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Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural–functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal–temporal functional connectivity, along with decreased structural–functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural–functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural–functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.

Stereotactic-electroencephalography (SEEG) is a common neurosurgical method to localize epileptogenic zone in drug resistant epilepsy patients and inform treatment recommendations. In the current clinical practice, localization of epileptogenic zone typically requires prolonged recordings to capture seizure, which may take days to weeks. Although epilepsy surgery has been proven to be effective in general, the percentage of unsatisfactory seizure outcomes is still concerning. We developed a method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state SEEG data. In a cohort of 43 drug resistant epilepsy patients, we estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. We hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. We found higher excitability in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions, probably reflecting inhibitory input from non-SOZ to prevent seizure initiation. Greater differences in information flow between SOZ and non-SOZ regions were associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, our method localized the SOZ with an accuracy of 85% and predicted the seizure outcome with an accuracy of 77% using clinically determined SOZ. Overall, our study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.

Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations; (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.

Background Precise suicide risk evaluation struggled in Major depressive disorder (MDD), especially for patients with only suicidal-ideation (SI) but without suicide attempt (SA). MDD patients have deficits in negative emotion processing, which is associated with the generation of SI and SA. Given the critical role of gamma oscillations in negative emotion processing, we hypothesize that the transition from SI to SA in MDD could be characterized by abnormal gamma interactions. Methods We recruited 162 participants containing 106 MDD patients and 56 healthy controls (HCs). Participants performed facial recognition tasks while magnetoencephalography data were recorded. Time-frequency-representation (TFR) analysis was conducted to identify the dominant spectra differences between MDD and HCs, and then source analysis was applied to localize the region of interests. Furthermore, frequency-specific functional connectivity network were constructed and a semi-supervised clustering algorithm was utilized to predict potential suicide risk. Results Gamma (50–70 Hz) power was found significantly increased in MDD, mainly residing in regions from fronto-parietal-control-network (FPN), visual-network (VN), default-mode-network (DMN) and salience-network (SN). Based on impaired gamma functional connectivity network between well-established SA group and non-SI group, semi-supervised algorithm clustered patients with only SI into two groups with different suicide risks. Moreover, Inter-network gamma connectivity between FPN and DMN significantly negatively correlated with suicide risk and not confounded by depression severity. Conclusion Inter-network gamma connectivity with FPN and DMN might be the key neuropathological interactions underling the progression from SI to SA. By applying semi-supervised clustering to electrophysiological data, it is possible to predict individual suicide risk.

Hongliang Zhou, 1 Lingling Hua, 1 Haiteng Jiang, 2 Zongpeng Dai, 2 Yinglin Han, 1 Pinhua Lin, 1 Haofei Wang, 1 Qing Lu, 2, 3 Zhijian Yao 1, 4 1Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China; 2School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, People’s Republic of China; 3Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, People’s Republic of China; 4Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, People’s Republic of ChinaCorrespondence: Zhijian YaoDepartment of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of ChinaTel +8625 8229600Fax +8625 82296000Email zjyao@njmu.edu.cnQing LuSchool of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, People’s Republic of ChinaTel/Fax +8625 83792349Email luq@seu.edu.cnObjective: To investigate the mechanism of interactions between autonomic nervous system (ANS) and cognitive function in Major depression (MD) with Magnetoencephalography (MEG) measurements.Methods: Participants with MD (n = 20), and Health controls (HCs, n = 18) were completed MEG measurements during the performance of a go/no-go task. Heart rate variability (HRV) indices (SDANN, and RMSSD) were derived from the raw MEG data. The correlation analysis of the HRV and functional connectivities in different brain regions was conducted by Pearson’s r in two groups.Results: The go/no-go task performances of HCs were better than MD patients; HRV indices were lower in the MD group. Under the no-go task, a brain MEG functional connectivity analysis based on the seed regions of the orbitofrontal cortex (OFC) displayed increased functional inter-region connectivity networks of OFC in MD group. HRV indices were correlated with different functional inter-region connectivity networks of OFC in two groups, respectively.Conclusion: ANS is related to inhibitory and control function through functional inter-region connectivity networks of OFC in MD. These findings have important implications for the understanding pathophysiology of MD, and MEG may provide an image-guided tool for interventions.Keywords: major depression, magnetoencephalography, heart rate variability, functional connectivity, the orbitofrontal cortex

Objectives In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level. Methods Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power. Results Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P Conclusions In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20–30 Hz) suppression, more visual gamma activity (40–80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Background Major depressive disorder (MDD) is a system-level disorder affecting multiple functionally integrated cerebral networks. Nevertheless, their temporospatial organization and potential disturbance remain mostly unknown. The present report tested the hypothesis that deficient temporospatial network organization separates MDD and healthy controls (HC), and is linked to symptom severity of the disorder. Methods Eyes-closed resting-state magnetoencephalographic (MEG) recordings were obtained from twenty-two MDD and twenty-two HC subjects. Beamforming source localization and functional connectivity analysis were applied to identify frequency-specific network interactions. Then, a novel virtual cortical resection approach was used to pinpoint putatively critical network controllers, accounting for aberrant cerebral connectivity patterns in MDD. Results We found significantly elevated frontolimbic and frontocentral connectivity mediated by gamma (30–48 Hz) activity in MDD versus HC, and the right amygdala was the key differential network controller accounting for aberrant cerebral connectivity patterns in MDD. Furthermore, this frontolimbic and frontocentral gamma-band hyper-connectivity was positively correlated with depression severity. Limitations The overall sample size was small, and we found significant effects in the deep limbic regions with resting-state MEG, the reliability of which was difficult to corroborate further. Conclusions Overall, these findings support a notion that the right amygdala critically controls the exaggerated gamma-band frontolimbic and frontocentral connectivity in MDD during the resting-state condition, which potentially constitutes pre-established aberrant pathways during task processing and contributes to MDD pathology.

OBJECTIVE Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear. METHODS We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively. RESULTS In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA. INTERPRETATION Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.

Seizure generation is thought to be a process driven by epileptogenic networks; thus, network analysis tools can help determine the efficacy of epilepsy treatment. Studies have suggested that low-frequency (LF) to high-frequency (HF) cross-frequency coupling (CFC) is a useful biomarker for localizing epileptogenic tissues. However, it remains unclear whether the LF or HF coordinated CFC network hubs are more critical in determining the treatment outcome. We hypothesize that HF hubs are primarily responsible for seizure dynamics. Stereo-electroencephalography (SEEG) recordings of 36 seizures from 16 intractable epilepsy patients were analyzed. We propose a new approach to model the temporal-spatial-spectral dynamics of CFC networks. Graph measures are then used to characterize the HF and LF hubs. In the patient group with Engel Class (EC) I outcome, the strength of HF hubs was significantly higher inside the resected regions during the early and middle stages of seizure, while such a significant difference was not observed in the EC III group and only for the early stage in the EC II group. For the LF hubs, a significant difference was identified at the late stage and only in the EC I group. Our findings suggest that HF hubs increase at early and middle stages of the ictal interval while LF hubs increase activity at the late stages. In addition, HF hubs can predict treatment outcomes more precisely, compared to the LF hubs of the CFC network. The proposed method promises to identify more accurate targets for surgical interventions or neuromodulation therapies.

Significance Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci., High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.

Brain–computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind–body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here, we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in electroencephalogram signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a breath counting task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients’ control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

The regulation of the autonomic nervous system (ANS) can improve cognitive function in major depressive disorders (MDD). Heart rate variability (HRV) derives from the dynamic control of the ANS and reflects the balance between the activities of the sympathetic and parasympathetic nervous systems by measuring tiny changes in adjacent heart beats. Task-related HRV may reflect the association between the flexibility of cognition and ANS function. The study was to investigate the neural mechanism of interactions between ANS and cognitive function in MDD with Magnetoencephalography (MEG) measurements. Participants included 20 MDD patients and 18 healthy controls (HCs). All participants were measured with a go/no-go task MEG. HRV indices, the standard deviation of the average normal-to-normal (NN) interval calculated over short periods (SDANN) and the square root of the mean squared differences of successive NN intervals (RMSSD), were derived from the raw MEG data. Results showed that MDD patients showed decreased SDANN and RMSSD. In MDD patients, both resting-state and task-related RMSSD were related to inhibitory and control dysfunction. In the go/no-go task, many areas in the prefrontal cortex (PFC) are responsible for an individual’s inhibitory function. A brain MEG functional connectivity analysis revealed that there were significant differences in four brain regions within the prefrontal cortex (PFC) between MDD patients and HCs. Task-related RMSSD in HCs were related to the functional connectivity between the left middle frontal gyrus and the anterior cingulate cortex (ACC), while in MDD patients, these values were not related to the above functional connectivity but were related to the functional connectivity between the left middle frontal gyrus and insula. However, the resting-state RMSSD value was not related to these significant difference functional connectivity networks in all participants. It concludes that the decreased task-related HRV is associated with inhibitory dysfunction through functional inter-region connectivity in the PFC in MDD, and the task-related HRV can be used as an index of the association between MDD and autonomic dysregulation.

Contains fulltext : 218743.pdf (Publisher’s version ) (Closed access) It is well-established that theta (~4-10 Hz) and gamma (~25-100 Hz) oscillations interact in the rat hippocampus. This cross-frequency coupling might facilitate neuronal coordination both within and between brain areas. However, it remains unclear whether the phase of theta oscillations controls the power of slow and fast gamma activity or vice versa. We here applied spectral Granger causality, phase slope index and a newly developed cross-frequency directionality (CFD) measure to investigate directional interactions between local field potentials recorded within and across hippocampal subregions of CA1 and CA3 of freely exploring rats. Given the well-known CA3 to CA1 anatomical connection, we hypothesized that interregional directional interactions were constrained by anatomical connection, and within-frequency and cross-frequency directional interactions were always from CA3 to CA1. As expected, we found that CA3 drove CA1 in the theta band, and theta phase-to-gamma power coupling was prominent both within and between CA3 and CA1 regions. The CFD measure further demonstrated that distinct directional couplings with respect to theta phase was different between slow and fast gamma activity. Importantly, CA3 slow gamma power phase-adjusted CA1 theta oscillations, suggesting that slow gamma activity in CA3 entrains theta oscillations in CA1. In contrast, CA3 theta phase controls CA1 fast gamma activity, indicating that communication at CA1 fast gamma is coordinated by CA3 theta phase. Overall, these findings demonstrate dynamic directional interactions between theta and slow/fast gamma oscillations in the hippocampal network, suggesting that anatomical connections constrain the directional interactions. 12 p.

Despite accumulating evidence suggesting improvement in one's well-being as a result of meditation, little is known about if or how the brain and the periphery interact to produce these behavioral and mental changes. We hypothesize that meditation reflects changes in the neural representations of visceral activity, such as cardiac behavior, and investigated the integration of neural and visceral systems and the spontaneous whole brain spatiotemporal dynamics underlying traditional Tibetan Buddhist meditation. In a large cohort of long-term Tibetan Buddhist monk meditation practitioners, we found distinct transient modulations of the neural response to heartbeats in the default mode network (DMN), along with large-scale network reconfigurations in the gamma and theta bands of electroencephalography (EEG) activity induced by meditation. Additionally, temporal-frontal network connectivity in the EEG theta band was negatively correlated with the duration of meditation experience, and gamma oscillations were uniquely, directionally coupled to theta oscillations during meditation. Overall, these data suggest that the neural representation of cardiac activity in the DMN and large-scale spatiotemporal network integrations underlie the fundamental neural mechanism of meditation and further imply that meditation may utilize cortical plasticity, inducing both immediate and long-lasting changes in the intrinsic organization and activity of brain networks.

Background In studies when exploring distinct patterns of functional abnormalities inherent in depression, experiments are generally repeated over many trials, and then the data are averaged across those trials in order to improve the signal to noise ratio. Repeated stimuli will lead to unpredictable impairment on signals, due to material familiarity or subjects׳ fatigue. In this consideration, signal processing tools powerful on small numbers of trials are expected to alleviate the work load on subjects, especially for mental disease studies. Methods Forty-four subjects, half-depressed patients and half-healthy subjects, were recruited for MEG scanning in response to sad facial stimuli. Multichannel matching pursuit (MMP) was implemented to manage the limited number of trials. The post-MMP MEG signals were utilized to calculate the power topography over the whole brain, as inputs for a Support Vector Machine (SVM) classifier. Standard ICA and conventional ensemble averaging plus Butterworth filtering were employed as well as benchmark studies for performance comparison. Results A limited number of trials were required via MMP to discriminate the depressive. Post-MMP discriminative analysis revealed a deficit theta pattern and an excessive alpha/beta pattern. Limitations The small sample size may impair the stability of the reported findings. The transient tiny variance of the signal was excluded from exploration. Conclusions The deficit theta pattern together with the excessive alpha/beta pattern in depression may indicate the dysfunction of the limbic-cortical circuit in a ‘top-down’ process. The post-MMP discrimination helps alleviate the scanning burden, facilitating the possibility for neuroimaging supporting the affective disorder clinical diagnosis.

Neuronal oscillations provide a window for understanding the brain dynamics that organize the flow of information from sensory to memory areas. While it has been suggested that gamma power reflects feedforward processing and alpha oscillations feedback control, it remains unknown how these oscillations dynamically interact. Magnetoencephalography (MEG) data was acquired from healthy subjects who were cued to either remember or not remember presented pictures. Our analysis revealed that in anticipation of a picture to be remembered, alpha power decreased while the cross-frequency coupling between gamma power and alpha phase increased. A measure of directionality between alpha phase and gamma power predicted individual ability to encode memory: stronger control of alpha phase over gamma power was associated with better memory. These findings demonstrate that encoding of visual information is reflected by a state determined by the interaction between alpha and gamma activity.

Contains fulltext : 157290_.pdf (Publisher’s version ) (Closed access) Objective: We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. Methods: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. Results: In MDD patients, decreased theta (4–8 Hz) and alpha (8–14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14–30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r = 0.68, p = 0.0005) and beta power (r = 0.56, p = 0.007) respectively. Conclusions: Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. Significance: The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process. 7 p.

Magnetoencephalography (MEG) power topography may be useful for obtaining discriminative brain activity patterns that can distinguish depressed patients from healthy control subjects at the individual level. However, the application is still limited due to the lack of adequate analysis strategies to remove artifacts from the MEG signals. In this study, the multichannel matching pursuit (MMP) method was designed; in this technique, a linear decomposition method that provides components by iteratively reanalysing a residual signal after removing previously found components,. Forty-four subjects, half depressed patients and half healthy subjects, were recruited for MEG scanning whilst watching a video of sad faces. MMP was implemented to manage multichannel, multi-trial MEG signals. The representative post-MMP analysis signals were utilised to calculate the power topography over the whole brain and designed as inputs for a Support Vector Machine (SVM) classifier. A statistically significant discriminative accuracy of 86% ( p = 0.002) after a permutation test was achieved. Comparing the system classification performance to that of the ensemble averaging method and the established Independent Component Analysis (ICA), we demonstrated the ability of MMP to represent critical MEG information and, in turn, to mark the abnormality of oscillatory activities under negative stimuli using images of sad faces.