Posts Tagged electroencephalography
Researchers used electroencephalography and artificial intelligence to identify individuals who would likely respond to sertraline, the antidepressant marketed as Zoloft.
Stanford researchers and their collaborators used electroencephalography, a tool for monitoring electrical activity in the brain, and an algorithm to identify a brain-wave signature in individuals with depression who will most likely respond to sertraline, an antidepressant marketed as Zoloft.
A paper describing the work was published today in Nature Biotechnology.
The study emerged from a decades-long effort funded by the National Institute of Mental Health to create biologically based approaches, such as blood tests and brain imaging, to help personalize the treatment of depression and other mental disorders. Currently, there are no such tests to objectively diagnose depression or guide its treatment.
“This study takes previous research showing that we can predict who benefits from an antidepressant and actually brings it to the point of practical utility,” said Amit Etkin, MD, PhD, professor of psychiatry and behavioral sciences at Stanford. “I will be surprised if this isn’t used by clinicians within the next five years.”
Instead of functional magnetic resonance imaging, an expensive technology often used in studies to image brain activity, the scientists turned to electroencephalography, or EEG, a much less costly technology.
Etkin shares senior authorship of the paper with Madhukar Trivedi, MD, professor of psychiatry at the University of Texas-Southwestern. Wei Wu, PhD, an instructor of psychiatry at Stanford, is the lead author.
The paper is one of several based on data from a federally funded depression study launched in 2011 — the largest randomized, placebo-controlled clinical trial on antidepressants ever conducted with brain imaging — which tested the use of sertraline in 309 medication-free patients. The multicenter trial was called Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care, or EMBARC. Led by Trivedi, it was designed to advance the goal of improving the trial-and-error method of treating depression that is still in use today.
“It often takes many steps for a patient with depression to get better,” Trivedi said. “We went into this thinking, ‘Wouldn’t it be better to identify at the beginning of treatment which treatments would be best for which patients?’”
Most common mental disorder
Major depression is the most common mental disorder in the United States, affecting about 7% of adults in 2017, according to the National Institute of Mental Health. Among those, about half never get diagnosed. For those who do, finding the right treatment can take years, Trivedi said. He pointed to one of his past studies that showed only about 30% of depressed patients saw any remission of symptoms after their first treatment with an antidepressant.
To diagnose depression, clinicians rely on a patient reporting at least 5 of 9 common symptoms of the disease. The list includes symptoms such as feelings of sadness or hopelessness, self-doubt, sleep disturbances — ranging from insomnia to sleeping too much — low energy, unexplained body aches, fatigue, and changes in appetite, ranging from overeating to undereating. Patients often vary in both the severity and types of symptoms they experience, Etkin said.
“As a psychiatrist, I know these patients differ a lot,” Etkin said. “But we put them all under the same umbrella, and we treat them all the same way.” Treating people with depression often begins with prescribing them an antidepressant. If one doesn’t work, a second antidepressant is prescribed. Each of these “trials” often takes at least eight weeks to assess whether the drug worked and symptoms are alleviated. If an antidepressant doesn’t work, other treatments, such as psychotherapy or occasionally transcranial magnetic stimulation, may also be tried. Often, multiple treatments are combined, Etkin said, but figuring out which combination works can take a while.
“People often feel a lot of dejection each time a treatment doesn’t work, creating more self-doubt for those whose primary symptom is most often self-doubt,” Trivedi said.
Looking for a biomarker
The EMBARC trial enrolled 309 people with depression who were randomized to receive either sertraline or a placebo.
For their study, Etkin and his colleagues set out to find a brain-wave pattern to help predict which depressed participants would respond to sertraline. First, the researchers collected EEG data on the participants before they received any drug treatment. The goal was to obtain a baseline measure of brain-wave patterns.
Next, using insights from neuroscience and bioengineering, the investigators analyzed the EEG using a novel artificial intelligence technique they developed and identified signatures in the data that predicted which participants would respond to treatment based on their individual EEG scans. The researchers found that this technique reliably predicted which of the patients did, in fact, respond to sertraline and which responded to placebo. The results were replicated at four different clinical sites.
Further research suggested that participants who were predicted to show little improvement with sertraline were more likely to respond to treatment involving transcranial magnetic stimulation, or TMS, in combination with psychotherapy.
“Using this method, we can characterize something about an individual person’s brain,” Etkin said. “It’s a method that can work across different types of EEG equipment, and thus more apt to reach the clinic.”
Etkin is on leave from Stanford, working as the founder and CEO of the startup Alto Neuroscience, a company based in Los Altos, California, that aims to build on these findings and develop a new generation of biologically based diagnostic tests to personalize mental health treatments with a high degree of clinical utility. “Part of getting these study results used in clinical care is, I think, that society has to demand it,” Trivedi said. “That is the way things get put into practice. I don’t see a downside to putting this into clinical use soon.”
When EMBARC was launched, it was part of a broader effort by the NIMH to push for improvements in mental health care by using advances in fields such as genetics, neuroscience and biotechnology, said Thomas Insel, MD, who served as director of that institute from 2002 to 2015.
“We went into EMBARC saying anything is possible,” Insel said. “Let’s see if we can come up with clinically actionable techniques.” He didn’t think it would take this long, but he remains optimistic.
“I think this study is a particularly interesting application of EMBARC,” he said. “It leverages the power of modern data science to predict at the individual level who is likely to respond to an antidepressant.”
In addition to improving care, the researchers said they see a possible side benefit to the use of biologically based approaches: It could reduce the stigma associated with depression and other mental health disorders that prevents many people from seeking appropriate medical care.
“I’d love to think scientific evidence will help to counteract this stigma, but it hasn’t so far,” said Insel. “It’s been over 160 years since Abraham Lincoln said that melancholy ‘is a misfortune, not a fault.’ We still have a long way to go before most people will understand that depression is not someone’s fault.” (President Lincoln suffered bouts of depression.)
Other Stanford co-authors of the paper are postdoctoral scholars Yu Zhang, PhD, and Jing Jiang, PhD; former postdoctoral scholar Gregory Fonzo, PhD; neuroscience graduate students Molly Lucas and Camarin Rolle; research assistants Carena Cornelssen and Kamron Sarhadi; clinical research coordinator Trevor Caudle; former clinical research coordinators Rachael Wright, Karen Monuszko and Hersh Trivedi; and former neuroscience graduate student Russell Toll. All Stanford authors, including Etkin, are affiliated with Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness, Research, Education and Clinical Center in Palo Alto.
Etkin is a member of the Wu Tsai Neurosciences Institute at Stanford.
Researchers at South China University of Technology, the Netherlands Research Institute, Harvard Medical School, the New York State Psychiatric Institute, Columbia University and the Netherlands neuroCare Group also contributed to the work.
Insel is an investor in Alto Neuroscience.
The EMBARC study data are publicly available through the NIMH Data Archive.
The study was funded by the National Institutes of Health (U01MH092221, U01MH092250, R01MH103324, DP1 MH116506), the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.
Stanford Medicine integrates research, medical education and health care at its three institutions – Stanford University School of Medicine, Stanford Health Care (formerly Stanford Hospital & Clinics), and Lucile Packard Children’s Hospital Stanford. For more information, please visit the Office of Communication & Public Affairs site at http://mednews.stanford.edu.
Background/Objective. We investigated interhemispheric interactions in stroke survivors by measuring transcranial magnetic stimulation (TMS)–evoked cortical coherence. We tested the effect of TMS on interhemispheric coherence during rest and active muscle contraction and compared coherence in stroke and older adults. We evaluated the relationships between interhemispheric coherence, paretic motor function, and the ipsilateral cortical silent period (iSP).
Methods. Participants with (n = 19) and without (n = 14) chronic stroke either rested or maintained a contraction of the ipsilateral hand muscle during simultaneous recordings of evoked responses to TMS of the ipsilesional/nondominant (i/ndM1) and contralesional/dominant (c/dM1) primary motor cortex with EEG and in the hand muscle with EMG. We calculated pre- and post-TMS interhemispheric beta coherence (15-30 Hz) between motor areas in both conditions and the iSP duration during the active condition.
Results. During active i/ndM1 TMS, interhemispheric coherence increased immediately following TMS in controls but not in stroke. Coherence during active cM1 TMS was greater than iM1 TMS in the stroke group. Coherence during active iM1 TMS was less in stroke participants and was negatively associated with measures of paretic arm motor function. Paretic iSP was longer compared with controls and negatively associated with clinical measures of manual dexterity. There was no relationship between coherence and. iSP for either group. No within- or between-group differences in coherence were observed at rest.
Conclusions. TMS-evoked cortical coherence during hand muscle activation can index interhemispheric interactions associated with poststroke motor function and potentially offer new insights into neural mechanisms influencing functional recovery.
via Role of Interhemispheric Cortical Interactions in Poststroke Motor Function – Jacqueline A. Palmer, Lewis A. Wheaton, Whitney A. Gray, Mary Alice Saltão da Silva, Steven L. Wolf, Michael R. Borich, 2019
[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation
2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.
3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.
4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.
5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.
6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.
7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.
8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.
9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.
10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.
11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.
12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.
13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.
14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.
15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.
16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)
17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.
18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.
19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.
[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation
This multidisciplinary research topic is a collection of contemporary advances in neuroimaging applied to mapping functional brain networks in epilepsy. With technology such as simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) now more readily available, it is possible to non-invasively map epileptiform activity throughout the entire brain at millimetre resolution. This research topic includes original research studies, technical notes and reviews of the field. Due to the multidisciplinary nature of the domain, the topic spans two journals: Frontiers in Neurology (Section: Epilepsy) and Frontiers in Neuroscience (Section: Brain Imaging Methods).
In this editorial we consider the outcomes of the multidisciplinary work presented in the topic. With the benefit of time elapsed since the original papers were published, we can see that the works are making a substantial impact in the field. At the time of writing, this topic had well over 27,000 full-paper downloads (including over 18,000 for the 15 papers in the Epilepsy section, and over 9,000 for the 8 papers in the Brain Imaging Methods section). Several papers in the topic have climbed the tier in Frontiers and received an associated invited commentary, demonstrating there is substantial interest in this research area.
The topic’s review papers set the scene for the original research papers and synthesise contemporary thinking in epilepsy research and neuroimaging methods. We see that Epilepsy, whether of a “generalised” or “focal” origin, is increasingly recognised as a disorder of large-scale brain networks. At one level it is self-evident that otherwise healthy functional networks are recruited during epileptic activity, as this is what generates patient perceptions of their epileptic aura. For example, the epileptic aura of mesial temporal lobe epilepsy can include an intense sensation of familiarity (déjà vu) associated with involvement of the hippocampus, and unpleasant olfactory auras which may reflect involvement of adjacent olfactory cortex. As seizures spread more widely throughout the brain, presumably along pre-existing neural pathways, patients lose control of certain functions; for example, their motor system in the case of generalised convulsions, or aspects of awareness in seizures that remain localised to non-motor brain regions. Yet these functions return when the seizure abates, implying involved brain regions are also responsible for normal brain function. What has been less clear, and difficult to investigate until the advent of functional neuroimaging, is precisely which brain networks are involved (especially in ‘generalised’ epilepsy syndromes), and the extent to which functional networks are perturbed during seizures, inter-ictal activity, and at other times.
Functional imaging evidence of brain abnormalities in temporal lobe epilepsy is explored in (Caciagli et al., 2014), including evidence of dysfunction in limbic and other specific brain networks, as well as global changes in network topography derived from resting-state fMRI. Archer et al systematically review the functional neuroimaging of a particularly severe epilepsy phenotype, Lennox-Gastaut Syndrome (LGS), illustrating well how different forms of brain pathology can manifest in a similar clinical phenotype, simply by the nature of the healthy networks that the underlying pathology perturbs (Archer et al., 2014). Similarly, the mechanisms of absence seizure generation are reviewed by (Carney and Jackson, 2014), revealing that it too has a signature pattern of large-scale functional brain network perturbation. The ability to make such observations has considerable clinical significance, as highlighted in the review by (Pittau et al., 2014).
The tantalising proposition that there may be a common treatment target for all focal epilepsy phenotypes is also explored in a review of the piriform cortex by (Vaughan and Jackson, 2014). The piriform cortex was first implicated as a common brain region associated with spread of interictal discharges in focal epilepsy in an experiment that analysed the spatially normalised functional imaging data of a heterogeneous group of focal epilepsy patients (Laufs et al., 2011). This finding, since replicated (Flanagan et al., 2014), led Vaughan & Jackson to explore in detail what is known of the piriform cortex. Their findings reveal the piriform has several features that likely predispose it to involvement in focal epilepsy, and features that also explain many of the peculiar symptoms experienced by patients, from olfactory auras to the characteristic nose-wiping that many patients perform postictally. This work points to the need for future studies to determine whether the piriform might be an effective target for deep brain stimulation or other targeted therapy to prevent the spread of epileptiform activity.
Temporal lobe epilepsy is investigated in several papers in this topic. One of these studies also introduces a new exploratory method, Shared and specific independent component analysis (SSICA), that builds upon independent component analysis to perform between-group network comparison (Maneshi et al., 2014). In application to mesial temporal lobe epilepsy (MTLE) and healthy controls, three distinct reliable networks were revealed: two that exhibited increased activity in patients (a network including hippocampus and amygdala bilaterally, and a network including postcentral gyri and temporal poles), and a network identified as specific to healthy controls (i.e. effectively decreased in patients, consisting of bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus). These finding give mechanistic clues to the cognitive impairments often reported in patients with MTLE. Further clues are revealed in a study of the dynamics of fMRI and its functional connectivity (Laufs et al., 2014). Compared to healthy controls, temporal variance of fMRI was seen to be most increased in the hippocampi of TLE patients, and variance of functional connectivity to this region was increased mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. More severe disruption of connectivity in these networks during seizures may explain patients’ cognitive dysfunction (Laufs et al., 2014). Yang and colleagues also show that it may be possible to use fMRI functional connectivity to lateralise TLE (Yang et al., 2015), which could be a useful clinical tool.
Mechanistic explanations of symptomatology beyond the seizure onset zone can also be revealed with conventional nuclear medicine techniques such as 18F-FDG-PET. This is demonstrated in a study of Occipital Lobe Epilepsy by Wong and colleagues, who observed that patients with automatisms have metabolic changes extending from the epileptogenic occipital lobe into the ipsilateral temporal lobe, whereas in patients without automatisms the 18F-FDG-PET was abnormal only in the occipital lobe (Wong et al., 2014).
The clinical significance of the ability to non-invasively study functional brain networks extends to understanding the impact of surgery on brain networks. This Frontiers research topic includes an investigation by Doucet and colleagues revealing that temporal lobe epilepsy and surgery selectively alter the dorsal, rather than the ventral, default-mode network (Doucet et al., 2014).
Another approach to better understand the mechanisms of seizure onset and broader symptomatology is computational modelling. It can track aspects of neurophysiology than cannot be readily measured: for example effective connectivity and mean membrane potential dynamics are shown by (Freestone et al., 2014) to be estimable using model inversion. In a proof-of-principle experiment with simulated data, they demonstrate that by tailoring the model to subject-specific data, it may be possible for the framework to identify a seizure onset site and the mechanism for seizure initiation and termination. Also in this topic, Petkov and colleagues utilise a computational model of the transition into seizure dynamics to explore how conditions favourable for seizures relate to changes in functional networks. They find that networks with higher mean node degree are more prone to generating seizure dynamics in the model, thus providing a mathematical mechanistic explanation for increasing node degree causing increased ictogenicity (Petkov et al., 2014).
Seizure prediction is an area of considerable research, and in this topic Cook and colleagues reveal intriguing characteristics in the long-term temporal pattern of seizure onset. They confirmed that human inter-seizure intervals follow a power law, and they found evidence of long-range dependence. Specifically, the dynamics that led to the generation of a seizure in most patients appeared to be affected by events that took place much earlier (as little as 30 minutes prior and up to 40 days prior in some patients) (Cook et al., 2014). The authors rightly note that this information could be valuable for individually-tuned seizure prediction algorithms.
Several methodological papers in this Frontiers Topic prove there remains considerable potential to improve neuroimaging methods as applied to the study of epilepsy. For example, (Mullinger et al., 2014) reveal the critical importance of the accuracy of physical models if one is to optimise lead positioning in functional MRI with simultaneous EEG. Confirming with computer modelling and phantom measurements that lead positioning can have a substantial effect on the amplitude of the MRI gradient artefact present on the EEG, they optimised the positions in a novel cap design. However, whilst this substantially reduced gradient artefact amplitude on the phantom, it made things worse when used on human subjects. Thus, improvement is required in model accuracy if one is to make accurate predictions for the human context.
Reduction of artefact, particularly cardioballistic and non-periodic motion artefact, remains a challenge for off-the-shelf MRI-compatible EEG systems. However, for over a decade, the Jackson group in Melbourne has dealt well with this issue using insulated carbon-fibre artefact detectors, physically but not electrically attached to the scalp (Masterton et al., 2007). In the present topic, they provide detailed instructions for building such detectors and interfacing them with a commercially available MRI-compatible EEG system (Abbott et al., 2015). This team also previously developed event-related ICA (eICA), to map fMRI activity associated with inter-ictal events observed on EEG (Masterton et al., 2013b). The method is capable of distinguishing separate sub-networks characterised by differences in spatio-temporal response (Masterton et al., 2013a). The eICA approach frees one from assumptions regarding the shape of the time-course of the neuronal and haemodynamic response associated with inter-ictal activity (which can vary according to spike type, can vary from conventional models and may include pre-spike activity (Masterton et al., 2010); issues explored further in the present topic by (Faizo et al., 2014) and (Jacobs et al., 2014)). However, the effectiveness of eICA can be affected by fMRI noise or artefact. In the present topic we see that application of a fully automated de-noising algorithm (SOCK) is now recommended, as it can substantially improve the quality of eICA results (Bhaganagarapu et al., 2014).
The ability to detect activity associated with inter-ictal events can also be improved with faster image acquisition. Magnetic Resonance Encephalography (MREG) is a particularly fast fMRI acquisition method (TR=100ms) that achieves its speed using an under-sampled k-space trajectory (Assländer et al., 2013; Zahneisen et al., 2012). This has now been applied in conjunction with simultaneous EEG, to reveal that the negative fMRI response in the default-mode network is larger in temporal compared to extra-temporal epileptic spikes (Jacobs et al., 2014).
The default mode network and its relationship to epileptiform activity is also examined in several other papers in this topic. In a pilot fMRI connectivity study of Genetic Generalised Epilepsy and Temporal Lobe Epilepsy patients, (Lopes et al., 2014) observed that intrinsic connectivity in portions of the default mode network appears to increase several seconds prior to the onset of inter-ictal discharges. The authors suggest that the default mode network connectivity may facilitate IED generation. This is plausible, although causality is difficult to establish and it is possible that something else drives both the connectivity and EEG changes (Abbott, 2015).
Complicating matters further is the question of what connectivity means. There are many ways in which connectivity can be assessed. Jones and colleagues have discovered that some of these do not necessarily correlate well with each other. They examined connectivity between measurements made with intracranial electrodes, connectivity assessed using simultaneous BOLD fMRI and intracranial electrode stimulation, connectivity between low-frequency voxel measures of fMRI activity, and a diffusion MRI measure of connectivity – an integrated diffusivity measure along a connecting pathway (Jones et al., 2014). They found only mild correlation between these four measures, implying they assess quite different features of brain networks. More research in this domain would therefore be valuable.
Whatever the measure of connectivity utilised, most evidence of alterations in connectivity in epilepsy has been obtained from comparison of a group of patients with a group of healthy controls. However, a new method called Detection of Abnormal Networks in Individuals (DANI) is now proposed by (Dansereau et al., 2014). This method is designed to detect the organisation of brain activity in stable networks, which the authors call modularity. The conventional definition of modularity refers to the degree to which networks can be segregated into distinct communities, usually estimated by maximising within-group nodal links, and minimising between group links (Girvan and Newman, 2002; Rubinov and Sporns, 2010). Dansereau take a novel approach to this concept, instead evaluating the stability of each resting state network across replications of a bootstrapped clustering method (Bellec et al., 2010). In the DANI approach, the degree to which an individual’s functional connectivity modular pattern deviates from a population of controls is quantified. Whilst application of the method to epilepsy patients is preliminary, significant changes were reported likely related to the epileptogenic focus in 5 of the 6 selected focal epilepsy patients studied. In several patients, modularity changes in regions distant from the focus were also observed, adding further evidence that the pervasive network effects of focal epilepsy can extend well beyond the seizure onset zone.
When it comes to application of EEG-fMRI to detect the seizure onset zone, there is typically a trade-off between specificity and sensitivity, with the added complication that activity or network changes may also occur in brain regions other than the ictal onset zone. The distant activity may be due to activity propagation from the onset zone, pervasive changes in functional networks creating a ‘permissive state’, or in some cases might be the brain’s attempt to prevent seizures. Specificity and sensitivity of EEG-fMRI to detect the ictal onset zone is explored by (Tousseyn et al., 2014). They determined how rates of true and false positives and negatives varied with voxel height and cluster size thresholds, both for the full statistical parametric map, and for the single cluster that contained the voxel of maximum statistical significance. The latter conferred the advantage of reducing positives remote from the seizure onset zone. As a result, it appeared to be more robust to variations in statistical threshold than analysis of the entire map. One needs to be cautious however, given the small numbers of patients studied, and the fact that the “optimal” settings were determined using receiver operator characteristic curves of the same study data. It remains to be seen how well this might generalise to a different study.
Perhaps the greatest potential for future advancement in EEG-fMRI is in methods to make the most of the all the information captured by each modality. This is highlighted by the work of Deligianni et al, demonstrating with a novel analysis framework the potential to obtain more information on the human functional connectome by utilising EEG and fMRI together (Abbott, 2016; Deligianni et al., 2014).
We hope that you enjoy this collection of papers providing a broad snapshot of advances in brain mapping methods and application to better understand epilepsy.
[ARTICLE] Transcranial direct current stimulation for the treatment of motor impairment following traumatic brain injury – Full Text
After traumatic brain injury (TBI), motor impairment is less common than neurocognitive or behavioral problems. However, about 30% of TBI survivors have reported motor deficits limiting the activities of daily living or participation. After acute primary and secondary injuries, there are subsequent changes including increased GABA-mediated inhibition during the subacute stage and neuroplastic alterations that are adaptive or maladaptive during the chronic stage. Therefore, timely and appropriate neuromodulation by transcranial direct current stimulation (tDCS) may be beneficial to patients with TBI for neuroprotection or restoration of maladaptive changes.
Technologically, combination of imaging-based modelling or simultaneous brain signal monitoring with tDCS could result in greater individualized optimal targeting allowing a more favorable neuroplasticity after TBI. Moreover, a combination of task-oriented training using virtual reality with tDCS can be considered as a potent tele-rehabilitation tool in the home setting, increasing the dose of rehabilitation and neuromodulation, resulting in better motor recovery.
This review summarizes the pathophysiology and possible neuroplastic changes in TBI, as well as provides the general concepts and current evidence with respect to the applicability of tDCS in motor recovery. Through its endeavors, it aims to provide insights on further successful development and clinical application of tDCS in motor rehabilitation after TBI.
Traumatic brain injury (TBI) is defined as “an alteration in brain function (loss of consciousness, post-traumatic amnesia, and neurologic deficits) or other evidence of brain pathology (visual, neuroradiologic, or laboratory confirmation of damage to the brain) caused by external force” . The incidence and prevalence of TBI are substantial and increasing in both developing and developed countries. TBI in older age groups due to falling has been on the rise in recent years, becoming the prevalent condition in all age groups [2, 3]. TBI causes broad spectrum of impairments, including cognitive, psychological, sensory or motor impairments [4, 5], which may increase the socioeconomic burdens and reduce the quality of life [6, 7]. Although motor impairment, such as limb weakness, gait disturbance, balance problem, dystonia or spasticity, is less common than neurocognitive or behavioral problems after TBI, about 30% of TBI survivors have reported motor deficits that severely limited activities of daily living or participation .
Motor impairment after TBI is caused by both focal and diffuse damages, making it difficult to determine the precise anatomo-clinical correlations [9, 10]. According to previous clinical studies, recovery after TBI also seems worse than that after stroke, although the neuroplasticity after TBI may also play an important role for recovery . Therefore, a single unimodal approach for motor recovery, including conventional rehabilitation, may be limiting, and hence, requiring a novel therapeutic modality to improve the outcome after TBI.
Transcranial direct current stimulation (tDCS) – one of the noninvasive brain stimulation (NIBS) methods – can increase or decrease the cortical excitability according to polarity (anodal vs. cathodal) and be used to modulate the synaptic plasticity to promote long-term functional recovery via long-term depression or potentiation [12, 13]. Recent clinical trials evaluating patients with stroke have reported the potential benefits of tDCS for motor recovery . Neuroplastic changes after TBI and results from animal studies also suggest that tDCS could improve the motor deficit in TBI, although clinical trials using tDCS for motor recovery in TBI are currently lacking .
In this review, we will cover (1) the pathophysiology and possible neuroplastic changes in TBI; (2) physiology of tDCS; (3) current clinical evidence of tDCS in TBI for motor recovery; (4) general current concept of tDCS application for motor recovery; and (5) the future developments and perspectives of tDCS for motor recovery after TBI. Although the scope of motor recovery is wide, this review will focus primarily on the recovery of limb function, especially that of the upper limb. We expect that this review can provide insights on further successful development and clinical application of tDCS in motor rehabilitation after TBI.[…]
[Abstract] Mozart’s music and multidrug-resistant epilepsy: a potential EEG index of therapeutic effectiveness.
[ARTICLE] Transcranial direct current stimulation (tDCS) for upper limb rehabilitation after stroke: future directions. – Full Text
Transcranial Direct Current Stimulation (tDCS) is a potentially useful tool to improve upper limb rehabilitation outcomes after stroke, although its effects in this regard have shown to be limited so far. Additional increases in effectiveness of tDCS in upper limb rehabilitation after stroke may for example be achieved by
(1) applying a more focal stimulation approach like high definition tDCS (HD-tDCS),
(2) involving functional imaging techniques during stimulation to identify target areas more exactly,
(3) applying tDCS during Electroencephalography (EEG) (EEG-tDCS),
(4) focusing on an effective upper limb rehabilitation strategy as an effective base treatment after stroke.
Perhaps going even beyond the application of tDCS and applying alternative stimulation techniques such as transcranial Alternating Current Stimulation (tACS) or transcranial Random Noise Stimulation (tRNS) will further increase effectiveness of upper limb rehabilitation after stroke.
Impaired arm function after stroke is both frequent and a considerable burden for people with stroke and their caregivers. An emerging approach for enhancing neural plasticity after acute and chronic brain damage, thus enhancing rehabilitation outcomes in the upper limb rehabilitation after stroke, is non-invasive brain stimulation (NIBS), for example delivered by transcranial direct current stimulation (tDCS) . tDCS is a potentially useful tool for facilitating neural plasticity, because it is relatively inexpensive, easy to administer and safe.
Many small trials regarding the effects of tDCS on arm motor function poststroke were undertaken in the past with partly promising but not conclusive results [2, 3]. Based on these trials a lot of research interest increased in the last 10 to 15 years which still persists. This considerable research interest is a bit surprising first, given the fact that this type of therapy is not used across the board in clinical routine and second, the largest multicenter randomized clinical trial with appropriate methodology including 96 patients did not find clear results in favor of this type of stimulation . A recent network meta-analysis of randomised controlled trials about the effectiveness of tDCS suggested only limited evidence for effectiveness of tDCS after stroke for arm rehabilitation . The optimal stimulation paradigm regarding polarisation, electrode location, amount of direct current applied and stimulation duration still has to be established in order to maximize clinical effectiveness of tDCS . Additionally, doubts emerged that the underlying rationale, the interhemispheric competition model, may be oversimplified or even incorrect . The interhemispheric competition model postulates that a stroke leads to an inhibition of the ipsilateral and to an (over-) excitation of the contralateral brain hemisphere. Hence its clinical implications are to inhibit the contralateral hemisphere and to excited ipsilateral hemisphere. Moreover, electrode positioning and the resulting direction of electric fields as well as variation in head anatomy also modulate stimulation effects [7, 8]. Hence, further approaches may be warranted beyond the approach of neuronavigation prior to stimulation: Additional increases in effectiveness of tDCS in upper limb rehabilitation after stroke may for example be achieved by (1) applying a more focal stimulation approach like high definition tDCS (HD-tDCS), (2) involving functional imaging techniques during stimulation to identify target areas more exactly, (3) applying tDCS during EEG (EEG-tDCS), (4) focusing on an effective upper limb rehabilitation strategy as an effective base treatment after stroke. Perhaps going even beyond the application of tDCS and applying alternative stimulation techniques such as transcranial Alternating Current Stimulation (tACS)  or transcranial Random Noise Stimulation (tRNS)  will further increase effectiveness of upper limb rehabilitation after stroke.[…]
[Abstract + References] Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface
In recent years, stroke has became one of the major health problems which significantly affect the daily life of the elderly, and hand rehabilitation is introduced as an auxiliary treatment. Though various kinds of mechanical devices for hand rehabilitation have been developed, some deficiencies still exist in the current rigid rehabilitation hand, such as the degrees of freedom is not enough, complexity, unsafe status, overweight, being uncomfortable, unfitness and so on. Therefore, with the growth of aging population, it is highly needed to develop some new devices to satisfy the comprehensive rehabilitation requirements. Meanwhile, inspired by the mollusks in nature, soft robot is made of soft materials that can withstand large strains. It is a new type of continuum robot with high flexibility and environmental adaptability. The soft robot has a broad application prospects in military detection techniques, such as instance search, rescue, medical application and other fields.
[Abstract] EEG predicts upper limb motor improvement after robotic rehabilitation in chronic stroke patients
Robotic rehabilitation is known to be at least as effective as conventional training for upper limb motor recovery after stroke; nevertheless, which patients could benefit from this treatment is unknown and finding markers that could predict rehabilitation outcome is a challenge.
We aimed at understanding the neural mechanisms of motor function recovery after upper limb robotic rehabilitation in chronic stroke patients using neurophysiological markers obtained by electroencephalography recording (EEG).
Material and method
Fourteen chronic stroke patients (M/F: 11/3; 59.5 ± 13 yrs) with mild to moderate upper limb paresis were subjected to 10 sessions of upper limb rehabilitation with a planar mobile robotic device (MOTORE, Humanware). Fugl–Meyer Assessment Scale (FMAS) and Wolf Motor Function Test (WMFT) were administered before (t0), at the end (t1) and at 1 month follow-up (t2); at the same timing 64-channals EEG was recorded.
We analyzed power spectrum density in different frequency bands of the affected and unaffected hemispheres with 64-ch EEG and their correlation with motor impairment as measured by clinical scales. Correlation analyses were performed to identify the indicators of good rehabilitative outcome.
Clinical assessment indicated a significant functional improvement in upper limb motor function at the end of rehabilitation as assessed with FMAS and WMFT score that is maintained at follow-up. We found a positive correlation between global Alpha activity at t0 and WMFT score variation (t0–t1) and between global Beta activity at t0 and WMFT time variation (t0–t1) and a positive correlation between Beta activity at t0 in the unaffected hemisphere and FMAS variation (t0–t1 and t0–t2).
Robotic rehabilitation improves upper limb motor performance in stroke patients even in the chronic phase. The amount of Alpha and Beta band power at t0 is suggestive of rehabilitation-related motor outcome. Our results suggest that EEG recording preliminarily to robotic rehabilitation could help identifying good responders to treatment thus optimizing results.