fatigue maximum finger flexion. MATERIALS AND METHODS: fMRI activation maps for each subject and during each condition were obtained by estimating the optimal model of the hemodynamic response function (HRF) out of four standard HRF models and an individual-based HRF model (ibHRF). RESULTS: ibHRF was selected as the optimal model in six out of nine subjects for minimum movement, in five out of nine for maximum movement, and in eight out of nine for post-fatigue maximum movement. As compared to maximum movement, a large reduction in the total number of active voxels (primary sensorimotor area, supplementary motor area and cerebellum) was observed in post-fatigue maximum movement. CONCLUSION: This is the first approach to the evaluation of long-lasting contraction effort in healthy subjects by means of the fMRI paradigm with the use of an individual-based hemodynamic response. The results may be relevant for defining a baseline in future studies on central fatigue in patients with neuropathological disorders.

Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements.

BACKGROUND: Robot-assisted therapy in patients with neurological disease is an attempt to improve function in a moderate to severe hemiparetic arm. A better understanding of cortical modifications after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Modifications of cortical activity in healthy subjects were evaluated during voluntary active movement, passive robot-assisted motor movement, and motor imagery tasks performed under unimanual and bimanual protocols. METHODS: Twenty-one channel electroencephalography (EEG) was recorded with a video EEG system in 8 subjects. The subjects performed robot-assisted tasks using the Bi-Manu Track robot-assisted arm trainer. The motor paradigm was executed during one-day experimental sessions under eleven unimanual and bimanual protocols of active, passive and imaged movements. The event-related-synchronization/desynchronization (ERS/ERD) approach to the EEG data was applied to investigate where movement-related decreases in alpha and beta power were localized. RESULTS: Voluntary active unilateral hand movement was observed to significantly activate the contralateral side; however, bilateral activation was noted in all subjects on both the unilateral and bilateral active tasks, as well as desynchronization of alpha and beta brain oscillations during the passive robot-assisted motor tasks. During active-passive movement when the right hand drove the left one, there was predominant activation in the contralateral side. Conversely, when the left hand drove the right one, activation was bilateral, especially in the alpha range. Finally, significant contralateral EEG desynchronization was observed during the unilateral task and bilateral ERD during the bimanual task. CONCLUSIONS: This study suggests new perspectives for the assessment of patients with neurological disease. The findings may be relevant for defining a baseline for future studies investigating the neural correlates of behavioral changes after robot-assisted training in stroke patients.

eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients.

While stroke is one of the leading causes of disability, the prediction of upper limb (UL) functional recovery following rehabilitation is still unsatisfactory, hampered by the clinical complexity of post-stroke impairment. Predictive models leading to accurate estimates while revealing which features contribute most to the predictions are the key to unveil the mechanisms subserving the post-intervention recovery, prompting a new focus on individualized treatments and precision medicine in stroke. Machine learning (ML) and explainable artificial intelligence (XAI) are emerging as the enabling technology in different fields, being promising tools also in clinics. In this study, we had the twofold goal of evaluating whether ML can allow deriving accurate predictions of UL recovery in sub-acute patients, and disentangling the contribution of the variables shaping the outcomes. To do so, Random Forest equipped with four XAI methods was applied to interpret the results and assess the feature relevance and their consensus. Our results revealed increased performance when using ML compared to conventional statistical approaches. Moreover, the features deemed as the most relevant were concordant across the XAI methods, suggesting good stability of the results. In particular, the baseline motor impairment as measured by simple clinical scales had the largest impact, as expected. Our findings highlight the core role of ML not only for accurately predicting the individual outcome scores after rehabilitation, but also for making ML results interpretable when associated to XAI methods. This provides clinicians with robust predictions and reliable explanations that are key factors in therapeutic planning/monitoring of stroke patients.

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas.

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1). A time-frequency analysis based on the wavelet method was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS. Single, paired, and transcallosal TMS applied on the sensorimotor areas induced rapid desynchronization over the frontal and central-parietal electrodes mainly in the alpha and beta bands, followed by a rebound of synchronization, and rapid synchronization of delta and theta activity. Wavelet analysis after a perturbation approach is a novel way to investigate modulation of oscillatory brain activity. The main findings are consistent with the concept that the human motor system may be based on networklike oscillatory cortical activity and might be modulated by single, paired, and transcallosal magnetic pulses applied to M1, suggesting a phenomenon of fast brain activity resetting and triggering of slow activity.

Integrating EEG and fMRI in epilepsy.

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes.

Wavelet analysis as a tool for investigating movement-related cortical oscillations in EEG-fMRI coregistration.

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) identifies blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG events. In this study we used EEG-fMRI to determine the possible correlation between topographical movement related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 12 subjects during eyes-closed condition inside a three T magnetic resonance (MR) scanner using an MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used a time-frequency approach to measure time by varying the energy in a signal at a given frequency band by the convolution of the EEG signal with a wavelet family in the alpha and beta bands. The correlation between the BOLD signal associated with the EEG regressor provides that sensory motor region is a source of the EEG. We conclude that combined EEG-fMRI can be used to investigate movement-related oscillations of the human brain inside an MRI scanner and wavelet analysis adds further details on the EEG changes. The movement-related changes in the EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.

Noise removal in resting-state and task fMRI: functional connectivity and activation maps.

OBJECTIVE: Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH: Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. MAIN RESULTS: Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE: This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.

Voxel-based morphometry and task functional magnetic resonance imaging in essential tremor: evidence for a disrupted brain network.

The pathophysiology of essential tremor (ET) is controversial and might be further elucidated by advanced neuroimaging. Focusing on homogenous ET patients diagnosed according to the 2018 consensus criteria, this study aimed to: (1) investigate whether task functional MRI (fMRI) can identify networks of activated and deactivated brain areas, (2) characterize morphometric and functional modulations, relative to healthy controls (HC). Ten ET patients and ten HC underwent fMRI while performing two motor tasks with their upper limb: (1) maintaining a posture (both groups); (2) simulating tremor (HC only). Activations/deactivations were obtained from General Linear Model and compared across groups/tasks. Voxel-based morphometry and linear regressions between clinical and fMRI data were also performed. Few cerebellar clusters of gray matter loss were found in ET. Conversely, widespread fMRI alterations were shown. Tremor in ET (task 1) was associated with extensive deactivations mainly involving the cerebellum, sensory-motor cortex, and basal ganglia compared to both tasks in HC, and was negatively correlated with clinical tremor scales. Homogeneous ET patients demonstrated deactivation patterns during tasks triggering tremor, encompassing a network of cortical and subcortical regions. Our results point towards a marked cerebellar involvement in ET pathophysiology and the presence of an impaired cerebello-thalamo-cortical tremor network.

EEG and FMRI coregistration to investigate the cortical oscillatory activities during finger movement.

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) may be used to identify blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG event. In this study we used EEG-fMRI to determine the possible correlation between topographical movement-related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI-BOLD cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 9 subjects during eyes-open condition inside a 1.5 T magnetic resonance (MR) scanner using a MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used the event-related-synchronization/desynchronization (ERS/ERD) approach to investigate where movement-related decreases in alpha and beta power are located. For image statistical analysis we used a general linear model (GLM) approach. There was a significant correlation between the positive-negative ratio of BOLD signal peaks and ERD values in the electrodes over the region of activation. We conclude that combined EEG-fMRI may be used to investigate movement-related oscillations of the human brain inside an MRI scanner and the movement-related changes in the EMG or EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.

Continuous EEG-fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG.

PURPOSE: To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG-functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source. METHODS: Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG-fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges). RESULTS: In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal. CONCLUSIONS: Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG-fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.

Dual-echo ASL based assessment of motor networks: a feasibility study.

OBJECTIVE: Dual-echo arterial spin labeling (DE-ASL) technique has been recently proposed for the simultaneous acquisition of ASL and blood-oxygenation-level-dependent (BOLD)-functional magnetic resonance imaging (fMRI) data. The assessment of this technique in detecting functional connectivity at rest or during motor and motor imagery tasks is still unexplored both per-se and in comparison with conventional methods. The purpose is to quantify the sensitivity of the DE-ASL sequence with respect to the conventional fMRI sequence (cvBOLD) in detecting brain activations, and to assess and compare the relevance of node features in decoding the network structure. APPROACH: Thirteen volunteers were scanned acquiring a pseudo-continuous DE-ASL sequence from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. The approach consists of two steps: (i) model-based analyses for assessing brain activations at individual and group levels, followed by statistical analysis for comparing the activation elicited by the three sequences under two conditions (motor and motor imagery), respectively; (ii) brain connectivity graph-theoretical analysis for assessing and comparing the network models properties. MAIN RESULTS: Our results suggest that cvBOLD and ccBOLD have comparable sensitivity in detecting the regions involved in the active task, whereas ASL offers a higher degree of co-localization with smaller activation volumes. The connectivity results and the comparative analysis of node features across sequences revealed that there are no strong changes between rest and tasks and that the differences between the sequences are limited to few connections. SIGNIFICANCE: Considering the comparable sensitivity of the ccBOLD and cvBOLD sequences in detecting activated brain regions, the results demonstrate that DE-ASL can be successfully applied in functional studies allowing to obtain both ASL and BOLD information within a single sequence. Further, DE-ASL is a powerful technique for research and clinical applications allowing to perform quantitative comparisons as well as to characterize functional connectivity.