Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments

Recent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted. Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. The effectiveness of filtering the data is one key preprocessing step to improve the decomposition that has been investigated previously. However, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high‐pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high‐pass filters of up to 2 Hz cut‐off frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA has been proved to be important and functional even with low‐density channel setups. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.TU Berlin, Open-Access-Mittel – 202

Human cortical dynamics during full-body heading changes

The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct spatial reference frames based on multi-sensory information. While invasive studies allow investigating heading computation in moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the role of the RSC in heading computation of actively moving humans, we used a Mobile Brain/Body Imaging approach synchronizing electroencephalography with motion capture and virtual reality. Data from physically rotating participants were contrasted with rotations based only on visual flow. During physical rotation, varying rotation velocities were accompanied by pronounced wide frequency band synchronization in RSC, the parietal and occipital cortices. In contrast, the visual flow rotation condition was associated with pronounced alpha band desynchronization, replicating previous findings in desktop navigation studies, and notably absent during physical rotation. These results suggest an involvement of the human RSC in heading computation based on visual, vestibular, and proprioceptive input and implicate revisiting traditional findings of alpha desynchronization in areas of the navigation network during spatial orientation in movement-restricted participants.TU Berlin, Open-Access-Mittel – 202

Mobile brain/body imaging of landmark‐based navigation with high‐density EEG

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion-constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark-based navigation in actively behaving young adults, solving a Y-maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state-of-the-art brain imaging literature of landmark-based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo-spatial processing and coding, we observed an alpha-power desynchronization while participants gathered visual information. We also hypothesized behavior-dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time-frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high-density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark-based navigation

Radio-frequency ablation as primary management of well-tolerated sustained monomorphic ventricular tachycardia in patients with structural heart disease and left ventricular ejection fraction over 30%

Aims Patients with well-tolerated sustained monomorphic ventricular tachycardia (SMVT) and left ventricular ejection fraction (LVEF) over 30% may benefit from a primary strategy of VT ablation without immediate need for a ‘back-up' implantable cardioverter-defibrillator (ICD). Methods and results One hundred and sixty-six patients with structural heart disease (SHD), LVEF over 30%, and well-tolerated SMVT (no syncope) underwent primary radiofrequency ablation without ICD implantation at eight European centres. There were 139 men (84%) with mean age 62 ± 15 years and mean LVEF of 50 ± 10%. Fifty-five percent had ischaemic heart disease, 19% non-ischaemic cardiomyopathy, and 12% arrhythmogenic right ventricular cardiomyopathy. Three hundred seventy-eight similar patients were implanted with an ICD during the same period and serve as a control group. All-cause mortality was 12% (20 patients) over a mean follow-up of 32 ± 27 months. Eight patients (40%) died from non-cardiovascular causes, 8 (40%) died from non-arrhythmic cardiovascular causes, and 4 (20%) died suddenly (SD) (2.4% of the population). All-cause mortality in the control group was 12%. Twenty-seven patients (16%) had a non-fatal recurrence at a median time of 5 months, while 20 patients (12%) required an ICD, of whom 4 died (20%). Conclusion Patients with well-tolerated SMVT, SHD, and LVEF > 30% undergoing primary VT ablation without a back-up ICD had a very low rate of arrhythmic death and recurrences were generally non-fatal. These data would support a randomized clinical trial comparing this approach with others incorporating implantation of an ICD as a primary strateg

BeMoBIL Pipeline Example Data

This is the example dataset for the BeMoBIL Pipeline for EEG and multimodal data processing: Code: https://github.com/BeMoBIL/bemobil-pipeline Preprint: https://www.biorxiv.org/content/10.1101/2022.09.29.51005

Mobile Brain/Body Imaging Data Heading Computation

This is Mobile Brain/Body Imaging data from 20 healthy adult participants in a heading computation experiment. Participants performed a spatial orientation task in a sparse virtual environment (WorldViz Vizard, Santa Barbara, USA) consisting of an infinite floor granulated in green and black. The experiment was self-paced and participants advanced the experiment by starting and ending each trial with a button press using the index finger of the dominant hand. A trial started with the onset of a red pole, which participants had to face and align with. Once the button was pressed the pole disappeared and was immediately replaced by a red sphere floating at eye level. The sphere automatically started to move around the participant along a circular trajectory at a fixed distance (30m) with one of two different velocity profiles. Participants were asked to rotate on the spot and to follow the sphere, keeping it in the center of their visual field (outward rotation). The sphere stopped unpredictably at varying eccentricity between 30° and 150° and turned blue, which indicated that participants had to rotate back to the initial heading (backward rotation). When participants had reproduced their estimated initial heading, they confirmed their heading with a button press and the red pole reappeared for reorientation. To ensure that the floor could not be used as an external landmark during the trials, it was faded out, turned randomly, and faded back in after each outward and backward rotation. The participants completed the experimental task twice, using i) a traditional desktop 2D setup (visual flow controlled through joystick movement; “joyR”), and ii) equipped with a MoBI setup (visual flow controlled through active physical rotation with the whole body; “physR”). The condition order was balanced across participants. To ensure the comparability of both rotation conditions, participants carried the full motion capture system at all times. In the joyR condition participants stood in the dimly lit experimental hall in front of a standard TV monitor (1.5m viewing distance, HD resolution, 60Hz refresh rate, 40″ diagonal size) and were instructed to move as little as possible. They followed the sphere by tilting the joystick and were thus only able to use visual flow information to complete the task. In the physical rotation condition participants were situated in a 3D virtual reality environment using a head-mounted display (HTC Vive; 2x1080x1200 resolution, 90 Hz refresh rate, 110° field of view). Participants’ movements were unconstrained, i.e., in order to follow the sphere they physically rotated on the spot, thus enabling them to use motor and kinesthetic information (i.e., vestibular input and proprioception) in addition to the visual flow for completing the task. If participants diverged from the center position as determined through motion capture of the head position, the task automatically halted and participants were asked to regain center position, indicated by a yellow floating sphere, before continuing with the task. Each movement condition was preceded by recording a three-minute baseline, during which the participants were instructed to stand still and to look straight ahead. The starting condition (visual flow only or physical rotation) was also counterbalanced for participants with different reference frame proclivities, such that five egocentric, four allocentric, and two mixed-strategy participants started with the joyR condition, and four egocentric, five allocentric participants started with the physR condition. In each rotation condition, participants practiced the experiment in three learning trials with instructions presented on screen. Subsequently, the main experiment started, including 140 experimental trials per rotation condition. The experimental trials in each condition were randomized and split into five blocks of 28 trials each. The breaks were self-paced and the next block was initiated with the push of a button. The sphere moved either clockwise or anticlockwise around the participant; this movement was either slow or fast (randomized), depending on two different velocity profiles. The eccentricities of the sphere’s end positions were clustered from -15° to +15° around the mean eccentric end positions of 45°, 90°, and 135° in steps of 3° (e.g., the cluster 45° eccentricity ranged from 30° and 60° with 11 trials covering all eccentricities). In addition, eccentricities of 67° and 112° (2 x 8 trials) were used to achieve a near-continuous distribution of end positions for the outward rotation in both rotation directions. Mobile Brain/Body Imaging (MoBI) setup. To allow for a meaningful interpretation of the data modalities and to preserve their temporal context, the EEG data, motion capture data from different sources, and experiment event marker data were time-stamped, streamed, recorded, and synchronized using the Lab Streaming Layer. Data Recordings: EEG. EEG data was recorded from 157 active electrodes with a sampling rate of 1000 Hz and band-pass filtered from 0.016 Hz to 500 Hz (BrainAmp Move System, Brain Products, Gilching, Germany). Using an elastic cap with an equidistant design (EASYCAP, Herrsching, Germany), 129 electrodes were placed on the scalp, and 28 electrodes were placed around the neck using a custom neckband (EASYCAP, Herrsching, Germany) in order to record neck muscle activity. Data were referenced to an electrode located closest to the standard position FCz. Impedances were kept below 10kΩ for standard locations on the scalp, and below 50kΩ for the neckband. Electrode locations were digitized using an optical tracking system (Polaris Vicra, NDI, Waterloo, ON, Canada). Data Recordings: Motion Capture. Two different motion capture data sources were used: 19 red active light-emitting diodes (LEDs) were captured using 31 cameras of the Impulse X2 System (PhaseSpace Inc., San Leandro, CA, USA) with a sampling rate of 90 Hz. They were placed on the feet (2 x 4 LEDs), around the hips (5 LEDs), on the shoulders (4 LEDs), and on the HTC Vive (2 LEDs; to account for an offset in yaw angle between the PhaseSpace and the HTC Vive tracking). Except for the two LEDs on the HTC Vive, they were subsequently grouped together to form rigid body parts of feet, hip, and shoulders, enabling tracking with six degrees of freedom (x, y, and z position and roll, yaw, and pitch orientation) per body part. Head motion capture data (position and orientation) was acquired using the HTC Lighthouse tracking system with 90Hz sampling rate, since it was also used for the positional tracking of the virtual reality view. Because the main focus of the study concerned the head movement-related modulation of neural dynamics in RSC, only data streams from the head motion capture data were used for the analysis.DFG, 240600905, Mobiles Bildgebungssyste

Cognitive performance and brain structural connectome alterations in major depressive disorder

BACKGROUND: Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks. METHODS: Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength. RESULTS: All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course. CONCLUSIONS: Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity

Cognitive performance and brain structural connectome alterations in major depressive disorder

BACKGROUND: Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks. METHODS: Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength. RESULTS: All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course. CONCLUSIONS: Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Background: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects. Methods: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory–based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices. Results: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate–corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis. Conclusions: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BACKGROUND: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects. METHODS: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices. RESULTS: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis. CONCLUSIONS: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders