MEG Working Memory N-Back Task Reveals Functional Deficits in Combat-Related Mild Traumatic Brain Injury

Case report: Neural timing deficits prevalent in developmental disorders, aging, and concussions remediated rapidly by movement discrimination exercises

Background

The substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a Traumatic Brain Injury (TBI) is presented.

Objective

When these timing deficits are remediated using low-level movement-discrimination training, then high-level cognitive skills, including reading, attention, processing speed, problem solving, and working memory improve rapidly and effectively.

Methods

In addition to the substantial evidence published previously, new evidence based on a neural correlate, MagnetoEncephalography physiological recordings, on an adult dyslexic, and neuropsychological tests on this dyslexic subject and an older adult were measured before and after 8-weeks of contrast sensitivity-based left-right movement-discrimination exercises were completed.

Results

The neuropsychological tests found large improvements in reading, selective and sustained attention, processing speed, working memory, and problem-solving skills, never before found after such a short period of training. Moreover, these improvements were found 4 years later for older adult. Substantial MEG signal increases in visual Motion, Attention, and Memory/Executive Control Networks were observed following training on contrast sensitivity-based left-right movement-discrimination. Improving the function of magnocells using figure/ground movement-discrimination at both low and high levels in dorsal stream: (1) improved both feedforward and feedback pathways to modulate attention by enhancing coupled theta/gamma and alpha/gamma oscillations, (2) is adaptive, and (3) incorporated cycles of feedback and reward at multiple levels.

Conclusion

What emerges from multiple studies is the essential role of timing deficits in the dorsal stream that are prevalent in developmental disorders like dyslexia, in aging, and following a TBI. Training visual dorsal stream function at low levels significantly improved high-level cognitive functions, including processing speed, selective and sustained attention, both auditory and visual working memory, problem solving, and reading fluency. A paradigm shift for treating cognitive impairments in developmental disorders, aging, and concussions is crucial. Remediating the neural timing deficits of low-level dorsal pathways, thereby improving both feedforward and feedback pathways, before cognitive exercises to improve specific cognitive skills provides the most rapid and effective methods to improve cognitive skills. Moreover, this adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person's quality of life rapidly and effectively.

Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients

OBJECTIVE

Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations.

METHODS

We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods.

RESULTS

The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity.

CONCLUSIONS

Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data.

SIGNIFICANCE

Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.

EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies

Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.

Beyond the Beta Rebound: Post-Task Responses in Oscillatory Activity follow Cessation of Working Memory Processes

Post-task responses (PTRs) are transitionary responses occurring for several seconds between the end of a stimulus/task and a period of rest. The most well-studied of these are beta band (13 - 30 Hz) PTRs in motor networks following movement, often called post-movement beta rebounds, which have been shown to differ in patients with schizophrenia and autism. Previous studies have proposed that beta PTRs reflect inhibition of task-positive networks to enable a return to resting brain activity, scaling with cognitive demand and reflecting cortical self-regulation. It is unknown whether PTRs are a phenomenon of the motor system, or whether they are a more general self-modulatory property of cortex that occur following cessation of higher cognitive processes as well as movement. To test this, we recorded magnetoencephalography (MEG) responses in 20 healthy participants to a working-memory task, known to recruit cortical networks associated with higher cognition. Our results revealed PTRs in the theta, alpha and beta bands across many regions of the brain, including the dorsal attention network (DAN) and lateral visual regions. These PTRs increased significantly (p < 0.05) in magnitude with working-memory load, an effect which is independent of oscillatory modulations occurring over the task period as well as those following individual stimuli. Furthermore, we showed that PTRs are functionally related to reaction times in left lateral visual (p < 0.05) and left parietal (p < 0.1) regions, while the oscillatory responses measured during the task period are not. Importantly, motor PTRs following button presses did not modulate with task condition, suggesting that PTRs in different networks are driven by different aspects of cognition. Our findings show that PTRs are not limited to motor networks but are widespread in regions which are recruited during the task. We provide evidence that PTRs have unique properties, scaling with cognitive load and correlating significantly with behaviour. Based on the evidence, we suggest that PTRs inhibit task-positive network activity to enable a transition to rest, however, further investigation is required to uncover their role in neuroscience and pathology.

Neural alterations in working memory of mild-moderate TBI: An fMRI study in Malaysia

Working memory (WM) encompasses crucial cognitive processes or abilities to retain and manipulate temporary information for immediate execution of complex cognitive tasks in daily functioning such as reasoning and decision-making. The WM of individuals sustaining traumatic brain injury (TBI) was commonly compromised, especially in the domain of WM. The current study investigated the brain responses of WM in a group of participants with mild-moderate TBI compared to their healthy counterparts employing functional magnetic resonance imaging. All consented participants (healthy: n = 26 and TBI: n = 15) performed two variations of the n-back WM task with four load conditions (0-, 1-, 2-, and 3-back). The respective within-group effects showed a right hemisphere-dominance activation and slower reaction in performance for the TBI group. Random-effects analysis revealed activation difference between the two groups in the right occipital lobe in the guided n-back with cues, and in the bilateral occipital lobe, superior parietal region, and cingulate cortices in the n-back without cues. The left middle frontal gyrus was implicated in the load-dependent processing of WM in both groups. Further group analysis identified that the notable activation changes in the frontal gyri and anterior cingulate cortex are according to low and high loads. Though relatively smaller in scale, this study was eminent as it clarified the neural alterations in WM in the mild-moderate TBI group compared to healthy controls. It confirmed the robustness of the phenomenon in TBI with the reproducibility of the results in a heterogeneous non-Western sample.

Functional near-infrared spectroscopy as a potential objective evaluation technique in neurocognitive disorders after traumatic brain injury

Most patients with neurocognitive disorders after traumatic brain injury (TBI) show executive dysfunction, in which the pre-frontal cortex (PFC) plays an important role. However, less objective evaluation technique could be used to assess the executive dysfunction in these patients. Functional near-infrared spectroscopy (fNIRS), which is a non-invasive technique, has been widely used in the study of psychiatric disorders, cognitive dysfunction, etc. The present study aimed to explore whether fNIRS could be a technique to assess the damage degree of executive function in patients with neurocognitive disorders after TBI by using the Stroop and N-back tasks in PFC areas. We enrolled 37 patients with neurocognitive disorders after TBI and 60 healthy controls. A 22-channel fNIRS device was used to record HbO during Stroop, 1-back and 2-back tasks. The results showed that patients made significantly more errors and had longer response times than healthy controls. There were statistically significant differences in HbO level variation in bilateral frontopolar, bilateral inferior frontal gyrus and left middle temporal gyrus during Stroop color word consistency tasks and in left frontopolar during Stroop color word inconsistency tasks. During 2-back tasks, there were also statistically significant differences in HbO level variation in bilateral frontopolar, bilateral inferior frontal gyrus, bilateral dorsolateral pre-frontal cortex. According to brain activation maps, the patients exhibited lower but more widespread activation during the 2-back and Stroop color word consistency tasks. The fNIRS could identify executive dysfunction in patients with neurocognitive disorders after TBI by detecting HbO levels, which suggested that fNIRS could be a potential objective evaluation technique in neurocognitive disorders after TBI.

Magnetoencephalography abnormalities in adult mild traumatic brain injury: A systematic review

BACKGROUND

The global incidence of traumatic brain injuries is rising, with at least 80% being classified as mild. These mild injuries are not visible on routine clinical imaging. The potential clinical role of a specific imaging biomarker be it diagnostic, prognostic or directing and monitoring progress of personalised treatment and rehabilitation has driven the exploration of several new neuroimaging modalities. This systematic review examined the evidence for magnetoencephalography (MEG) to provide an imaging biomarker in mild traumatic brain injury (mTBI).

METHODS

Our review was prospectively registered on PROSPERO: CRD42019151387. We searched EMBASE, MEDLINE, trial registers, PsycINFO, Cochrane Library and conference abstracts and identified 37 papers describing MEG changes in mTBI eligible for inclusion. Since meta-analysis was not possible, based on the heterogeneity of reported outcomes, we provide a narrative synthesis of results.

RESULTS

The two most promising MEG biomarkers are excess resting state low frequency power, and widespread connectivity changes in all frequency bands. These may represent biomarkers with potential for diagnostic application, which reflect time sensitive changes, or may be capable of offering clinically relevant prognostic information. In addition, the rich data that MEG produces are well-suited to new methods of machine learning analysis, which is now being actively explored.

INTERPRETATION

MEG reveals several promising biomarkers, in the absence of structural abnormalities demonstrable with either computerised tomography or magnetic resonance imaging. This review has not identified sufficient evidence to support routine clinical use of MEG in mTBI currently. However, verifying MEG's potential would help meet an urgent clinical need within civilian, sports and military medicine.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury

Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

Enhanced Fast-VESTAL for Magnetoencephalography Source Imaging: From Theory to Clinical Application in Epilepsy

A novel magnetoencephalography source imaging approach called Fast Vector-based Spatio-Temporal Analysis (Fast-VESTAL) has been successfully applied in creating source images from evoked and resting-state data from both healthy subjects and individuals with neurological and/or psychiatric disorders, but its reconstructed source images may show false-positive activations, especially under low signal-to-noise ratio conditions. Here, to effectively reduce false-positive artifacts, we introduced an enhanced Fast-VESTAL (eFast-VESTAL) approach that adopts generalized second-order cone programming. We compared the spatiotemporal characteristics of the eFast-VESTAL approach to those of the popular distributed source approaches (e.g., the minimum L2-norm/ mixed-norm methods) using computer simulations and auditory experiments. More importantly, we applied eFast-VESTAL to the presurgical evaluation of epilepsy. Our results demonstrated that eFast-VESTAL exhibited a lower dipole localization error and/or a higher correlation coefficient (CC) between the estimated source time series and ground truth under various conditions of source waveforms. Experimentally, eFast-VESTAL displayed more focal activation maps and a higher CC between the raw and predicted sensor data in response to auditory stimulation. Notably, eFast-VESTAL was the most accurate method for noninvasively detecting the epileptic zones determined using more invasive stereo-electroencephalography in the comparison.

Altered brain network topology related to working memory in internet addiction

BACKGROUND AND AIMS

The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.

METHODS

A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.

RESULTS

We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.

CONCLUSIONS

In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

Disrupted brain functional hub and causal connectivity in acute mild traumatic brain injury

There have been an increasing number of functional magnetic resonance imaging (fMRI) reports on brain abnormalities in mild traumatic brain injury (mTBI) at different phases. However, the neural bases and cognitive impairment after acute mTBI are unclear. This study aimed to identify brain functional hubs and connectivity abnormalities in acute mTBI patients and their correlations with deficits in cognitive performance. Within seven days after brain injury, mTBI patients (n=55) and age-, sex-, and educational -matched healthy controls (HCs) (n=41) underwent resting-state fMRI scans and cognitive assessments. We derived functional connectivity (FC) strength of the whole-brain network using degree centrality (DC) and performed Granger causality analysis (GCA) to analyze causal connectivity patterns in acute mTBI. Compared with HCs, acute mTBI patients had significantly decreased network centrality in the left middle frontal gyrus (MFG). Additionally, acute mTBI showed decreased inflows from the left MFG to bilateral middle temporal gyrus (MTG), left medial superior frontal gyrus (mSFG), and left anterior cingulate cortex (ACC). Correlation analyses revealed that changes in network centrality and causal connectivity were associated with deficits in cognitive performance in mTBI. Our findings may help to provide a new perspective for understanding the neuropathophysiological mechanism of acute cognitive impairment after mTBI.

Prefrontal high gamma during a magnetoencephalographic working memory task

In human electrophysiology research, the high gamma part of the power spectrum (~>60 Hz) is a relatively new area of investigation. Despite a low signal-to-noise ratio, evidence exists that it contains significant information about activity in local cortical networks. Here, using magnetoencephalography (MEG), we found high gamma activity when comparing data from an n-back working memory task to resting data in a large sample of normal volunteers. Initial analysis of power spectra from 0-back, 2-back, and rest trials showed three frequency bands exhibiting task-related differences: alpha, beta, and high gamma. Unlike alpha and beta, the high gamma spectrum was broad, without a peak at a single frequency. In addition, power in high gamma was highest for the 2-back and lowest during rest, while the opposite pattern occurred in the other bands. Beamformer source localization of each of the three frequency bands revealed a distinct set of sources for high gamma. These included several regions of prefrontal cortex that exhibited greater power when both n-back conditions were compared to rest. A subset of these regions had more power when the 2-back was compared to 0-back, which indicates a role in working memory performance. Our results show that high gamma will be important for understanding cortical processing during cognitive and other tasks. Furthermore, data from human intracortical recordings suggest that high gamma is the aggregate of spiking in local cortical networks, which implies that MEG could serve to bridge experimental modalities by noninvasively observing task-related modulation of spiking rates.